The following paper, "Unique insights: the mediating role of learned helplessness on the influence of perceived dirty work on career transition intentions, and the moderating role of job crafting and career calling" (International Journal of Economics and Business Research 19(11) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following paper, "One-shot transfer learning with limited data sample for bearing component fault diagnosis" (International Journal of Hydromechatronics 8(6) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following paper, "Secure addition of floating points" (International Journal of Applied Cryptography 5(5) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
A newly developed framework could reshape the way scholars and professionals understand the complex processes behind human decision-making. Developed through a multidisciplinary effort and validated with data from German professionals across sectors, the RIDMS (Rational and Intuitive Decision-Making Styles) model offers a comprehensive, evidence-based structure for assessing both analytical and instinctive aspects of how people make choices. The new framework is discussed in detail in the International Journal of Economics and Business Research.
Markus A. Launer of the Ostfalia University of Applied Sciences, Ostfalia, Suderburg, Germany, and Fatih Çetin of the Universite Caddesi in Ankara, Turkey introduce RIDMS as having ten distinct dimensions. Together, these characteristics map out the range of rational and intuitive strategies people might use when making decisions. These dimensions are designed to be applied across disparate areas of decision-making, from healthcare and education to emergency response and organizational leadership. As such, they provide researchers and practitioners with a more nuanced, adaptable tool than has previously been available.
Intuitive decision-making has remained little studied. While individuals frequently rely on gut feelings, emotional cues, or accumulated experience, especially in high-pressure or uncertain environments, many existing models of decision-making have only vaguely defined such intuitive decision-making. RIDMS could change that by breaking down intuition into its components and recognising how each is influenced by context and experience.
Among its ten dimensions, RIDMS includes not only the typical logical-planning methods, but also less linear processes, such as the phenomenon of incubation, where decisions seem to crystallise gradually over time. It also accounts for socially distributed decision-making, in which individuals draw heavily on trusted peers or mentors. In doing so, the model recognises that decision-making is often embedded in a social context, rather than occurring in isolation.
The RIDMS model incorporates insights from a broad spectrum of disciplines, including cognitive psychology, neuroscience, management theory, and behavioural science. The team thus explains that it is empirically grounded and has been validated statistically.
Launer, M.A. and Çetin, F. (2025) ‘A new instrument for the rational and intuitive decision-making styles – RIDMS’, Int. J. Economics and Business Research, Vol. 29, No. 11, pp.31–51.
The following paper, "Methodological strategies for control experiments in independent teaching and learning environment" (International Journal of Computational Systems Engineering 9(7) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
A study in the International Journal of Business and Globalisation has looked at the various factors contributing to job satisfaction among manual workers in Vietnam’s garment manufacturing sector. While much research on employee satisfaction has traditionally focused on white-collar workers, this study focuses on blue-collar labour, a crucial demographic in a rapidly developing economy such as that of Vietnam.
Hanh Vu Thi and Huong Pham Thu of the Foreign Trade University, Lan Anh Nguyen Thi of Fujifilm Business Innovation Co., Ltd in Hanoi, Vietnam, and Tra Ly Thu of NextWave Partners, Singapore, found that among the most important factors associated with job satisfaction were the quality of an employee’s relationships with supervisors, workplace benefits, and occupational safety. Perhaps surprisingly, the level of pay, which is typically thought of as being central to job satisfaction, was found to be minor factor. The researchers suggest that for workers in jobs requiring a lower educational level, job security and a positive work environment may outweigh financial incentives.
Leadership styles in Vietnamese companies appears to play a crucial role in shaping job satisfaction, the team found. Their results highlight the prevalence of autocratic leadership and family-based management models in many businesses. These models, while offering powerful authority structures, can sometimes hinder open communication and democratic participation in the workplace. The research thus calls for a reassessment of leadership strategies in order to create a more inclusive and supportive environment for blue-collar workers.
The work is important in the light of Vietnam’s socio-economic context. Since the 1980s, Vietnam has been developing rapidly and its economy growing, partly driven by foreign direct investment (FDI) in various sectors, including manufacturing, and in particular garment manufacturing.
Thi, H.V., Thu, H.P., Thi, L.A.N. and Thu, T.L. (2025) ‘Analysis of factors affecting job satisfaction of manual workers: a case study from a garment factory in Northern Vietnam’, Int. J. Business and Globalisation, Vol. 39, No. 5, pp.1–16.
A review in the International Journal of Business Innovation and Research has looked at the recent literature to see how large companies can become more resilient by using digital tools and technology. The researchers found that being flexible, making decisions based on data, and involving all stakeholders are the main ways to boost resilience. They add that the use of good knowledge-sharing systems also makes a big difference, rather than simply using advanced technology.
Large companies face a paradox: while digital transformation holds enormous promise for strengthening their resilience, many, nevertheless, fail to achieve their intended outcomes. To find out why this might be, Moris Krismas Tarigan, Togar Mangihut Simatupang, and Yuni Ros Bangun of the Institut Teknologi Bandung, Indonesia, have carried out a comprehensive review of research published between 2020 and 2024. The team used the digital maturity model, the Industry 4.0 framework, and dynamic capabilities theory to help them reveal the various factors affecting organisational resilience.
Organisational resilience refers to a company’s ability to anticipate, adapt to, and recover from disruptions, whether triggered by economic shocks, global pandemics, supply chain breakdowns, or rapid technological changes. Digital transformation involves a fundamental rethinking of business operations through the adoption and integration of digital technologies. Digital transformation can have powerful effects, but this review shows that the true value lies not in technical capabilities alone, but in how effectively they are embedded within an organisation’s culture, strategy, and decision-making systems.
The researchers found that there are three important mechanisms through which digital transformation boosts organisational resilience in large companies. The first is a marked improvement in operational flexibility, meaning companies can adjust processes, production, and service delivery more rapidly in response to changing conditions. The second is that data-driven decision-making is improved, allowing companies to make faster, more informed, and adaptive choices during times of uncertainty. The third is that stakeholder engagement is increased, which strengthens collaboration and communication with partners, suppliers, and customers. Overall, these factors help companies respond more effectively to disruption and foster long-term adaptability.
