Machine learning in education could tune up the way in which music is taught and learned. Such tools might offer instant, precise feedback and tailored support to students. They do, however, raise some interesting ethical questions about pedagogy, accessibility, and cultural integrity.

Historically, music instruction has always been about live teaching, often one-on-one, where students receive guidance based on their teacher's observations and interpretations of their abilities. Such direct mentoring is inherently limited by time, subjectivity, and the delays between performance and feedback. In contrast, machine learning (ML) systems now being developed promise to provide real-time, data-driven responses to a student's playing. Moreover, work in the International Journal of Information and Communication Technology, suggests that such tools might even detect issues that even the most attentive human teacher might miss and so provide important advice to the student.

These intelligent systems combine audio signal processing with predictive modelling rooted in deep learning. Deep learning is a type of artificial intelligence (AI) inspired by how the human brain processes information. It allows software to detect patterns in large datasets, such as musical performances, and make judgements or predictions based on those patterns. Once trained, these models can assess technical elements such as pitch accuracy, rhythm and tempo precision, dynamics, and even expressive phrasing.

For music students, such tools can offer feedback instantaneously, perhaps between lessons with human teachers. A student practising a piece can be alerted mid-performance about inconsistent tempo or missed notes and can see suggestions for improvement immediately, and so be ahead of the score when it comes to playing for their tutor or in an examination setting.

The systems also offer personalisation at a level difficult to achieve in traditional teaching. By monitoring a student's progress over time, machine learning tools can detect persistent challenges, such as uneven articulation or imprecise rhythm, and adapt future exercises accordingly. This responsive, data-informed support is especially valuable in contexts where access to expert instruction is limited, allowing high-quality feedback to reach learners beyond the conservatoire or music school.

Lu, L. (2025) 'AI-powered intelligent music education systems for real-time feedback and performance assessment', Int. J. Information and Communication Technology, Vol. 26, No. 18, pp.33–47.
DOI: 10.1504/IJICT.2025.146690

A new approach to career decision-making could boost students' confidence in their choices by not only offering information and guidance, but by helping them believe in their ability to choose wisely. The research International Journal of Computational Systems Engineering draws on social cognitive theory. This theory emphasizes how individuals learn and make choices by observing others and reflecting on personal experiences. By incorporating this theory into the new model for offering careers advice, the researchers say their approach can strengthen student self-efficacy, their level of confidence in their own abilities.

The new model builds on what the researchers call the "implicit feature matrix". These are the often unspoken, unconscious beliefs and assumptions that shape how students perceive different careers. These internal influences, which may arise from family expectations, past successes or failures, or cultural narratives, can nudge students towards particular choices and away from others. By identifying and incorporating these hidden factors into the new model, it is possible to produce more nuanced and accurate careers advice. This effectively overrides the assumptions and biases that might otherwise guide a student down a potentially unsuitable career path.

Conventional careers advice focuses on external characteristics, such as a student's grades, their interests, and their stated preferences. It then attempts to match the student's profile to suitable occupations. Unfortunately, such an approach can rely too heavily on stereotypes or statistical clustering and ignores the deeper motivational dynamics. The new model demonstrates has greater "structural validity" and captures how various psychological and social influences interact to shape choices. One of the distinctive features of the model is that it uses particle swarm optimization, a computational technique inspired by the coordinated movement of social animals such as bees.

In this technique, individuals learn how to behave by observing and adapting to the behaviour of others in the group. In the educational context, students are the individuals, or agents, in the swarm, and can strengthen their self-efficacy by engaging with peers, learning from each other's successes and strategies, and refining their own paths accordingly. This approach thus introduces a social-learning component to careers advice that goes beyond simple self-reflection and encourages communication skills, empathy, and collaborative problem-solving.

Zhou, Q. (2025) 'Particle swarm optimisation-based self-efficacy model for student learning and decision-making capabilities', Int. J. Computational Systems Engineering, Vol. 9, No. 10, pp.1–9.
DOI: 10.1504/IJIJCSYSE.2025.146794

A new approach to the storage and processing of large-scale data, called Hadoop-OptiStor is discussed in the International Journal of Reasoning-based Intelligent Systems. The optimisation framework is built on top of the widely used open-source system Hadoop and could deliver a more responsive and efficient backbone for big data infrastructure.

