Mobile marketing is having an increasing influence on the purchasing decisions of Generation Z in Poland, according to International Journal of Economic Policy in Emerging Economies. Gen Z, or Generation Z, is the demographic born between 1997 and 2012 and usually considered the offspring of Generation X (those born between 1965 and 1980 and themselves generally speaking the children of the Baby Boomer generation (born 1946 to 1964).

Bogdan Mróz of Warsaw School of Economics SGH and Barbara Grabiwoda of Publicis Commerce Poland in Warszawa, Poland, explain that Gen Z represents a group of young consumers whose lives are deeply integrated with digital technology. Their research, which combines an extensive review of existing literature with empirical statistical analysis, provides insights into how mobile devices are shaping the way in which this demographic interacts with brands and makes choices about what products and services on which it wishes to spend its money.

For Gen Z, typically smartphones, tablets, and other mobile devices are not just tools for communication but an essential part of daily life. Those tools are essential for other generations too, but Gen Z has never known a time without them, broadly speaking.

According to the current study, more than half of this demographic actively engages with mobile marketing communications, and a large proportion has a positive attitude toward brands that connect with them through the various digital platforms and social media. Indeed, the study reveals a clear trend: the more branded content Gen Z encounters on mobile devices, the more positive is their view of the companies involved.

For Gen Z, this great affinity for mobile technology has blurred the lines between the physical and digital worlds. Unlike previous generations, mobile devices seem intrinsically wired into their lives and even identity, playing a central role not only in social interactions but also in their shopping habits. For many, mobile platforms and social media represent essential spaces for discovering new products and brands. As such, the various platforms are pivotal for marketers seeking to engage with Gen Z.

The research suggests that conventional advertising on old-school media, such as print, TV, and radio, are becoming less effective at reaching this audience and many Gen Z individuals may never see or hear anything from the traditional media. For businesses, this shift in consumer behaviour means they need to adapt to the ongoing changes if they are to survive. Companies must be proactive in rethinking their marketing strategies to cater to the preferences and expectations of Gen Z.

Mróz, B. and Grabiwoda, B. (2025) 'Generation Z: the new mobile consumers. Empirical evidence from Poland', Int. J. Economic Policy in Emerging Economies, Vol. 21, No. 1, pp.1–20.
DOI: 10.1504/IJEPEE.2025.145412

Research from Kuwait, published in the International Journal of Public Sector Performance Management, has looked at the financial challenges faced by small and medium-sized enterprises (SMEs) there. Economic disruption and the post-pandemic environment have increased the risk of financial default among such businesses. Kuwait is among the richest nations, but its dependence on oil exports has made its economy highly vulnerable to global shifts, and SMEs have had to bear much of the burden.

Financial default, a situation in which businesses are unable to meet their debt obligations, is a growing concern for SMEs in Kuwait. The research, based on interviews with numerous SME managers, has identified several factors that have contributed to the financial instability of these businesses. The economic fallout from the COVID-19 pandemic, disruptions to daily operations, and changes in consumer behaviour were among the most significant factors, the researchers found.

In the wake of these crises, many SMEs found it difficult to access funding from traditional sources such as banks, which typically offer loans to support business operations. This lack of access to funding has left many companies with limited resources to adapt or recover. The researchers suggest that their findings point to a need for SMEs to adopt better financial management practices in order to mitigate the risk of default in future crises. SMEs in Kuwait, the research suggests, need to place greater emphasis on sound financial practices like budgeting, cost management, and cash flow forecasting. They say that by improving these areas, businesses may be better equipped to handle unexpected challenges.

Additionally, there is a need for businesses to explore alternative funding methods such as crowdfunding and angel investments rather than relying on bank loans. Of course, those approaches may not be appropriate for every kind of SME.

These findings also point to a need for increased government support and regulatory reform. The research suggests that greater transparency, regulatory simplification, and stronger public-private partnerships could help create a more stable financial environment for SMEs in Kuwait.

Alhaimer, R., Alshami, A., Alkhaldi, A., Alsadeeqi, A., Aloumi, D. and Malik, S. (2025) 'Evaluating public policy interventions in mitigating financial default risk among SMEs', Int. J. Public Sector Performance Management, Vol. 15, No. 5, pp.1-18.
DOI: 10.1504/IJPSPM.2025.145590

Research in the International Journal of Entrepreneurship and Small Business has identified the ingredients that lead to financial success among women-owned small-scale culinary businesses in Riau, Indonesia. The research used a combination of theoretical frameworks to shed light on how women entrepreneurs in this sector can overcome significant challenges to achieve greater financial stability and growth.

Okta Karneli, Harlen, and Yusni Maulida of the Universitas Riau, and Muammar Revnu Ohara of the Universitas Lancang Kuning, also in Riau, and Pratiwi Dwi Suhartanti of the Institut Bisnis dan Teknologi Kalimantan in South Kalimantan, Indonesia, explain that women are playing an increasingly important role in Indonesia's local economy. Understanding the recipe for their success is important to understanding the sector and how others might grow their businesses within it.

Women entrepreneurs in Indonesia's culinary sector face various many barriers, such as limited access to financial resources, insufficient education in financial management skills, and difficulties scaling operations. The researchers surveyed 355 women running business in the sector in Riau to understand how entrepreneurial orientation, adaptive capacity, and social networks might improve the bottom line for such businesses.

One of the key findings of the study is the strong link between entrepreneurial orientation and financial success. Innovative women willing to take risks and working proactivity within the sector proved to the be most successful, as one might expect. The entrepreneurs displaying these characteristics were better positioned to seize new opportunities, expand their market reach, and improve financial performance. The finding suggests that cultivating an entrepreneurial mindset is important to success for business operating in this dynamic sector.

The research also revealed that adaptive capacity is important. Businesses that could adjust to changing market conditions were generally more successful and could sustain long-term profitability. In the face of changing consumer tastes and economic uncertainties, being adaptable is critical to success. Finally, the team found that social networks – connections with suppliers, customers, and other entrepreneurs – helps provide the underlying support, resources, and information need to help businesses thrive.

Karneli, O., Harlen, Maulida, Y., Ohara, M.R. and Suhartanti, P.D. (2025) 'Women entrepreneurs in Indonesia's culinary sector: a study on entrepreneurial orientation and financial capability', Int. J. Entrepreneurship and Small Business, Vol. 55, No. 5, pp.1–28.
DOI: 10.1504/IJESB.2025.145581

In Lisbon's busy streets, tuk tuk companies have been slow to adapt to the digital age. Many rely on what researchers consider outdated and error-prone manual processes for managing their fleets of tourist transport vehicles. Research in the International Journal of Business Information Systems could help them navigate their way to a more efficient and effective future.

Eduarda Perdigão and Bráulio Alturas of the Instituto Universitário de Lisboa, in Lisbon, Portugal, have focused on how a tailored information system that could transform the daily management of companies such as Citytuk, one of the popular guided tour services in Lisbon.

Previously, tuk tuk companies relied on drivers to fill out daily service sheets by hand. This data was then transcribed manually into a central system by managers. Such an approach is inherently slow and prone to mistakes. For a growing business in the competitive tourism sector, such inefficiencies are not sustainable, the team suggests. The researchers have now identified an opportunity to enhance productivity and improve decision-making by replacing the manual process with a more streamlined, automated system.

The result of this research is Tuksy, a new application designed to simplify and modernize tuk tuk operations. Tuksy consists of two components: a mobile app for drivers to input service data directly, and a desktop app for managers to track and analyse that data in real-time. This digital solution eliminates the need for paper records, so reduces errors and frees up valuable time for both drivers and managers.

The system represents more than just a solution for one company's operational challenges, it represents a model for how other small businesses in the tourism sector might embrace technology and boost their efficiency.

Perdigão, E. and Alturas, B. (2025) 'Developing an app proposal for tuk tuk service management', Int. J. Business Information Systems, Vol. 48, No. 4, pp.433–451.
DOI: 10.1504/IJBIS.2025.145549

Virtual reality (VR) has steadily become a key tool in sports training, offering immersive environments that simulate real-world physical exercises. However, its application in aerobics training has faced significant challenges, particularly in accurately capturing and recognizing complex body movements. A study in the International Journal of Computational Systems Engineering has overcome some of these barriers obstacles by improving the precision of motion capture and the recognition of different actions during aerobics exercises.

Conventional VR motion capture systems rely on algorithms that align 3D representations of physical objects, so-called point cloud data, to track body movements. However, these systems often struggle with two critical issues: noise and incomplete data. Noise refers to unwanted interference that can distort the data collected by sensors, while incomplete data arises when certain body movements are not fully captured. In aerobics, where precision in movement is key to safe and effective exercising, these issues compromise the effectiveness of VR-based training systems.

Hui Wang of the School of Physical Education at Yan'an University in Yan'an, China, has addressed these challenges by developing two new models. The first focuses on enhancing the Iterative Closest Point (ICP) algorithm, which is used to align point cloud data. ICP is a well-established method, but it is prone to inefficiencies, particularly when faced with noisy or incomplete data. By optimizing the algorithm, Wang has improved accuracy and speed of capture.

The second model focuses on refining action recognition. A neural network is used to analyse the complex relationships between body joints over time by tracking the interactions between different body parts. Wang improved the neural network used by incorporating a perturbation mechanism to deal with noise, which further improved its ability to capture subtle movements and interdependencies between non-adjacent joints during aerobics.

Accuracy up to 99 percent was achieved, indicating a remarkable ability to recognize and classify aerobics movements with minimal error. Moreover, the experimental group using these advanced models outperformed the control group in various performance metrics, particularly in terms of the standardization of movements, the work explains. This enhanced motion recognition technology could significantly improve both the learning experience for students and the ability of instructors to offer targeted feedback, leading to more efficient and personalized training in aerobics.

Wang, H. (2025) 'The application of VR-based fine motion capture algorithm in college aerobics training', Int. J. Computational Systems Engineering, Vol. 9, No. 6, pp.1–10.
DOI: 10.1504/IJCSYSE.2025.145446

Hashtags, the keywords preceded by the "#" symbol, are widely used on social media platforms like Instagram to categorize content and increase its visibility. While their primary function is to help posts reach broader audiences, a study in the International Journal of Web Based Communities shows that hashtags also play a significant role in shaping how users perceive the trustworthiness of the post's source. This research challenges the common practice of "hashtag stuffing", the use of excessive or irrelevant hashtags to boost engagement. It then explores the unintended consequences it may have on the credibility of a given post and the person or company using them.

On Instagram, as with other platforms, hashtags are often used to tap into trending topics or relevant themes, enabling users to increase the visibility of their posts. This study suggests that beyond increasing visibility, hashtags play a significant role in how users judge the credibility of a post.

Ye Han and Peter Haried of the University of Wisconsin-La Crosse, La Cross, Wisconsin, Shuang Wu of Rowan University, Glassboro, New Jersey, USA, carried out experiments and found that hashtags act as "heuristic cues." In psychological terms, a heuristic is a mental shortcut people use to quickly make decisions or judgements without having to analyse every piece of information. In this context, hashtags serve as cues that shape how trustworthy a post seems, even if the viewer does not scrutinize the content itself in detail.

When a post includes hashtags, users tend to assume that the source is more likely to share additional information or similar content. This perception increases the post's credibility, reinforcing trust. However, this trust is undermined when hashtags are deemed irrelevant or excessive, as is the case with hashtag stuffing. Users may begin to question the authenticity of the post, leading them to engage in more critical analysis of the content, ultimately reducing the post's perceived trustworthiness.

This finding underscores a critical tension for Instagram users, particularly commercial enterprises and so-called influencers who all rely on visibility and reach. While using more hashtags may help posts reach a wider audience, the study suggests that excessive or irrelevant hashtags can backfire. Users may interpret such posts as less credible, as the hashtag choices can signal an attempt to manipulate engagement rather than offer valuable or pertinent content.

The research also suggests that the visual nature of Instagram posts affects how users interact with hashtags. If the image is clear and straightforward, users are more likely to engage with hashtags, trusting that the content is well-supported by relevant tagging. In other words, hashtags should be directly related to the post's content to maintain both trust and engagement. This balanced approach prevents users from feeling overwhelmed by irrelevant information and ensures a more authentic connection with the audience, the research suggests.

Han, Y., Wu, S. and Haried, P. (2025) 'The hidden impact of hashtags on Instagram: navigational heuristics on source trustworthiness', Int. J. Web Based Communities, Vol. 21, Nos. 1/2, pp.155–185.
DOI: 10.1504/IJWBC.2025.145135

A study in the International Journal of Knowledge and Learning has looked at how individual personality traits influence how much users disclose personal information on social networking sites. Self-disclosure, revealing personal details to others, is generally considered a key component of online social networking interaction.

Understanding what motivates people to share in this way could help platform providers improve the user experience and engagement. The work might also have applications in psychology, social media studies, but perhaps also it could ultimately benefit the bottom-line for the platforms.

Nam Tien Duong of Ho Chi Minh City University of Economics and Finance, Vietnam, has looked at the intersection of personality, self-presentation, and social networking behaviour. He found that social network users are driven by specific interpersonal needs that shape how much they reveal about themselves. These needs, grounded in Maslow's hierarchy of needs, emphasize the social and emotional drive for connection and affection. Social networking platforms have offered us a unique space to meet these needs through active self-expression online.

The research has drawn on two primary interpersonal needs that shape behaviour: the need for belonging and the need for self-presentation. The need for belonging involves the desire to connect with others and feel recognized, while the need for self-presentation is about managing the image we project to others. The study emphasizes that self-presentation plays an important part in motivating self-disclosure, though its impact varies depending on an individual's personality traits, particularly extraversion and narcissism.

Extraversion refers to a person's tendency to seek out social interaction and enjoy group activities. According to the findings, individuals with high levels of extraversion are more likely to disclose personal information. Their enthusiasm for engaging with others translates into a greater willingness to share personal details. In contrast, introverts, who are less inclined toward social interactions, tend to disclose less about themselves, even when they may still have a strong desire for social inclusion.

Another personality trait that significantly influences self-disclosure is narcissism. Narcissists, who possess a strong desire for admiration and validation, often share more personal information to highlight their perceived individuality. This behaviour is driven by a need to garner attention and reinforce their sense of self-importance, which stands in contrast to those who may share less for more intimate or relational reasons.

Duong, N.T. (2025) 'Why do people disclose themselves on social networking sites? Evidence from Vietnamese Facebook users', Int. J. Knowledge and Learning, Vol. 18, No. 2, pp.186–203.
DOI: 10.1504/IJKL.2025.145086

As digital devices become more integrated into children's lives, concerns about their impact on physical and mental health continue to grow. In modern households, smartphones, tablets, and computers are now commonplace, leading to increased exposure to online content. This shift has raised important questions about how much screen time is appropriate and what effects it has on children's well-being.

The issue of screen time has been widely debated, with research pointing to both potential risks and benefits. Excessive screen use has been linked to physical issues such as eye strain, headaches, and sleep disruption. There are also concerns about the relationship between increased screen time and physical inactivity, as children who spend more time on devices might be less engaged in outdoor play and exercise, both essential for their physical development.

On the other hand, the online world offers numerous opportunities for learning, creativity, and socialization. Educational apps, online learning platforms, and digital games can stimulate intellectual growth, promote critical thinking, and even foster social connections with peers across the globe. The challenge is finding a balance that maximizes the benefits of digital engagement while mitigating the potential negative effects on health and well-being.

Beyond physical health, the psychological effects of digital media are also a growing concern. Research indicates that extended use of devices, particularly those providing access to social media, can influence children's emotional well-being, intellectual development, and sense of identity. While some cases have linked excessive screen time to negative outcomes, the full psychological impact of digital media remains an area of ongoing research. It is important to also acknowledge the positive effects, such as improved cognitive skills and the opportunity for global social connections.

Given these concerns, researchers are exploring more personalized methods of regulating screen time, such as the use of fuzzy logic inference systems. These systems, a type of artificial intelligence, can evaluate complex and imprecise data, making them ideal for tailoring screen time recommendations and restrictions based on a child's unique characteristics.

Parents, guardians, or teachers could input data about a child's age, health, and psychological profile into the system, which would then use this information to determine appropriate screen time and content limits. Unlike generic restrictions, which may be difficult to enforce or inappropriate for all young users, fuzzy logic systems offer a more customized and flexible approach to managing screen use.

While there are existing tools that restrict screen time and block content, an adaptive approach, could be key to managing both the quantity and quality of screen time. Younger, more vulnerable users would have stricter controls and limits, while older, more mature children could access a wider range of appropriate resources, all based on their individual developmental profiles.