In addition, the review also found that organisational learning has an important role. Companies that invest in knowledge management system, tools and practices that enable the capture, sharing, and application of knowledge, can enhance the positive effects of digital transformation. In other words, technological sophistication alone is not enough to build resilience, but rather an organisation needs to have the capacity to learn from experience.
Tarigan, M.K., Simatupang, T.M. and Bangun, Y.R. (2025) ‘Building resilience through digital transformation: a systematic literature review and comprehensive framework for large enterprises’, Int. J. Business Innovation and Research, Vol. 36, No. 5, pp.1–28.
Research in the International Journal of Computational Systems Engineering has implications for how advanced technology is integrated into the modern classroom. The work shows how education is moving way beyond the traditional lecture-based teaching model and touches on how the “smart” classroom environment might make use of many of the much-hyped tools, such as artificial intelligence (AI), big data analytics, virtual reality (VR), and cloud computing to enhance student-centred, independent learning.
According to the work from Qingping Luo of Changsha Social Work College in ChangSha, China, smart classrooms might be described as technology-enhanced learning spaces designed to adapt to the individual needs of students. These environments encourage learners to explore, research, and engage with content at their own pace using digital platforms that offer interactive media, online assessments, and live collaboration tools. The flexibility offered by the smart classroom not only sits well with current educational theory but also reflects a broader shift in teaching towards active and personalized learning.
At the heart of the digital transformation of education is the integration of internet-connected equipment such as tablets and laptops into the educational milieu. This connectivity allows students to carry out real-time research, collaborate between different locations, and access learning resources that would otherwise be unavailable to them.
The research emphasizes that these changes are not merely a matter of convenience. In fact, it demonstrates measurable improvements in learning outcomes. For instance, subject-specific platforms are being developed to enhance instruction in areas such as music education, using AI-driven systems that support interactive learning. These systems not only present information but also analyse student responses through tools such as classification, keyword retrieval, and visual mapping. These are all techniques that can help reinforce comprehension and encourage critical analysis.
Importantly, the research also introduces a framework that might allow the impact on education of these new approaches to be evaluated. Indeed, controlled experiments comparing students in smart classrooms with peers receiving traditional instruction suggest that those using the technology-enhanced methods do perform better, especially where self-guided exploration and inquiry are encouraged and facilitated by the technology.
Luo, Q. (2025) ‘Methodological strategies for control experiments in independent teaching and learning environment’, Int. J. Computational Systems Engineering, Vol. 9, No. 7, pp.1–10.
Mobile marketing is having an increasing influence on the purchasing decisions of Generation Z in Poland, according to International Journal of Economic Policy in Emerging Economies. Gen Z, or Generation Z, is the demographic born between 1997 and 2012 and usually considered the offspring of Generation X (those born between 1965 and 1980 and themselves generally speaking the children of the Baby Boomer generation (born 1946 to 1964).
Bogdan Mróz of Warsaw School of Economics SGH and Barbara Grabiwoda of Publicis Commerce Poland in Warszawa, Poland, explain that Gen Z represents a group of young consumers whose lives are deeply integrated with digital technology. Their research, which combines an extensive review of existing literature with empirical statistical analysis, provides insights into how mobile devices are shaping the way in which this demographic interacts with brands and makes choices about what products and services on which it wishes to spend its money.
For Gen Z, typically smartphones, tablets, and other mobile devices are not just tools for communication but an essential part of daily life. Those tools are essential for other generations too, but Gen Z has never known a time without them, broadly speaking.
According to the current study, more than half of this demographic actively engages with mobile marketing communications, and a large proportion has a positive attitude toward brands that connect with them through the various digital platforms and social media. Indeed, the study reveals a clear trend: the more branded content Gen Z encounters on mobile devices, the more positive is their view of the companies involved.
For Gen Z, this great affinity for mobile technology has blurred the lines between the physical and digital worlds. Unlike previous generations, mobile devices seem intrinsically wired into their lives and even identity, playing a central role not only in social interactions but also in their shopping habits. For many, mobile platforms and social media represent essential spaces for discovering new products and brands. As such, the various platforms are pivotal for marketers seeking to engage with Gen Z.
The research suggests that conventional advertising on old-school media, such as print, TV, and radio, are becoming less effective at reaching this audience and many Gen Z individuals may never see or hear anything from the traditional media. For businesses, this shift in consumer behaviour means they need to adapt to the ongoing changes if they are to survive. Companies must be proactive in rethinking their marketing strategies to cater to the preferences and expectations of Gen Z.
Mróz, B. and Grabiwoda, B. (2025) ‘Generation Z: the new mobile consumers. Empirical evidence from Poland’, Int. J. Economic Policy in Emerging Economies, Vol. 21, No. 1, pp.1–20.
Research from Kuwait, published in the International Journal of Public Sector Performance Management, has looked at the financial challenges faced by small and medium-sized enterprises (SMEs) there. Economic disruption and the post-pandemic environment have increased the risk of financial default among such businesses. Kuwait is among the richest nations, but its dependence on oil exports has made its economy highly vulnerable to global shifts, and SMEs have had to bear much of the burden.
Financial default, a situation in which businesses are unable to meet their debt obligations, is a growing concern for SMEs in Kuwait. The research, based on interviews with numerous SME managers, has identified several factors that have contributed to the financial instability of these businesses. The economic fallout from the COVID-19 pandemic, disruptions to daily operations, and changes in consumer behaviour were among the most significant factors, the researchers found.
In the wake of these crises, many SMEs found it difficult to access funding from traditional sources such as banks, which typically offer loans to support business operations. This lack of access to funding has left many companies with limited resources to adapt or recover. The researchers suggest that their findings point to a need for SMEs to adopt better financial management practices in order to mitigate the risk of default in future crises. SMEs in Kuwait, the research suggests, need to place greater emphasis on sound financial practices like budgeting, cost management, and cash flow forecasting. They say that by improving these areas, businesses may be better equipped to handle unexpected challenges.
