Thermal, infrared (IR), facial recognition technology has advanced apace recently. Research in the International Journal of Information and Communication Technology, moves us another step towards a tenable system that overcomes some of the limitations of traditional visible-light systems.

Naser Zaeri of the Faculty of Computer Studies at the Arab Open University in Ardiya and Rusul R. Qasim of Kuwait Technical College in Abu-Halifa, Kuwait, explain how IR imaging sidesteps the problem of ambient lighting conditions and variations in skin tone seen with visible-light facial recognition. The use of thermal imaging relies on capturing the unique heat patterns emitted by the face rather than reflected light. The heat pattern observed is determined almost wholly by a person's facial vasculature and tissue structures beneath the skin. These are consistent, broadly speaking, regardless of environmental lighting and skin tone. This could make thermal IR a much more reliable alternative to visible-light imaging for biometric identification.

However, thermal recognition has faced challenges. The technology often has to cope with degraded image quality due to factors such as noise, blurring, reduced spatial resolution, and temperature drift. Additionally, variations in facial expression and pose can complicate the recognition process. Overcoming these issues requires advanced methods capable of accurately processing and recognizing faces even in less-than-ideal conditions.

Zaeri and colleagues have demonstrated the potential of Convolutional Neural Networks (CNNs) in enhancing the recognition of degraded thermal face images. CNNs are a class of deep learning models that have made a significant impact on the field of computer vision, thanks to their ability to automatically extract and learn complex features from raw images without requiring extensive pre-processing. This capability makes CNNs particularly well-suited to face the biometric challenge.

The team has worked with the well-known ResNet-50 CNN architecture. They applied it to a database of 7500 thermal images in order to evaluate performance with images of different quality and where facial expression and pose are different. The promising results show that this CNN-based system can achieve better recognition accuracy even with degraded thermal images and works across a range of scenarios. The work will have applications in security and the military world.

Zaeri, N. and Qasim, R.R. (2024) 'Resilient recognition system for degraded thermal images using convolutional neural networks', Int. J. Information and Communication Technology, Vol. 25, No. 5, pp.50–71.
DOI: 10.1504/IJICT.2024.140327

Research published in the European Journal of International Management has looked at how positive attitudes towards cultural diversity can significantly enhance team performance. The study, conducted with over 1000 leaders from highly globalized academic research teams in the Nordic region, shows that teams open to diverse cultural values, especially when combined with openness to language diversity, perform better and are more creative than others.

The findings from Jakob Lauring of Aarhus University, Denmark, Christina L. Butler of Kingston Business School, London, UK, Minna Paunova of Copenhagen Business School in Copenhagen, Denmark, Timur Uman of Jönköping University, Sweden, and Lena Zander of Uppsala University, Sweden, have some implications for better management of multicultural teams across various sectors, particularly in an increasingly globalized work environment.

It is important from the management perspective to understand how cultural values and language interact and how they affect workplace behaviour and success. "Cultural values" usually refers to the underlying principles that guide decisions in different societies, such as beliefs about hierarchy, individualism, and communication styles. Language diversity, by contrast, refers to the inclusion of multiple languages within a team, reflecting the varied backgrounds of its members. Both factors are critical to success in increasingly international teams.

Openness to cultural values it appears influences team performance positively. Moreover, teams that are receptive to both cultural and language diversity are commonly better positioned to capitalize on the benefits of each. Essentially, a willingness to embrace different languages within a team enhances the positive effects of being open to various cultural values, leading to improved collaboration, creativity, and overall team outcomes. The impact is synergistic, whereas earlier studies had not necessarily demonstrated that these two factors work together and were perhaps considered as operating independently. It is therefore time to refine research models concerning the way teams work Different types of diversity attitude and how they interact and influence each other must be embedded in such models.

Lauring, J., Butler, C.L., Paunova, M., Uman, T. and Zander, L. (2024) 'Openness towards language differences and cultural differences in multicultural teams: how do they interact?', European J. International Management, Vol. 24, No. 1, pp.1–24.
DOI: 10.1504/EJIM.2024.140297

Research published in the International Journal of Healthcare Technology and Management has looked at how additive manufacturing, colloquially referred to as "3D printing", is changing healthcare. This transformative technology, long associated with advances in traditional manufacturing, is increasingly being recognized for its capacity to produce highly customized, patient-specific medical models. Such models can be used in surgical planning, training, and the production of custom prosthetics and other medical devices.