Alguliyev, R.M., Abdullayeva, F.J. and Ojagverdiyeva, S.S. (2024) 'Fuzzy expert system for access control of children to the internet', Int. J. Reasoning-based Intelligent Systems, Vol. 16, No. 6, pp.455–462.
DOI: 10.1504/IJRIS.2024.144062

Urban music, which originated in marginalized communities in The Caribbean and the USA, has found a global audience, resonating especially with young people, as is often the case with emerging music genres. Urban music has evolved into more than just a genre of entertainment, it has become a significant cultural force that shapes the identities, behaviour, and educational experiences of young people.

A study in the International Journal of Knowledge and Learning has examined the impact of urban music on secondary school students in Peru. The work sheds new light on its multifaceted role in adolescent life, which may well have wider implications. The findings suggest that urban music, encompassing styles such as hip-hop and reggaetón can serve as a platform for cultural expression and social belonging, influencing students in ways that are deeply linked to their socio-economic environments.

Agustin Angel Roberto Chumpitaz-Avila and Luis Fernando Castro-Llacsa of the National University of San Agustín of Arequipa in Arequipa, Peru, highlight how this musical genre has penetrated schools across Peru, including state, private, and religious institutions. This reflects the wide-reaching influence of urban music. While critics have long asserted that urban music might somehow promote antisocial behaviour, the research suggests that its influence on youth is not so easily categorized and indeed can have a strong positive influence.

Urban music does commonly have explicit lyrics that often feature violence, overtly sexual imagery, and drug use. Those social observers who malign it for these characteristics suggest that young listeners may internalize these messages. However, the current study found that while some students might adopt attitudes reflected in the music, the broader socio-economic and familial context plays a more significant role in determining their behaviour. Urban music, it seems, is a tool for young people to interpret their surroundings rather than an inherently harmful influence.

Chumpitaz-Avila, A.A.R. and Castro-Llacsa, L.F. (2025) 'Comparative analysis of the impact of urban music on students of state, private and parochial educational institutions', Int. J. Knowledge and Learning, Vol. 18, No. 2, pp.170–185.
DOI: 10.1504/IJKL.2025.145085

As digital music libraries continue to expand, the challenge of accurately categorizing musical genres remains high on the agenda. A study in the International Journal of Information and Communication Technology introduces a deep learning model designed to improve the classification of classical music genres.

By employing multi-channel learning (MCL) and Mel-spectrogram analysis, the model, known as MC-MelNet, offers what the research suggests is a more nuanced and efficient approach to genre identification. Tests carried out by its developer, Lei Zhang of the Henan Academy of Drama Arts at Henan University in Zhengzhou, China, show that it outperforms traditional classification methods.

The ability to classify music automatically has far-reaching implications for streaming services, music recommendation algorithms, and digital archiving. Classical music, with its intricate structures and subtle variations, presents a particular challenge for automated classification. Zhang explains that MC-MelNet addresses these issues by integrating multiple layers of analysis, capturing both the tonal and temporal characteristics of a composition.

At the core of MC-MelNet's innovation is its multi-channel learning framework, which processes multiple audio features simultaneously. Conventional approaches rely primarily on Mel-spectrograms, which break down an audio signal into different frequency components in a way that mimics human hearing. However, while effective in capturing tonal elements, Mel-spectrograms alone do not fully represent the temporal dynamics of music.

MC-MelNet overcomes this limitation by incorporating additional audio features such as Mel-frequency cepstral coefficients (MFCC) and Chroma features. MFCCs capture the timbral qualities of a sound, making them useful for distinguishing between different instruments or playing styles. Chroma features, on the other hand, focus on pitch content and harmonic structure. By combining these elements, MC-MelNet creates a richer and more detailed representation of musical compositions, allowing it to distinguish between closely related classical genres with greater accuracy.

Unlike conventional classification methods, which require manual feature extraction, MC-MelNet uses an end-to-end deep learning approach. It utilizes convolutional neural networks (CNNs) to detect spatial patterns in audio data and recurrent neural networks (RNNs), specifically long short-term memory (LSTM) networks, to process sequential musical information.

MC-MelNet might have applications beyond classical music classification. It could, for instance, be adapted for real-time sound processing and audio event detection. Enhancing the model's generalizability by training it on a more diverse dataset could make it applicable to a wider range of genres, improving automated music classification for commercial streaming platforms.

Zhang, L. (2025) 'Classification of classical music genres based on Mel-spectrogram and multi-channel learning', Int. J. Information and Communication Technology, Vol. 26, No. 5, pp.39–53.
DOI: 10.1504/IJICT.2025.145153

A distribution model designed to streamline spare parts delivery to roadside assistance vehicles could cut costs in half, according to work in the International Journal of Shipping and Transport Logistics. The model builds a solution to the well-known Travelling Salesman Problem, a complex optimisation problem that involves finding the shortest route that visits each city once and ends at the starting point. The model was tested on real data from a roadside assistance company operating a fleet of service vehicles.

Abolfazl Shafaei, Mohammad Reza Akbari Jokar, and Majid Rafiee of Sharif University of Technology in Tehran, Iran, and Ahmad Hemmati of the University of Bergen in Bergen, Norway, explain that the major logistical challenge for roadside assistance fleets is balancing inventory space with repair capabilities. Service vehicles have limited space onboard, so they must prioritize particular spare parts and specific tools. Service vehicles usually visit a central warehouse on a regular schedule to restock on spare parts every few days. This adds to overall fuel costs, vehicle wear and tear, and lost servicing time. The new system replaces these frequent trips with a centralized delivery truck that optimizes the frequency and route of spare part deliveries.

However, drivers everywhere expect fast, efficient service from the company with which they entrust their vehicle's roadside maintenance, They also expect it to be inexpensive and a high-quality service.

The team tested several delivery schedules, including daily and every five days, and found that the most efficient option for this roadside assistance company was an optimized cycle on the first, second, and fourth days. This approach reduced costs by 56%.

The new model reduces the need to stockpile items by ensuring regular deliveries to the service fleet out on the road. This frees up space for repair equipment that allows for a wider variety of roadside fixes.

Beyond the immediate time and cost savings to companies running roadside assistance fleets, the model also promises significant environmental benefits. With fewer vehicles returning to a central warehouse to restock, fuel consumption and carbon emissions can be greatly reduced. Indeed, for the test case, the team found that annual carbon dioxide emissions could be reduced by 75 percent.

Shafaei, A., Akbari Jokar, M.R., Rafiee, M. and Hemmati, A. (2025) 'Using the route planning for supplying spare parts to reduce distribution costs: a case study in a roadside assistance company', Int. J. Shipping and Transport Logistics, Vol. 20, No. 1, pp.131-158.
DOI: 10.1504/IJSTL.2025.144995

Recent work in 6D object pose estimation holds significant promise for advancing robotics, augmented reality (AR), virtual reality (VR), as well as autonomous navigation. The research, published in the International Journal of Computational Science and Engineering, introduces a method that enhances the accuracy, generalization, and efficiency of determining an object's rotation and translation from a single image. This could significantly improve robots' ability to interact with objects, especially in dynamic or obstructed environments.

In robotics, 6D object pose estimation refers to determining both the orientation (rotation) and position (translation) of an object in three-dimensional space. "6D" describes six degrees of freedom: three for translation (X, Y, Z axes) and three for rotation (around those axes). Accurate pose estimation is critical for autonomous systems, including robots and AR/VR systems.

Challenges arise due to variations in object shapes, viewpoints, and computational demands. Current methods rely on deep-learning techniques using large datasets of objects viewed from various angles. These models struggle with unseen objects or those with shapes different from training data.

The new technique discussed by Zhizhong Chen, Zhihang Wang, Xue Hui Xing, and Tao Kuai of the Northwest Institute of Mechanical and Electrical Engineering in Xianyang City, China, addresses the various challenges by incorporating rotation-invariant features into an artificial intelligence system known as a 3D convolutional network. This allows the system to process an object's 3D point cloud, regardless of its orientation, leading to more accurate pose predictions even when the object is rotated or seen from unfamiliar angles. The network uses a consistent set of coordinates, known as canonical coordinates, which represent the object in a frame of reference unaffected by rotation. This innovation improves the system's ability to generalize to new poses, overcoming a limitation of conventional methods.

Not only is the new approach more accurate, it is more efficient and so needs less training data and less computer power, making it more suited for real-time, real-world applications.

Chen, Z., Wang, Z., Xing, X.H. and Kuai, T. (2025) 'Rotation-invariant 3D convolutional neural networks for 6D object pose estimation', Int. J. Computational Science and Engineering, Vol. 28, No. 8, pp.1–9.
DOI: 10.1504/IJCSE.2025.145133

Separating the human voice from the music in an audio recording has long been a challenge in signal processing. There are numerous so-called artificial intelligence (AI) tools around that can do this now with varying degrees of accuracy. The task is difficult due to the complexity of music, which involves multiple overlapping sources across the audible frequency spectrum. There is a need to increase the resolution and clarity of systems that can separate a vocal from the instrumental for a wide range of applications, such as post-production remixes of music, singing instruction and rehearsing.

A new method is reported in the International Journal of Reasoning-based Intelligent Systems. The researchers, Maoyuan Yin and Li Pan of the School of Music and Dance at Mudanjiang Normal University in Mudanjiang, China, have, they say, improved upon existing techniques by combining several advanced signal processing techniques. Their starting point is the use of a virtual microphone array. This virtual setup helps them localize the human voice within the overall sound and isolate it from the background.

The virtual microphone array creates a spatial representation of the sound, the team explains. To further improve on the results, the team also used near-field and far-field models to simulate the propagation of sound from sources at different distances. This gives them even more precision in localising the vocal within the sound.

Once the voice is accurately located, the system constructs a time-frequency spectrum for both the human voice and the background music. The time-frequency spectrum tracks how the energy of sound signals shifts along the frequency axis over time. The system can then analyse these changes and distinguish between vocal and instrumental, isolating them from one another.

The process is further refined by the use of a sophisticated algorithmic technique – the Hamming window function, which improves the efficiency of the requisite two-dimensional fast Fourier transform (2DFT) processing of the data. This step reduces the number of dimensions of the various extracted sound signals, simplifying the final extraction of vocal from music.

Test results demonstrate the effectiveness of this new approach with a localization error of just 0.50%. For background music, the feature extraction error is reduced to 0.05%. Overall, the team could reach almost 99 percent accuracy in separating vocal from instrumental. The same approach should also work in isolating a human voice from non-musical background noise. It could thus be used to improve automated spoken-word transcription services and help in the development of better hearing aids.

Yin, M. and Pan, L. (2025) 'Separating voice and background music based on 2DFT transform', Int. J. Reasoning-based Intelligent Systems, Vol. 17, No. 1, pp.50–57.
DOI: 10.1504/IJRIS.2025.145050

Fractals are intricate geometric shapes that exhibit self-similarity, meaning their patterns repeat at different scales, no matter how much they are magnified. Unlike traditional geometric figures such as circles or squares, which can be described with simple equations, fractals are generated through iterative mathematical processes, producing infinitely complex and detailed structures.

We see fractals all around us, in the branching structure of a tree, in clouds, snowflakes, coastlines, in the system of blood vessels and nerves in our bodies. Fractals can thus be used as a scientific model for many natural phenomena, However, their inherent beauty and intrigue can be a source of artistic inspiration too.

A study in the International Journal of Information and Communication Technology introduces an advanced approach that can be used to create novel images based on fractals. The optimisation algorithm, developed by Junli Wang of the School of Digital Arts at Wuxi Vocational College of Science and Technology in Wuxi, China, and known as the Equilibrium Optimiser (EO), significantly improving efficiency and design diversity.

Fractal geometry was first formalised by Benoît Mandelbrot in the 1970s and has influenced fields ranging from architecture to computer graphics and even music composition. The challenge in fractal art generation has traditionally been the reliance on manual input, requiring expertise and time-consuming adjustments. The new research overcomes some of those limitations through the EO algorithm, which enables a more efficient, diverse, and aesthetically rich exploration of fractal forms, according to the study.

The EO algorithm is an advanced optimisation algorithm based on how natural physical systems balance themselves. Unlike Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO), the EO algorithm adjusts its search strategies dynamically to avoid becoming trapped in local optimisation points, a common problem of many mathematical models. This means that the EO algorithm can fine-tune the parameters needed to generate fractal patterns, producing designs with greater symmetry, complexity, and structural variation than traditional approaches. Wang's tests show that the EO algorithm works better than older algorithms in terms of the speed with which it converges on a solution and the visual quality and stability it produces.

Beyond its technical contributions, this research raises important questions about the intersection of technology and art. The ability to generate intricate fractal patterns automatically expands the creative possibilities available to artists, designers, and researchers. Unlike hand-drawn or physically painted works, digital fractal art is created through computation, challenging conventional ideas of authorship and artistic intent.

Wang, J. (2025) 'An alternative method for generating fractal art patterns based on the balanced optimiser algorithm', Int. J. Information and Communication Technology, Vol. 26, No. 5, pp.54–68.
DOI: 10.1504/IJICT.2025.145150

Can money buy happiness? An age-old question with as yet no definitive answer. The ancient philosophers could not find it, nor can modern economists with their spreadsheets and algorithms. A study in the International Journal of Happiness and Development has explored the complex relationship between income and happiness and provides some new insights into the debate.

Ling Zhang China Agricultural University in Beijing, China, and Sajal Lahiri of Southern Illinois University Carbondale in Carbondale, Illinois, USA, used data from the Panel Study of Income Dynamics, a comprehensive dataset that tracks individuals over time, and found that having a higher income does seem to correlate with increased life satisfaction. Importantly, the data is longitudinal, which means it tracks the same individuals over several years, allowing the researchers to control for unchanging factors such as personality traits or family background.

The study also found that for those with rising income, their happiness tends to rise too. Conventionally, the Easterlin Paradox has suggested that although wealthier people are generally happier at any given time, happiness does not necessarily increase as individuals become wealthier over their lifetime. This new research somewhat overturns that notion.

Of course, there are always exceptions to any rule, poorer people with seemingly plenty to smile about, who accept their lot and enjoy life regardless and conversely the super-rich individual who never smiles and seems perpetually burdened by their wealth.

The study used subjective well-being (SWB) scales, where individuals rate their life satisfaction on a scale from 1 to 10 to measure happiness. The results were telling: income was positively linked to life satisfaction, and the effect of income on happiness has become more pronounced in recent years. The study also showed that individuals whose income decreased in real terms, often because of factors such as technological progress or globalization, reported lower levels of life satisfaction. This suggests that declines in income, particularly among lower-income individuals, can negatively affect well-being.

While the relationship between money and happiness remains a nuanced topic, the evidence from this study adds weight to the argument that economic well-being plays a significant role in determining overall life satisfaction. These findings emphasise the importance of addressing income inequality and supporting policies that help individuals increase their earnings. As the study suggests, fostering economic fairness and opportunity may not only help individuals thrive, but could also enhance collective happiness.

Zhang, L. and Lahiri, S. (2025) 'Income and happiness: a study of a panel of US residents', Int. J. Happiness and Development, Vol. 9, No. 1, pp.1–14.
DOI: 10.1504/IJHD.2025.144959

Artificial intelligence (AI) and deep-learning technologies have led to the development of so-called deepfakes. These are generated or manipulated video or audio recordings that can alter a person's facial expressions, voice, or even their entire identity. While deepfakes have some legitimate uses in areas such as entertainment and art, their potential for misuse and for the spread of misinformation or damaging reputations has been recognised for several years. As the technology used to create deepfakes becomes more sophisticated, so there is a growing need to develop methods for deepfake detection.

Research described in the International Journal of Computational Science and Engineering introduces a hybrid deep-learning model that can itself improve the detection of deepfake content. The model combines two convolutional neural network (CNN) architectures, Inception ResNetV2 and Xception, along with long short-term memory (LSTM) networks. LSTM networks can process sequential data from video or audio segments and are particularly useful in spotting inconsistencies in manipulated media.

Shourya Chambial, Tanisha Pandey, Rishabh Budhia, and Balakrushna Tripathy of the Vellore Institute of Technology in Tamil Nadu, India, and Anurag Tripathy of Carnegie Mellon University in Pittsburgh, Pennsylvania, USA, trained their deepfake detector on a large dataset containing both real and manipulated video data. They were able to achieve an accuracy of almost 97 percent in tests. This suggests that the hybrid approach is capable of identifying subtle signs of manipulation in digital media and so decide whether a video is real or fake.

An important aspect of the research is that it emphasizes the importance of fine-tuning deep-learning models so that they work well with real-world data. An issue that commonly arises with certain types of AI model is that of "overfitting", where the algorithm is too closely tied to the specific characteristics of its training data and struggles to perform on new, unseen data. In the current work, the team monitored performance and adjusted it to ensure it remained effective with a wide range of video content.

Chambial, S., Pandey, T., Budhia, R., Tripathy, B. and Tripathy, A. (2025) 'Unlocking the potential of deepfake generation and detection with a hybrid approach', Int. J. Computational Science and Engineering, Vol. 28, No. 2, pp.151–165.
DOI: 10.1504/IJCSE.2025.144802

Better technology for keeping track of glass beer bottles could be important in improving health and safety and operational efficiency in the alcoholic beverage sector, according to work published in the International Journal of Productivity and Quality Management.

