The earliest stage of drug discovery is governed by a simple constraint: there are far more possible drug-like molecules than any pharmaceutical laboratory could ever test. A new deep learning system, reported in the International Journal of Reasoning-based Intelligent Systems, offers a way to speed up research and could unblock industry bottlenecks.

Bringing a new pharmaceutical to market can take more than a decade and will inevitably cost billions of dollars in research and development, testing, regulatory compliance, and marketing. A large share of that investment is spent on identifying compounds that bind to biological targets, these are commonly proteins involved in disease, whether a protein found in a pathogen or a protein in our bodies involved in the disease. Virtual screening, so-called in silico studies, has for decades used computer models to predict which molecules from a library of candidates might be suitable for testing in vitro (in the laboratory) and ultimately in vivo (in animals, then humans).

That said, established methods fall into two categories. The first are receptor-based approaches, such as molecular docking, that simulate how a molecule fits into a protein's three-dimensional binding site and estimate the strength of the bond that forms between. The accuracy of this approach depends on high-quality protein structures and simplified scoring formulae. A second approach is the ligand-based approach and this instead looks for compounds resembling known active molecules, using predefined chemical features, or descriptors.

These techniques can be computationally efficient and heavily successfully led to many pharmaceuticals on the market today. However, they rely heavily on prior knowledge and expert assumptions. In both cases, human-designed rules limit how much chemical complexity can be captured. The advent of deep learning systems is opening up a new approach.

Instead of manual feature selection, deep learning, a form of machine learning that uses multi-layered neural networks to detect patterns directly from raw data, can treat drug candidate molecules as graphs, with atoms as nodes and chemical bonds as edges. A graph neural network updates each atom's representation based on its neighbours, allowing the model to learn subtle structural relationships.

Crucially, this new approach uses another information channel in addition to the graph. It handles the drug candidate's SMILES string. A SMILES string is a unique text-based representation of the chemical structure of a molecule. By using structural and sequential representations together, the researchers could improve performance significantly. In tests on standard public benchmarks, the model achieved a score of 0.889; where 1.000 would be a perfect score. This score is a measure of how well the system distinguishes between active and inactive drug candidates. A score of 1 is ideal prediction whereas 0.5 reflects a 50:50 chance, a guess. Incredibly, the system could screen one million molecules in a quarter of an hour, which is 80 per cent faster than conventional approaches.

Electricity pylons, or transmission towers, have been a critical component of energy infrastructure for decades. The structural integrity of these power towers, which stride across landscapes the world over, is vital to power supply and public safety.

A study in the International Journal of Energy Technology and Policy has investigated a novel, more precise and efficient way to inspect pylons using advanced 3D scanning and geometric analysis. The approach might speed up the shift from labour-intensive field checks to what might be referred to as a fully digital inspection regime.

The researchers explain that a laser system can be used to scan a pylon's geometry in minute detail to generate a "point cloud". This is a collection of millions of spatial points representing the pylon's surface. To assess structural integrity, multiple scans are taken from different angles and then must be aligned into a single coordinate system in a registration process. This typically occurs in two stages: coarse registration, which provides an initial alignment, and fine registration, which refines it to high precision.

The lattice frameworks of pylons with their intersecting beams and sharp edges generate extremely large datasets and create ambiguities when identifying matching features, so registration even with the best algorithms is tough and consequently error-prone. In the IJETP paper, the researchers propose the use of Gaussian curvature in the feature-extraction process required for registration. Gaussian curvature is a mathematical measure of how a surface bends at a given point: flat areas have near-zero curvature, while sharp edges or corners have higher values. Because beam intersections and joints exhibit high curvature, they provide distinctive geometric markers for alignment.

Once aligned, the digital model of the pylon can then be compared with a high-precision reference design to identify geometric deviations. This allows engineers to detect misalignments or structural problems with confidence and so prioritise maintenance and repair across the power grid.

Educators are using digital platforms more and more alongside conventional classroom teaching. A study in the International Journal of Continuing Engineering Education and Life-Long Learning has taken a look at the important question of whether or not this "blended" educational model enhances learning.

Blended online-offline education, sometimes referred to as smart education, combines face-to-face instruction with tools such as learning management systems, digital resources, and data-driven feedback. It was already being used prior to the covid pandemic, but in 2020 it became a critical part of educational life and since then has become embedded in education. It is flexible and holds the promise of personalised learning. However, systematic research into how students experience offline-online education has not kept pace with digital developments.

The research in IJCEELL identified 14 different factors that could shape the student learning experience. They grouped these into five broad dimensions: course environment and platform, course design, teacher characteristics, learner characteristics and social interaction. The factors included the reliability and usability of digital systems, the clarity and coherence of course structure, the responsiveness of teachers, the capacity of students for self-directed learning, and the quality of peer engagement.

Rather than treating these factors as separate variables, the researchers examined how they interact to give particular outcomes. As such, they used an interpretive structural model to find the hierarchical relationships. In practical terms, this approach can distinguish between foundational elements, intermediary influences, and the educational outcomes.

Their structural model has course content and resource infrastructure at its foundations. Teaching interaction and learner-related factors such as motivation and self-regulation then sit on top of these foundations. The layer above that is the learning outcomes, including satisfaction and performance. As one might expect, the model showed that student experience emerges from interconnected factors from the base to the top, rather than isolated inputs.

The framework demonstrated more than 95 per cent accuracy and performed better than earlier approaches that used static surveys or business-derived models where factors are all treated independently. Ultimately, it showed that investment in digital technology alone is unlikely to transform learning outcomes without close attention also being paid to course design and teacher development.

Fang, Y. and Hu, J. (2026) 'Analysis of factors influencing student learning experience in the blended online-offline smart education model', Int. J. Continuing Engineering Education and Life-Long Learning, Vol. 36, No. 7, pp.23–34.
DOI: 10.1504/IJCEELL.2026.151814

For decades, China's ascent to become the world's second-largest economy was powered by coal-fired energy, steel mills, and chemical plants. The environmental toll grew increasingly visible. In 2016, Beijing launched one of its most sweeping regulatory interventions, the centralised Environmental Protection Inspection (EPI) programme. This would dispatch inspection teams to scrutinise the activities of local governments and major polluters.

Research in the International Journal of Environment and Pollution suggests that the programme has done more than curb emissions. It has improved what economists call green total factor productivity (GTFP) among some of the country's heaviest polluters.

Traditional productivity measures assess how efficiently companies turn inputs such as labour and capital into output. They ignore pollution generated in the process. GTFP adjusts for this by counting emissions and other environmental damage as undesirable outputs. In effect, it measures not only how much a firm produces but also how cleanly it does so. A rise in GTFP means a company is generating more economic value for the same environmental cost or maintaining output while reducing pollution.

The research analysed almost a decade's worth of data from Chinese A-share listed companies across heavily polluting industries. The researchers tracked changes over time using specialist efficiency models, which incorporated environmental factors into their productivity calculations. They then used statistics to compare firms subject to inspections with those that were not, before and after the introduction of the policy. This approach would isolate the effect of the inspections from other economic trends.

The results show a statistically significant increase in GTFP among heavily polluting enterprises following the inspections. Importantly, the gains do not appear to stem primarily from temporary production cuts to meet emissions targets. Instead, the evidence points to increased green technological innovation. Firms invested in cleaner technologies, energy-efficiency upgrades, and process improvements that reduced their environmental footprint in the long term.

A study of university students has demonstrated a link between heavy smartphone use, forward head posture, and neck pain. The work, published in the International Journal of Medical Engineering and Informatics, highlights growing concerns about the physical costs of constant digital connectivity among young adults.

The researchers surveyed 404 students in Malaysia aged between 17 and 30 years old in what is referred to as a cross-sectional study. In such a study, data are collected at a single point in time rather than over an extended period of months or years. The students, 216 male and 188 female, completed an online questionnaire detailing their smartphone habits and any physical problems they experienced, such as backache or neck pain.

The team's statistical analysis revealed that those using their smartphones for prolonged periods tended to have a forward neck posture and suffer neck pain. The analysis suggests that just 1 per cent of those had neck pain purely by coincidence and that it was unconnected to posture and smartphone use.