Additionally, there is a need for businesses to explore alternative funding methods such as crowdfunding and angel investments rather than relying on bank loans. Of course, those approaches may not be appropriate for every kind of SME.
These findings also point to a need for increased government support and regulatory reform. The research suggests that greater transparency, regulatory simplification, and stronger public-private partnerships could help create a more stable financial environment for SMEs in Kuwait.
Alhaimer, R., Alshami, A., Alkhaldi, A., Alsadeeqi, A., Aloumi, D. and Malik, S. (2025) ‘Evaluating public policy interventions in mitigating financial default risk among SMEs‘, Int. J. Public Sector Performance Management, Vol. 15, No. 5, pp.1-18.
The following paper, "Evaluating public policy interventions in mitigating financial default risk among SMEs" (International Journal of Public Sector Performance Management 15(5) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Research in the International Journal of Entrepreneurship and Small Business has identified the ingredients that lead to financial success among women-owned small-scale culinary businesses in Riau, Indonesia. The research used a combination of theoretical frameworks to shed light on how women entrepreneurs in this sector can overcome significant challenges to achieve greater financial stability and growth.
Okta Karneli, Harlen, and Yusni Maulida of the Universitas Riau, and Muammar Revnu Ohara of the Universitas Lancang Kuning, also in Riau, and Pratiwi Dwi Suhartanti of the Institut Bisnis dan Teknologi Kalimantan in South Kalimantan, Indonesia, explain that women are playing an increasingly important role in Indonesia’s local economy. Understanding the recipe for their success is important to understanding the sector and how others might grow their businesses within it.
Women entrepreneurs in Indonesia’s culinary sector face various many barriers, such as limited access to financial resources, insufficient education in financial management skills, and difficulties scaling operations. The researchers surveyed 355 women running business in the sector in Riau to understand how entrepreneurial orientation, adaptive capacity, and social networks might improve the bottom line for such businesses.
One of the key findings of the study is the strong link between entrepreneurial orientation and financial success. Innovative women willing to take risks and working proactivity within the sector proved to the be most successful, as one might expect. The entrepreneurs displaying these characteristics were better positioned to seize new opportunities, expand their market reach, and improve financial performance. The finding suggests that cultivating an entrepreneurial mindset is important to success for business operating in this dynamic sector.
The research also revealed that adaptive capacity is important. Businesses that could adjust to changing market conditions were generally more successful and could sustain long-term profitability. In the face of changing consumer tastes and economic uncertainties, being adaptable is critical to success. Finally, the team found that social networks – connections with suppliers, customers, and other entrepreneurs – helps provide the underlying support, resources, and information need to help businesses thrive.
Karneli, O., Harlen, Maulida, Y., Ohara, M.R. and Suhartanti, P.D. (2025) ‘Women entrepreneurs in Indonesia’s culinary sector: a study on entrepreneurial orientation and financial capability’, Int. J. Entrepreneurship and Small Business, Vol. 55, No. 5, pp.1–28.
The following paper, "Women entrepreneurs in Indonesia's culinary sector: a study on entrepreneurial orientation and financial capability" (International Journal of Entrepreneurship and Small Business 55(5) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
In Lisbon’s busy streets, tuk tuk companies have been slow to adapt to the digital age. Many rely on what researchers consider outdated and error-prone manual processes for managing their fleets of tourist transport vehicles. Research in the International Journal of Business Information Systems could help them navigate their way to a more efficient and effective future.
Eduarda Perdigão and Bráulio Alturas of the Instituto Universitário de Lisboa, in Lisbon, Portugal, have focused on how a tailored information system that could transform the daily management of companies such as Citytuk, one of the popular guided tour services in Lisbon
Previously, tuk tuk companies relied on drivers to fill out daily service sheets by hand. This data was then transcribed manually into a central system by managers. Such an approach is inherently slow and prone to mistakes. For a growing business in the competitive tourism sector, such inefficiencies are not sustainable, the team suggests. The researchers have now identified an opportunity to enhance productivity and improve decision-making by replacing the manual process with a more streamlined, automated system.
The result of this research is Tuksy, a new application designed to simplify and modernize tuk tuk operations. Tuksy consists of two components: a mobile app for drivers to input service data directly, and a desktop app for managers to track and analyse that data in real-time. This digital solution eliminates the need for paper records, so reduces errors and frees up valuable time for both drivers and managers.
The system represents more than just a solution for one company’s operational challenges, it represents a model for how other small businesses in the tourism sector might embrace technology and boost their efficiency.
Perdigão, E. and Alturas, B. (2025) ‘Developing an app proposal for tuk tuk service management’, Int. J. Business Information Systems, Vol. 48, No. 4, pp.433–451.
Virtual reality (VR) has steadily become a key tool in sports training, offering immersive environments that simulate real-world physical exercises. However, its application in aerobics training has faced significant challenges, particularly in accurately capturing and recognizing complex body movements. A study in the International Journal of Computational Systems Engineering has overcome some of these barriers obstacles by improving the precision of motion capture and the recognition of different actions during aerobics exercises.
Conventional VR motion capture systems rely on algorithms that align 3D representations of physical objects, so-called point cloud data, to track body movements. However, these systems often struggle with two critical issues: noise and incomplete data. Noise refers to unwanted interference that can distort the data collected by sensors, while incomplete data arises when certain body movements are not fully captured. In aerobics, where precision in movement is key to safe and effective exercising, these issues compromise the effectiveness of VR-based training systems.
Hui Wang of the School of Physical Education at Yan’an University in Yan’an, China, has addressed these challenges by developing two new models. The first focuses on enhancing the Iterative Closest Point (ICP) algorithm, which is used to align point cloud data. ICP is a well-established method, but it is prone to inefficiencies, particularly when faced with noisy or incomplete data. By optimizing the algorithm, Wang has improved accuracy and speed of capture.