Apache Hadoop is an open-source framework for scalable, distributed computing. It can store and process large datasets across computer clusters using the MapReduce programming model. It was originally developed for inexpensive, off-the-shelf machines, but is now used to support high-end systems. Importantly, Hadoop can handle hardware failures automatically, ensuring reliable operation in large-scale environments.

Despite its benefits, Hadoop's traditional mechanisms, such as its approach to data replication for reliability and its task scheduling strategy, it has some limitations when it comes to huge data volumes. Processing delays, inefficient resource allocation, and data traffic bottlenecks are becoming more common in many real-world deployments.

Hadoop-OptiStor addresses these issues by reworking how Hadoop handles three core operations: data distribution, replica management, and task scheduling. The developers have carried out a practical deployment within a major internet company. Their new model achieved a 30% reduction in task execution time, a 20% decrease in system load, and a 40% increase in data throughput. These results reflect a more balanced use of computational resources and a notable gain in overall system efficiency.

Additionally, Hadoop-OptiStor uses machine learning to anticipate data access patterns and optimise scheduling. It can learn from historical usage data and make proactive adjustments, further reducing delays and inefficiencies.

Industries that rely on real-time data analysis, such as healthcare, finance, transportation, and energy, need infrastructure that can handle both the scale and speed of modern data streams. Hadoop-OptiStor offers a practical route to improving performance without making existing systems redundant. Its suitability for low-latency, high-throughput scenarios also makes it well-aligned with the needs of Internet of Things (IoT) environments, where devices continuously generate and respond to data.

Qi, Y. (2025) 'Optimisation of distributed storage technology for large-scale data based on Hadoop technology', Int. J. Reasoning-based Intelligent Systems, Vol. 17, No. 7, pp.11–20.
DOI: 10.1504/IJRIS.2025.146672

A research-led installation entitled Move in Tempo could reshape how we understand the intersection of human movement, machine learning, and artistic experience. The artwork was developed as part of a growing field at the convergence of interactive media art and computational design. As such, the project functions as both an artwork and an experimental platform. Those involved are thus using it to probe the ways in which physical motion, algorithmic systems, and audience engagement can be integrated into a single responsive environment. They discuss their findings in the International Journal of Arts and Technology.

At the centre of Move in Tempo is a dynamic interaction model. Visitors to the installation are invited either to move within the space, triggering algorithmic responses in light, sound, and digital imagery, or to observe choreographed performers navigating the same system. Crucially, both modes engage with the same technological infrastructure, designed to sense, interpret, and respond to movement in real time.

This infrastructure involves motion-sensing technologies, including depth cameras and inertial sensors. These are coupled to adaptive machine learning algorithms that process gestural input as a form of data. These inputs do not simply control pre-programmed responses, instead they contribute to the live construction of the installation's aesthetic and structural outputs. In effect, human movement becomes both the medium and the message.

The research team behind Move in Tempo frames the work as part of a broader inquiry into temporality in digital systems. Specifically, they investigate how the rhythms and pacing of human movement can be synchronized with computational processes. Rather than treating time as a background condition, the installation makes it an explicit parameter: tempo, duration, repetition, and variation all shape how the system responds and evolves. This has implications not just for the arts, but for any field where human-computer interaction relies on timing, gesture, or non-verbal cues.

Nogueira, M.R., Simões, J.B., de Carvalho, J.M. and Menezes, P. (2025) "'Move in Tempo': involving the audience through their movement in installation art", Int. J. Arts and Technology, Vol. 15, No. 5, pp.1–22.
DOI: 10.1504/IJART.2025.146787

The integration of large language models (LLMs), commonly referred to as generative artificial intelligence (AI), into healthcare systems is increasingly common. The hope is that LLMs will reduce the workload on clinicians, streamline administrative tasks, and improve access to medical information. A new analysis of the output of LLMs published in the International Journal of Electronic Healthcare urges caution in their use, however. The work highlights the need for a nuanced understanding of what these tools actually do, and more importantly, what they do not.