The cervical spine has seven vertebrae that support the head and protect the spinal cord. Forward neck posture describes the common position adopted while looking down at a phone, in which the head tilts forward and downwards. This posture increases the effective weight borne by the neck, placing added strain on muscles, ligaments, and joints. Over time, such strain can lead to irritation of soft tissues, cause nerve compression, and even affect the natural curvature of the spine detrimentally.

Although the study does not establish cause and effect, the strength of the association and its consistency with previous research point to the obvious conclusion that forward head posture during smartphone use is a modifiable risk factor for mechanical neck pain. Given that this problem is reportedly on the increase among younger people, the suggestion is that a little education and guidance on posture and reducing smartphone use would be well placed to preclude an epidemic of chronic spinal problems in this demographic.

Thick cloud cover can completely obscure the surface of the earth from satellite view, while thinner haze and shadows distort the image of rural and urban regions. As such, many remote sensing images for monitoring climate, crops, and urban growth are only partially usable.

Research in the International Journal of Bio-Inspired Computation offers a way for satellites to see through clouds using a hybrid artificial intelligence system. The system essentially removes clouds from the images sent back by the satellite and reconstructs the land surface beneath with greater fidelity than is possible with earlier techniques. Almost all optical satellite images are affected by clouds to some degree, so improvements in AI cloud removal could expand the reliability of high-resolution Earth observation data.

Traditional approaches have relied either on physical models of atmospheric light scattering or on image-processing techniques that compare multiple images through time or across different wavelengths of light. Those methods are useful but struggle with varying cloud thickness or large, fully obscured areas. More recent machine learning systems, in which algorithms learn patterns from large datasets, have improved results, but they need clear reference images, without them, they simply produce blurred areas where the landscape was obscured by clouds.

The new approach is a deep denoising application known as SenseNet. It treats those image pixels with clouds or haze as being structured noise that can be removed. The system uses a model inspired by nature called a hybrid Coyote Fox Optimisation algorithm, which works by modelling the social, cooperative behaviour in canines to take the input data and process it to find the optimal solution. In computational terms, it helps tune the network's internal parameters so that training does not stall on suboptimal solutions that would otherwise confound the learning algorithm.

Compared with existing denoising approaches, the system improved signal-to-noise ratios by more than two decibels and reduced residual errors. An improvement of just 2 dB is an almost 60 per cent improvement.

By clearing the clouds away, the system can more readily delineate agricultural boundaries and map road networks and bodies of water so that phenomena such as deforestation, crop yields, and infrastructure can be viewed with more detail. In persistently cloudy regions, including much of the tropics, more reliable cloud removal could reduce data gaps, supporting climate adaptation and disaster response strategies that increasingly depend on near-real-time satellite intelligence.

As cities build upwards to accommodate growing populations, the safety of deep excavation, the process of digging large foundation pits to anchor high-rise buildings, has become a significant challenge in the construction industry. These pits must withstand the problem of shifting of the underlying earth, changes in groundwater pressure, and the heavy machinery while remaining stable enough to protect workers and nearby structures. Failures at this stage can trigger collapses, flooding or structural damage.

Work in the International Journal of Critical Infrastructures discusses an AI (artificial intelligence) system designed to improve safety monitoring at deep foundation pit support sites. The system aims to identify abnormal behaviour, such as unsafe actions, improper equipment use, or entry in restricted zones without protective gear, in close to real time so that warnings can be sounded in a timely manner.

Construction sites have traditionally relied on manual supervision and earlier generations of automated monitoring. But these approaches often struggle to detect unsafe behaviour quickly and accurately. Many systems record high false acceptance rates, meaning they mistakenly classify dangerous actions as safe. Others process video feeds too slowly to intervene effectively in rapidly changing environments.

The new system combines several advanced AI techniques to address those weaknesses. It begins by extracting key frames from surveillance footage using the fractional Fourier transform. This is a mathematical method that analyses data across different domains. By identifying the most informative frames rather than scanning every second of video, the system reduces computational load but still retains critical information.

The system then uses a spatiotemporal graph convolutional network, a form of deep learning that analyses both space and time data. The spatial analysis examines how workers and machinery are positioned relative to one another, while the temporal analysis tracks how movements change over time. Unlike conventional image-recognition models that treat frames in isolation, this approach captures sequences of actions and interactions. This is vital for working out what is happening moment to moment on the construction site.

The final step is to use a hybrid model that combines a convolutional neural network (CNN) with a so-called long short-term memory network (LSTM). The CNN can recognise visual features such as body posture or equipment shape. The LSTM can detect patterns in sequences of data. Working together, those two tools allow the system to determine not only what is happening in a single frame, but whether a series of movements constitutes a safety violation.

In their tests on active deep excavation sites, the researchers got a minimum false acceptance rate of 2.43 per cent and a peak abnormal behaviour recognition accuracy of 99.12 per cent. Processing time was as low as 0.19 seconds per analysis cycle, allowing near real-time monitoring.

A large-scale study published in theInternational Journal of Business Innovation and Research has looked at what factors lead to sustained gains in the construction industry. The team looked at 226 nationally registered firms and found that operational efficiency and collaboration, long seen as the sector's primary remedies for underperformance, are insufficient on their own to lead to sustained gains. Instead, the decisive factor is whether companies fundamentally rethink how they create, deliver, and capture value.

The research used a statistical tool known as Partial Least Squares Structural Equation Modelling to analyse information from the 226 companies and to look for any relationships between various organisational factors. The approach allowed them to look at how lean construction practices and strategic partnerships affect performance. It was also possible to discern whether business model innovation acts as a bridge between these strategies and measurable outcomes such as profitability, operational efficiency and competitive position.

Lean construction is a systematic project management approach designed to eliminate waste and maximise value throughout a project's lifecycle. Waste includes excess materials, redundant labour, delays, reworking, and poor coordination between contractors. Unlike simple cost-cutting, lean methods emphasise continuous improvement, integrated workflows, and delivering greater value to clients.

The study confirms that those companies that adopt lean practices do tend to perform better. However, the most significant improvements did not stem solely from streamlining their processes. Instead, lean thinking proved most powerful when it also prompted broader strategic change.

That broader shift is captured in the concept of business model innovation. A business model defines how a company creates value for customers, how it delivers that value, and how it generates revenue. Innovation in this context involves reconfiguring those core elements. For example, this might include moving from one-off, project-based contracts to long-term integrated service models, adopting digital coordination platforms, redesigning revenue structures, and embedding sustainability into what the company offers to clients.

Business model innovation was found to have a strong and direct positive effect on performance. More importantly, it amplified the impact of lean construction. When lean methods were embedded within a redesigned business model, performance gains were significantly greater than when lean was treated as a stand-alone efficiency tool. The research found that partnerships boosted performance only when it allowed companies to innovate in their business models. Access to shared knowledge, resources, and trust-based relationships yielded gains only if companies used them to reconfigure how they compete and deliver value.

In the age of global branding, instantaneous communication, and generative AI images, the symbols that we see in our daily lives circulate at an unprecedented rate. A study in the International Journal of Information and Communication Technology argues that if the symbols we share are to foster understanding rather than confusion, designers must treat them as carriers of cultural meaning, not mere decoration.

The team has used communication science, design theory, and semiotics, the study of signs and how they create meaning, to propose a systematic, evidence-based framework to identify, refine and test traditional cultural symbols. Their concept echoes an insight by Ferdinand de Saussure that suggests that a sign is not simply a form but a form bound to shared content. A flower or mythical creature, in this view, evokes memories, values and beliefs as much as it depicts the object it illustrates.

As digital platforms accelerate the circulation and mutation of images, we experience the fragmentation of symbols and signs. Moreover, in the age of generative artificial intelligence, almost all content is being cannibalised and regurgitated as derivative works, visual motifs are thus losing their inherited symbolism or, at best, being misappropriated or diluted. In the face of these changes, the researchers suggest that semiotics has now become a necessary part of creativity and perhaps the only hope of our conserving our symbols and their significance.

In their paper, the researchers discuss a five-step process beginning with systematic data collection and identification of culturally significant symbols. They followed this with a cross-cultural analysis, design refinement, and empirical testing. Statistical analysis together with expert review allowed them to look at specific symbols, such as the blue-and-white porcelain motifs featuring the lotus, peony, and plum blossom. As a good example of symbolic art, these patterns scored highly for clarity, adaptability, and perceived authenticity. The lotus is widely associated in East Asia with purity and renewal, the peony with prosperity and honour, and the plum blossom with resilience in adversity. Their visual simplicity combined with layered symbolism appears to aid translation into contemporary branding, the analysis found. More complex imagery failed to ignite the imagination of general audiences, although it was recognised as culturally significant by the experts.