A team in Brazil has evaluated three technologies, laser, carbon dioxide laser, and QR code systems and considered basic criteria such as security, cost, performance, and social impact. Carolina Xavier da Silva Seixas Rocha, Aldara da Silva César, and Cecilia Toledo Hernández, and Ualison Rébula de Oliveira of the Fluminense Federal University in Volta Redonda, and Fabiane Letícia Lizarelli of the Universidade Federal de São Carlos in São Carlos, Brazil, suggest that QR code technology offers the best balance between the various factors, particularly in terms of safety and broader social implications.

Traceability is the ability to track products through the supply chain and has become a critical issue in the food industry due to the increasing frequency of health problems associated with foodborne pathogens. According to the World Health Organization, millions of people are affected by foodborne illnesses each year, underlining the importance of systems that can trace the origin and movement of food products. Effective traceability can help quickly identify and remove products that pose a safety risk, which is vital for consumer protection.

The team focus on Brazil, where beer consumption is on the rise, and highlight the need for improved traceability in the beer industry. The team has identified how Brazilian glass bottle manufacturers lacking the ability to trace individual bottles. This limitation inevitably complicates the resolution of customer complaints and the identification of production issues.

While there are sophisticated methods for analysing a given bottle, the team suggests that the two-dimensional bar code system, known as the QR (Quick-response) code, offers a promising solution to traceability. QR codes are relatively inexpensive to implement, easy to use, and capable of providing real-time data on product movements. These features make them a strong choice for companies looking to meet both safety requirements and consumer demand for transparency. Additionally, QR codes align with increasing regulatory pressures in markets like Brazil, where food safety standards are becoming more stringent.

Rocha, C.X.d.S.S., César, A.d.S., Hernández, C.T., de Oliveira, U.R. and Lizarelli, F.L. (2025) 'Analysis and selection of glass bottle traceability technologies in the beer production chain', Int. J. Productivity and Quality Management, Vol. 44, No. 2, pp.178–204.
DOI: 10.1504/IJPQM.2025.144340

Independent metering control systems (IMCS) are an advanced electro-hydraulic technology used in mobile machinery for construction, agriculture, and mining equipment. They represent something of a revolution in mobile machinery, offering the potential to drastically improve energy efficiency, reduce operational costs, and cut carbon emissions, so contributing to environmental sustainability. However, the path to their widespread adoption faces various challenges. A critical review of the state-of-the art International Journal of Hydromechatronics looks at hos these challenges might be addressed.

In standard hydraulic systems, fluid flow is controlled through multiway valves that adjust flow in a single direction. These usually rely on mechanical components to manage the flow. By contrast, IMCS decouples the inlet and outlet of the valve, enabling independent control of fluid flow both into and out of hydraulic actuators. This innovation allows for finer control over hydraulic functions, enhancing system performance and providing more precise energy management.

The primary advantage of IMCS lies in its potential to significantly reduce energy consumption. Traditional hydraulic systems often suffer from inefficiencies due to throttling whereby fluid flow is necessarily restricted to control speed, which leads to energy loss. By providing more precise regulation of fluid pressure and flow, IMCS can minimize these losses. This and other benefits are discussed by Ruqi Ding, Guohua Sun, and Ling Peng of East China Jiaotong University in Nanchang, Min Cheng of Chongqing University in Chongqing, and Junhui Zhang, Bing Xu, and Huayong Yang of Zhejiang University, in Hangzhou, China.

The researchers point out that all industries are facing increased pressure to meet stringent environmental standards. Improving hydraulic system efficiency by just 15 percent could save these industries billions of dollars annually, while also significantly lowering carbon emissions.

One of the main obstacles to more widespread adoption is the complexity associated with the integration of IMCS into existing machinery. Traditional hydraulic systems use mechanical controls that are relatively simple, whereas IMCS relies on sophisticated electronics and software to manage the complex control of multiple fluid inputs and outputs. To take on IMCS, industry will need to invest heavily in the new technology and on training and trust that the payback will be quick given the improvements they will see in the efficiency of their equipment.

Ding, R., Sun, G., Zhang, J., Peng, L., Cheng, M., Xu, B. and Yang, H. (2025) 'A review of independent metering control system for mobile machinery', Int. J. Hydromechatronics, Vol. 8, No. 5, pp.1–39.
DOI: 10.1504/IJHM.2025.144958

India has one of the largest legal professions in the world, with more than 600,000 law professionals. Yet, women remain underrepresented in the upper echelons of legal practice and the judiciary. Writing in the International Journal of Process Management and Benchmarking, P. Nisha and A. Vasumathi of the Vellore Institute of Technology in Vellore, India, explain how this gender disparity is particularly stark in India's Supreme Court and High Courts. Here, women represent only a small fraction of judges. The team points out that while many women have made notable progress in the legal field, they still face persistent barriers to career advancement and promotion opportunities, often referred to as the "glass ceiling".

The concept of the glass ceiling is not a new phenomenon, nor one restricted to India. Historically, women around the world have always faced significant obstacles in entering and progressing within the legal profession. Even as more women entered the profession, social expectations and family responsibilities have continued to stifle career progression to senior positions. This imbalance has persisted despite the fact that women now make up a significant proportion of law school graduates and practicing professionals.

Gender stereotypes have played a detrimental role in shaping the professional lives of women in law. This bias in the systems affects what kinds of cases women are assigned, the networking opportunities available to them, and their overall access to career-advancing resources. Faced with fewer high-profile cases and lower-paying ones at that exacerbates the problems they face, often by robbing them of the very opportunities that would allow them to gain the necessary experience to be considered for leadership roles.

The researchers suggest that institutional support could play a major role in addressing these disparities. They suggest that daycare facilities for the children of court employees might alleviate at least some of the burden and obligation on women professionals with offspring, where society often expects them to be the primary carer. However, such measures are not enough. Real progress will only happen when there is a cultural shift within law firms, judicial bodies, and educational institutions, the research suggests. Gender-neutral policies must be implemented at every level of the profession to ensure that women are not held back by biases or by societal expectations.

Nisha, P. and Vasumathi, A. (2025) 'The impact of personality traits of women advocates towards glass ceiling beliefs for career development', Int. J. Process Management and Benchmarking, Vol. 19, No. 2, pp.147–192.
DOI: 10.1504/IJPMB.2025.143986

Social media has become a very useful tool for companies hoping to boost brand awareness, engagement, and loyalty among consumers. A study in the International Journal of Business Performance Management has looked at its role in detail and found that many companies are directly interacting with consumers, building stronger brand connections, and gaining a competitive edge.

Radhika Madan, a Soft Skills Trainer, in Gurgaon, Haryana and Manmohan Rahul of Sharda University in Greater Noida, Uttar Pradesh, India, surveyed 350 internet users to help them establish a reliable scale for measuring the effectiveness of brand communication via social media. They used Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) to analyse their survey results and found that social media offers a more direct, cost-effective, and rapid means of reaching a wider customer base when compared with conventional media, such as television, papers and magazines, and even email.

The researchers emphasize that social media platforms allow businesses to undertake ongoing dialogues with consumers, which in turn boosts both brand visibility and loyalty. This is particularly important in hospitality, air travel, banking, telecommunications, and e-commerce.

One aspect of social media's power is the potential to "go viral" when users share information about a brand or product with their networks, and the reach of that message expands quickly across the platform. Going viral can spread a brand message far wider and far more quickly than conventional advertising in many cases.

Of course, the concept of branding itself has evolved alongside the rise of social media. Traditionally, branding referred to creating a distinct identity for a company, highlighting its core values and differentiating it from competitors. Branding is no longer one-way traffic, the consumers themselves are part of the message and can have two-way, real-time conversations with businesses. This allows consumers to offer instantaneous feedback about products and services and gives businesses the means to respond to that feedback, whether positive or negative, just as quickly and to change their "offering" accordingly, if appropriate.

Madan, R. and Rahul, M. (2025) 'The role of social media as a brand communication tool: an exploratory work', Int. J. Business Performance Management, Vol. 26, No. 2, pp.228–249.
DOI: 10.1504/IJBPM.2025.144689

The integration of Artificial Intelligence (AI) into manufacturing processes has huge potential for improving productivity, efficiency, and safety. Machine learning models are already used to monitor equipment health and others predict supply-chain issues and consumer demand. However, research in the International Journal of Mechatronics and Manufacturing Systems suggests that there remain barriers to the more widespread adoption of AI in production environments. In particular, there are obstacles to incorporating AI in the early design phase.

Yuji Yamamoto and Kristian Sandström of Mälardalen University in Eskilstuna, and Aranda Muñoz Álvaro of the Research Institutes of Sweden in Västerås, Sweden, explain how the early design phase is fundamental in determining how AI might ultimately be embedded into the manufacturing workflow. They point out that it is during this period that engineers, data scientists, production staff, and other stakeholders have to align their goals with functionality and outcomes. However, this process can be stymied if there is a misalignment between the technical expertise of the data scientists and the practical knowledge of the manufacturing professionals. Poor communication and unrealistic expectations then lead to the installation of an AI system that does not meet the operational needs of the factory floor.

One of the biggest problems the researchers found is that of cognitive overload, where those involved are overwhelmed by the complexity of the tasks at hand. The technical jargon of machine learning and AI, for example, is often inaccessible to those with expertise in production management but not in data science.

Conversely, data scientists may struggle to understand the intricacies of manufacturing operations, such as workflow design and the real-time adjustments needed to address unpredicted challenges. This knowledge gap between the two groups can lead to failure especially if the AI system does not take into account the very dynamic nature of manufacturing.

Yamamoto, Y., Álvaro, A.M. and Sandström, K. (2024) 'Challenges in designing a humancentred AI system in manufacturing', Int. J. Mechatronics and Manufacturing Systems, Vol. 17, No. 4, pp.351–369.
DOI: 10.1504/IJMMS.2024.144289

New insights into the design and performance of combined weir-gate hydraulic structures, a crucial component of modern water-management systems, are discussed in the International Journal of Hydrology Science and Technology. Weir-gate hybrid structures merge the functions of a traditional weir and a sluice gate and are now being widely used to regulate water flow, control flood risks, and aid in flow measurement.

Noor I. Khattab, Azza N. Altalib, and Arwa A. Mullah of the University of Mosul, Mosul, Iraq, have now looked at a novel design for such structures, which incorporates a triangular shape with interior angles that can range from 60 to 180 degrees. Their findings explain how the configuration of these structures impacts their efficiency in managing water flow.

A weir is typically used to direct water over a barrier to control flow, while a sluice gate is designed to regulate flow beyond the barrier. By combining both functions into a single hybrid structure, engineers can maximize the benefits of each. The new work demonstrates how varying the angle within a triangular opening affects the flow of water and the efficiency of discharge measurement. The team used a key performance indicator, the discharge coefficient, is used to quantify the efficiency of the structure in controlling and measuring flow.

The researchers found that as the interior angle of the hybrid structure decreases, so the discharge coefficient increases. Under constant upstream head values, the discharge coefficient showed an average increase of 27% to 54% as the interior angle became more acute. The study also found that the shape and configuration of the structure, including the length of the crest and the specific type of flow, whether it flows over, under, or through the structure, affect overall performance. One of the important applications of these hybrid structures is the reduction of sediment accumulation beneath gates. The weir pushes materials out while the gate controls flow.

Weir-gate structures play a role in managing water: flood control, irrigation, water supply, and energy generation. If the design of these structures can be improved and optimized for specific purposes, then efficient and cost-effective infrastructure might be developed that is better equipped to handle fluctuating water conditions.

Khattab, N.I., Altalib, A.N. and Mullah, A.A. (2025) 'Hydraulic characteristics of combined weir-gate structure', Int. J. Hydrology Science and Technology, Vol. 19, No. 5, pp.1–17.
DOI: 10.1504/IJHST.2025.144936

Research in the International Journal of Management in Education has looked at the various factors that affect a teacher's behaviour in terms of innovation. Innovation, the research explains, is an important part of improving educational performance in an increasingly competitive environment.

Jimmy Ellya Kurniawan, Kuncoro Dewi Rahmawati, and Evan Tanuwijaya of the Universitas Ciputra Surabaya in Surabaya, East Java, Indonesia, carried out research across private schools there and their findings offer a clearer understanding of how the attitudes of teachers as well as their motivation and the organizational culture within which they work can shape their willingness to engage in innovative practices.

Educational innovation can drive teaching quality and student outcomes, the team adds. However, the factors that lead to what drives teachers to be innovative has not been researched in detail until now. The current work focuses on two key elements: learning orientation culture and self-determination. The researchers used the theory of planned behaviour (TPB), a psychological framework that links behaviour to one's attitudes, social pressures, and perceived control, to show how these elements influence teachers.

TPB shows that our behaviour is affected not only by personal beliefs but also by societal norms and how much we feel in control of our actions. In the context of education, the researchers showed that the organizational culture of a school can help foster innovation. A learning orientation culture, where continuous learning and knowledge application are prioritized, encourages teachers to embrace this. Moreover, when coupled with self-determination or a sense of autonomy there is an even greater likelihood of engaging in innovative practices.

From a practical perspective, the study offers valuable insights for school administrators. It suggests that if they can create a positive environment that emphasizes a learning-oriented culture and support the autonomy of their teachers, they might significantly enhance innovative behaviour and so student outcomes as well as the job satisfaction of their teachers.

Kurniawan, J.E., Rahmawati, K.D. and Tanuwijaya, E. (2025) 'School teacher's innovative work behaviour model', Int. J. Management in Education, Vol. 19, No. 7, pp.1–36.
DOI: 10.1504/IJMIE.2025.144925

Digital tools continue to redefine much of modern student life and learning. Educational administrators could better serve their student communities if they had a clearer view of the emotions and opinions those students are expressing online. Research in the International Journal of Information and Communication Technology, describes a deep learning-based method to analyse and categorize student sentiment in online content. The tools could offer invaluable insights for managing campus dynamics and enhancing the academic environment.

Dan Wang and Li Wang of the Gingko College of Hospitality Management in Chengdu, China, explain how deep learning techniques can be though of a subset of artificial intelligence (AI) technologies with a focus on understanding human language. By analysing content from different online platforms, such as social media, discussion forums, and website comment sections, the team suggests that it is possible to extract a clearer picture of the emotional and ideological landscape of student population.

The approach uses Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. CNNs can identify patterns and extract key features from textual data, while LSTMs are used to understand the relationships between words in long passages of text. By combining the strengths of these tools, it is possible to extract the nuance of ideas and emotions being shared online in the wider student discourse.

A key aspect of the new analytical model is the introduction of an "attention mechanism". This improves the model's ability to accurately interpret complex emotional expressions. In online communication, students often use irony, sarcasm, or metaphor to convey sentiments, as do we all. This is difficult to grasp with a simple analytical tool. The attention mechanism allows the system to focus on the most critical words or phrases in a given piece of text and this improves its ability to detect and decode these subtle emotional cues. For instance, the phrase "yeah, right" is familiar American vernacular and is commonly used as a sarcastic riposte to an apparently unbelievable comment. Taken literally, however, it would simply be interpreted as confirmation of the person reading the unbelievable and confirming their acceptance of it.

In addition to the nuances of the model and the AI tools on which it is built, the team has also created a large-scale, annotated dataset of student-generated content. This dataset, drawn from a wide range of platforms, allowed the team to train and validate their model with real data. The same data and model might be used off campus too, to analyse public online sentiment or perhaps within the corporate environment.

Wang, D. and Wang, L. (2024) 'Deep learning semantic understanding and classification of student online public opinion for new media', Int. J. Information and Communication Technology, Vol. 25, No. 10, pp.62–76.
DOI: 10.1504/IJICT.2024.143335

A new approach to gear skiving, a specialized machining technique for producing internal gears, could improve the speed and accuracy with which gear teeth are formed. The work, described in the International Journal of Abrasive Technology, could be useful to industries reliant on high-precision gears, such as automotive and aerospace engineering.

Traditionally, manufacturing internal gears, whether spur gears, which have straight teeth, or helical gears, which have angled teeth, has been a complex, time-consuming process. Gear skiving, an advanced technique that uses a specialized cutting tool called a skiving cutter, has emerged as an effective solution.

Hiroki Yonezawa, Jun'ichi Kaneko, and Takeyuki Abe of Saitama University, and Naruhiro Irino, Yuta Shinba, and Yasuhiro Imabeppu of DMG MORI Co, Ltd., Japan, explain that unlike conventional machining methods, which often struggle with the precise geometry of gear teeth, gear skiving synchronizes the rotation of the workpiece with the motion of the cutter. This allows for a continuous and efficient cutting action. However, predicting the exact geometry of the tooth flanks, the surfaces that form the teeth of the gear, has long been a major challenge.

