The following International Journal of Automotive Technology and Management article, "Applying the Jidoka concept to white-collar departments: an examination of the JKK initiative at Toyota Motors", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Underground metro (subway) stations are no longer merely points of departure and arrival. As cities grow denser and transit networks expand, these spaces have the potential to function as some of the most widely shared public interiors in urban life. They are places where millions pass daily, cutting across age, income, and neighbourhood. They offer a rare platform for collective cultural experience. Stations can, suggests research in the International Journal of Environment and Sustainable Development, anchor local identity, narrate a city’s history, and shape how residents and visitors alike perceive the character of the urban environment.
The research addresses a practical question confronting transport authorities and urban designers: how can large-scale public art projects fit into this infrastructure as it changes? Traditional artist-led design processes, though highly creative, can be time-intensive. By contrast, deep learning has allowed computers to generate high-quality images at speed. The missing link is that the computer-generated images may not understand the cultural meaning that the images need to convey. There is also a need to take into account how well a design might be installed in a real site.
The researchers hope to bridge this gap and have developed a multi-stage framework that integrates cultural analysis, visual cognition modelling, and spatial feasibility testing into a single pipeline.
Their approach is based on a semantic labelling system. The system can organise cultural concepts, such as local history, regional traditions, and environmental identity, into a knowledge graph. This graph can map relationships between ideas, enabling the computer to understand individual symbols and how they fit with broader narratives.
The framework then uses Contrastive Language-Image Pretraining, CLIP, is a deep neural network trained on vast datasets of containing pairings of images and text. An additional layer simulates human perception through a visual attention prediction network, considering composition, spatial layout, and pedestrian flow. By predicting where passengers are likely to focus while moving through a station, the system can position key symbolic elements in high-attention zones. The researchers suggest this could improve not only the aesthetic impact of the art installation but also the way in which pedestrians navigate the subway stations.
Wang. Q. (2026) ‘Application of deep learning algorithms in the design of urban subway public art space’, Int. J. Environment and Sustainable Development, Vol. 25, No. 5, pp.44–72.
In a country where the physical scars of war remain visible in shattered buildings and disrupted markets, research in the International Journal of Diplomacy and Economy suggests that the moral architecture of business may be just as important to recovery in Syria as capital investment and bricks & mortar.
A study of 200 business leaders working in international companies in Aleppo and Damascus finds that ethical decision-making in Syria can be explained, to a significant degree, by a well-established psychological framework known as the Theory of Planned Behaviour. This theory suggests that human behaviour is primarily shaped by intention, a person’s conscious plan or readiness to act. Those intentions, in turn, are influenced by three factors: personal attitudes, perceived social expectations, and perceived control over whether the behaviour is realistically achievable.
In practical terms, individuals are more likely to act ethically if they believe ethical conduct is right, think that others expect it of them, and feel capable of acting accordingly.
The researchers applied this theory in the context of Syria as part of an effort to understand how business leaders make ethical choices amid conflict, economic disruption, and institutional fragility. Their focus was Syria’s post-war reconstruction drive, a national strategy aimed at restoring infrastructure, reviving markets, and rebuilding social trust after years of violence.
Trust, the study notes, is not an abstract virtue in such an environment. It is a prerequisite for attracting investment, stabilising supply chains, and enabling cooperation between domestic firms and international partners. Ethical business conduct is thus a functional prerequisite of economic recovery.
For practitioners, the implications are concrete. The findings indicate that organisations seeking to strengthen ethical leadership cannot rely solely on written rules. Codes of ethics must be actively communicated and embedded within organisational culture, the shared values and practices that shape everyday work. When ethical expectations become part of that culture, they function as powerful social norms, guiding behaviour even in the absence of direct oversight.
Amoozegar, A., Lata, A., Falahat, M., Shakib, S., Kumar, M., Ramzani, S.R. and Yadav, M. (2026) ‘Mediating role of ethical intention between social norms, code of ethics and ethical decision-making’, Int. J. Diplomacy and Economy, Vol. 12, No. 5, pp.1–20.
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.
Zhang, C. (2026) ‘Deep learning-based virtual screening system for drug molecules’, Int. J. Reasoning-based Intelligent Systems, Vol. 18, No. 8, pp.44–55.
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.
Qi, X., Yan, H., Tu, X., Liu, Y. and Ding, W. (2025) ‘Quality inspection of power transmission towers based on point cloud registration’, Int. J. Energy Technology and Policy, Vol. 20, No. 7, pp.3–22.
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.
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.
Gu, Y., and Liu, C. (2026) ‘Empowering sustainable growth: the transformative impact of environmental protection inspections on heavy polluters’, Int. J. Environment and Pollution, Vol. 76, Nos. 1/2, pp.40–56.
The following International Journal of Diplomacy and Economy article, "Mediating role of ethical intention between social norms, code of ethics and ethical decision-making", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Environment and Pollution article, "Research on distribution network fault identification based on active transfer learning and autoencoder", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
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.
Antoniraj, S., Hassan, H.C. and Baleswamy, K. (2026) ‘Forward neck posture on cervical pain among university students: effect of smartphone addiction’, Int. J. Medical Engineering and Informatics, Vol. 18, No. 2, pp.198–205.
The following International Journal of Environment and Pollution article, "Research on green trade data prediction under global economic shock based on ConvLSTM model oriented towards reducing carbon emissions", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Environment and Pollution article, "Digital economy and global value chain restructuring from a cross-country sustainable industry analysis", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