Quantitative evaluation thus shows the different priorities associated with authenticity and meaning, challenging assumptions of universal interpretation for even familiar symbols that might be used in marketing and branding.

Mental health problems are among the most pressing of public health challenges, affecting millions across different age groups and societies. Depression, anxiety, and stress-related conditions rank among the leading causes of diminished quality of life worldwide. They exact a heavy social toll and economic cost. Yet diagnosis still relies largely on self-reported symptoms and intermittent clinical interviews, which means diagnosis is vulnerable to memory lapse, stigma, and limited access to trained professionals.

Research in the International Journal of Networking and Virtual Organisations discusses an artificial intelligence (AI) diagnostic system that can spot early signs of various mental health conditions by analysing how people write online. The model, known as a Fossa-based Graph Neural Network (FbGNN), examines language patterns in text drawn from social media platforms and online forums. Instead of relying solely on questionnaires, it studies sentiment-driven textual information, the emotional tone, word choices and behavioural cues embedded in a person's online writing.

The researchers explain that their system combines two advanced computational techniques. The first is the Fossa optimisation, a feature-selection method based on search strategies seen in nature. In machine learning, features are identifiable pieces of information, specific words, phrases or emotional markers. By applying Fossa optimisation, the system can filter out any irrelevant data from those features and identify pertinent indicators of mental distress.

The second component is a Graph Neural Network, a GNN. A GNN analyses relationships by representing information as a network of nodes and connections. Here, nodes correspond to features, and the connections are the interactions between them. This allows the model to detect complex patterns, such as recurring combinations of emotional expression and behavioural signals.

By training the system to classify text based on categories such as depression, anxiety, stress, bipolar disorder, suicidal ideation, and personality disorders, the team was able to then test its accuracy against known sample data. It was able to predict a person's mental health status with an accuracy of almost 99 per cent in the trials. Such accuracy would be useful in screening for mental health problems among a cohort of users, such as students, employees, or any other group. It would allow healthcare follow-ups to be directed at those most likely to have problems that might be addressed and would only miss one in a hundred. Further refinements of the system could bring that accuracy closer to 100 per cent.

Digital payments are a routine part of daily life for many people. As such, the risk of online fraud is rising alongside this convenience. Identity theft, email compromise, scams, and misleading investment schemes all exploit technological weaknesses and often user naivety and can lead to big financial losses.

Research in the American Journal of Finance and Accounting has looked at technological threat avoidance theory (TTAT), a framework used to understand how individuals respond to technology-related risks. The study sheds new light on what motivates users to protect themselves from online financial threats, if they do at all. It considers user attitudes towards fraud and the perception of potential financial loss with the aim of identifying the specific influences that lead to a user taking protective action.

The team surveyed users of online payment platforms and found that rather than an abstract fear of fraud, the decisive factor in whether or not people took preventative measures was simply the perceived financial loss. This finding suggests that awareness campaigns focused on general threats may be less effective than approaches that point out the direct financial consequences of online fraud.

Online fraud costs us roughly US$1 trillion per annum, and it is likely that figure is rising year on year. There are millions of reported cases and probably many more that are never reported. The losses that people bear when a victim of online fraud erodes overall trust in the digital systems on which we rely. Moreover, widespread, organised fraud can disrupt financial infrastructure, threatening broader economic stability and making it almost impossible for regulators to maintain oversight and control.

Facing such problems, the digital economy needs technological innovation in payment systems to incorporate effective strategies to influence user behaviour. Such strategies need to make it difficult for users to compromise themselves through technological naivety. Policymakers, platform developers, and financial educators also need to help in the design of interventions that align perceived risk with actual behaviour and so strengthen the individual against threats as well as help maintain trust in digital financial systems.

The migration to electronic medical records, used by healthcare providers, hospitals, and medical insurers, continues. However, this switch from paper records is leading to an accumulation of data, a lot of which is in free-text form that cannot be processed easily by an algorithm searching for knowledge and looking for patterns.

A study in the International Journal of Business Process Integration and Management has looked at using basic text-mining methods to convert this free text, which might be as unsophisticated as the jottings of a doctor or nurse, into something more organised. This kind of processing could make decisions in medicine faster and more consistent as well as potentially opening up new avenues for medical research and epidemiology.

The research focused on the specific medical condition of lower back pain and the reports associated with it. Lower back pain is a big problem for a lot of people and a major reason people miss days in work or file for disability. Experts can evaluate symptoms and consider what medical scans show and make a diagnosis and offer a prognosis. Administrators have to read through reports manually to determine fees and payments. A system to convert free text to structured text would be a boon, allowing dates and diagnoses to be searched, checked, and analysed much more easily.

The team used pattern-matching rules to look for regular expressions that allow software to detect specific phrases or formats in text. This could then be used to extract clinical and administrative details. This rule-based text mining was combined with machine learning algorithms that can learn from past data and make predictions about new cases.

The researchers tested their system on 255 anonymised reports. Medical specialists validated the extracted information, confirming a precision rate of 98 per cent. The structured information was then used to train three established predictive models: AdaBoost, which combines multiple simple models to improve accuracy; Random Forest, which aggregates the results of many decision trees; and Support Vector Machines, which identify boundaries between categories in complex datasets.

In tests, AdaBoost achieved perfect accuracy in predicting when rest should be prescribed. Random Forest reached 91 per cent accuracy and 93 per cent recall, a measure of how many relevant cases are correctly identified, in return-to-work assessments. The Support Vector Machine recorded a 98 per cent recall rate in classifying disability cases.

Beyond performance metrics, the researchers argue that the approach reduces processing time and limits transcription errors. Because the extraction rules are explicit, the system remains interpretable. This is important, as decisions still need to be explained to patients and others regardless of how structured or unstructured the data is.

As self-driving, autonomous, vehicles head out on to public roads, one of the field's most persistent challenges remains collision avoidance in unpredictable traffic. A study in the International Journal of Vehicle Design discusses an artificial intelligence (AI) control system that has a 97 per cent success rate in avoiding obstacles, with a maximum response time of about half a second.

Urban roads present a shifting landscape of pedestrians, stalled vehicles, roadworks and erratic drivers. For a self-driving car, safe operation depends not only on accurate sensors but also on rapid decisions made under such uncertain conditions. Conventional obstacle-avoidance systems often rely on fixed rules or straightforward processing of sensor data. These approaches can sometimes fail in heavy rain, fog, or headlight glare.

Other systems that use reinforcement learning, a branch of AI in which the algorithm learns by trial and error, such as Deep Deterministic Policy Gradient, need a lot of computing power and often struggle to work quickly enough for real-world driving conditions.

The new approach described in IJVD builds on a reinforcement learning framework called Soft Actor-Critic, or SAC. In this computing system, the software actor proposes driving actions while the software critic evaluates whether or not the given manoeuvre would be sensible or not. SAC is designed to learn so that positive outcomes boost the actor-critic interactions that led to them. The system also embeds entropy, a statistical measure of randomness that allows it to continue to explore the best manoeuvres rather than settling prematurely on a single solution. This helps the system remain adaptable in uncertain environments.

The model also incorporates a self-organising cluster mechanism inspired by the collective movement of a flock of birds, that famously avoid mid-air collisions. At close range, a mathematically defined repulsion force pushes vehicles apart to prevent impact. At medium distances, a velocity calibration rule aligns speed with an ideal braking curve to reduce the risk of rear-end collisions. Additional rules govern wall and obstacle avoidance. This layered design allows multiple autonomous vehicles to coordinate their movements without relying on a single lead vehicle.

A study in the Journal of Business and Management has shown that self-esteem plays an important part in determining whether someone wishes to pursue a leadership role. The findings have implications for both organisational success and career development, underscoring, as they do, how self-esteem affects personal motivation.

The research suggests that self-esteem affects a person's regulatory focus, a psychological framework that influences how individuals approach challenges and goals. There are two main types of regulatory focus: promotion focus and prevention focus. Promotion focus is characterised by a drive for growth, achievement, and opportunity-seeking. In contrast, prevention focus is concerned more with the avoidance of failure, staying safe, and fulfilling one's basic duties and no more.