The new research introduces an innovative method for predicting the tooth profile after the skiving process is complete. The team analyses the shape of the material removed during machining, projecting the removal area from the perspective of the workpiece's "tooth lead" direction. The term "tooth lead" refers to the angle at which the teeth of the gear are shaped. This projection-based approach simplifies the analytical process, significantly reducing the computational resources needed to do the calculations when compared to conventional methods. The new approach focuses on the projection of the removal area, factoring in the rotation of the workpiece around its axis to estimate the final tooth profile more quickly and accurately than was previously possible.

The team explains that by providing more accurate and faster predictions of how gear teeth will form after the skiving process, manufacturers can improve the design of specialized tools such as profile crowning tools and chamfering tools. These tools are critical in ensuring the final gear has the desired geometry and performance characteristics for high-tech engineering applications. In addition, the same method can be used to assess the effects of tool wear or mounting errors on the gear's final tooth profile, allowing for better control over the production process.

Yonezawa, H., Kaneko, J., Abe, T., Irino, N., Shinba, Y. and Imabeppu, Y. (2024) 'Development of precision analysis method of tooth profile in gear skiving process with shape projection of removal area', Int. J. Abrasive Technology, Vol. 12, No. 5, pp.1–14.
DOI: 10.1504/IJAT.2024.144424

As we endeavour to address climate change issues, businesses must play an increasingly important role in reducing greenhouse gas emissions. A study in the International Journal of Business Performance Management, has looked at the net-zero emissions target and identified the difficulties businesses are encountering and suggests a tailored approach to solving the problems different sectors face.

Net-zero emissions mean balancing the amount of greenhouse gases – carbon dioxide and other compounds – a business releases with those it removes or offsets. Achieving a balance is part of a broader effort to curb climate change and lead us into a sustainable future. The study, by Luisa Huaccho Huatuco and Juan Ramon Candia of the University of York, Ruby Christine Mathew of York St John's University, and Graciela Zevallos Porles of the University of East Anglia, UK, included interviews with senior managers from various industries. The team found that while many organizations are taking steps towards net-zero, there are many obstacles in their way that are stymieing efforts in many instances.

The team found that businesses rely heavily on technological improvements and management changes as part of their strategy to reduce emissions. However, infrastructure limitations, a lack of government guidance, and insufficient funding are holding back the transition to a greener future. As such, targets are not being hit.

The team points out that classifying businesses according to their efforts can help identify the problems and perhaps help them in their greening efforts. For instance, most of the organizations referenced in the study were classified as 'opportunity-seeking,' meaning they view the transition to net-zero emissions as a chance to innovate and grow. By contrast, businesses in the agricultural sector were seen to be taking a 'conformance' approach. In other words, they were focused on meeting regulatory requirements rather than pursuing new opportunities through the transition. Fortunately, it seems that no businesses were actively resisting or delaying their net-zero efforts.

The bottom line seems to be that there is no off-the shelf approach to suit all types of business. In other words, the study suggests that businesses might benefit from more tailored support systems, with policies designed to address the unique needs of different industries.

Huaccho Huatuco, L., Candia, J.R., Mathew, R.C. and Zevallos Porles, G. (2025) 'Unravelling net zero practices, strategies and barriers among businesses in a UK region', Int. J. Business Performance Management, Vol. 26, No. 8, pp.1–22.
DOI: 10.1504/IJBPM.2025.144423

A study in the International Journal of Economics and Business Research has looked at the challenges facing one post-communist nation, Albania, in terms of its business start-up culture. The research investigates the role played by the country's business ecosystems but shows that while globally, start-ups are perceived as engines of economic growth, innovation, and job creation, Albania is lagging behind its European counterparts, particularly Estonia and Lithuania, in nurturing this part of its economy.

Valbona Mehmeti, Bajram Korsita, and Erisa Musabelli of the Aleksander Moisiu University of Durres in Albania, have compared the business environments of Albania, Estonia, and Lithuania, and specifically the relationship between each country's business ecosystem and the number of active start-ups. The researchers used time-series data, to rank ecosystem quality and innovation capacity, as well as the total number of active start-ups in each country. The findings reveal a strong, and perhaps not surprising, positive correlation between how conducive a country's business environment is to start-ups and the number of such active businesses.

Other post-communist nations such as Estonia and Lithuania have 1100 and 500 active startup companies per one million population. For Albania, the number is rather sobering. Albania hosts just 88 startups per million inhabitants. This enormous gap could be of great concern given the increasing importance of start-ups in driving economic growth globally. Indeed, the research suggests that this low level of startup activity is stifling economic development in Albania.

The researchers suggest that development could be nurtured by a more favourable business ecosystem, but this will take a lot of effort from government, policymakers, the extant businesses, and other stakeholder, perhaps even international partners. The point is that it is not simply about the raw data, but about the quality of start-ups. The study suggests that public institutions, the private sector, and society at large must now collaborate to create the right conditions for start-ups to flourish in Albania. Funding, supportive regulatory frameworks, and strong collaboration between universities, industry, and government are needed.

Mehmeti, V., Korsita, B. and Musabelli, E. (2025) 'Business ecosystems and development of start-ups in Albania: a correlational analysis', Int. J. Economics and Business Research, Vol. 29, No. 9, pp.1–12.
DOI: 10.1504/IJEBR.2025.144738

Family-run micro-enterprises in India rely on more than just business acumen for financial success, according to a study in the International Journal of Social and Humanistic Computing. Researchers have explored the key factors contributing to the success of such businesses and have found that social networks, financial literacy, and entrepreneurial ambition play important roles in driving financial performance.

S. Bharathithasan and K. Sakthi Srinivasan of VIT University in Tamil Nadu, India, looked at the interactions between these factors and found that entrepreneurs with the strongest social networks, including connections with family, friends, and their local community, were more likely to achieve financial stability. Financial stability in this context being represented by a steady income, manageable debt, and overall financial security. Such stability then allowed those business owners to make better decisions to drive their company's long-term growth.

The researchers suggest that the importance of social networks in this context cannot be overstated. In environments such as those in which many Indian micro-enterprises find themselves, resources are limited and competition is fierce. Social capital, an essentially intangible asset drawn from one's personal and business relationships, plays a vital role in allowing business owners to face the many challenges. Strong connections within the community provide entrepreneurs with access to financial resources, business advice, and emotional support. Moreover, family and community ties offer micro-business owners a distinct advantage, helping them to mitigate risks, adopt new technologies, and respond more readily to market changes.

While social networks are clearly important, the researchers also showed that financial intelligence plays an important part in the success of micro-businesses. Financial intelligence refers to the ability to manage money effectively in terms of budgeting, investing, and making informed financial decisions. That said, the study found that the importance of financial literacy was not in terms of greater long-term financial success directly but rather in working synergistically with social capital.

The team adds that entrepreneurs with obvious ambition are more likely to take calculated risks and make decisions that contribute to sustainable growth. However, it is the foundational support of family and community that sets them up to realise their ambitions.

Bharathithasan, S. and Srinivasan, K.S. (2024) 'Building your network, building your wealth', Int. J. Social and Humanistic Computing, Vol. 4, No. 3, pp.253–276.
DOI: 10.1504/IJSHC.2024.143669

Emoji are cartoon representations of human faces, animals, and various objects that were in some sense an extension of the text-character based representations known as emoticons or smileys. The term derives from Japanese – e "picture" + moji "character", so the "emo" is a happy, and ironic, coincidence.

Emoji have become an integral part of digital communication, especially in the age of social media, helping convey emotions and tone in text-based interactions that often lack the nuances of face-to-face conversation. However, research in the International Journal of Social and Humanistic Computing suggests that inconsistency in the use of emoji can lead to confusion, frustration, and negative reactions, particularly in the context of social media.

Emmanuel Adu-Mensah, Solomon Odei-Appiah, and Raphael Amponsah of the Ghana Institute of Management and Public Administration in Accra, Ghana, surveyed 400 users to see how exaggeration, misapplication, and excessive use of emoji might distort communication and provoke unintended emotional responses. The team used the Cognitive Dissonance Process Model (CDPM) and found that discrepancies between a sender's intended meaning and the recipient's interpretation of a given emoji can create a sense of psychological discomfort, known as cognitive dissonance.

Cognitive dissonance occurs when there is a mismatch between a person's attitudes or beliefs and their behaviour, leading to mental discomfort. In this case, when the use of emoji does not align with the message being conveyed, it triggers negative emotions such as frustration, confusion, and irritation. The study reveals that these emotional responses not only affect the quality of communication but also have the potential to affect detrimentally the relationships between sender and recipient. Emoji are often used as shorthand for expressing one's feelings, but the study shows that their overuse or misapplication can cloud the original message.

A common issue that can arise is when a given user imagines a certain meaning for a specific emoji that isn't the usual interpretation and their correspondent understands the emoji to mean something else. In the language of text messaging a similar issue arises with the use of LOL for instance, most people understand the meaning to be "laugh out loud", but others infamously took it erroneously to mean "lots of love". Similarly, in the world of emoji, a sender might that sharing the aubergine/eggplant, cucumber, banana, avocado, peach, or pineapple emoji that they are innocently discussing fruit and veg, whereas others would place an entirely NSFW (not safe for work) interpretation on the use of those emoji. Other examples of potentially confusing emoji are given in the footnote.

When things go wrong the "face with tears of joy" may well go all "sad face" and nobody will be "LOL" any more.

Adu-Mensah, E., Odei-Appiah, S. and Amponsah, R. (2024) 'When emojis go bad: emotional and cognitive concerns on their exaggeration, mis-application and excessive usage', Int. J. Social and Humanistic Computing, Vol. 4, No. 3, pp.277–306.
DOI: 10.1504/IJSHC.2024.143659

Footnote

More putatively confusing emoji

We could all get by with a little help from our friends, a new study on a novel networking protocol suggests. Research in the International Journal of Social and Humanistic Computing has looked at Friend-to-Friend (F2F) systems, which are decentralized networks that allow individuals to exchange computing power and storage. F2F systems are the kissing cousins of P2P, peer-to-peer networks that allow files to be shared. They allow resources, rather than simply digital entities (images, documents, video etc) to be shared without the need for a central server or any intermediaries.

The research by Pramod C. Mane of the Indian Institute of Management Rohtak in Haryana, India, highlights the role of network effects, known as externalities, in determining the flow of resources and how the formation of new connections between peers influences resource availability across the system.

For F2F systems, the value of a network increases as more participants join or interact within the system. In other words, each new friend has the potential to enhance the availability of shared resources. The research investigates both local and global network effects and the impact of new connections not only on the peers directly involved but also on other members of the broader network.

Mane has found that a crucial effect on the network is the distance between peers when a new link is formed. If the physical or logical distance between two connected peers is greater, so the local network effect, the benefits to friends, becomes weaker. Conversely, when peers are more closely connected, the best of friends one might say, there is more chance that the new link will positively impact the broader network, increasing the availability of resources to nearby participants.

However, it is worth noting that one can have too many friends. The density of the network, the number of active connections, has a complex relationship with resource availability. While adding more connections increases value it also introduces unpredictability. In highly dense networks, the creation of new links can negatively affect resource distribution. The research thus suggests that it is worth controlling network density or prioritizing connections that reduce the distance between peers. If no one told you life was going to be this way, keep your friends close, but your enemies closer to ensure the F2F system can continue to distribute resources reliably.

Mane, P.C. (2024) 'Network effects in friend-to-friend resource sharing network', Int. J. Social and Humanistic Computing, Vol. 4, No. 3, pp.232–252.
DOI: 10.1504/IJSHC.2024.143655

Research in the International Journal of Innovation and Learning has looked at the rapid transition to online learning at Hong Kong's tertiary institutions. The study sheds new light on the problems and opportunities presented by digital education and reveals that students from lower-income households face particular challenges.

Jessie Ming Sin Wong, William Ko Wai Tang, and Kam Cheong Li of Hong Kong Metropolitan University in Homantin surveyed 400 students in higher education to uncover what factors, such as access to technology, educator competency, learning environments, and privacy concerns, influenced the student experience.

One of the most striking findings is the disparity in the ability of different students to access and benefit from online learning. While most participants had the necessary devices, issues such as poor internet connectivity and disruptive home environments emerged as significant barriers to effective learning, particularly for students from lower-income families. These students were more likely to experience problems that hindered their academic performance. Many students noted that while they valued the flexibility of online classes, they struggled to maintain focus without the structure of in-person teaching. Social interaction, a key component of traditional classrooms, was another missing element that students cited as negatively impacting their overall learning experience.

Nevertheless, the team found that most students surveyed found that their instructors were reasonably proficient with the digital tools required for online teaching. Concerns about privacy were, however, often mentioned. Students expressed unease about the use of webcams and the security of their online interactions, particularly regarding data privacy. This finding underscores the need for institutions to not only address educational quality but also ensure that student privacy is protected.

The team suggests that a hybrid or agile-blended learning model, one that combines online education with in-person sessions, would give students more balance in their learning. This approach would allow universities to take advantage of the flexibility of online learning while also providing face-to-face interaction.

Wong, J.M.S., Tang, W.K.W. and Li, K.C. (2025) 'Digital transformation in higher education: tertiary students' perspectives on online learning and its implications for the future', Int. J. Innovation and Learning, Vol. 37, No. 5, pp.1-18.
DOI: 10.1504/IJIL.2025.144600

A new tool known as abnormalest has been developed to help researchers more easily identify unusual financial patterns, a task that plays an important role in both accounting and social science research. By automating the estimation of unusual accruals, the tool, discussed in the International Journal of Data Analysis Techniques and Strategies, could overcome the inefficiencies of traditional methods, which are often time-consuming and error-prone.

Francesca Rossignoli and Nicola Tommasi of the University of Verona, Italy, explain that abnormal accruals refer to the discrepancies between what is reported in a financial statement and what the actual finances are. Such discrepancies can be indicative of manipulation, where managers alter financial reports for personal benefit. The conventional approach to estimating abnormal accruals is a complex process involving manual calculations, the selection of control samples, and the application of specific conditions to detect the problems. abnormalest has been developed to automate the key steps.

The tool can select appropriate control samples, carry out entropy balance pre-processing, and then use predictive models such as regression-based techniques to estimate abnormal accruals. This kind of automation is much faster than manual approaches and makes far fewer mistakes.

The abnormalest system provides a more detailed output than traditional methods. It includes valuable information such as the abnormal accrual measure, degrees of freedom, and explanations for any estimation failures. This is information that conventional approaches cannot easily provide.

Although originally designed with accounting research in mind, abnormalest might also be used in the social sciences. Its flexibility allows it to be used in a variety of contexts, from identifying fraud to assessing unusual business performance or examining behaviour that deviates from the norm.

The researchers have successfully tested the system using real-world financial datasets. They found that it performs better than existing models used in academic research.

Rossignoli, F. and Tommasi, N. (2025) 'Abnormal accrual estimation: an automation data analysis technique', Int. J. Data Analysis Techniques and Strategies, Vol. 17, No. 5, pp.1–18.
DOI: 10.1504/IJDATS.2025.144576

Research published in the International Journal of Services and Standards has looked at the factors that driving brand loyalty around "green" products in Vietnam. The work by Truong Thi Hue and Pham Thi Thanh Hang of the Vietnam National University in Hanoi, and Tran Anh Phuong of Hanoi University of Industry, Vietnam, surveyed hundreds of consumers in depth and offers several insights into what makes consumers stick to such environmentally friendly electronic products. Given that environmental concerns play an important role in shaping consumer choices, the research highlights how businesses might tap into the growing demand for sustainable products.

The team explains that their research finds an important factor in consumer buying decisions – green perceived value. This is a measure of the benefits consumers believe they gain from a product's environmental attributes. Indeed, this is the most important driver of purchasing decisions followed by "green trust," or the confidence consumers place in a brand's environmental claims.

Green perceived value and green trust function synergistically and can even outweigh conventional brand image and so change consumer behaviour. In other words, the researchers suggest, consumers are more likely to remain loyal to a green brand if they believe the product offers real value and if they trust that the company is genuinely committed to sustainability.

The findings are particularly poignant for businesses in the electronics sector like electronics, where consumers often place high importance on product quality and reliability. If environmental credibility is now becoming an important factor in buying decisions, those companies need to move quickly if they are to benefit. Those that do not adapt to the green demands of consumers will inevitably be left behind. Quality and brand remain important but sustainability and green credibility are overtaking those as more important for the modern consumer.

Hue, T.T., Phuong, T.A. and Hang, P.T.T. (2024) 'A combined approach to explore the drivers of green brand loyalty', Int. J. Services and Standards, Vol. 14, No. 3, pp.268–289.
DOI: 10.1504/IJSS.2024.143434

The healthcare sector is increasingly turning to innovative solutions to meet the needs of an ageing population. A new framework based on Fog-to-Cloud (F2C) computing, promises to revolutionize healthcare for older people by enabling real-time, remote monitoring of health metrics while addressing concerns surrounding data privacy and security.