However, despite its promise, Ethan Sanekane, Jill Speece, Mohamed Awwad, and Xuan Wang of California Polytechnic State University in Obispo and Sara Moghtadernejad California State University Long Beach, California, USA, suggest that access to this technology in healthcare is rather limited. There is an information gap that the current research seeks to fill.

Additive manufacturing, as the name suggests, involves the creation of objects by adding material layer by layer. This approach, pioneered in the 1980s, but having come to the fore in many areas in the last couple of decades, can be used to produce highly complex and detailed structures that would be beyond economic viability in conventional manufacturing. In healthcare, this technology enables the creation of models that might be an exact replica of a patient's anatomy, for instance. Surgeons could, for example, then use such a model of a disease site to plan a sophisticated procedure with unprecedented precision. Such a model might be even more useful with robotic surgery, where the model could be used to train the robot with no risk to the patient.

The same technology could be used to craft bespoke orthotics and prosthetics that are precisely tailored to the patient's unique needs, rather than being off-the shelf components that might be cut to fit, as it were.

The research has taken an important step forward in identifying the full potential of additive manufacturing in healthcare. By addressing the barriers to access and strategically locating additive manufacturing hubs, the researchers have perhaps paved the way for greater adoption of this transformative technology.

Sanekane, E., Speece, J., Awwad, M., Wang, X. and Moghtadernejad, S. (2024) 'Healthcare industry input parameters for a deterministic model that optimally locates additive manufacturing hubs', Int. J. Healthcare Technology and Management, Vol. 21, No. 2, pp.111–128.
DOI: 10.1504/IJHTM.2024.140392

Research in the International Journal of Computational Systems Engineering has demonstrated a new image compression tool that combines recursive algorithms with convolutional neural networks (CNNs) to out-perform other approaches to the compression of images from computer art and interaction design. Digital art and design increasingly rely on large volumes of visual data, so effective image compression is important for reducing the computer storage requirements without compromising quality. Duan Song of the Department of Fine Arts at Hebei Vocational Art College in Shijiazhuang, China, has proposed an algorithm that works to address the issues by integrating traditional and modern techniques.

Recursive algorithms, which simplify complex problems through repeated application of rules. Song explains that the approach works by breaking an image down into simpler components. By applying the process iteratively, quality can be maintained. The integration of CNNs into the compression approach builds on the way in which such systems were initially inspired by the way the human brain processes visual information. They are widely used in deep learning for image recognition and processing. Song's innovative merging of the recursive methods with CNNs allows him to overcome some of the limitations of earlier image compression techniques, which commonly struggle to achieve useful compression ratios because of the increasing complexity and scale of modern image data.

Song has tested the algorithm on two well-known image datasets, Kodak1 and Kodak2, to evaluate its performance. The results indicate that the algorithm consistently reduced the mean square error (MSE) between the original and compressed images. A lower MSE means better conservation of image quality. After 800 iterations, the algorithm achieved the lowest MSE compared to other methods and also performed well in terms of peak signal-to-noise ratio and multi-scale structural similarity. These results suggest that the proposed method can compress images effectively with no significant loss of quality.

The approach will be useful in the field of computer art but might also be useful in animation modelling, art interface design, and medical imaging.

Song, D. (2024) 'Recursive quantitative analysis modelling of computer art design interaction', Int. J. Computational Systems Engineering, Vol. 8, No. 5, pp.1–11.
DOI: 10.1504/IJCSYSE.2024.139715

A paper in the International Journal of Healthcare Technology and Management, has highlighted the potential benefits of adopting a new approach to collaboration in eHealth initiatives. The approach suggested by Maria Qvarfordt, Stefan Lagrosen, and Lina Nilsson of Linnaeus University in Kalmar, Sweden, braids together the four strands of stakeholder relationships – academia, business, the public sector, and citizens – into what the team calls a quadruple helix (QH).

Digitalisation in healthcare encompasses the adoption of digital technologies across various sectors and is crucial for global healthcare advancements. eHealth specifically refers to the use of electronic tools and methods to improve healthcare delivery and outcomes. For eHealth to be effective, collaboration among various stakeholders is critical. Previous studies have shown that the involvement of different stakeholders can be understood and developed with a traditional triple helix model (academia, government, and industry).