The second model focuses on refining action recognition. A neural network is used to analyse the complex relationships between body joints over time by tracking the interactions between different body parts. Wang improved the neural network used by incorporating a perturbation mechanism to deal with noise, which further improved its ability to capture subtle movements and interdependencies between non-adjacent joints during aerobics.
Accuracy up to 99 percent was achieved, indicating a remarkable ability to recognize and classify aerobics movements with minimal error. Moreover, the experimental group using these advanced models outperformed the control group in various performance metrics, particularly in terms of the standardization of movements, the work explains. This enhanced motion recognition technology could significantly improve both the learning experience for students and the ability of instructors to offer targeted feedback, leading to more efficient and personalized training in aerobics.
Wang, H. (2025) ‘The application of VR-based fine motion capture algorithm in college aerobics training’, Int. J. Computational Systems Engineering, Vol. 9, No. 6, pp.1–10.
The following paper, "The application of VR-based fine motion capture algorithm in college aerobics training" (International Journal of Computational Systems Engineering 9(6) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Hashtags, the keywords preceded by the “#” symbol, are widely used on social media platforms like Instagram to categorize content and increase its visibility. While their primary function is to help posts reach broader audiences, a study in the International Journal of Web Based Communities shows that hashtags also play a significant role in shaping how users perceive the trustworthiness of the post’s source. This research challenges the common practice of “hashtag stuffing”, the use of excessive or irrelevant hashtags to boost engagement. It then explores the unintended consequences it may have on the credibility of a given post and the person or company using them.
On Instagram, as with other platforms, hashtags are often used to tap into trending topics or relevant themes, enabling users to increase the visibility of their posts. This study suggests that beyond increasing visibility, hashtags play a significant role in how users judge the credibility of a post.
Ye Han and Peter Haried of the University of Wisconsin-La Crosse, La Cross, Wisconsin, Shuang Wu of Rowan University, Glassboro, New Jersey, USA, carried out experiments and found that hashtags act as “heuristic cues.” In psychological terms, a heuristic is a mental shortcut people use to quickly make decisions or judgements without having to analyse every piece of information. In this context, hashtags serve as cues that shape how trustworthy a post seems, even if the viewer does not scrutinize the content itself in detail.
When a post includes hashtags, users tend to assume that the source is more likely to share additional information or similar content. This perception increases the post’s credibility, reinforcing trust. However, this trust is undermined when hashtags are deemed irrelevant or excessive, as is the case with hashtag stuffing. Users may begin to question the authenticity of the post, leading them to engage in more critical analysis of the content, ultimately reducing the post’s perceived trustworthiness.
This finding underscores a critical tension for Instagram users, particularly commercial enterprises and so-called influencers who all rely on visibility and reach. While using more hashtags may help posts reach a wider audience, the study suggests that excessive or irrelevant hashtags can backfire. Users may interpret such posts as less credible, as the hashtag choices can signal an attempt to manipulate engagement rather than offer valuable or pertinent content.
The research also suggests that the visual nature of Instagram posts affects how users interact with hashtags. If the image is clear and straightforward, users are more likely to engage with hashtags, trusting that the content is well-supported by relevant tagging. In other words, hashtags should be directly related to the post’s content to maintain both trust and engagement. This balanced approach prevents users from feeling overwhelmed by irrelevant information and ensures a more authentic connection with the audience, the research suggests.
Han, Y., Wu, S. and Haried, P. (2025) ‘The hidden impact of hashtags on Instagram: navigational heuristics on source trustworthiness’, Int. J. Web Based Communities, Vol. 21, Nos. 1/2, pp.155–185.
The following paper, "Construction of a CS-ELM-based assessment model for civic education within a multidimensional analysis framework" (International Journal of Information and Communication Technology 26(6) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following paper, "Sentiment analysis for tourism reviews based on dual-stream graph attention fusion network" (International Journal of Information and Communication Technology 26(6) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
A study in the International Journal of Knowledge and Learning has looked at how individual personality traits influence how much users disclose personal information on social networking sites. Self-disclosure, revealing personal details to others, is generally considered a key component of online social networking interaction.
Understanding what motivates people to share in this way could help platform providers improve the user experience and engagement. The work might also have applications in psychology, social media studies, but perhaps also it could ultimately benefit the bottom-line for the platforms.
Nam Tien Duong of Ho Chi Minh City University of Economics and Finance, Vietnam, has looked at the intersection of personality, self-presentation, and social networking behaviour. He found that social network users are driven by specific interpersonal needs that shape how much they reveal about themselves. These needs, grounded in Maslow’s hierarchy of needs, emphasize the social and emotional drive for connection and affection. Social networking platforms have offered us a unique space to meet these needs through active self-expression online.
The research has drawn on two primary interpersonal needs that shape behaviour: the need for belonging and the need for self-presentation. The need for belonging involves the desire to connect with others and feel recognized, while the need for self-presentation is about managing the image we project to others. The study emphasizes that self-presentation plays an important part in motivating self-disclosure, though its impact varies depending on an individual’s personality traits, particularly extraversion and narcissism.
Extraversion refers to a person’s tendency to seek out social interaction and enjoy group activities. According to the findings, individuals with high levels of extraversion are more likely to disclose personal information. Their enthusiasm for engaging with others translates into a greater willingness to share personal details. In contrast, introverts, who are less inclined toward social interactions, tend to disclose less about themselves, even when they may still have a strong desire for social inclusion.
Another personality trait that significantly influences self-disclosure is narcissism. Narcissists, who possess a strong desire for admiration and validation, often share more personal information to highlight their perceived individuality. This behaviour is driven by a need to garner attention and reinforce their sense of self-importance, which stands in contrast to those who may share less for more intimate or relational reasons.