LLMs, are statistical algorithms trained on large amounts of text and used to generate plausible sequences of words based on known patterns. They may appear to have what is commonly referred to as artificial intelligence (AI), but they do not understand the content of their training datasets nor their output. Indeed, the output is shaped not by reasoning but by mathematical correlations between words and phrases in the data on which it was trained and so, in a sense, LLMs are really just very sophisticated autocomplete systems.

The distinction between the received wisdom concerning AI and the workings of LLMs is critical, especially in the context of healthcare. The uncritical reliance on seemingly fluent and authoritative output from an LLM could be a matter of life or death when that output contains errors or worse, wholly incorrect 'hallucinations'.

There are also concerns regarding inherent bias in LLMs. Given that they are built on human-generated data, there is the possibility that said data contains embedded social, cultural, and institutional biases. These can manifest in outputs that inadvertently reinforce health disparities, particularly for patients from underrepresented or marginalised groups. For instance, if a model is trained primarily on data from Western clinical populations, its diagnostic suggestions or educational content may be less relevant, or even misleading, for patients with different backgrounds or needs.

Healthcare is not a static field; it is what scholars call a complex adaptive system, meaning that small changes can produce wide-reaching effects. The introduction of LLMs could subtly reshape how care is delivered, how records are kept, or even how professional expertise is perceived. Over time, increased reliance on AI-generated documentation or advice could deskill clinicians, shifting responsibility from trained professionals to automated systems. Meanwhile, patients may begin to see AI as a substitute for human attention, altering expectations around trust, empathy, and accountability in care.

In the light of these various risks, the research advocates for a model of reflexive governance, a flexible and responsive framework for monitoring and regulating AI in healthcare. This approach would replace rigid, one-time assessments with ongoing oversight that can adapt to new uses, emerging harms, and shifting ethical considerations. Crucially, such governance could and should go beyond technical safety to include values like equity, transparency, and patient autonomy.

Salzmann-Erikson, M., Göras, C., Lindberg, M., Arakelian, E. and Olsson, A. (2025) 'ChatGPT in complex adaptive healthcare systems: embrace with caution', Int. J. Electronic Healthcare, Vol. 14, No. 6, pp.1–17.
DOI: 10.1504/IJEH.2025.146764

As entrepreneurial firms expand into global markets, their success hinges not only on sound strategy or financial performance, but on a more elusive asset: legitimacy. A review in the International Journal of Entrepreneurship and Small Business has looked at a decade of academic research and reveals how boards of directors play a pivotal role in helping firms gain the acceptance and trust they need to operate and succeed in foreign environments.

Legitimacy, in this context, refers to the perception among local stakeholders, such as regulators, customers, business partners, and society in general, that any foreign firm is competent and fit to operate in the host nation. The concept goes beyond legal compliance or profitability. Without legitimacy, companies entering a host market will encounter the problem of what is referred to as the "liability of foreignness", a cluster of disadvantages noted by academics that arise from being perceived as outsiders. These include cultural missteps, limited networks, and suspicion from the local actors.

The review explores a little-studied dimension of internationalisation – the role of the Board in building legitimacy. The work draws on research from management studies, sociology, and political science. It then offers a cohesive framework that explains how Boards can help firms navigate and adapt to new institutional environments internationally.

Boards of directors are often comprised of seasoned professionals with governance duties and are traditionally viewed as vehicles for strategic oversight. But this review highlights a broader, dual function. Internally, boards influence legitimacy through their composition, ethical leadership, and adherence to good governance. Externally, they serve as conduits for reputational capital and local engagement, building connections with the political and economic hierarchy and aligning the company with the local societal norms.

Godley, A., Bolade-Ogunfodun, Y., Lodorfos, G., Nasr, R., Konstantopoulou, A., Soga, L.R. and Amankwah-Amoah, J. (2025) 'The role of governing boards in building legitimacy for new entrepreneurial ventures in host markets: a systematic literature review', Int. J. Entrepreneurship and Small Business, Vol. 55, No. 7, pp.1–36.
DOI: 10.1504/IJESB.2025.146677

As e-commerce platforms grow ever more reliant on cloud computing, efficiency and sustainability have come to the fore as urgent pressures on development. A study in the International Journal of Reasoning-based Intelligent Systems, has introduced an innovative approach to the problem based on a slime mould algorithm (SMA). The work could improve both performance and energy efficiency for e-commerce systems.