By incorporating a fourth strand – the public – which we might more formally refer to as civil society, an emphasis on the importance of the end-user perspective can be incorporated into eHealth solutions. Each stakeholder group brings unique knowledge, resources, and perspectives and so can benefit the outcomes as a whole, with that whole being more than the sum of its parts in some instances.

To develop the QH approach, the researchers used a grounded theory methodology and collected and analysed stakeholder perspectives on eHealth collaboration. They then aligned their findings from the study with an actor-resource-activity (ARA) model – a framework designed to understand business relationships. The team emphasise the importance of promoting value and quality in eHealth development collaborations. They highlight the role of knowledge and competence.

Overall, the QH approach is more inclusive and participatory and will hopefully lead to more effective and more widely accepted eHealth implementations. The paper thus offers an invaluable framework for understanding and improving stakeholder collaboration in the digitalisation of healthcare.

Qvarfordt, M., Lagrosen, S. and Nilsson, L. (2024) 'Quadruple helix collaboration for eHealth: a business relationship approach', Int. J. Healthcare Technology and Management, Vol. 21, No. 2, pp.89–110.
DOI: 10.1504/IJHTM.2024.140387

We are living in an era of astonishing data proliferation and the sharing of user-created content across all kinds of media, from social networks to news sites, e-commerce reviews to endless forums for every kind of interest and niche. Being able to accurately interpret emotions conveyed through such messages is increasingly important for social science and politics, in marketing, business, and economics, and elsewhere.

Recent advancements in the field of so-called "sentiment analysis" have led to the development of more sophisticated models capable of extracting and interpreting emotional subtleties in textual data. One such model is the BERT-ABiLSTM – Bidirectional Encoder Representations from Transformers, Attention Bidirectional Long Short-Term Memory. Research in the International Journal of Information and Communication Technology reports on how this large-scale pre-trained algorithmic can be used for sentiment analysis. However, as author Zhubin Luo, of the Hunan University of Humanities, Science and Technology in China, points out the system's use of ABiLSTM, means there are some limitations as it focuses on global features and can overlook nuance.

BERT, Luo explains, can learn language representations from extensive bodies of text. The ABiLSTM, a recurrent neural network, processes text sequences. Luo has now added TextCNN (Text Convolutional Neural Network) to the system to make BERT-CNN-ABiLSTM, a more sophisticated version of the model.

Overall, the underlying bidirectional approach allows the model to understand context from both past-to-future and future-to-past segments of text. This is important for capturing long-term dependencies in text. The attention mechanism within ABiLSTM further refines this by enabling the model to focus on the most pertinent parts of the text when making predictions, thus improving the accuracy of sentiment analysis.

The TextCNN component then uses convolutional kernels of various sizes to detect different granularities of features within the text. This allows the model to capture much more subtle local patterns within the text that would have been missed by simpler models, thus providing a yet more detailed analysis of textual content.

The improvements reported by Luo are particularly relevant for scenarios that require detailed text classification and recognition. This might include sentiment analysis on social media, evaluating customer feedback in e-commerce platforms, or empowering "intelligent" online question-and-answer systems.

Luo, Z. (2024) 'A study into text sentiment analysis model based on deep learning', Int. J. Information and Communication Technology, Vol. 24, No. 8, pp.64–75.
DOI: 10.1504/IJICT.2024.139869

In 2015, the United Nations initiated the Sustainable Development Goals (SDGs) an ambitious plan to end poverty, safeguard the environment, and promote prosperity for all by 2030. These 17 interconnected goals recognise that progress in one area can affect another area in positive ways. For instance, improving water quality (SDG 6) can have a ripple effect, enhancing health (SDG 3) and education (SDG 4). It is important to understand the connections so that effective policies can be put in place to help us achieve the goals in what might be referred to as a holistic manner.

A review in the International Journal of Sustainable Development has looked at the state of research in this area and provide a systematic summary, as well as incorporating insights from a workshop with SDG experts. The findings show that work is still in its infancy and focuses largely on statistical and conceptual associations rather than causal relationships. The reviewers suggest that this emphasis on correlation rather than causation means it is difficult to reproduce findings and apply them to policymaking. This, they add, is a particular issue at the local level.