Writing in the International Journal of Medical Engineering and Informatics, Hafida Saidi and Nabila Labraoui of the University of Abou Bekr Belkaid in Chetouane Tlemcen, Algeria, and Ado Adamou Abba Ari of the University Paris-Saclay in Versailles, France, explain that healthcare for seniors has conventionally involved direct visits to or from healthcare providers. Regular checkups, tests, and diagnoses have usually been done face-to-face. However, with a growing number of older people with a range of health problems, this model is becoming unsustainable. Innovations such as the medical equivalent of the Internet of Things (IoT), the Internet of Medical Things (IoMT) could change all that.

Smart medical devices, sensors, and health applications connected through the internet offer the possibility of remote monitoring. This would allow physicians and other healthcare providers to track their patients' conditions from remotely, as well as potentially prescribing and administering treatments.

While this technology has the potential to reduce clinic and hospital visits and improve access to healthcare overall, it comes with significant challenges around the safeguarding of sensitive medical data.

The new framework address these concerns by combining the strengths of cloud and fog computing. Fog computing, which places computational power closer to the source of data (such as at the patient's home or nearby healthcare facilities), reduces the amount of data that needs to be transmitted to distant cloud servers. This allows for faster processing times and reduces the strain on networks, which is crucial for real-time applications like health monitoring. The proposed Fog-to-Cloud computing builds on this idea by enhancing storage capabilities, minimizing network traffic, and lowering latency, that are safe from hackers, data breaches, and inadvertent access by third parties within healthcare.

Saidi, H., Labraoui, N. and Ari, A.A.A. (2025) 'A secure health monitoring system based on fog to cloud computing', Int. J. Medical Engineering and Informatics, Vol. 17, No. 1, pp.30–43.
DOI: 10.1504/IJMEI.2025.143283

As social media becomes increasingly a part of marketing strategies, businesses are investing heavily in the platforms to reflect the ability to reach and engage with target audiences. However, according to work in the International Journal of Internet Marketing and Advertising one aspect of social media marketing remains relatively underexplored, the impact on building long-term brand loyalty.

Sohail Ahmad and Li Liang of Southwest Jiaotong University in Chengdu, China, Ahmad Iqbal of The Islamia University of Bahawalpur, and Irshad Hussain Sarki of the National College of Business Administration and Economics both in Pakistan, have considered this gap in our knowledge and focused on the role of community engagement as a mediating factor in the development of brand loyalty through social media. By examining how consumer participation in online communities influences loyalty, the team shows how companies might better improve engagement and increase loyalty through judicious choices surrounding digital channels.

The concept of brand loyalty can be central to the success of any business. Loyal customers are more likely to make repeat purchases as well as advocating for the brand. Marketers have long recognized the importance of loyalty, but fostering such loyalty has become more complex in the age of social media. The study shows that while social media marketing activities can directly influence brand loyalty, this influence is most effective when mediated by active engagement within online communities. The findings were built on three key theoretical frameworks: Stimulus-Organism-Response theory, Service-Dominant logic, and the concept of privacy calculus. S-D logic has perhaps the greatest relevance in showing how the collaborative nature of value creation, where brands and consumers co-create value through interactions, affects loyalty.

Fundamentally, consumers who engage more with brands and other community members on social media are more likely to feel a stronger connection to the brand, which leads to greater loyalty over time.

Ahmad, S., Liang, L., Iqbal, A. and Sarki, I.H. (2025) 'Beyond likes and shares: the secret to building stronger brands on social media from a privacy calculus perspective', Int. J. Internet Marketing and Advertising, Vol. 22, No. 1, pp.72–97.
DOI: 10.1504/IJIMA.2025.144205

As the number of digital resources expands and expands it becomes increasingly difficult to recommend reading matter. Research in the International Journal of Information and Communication Technology has led to a new artificial intelligence (AI) system that improve on precision and variety of book recommendations for online library goers. The new approach blends two established techniques, content-based filtering (CBF) and collaborative filtering (CF), and then has its roots in an advanced machine learning algorithm – Extreme Learning Machine (ELM) – which allows it to come up with the perfect personalized recommendation for the reader.

In traditional recommendation systems, Tianhao Wu of Changchun University of Technology, China explains, content-based filtering suggests books based on a book's attributes, such as its title, author, and genre. Collaborative filtering by contrast makes recommendations based on user behaviour, what books they have read previously and how they rated them. By combining both systems with ELM, the new hybrid model aims to improve the accuracy of suggestions while also increasing their diversity, better reflecting a user's unique preferences and opening them up to new books they may not have encountered otherwise but will hopefully enjoy.

ELM can process large datasets quickly and efficiently, which is particularly useful in the context of online libraries, where both the number of books and user interactions can be immense. Unlike traditional neural networks, ELM reduces the complexity of training by randomly generating weights for each entry. This allows it to adapt to new data much more quickly than other approaches and with greater accuracy.

As digital libraries continue to grow, this new hybrid system holds the potential to transform how books are recommended to users, making their library experiences more personalized and efficient. The team will attempt to address remaining challenges such as the cold-start problem facing new users about which there is initially no reading experience data and similarly with new books from new authors that equally lack a data history.

Wu, T. (2025) 'An ELM-based approach to promoting reading of library books', Int. J. Information and Communication Technology, Vol. 26, No. 2, pp.82–95.
DOI: 10.1504/IJICT.2025.144057

Small and medium-sized enterprises (SMEs) play an important role in the economy of the United Arab Emirates. Indeed, SMEs represent 94% of businesses and employ 86% of the workforce. When the COVID-19 pandemic struck, these businesses faced numerous challenges, including closures, financial instability, and disruptions to supply chains. A study in the International Journal of Entrepreneurship and Small Business has looked at how those SMEs addressed the issues and the role new media technologies played in how they were able to adapt and recover from the pandemic.

Bharti Pandya, Shreesha Mairaru, Asma Buhannad, and Leena Daroo of the Higher Colleges of Technology, Abu Dhabi, United Arab Emirates, highlight the immediate impact the pandemic had and how widespread this was as well as looking at the significant longer-term consequences for many SMEs. The team explains that these businesses were forced to adjust their operations quickly in order to survive. The team showed that social media, e-commerce platforms, and other digital tools were critical in this, helping businesses shift their strategies during the crisis. Once lockdowns and social distancing measures had begun to limit traditional business practices, those technologies allowed businesses to continue reaching customers, marketing their products, and managing operations remotely.

However, although new media technologies were important in the short term, the study shows that they led to longer-term changes in SMEs. Once these digital tools were integrated into the core business functions of those companies, they became critical to sustaining competitiveness as the economy changed after the pandemic. The researchers suggest that digital adoption was not simply a sticking plaster for the pandemic times, but a necessary treatment for the ongoing health of SMEs. Indeed, they explain that the integration of digital tools into communication and customer outreach have helped sustain growth beyond the initial crisis.

Of course, some SMEs needed different digital strategies and not all were able to adapt and survive. Response depended on sector and company size, with some SMEs needing more time and resources to adopt the digital tools and others finding that there were no platforms to meet their specific needs. The team concludes that any business needs to consider which digital tools are best suited to its objectives and resources carefully, and particularly in responding to a global crisis.

Pandya, B., Mairaru, S., Buhannad, A. and Daroo, L. (2025) 'New media technologies and small and medium enterprises: evidence from the COVID-19 period', Int. J. Entrepreneurship and Small Business, Vol. 54, No. 3, pp.403–422.
DOI: 10.1504/IJESB.2025.144230

Research in the International Journal of Computational Systems Engineering has looked at the challenges facing online education systems in terms of improving efficiency and avoiding redundancy in cloud computing platforms. Muchao Zhang of Nanjing Xiaozhuang University, China, offers a new approach to integrating educational data from various sources, models, and formats, all with the aim of improving cloud the efficiency.

Zhang shows how cloud platforms, known for their scalability, flexibility, and security, have already become an essential component of online education. However, the diverse nature of educational data, video lectures and digital textbooks, for instance, creates problems. Different data formats and structures can lead to redundancy, confusion, and inefficient allocation of computing resources. This then reduces the potential for the educational content to be as streamlined as possible.

To address this, Zhang has developed an approach that combines algorithms to help integrate the disparate data types. The various algorithms can each resolve a different issue associated with data integration. For example, the PMI-Simhash algorithm helps identify similarities between data sets, the BSM model aids in classifying the information more accurately, and the US-EM algorithm improves the matching of entities across different systems without needing manual intervention. The result is an integrated approach that minimizes redundancy and ensures that educational resources are much better organized than they otherwise would be.

Zhang has how these algorithms can work together using an online painting course for art students. The approach merges multimodal data, text, images, and video, and proved highly effective in terms of accuracy, speed, and resource usage. By improving the accuracy of data matching, Zhang's approach could ensure that students access the right resources at the right time, improving both the learning experience and resource management.

Zhang, M. (2025) 'Online education resource integration method for painting teaching of art majors based on cloud platform', Int. J. Computational Systems Engineering, Vol. 9, No. 5, pp.1–10.
DOI: 10.1504/IJCSYSE.2025.144358

A team from India, Netherlands, Poland, and Switzerland has looked at how to improve data analysis and to reduce the inherent bias in social network analysis. Writing in the International Journal of Applied Management Science, the researchers recognise that in quantitative surveys and social network analysis, the accuracy of data can often be skewed by biases in how respondents answer the questions. One particular form of bias, known as declarative bias, poses a significant threat to the reliability of survey results, particularly when addressing complex social issues.

Declarative bias occurs when survey participants, consciously or unconsciously, provide answers influenced by social expectations, fatigue, or external pressures rather than reflecting their true attitudes or beliefs. This type of bias is particularly problematic when the research seeks to inform public policy, as it can lead to misleading conclusions about society's attitudes and behaviour and thus inappropriate policies.

Response time testing could offer an answer. The assumption is that a more immediate response tends to reflect a stronger, more internalized opinion, while a slower response may reflect uncertainty or a response swayed by external factors, such as social desirability or reading into the questions themselves to work out what the right answer might be. By distinguishing between these types of responses, the researchers suggest that it might be possible to segregate strong answers from the flimsy.

They tested their approach on an international survey conducted in Spain and Sweden to explore attitudes toward the COVID-19 pandemic. Their results were striking. By homing in on high-confidence, fast responses, the team could see a much greater diversity of opinion. By contrast, a conventional analysis, where declarative bias was present, showed much more homogeneous opinions.

The findings have implications for public policy and health interventions based on surveys of the public or stakeholders on a given topic. For instance, public health policies based on the assumption of uniform public opinion on issues such as the pandemic might fail to address the subtleties of diverse opinions from different groups. By reducing declarative bias in the analysis of surveys, it should be possible to form policy that takes into account diverse opinions and needs.

Fernandez, G.P., Norré, B.F., Reykowska, D., Dutta, K., Nguyen-Phuong-Mai, M., Fernandez, J. and Ohme, R. (2024) 'Social network of confident attitudes with response time testing', Int. J. Applied Management Science, Vol. 16, No. 5, pp.1–31.
DOI: 10.1504/IJAMS.2024.144419

Research in the International Journal of Shipping and Transport Logistics has looked at the environmental sustainability of the Yangtze River Economic Belt (YREB) and raises important points about the region's ability to balance rapid economic growth with ecological preservation. Zhimei Lei, Shanshan Cai and Shaoxin Zhuo of Kunming University of Science and Technology, Yui-yip Lau of The Hong Kong Polytechnic University, and Ming Kim Lim of the University of Glasgow, UK, explain that the YREB encompasses eleven provinces and cities. The region thus plays a pivotal role in the national economy of China. However, its development has often been marred by significant environmental challenges, such as pollution, resource depletion, and ecological degradation.

The team examined almost two decades of data on sustainability levels across the YREB, using an innovative evaluation framework and a "pressure-state-response" (PSR) model. This latter tool allowed the team to link environmental pressures to the condition of the environment and the responses to the problems set in motion by policymakers. As such, the work integrates both qualitative indicators, such as government policies and key speeches, and quantitative data, making it particularly well-suited for the complex realities of the YREB.

Improving environmental sustainability over the study period could be seen in the data with the middle and upper regions of the YREB showing the most progress. However, the research also showed that there are persistent regional disparities. The lower regions of the YREB, in particular, lag behind in terms of environmental sustainability, which could have long-term implications for the overall ecological health of the area. Moreover, despite some obvious progress, there is no clear improvement in sustainability levels even between neighbouring provinces.

This, the researchers suggest, implies that effective collaboration across the YREB is not occurring. The team explains the disparities as perhaps being due to a combination of intra-regional and inter-regional factors: levels of industrialization, policy implementation approaches, and investment in green technologies. The implication is that there is a pressing need for more coordinated action between the YREB's provinces and cities. The team adds that the creation of a platform for sharing environmental data and research could be used to improve governance and decision-making across the whole region.

Lei, Z., Cai, S., Zhuo, S., Lau, Y-y. and Lim, M.K. (2024) 'Analysis of the differences and spatial-temporal dynamic evolution of the environmental sustainability of the Yangtze River Economic Belt in China', Int. J. Shipping and Transport Logistics, Vol. 19, No. 5, pp.1–41.
DOI: 10.1504/IJSTL.2024.144404

Research in the International Journal of Economics and Business Research has looked at the relationship between employee empowerment and job satisfaction, with a particular focus on the banking sector in Greece. As digital technologies reshape the modern workplace, are traditional concepts of empowerment being put to the test, the study asks. George Papageorgiou, Kyriakos Christofi, Aikaterini Gelinou, Andreas Efstathiades, and Elena Tsappi of the European University Cyprus in Nicosia, Cyprus, found which strategies can boost job satisfaction in an increasingly digitalized environment and offer managers insights for navigating this transformation.

The team identified four important empowerment practices that apparently contribute positively to an employee's level of job satisfaction. First, a well-defined organizational mission, combined with performance-based rewards, strengthens how much the employee aligns themselves in a positive way with company goals, thus giving them more of a sense of purpose. Secondly, organisations that allow employees a degree of autonomy in decision-making gives them a sense of so-called ownership over their role. This too increases engagement and involvement in the organisation's success. Thirdly, by delegating certain managerial responsibilities to lower-level employees, an organisation can promote a sense of trust and accountability even in more junior employees. Finally, effective communication between departments ensures that employees feel informed and supported by the organisation and their colleagues above and below them in the hierarchy.

However, the team also found that problems can arise when there is excessive standardization. While consistency and efficiency are important to success within an organisation, overly rigid structures can stymie initiative and limit career growth opportunities. The team suggests that as workplaces become more digitalized, organisations must find the right balance between structured processes and allow sufficient flexibility to encourage innovation and employee development.

The team adds that job-enrichment strategies, such as decentralization, team-based collaboration, and the use of digital tools, can boost engagement and job satisfaction. Specifically, with regard to the latter, technologies that allow flexible work arrangements and facilitate communication across different locations can improve engagement and satisfaction.

Papageorgiou, G., Christofi, K., Gelinou, A., Efstathiades, A. and Tsappi, E. (2025) 'Employee empowerment and job satisfaction in the evolving digital banking workplace', Int. J. Economics and Business Research, Vol. 29, No. 8, pp.41-60.
DOI: 10.1504/IJEBR.2025.144288

Electronic waste, including PCBs, is a rapidly growing problem as consumers endlessly replace their electronic gadgets. Regulations can go so far to nudge this waste into a recycling stream, but there is still the pressing need for the technology to process the waste.

The retrieval and extraction of useful metals from electronic waste will be a critical part of creating a sustainable future if that is to be technology led. Many metals are relatively rare or found only in geopolitically sensitive regions of the world. More to the point, we have tonnes of discarded devices, circuit boards, and wiring sitting in recycling dumps and landfills. If there were a simple way to extract metals, such as copper, from these resources, that use less energy and fewer resources than mining the ores, then that would offer us a more environmentally friendly option to sourcing copper.

Jayashree Mohanty, Puspita Biswal, Subhashree Subhasmita Mishra, and Tamasa Rani Das Mohapatra of the C.V. Raman Global University in Bhubaneswar, Odisha, India, have now demonstrated an approach to extracting copper from printed circuit boards that does not require the PCBs to be dismantled. Their approach, reported in the International Journal of Environmental Engineering, uses pieces of chopped up PCBs as one electrode in an acidic solution, the electrolyte, with the other electrode is a stainless steel plate. By passing an electric current through the electrodes and the solution it is possible to dissolve the copper as positive ions into the solution. The current then drives these ions towards the negative electrode, the steel plate, where they are deposited as metallic copper. This copper plating can be readily removed from the steel electrode.