Individuals with high self-esteem are more likely to be promotion focused, which then drives them to seek leadership roles. Those with lower self-esteem tend to lean towards prevention focus, which makes them less inclined to pursue leadership roles.

The effect is not solely down to the individual's personality, however. The work also showed that career encouragement and support from supervisors and peers can affect a person's focus and the motivational pathways they might take. Encouragement can boost the positive effects of promotion focus, motivating individuals to pursue leadership. However, for those with lower self-esteem, encouragement can have the opposite effect, reinforcing their reluctance to take on leadership responsibilities due to their prevention focus. The research thus highlights a need to consider individual psychological states when offering career support so that talented people who have leadership potential are not lost to those roles because of their lower self-esteem.

The team adds that unlike static predictors, such as personality traits or gender, regulatory focus can be affected by one's experiences and external support. This makes it a more pliable characteristic that might be influenced to the person's benefit through good career development advice for those with the potential for leadership.

Research into the COVID-19 crisis, which began in December 2019, suggests that although there was widespread loss and disruption, the international crisis also planted the seeds for grassroots innovation and resilience. A study in the International Journal of Entrepreneurial Venturing of one hundred initiatives that emerged in Belgium during the pandemic finds that when established institutions struggled to respond quickly, individuals and organisations were able to step up to create new economic and social value.

The research focuses on initiatives defined broadly to include both newly created ventures and existing organisations that adapted their activities. These ranged from informal mutual aid efforts to repurposed businesses and newly launched services. Some were started by people with no prior experience of entrepreneurship. Other initiatives were started by established entrepreneurs responding to the sudden changes in demand and regulation. What they shared was a capacity to adjust rapidly under pressure.

The pandemic created conditions of extreme uncertainty. Lockdowns and business closures, imposed to limit the spread of the virus, caused sharp falls in income, consumption, and investment. Many people perceived formal support systems as too slow or rigid to meet urgent needs. This gap became the space in which these initiatives emerged, often spontaneously and with limited resources.

The study looks at this kind of resilience and rather than treating it simply as endurance in the face of a crisis, defines it as a dynamic process of recovery, adjustment, and innovation. Resilience was, during the pandemic and in its aftermath, both the route through which initiatives developed and the results they produced. The researchers argue that action was not driven solely by compassion or urgency, but by the ability to reframe the crisis as an opportunity to meet unmet needs.

The study suggests that locally driven, resilience-based initiatives can complement government and aid responses, particularly in the early stages of a crisis. As such, for policymakers, the challenge is how to recognise and sustain such efforts without undermining their flexibility. We will face pandemic and other shocks in the future, our ability to adapt and innovate in these conditions will be key to an effective disaster response.

A study in the International Journal of Global Environmental Issues has looked at "ritualistic" hunting practices in eastern India. It finds that they are contributing markedly to a worrying decline in wildlife and forest health. The work raises difficult questions about how cultural traditions can coexist with modern conservation goals.

The research focuses on Jungle Mahal, a forested region in western West Bengal, where hunting remains an integral part of religious and social life for several communities, particularly the Santhal. Ritualistic hunting, defined in the study as the killing of wild animals for ceremonial rather than commercial or subsistence purposes, is shown to be placing increasing pressure on ecosystems that are inherently vulnerable.

West Bengal hosts a range of ecologically significant species, including pangolins, fishing cats, and diverse bird populations. Such animals play crucial roles in the functioning of the ecosystems across the region. They help to regulate prey populations, disperse seeds, and recycle nutrients, among other things. The study reports a clear reduction in wildlife richness, biodiversity. It also notes a marked decline in forest density in Jungle Mahal. It is worth noting, that residents and hunters are well aware of these changes to their local environment, however, there is the paper reports, little inclinations towards matters of conservation.

Hunting in the region employs traditional techniques such as bow-and-arrow, traps, nets, and the use of smoke to flush animals from burrows. It occurs throughout the year, but intensifies during festival periods between March and June. During this period, large communal hunts with hundreds or even thousands of participants take place and huge numbers of animals are killed in a very short time.

India's Wildlife Protection Act of 1972 prohibits the hunting of wild animals, but the researchers found that enforcement is weak in remote forest areas. Awareness of conservation laws among local communities is limited, and illegal hunting continues unchecked. The study highlights the fact that there is great mistrust of authorities in such regions and a general perception that conservation policies are detrimental to indigenous values and livelihoods. It remains an open-ended question as to how this disconnection between culture and conservation might be remedied.

Climate change and worsening environmental conditions have brought into sharp relief how we must reconcile development with sustainability. This issue is nowhere more starkly relevant than among the fastest-growing economies. Research in the International Journal of the Energy-Growth Nexus that examined the BRICS countries, Brazil, Russia, India, China and South Africa, suggests that investment in education and training might play a significant role in reducing environmental harm, a role that has often been overlooked.

The researchers analysed several years worth of data from the BRICS countries. These nations account for a large proportion of the world's population, energy use, and greenhouse gas emissions. The analysis found a close relationship between higher levels of human capital and lower levels of environmental degradation, measured primarily through carbon emissions. Human capital refers to the stock of education, skills and knowledge embodied in a workforce, commonly captured through indicators such as schooling level and training.

According to the analysis, improvements in human capital is associated with reduced emissions across the BRICS economies. The results hold across several statistical techniques designed to address common problems in these kinds of studies, such as cultural and social differences, the differing impact of global shocks, and the two-way causality between growth and pollution.

The findings are rooted in endogenous growth theory that says long-term economic progress depends on knowledge, innovation, and research rather than on physical inputs alone. In environmental terms, a more educated and skilled workforce is better able to develop and adopt cleaner technologies, improve energy efficiency, and comply with environmental regulations. Innovation, measured in this study by patent activity, is also associate with better environmental outcomes. This latter point reinforces the idea that technological progress can decouple growth from emissions.

The team adds that globalisation emerges as another factor associated with improved environmental quality. This phenomenon perhaps reflects technology transfer and the sharing of cleaner production methods across borders. Trade openness itself, however, has the opposite effect. More international trade means higher levels of environmental degradation in the BRICS countries. This is consistent with concerns that trade can encourage the expansion of pollution-intensive industries or the import of environmentally inefficient technologies.

As emerging economies continue to drive global growth and emissions, this study shows how education and training are key to climate and environmental strategy. Policies that open up access to high-quality education, raise average years of schooling, and support research and development could lead to environmental benefits as well as providing an economic boost. There is a need, however, to improve trade policy and environmental regulation so that economic development is not to the detriment of environmental sustainability.

Public sector organisations in emerging economies could improve their performance and resilience by taking a more systematic approach to knowledge management, according to a review in the International Journal of Business Excellence.

The review examined research into how government institutions create, share and retain knowledge. It also considered why these practices are important to the institutions' ability to deliver their services, adapt to change, and withstand disruption. The main conclusion is that effective knowledge management should not be considered as a peripheral administrative exercise, but must be seen as an essential strategic component of governance.

Knowledge management refers to the systematic processes through which organisations generate, store, and use knowledge. This includes policy documents, reports, and databases. It also includes tacit knowledge, the experience, skills, and judgement of individual members of the institution. In the public sector, much of this kind of tacit knowledge can be lost through staff turnover and political change if it is not deliberately captured and shared in a timely manner.

Across the research papers covered in this IJBE review, there is strong evidence that public organisations that invest in knowledge management perform better. They are more able to innovate, they can respond more effectively to new social and economic challenges, and they can maintain continuity during periods of political and social upheaval. This kind of institutional resilience is strengthened by effective knowledge management.

However, the review also shows that too many public bodies rely on informal or fragmented approaches to knowledge management. This tends to limit its long-term impact. The underlying problem is often inadequate technological infrastructure. If the digital platforms for storing information and enabling collaboration are not present, then there is no functionality within the institution to allow for effective knowledge management. In addition, cultural and organisational barriers often stymie efforts to share knowledge in institutions with rigid hierarchies and siloed departments, and low levels of trust among employees in different areas within the institution.

Good leadership is the decisive factor in overcoming these various obstacles. Indeed, the review found that ethically inclined and committed leaders who actively promote collaboration and learning can embed knowledge management into everyday practice. Technology helps but human factors such as motivation and skills can make all the difference.