The SDGs are the successor to the Millennium Development Goals and emerged from a participatory process that required consensus among UN member states. Unlike those earlier projects, the SDGs are broader in their scope and more integrated. They emphasize the need to understand how progress in one goal might affect other goals. This, the team suggests, means that policymakers and other stakeholders must recognise the interconnectedness of the goals and dismantle the old policy silos to help promote integrated approaches to sustainable development.

A new framework to address the gaps in research and understanding, emerges from this current review. The framework stresses the importance of the local context, as well as the interconnections that might vary critically because of regional socio-economic and environmental conditions. As the 2030 deadline approaches, there is an increasing urgency in attempting to achieve the SDGs. The consolidation of research findings and the standardization of data collection could help us implement real-world action that works to achieve those goals.

Chaniotakis, E., Siragusa, A., Tzanis, D. and Stamos, I. (2024) 'Scoping SDG interlinkages and methods to infer them', Int. J. Sustainable Development, Vol. 27, No. 5, pp.1–54.
DOI: 10.1504/IJSD.2024.140326

Wound infections, particularly associated with burns, are a serious health problem causing high morbidity and mortality. Aside from hygiene and basic dressings, antibiotics are the standard treatment for serious wounds. However, cost, access, and emerging bacterial resistance, make their use difficult and ineffective, especially when a course of treatment is not completed. Globally, a huge number of deaths occur because of infected burns especially in low- and middle-income countries, and most commonly in rural areas.

Treating burn wounds is complex due to various factors. Burns disrupt the skin barrier, exposing fluid from the wound to opportunistic bacteria that thrive on the exuded nutrients. Such wounds also compromise blood supply and affect the local immune response. Moreover, a large burn, covering more than a fifth of the skin will often lead to systemic inflammatory response syndrome (SIRS), further complicating infection management.

Research in the International Journal of Biomedical Nanoscience and Nanotechnology, has looked at how silver-containing antimicrobial nanoparticle preparations might be used, not as topical antiseptic creams, but as a sustained-release component of an advanced wound dressing. The cost of such a dressing would likely make it unviable in normal circumstance. However, the team involved from KLE University in Belagavi, India, has developed a low-cost, antimicrobial starch-based polymer film within which they can embed silver nanoparticles, synthesized using a simple method from tea extracts.

The team's environmentally friendly approach also benefits from using those plant extracts as they contain polyphenolic compounds, which have an additional antimicrobial character as they are antioxidants, anti-inflammatory agents, and antimicrobial.

In tests, the researchers – Sambuddha Dinda, Anuradha B. Patil, Sumati Annigeri Hogade, and Abhishek Bansal – showed that their starch-based film showed significant antimicrobial activity against various types of bacteria, including the ever-troublesome Staphylococcus aureus and Pseudomonas aeruginosa.

"This study showed anti-microbial efficacy of a low-cost starch-based polymer film containing Ag-NP with antioxidant biomolecules of green tea which can be easily fabricated and used for wound dressing," the researchers conclude.

Dinda, S., Patil, A.B., Hogade, S.A. and Bansal, A. (2024) 'Development of an anti-microbial starch-based polymer film embedded with silver nanoparticles by green synthesis from tea extract: a potential low cost wound dressing for rural population of developing countries', Int. J. Biomedical Nanoscience and Nanotechnology, Vol. 5, No. 1, pp.1–14.
DOI: 10.1504/IJBNN.2024.139295

Research in the journal Interdisciplinary Environmental Review, which draws on data from the "Climate Change in the American Mind: National Survey," offers new insights into generational attitudes towards climate change and discusses the implications for businesses.

The survey itself captured responses from various generations born after World War II – The Baby Boomer generation (born approximately 1946 to 1964), Generation X (~1965 to 1980), the Millennials (born ~1981 to 1996), and Generation Z (born ~1997 to 2012). The research unravels common threads of concern about global warming that spans all age groups. This, the researchers suggest, means there is an evolving awareness among consumers across the generations that is means businesses must adapt to new expectations regarding climate change and sustainability.

