2025 Research news

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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