Social media influencers have become a prominent part of modern advertising. They can shape how brands communicate with consumers and how people decide what to buy in ways that conventional marketing perhaps never achieved in the past. A review of research into this phenomenon published in the International Journal of Business Excellence suggests that the impact influencers have is now sufficiently well established that there is a need to study their commercial effectiveness, as well as looking into any ethical or regulatory questions that arise.

The study systematically examined peer-reviewed research in this area to assess what is known about influencer marketing and what information is lacking. Influencer marketing refers to the promotion of products, services or ideas by individuals who have built large and engaged followings on social media platforms. Unlike conventional celebrities, influencers are typically perceived as ordinary people who share their daily lives or specialist interests online. This quality fosters a degree of trust in what they say and what they promote that might be elusive to the traditional scripted advertising and endorsements made by actors, pop stars, and other such well-known individuals.

Researchers consistently find that influencers affect consumer behaviour, particularly purchasing decisions. Many studies measure purchase intention, a term used to describe how likely a consumer is to buy a product after encountering marketing content. Influencers appear to shape purchase intention in several ways, through credibility, meaning how knowledgeable and trustworthy they seem, attractiveness, encompassing both physical appeal and likeability, and the fit between influencer and product, which refers to how closely an influencer's image aligns with the brand they promote.

Influencer endorsements might be referred to as electronic word of mouth, digitally mediated opinions that consumers often perceive as more authentic than traditional advertising. Beyond individual purchases, the research literature suggests that there is a broader effect on brand awareness, improved brand perception, and stronger engagement for the company being promoted with their target audience.

By reviewing what is considered to be a rather fragmented body of research, the paper suggests that influencer marketing is now a permanent feature of contemporary commerce, at least as permanent as any phenomenon might be in such a fickle world as marketing. The researchers say that there is now a need for a more context-specific analysis of this evolving industry, one that also takes into account the ethics, such as those surrounding children and vulnerable adults.

Climate change and sustainability issues are high on the agenda, and the fashion industry is facing increased scrutiny over its practices with regard to their environmental impact. Research in the International Journal of Sustainable Society has looked at how fast-fashion and luxury brands communicate their purported sustainability efforts. The findings reveal a sector grappling with progress and persistent shortcomings that suggests consumers need more dyed-in-the-wool greenwashing from manufacturers.

The research analysed 42 scholarly and industry papers focusing on corporate social responsibility disclosures, website content, and other public reports. Corporate social responsibility refers to the ways in which companies report their efforts to act responsibly towards the environment, society, and stakeholders. The study highlights a growing tension between brand messaging and actual environmental impact, particularly in the form of what is often called "greenwashing". Whereas whitewashing is a metaphor for painting over problems, greenwashing refers to companies exaggerating or misrepresenting their environmental credentials and eco-friendliness of their products.

Experts argue that greenwashing is symptomatic of a larger issue and that is the absence of clear, enforceable standards defining sustainable fashion. In other sectors, such as the food industry, terms such as "organic" are strictly regulated, but in the fashion industry, claims of sustainability are not monitored nor regulated in the same way. This regulatory gap allows companies to gain reputational benefits without verifiable proof, placing the onus on consumers to check their green credentials before buying.

The IJSS paper recommends various measures that could be used to improve transparency and accountability, including obtaining third-party certifications, sharing detailed production processes, and educating consumers on the complexities of sustainable clothing. Of course, there are obstacles in that overproduction and continuous consumption underpin the fashion economy, making the notion of sustainability difficult to achieve.

It is suggested that regulatory oversight could both protect consumers and encourage systemic reform in the industry. For consumers, policymakers, and industry professionals, there is a need for critical assessment of sustainability claims and for structural reform that will help the industry achieve meaningful environmental responsibility.

A study of business school graduates, published in the International Journal of Management Concepts and Philosophy, challenges the widely held belief that such students enter the workforce ill-prepared for the world of work. The finding is based on in-depth interviews with employers in Estonia and the research overall takes a broader view than earlier studies rather than focusing on specific skills such as communication, leadership, and problem-solving. Fundamentally, the research found a much smaller gap between what business schools provide and what employers want than is commonly assumed.

The paper explains that employers cited three main factors supporting this conclusion. First, holding a business degree itself serves as a reliable signal of employability. The credential indicates a graduate has the capacity to learn and adapt, traits employers value highly. Secondly, any deficiencies in technical knowledge or practical experience can often be addressed through on-the-job training. Thirdly, the qualities employers seek, such as adaptability, critical thinking, and ethical awareness, largely align with what business schools already cultivate in their students.

The research has implications for higher education leaders who may have been developing new curricula on the basis of a misconception. The work suggests that a complete overhaul of business school programmes is not needed. They might better focus on improving how their courses develop a graduate's ability to learn continuously. This would then allow employees to adapt as job requirements evolve. For students, the study reinforces the value of a business degree not only as a basic academic credential but also as the foundation for their ongoing professional development.

Of course, the work focused on graduates and employers in Estonia. Future work might increase the sample size, adjust the interview methodology, and widen the reach of the work to other countries.

Farmers in Kazakhstan's steppe region make production decisions based not only on potential profit, but must weigh expected income against the risk of economic volatility. That's according to research in the International Journal of Business Information Systems, which has examined agricultural decision-making in the region. The researchers analysed detailed farm survey data and found that a combination of government subsidies and farmers' attitudes toward risk also have an effect on the structure of agricultural production.

Risk aversion is a preference for more predictable outcomes over uncertain but potentially higher returns. In the agricultural context, this usually involves diversification. Spreading activities across different crops or livestock to reduce the likelihood that a single shock, such as a failed harvest or price drop, will severely damage income. The study confirms that diversification remains a central strategy for Kazakh farmers. However, it also found that even a limited degree of diversification into complementary activities could reduce risk especially for farms with limited resources.

The research found a big difference between outcomes from crop and livestock production. Crop farming is generally producing higher and more stable returns under current market conditions and needs less government support. Beef and dairy farming, on the other hand, often rely heavily on subsidies to remain viable.

The researchers point out that subsidies do more than simply raise income, they affect how willing a farmer might be to engage in riskier activities that might be beneficial in the long-term. While the work focused on Kazakhstan, it could have similar implications for other developing regions where governments might actively intervene to stabilise food supplies and farm incomes. Of course, it is worth noting that state support does introduce additional uncertainties, since subsidy schemes and price supports can change abruptly with policy shifts.

Digital payments, online banking, investment apps, and automated credit assessments have become routine parts of our everyday financial lives. A study in the International Journal of Business Information Systems argues that because of this the money management skills we need have changed fundamentally.

Financial literacy, the research suggests, is no longer simply about budgeting or understanding interest rates, we need digital skills to cope as well as psychological preparedness and the ability to make sensible economic decisions when faced with always-on apps and notifications. This means we need improved financial education that takes into account the digital tools so that no one is excluded for lack of understanding or access.

The study has reviewed the academic research and found that access to digital financial tools does not automatically lead to better financial outcomes. While financial technology, often referred to as "fintech", promises convenience and wider access to services, it also exposes users to unfamiliar risks. Such risks might include online fraud, shape lending, and inappropriate investment opportunities. People who lack confidence with digital systems are particularly vulnerable.

The work found that digital competence, the ability to use digital tools effectively and safely, can change financial behaviour by affecting a person's perceived control. In practice, this means that people who feel capable and in control can use their technical skills to make better financial decisions. That said, even when individuals have access to digital services and the skills to use them, positive results depend on their motivation, self-confidence and their sense of agency.

In modelling the findings from their review, the team saw a reciprocal relationship between motivation and capability. Stronger skills build confidence and intention, while higher motivation encourages additional skill development. The implications are that initiatives that focus solely on technical training may not work well, there needs to be a component of behavioural nudging too to help deliver better results.

Global shipping has a large carbon wake and as such the industry is pushing to reduce emissions. One approach has been to turn to a non-carbon fuel, ammonia as an alternative fuel. Research in the International Journal of Shipping and Transport Logistics, however, warns that international maritime law has not kept pace with the speed at which ammonia-powered vessels are being designed, tested, and promoted. This, the researchers suggest, might leave unresolved safety and liability questions unanswered, which could stall the transition to cleaner shipping.