Duong, N.T. (2025) ‘Why do people disclose themselves on social networking sites? Evidence from Vietnamese Facebook users’, Int. J. Knowledge and Learning, Vol. 18, No. 2, pp.186–203.
The following paper, "3D image reconstruction using an improved BEV model and global convolutional attention fusion" (International Journal of Information and Communication Technology 26(6) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following paper, "Electric vehicle charging station planning based on the development of distribution networks and coupled charging demand" (International Journal of Information and Communication Technology 26(6) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
As digital devices become more integrated into children’s lives, concerns about their impact on physical and mental health continue to grow. In modern households, smartphones, tablets, and computers are now commonplace, leading to increased exposure to online content. This shift has raised important questions about how much screen time is appropriate and what effects it has on children’s well-being.
The issue of screen time has been widely debated, with research pointing to both potential risks and benefits. Excessive screen use has been linked to physical issues such as eye strain, headaches, and sleep disruption. There are also concerns about the relationship between increased screen time and physical inactivity, as children who spend more time on devices might be less engaged in outdoor play and exercise, both essential for their physical development.
On the other hand, the online world offers numerous opportunities for learning, creativity, and socialization. Educational apps, online learning platforms, and digital games can stimulate intellectual growth, promote critical thinking, and even foster social connections with peers across the globe. The challenge is finding a balance that maximizes the benefits of digital engagement while mitigating the potential negative effects on health and well-being.
Beyond physical health, the psychological effects of digital media are also a growing concern. Research indicates that extended use of devices, particularly those providing access to social media, can influence children’s emotional well-being, intellectual development, and sense of identity. While some cases have linked excessive screen time to negative outcomes, the full psychological impact of digital media remains an area of ongoing research. It is important to also acknowledge the positive effects, such as improved cognitive skills and the opportunity for global social connections.
Given these concerns, researchers are exploring more personalized methods of regulating screen time, such as the use of fuzzy logic inference systems. These systems, a type of artificial intelligence, can evaluate complex and imprecise data, making them ideal for tailoring screen time recommendations and restrictions based on a child’s unique characteristics.
Parents, guardians, or teachers could input data about a child’s age, health, and psychological profile into the system, which would then use this information to determine appropriate screen time and content limits. Unlike generic restrictions, which may be difficult to enforce or inappropriate for all young users, fuzzy logic systems offer a more customized and flexible approach to managing screen use.
While there are existing tools that restrict screen time and block content, an adaptive approach, could be key to managing both the quantity and quality of screen time. Younger, more vulnerable users would have stricter controls and limits, while older, more mature children could access a wider range of appropriate resources, all based on their individual developmental profiles.
Alguliyev, R.M., Abdullayeva, F.J. and Ojagverdiyeva, S.S. (2024) ‘Fuzzy expert system for access control of children to the internet’, Int. J. Reasoning-based Intelligent Systems, Vol. 16, No. 6, pp.455–462.
The following paper, "Pseudo-coordinates graph convolutional generative adversarial network for art style transfer" (International Journal of Information and Communication Technology 26(6) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following paper, "Research on over-the-air programming and real-name authentication technology of eSIM based on 5G communication technology" (International Journal of Information and Communication Technology 26(6) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following paper, "Real-time frequency adaptation in carrier communication algorithm based on 2sVCNet network" (International Journal of Information and Communication Technology 26(6) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Urban music, which originated in marginalized communities in The Caribbean and the USA, has found a global audience, resonating especially with young people, as is often the case with emerging music genres. Urban music has evolved into more than just a genre of entertainment, it has become a significant cultural force that shapes the identities, behaviour, and educational experiences of young people.
A study in the International Journal of Knowledge and Learning has examined the impact of urban music on secondary school students in Peru. The work sheds new light on its multifaceted role in adolescent life, which may well have wider implications. The findings suggest that urban music, encompassing styles such as hip-hop and reggaetón can serve as a platform for cultural expression and social belonging, influencing students in ways that are deeply linked to their socio-economic environments.
Agustin Angel Roberto Chumpitaz-Avila and Luis Fernando Castro-Llacsa of the National University of San Agustín of Arequipa in Arequipa, Peru, highlight how this musical genre has penetrated schools across Peru, including state, private, and religious institutions. This reflects the wide-reaching influence of urban music. While critics have long asserted that urban music might somehow promote antisocial behaviour, the research suggests that its influence on youth is not so easily categorized and indeed can have a strong positive influence.
Urban music does commonly have explicit lyrics that often feature violence, overtly sexual imagery, and drug use. Those social observers who malign it for these characteristics suggest that young listeners may internalize these messages. However, the current study found that while some students might adopt attitudes reflected in the music, the broader socio-economic and familial context plays a more significant role in determining their behaviour. Urban music, it seems, is a tool for young people to interpret their surroundings rather than an inherently harmful influence.
Chumpitaz-Avila, A.A.R. and Castro-Llacsa, L.F. (2025) ‘Comparative analysis of the impact of urban music on students of state, private and parochial educational institutions’, Int. J. Knowledge and Learning, Vol. 18, No. 2, pp.170–185.
As digital music libraries continue to expand, the challenge of accurately categorizing musical genres remains high on the agenda. A study in the International Journal of Information and Communication Technology introduces a deep learning model designed to improve the classification of classical music genres.
By employing multi-channel learning (MCL) and Mel-spectrogram analysis, the model, known as MC-MelNet, offers what the research suggests is a more nuanced and efficient approach to genre identification. Tests carried out by its developer, Lei Zhang of the Henan Academy of Drama Arts at Henan University in Zhengzhou, China, show that it outperforms traditional classification methods.
The ability to classify music automatically has far-reaching implications for streaming services, music recommendation algorithms, and digital archiving. Classical music, with its intricate structures and subtle variations, presents a particular challenge for automated classification. Zhang explains that MC-MelNet addresses these issues by integrating multiple layers of analysis, capturing both the tonal and temporal characteristics of a composition.