At the core of the work is the development of BOSMA – the Balanced Optimisation Slime Mould Algorithm. The SMA is a heuristic optimisation technique inspired by the natural behaviour of slime moulds.

Slime moulds are useful models for algorithms because they excel at finding efficient paths through complex environments and adapting to changing conditions. Moreover, they do so without any central control system. They can explore their surroundings by sending out multiple tendrils, pseudopodia, in different directions, adjusting their shape and connections in response to feedback such as nutrient availability or obstacles.

This decentralised, probabilistic behaviour helps maintain a balance between exploring new possibilities and refining promising ones. By translating these adaptive behaviours into mathematical rules, researchers have designed algorithms that solve complex computational problems, such as network routing, task scheduling, and data migration, where the goal is to find efficient solutions in large, dynamic, and uncertain search spaces.

BOSMA addresses the issues that have become apparent with the simple SMAs by integrating two key enhancements. First, the researchers have added a balanced optimisation operator, which fine-tunes the algorithm's balance between exploration (seeking out new possible solutions) and exploitation (refining known good ones). The second enhancement is the addition of a stochastic difference variance operator, which injects some randomness into the search process and so helps avoid the kind of early convergence on an inferior solution to which simpler SMAs are prone. Together, these modifications make BOSMA more agile and more efficient in solving problems.

In this latest research, the team uses BOSMA to tackle the data migration problem. The transfer of data between different cloud systems or storage environments is complex especially in high-volume e-commerce operations. If data migration is not optimised then energy costs and so economic costs rise. BOSMA recruits mobile devices and edge computing terminals to reduce the load on the cloud systems. By dynamically adjusting for communication delays and other operational constraints, BOSMA thus reassigns tasks to whichever processing environment delivers the greatest energy saving.

Li, Y., Liu, N., Dang, L. and Huang, Y. (2025) 'E-commerce cloud computing data migration method based on improved slime mould algorithm', Int. J. Reasoning-based Intelligent Systems, Vol. 17, No. 7, pp.1–10.
DOI: 10.1504/IJRIS.2025.146670

An innovative design for electric torque motors that could reshape how heavy-duty machinery, industrial automation, and electric vehicles are built is reported in the International Journal of Hydromechatronics. By rethinking how permanent magnets are embedded within the rotating component responsible for generating motion, the researchers have developed a method that increases torque without compromising durability or thermal stability.

Torque motors are a category of electric motor engineered specifically to generate high levels of torque, or rotational force, particularly at low speeds. This makes them ideal for applications such as hydraulic pumps, industrial robotics, and aerospace actuators. Unlike conventional motors that require gearboxes to achieve sufficient torque, torque motors often use a direct-drive approach, eliminating mechanical complexity and potential points of failure. However, enhancing torque output without risking overheating or structural failure remains a central engineering challenge.

Traditional torque motor designs commonly mount permanent magnets directly on the surface of the rotor. While this arrangement can strengthen the motor's magnetic field and thereby improve torque, it introduces a serious vulnerability: magnets can detach under stress, particularly at high rotational speeds or under heavy loads. To mitigate against this, engineers commonly use carbon fibre sleeves to encase the magnets. But these protective layers increase the electromagnetic air gap, i.e. the space between rotor and stator where magnetic interaction occurs, and this reduces magnetic efficiency and so the torque.

This research focuses on a novel semi-inset permanent magnet pole rotor structure. Such a design offers a more integrated solution by partially embedding the magnets into notched sections of the rotor itself, the new structure locks the magnets securely in place reducing the risk of detachment without the need for bulky external reinforcement. The approach allows the electromagnetic air gap to remain tight. In turn, this supports a stronger magnetic flux for greater torque generation.

The team has simulated their design using Finite Element Analysis (FEA), a computational modelling technique that factors in physical forces, stress, and heat. Such simulations allow for the fine-tuning of the magnet shape and position to optimize magnetic flux density and structural integrity. Particular attention was paid to the thermal characteristics of the motor, since greater torque typically demands more current, and thus produces more heat. Heat dissipation features were integrated to prevent overheating, and mechanical tests showed the structure could endure operational stress without magnet failure or demagnetization.