Shipping accounts for a significant three percent of global carbon dioxide emissions. In 2023, the International Maritime Organization (IMO), the United Nations body that regulates global shipping, adopted a strategy committing the sector to net-zero greenhouse gas emissions by 2050. The aim is to achieve substantial reductions by 2030 and 2040. Achieving these goals will require a large-scale shift from heavy fuel oil to zero-carbon fuels.

Ammonia is a promising candidate. It contains no carbon, so releases no carbon dioxide when used as a fuel. It can also be produced efficiently using renewable electricity rather than fossil fuels. Indeed, it is anticipated that ammonia use as a fuel will expand rapidly in the next few years. Ammonia has an additional benefit over hydrogen as a fuel is it can be liquefied at more moderate temperature and pressure, which means it is compatible with much of the existing infrastructure used to transport liquefied gases over long distances. Hydrogen as fuel would require entirely new transport and storage infrastructure.

Despite the many advantages of ammonia as fuel, there are legal and safety complications. Ammonia is a highly toxic and corrosive substance. This creates engineering challenges that require specialised engine designs to ensure reliable ignition and efficiency.

Ammonia has been transported by sea for many years and the regulations around its transport are well established but do not account for it actually being used as a fuel on ships. The International Gas Carrier Code sets standards for ships carrying ammonia as cargo, while the International Code of Safety for Ships Using Gases or Other Low-Flashpoint Fuels was developed primarily with liquefied natural gas (methane) in mind and offers no specific guidance for ammonia as a fuel.

The regulatory gaps and internal legal inconsistencies urgently need to be closed so that shipbuilders, operators, flag states, and port authorities can have certainty in the building and use of ammonia-fuelled vessels.

University libraries hold vast collections of scholarly work, yet most academic books are borrowed only a handful of times each year. A study in the International Journal of Information and Communication Technology suggests that the problem lies less in library logistics than in the lack of a sophisticated recommendation system available to readers. The team behind the research have come up with a new approach to library recommendation systems that replaces the static models with an approach that adapts to the readers' changing learning needs.

For decades, most library and commercial platforms have relied on collaborative filtering, a technique that recommends items based on aggregated past behaviour, such as borrowing or purchasing patterns. While effective at scale, the method treats readers as having a fixed profile. It ignores the level of difficulty of material relative to a reader's ability. Moreover, it does not work well with a cold-start, where little data exist for new users or new books. This latest research suggests that overcoming such limitations could open up knowledge to more readers and stop those books gathering dust on the library shelves.

The new system models readers as learners whose knowledge changes over time. It uses a gated recurrent unit, a form of neural network designed for time-series data. This tracks changes in a reader's mastery of a subject and so can produce what the researchers refer to as a continuously updated "cognitive state matrix". This analysis reflects what a reader is likely to understand at any given moment in their education of research.

The team adds that their model incorporates behavioural signals, such as borrowing rhythms and search intent, and an environmental feedback mechanism that adjusts recommendations to balance a resource's difficulty against its popularity.

The approach was tested using real borrowing data from a university library. The team found improvements over established baselines in ranking quality and measured learning gains, while maintaining low response times compatible with live deployment.

Research in the International Journal of Data Science has looked at how network security technologies can be integrated into the redesign of ordinary homes for older adults with a view to improving their quality of life.

The approach could offer an alternative to institutional care for members of an ageing population. The research suggests that conventional housing could be adapted for older residents with a new ethos that overcomes the limitations of earlier approaches. Some of those earlier approaches could not address the complex and evolving risks associated with later-life living.

Home-based care of older people is commonly the preferred choice, but it is often stymied by interiors that were designed for younger, mobile individuals rather than those with reduced mobility, sensory impairment, or cognitive changes. The work suggests that network security systems that protect personal data as well as the interconnected sensors and monitoring systems that manage risk, detect hazards, and respond to changes in a resident's condition or environment.

The researchers have considered a standard two-bedroom flat and tested an approach that combined a useful setup with intelligent monitoring. The redesigned interior used networked sensors to identify potential dangers, support adaptable layouts, and define functional zones that could change according to daily routines and care needs.

The work highlights how the integration of useful technology into the home can be done so as not to detract from domestic comfort or visual aesthetics, which are also important to quality of life. The modified flat demonstrated how improvements not only in safety but also in usability and overall livability could be undertaken.

The findings have implications for social policy and public finance. Safer, more adaptable homes could allow older adults to remain independent for longer, reducing society's reliance on residential care facilities. This could thus reduce the pressure on public care budgets and pension schemes. The next step will be to look at other forms of housing and to investigate whether the approach is scalable.

The interaction of silver materials with light is well-known as the basis of film photography. But, there are much more sophisticated interactions when we consider very, very small particles of silver that could have applications in a wide range of technologies.

Research in the International Journal of Nanoparticles has looked at the behaviour of the tiniest of silver particles, just billionths of a metre in diameter when exposed to light. The behaviour of these silver nanoparticles when exposed to light is different depending on the exact size of the particles.

The team has modelled the absorption, scattering, and quenching of light of different wavelengths with silver nanoparticles from 10 to 240 nanometres in diameter. They found that the smaller nanoparticles primarily absorb light. This could be useful in boosting photothermal effects used in targeted medical therapies for cancer.

By contrast, larger particles, rather than absorbing light, scatter it. This phenomenon might be used to make reflective coatings and solar energy capture devices.

Those particles that are of intermediate size, 40 to 60 nanometres, displayed a third type of behaviour, plasmonic resonance. In this phenomenon, the incident light causes the conducting electrons in the silver to oscillate. This action could be used to detect chemicals in medical or environmental samples, as the presence of chemicals of interest even in very low concentration will change the pattern of these oscillations.

This new understanding of the behaviour of silver nanoparticles could thus open up a range of applications in medicine, biomedical, chemical, and environmental research. The team adds that not all silver nanoparticles are created equal, and this could also be useful in different technologies. For example, silver nanoparticles with complex geometries such as internal layers, like an onion or hollow nanoparticles might behave differently again. Their model could open up the exploration of such complex silver nanoparticles, which might be even more amenable to fine-tuning for specific applications.

Entrepreneurial success can emerge through the gradual development of reflexive decision-making rather than linear planning or favourable starting conditions, according to research in the International Journal of Management and Enterprise Development. The research looked at how a business moved from stalled operations to sustained competitiveness by navigating structural constraints in Britain's health and social care market over more than a decade.

The study follows a single enterprise, a London-based social enterprise founded by an African refugee woman over the course of thirteen years. The research was a longitudinal case study that tracked change over an extended period of time rather than capturing a simple snapshot of activity at a specific time. Moreover, it founded in a critical realist framework, which examined how an individual organisation operates within, and is shaped by, wider social and institutional structures. Central to the analysis is the notion of reflexivity, which is defined as the internal process through which an individual evaluates their circumstances, reassess their goals and adjust their actions in response to changing conditions.

In their case study, the team notes an early period of fractured reflexivity. Social ambition was strong, but strategic focus was limited. This resulted in zero measurable performance outcomes. Progress followed only as the entrepreneur developed autonomous reflexivity, enabling more disciplined decision-making, engagement with local business networks, and ultimately the establishment of operational credibility.

As the enterprise matured, communicative reflexivity became more and more important. Where there was dialogue with public-sector bodies then stats improved and access to competitively funded contracts opened up. Moreover, there was gradual recognition within London's regulated health and social care system. This later phase coincided with the building of reputation, quality certifications, and even national awards. In turn, these all further supported access to the market.

More recently, the entrepreneur involved has demonstrated what we might call meta-reflexivity, continually evaluating the enterprise's social mission along with its financial performance. She has reinvested profits into free training programmes for refugee women, embedded social value creation directly into the business model but still maintained commercial viability.

Given that conventional narratives often frame refugee entrepreneurs in terms of barriers and vulnerabilities, this case study demonstrates that refugee entrepreneurship within broader debates on migration, urban economies, and demographic change, can be framed far more positively.

A novel facial expression recognition system designed to overcome the conflict between accuracy and real-world use is discussed in the International Journal of Applied Pattern Recognition. The approach performs well while remaining computationally lightweight and addresses one of the main challenges facing emotion-aware technologies for vehicles, consumer devices, and healthcare applications.

Facial expression recognition involves classifying human emotions based on a visual analysis of the face. It has benefited from deep learning technology that use multilayered neural networks to examine an image. But, such technology generally requires a lot of computational power. The new work combines classical image analysis with a streamlined deep-learning architecture that preserves performance while lowering computational requirements.