At the core of MC-MelNet’s innovation is its multi-channel learning framework, which processes multiple audio features simultaneously. Conventional approaches rely primarily on Mel-spectrograms, which break down an audio signal into different frequency components in a way that mimics human hearing. However, while effective in capturing tonal elements, Mel-spectrograms alone do not fully represent the temporal dynamics of music.
MC-MelNet overcomes this limitation by incorporating additional audio features such as Mel-frequency cepstral coefficients (MFCC) and Chroma features. MFCCs capture the timbral qualities of a sound, making them useful for distinguishing between different instruments or playing styles. Chroma features, on the other hand, focus on pitch content and harmonic structure. By combining these elements, MC-MelNet creates a richer and more detailed representation of musical compositions, allowing it to distinguish between closely related classical genres with greater accuracy.
Unlike conventional classification methods, which require manual feature extraction, MC-MelNet uses an end-to-end deep learning approach. It utilizes convolutional neural networks (CNNs) to detect spatial patterns in audio data and recurrent neural networks (RNNs), specifically long short-term memory (LSTM) networks, to process sequential musical information.
MC-MelNet might have applications beyond classical music classification. It could, for instance, be adapted for real-time sound processing and audio event detection. Enhancing the model’s generalizability by training it on a more diverse dataset could make it applicable to a wider range of genres, improving automated music classification for commercial streaming platforms.
Zhang, L. (2025) ‘Classification of classical music genres based on Mel-spectrogram and multi-channel learning’, Int. J. Information and Communication Technology, Vol. 26, No. 5, pp.39–53.
The following paper, "An objective comparison of two prominent virtual actor frameworks: Proto.Actor and Orleans" (International Journal of Communication Networks and Distributed Systems 30(3) 2024), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following paper, "Both right nearby and far away: Rural Sámi entrepreneurs' engagement with spatial contexts" (International Journal of Management and Enterprise Development 24(5) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
A distribution model designed to streamline spare parts delivery to roadside assistance vehicles could cut costs in half, according to work in the International Journal of Shipping and Transport Logistics. The model builds a solution to the well-known Travelling Salesman Problem, a complex optimisation problem that involves finding the shortest route that visits each city once and ends at the starting point. The model was tested on real data from a roadside assistance company operating a fleet of service vehicles.
Abolfazl Shafaei, Mohammad Reza Akbari Jokar, and Majid Rafiee of Sharif University of Technology in Tehran, Iran, and Ahmad Hemmati of the University of Bergen in Bergen, Norway, explain that the major logistical challenge for roadside assistance fleets is balancing inventory space with repair capabilities. Service vehicles have limited space onboard, so they must prioritize particular spare parts and specific tools. Service vehicles usually visit a central warehouse on a regular schedule to restock on spare parts every few days. This adds to overall fuel costs, vehicle wear and tear, and lost servicing time. The new system replaces these frequent trips with a centralized delivery truck that optimizes the frequency and route of spare part deliveries.
However, drivers everywhere expect fast, efficient service from the company with which they entrust their vehicle’s roadside maintenance, They also expect it to be inexpensive and a high-quality service.
The team tested several delivery schedules, including daily and every five days, and found that the most efficient option for this roadside assistance company was an optimized cycle on the first, second, and fourth days. This approach reduced costs by 56%.
The new model reduces the need to stockpile items by ensuring regular deliveries to the service fleet out on the road. This frees up space for repair equipment that allows for a wider variety of roadside fixes.
Beyond the immediate time and cost savings to companies running roadside assistance fleets, the model also promises significant environmental benefits. With fewer vehicles returning to a central warehouse to restock, fuel consumption and carbon emissions can be greatly reduced. Indeed, for the test case, the team found that annual carbon dioxide emissions could be reduced by 75 percent.
Shafaei, A., Akbari Jokar, M.R., Rafiee, M. and Hemmati, A. (2025) ‘Using the route planning for supplying spare parts to reduce distribution costs: a case study in a roadside assistance company‘, Int. J. Shipping and Transport Logistics, Vol. 20, No. 1, pp.131-158.
The following paper, "Piano teaching-assisted beat recognition based on spatio-temporal two-branch attention" (International Journal of Information and Communication Technology 26(5) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following paper, "Intelligent fault diagnosis of mechanical equipment based on industrial big data" (International Journal of Information and Communication Technology 26(5) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Recent work in 6D object pose estimation holds significant promise for advancing robotics, augmented reality (AR), virtual reality (VR), as well as autonomous navigation. The research, published in the International Journal of Computational Science and Engineering, introduces a method that enhances the accuracy, generalization, and efficiency of determining an object’s rotation and translation from a single image. This could significantly improve robots’ ability to interact with objects, especially in dynamic or obstructed environments.
In robotics, 6D object pose estimation refers to determining both the orientation (rotation) and position (translation) of an object in three-dimensional space. “6D” describes six degrees of freedom: three for translation (X, Y, Z axes) and three for rotation (around those axes). Accurate pose estimation is critical for autonomous systems, including robots and AR/VR systems.
Challenges arise due to variations in object shapes, viewpoints, and computational demands. Current methods rely on deep-learning techniques using large datasets of objects viewed from various angles. These models struggle with unseen objects or those with shapes different from training data.
The new technique discussed by Zhizhong Chen, Zhihang Wang, Xue Hui Xing, and Tao Kuai of the Northwest Institute of Mechanical and Electrical Engineering in Xianyang City, China, addresses the various challenges by incorporating rotation-invariant features into an artificial intelligence system known as a 3D convolutional network. This allows the system to process an object’s 3D point cloud, regardless of its orientation, leading to more accurate pose predictions even when the object is rotated or seen from unfamiliar angles. The network uses a consistent set of coordinates, known as canonical coordinates, which represent the object in a frame of reference unaffected by rotation. This innovation improves the system’s ability to generalize to new poses, overcoming a limitation of conventional methods.
Not only is the new approach more accurate, it is more efficient and so needs less training data and less computer power, making it more suited for real-time, real-world applications.