He, X., Xiang, P., Xu, Z., Wei, X. and Li, Y. (2025) 'Development of a torque motor with enhanced performance employing novel semi-inset PM pole', Int. J. Hydromechatronics, Vol. 8, No. 5, pp.40–54.
DOI: 10.1504/IJHM.2025.144973

The indirect, long-term effects of armed conflict, particularly worsening food insecurity and reduced access to education, are discussed in the International Journal of Economic Policy in Emerging Economies. The study reveals that conflicts are more economically damaging than the immediate physical destruction of war. The conclusion is drawn from data covering 169 countries over an almost three-decade period beginning in 1990. The work could give pause for thought for rethinking how we design humanitarian aid and post-conflict recovery strategies.

By using a path-analytical approach, the study maps out the interconnected ways conflict influences national outcomes, tracing how disruptions to food systems and education ultimately reduce future income. This approach stands in contrast to standard analyses that examine the direct effects of conflict in isolation, such as infrastructure damage or population displacement. As such, it explores how war erodes a nation's human capital, the health of its citizens, the national knowledge and skills base, and how this occurs long before those detrimental effects become visible in the simplistic economic accounting of gross domestic product.

The findings are overwhelming. The work shows that over time, the economic harm caused by deteriorated nutrition and lost schooling is about 1.5 times greater than the damage wrought by bombed buildings or burned-out factories. In other words, while war's immediate effects grab headlines, its most enduring costs are often borne quietly by the young and vulnerable.

Central to this argument are the three D's of conflict: destruction, diversion, and disruption. "Destruction" refers not just to physical infrastructure loss but also to public health degradation, a phenomenon sometimes described as the third army of war. Diversion involves the redirection of public resources away from essential services like schools and hospitals toward military spending. Disruption captures the systemic breakdowns, such as severed supply chains or institutional collapse, which make it difficult to sustain basic services even where they still exist.

By interfering with food security and access to education, these mechanisms have compounding effects on a population's long-term productivity. Children who are malnourished or out of school during conflict are less likely to contribute effectively to the economy years later, weakening the foundation for recovery. The findings thus challenge the emphasis in humanitarian response, which prioritizes short-term survival for obvious reasons. However, a broader approach is need to safeguard education and nutrition, particularly for children, and so the national future.

Marktanner, M. and Merkel, A. (2025) 'From conflict to empty stomachs, empty classrooms, and empty wallets', Int. J. Economic Policy in Emerging Economies, Vol. 21, No. 3, pp.244–264.
DOI: 10.1504/IJEPEE.2025.146573

A study in the International Journal of Environment and Waste Management found a direct link between Ghana's mounting urban flooding challenges and unregulated waste disposal practices. The findings have significant consequences for public health, the real estate market, and broader urban development. The researchers focused on the cities of Kumasi and Tamale and found that open dumping, fly-tipping as it is known in some parts of the world, where households discard waste indiscriminately in streets, drains, and vacant lots, has become a serious problem associated with urban vulnerability. Four out of five households surveyed admitted to open dumping.

The research used waste management theory alongside statistical regression modelling to look at the prevalence of waste mismanagement and showed that it is a central factor in recurring flood events. The number of households engaging in open dumping is surprising, as the infrastructure for waste management does exist in many of the areas where this occurs. The behaviour is perhaps rooted more in public attitudes and a lack of civic awareness than problems with the waste management systems.

Increased flood risk associated with waste mismanagement has led to a reduction in residential property values. Moreover, the researchers found that tenants are reluctant to pay higher rents in areas that are vulnerable to floods, even when the architectural standards of the buildings are high. The team quantified this market reaction using statistical analysis and found a consistent downward pressure on rental prices in flood-prone zones.

Falling property values is important economically to landlords and developers, but also to the financial viability of investing in urban housing, particularly in lower-income neighbourhoods. The researchers warn that this downward pressure on prices might threaten the health of mortgage markets and reduce local government revenues that depend on property-related taxes—creating a cycle of underinvestment and further degradation.

Ofori, P. (2025) 'Indiscriminate disposal of waste challenges and implication for urban flooding and property values in emerging cities', Int. J. Environment and Waste Management, Vol. 37, No. 1, pp.88–133.
DOI: 10.1504/IJEWM.2025.146388