This simplified electrochemical copper extraction process avoids the usually energy-intensive mechanical shredding or chemical leaching process used in recycling and so uses less energy overall as well as minimizing processing waste and chemical pollutants. It thus has the potential to extract copper from the electrical waste stream much more effectively than was previously possible.

The team add a not-so-secret sauce to their copper extraction recipe, a salt called sodium sulfate. This substance, added to the electrolyte, buffers the solution and at a certain concentration improves the current density and efficiency increasing the amount of copper dissolved from the PCBs and deposited on to the steel cathode. The researchers found that a concentration of 0.03 molar sodium sulfate gave them the highest current efficiency, at 77%, However, the highest copper purity (99%) was obtained at 0.02 molar. There will thus be a compromise in process efficiency and retrieval rates using this additive.

Mohanty, J., Biswal, P., Mishra, S.S. and Mohapatra, T.R.D. (2025) 'Electrochemical recovery of copper from the waste computer printed circuit board', Int. J. Environmental Engineering, Vol. 13, No. 1, pp.1–11.
DOI: 10.1504/IJEE.2025.143562

Research in the International Journal of Information and Communication Technology has examined the relationship between local government debt and economic growth. Lian Pan of Hunan International Economics University in Hunan, China, used the Panel Smooth Transition Regression (PSTR) model to analyse data in combination with a federated learning data enhancement algorithm. Pan could thus explore how different economic structures influence the effects of borrowing. The findings suggest that while local government debt can support growth, its impact depends on the structure of the local economy. This raises important questions for policymakers.

One of the findings from the research is that industrial composition can shape the outcomes of government borrowing. In areas with well-established industries, debt-financed investment can contribute to economic expansion. However, in less diversified economies, the benefits are less obvious. Indeed, debt may place additional strain on financial resources. The research indicates that simply managing the level of debt is not enough, it is equally as important to define clearly the allocation of borrowed funds.

The findings come at a time when many local governments are facing increasing financial pressures. Economic shifts, rising borrowing costs, and "changing revenue structures" have made fiscal planning even more complex than it was ever before. Some authorities, facing shortfalls, turn to less sustainable sources of revenue, such as land sales or off-budget financing. The study highlights the risks associated with such approaches and stresses the need for greater transparency and more structured debt management practices.

It is worth noting, that the use of federated learning, a machine learning method, has allowed for more precise analysis while maintaining data privacy. By integrating this approach with the PSTR model, Pan's work has enhanced our ability to assess financial relationships without exposing sensitive information. The method could be further refined through vertical federated learning. This would account for variations in the data distribution across different regions. Addressing these differences could improve the accuracy of economic models and their application to policymaking.

Pan, L. (2024) 'Correlation analysis between local government debt and economic growth combined with PSTR model', Int. J. Information and Communication Technology, Vol. 25, No. 9, pp.22–42.
DOI: 10.1504/IJICT.2024.143319

Facial emotion recognition could have broad applications across healthcare, education, marketing, transportation, and entertainment. It might be used to help monitor patients remotely or in over-stretched hospitals or emergency response settings, or patients unable to communicate well for any number of reasons. It could be used to personalize learning, allowing a computerised training system to respond more appropriately to the user. Similarly, such a system could improve customer service and might even be used to create immersive entertainment experiences.

Computer systems that can identify emotions from our facial expressions are in development, but still face man challenges. The earliest systems relied on a single method, such as mapping a person's face and matching it to a database of annotated expressions. Some approaches based on this simplified method are more accurate than others, but none yet captures all the nuance of human emotion as it is expressed in our faces.

Research in the International Journal of Biometrics introduces a new approach based on machine learning that could address this problem and make an emotion detector viable for a wide range of applications. The biggest issue that is addressed by the new work is that it can extract a complex emotion from real-world situations where environmental factors, incomplete data, or complex emotions might affect the accuracy of the results. However, the new approach brings together facial expression recognition and uses the person's speech and tone of voice or even what they might be writing to give a more accurate result.

In their experiments, researchers Jian Xie and Dan Chu of Fuyang Normal University in Anhui, China, achieved a recognition accuracy of 98.6% with their approach. The system was particularly adept at identifying happiness or a neutral emotional state when compared with earlier systems. The system could not cope quite as well with the identification of disgust and surprise, however.

Xie, J. and Chu, D. (2025) 'Character emotion recognition algorithm in small sample video based on multimodal feature fusion', Int. J. Biometrics, Vol. 17, Nos. 1/2, pp.1–14.
DOI: 10.1504/IJBM.2025.143720

In an evolving job market shaped by technological disruption and changing industry demands, there is a pressing demands to ensure that higher education aligns with workforce needs. Research in the International Journal of Information and Communication Technology introduces a predictive model designed to address this issue. It offers an adaptable approach to talent demand forecasting and job matching. By integrating artificial intelligence (AI) with structured data analysis, the work of Xiaoli Mei of Jiangxi University of Technology in Jiangxi, China, offers an approach that could help educators, employers, and policymakers respond to labour market trends.

Mei's work builds a knowledge graph, a structured representation of information, to organize and integrate vast amounts of data from online recruitment platforms. The new approach uses graph neural networks to spot relationships between various factors in the job market. This should improve understanding of the relationships between job requirements, candidate qualifications, and industry trends. This new model can process complex employment patterns with greater precision than earlier manual methods. Those earlier methods were limited to relying on rigid keyword-based systems that might overlook the broader context of job descriptions and skill requirements.

The new model is armed with high fault tolerance, which means it is effective even when dealing with incomplete or inconsistent data. This will be invaluable in real-world applications, where missing or ambiguous information is common. By maintaining strong performance despite data gaps, the system offers a more reliable tool for workforce planning, recruitment, and career guidance.

Ultimately, the research could help close the gap between higher education supply and employment demand. There is thus the potential to train undergraduates, particularly on more vocational courses, who might then be better prepared for industry roles. Policymakers will benefit from the research, as it will allow them to spot emerging skill demands and workforce trends, governments might then develop targeted labour market policies to address shortages in specific sectors. Additionally, jobseekers themselves might gain from more intelligent job recommendations, which will hopefully lead to better employment outcomes and reduced mismatches between their qualifications and the available jobs.

Mei, X. (2024) 'Prediction of talent demand and job matching based on knowledge graph and attention mechanisms', Int. J. Information and Communication Technology, Vol. 25, No. 9, pp.76–87.
DOI: 10.1504/IJICT.2024.143327

A study in the International Journal of Business Performance Management has looked closely at how digital marketing strategies have influenced business performance in Laos, especially among small and medium-sized enterprises (SMEs). The research focuses on tools such as online advertising, social media marketing, content marketing, and mobile marketing.

Viengsavang Thipphavong and Xayphone Kongmanila of the National University of Laos in Vientiane, Laos, used a structural equation model (Smart PLS4) to analyse their data and found that online advertising has a clear impact on both financial and operational performance. Social media marketing, on the other hand, had an broader influence as it positively affects financial performance, operational efficiency, and a company's IT capabilities.

The study showed that content marketing was linked primarily to improvements in the companies' IT infrastructure, while mobile marketing, while beneficial to operational and IT performance, did not directly impact financial outcomes. This has implications for smaller companies that might do better to not invest too heavily in the kind of digital tools that will not help them generate greater profits.

The researchers suggest that businesses in Laos, SMEs in particular, should focus on using online advertising and digital marketing tools to improve their financial and operational performance. They add that government might play a role too by improving digital infrastructure, supporting online marketing education, and encouraging the growth of e-commerce. Such steps would, the team suggests, create a more favourable environment for businesses to adopt digital marketing strategies and enhance their overall performance.

As digital tools become more accessible, companies in emerging markets such as Laos are increasingly able to reach wider audiences and streamline operations without incurring significant marketing costs. For Laos, where internet penetration and digital adoption are yet to mature, this presents a clear opportunity. As more people access the mobile internet, businesses have the potential to expand their customer base and improve operational efficiency with relatively modest investment.

Thipphavong, V. and Kongmanila, X. (2025) 'The impact of digital marketing on the business performance of firms In Laos', Int. J. Business Performance Management, Vol. 26, No. 7, pp.1–22.
DOI: 10.1504/IJBPM.2025.144089

An examination of Vietnam's financial sector for the period 1990 to 2022 provides empirical evidence of the relationship between banking development, trade openness, inflation, and economic growth. The findings, published in the International Journal of Economics and Business Research, suggest that a well-functioning banking system plays an important role in supporting economic activity. They also highlight some of the challenges facing developing nations associated with financial sector expansion in a globalized economy.

Thao Huong Phan and Thao Viet Tran of Thuongmai University and Trang Mai Tran of the Vietnam Academy of Social Sciences, in Ha Noi, Vietnam, discuss how Vietnam's banking sector remains the dominant channel for capital allocation, given the relatively underdeveloped nature of its financial markets. Banks provide credit to businesses and individuals, facilitating investment and economic activity. Their research found a positive relationship between banking sector growth and economic expansion, both in the short and long term.

Trade openness, defined as the extent to which an economy engages in international trade, has previously been linked to economic growth. By participating in global markets, businesses gain access to new customers, technologies, and competitive pressures that can improve their overall productivity and their bottom line.

Of course, this kind of international exposure also comes with risks, particularly if domestic financial institutions are not well-equipped to manage the inevitable external shocks. The researchers suggest that Vietnam's banking sector needs to strengthen its ability to address such problems through improved risk management and regulatory oversight.

Inflation, another key factor in economic stability, also plays a role in financial sector performance. While moderate inflation can signal a growing economy, excessive inflation undermines purchasing power and creates uncertainty for investors. The study suggests that sound monetary policy, including responsible credit expansion and liquidity management, will also be important in ensuring financial stability.

As Vietnam continues to integrate into the global economy, its financial sector will need to adapt to new demands. Strengthening banking regulations, enhancing risk management practices, and ensuring adequate liquidity controls will be important in maintaining financial stability, the work suggests.

Phan, T.H., Tran, T.V. and Tran, T.M. (2025) 'Banking development contributes to economic growth and inflation control in Vietnam', Int. J. Economics and Business Research, Vol. 29, No. 7, pp.1-16.
DOI: 10.1504/IJEBR.2025.144102

Research in the International Journal of Automotive Technology and Management has looked at digital transformation in the German and Japanese automotive industries. The study highlights key differences in how companies in each country have adopted digital technology.

Martin Schröder of Ritsumeikan University in Osaka, Takefumi Mokudai of Kyushu University in Fukuoka, Japan, and Hajo Holst of the University of Osnabrück, Germany, explain how digital transformation in the automotive industry is an ongoing process. It is encompassing a range of technological developments, including automation, smart manufacturing, mobility-as-a-service (MaaS), and the broader shift towards new business models.

One might talk of "Industry 4.0" as being the state-of-the-art where the emphasis is on automation and data exchange in manufacturing technologies. It is this that has been particularly influential in shaping how companies innovate and adapt and how they make the most of new opportunities.

The researchers found some notable distinctions between German and Japanese companies and their approach digitalization. German companies tend to adopt top-down, systematic approaches, implementing digital technologies across entire production lines. This, the team explains, is done in order to optimize manufacturing processes. In contrast, Japanese firms take a bottom-up approach, integrating digital tools incrementally into existing systems. This, the research suggests has led to "island solutions," or individual digital enhancements that are not necessarily integrated fully.

Nevertheless, firms in Germany and Japan are both evolving. Japanese firms are adopting more comprehensive and systematic digitalization models. While their German counterparts are increasingly focusing more on operational flexibility, reducing downtime, and improving product quality, rather than simply pursuing extensive automation. The changes reflect a broader shift in the automotive sector, as companies in both countries adapt to the challenges posed by digital technologies, the transition to electric vehicles, for instance.

Schröder, M., Mokudai, T. and Holst, H. (2024) 'Industry 4.0 and lean augmentation? Digital transformation in the German and Japanese automotive industry', Int. J. Automotive Technology and Management, Vol. 24, No. 6, pp.1–27.
DOI: 10.1504/IJATM.2024.144148

A lot has been said about the tragic, and ongoing outcomes of the COVID-19 pandemic. There has also been much discussion about the economic impact and how the pandemic led to a dramatic shift in work culture for many people. Remote working and working-from-home, while having been part of many people's day-to-day routines for decades, emerged more obviously for others from the emergency measures such as lockdowns and quarantines.

Research in the International Journal of Business Performance Management discusses how what began as a response to health and safety concerns for many people has since become the norm and an essential component of modern work structures for many organisations. Simanchala Das, Sanam Jaswanth, Nethi Sandhya, Ponnada Satya Sumanth, and Pattem Gayathri of the KL Business School at the Koneru Lakshmaiah Education Foundation in Andhra Pradesh, India, point out that while remote work and working-from-home offer many advantages for lots of workers they also present challenges that organisations must address to maintain both productivity and employee well-being.

For many workers, the benefits of working-from-home are obvious. The flexibility to manage one's own schedule and work environment has contributed to an improved work-life balance for so many people. Moreover, without the need to commute, employees can save time and reduce stress, factors which have been linked to increased job satisfaction. Remote work offers autonomy, allowing employees to structure their day around personal priorities within limits, and this has led to greater perceived control over their work.

Employers have recognized many advantages, including reduced overheads associated with reduced facilities and utilities needs. Remote work also opens up the possibility of hiring talent beyond the local area, increasing access to a more diverse pool of candidates.

However, the widespread adoption of working-from-home has given rise to several challenges, particularly concerning employee well-being. Isolation is a recurring issue, with many remote workers reporting feelings of loneliness and a lack of connection to their colleagues. The absence of casual, in-person interactions, can make it harder to maintain team cohesion and effective communication. This lack of face-to-face contact can hinder collaboration and may reduce creativity and innovation, which thrive in environments where ideas can be shared informally. Additionally, there are suspicions among employers and industry leaders that staff working-from-home might in some ways lead to lower productivity without the pressure of one's boss keeping a weather eye on an employee's work in the office, for instance.

In response to challenges associated with well-being and mental health, many organisations are recognizing the importance of creating a supportive work culture in a remote setting. This includes not only providing the necessary digital tools to facilitate communication and productivity but also fostering an environment where employees feel connected and valued. Regular virtual check-ins, team-building exercises, and informal conversations are some of the strategies that can help mitigate the sense of isolation many remote workers experience.

However, if there is a shift in emphasis to outcomes rather than hours worked, then employee and employer can benefit greatly, it seems. A results-oriented approach allows businesses to strike a balance between offering flexibility to employees while ensuring that the goals of the organisation are still being met.

Das, S., Jaswanth, S., Sandhya, N., Sumanth, P.S. and Gayathri, P. (2025) 'Active and passive links between work from home and employee well-being: a post-COVID performance perspective', Int. J. Business Performance Management, Vol. 26, No. 1, pp.46–58.
DOI: 10.1504/IJBPM.2025.143644

The COVID-19 pandemic left few facets of life untouched tragically in so many cases. It also had a major impact on economics and shopping habits in particular. While e-commerce emerged at a time when the children of the Baby Boomer generation, Gen X, were first logging on, before the Millennials ever had a bank card and before Gen Z was even born, perhaps even before silver surfers were to be minted, it became the domain of the younger tech-savvy users. See footnote for generational definitions.

As the pandemic hit, Gen X and the Baby Boomers, many of whom had opted out after the dot-com bubble burst, found themselves opting back in out of necessity especially as online pharmaceutical platforms became de rigueur for dealing with the aches and ailments of the ageing internet players.

A study in the International Journal of Business Information Systems has looked closely at specific elements that inspire trust among older consumers, especially when purchasing medicines online. After all, this is an area of e-commerce fraught with safety concerns. Trust in this sector is more than just a buzzword. It does not matter so much if the latest gadget or fashion accessory does not live up to expectations, but when your life-saving pills and potions fall short…well, it could be game over.

It has to be emphasised that for consumers who spent decades relying on face-to-face interactions at local pharmacies, for many making the digital leap to online transactions requires overcoming a lifetime of ingrained habits. The researchers conducted a detailed analysis of survey data from 314 respondents. They used structural equation modelling, a sophisticated statistical method, to identify relationships between variables emerging from the survey answers.

The team has found that three factors are associated with reliably building trust among older e-commerce users: brand image, monetary value, and offline presence.

Brand image emerges as a powerful influence. A vendor with a strong, positive reputation can reassure wary customers by reducing perceived risks, a critical concern for individuals used to assessing products in person. Whether through word-of-mouth, advertising, or long-standing credibility, a trusted brand becomes a dead cert, if you'll pardon the allusion.

Equally important, the team found, was value for money. Competitive pricing and well-crafted discounts are not mere enticements. For older consumers, often living on fixed incomes, such financial incentives can make online shopping more appealing and more accessible.