Chen, Z., Wang, Z., Xing, X.H. and Kuai, T. (2025) ‘Rotation-invariant 3D convolutional neural networks for 6D object pose estimation’, Int. J. Computational Science and Engineering, Vol. 28, No. 8, pp.1–9.
The following paper, "An alternative method for generating fractal art patterns based on the balanced optimiser algorithm" (International Journal of Information and Communication Technology 26(5) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following paper, "Knowledge creation in vocational education using multi-source data fusion under big data environment" (International Journal of Information and Communication Technology 26(5) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Separating the human voice from the music in an audio recording has long been a challenge in signal processing. There are numerous so-called artificial intelligence (AI) tools around that can do this now with varying degrees of accuracy. The task is difficult due to the complexity of music, which involves multiple overlapping sources across the audible frequency spectrum. There is a need to increase the resolution and clarity of systems that can separate a vocal from the instrumental for a wide range of applications, such as post-production remixes of music, singing instruction and rehearsing, .
A new method is reported in the International Journal of Reasoning-based Intelligent Systems. The researchers, Maoyuan Yin and Li Pan of the School of Music and Dance at Mudanjiang Normal University in Mudanjiang, China, have, they say, improved upon existing techniques by combining several advanced signal processing techniques. Their starting point is the use of a virtual microphone array. This virtual setup helps them localize the human voice within the overall sound and isolate it from the background.
The virtual microphone array creates a spatial representation of the sound, the team explains. To further improve on the results, the team also used near-field and far-field models to simulate the propagation of sound from sources at different distances. This gives them even more precision in localising the vocal within the sound.
Once the voice is accurately located, the system constructs a time-frequency spectrum for both the human voice and the background music. The time-frequency spectrum tracks how the energy of sound signals shifts along the frequency axis over time. The system can then analyse these changes and distinguish between vocal and instrumental, isolating them from one another.
The process is further refined by the use of a sophisticated algorithmic technique – the Hamming window function, which improves the efficiency of the requisite two-dimensional fast Fourier transform (2DFT) processing of the data. This step reduces the number of dimensions of the various extracted sound signals, simplifying the final extraction of vocal from music.
Test results demonstrate the effectiveness of this new approach with a localization error of just 0.50%. For background music, the feature extraction error is reduced to 0.05%. Overall, the team could reach almost 99 percent accuracy in separating vocal from instrumental. The same approach should also work in isolating a human voice from non-musical background noise. It could thus be used to improve automated spoken-word transcription services and help in the development of better hearing aids.
Yin, M. and Pan, L. (2025) ‘Separating voice and background music based on 2DFT transform’, Int. J. Reasoning-based Intelligent Systems, Vol. 17, No. 1, pp.50–57.
Fractals are intricate geometric shapes that exhibit self-similarity, meaning their patterns repeat at different scales, no matter how much they are magnified. Unlike traditional geometric figures such as circles or squares, which can be described with simple equations, fractals are generated through iterative mathematical processes, producing infinitely complex and detailed structures.
We see fractals all around us, in the branching structure of a tree, in clouds, snowflakes, coastlines, in the system of blood vessels and nerves in our bodies. Fractals can thus be used as a scientific model for many natural phenomena, However, their inherent beauty and intrigue can be a source of artistic inspiration too.
A study in the International Journal of Information and Communication Technology introduces an advanced approach that can be used to create novel images based on fractals. The optimisation algorithm, developed by Junli Wang of the School of Digital Arts at Wuxi Vocational College of Science and Technology in Wuxi, China, and known as the Equilibrium Optimiser (EO), significantly improving efficiency and design diversity.
Fractal geometry was first formalised by Benoît Mandelbrot in the 1970s and has influenced fields ranging from architecture to computer graphics and even music composition. The challenge in fractal art generation has traditionally been the reliance on manual input, requiring expertise and time-consuming adjustments. The new research overcomes some of those limitations through the EO algorithm, which enables a more efficient, diverse, and aesthetically rich exploration of fractal forms, according to the study.
The EO algorithm is an advanced optimisation algorithm based on how natural physical systems balance themselves. Unlike Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO), the EO algorithm adjusts its search strategies dynamically to avoid becoming trapped in local optimisation points, a common problem of many mathematical models. This means that the EO algorithm can fine-tune the parameters needed to generate fractal patterns, producing designs with greater symmetry, complexity, and structural variation than traditional approaches. Wang’s tests show that the EO algorithm works better than older algorithms in terms of the speed with which it converges on a solution and the visual quality and stability it produces.
Beyond its technical contributions, this research raises important questions about the intersection of technology and art. The ability to generate intricate fractal patterns automatically expands the creative possibilities available to artists, designers, and researchers. Unlike hand-drawn or physically painted works, digital fractal art is created through computation, challenging conventional ideas of authorship and artistic intent.
Wang, J. (2025) ‘An alternative method for generating fractal art patterns based on the balanced optimiser algorithm’, Int. J. Information and Communication Technology, Vol. 26, No. 5, pp.54–68.
The following paper, "Classification of classical music genres based on Mel-spectrogram and multi-channel learning" (International Journal of Information and Communication Technology 26(5) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following paper, "Ideological opinion clustering identification based on Gibbs sampling in social new media environment" (International Journal of Information and Communication Technology 26(5) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Can money buy happiness? An age-old question with as yet no definitive answer. The ancient philosophers could not find it, nor can modern economists with their spreadsheets and algorithms. A study in the International Journal of Happiness and Development has explored the complex relationship between income and happiness and provides some new insights into the debate.
Ling Zhang China Agricultural University in Beijing, China, and Sajal Lahiri of Southern Illinois University Carbondale in Carbondale, Illinois, USA, used data from the Panel Study of Income Dynamics, a comprehensive dataset that tracks individuals over time, and found that having a higher income does seem to correlate with increased life satisfaction. Importantly, the data is longitudinal, which means it tracks the same individuals over several years, allowing the researchers to control for unchanging factors such as personality traits or family background.