The team has used a convolutional neural network, a type of model well suited to image processing. And, rather than solely learning from training data, the system uses traditional texture descriptors and grey levels. By combining these well-used computer vision techniques with the neural network outputs that can analyse fine-grained facial detail at low computational cost.

The team has tested their approach using two benchmark data sets, large collections of facial images annotated for emotional content. The system achieved recognition accuracies of almost 80 per cent for one and almost 87 per cent for the other. Real-world type tests on still images, recorded video, and live camera feeds in real time also showed how well the system can perform.

Such work is part of a broad area known as affective computing, the discipline concerned with recognising and responding to human emotion. By showing that hybrid designs can offset the computational resource demands of deep learning, the work opens up the possibility of developing emotion recognition that can be integrated into public infrastructure, mobile devices, and clinical environments for a wide range of applications.

A review in the International Journal of Business Excellence of half a century of scholarship has found that academic interest in why migrants return to their countries of origin has expanded sharply over the past decade. The review reframes return migration as a central feature of the global circulation of skills, rather than a marginal or corrective movement.

The researchers studied 375 peer-reviewed papers published during the period 1972 to 2022. The work thus offers the most comprehensive mapping to date of how this field of social science has evolved in recent decades. The study used bibliometric analysis, a quantitative method that examines patterns in academic publishing such as citation trends, collaboration networks, and thematic clustering. The analysis revealed a steady growth in output, with publication rates rising particularly quickly after 2010. Total citations increases continuously, but the average citations per article declined from 2015 onwards. The authors suspect that this change was down to rapid diversification and specialisation within the field at that time.

They point out that high-ranking journals in migration studies, business, and management dominated the output, as one might expect. This, they suggest, highlights the relevance of return migration to organisational strategy, economic performance, and institutional governance. Scholarly leadership is concentrated in Canada, Spain, the UK, and the USA, although many papers have international authorship.

The review also shows that the focus in this area of research has changed. In the early years covered by the review, research largely addressed aggregate population movements, demographic change, and macro-level migration flows. However, in the two most recent decades covered, research has moved towards the lived experience of return. Gender emerges as a central analytical category, while education, particularly higher education and international student mobility, form a core thematic pillar. The team believes that this reflects a growing engagement with human capital theory, an economic framework that views education and skills as investments shaping productivity and earnings.

A new forensic framework designed specifically for the Internet of Things (IoT) is discussed in the International Journal of Electronic Security and Digital Forensics. This deep learning-driven system offers benefits over earlier approaches in detecting and reconstructing cyberattacks on components of the vast network of connected sensors, appliances and machines. It achieves an accuracy of almost 98 percent, according to the researchers, and cuts analysis time by more than three quarters.

There has been a sharp rise in malware aimed at IoT environments. Standard digital forensics tools struggle in this space with the volume, diversity, and the enormous and constant flow of data. The researchers suggest that existing methods, built for relatively static computers and servers, are increasingly mismatched to the IoT world. Given that IoT systems now underpin a lot of transport networks, domestic technologies, and urban infrastructure they will be increasingly vulnerable unless security systems can keep up.

At the heart of this new approach is a hybrid deep learning model that combines a convolutional neural network. This can identify patterns in data using its long short-term memory architecture. When applied to IoT network traffic, the system can detect the subtle signatures of a cyberattack as they evolve over time, rather than simply spotting isolated events.

The team has improved performance by refining the detection approach with a so-called particle swarm optimisation. This technique was inspired by collective behaviour in nature, such as starling murmurations, and honeybee swarming. It can dynamically adjust the detection parameters to home in on the optimal approach without heavy increasing computational cost. This is particularly important for protecting IoT devices, many of which operate with limited processing power and low energy budgets.

Tests conducted across simulated vehicle networks, smart homes, and smart city infrastructures showed that the model works better than existing forensic tools. It is faster and more accurate, but also has the ability to trace and classify multiple forms of cyberattack.

Urban roof gardens can help with removal of atmospheric pollutants at measurable, controllable rates, according to a study in the International Journal of Environment and Pollution. The research suggests that rather than simply being decorative, recreational features, such gardens can become part of an active and living environmental infrastructure.

The team report that a dynamically managed rooftop system can be established to absorb hazardous fine particulate matter from the cityscape including (PM2.5, airborne particles that are smaller than 2.5 micrometres) at a rate of about 42.5 micrograms per square metre per hour. It can also absorb nitrogen oxides (NOx, toxic combustion gases) at a rate of 15.6 micrograms per square metre per hour.

The work begins to address growing concern that conventional urban greening, typically static plantings designed for visual appeal, has limited capacity to respond to pollution or climate change. More adaptive and responsive planting, on the other hand, to construct layered plant communities in roof gardens could be functional as well as aesthetic. The team suggests that by grouping species together according to their known capacity to absorb different pollutants, it should be possible to address the problem of different contaminants in the same growing patch. They have carried out tests in an environmental chamber and found that such coordinated but mixed planting can be more effective than single-species approaches given the common mix of urban pollution.

The work also demonstrated that by using a lightweight, bioactive growing substrate containing activated carbon (pollutant absorbing) and vermiculite (for aeration and moisture retention), such planting could improve the rate of pollutant mineralisation.

A set of indicators, natural chemicals found in the blood, known as biomarkers, can help predict when an aneurysm in the brain might rupture. The work, published in the International Journal of Data Mining and Bioinformatics, looks at the risk of rupture associated with the ballooning out of a weakened blood vessel in the brain, that can lead to catastrophic bleeding. By analysing genetic data from three groups of patients, the team has identified characteristics associated with increased instability of an aneurysm.

The researchers used genetic profiling to look at activity associated with stable and ruptured aneurysms, as well as interactions between proteins that were linked to the latter and not the former. Across all the data, they found two interactions that were active in aneurysms prone to rupture. Then, by using machine learning techniques, specifically Least Absolute Shrinkage and Selection Operator (LASSO) regression, they were able to develop a prediction curve that gives a patient rupture risk based on the presence of the biomarkers.

The findings highlight an underlying mechanism that links chronically raised elevated blood pressure, hypertension, and inflammation in the vasculature of the brain. Hypertension puts mechanical strain on the walls of the blood vessels, while, at the same, activating a hormone-driven regulator of blood pressure, known as the local renin-angiotensin system. This system triggers inflammation and can weaken blood vessels. The research suggests that those genes associated with these biological systems come together to increase a person's risk of a ruptured aneurysm. As such, they also now become targets for the development of novel therapies that are aimed at reducing mechanical stress in the brain's blood vessels as well as lowering local inflammation.

This new understanding might improve the medical outcome for at-risk patients as well as precluding unnecessary medical intervention for those at lower risk who happen to have other risk factors. Given the prevalence of intracranial aneurysms and the high morbidity associated with rupture, such strategies could shift management from reactive emergency treatment to proactive, targeted prevention.

Researchers have developed a new algorithmic model that can improve predictions of cooling demand for greener buildings. This kind of control will be a key factor in energy efficiency, allowing interior climate control systems to optimise cooling periods and so reduce energy demands.

The framework for the new model is based on a probabilistic neural network (PNN), which has been tested across varied climatic conditions. According to the research published in the International Journal of Environment and Pollution, it delivers accurate forecasts and quantifies the uncertainty in a way that conventional models do not.

Cooling systems account for a substantial proportion of a building's energy consumption in the hottest parts of the world. Their operation is dependent on outside temperature, humidity, building characteristics, and occupant behaviour. The standard control models usually assume linear relationships and so cannot capture the nonlinear dynamics of climatic variability and requirements. The PNN approach overcomes this problem by modelling the nonlinear relationships. This allows the system to understand the intricacies of the building-specific data and to provide better predictions to optimise climate control. The team was able to demonstrate almost 97 percent reliable control across various scenarios.

Such a system could be used by policymakers, developers, and energy managers hoping to optimise cooling in hot climates and to reduce the carbon footprint of air-conditioning systems. By providing a more subtle understanding of cooling load variability, the PNN allows for accurate data-driven decisions regarding system design, operational scheduling, and regulatory compliance. The team explains that plans can be put in place for both typical and extreme conditions with greater assurance, reducing energy waste while maintaining occupant comfort.