Finally, the existence of a physical shop, somewhere in town or a not-too-distant location, offers additional reassurance. An offline location tethers the online operation to the real world. This makes it tangible and legitimate, almost suggesting that if one really had to, one could drive to the shop and discuss any concerns face to face with the manager. Ultimately, this notion bridges any gap in the trust might one have in a virtual as opposed to a physical shop.

What began as a necessary adjustment during the pandemic, is evolving into a permanent shift, with many older shoppers who may well not have had a prior digital life, proving that it can be, for them just as with any Gen Z, all about the clicks.

Maddodi, B., Shetty, D.K., Tatkar, N.S., Parthasarathy, K., Shridutt, B., Prasad, S.K., Pavithra, S., Naik, N., Mahdaviamiri, D. and Patil, V. (2025) 'Factors influencing online purchase decisions of pharmaceutical products by baby boomers: mediating effect of consumer behaviour and attitude on trust development', Int. J. Business Information Systems, Vol. 48, No. 1, pp.118–135.
DOI: 10.1504/IJBIS.2025.144077

The term artificial intelligence (AI) has perhaps been much misused, not least in hyperbolic reports in the media of its potential to destroy the creative industries and to wreak havoc on the job market. However, AI encompasses so many disparate tools not just the generative software that magics up images, music, video, and text from user prompts but also the analytical tools that can spot latent patterns in data whether that's financial reports or medical scans.

Despite the hyperbole, it can be said that AI and related tools are changing the way many processes across industries and academia are carried out. Sometimes the transformation is certainly for the better when the AI tools can detect patterns that would normally be missed by human or even conventional software analysis. Research in the International Journal of Behavioural Accounting and Finance has looked at how AI might benefit corporate operations in terms of financial reporting, decision-making, and stakeholder engagement.

Adel Almasarwah of Georgia College and State University in Milledgeville, Georgia, Assyad Al-Wreikat of Frostburg State University in Frostburg, Maryland, USA, Yahya Marei of Seneca College, Toronto, Ontario, Canada, and Nizar Alsharari of Jackson State University in Jackson, Missouri, USA, point out that conventional labour-intensive tasks can be automated using machine-learning tools, neural networks, algorithms. These could allow businesses to handle data, make decisions, and communicate transparency more readily than previously.

The shift reflects the ability of AI tools to process enormous quantities of data quickly and accurately. Given that financial reporting is usually an arduous task prone to human error, the refinements offered by AI's capacity to identify trends and anomalies could ensure greater accuracy in corporate disclosures. This should allow companies to meet increasingly stringent regulatory requirements and the expectations of investors and other stakeholders more effectively.

Accurate and timely financial reporting, supported by AI, has the potential to foster trust among stakeholders and strengthen corporate governance practices. For investors, in particular, the ability to rely on clear, data-driven insights should enhance confidence in a company's management and operations.

Almasarwah, A., Al-Wreikat, A., Marei, Y. and Alsharari, N. (2024) 'AI's influence on corporate transparency and financial performance: a new era', Int. J. Behavioural Accounting and Finance, Vol. 7, No. 3, pp.233-253.
DOI: 10.1504/IJBAF.2024.143833

A study in the International Journal of Agriculture Innovation, Technology and Globalisation looks at a little-researched factor in pig farming: the libido of boars and the impact this has on sow fertility. Tshepo Teele of the Center of Agriculture and Environmental Sciences at the University of South Africa, has looked at indigenous pig breeds in South Africa and identified the sex drive of the boar as having a big impact on litter size. Obviously, litter size has a big effect on the efficiency and sustainability of pig-farming operations.

Teele points out that Southern African indigenous pig breeds have not generally undergone the same genetic selection processes as other more widely held porcine stock. As such, they have unique reproductive characteristics. Moreover, they are commonly adaptable and have resistance to troublesome diseases. Given that pork is a significant source of relatively low-cost protein, these breeds could have an even more important role to play in the market for pork. However, attention needs to be paid to their reproductive capacity and breeding.

Efficient breeding systems are important for meeting demand, keeping costs down, and ensuring breeders and farmers make a sustainable living from their livestock. Teele explains that conventional breeding programmes tend to focus on growth rate and carcass quality, reproductive factors, particularly boar libido, deserve closer attention for facile ways to improve yields.

Porcine libido can be measured in terms of reaction time (the interval from mounting to ejaculation). It can have a direct impact on sow fertility, not least because boars with a higher libido can through their behaviour and pheromone release stimulate earlier maturity in gilts, young female pigs, and trigger the development of larger litters.

The work argues for the inclusion of libido-focused estimated breeding values as a statistical tool for predicting genetic potential in breeding strategies. By doing so, farmers can build on the natural strengths of their pigs to improve yields.

Reproductive traits in pigs are inherited at quite a low rate. However, dietary supplements such as zinc and selenium are known to boost testosterone levels, which may improve boar libido. Given the correlation between boar libido and sow fertility, there are obvious practical interventions that could complement any breeding efforts to boost reproductive outcomes.

Teele, T. (2024) 'Analysis of the reproduction components trait litter size in sows and interaction with boar libido in indigenous pigs', Int. J. Agriculture Innovation, Technology and Globalisation, Vol. 4, No. 3, pp.217–226.
DOI: 10.1504/IJAITG.2024.143902

The Tobacco Cutworm, or Cotton Cutworm, is a moth species native to Asia, it is considered a serious agricultural pest. The larvae of Spodoptera litura, to give the species its scientific binomial, are responsible for significant damage to economically vital crops such as vegetables, grains, and cotton, particularly. It can adapt easily to different environments and has developed resistance to conventional pesticides. These and other factors have made it a persistent and costly problem for farmers worldwide.

Research in the International Journal of Agriculture Innovation, Technology and Globalisation introduces a new system based on the Internet of Things (IoT) that might be able to address this agricultural threat by improving monitoring and allowing more targeted response to the species.

Jheng-Hong Hu, Ming-Yao Chiang, Jenn-Kuo Tsai, and Chiling Chen of the Ministry of Agriculture in Taichung City and Chau-Chin Lin of the Society of Subtropical Ecology in Taipei City, have suggested that by using an IoT system that brings together infrared automatic counting devices, low-power LoRa (Long Range) wireless data transmission and mobile platforms, it should be possible to monitor Tobacco Cutworm infestations in real time. Such an automated approach would provide timely alerts, allowing farmers to act quickly and prevent widespread crop damage.

The team has conducted field trials in partnership with the Taiwan Agricultural Research Institute and local farmers and demonstrated the system's effectiveness when compared with manual monitoring as well as its adaptability for practical use. Fundamentally, the approach allows for a more timely response that avoids the use of blanket pesticide spraying and uses more focused treatment with effective materials. It will be effective in a wide range of agricultural settings, from small farms to large commercial enterprises.

Hu, J-H., Chiang, M-Y., Tsai, J-K., Lin, C-C. and Chen, C. (2024) 'Internet of things technology applied in monitoring and warning of Spodoptera litura Fabricius (tobacco cutworm) occurrences', Int. J. Agriculture Innovation, Technology and Globalisation, Vol. 4, No. 3, pp.257–272.
DOI: 10.1504/IJAITG.2024.143907

The Anthropocene is a relatively recent term, coined to define the epoch in which human activity is increasingly dictating environmental and biological change on earth as previous periods driven by natural occurrences did in pre-history; during the Pleistocene, for instance. Technically, the current epoch is the Holocene, but human activity has altered the world so significantly, that, with our usual species-centric perspective, we have shunned hubris and given the current epoch this new name in a fit of unaccustomed self-awareness.

Writing in the Interdisciplinary Environmental Review, Miti Mallick of Bankura University in Purandarpur, West Bengal, India, discusses how the concept of the Anthropocene plays out across the economic landscape too. While the Anthropocene has brought major improvements in living conditions for the wealthier nations, it is becoming ever clearer that the challenges of climate change and environmental degradation will demand more drastic measures from these same nations in terms of sustaining their own living standards and improving those of the majority that live in poverty.

At the heart of any such discourse is the concept of capitalism. This is the dominant global economic force that organizes production, labour, and the distribution of wealth. Capitalism is driven by the principles of private ownership and the pursuit of profit. It has been instrumental in driving what we consider economic growth but has also contributed to social inequality, environmental destruction, and a growing sense of disconnection between the economy and the planet's ecological limits.

Capitalism functions in liberal market economies, which emphasize decentralized markets, as well as in state-coordinated models, where government plays a more prominent role.

The consequences of capitalism have become increasingly difficult to ignore as historically the maximization of profits has been at the long-term cost of environmental and social considerations, the research argues. The rise of oligarchic capitalism, which benefits a select few and see multibillionaires in powerful positions within society, and the focus on technological innovation, have further worsened the social and environmental toll.

In the context of the Anthropocene, this economic model is coming under increasing scrutiny. It seems that capitalism as we know it may be at a pivot point. Given that scholars, activists, and policymakers are beginning to challenge the assumption that economic growth and ecological sustainability are inherently incompatible, there is a need for a new capitalism. One that redefines value in terms that extend beyond profit margins. This reimagined model of capitalism might centre on the well-being of individuals, communities, and the environment. Investments would no longer solely be evaluated on their financial returns but also on their potential to reduce inequality and promote sustainable development.

This putatively idyllic world may not be to everyone's taste especially some of those multibillionaires. While entrepreneurs, investors, and policymakers are increasingly being called to task, there is not necessarily the political will nor the motivation for egocentric oligarchs to imagine such a world. Plus ça change, plus c'est la même chose.

Mallick, M. (2025) 'How capitalism could be the new market in the Anthropocene era: a review', Interdisciplinary Environmental Review, Vol. 24, No. 1, pp.1–15.
DOI: 10.1504/IER.2025.143620

Research in the International Journal of Computational Vision and Robotics could lead to faster and more accurate robots for high-precision tasks in factories.

Delta robots are parallel computer-controlled machines that have a fixed base and a set of three arms connected to a platform. They are typically used for pick-and-place applications in industries like packaging, assembly, electronics fabrication, pharmaceutical production, and food processing. They can work very quickly, making precise movements for even delicate tasks. Unlike serial robots, the parallel kinematics of delta robots means arms and actuators work together to move the platform.

Riyadh A. Sarhan, Zaid H. Rashid, and Mohammed S. Hassan of the Technical University in Babylon, Iraq, are working to make delta robots even more reliable and have developed a novel control system that boosts their ability to make swift, precise movements. In their paper, they integrate fuzzy logic with an adaptive neuro-fuzzy inference system (ANFIS). This hybrid technology combines the best aspects of artificial neural networks and fuzzy logic to manage the complex kinematics, the mathematical description of the robot's movements, in order to improve performance significantly.

The improvement in control of precision delta robots should allow manufacturers to increase speed, quality, and overall efficiency on their production lines. Moreover, there is the potential in this hybrid control approach to allow delta robots to be more responsive to and to compensate for changes in their environment.

As industries continue to look for ways to improve automation, the research offers step towards faster, more accurate robotic systems.

Sarhan, R.A., Rashid, Z.H. and Hassan, M.S. (2025) 'Motion control of 3-DoF delta robot using adaptive neuro fuzzy inference system', Int. J. Computational Vision and Robotics, Vol. 15, No. 7, pp.1–16.
DOI: 10.1504/IJCVR.2025.143990

Digital therapeutics allow healthcare workers and patients use software is in the management and treatment of disease. The idea spans various healthcare areas, including mental health, chronic disease management, neurological disorders, addiction treatment, and rehabilitation.

Software-based interventions often offer personalized therapies through apps or digital platforms, using techniques like cognitive behavioural therapy, symptom tracking, and virtual exercises to help manage conditions such as mental health problems, diabetes, substance use, and recovery from physical injuries.

Research in the International Journal of Technology Transfer and Commercialisation, suggests that digital therapeutics have changed the healthcare landscape Of course, the rapid commercialisation of these products has continued apace but equally important is the challenge of the internationalisation of such systems allowing them to be expanded into foreign markets. Amy Lee and Grigorij Ljubownikow of The University of Auckland, New Zealand, have highlighted how these processes commercialisation and internationalisation, traditionally seen as separate, are deeply interconnected for companies that start out as born-digital enterprises.

These companies all operate in highly regulated environments. What sets them apart from conventional healthcare companies is their use of wholly digital solutions. The shift from conventional to digital was happening steadily up to around 2020 but was accelerated enormously by the pandemic and the urgent need for remote, or virtual, care.

The researchers point out that while traditional companies might commercialise their product domestically first and then branch out internationally, digital therapeutics firms have had to rethink this linear path because in the digital world global is essentially just as immediate and local a market as the domestic one. The research reveals that for these companies, international expansion is not a separate concern to be tackled later, but has to be a key factor in the broader strategy from the outset.

The research emphasises how collaboration, networking, and continuous learning within these companies can help them address the additional challenges of regulatory and reimbursement hurdles across international markets. While global may be perceived as the new local, there are still enormous differences in the socio-political and economic environments between countries. Navigating the diverse institutional and international frameworks requires not only innovation in product development but also flexibility in business models. Lee and Ljubownikow's findings thus offer insights into how firms can refine their strategies for global growth.

Lee, A. and Ljubownikow, G. (2024) 'The road to commercialisation: expanding digital therapeutics across international markets', Int. J. Technology Transfer and Commercialisation, Vol. 21, No. 5, pp.1–25.
DOI: 10.1504/IJTTC.2024.143991

Research in the International Journal of Computational Science and Engineering has developed a new approach to addressing ideological polarisation on social media. The problem of users generally encountering only like-minded perspectives and so reinforcing their own beliefs even in the face of conflicting evidence is highly divisive.

The phenomenon, known as the "echo chamber" effect or referred to as "filter bubbles", arises in part because the algorithms driving the position of content in one's social media apps. This, in turn, is driven largely by the need to keep users active and engaged on a particular platform. Too many contrary updates might drive users away, and that will ultimately reflect negatively on the advertising and other revenue streams for the companies that operate the platforms. By contrast, an echo chamber effect that reinforces their viewpoints will, for many people, be more attractive than one that doesn't.

Zaka Ul Mustafa and Muhammad Amir of the International Islamic University Islamabad, Manal Mustafa of Zaman Technologies Pvt Limited, Pakistan, and Muhammad Adnan Anwar of Ulisboa, Portugal, suggest that the social media platforms could benefit from the use of genetic algorithms (GAs). Such computational techniques inspired by the principles of evolutionary natural selection could reduce polarisation and the echo chamber effect but still respect the organic nature of online interactions, and so keep users engaged without being so divisive.

The team explains that current strategies to counter polarisation often involve connecting disparate groups (edge addition) or altering expressed views (opinion flipping). These methods are not only static, but also raise ethical concerns about the platforms interfering with user autonomy. A GA-based approach instead identifies influential nodes in the online social network and only subtly adjusts their highlighted connections to reduce polarisation. The critical contribution of the work lies in identifying network elements that disproportionately contribute to ideological divides, and then encouraging more diversity of interaction with minimal disruption to the organic nature of social media.

The team has tested their approach on real-world datasets that focus on polarised US political discourse. The datasets have communities clustered around distinct ideological groups, and so can provide a useful test for how well the method precludes polarisation and division. The results showed that the GA approach could foster connections between disparate groups, and this led to a measurable decrease in polarisation without fundamentally altering the network's overall structure.

Ul Mustafa, Z., Amir, M., Mustafa, M. and Anwar, M.A. (2025) 'Harmony amidst division: leveraging genetic algorithms to counteract polarisation in online platforms', Int. J. Computational Science and Engineering, Vol. 28, No. 7, pp.1–17.
DOI: 10.1504/IJCSE.2025.143956

As international trade and global security become more reliant on marine resources, the demand for advanced maritime surveillance and port management has never been greater. One of the big challenges in this area is the detection of ships in complex environments, a task that has traditionally relied on manual techniques. These methods, while functional, are often inadequate in dynamic, cluttered marine conditions, where varying sea states, weather patterns, and ship sizes can easily confound detection efforts.

Research in the International Journal of Information and Communication Technology has introduced a new approach to ship target detection. The research combines several cutting-edge deep learning techniques, "You Only Look Once" version 4 (YOLOv4), the Convolutional Block Attention Module (CBAM), and the transformer mechanism. The team of Weiping Zhou, Shuai Huang, and Qinjun Luo of Jiangxi Polytechnic University in JiuJiang, and Lisha Yu of Shanghai Cric information Technology Co. Ltd. In Shanghai, China, have combined these into a single algorithmic program that is both accurate and reliable in the identification of vessels in challenging conditions.

Modern, fast deep-learning models such as YOLOv4 out-class traditional methods by cutting out the multiple steps needed to process an image. YOLOv4 can scan and classify objects in a single pass, making it ideal for real-time surveillance over large expanses.

CBAM is a feature-enhancing technique that works by focusing the model's attention on the most important elements within a given image. This allows the hybrid system to identify ships even if they are surrounded by other vessels, docks, flotsam, and even rough seas. Conventional techniques often failed in distinguishing vessel from background in such images. The transformer mechanism is a powerful system that further improves the capacity of the model to process features at different levels, ensuring that important detail are not missed.