Global warming, a term often interchanged with climate change but technically distinct, refers to the long-term rise in Earth's average surface temperature due to increasing concentrations of greenhouse gases. In contrast, climate change encompasses a broader range of shifts in weather patterns. Both phenomena, driven primarily by human activities such as fossil fuel combustion, deforestation, and industrial processes have led to a rise in the frequency of extreme weather events and an increase in their severity. Hurricanes, droughts, wildfires, and floods, have been with us for millennia but their increasing rage is leading to human tragedy as ell as substantial destruction and disruption for communities and businesses alike.

Angelina Kiser and Tracie Edmond of the University of the Incarnate Word in San Antonio, Texas, USA, discuss the international response to the present environmental challenges, such as the 1992 United Nations Framework Convention on Climate Change, the 1997 Kyoto Protocol, and the 2015 Paris Climate Agreement. However, they add that despite these efforts, countries vary in their levels of commitment and activity is heavily influenced by economic considerations and political pressure rather than the science.

They point out that while all generations have awareness of the issues, the younger generation is perhaps more keenly attuned to the growing crises and is therefore more demanding of business and the need for sustainable business practices. They add that a significant number of younger Americans support a transition to renewable energy sources, indicating a potential shift in market demands.

Businesses now face the challenge of balancing their diverse stakeholder interests with consumer values. Finding the balance between what shareholders, employees, and the public need and want of them is perhaps the most pressing issue they face because they may all hold potentially conflicting expectations and mutually exclusive demands. While sustainability initiatives may incur higher costs, leading to increased prices or reduced profits, neglecting consumer demands for climate-friendly practices could result in a loss of market share.

Kiser, A. and Edmond, T. (2024) 'From baby boomers to Gen Z: global warming and business', Interdisciplinary Environmental Review, Vol. 23, No. 4, pp.301–316.
DOI: 10.1504/IER.2024.140246

Nature has provided inspiration for many innovations. In recent years, the development of algorithms that emulate the problem-solving ability of the natural world have come to the fore. Such algorithms, computer programs that are modelled on various natural behaviours, are known collectively as nature-inspired algorithms. They are designed by studying the dynamics of a natural or social system, such as those observed in ants and bees or the movements and skills of bats and birds. There are several classes defined by the behaviour on which they are modelled, including swarm intelligence, biological systems, and physical or chemical processes.

Swarm intelligence is a particularly useful part of nature-inspired algorithms. It is derived from the collective behaviour of groups of animals, such as flocks of birds or schools of fish. The principle behind these algorithms is the concept of self-optimization, a hallmark of natural systems that efficiently manage resources and adapt to changing environments to solve seemingly complex problems. By transferring these natural skills into an algorithm, researchers are finding ways to develop self-optimizing systems for some of the problems we face.

Writing in the International Journal of Advanced Intelligence Paradigms, S. Thanga Revathi of the Misrimal Navajee Munoth Jain Engineering College in Chennai and N. Ramaraj of Vignan University in Guntur, India, explain how nature-inspired algorithms can give us an efficient and adaptable way to approach difficult and perhaps otherwise intractable problems.

They cite some of the most notable, such as the ant colony optimization (ACO), particle swarm optimization (PSO), cuckoo search, and the bat algorithm. Each of these algorithms uses characteristics of natural collective behaviour to converge on a solution to a problem. For instance, within a bird flock, each bird follows simple rules without any single leader that then gives rise to the complex system that is a starling murmuration, for instance. Flocking behaviour like a murmuration is commonly a collective predator avoidance technique. The birds' movements are influenced by their closest neighbours organization. Critical avoiding collisions, matching velocities, and maintaining proximity to the group are what lead to this coordinated and cohesive movement of the flock.

The practical applications of swarm-based algorithms span a wide array of fields. In biomedicine, for example, they can be used in diagnosis, genetics, and protein structure prediction. Other algorithms can be used to manage networks, classify data, and managing queuing systems. The review suggests that we have only just begun to develop nature-inspired systems and that there is great potential to model many different systems in the natural world for addressing a wide range of the problems facing humanity.

Revathi, S.T. and Ramaraj, N. (2024) 'A brief study about nature inspired optimisation algorithms', Int. J. Advanced Intelligence Paradigms, Vol. 28, Nos. 1/2, pp.1–15.
DOI: 10.1504/IJAIP.2024.139952