The same framework might have broader energy management use, allowing for short-term control as well as long-term planning of infrastructure in low-carbon developments. The construction industry must incorporate green systems, and such tools as PNN-managed climate control could play an important role in the development of sustainable buildings.

Diapers (nappies) and feminine hygiene products (menstrual pads and tampons) are emerging as a critical challenge in the waste management. They account for a disproportionate share of municipal waste, according to work in the International Journal of Sustainable Society.

An analysis conducted across 31 Slovak cities showed that these products alone make up 10% of total mixed waste in both urban and rural settings. There have been recent efforts to improve waste reduction and recycling. However, addressing the environmental impact of this waste stream remains a significant challenge in the Slovak Republic and elsewhere and is hindering sustainability efforts in many places.

The research points out that diapers and sanitary products, though comprising a substantial propportion of waste, are not covered by current waste legislation. These items, primarily comprising plastics and superabsorbent polymers as well as biological materials after use, represent a major problem in recycling and are generally fed into landfill or incinerated rather than being recycled in most places.

While their contents after use will degrade biologically, the materials from which they are manufactured might take centuries to decompose in landfills. While the global market produces billions of units annually, the lack of regulation and effective recycling for these products exacerbates the waste management issues, especially in the Slovak Republic, where recycling rates overall are below EU averages.

The study points to potential solutions, including composting, which has been shown to reduce the volume of diaper waste. However, these methods are limited by the non-biodegradable materials involved. Emerging technologies, such as vermicomposting and thermal pyrolysis, offer promising alternatives by recycling used diapers into usable materials. However, these technologies require proper infrastructure and legislative support to be fully effective.

The modern network is a place where danger whispers rather than shouts. Corporate systems, public services, and critical infrastructure are increasingly complex and increasingly vulnerable to more subtle cyberattack. Where an old-school hacker might try brute-force techniques or an army of bots that pound the system until it breaks, modern threats can work more insidiously. They might masquerade as ordinary server traffic, draining resources or slowly siphoning off data, while the anti-malware systems and firewalls are focused on the brutes.

New intrusion-detection models are needed, according to the author of work published in the International Journal of Reasoning-based Intelligent Systems. While it is generally easy to hear the alarm bells ringing when the brutes are pounding the servers, the sinister-but-subtle attackers need a different approach, one that listens out for the whispers.

In the work, a new model, called ST-CCNet, promises this kind of protection. In tests against standard benchmarks, it accurately – 98.2 percent – identified covert attacks better than existing approaches. More specifically, it was able to spot low-rate distributed denial-of-service (dDOS) attacks, botnet activity, and subtle web intrusions that had been designed to look like legitimate behaviour. The model can now detect slow-burn attacks that exhaust server capacity over long periods, or threats that unfold over weeks or months. Such attacks have long been the nemesis of network security systems.

One part of the ST-CCNet system uses causal convolution to analyse traffic in temporal order, capturing tiny, momentary deviations that may appear only for microseconds but can mark the opening move of an attack. In parallel with this, a spatio-temporal transformer scans across much longer timescales, identifying patterns that only become meaningful when viewed in context, such as the rhythmic exchanges between compromised machines and their controllers.

This balanced approach addresses the shortcomings of conventional security. By combining short-term acuity with long-term memory, ST-CCNet aligns with the way real sinister-but-subtle attacks operate.

Sustainable entrepreneurship in Nigeria is being stymied by a lack of engagement among business owners because of structural economic and institutional barriers, according to research in the World Review of Entrepreneurship, Management and Sustainable Development that has studied one of Africa's largest entrepreneurial ecosystems.

The research used quantitative data from 310 entrepreneurs across manufacturing, sales, and food services. An analysis of this data showed that unstable macroeconomic conditions, limited access to finance, weak technological infrastructure, and inconsistent government support are the main barriers faced by entrepreneurs hoping to adopt environmentally responsible business practices. Moreover, they found that many entrepreneurs operate under conditions in which immediate cash-flow pressures outweigh long-term environmental considerations. The result is that sustainability initiatives are difficult to get underway and even harder to maintain.

Entrepreneurs in Nigeria, the study found, are somewhat aware of sustainability principles, but currency volatility, high inflation, and unreliable public services restrain action. The researchers add that access to affordable credit remains limited, particularly for micro, small and medium-sized enterprises (MSMEs). Such companies with fewer than 250 employees form the backbone of the Nigerian economy. Without financial buffers or capital, investments in cleaner technologies or resource-efficient processes are often postponed indefinitely. There is thus an urgent need to improve conditions for entrepreneurs to encourage those that are less than willing to engage that there are long-term benefits, and to nudge the more engaged further towards sustainability. Regulatory incentives and green technologies that have remained largely inaccessible to smaller companies need to be opened up to Nigeria's MSMEs.

There are obvious implications for other emerging economies facing similar constraints, which also risk missing out on the economic, environmental, and social benefits associated with sustainable enterprise. There is a need to align financial systems, policy instruments, and educational initiatives with sustainability objectives across the whole of the developing world, the research would suggest.

The future of urban green space might be written in code, according to research in the International Journal of Reasoning-based Intelligent Systems. The age-old image of the landscape architect, sketchbook in hand, guided by intuition and a feel for the land, is being dug over by digital disruption. The work suggests that for city and town planners facing increasingly dense populations and the problems that climate change brings, the art of urban garden design needs reseeding with modern tools to fertilise new ideas.

Urban green spaces are now recognised as increasingly important for the recreation, enjoyment, and wellbeing of city dwellers, Moreover, such as spaces and in particular the protective effects of trees during scorching summers and the atmospheric cleansing they bring are no longer an aesthetic luxury but an essential part of the modern cityscape. The concrete jungle needs to go green, and an algorithmic augmentation of human intuition can help balance the competing pressures in landscaping our urban spaces.

The researchers talk of "landscape optimization" wherein a green space or garden is not simply a canvas on which to paint trees, lawns and shrubberies, but a complex data problem that can be more effectively solved algorithmically without compromising art nor beauty. The team merging aesthetics and ecology reframe the problem into a "rationality index" which considers the terrain profile, soil health, and the local climate to provide the computer with a unified metric it can interpret and from which it can provide novel design solutions using various algorithms based on natural systems such as honeybee behaviour and ant colonies.

In preliminary tests, the team found that their hybrid algorithmic approach worked better than conventional methods used to calculate land-use efficiency. They emphasise that by treating landscape design as an optimizable process, city planners can produce evidence-based layouts that are reproducible, resilient, and reliable. While the immediate focus is on gardens, the implications for wider urban planning are significant. As public authorities face mounting pressure to meet sustainability targets, the "intuition" of the past may soon give way to the "optimization" of the future.

In terms of sustainability and competitiveness, modern agriculture depends on information across the whole of food-production. Research in the International Journal of Agricultural Resources, Governance and Ecology has looked at how data, innovation, and collaboration shape farm performance in the facing of growing climate change issues and under diverse market pressures. The work suggests that without knowledge frameworks, policies and technologies designed to improve resilience are likely to underperform.

The researchers show that information quality is a decisive factor linking farm-level decisions to wider economic and environmental outcomes. Data on production volumes, input costs, as well as resource use can be combined with national statistics and market intelligence to help farmers and policymakers to respond to price signals, supply chain disruption, and climate stress. Unfortunately, many farms still operate without formal accounting systems or even consistent record-keeping, which means their decision-making is not clear. Moreover, a lack of detailed economic awareness might be limiting the capacity of many farms to adapt production in response to changing conditions.

The detrimental effects of this information gap are worsened by social and organisational factors within the sector. Farmers' associations, cooperatives, and informal networks can play a role in knowledge exchange, but many farmers do not make full use of such networks, with differences in uptake being linked to farm size, education level, and age. The team adds that the retention of younger people in rural areas emerges is a major concern, as demographic decline threatens the sector's capacity to absorb new skills and sustain innovation over time.

The bottom line is that digitalisation, used systematically, rather than casually, might offer a structural shift in how agriculture is managed that could help overcome some of these problems. Digital systems can reduce wasted resources and wasted effort. With improved resource efficiency and decision-making supported by data in a sector where timing is often critical, farming practices might be improved. Ultimately, there is a need to embed this digitalisation within farming networks supported by leadership, coherent policy and trained personnel.