The team explains that this combined effort allows their system to outperform earlier models, particularly in the detection of smaller vessels and ships in complex maritime environments. They tested the approach on the Ship Sea Detection Dataset (SSDD), which includes remote sensing images of various marine conditions. Their results demonstrated superior speed and precision, especially when identifying minor or obscured targets. Given the critical importance of timely and accurate detection in maritime security, the implications of this improvement are significant.

Zhou, W., Huang, S., Luo, Q. and Yu, L. (2024) 'Research on a ship target detection method in remote sensing images at sea', Int. J. Information and Communication Technology, Vol. 25, No. 12, pp.29–45.
DOI: 10.1504/IJICT.2024.143631

Architects and industrial designers play an important part in what we might term the circular economy (CE). This is a sustainability framework that aims to minimize waste by reusing and regenerating resources. Research in the Journal of Design Research has surveyed practitioners in The Netherlands and Sweden to see whether there is growing enthusiasm for circular design strategies and what significant challenges remain to be overcome.

Giliam Dokter, Jonathan Edgardo Cohen, Sofie Hagejärd, Oskar Rexfelt, and Liane Thuvander of Chalmers University of Technology, Gothenburg, Sweden, surveyed 114 professionals. They found that almost two-thirds of them engaged with CE-related projects, while a similar proportion reported that there were shifts within their organizations to support such initiatives.

The team reports that techniques such as "design for disassembly", the crafting products or buildings for easy dismantling and reuse, are all part of this move towards greater sustainability. They point out that circular business models, emphasize regeneration over consumption and the associated principles are commonly applied in CE-focused projects undertaken by the survey participants.

It was found that architects tend to prioritize material reuse at the building level, while industrial designers have more of a focus on making it possible to disassemble products. Both groups are advancing creative solutions that reflect the principles of CE, however, even if their approaches are different and the substantial barriers they face are apparent.

The survey revealed that a lack of reliable knowledge about materials and the tools needed to evaluate environmental and economic impacts during design is one of the biggest barriers to adopting the principles of the CE in both architecture and industrial design. The research points out that choosing sustainable materials requires precise data about the lifecycle of these materials and their potential reuse. However, such information is often scarce or fragmented.

In addition to this dearth of relevant information there are also factors such as regulatory and market challenges that are beyond the immediate control of those working to CE principles and such barriers might hamper their efforts towards sustainability regardless of their efforts and focus.

Dokter, G., Cohen, J.E., Hagejärd, S., Rexfelt, O. and Thuvander, L. (2024) 'Mapping the practice of circular design: a survey study with industrial designers and architects in the Netherlands and Sweden', J. Design Research, Vol. 21, Nos. 3/4, pp.177–209.
DOI: 10.1504/JDR.2024.143685

Online shopping in China, particularly among young people, is a vast enterprise. Online retail sales amounted to about 16 trillion yuan in 2024, approximately 2 trillion US dollars. Indeed, online shopping has transformed the way youngsters approach buying everything from clothing to gadgets, especially in the post-pandemic era where old shopping habits have been abandoned by many people.

Much of the research into online consumer behaviour has focused on the after-sales experience. Now, a study in the International Journal of Data Science, turns the research lens to look more closely at the pre-purchase stage. In so doing, Nanhua Duan and Jingwen Zhang of Northwestern Polytechnical University in Shaanxi, China, hoped to understand how young Chinese consumers perceive value before they hit the all-important "buy now" button when shopping online.

The team explains that the concept of Customer Perceived Value (CPV) is at the core of their research. CPV refers to the overall worth a consumer assigns to a product based on the benefits they expect in relation to the cost. For experiential products, this perception is even more complex because the product's value is influenced by a variety of factors that may not be immediately obvious. The same is true for clothing when one cannot touch or try on an item before making a buying decision.

To home in on the factors involved, the team has proposed a new framework, which identifies six key dimensions that influence CPV when young Chinese consumers shop online for clothing and similar items. These are: word-of-mouth value, service value, aesthetic value, cost value, quality value, and brand value. Each of these, they found, plays a critical role in shaping the consumer's expectations prior to purchase.

The findings are particularly relevant to China's booming apparel market, which has seen rapid growth among digitally consumers. The research emphasizes that young buyers are not just concerned with the price tag or material quality alone. Indeed, they also consider factors like the reputation of the brand, the service experience, and how well a product aligns with their personal style or social status. This is where the online shopping environment differs from traditional brick-and-mortar shops, where the tactile nature of the shopping experience provides more immediate and obvious feedback and the potential for impulse buys or purchases prompted by an enthusiastic sales assistant.

For retailers and brands looking to tap into the ever-growing online market, understanding the six dimensions of CPV could offer insight into how to develop a more compelling online experience. It is, the research suggests, no longer sufficient to highlight the physical attributes of a product, companies must also now showcase the brand and its reputation as well as the quality of service.

In practical terms, the findings could mean that companies could benefit from focusing on positive reviews, clear and appealing product images, and smooth, customer-friendly websites. There might even be potential for developing innovative ways to display the products that might involve interactive elements, such as changing viewing angles, product colours and styles, and perhaps even offering options to see different models wearing the items. There is huge potential for the marketers that learn how to persuade people to click that "buy now" button.

Duan, N. and Zhang, J. (2025) 'The development of a product-layer perceived value scale for the online experience products of young Chinese consumers: take online apparel as an example', Int. J. Data Science, Vol. 10, No. 5, pp.1–21.
DOI: 10.1504/IJDS.2025.143886

Many work-related activities come with a risk of musculoskeletal problems, not least working at a desk. They are perhaps more commonly seen in the industrial or manual labour settings where repetitive movements, awkward postures, considerable muscular force and vibration, and lifting heavy objects are problematic.

A new study in the International Journal of Human Factors and Ergonomics introduces a tool that could be used by employers to assess the risk of such problems to their workers. The tool, the Ergonomist Assistant for Evaluation (ERAIVA), could streamline the process of identifying risky postures, which might lead to chronic pain and issues such as repetitive strain injury over time.

Where workers perform tasks that involve awkward body positions, repetitive movements, and heavy lifting there is an increased risk of debilitating conditions such as back pain and injury, carpal tunnel syndrome, and tendinitis. Previously, assessing such risks was done only on an ad hoc basis and not necessarily systematically, to the detriment of workers moreover the assessment itself was labour and time intensive, requiring experts to visually monitor workers or examine video footage of their activities.

Veeresh Elango, Lars Hanson, and Anna Syberfeldt of the University of Skövde, Staffan Hedelin and Johan Sandblad of Scania CV AB in Södertälje, and Mikael Forsman of the KTH Royal Institute of Technology in Stockholm, Sweden, explain that ERAIVA addresses these shortcomings by offering an automated way to analyse and annotate video recordings of industrial tasks. The technology could avoid human error in assessing work tasks and the posture and activity of individuals carrying out those tasks. Such a system could allow posture and other problems to be corrected and reduce the risk of musculoskeletal problems.

The system is easy to use and so reduces the need for expert assessment and remediation. Engineers and operators, as well as risk assessors, can all work together with the results it provides to identify and mitigate risks in the workplace.

Elango, V., Hedelin, S., Hanson, L., Sandblad, J., Syberfeldt, A. and Forsman, M. (2024) 'Evaluating ERAIVA – a software for video-based awkward posture identification', Int. J. Human Factors and Ergonomics, Vol. 11, No. 6, pp.1–16.
DOI: 10.1504/IJHFE.2024.143861

Online education is now ubiquitous and in recent years has changed fundamentally the way many people learn. Various platforms have opened up access to knowledge for millions of people. However, there remains an ongoing challenge: how to accurately measure and enhance the quality of teaching in these digital spaces.

Conventional evaluation tools focus on test scores and student satisfaction surveys. However, these often overlook the students' emotional experience of the course. Research in the International Journal of Information and Communication Technology, proposes a new solution that could change the way online teaching is assessed, getting closer to the heart of emotional matters.

The new work by Ruiting Bai of Puyang Medical College in Puyang, China, introduces the EduSent-Dig model, which can carry out advanced sentiment analysis and use big data techniques to evaluate teaching quality. By analysing the student emotional response given in their course feedback, the model can extract the nuances of online teaching that work most effectively. Rather than flagging the feedback as simply "positive" or "negative", EduSent-Dig identifies specific emotional undercurrents such as joy, frustration, or surprise. It does so by using analytical tools such as Bi-LSTM, a deep learning framework, and Word2Vec, which converts words into numerical representations for computational analysis.

The study reveals that emotional experiences are not just peripheral to learning; they are central to it. How students feel about their coursework directly affects their motivation, engagement, and whether they complete a course. As such, the new model in identifying and interpreting sentiment accurately, can provide educators and course designers with insights into how to improve their educational offering. Moreover, real-time sentiment analysis undertaken as a course progresses might even allow teachers to fine tune their teaching dynamically, tailoring lessons to student needs on an ad hoc basis. This could transform the way courses are designed and how they are developed as the students progress through them. All in, the insights could foster a more empathetic and effective learning environment.

Bai, R. (2024) 'Big data-driven deep mining of online teaching assessment data under affective factor conditions', Int. J. Information and Communication Technology, Vol. 25, No. 11, pp.35–51.
DOI: 10.1504/IJICT.2024.143412

Increasing complexity, evolving consumer expectations, and tightened development timelines means that physical product development increasingly comes unstuck when conventional methodologies are used. The predominant systems engineering frameworks have structure and predictability, but often falter when innovation is needed to fill the gap in modern markets. Companies have turned to agile approaches to help them transform their approach to software development, for instance. But, there are major obstacles to the adoption of that kind of approach for the development of physical products, where material constraints, prototyping costs, and supply chain integration are always critical factors.

A new hybrid framework is discussed in the Journal of Design Research that might address some of the issues. Frank Koppenhagen, Tobias Held, of Hamburg University of Applied Sciences in Hamburg, Tim Blümel of Porsche AG in Weissach, Paul D. Kollmer of the University of Hamburg, Germany, and Christoph H. Wecht of the New Design University in St. Pölten, Austria, describe a new model, Systematic Engineering-Design-Thinking (SEDT). In this approach, the strengths of systems engineering is combined with the user-centric, principles of design thinking to create a more adaptive and innovative product development pathway. SEDT builds on the Stanford University ME310 process, which has proven itself to some degree in academia and industry, but an expansion was always needed.

By integrating systematic exploration techniques from systems engineering, SEDT refines the ME310 framework to better support the development of solutions to problems. The result is a process capable of accommodating greater degrees of uncertainty and complexity, enabling teams to pursue transformative innovation rather than simply incremental improvement. The approach reimagines project structures to emphasize collaboration, fluidity, and cross-disciplinary interaction.

The next step is to test SEDT in both academic and industrial environments to determining its usefulness as a comprehensive framework for physical product innovation.

Koppenhagen, F., Blümel, T., Held, T., Wecht, C.H. and Kollmer, P.D. (2024) 'Hybrid development of physical products based on systems engineering and design thinking: towards a new process model', J. Design Research, Vol. 21, Nos. 3/4, pp.210–261.
DOI: 10.1504/JDR.2024.143686

Research in the International Journal of Information and Communication Technology suggests that machine learning tools might be used to detect and so combat financial fraud.

According to Weiyi Chen of the Monitoring and Audit Department of the Financial Shared Center at the National Energy Group Qinghai Electric Power Co., Ltd. In Xining, China, financial fraud is a constant challenge for capital markets, especially in developing economies where regulatory systems are still not fully mature. Fraudsters use sophisticated techniques to outpace conventional detection methods, which can leave investors exposed to potentially devastating risks beyond the everyday risks of investments! Chen's work offers a promising new approach to fraud detection by combining machine learning and deep learning to bridge the gap between financial data and the information found in corporate reports.

Financial fraud has long afflicted markets, distorted investment decisions, and weakened public trust in financial systems. Manual audits and statistical models can detect some fraudulent activities, but they can be inefficient when faced with increasingly complex fraud in the digital age. The problem is especially obvious in developing markets, including China, where financial fraud is widespread, and the regulatory structures have not necessarily kept pace with the fraudsters.

Machine learning can analyse vast datasets more quickly and accurately than traditional methods. However, it struggles with the non-linear aspects of financial data and in particular textual rather than numeric information. As such, applying advancements in deep learning could bolster machine learning and allow qualitative text found in corporate reports, such as the Management Discussion and Analysis (MD&A) section to be "understood" by fraud-detecting algorithms that might then spot the telltale signs of problematic corporate activity.

Chen's dual-layer approach brings together financial data analysis and sentiment analysis. The use of bidirectional long short-term memory (BiLSTM) networks allows the system to interpret sequences of data, while a parallel network refines the key financial indicators using a convolutional neural network (CNN). Inconsistencies between the sentiment and the financial data can then be revealed. Tests showed a fraud-detection accuracy of 91.35%, with an "Area Under the Curve" of 98.52%. This surpasses traditional fraud-detection methods by a long way, Chen's results suggest.

Chen, W. (2024) 'Financial fraud recognition based on deep learning and textual feature', Int. J. Information and Communication Technology, Vol. 25, No. 12, pp.1–15.
DOI: 10.1504/IJICT.2024.143633

A new method for classifying calligraphy and painting images could be used in the management of cultural heritage, according to research published in the International Journal of Information and Communication Technology.

Nannan Xu OF Suzhou University in Suzhou, China, explains how technology is playing an ever useful role in the preservation and study of artwork and so there is a growing need to find recognition and categorisation tools. The work points out how there is an imbalance in the sample categories that can skew classification models, making it harder to achieve accuracy, and offers a novel solution to this problem. One that could improve accuracy and increase the versatility of image classification for artworks.

Xu introduces a classification method that builds on the AdaBoost algorithm. This machine learning tool works by combining multiple weak classifiers into a strong model and is bolstered by a dynamic training subset construction strategy (DWSCS). According to the research, this approach overcomes the imbalance wherein certain artistic styles are underrepresented. By using sample weights and adjusting how the model is trained on each subset of data, the new method overcomes this bias and so allows a more generalized approach to categorisation where rare artistic styles can be considered.

In cultural heritage, the management and preservation of artworks is critical. This new approach could streamline the cataloguing process for museums and galleries by automating the classification of diverse images. The potential is there for institutions to be able to handle large volumes of calligraphy and paintings efficiently. The same technology might also be useful not only in conservation but in education, offering art historians and students an easier way to analyse and understand the diverse techniques used across different periods and cultures.

Beyond the galleries, the technology might also be used in provenance and authenticity. The system could offer an objective, technology-driven method for verifying the origins of artworks, supporting trust in transactions and authentication processes for art collectors and investors.

Xu, N. (2024) 'Intelligent judgement of calligraphy and painting image categories based on integrated classifier learning', Int. J. Information and Communication Technology, Vol. 25, No. 11, pp.1–20.
DOI: 10.1504/IJICT.2024.143414

A new method for managing urban traffic at multi-intersection networks is discussed in the International Journal of Information and Communication Technology. The research promises improvements in efficiency and adaptability, and by combining technologies could address the long-standing challenges of congestion and unpredictable traffic patterns in dense urban areas.

Renyong Zhang, Shibiao He, and Peng Lu of the Chongqing Institute of Engineering in Chongqing, China, suggest the use of vehicle-to-everything (V2X) technology could allow vehicles and infrastructure to exchange real-time data about road conditions and traffic. This continuous sharing of data would improve the way in which traffic management systems control traffic lights and speed and lane restrictions to smooth the flow of vehicles safely.

The system suggested by the team uses an improved long short-term memory (LSTM) model, a type of artificial intelligence designed for recognizing patterns and making predictions. By using a "sliding time window" update mechanism, the model can learn from real-time data while maintaining historical context. By balancing the two, faster adjustments to traffic flow can be made while reducing the overall computational load on the system and cutting prediction times in half.

The team has carried out simulations and demonstrated that such an approach might reduce average vehicle delays by just under a third and increase road "throughput" by almost 15 percent. The result would be shorter travel times and smoother traffic flow. This should also improve fuel consumption and reduce overall vehicle emissions.

Conventional traffic management systems use historical data or limited real-time inputs, and so cannot respond to actual road conditions at a given time without manual input. Such systems are useful in less complex traffic scenarios, but struggle to handle rapid and unpredictable changes in traffic, particularly in larger, interconnected networks. The newly proposed system addresses these limitations by offering more responsive and precise adjustments.

Zhang, R., He, S. and Lu, P. (2024) 'Multi-intersection traffic flow prediction control based on vehicle-road collaboration V2X and improved LSTM', Int. J. Information and Communication Technology, Vol. 25, No. 11, pp.52–68.
DOI: 10.1504/IJICT.2024.143411