The study also found that for those with rising income, their happiness tends to rise too. Conventionally, the Easterlin Paradox has suggested that although wealthier people are generally happier at any given time, happiness does not necessarily increase as individuals become wealthier over their lifetime. This new research somewhat overturns that notion.
Of course, there are always exceptions to any rule, poorer people with seemingly plenty to smile about, who accept their lot and enjoy life regardless and conversely the super-rich individual who never smiles and seems perpetually burdened by their wealth.
The study used subjective well-being (SWB) scales, where individuals rate their life satisfaction on a scale from 1 to 10 to measure happiness. The results were telling: income was positively linked to life satisfaction, and the effect of income on happiness has become more pronounced in recent years. The study also showed that individuals whose income decreased in real terms, often because of factors such as technological progress or globalization, reported lower levels of life satisfaction. This suggests that declines in income, particularly among lower-income individuals, can negatively affect well-being.
While the relationship between money and happiness remains a nuanced topic, the evidence from this study adds weight to the argument that economic well-being plays a significant role in determining overall life satisfaction. These findings emphasise the importance of addressing income inequality and supporting policies that help individuals increase their earnings. As the study suggests, fostering economic fairness and opportunity may not only help individuals thrive, but could also enhance collective happiness.
Zhang, L. and Lahiri, S. (2025) ‘Income and happiness: a study of a panel of US residents’, Int. J. Happiness and Development, Vol. 9, No. 1, pp.1–14.
The following paper, "Implementation of gesture recognition technology optimised by neural networks in OpenMV" (International Journal of Information and Communication Technology 26(5) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following paper, "Internal promotion in companies in El Salvador" (International Journal of Economics and Business Research 29(10) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following paper, "Rotation-invariant 3D convolutional neural networks for 6D object pose estimation" (International Journal of Computational Science and Engineering 28(7) 2025), is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Artificial intelligence (AI) and deep-learning technologies have led to the development of so-called deepfakes. These are generated or manipulated video or audio recordings that can alter a person’s facial expressions, voice, or even their entire identity. While deepfakes have some legitimate uses in areas such as entertainment and art, their potential for misuse and for the spread of misinformation or damaging reputations has been recognised for several years. As the technology used to create deepfakes becomes more sophisticated, so there is a growing need to develop methods for deepfake detection.
Research described in the International Journal of Computational Science and Engineering introduces a hybrid deep-learning model that can itself improve the detection of deepfake content. The model combines two convolutional neural network (CNN) architectures, Inception ResNetV2 and Xception, along with long short-term memory (LSTM) networks. LSTM networks can process sequential data from video or audio segments and are particularly useful in spotting inconsistencies in manipulated media.
Shourya Chambial, Tanisha Pandey, Rishabh Budhia, and Balakrushna Tripathy of the Vellore Institute of Technology in Tamil Nadu, India, and Anurag Tripathy of Carnegie Mellon University in Pittsburgh, Pennsylvania, USA, trained their deepfake detector on a large dataset containing both real and manipulated video data. They were able to achieve an accuracy of almost 97 percent in tests. This suggests that the hybrid approach is capable of identifying subtle signs of manipulation in digital media and so decide whether a video is real or fake.
An important aspect of the research is that it emphasizes the importance of fine-tuning deep-learning models so that they work well with real-world data. An issue that commonly arises with certain types of AI model is that of “overfitting”, where the algorithm is too closely tied to the specific characteristics of its training data and struggles to perform on new, unseen data. In the current work, the team monitored performance and adjusted it to ensure it remained effective with a wide range of video content.
Chambial, S., Pandey, T., Budhia, R., Tripathy, B. and Tripathy, A. (2025) ‘Unlocking the potential of deepfake generation and detection with a hybrid approach’, Int. J. Computational Science and Engineering, Vol. 28, No. 2, pp.151–165.
Better technology for keeping track of glass beer bottles could be important in improving health and safety and operational efficiency in the alcoholic beverage sector, according to work published in the International Journal of Productivity and Quality Management.
A team in Brazil has evaluated three technologies, laser, carbon dioxide laser, and QR code systems and considered basic criteria such as security, cost, performance, and social impact. Carolina Xavier da Silva Seixas Rocha, Aldara da Silva César, and Cecilia Toledo Hernández, and Ualison Rébula de Oliveira of the Fluminense Federal University in Volta Redonda, and Fabiane Letícia Lizarelli of the Universidade Federal de São Carlos in São Carlos, Brazil, suggest that QR code technology offers the best balance between the various factors, particularly in terms of safety and broader social implications.
Traceability is the ability to track products through the supply chain and has become a critical issue in the food industry due to the increasing frequency of health problems associated with foodborne pathogens. According to the World Health Organization, millions of people are affected by foodborne illnesses each year, underlining the importance of systems that can trace the origin and movement of food products. Effective traceability can help quickly identify and remove products that pose a safety risk, which is vital for consumer protection.
The team focus on Brazil, where beer consumption is on the rise, and highlight the need for improved traceability in the beer industry. The team has identified how Brazilian glass bottle manufacturers lacking the ability to trace individual bottles. This limitation inevitably complicates the resolution of customer complaints and the identification of production issues.
While there are sophisticated methods for analysing a given bottle, the team suggests that the two-dimensional bar code system, known as the QR (Quick-response) code, offers a promising solution to traceability. QR codes are relatively inexpensive to implement, easy to use, and capable of providing real-time data on product movements. These features make them a strong choice for companies looking to meet both safety requirements and consumer demand for transparency. Additionally, QR codes align with increasing regulatory pressures in markets like Brazil, where food safety standards are becoming more stringent.
Rocha, C.X.d.S.S., César, A.d.S., Hernández, C.T., de Oliveira, U.R. and Lizarelli, F.L. (2025) ‘Analysis and selection of glass bottle traceability technologies in the beer production chain’, Int. J. Productivity and Quality Management, Vol. 44, No. 2, pp.178–204.
