The following International Journal of Information and Communication Technology article, "Identification of translation bias in Chinese-Korean Confucian texts based on pre-trained language models", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Energy efficiency analysis and optimisation strategies for green building design based on gravitational search algorithm", is freely available for download as an open access article.
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Railway infrastructure could be made safer and more reliable using AI, artificial intelligence, according to research in the International Journal of Information and Communication Technology. The research outlines a new automated, real-time fault detection system based on deep learning that can identify problems with track, bridges, tunnels, and signalling equipment. The work could address long-standing challenges in maintaining complex transportation networks.
Faults in railway infrastructure arise through wear and tear, ageing, and unexpected failures. Conventional inspection methods remain largely manual and periodic and as such are costly, time-consuming, and prone to human error. This limits their ability to detect problems early. The new AI system can process vast amounts of operational data and quickly identify patterns and anomalies with high precision that can then be followed up by maintenance staff.
One of the major obstacles to the development of automated tools for fault detection has been the scarcity and imbalance of the requisite data. Some types of failures are so rare that training machine-learning models is almost impossible, as there is a dearth of sample data on which to train the model. The new research tackles this by combining an enhanced Synthetic Minority Over-sampling Technique (ESMOTE) with a class-conditional Generative Adversarial Network (CSGAN). ESMOTE improves data diversity by clustering similar samples and interpolating between them, while CSGAN generates synthetic data that reflects the characteristics of different fault categories. This dual approach creates a more balanced dataset, reducing reliance on expert-labelled data and improving model stability.
Once the data is prepared, the system extracts detailed features from operational signals using a multiscale residual network (ResNet), a type of neural network that captures fine-grained patterns while taking into account variations in operating conditions. A subdomain-adaptive transfer learning strategy allows insights gained from one dataset to be applied to others, enabling accurate fault identification across different environments.
Tests on the new system gave a diagnostic accuracy of almost 94 percent, which is better than previous models that struggled with manual feature extraction or unbalanced datasets. The improved precision promises practical benefits for railway operators. Earlier fault detection means limited maintenance resources can be prioritized better and the number of disruptions to service minimized.
An, Q. (2025) ‘Intelligent fault diagnosis system for railway infrastructure based on deep learning’, Int. J. Information and Communication Technology, Vol. 26, No. 41, pp.107–122.
The following International Journal of Information and Communication Technology article, "Real-time detection of Business English grammar errors driven by transfer learning", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "M-DRAMA: a multimodal-driven framework for classical drama short video promotion", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Sparse coding-based vocal music feature extraction and real-time transmission", is freely available for download as an open access article.
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Financial technology can significantly boost the environmental performance of small and medium-sized enterprises (SMEs) in Pakistan’s manufacturing sector. However, this boost only arises when coupled with organisational innovation and access to green finance, according to new research in the International Journal of Business Innovation and Research. The work has looked at the pressures facing SMEs with limited resources in emerging economies in this context.
The research used data from 340 manufacturing SMEs in Pakistan and found, using structural equation modelling, that those companies adopting digital financial tools, fintech, showed clear improvements in sustainability performance. This is a broad measure covering cleaner production methods, reduced waste, and more efficient use of resources.
The structural equation modelling can tease out complex causal relationships, and so was able to determine how fintech interacts with internal innovation practices and environmentally focused financial instruments. The researchers thus found that technology alone was insufficient to boost SMEs, but its effects were strongest when it prompted firms to redesign processes, improve transparency, and make operational decisions based on real-time data.
The team adds that access to green finance, such as loans linked to sustainability efforts or green bonds, was just as important. Such financial products, earmarked for environmental improvements, are usually out of reach of many SMEs because of limited collateral or weak reporting systems. Their use of fintech helps them overcome such barriers by standardising data, streamlining due-diligence checks, and widening the pool of potential lenders.
As a result, firms using digital financial tools were better able to secure funding for cleaner technologies that they could not otherwise afford. Organisational innovation and green finance affect the link between adopting fintech tools and sustainability outcomes. This means that technology improves environmental performance partly by enabling these other capabilities. Conversely, fintech itself mediates the relationship between green finance and sustainability, acting as the mechanism that converts earmarked funding into measurable environmental gains.
Rashid, S., Ejaz, S., Alwadi, B.M., Kumar, A., Ejaz, F. and Hossain, M.B. (2025) ‘Linking financial technology, innovation, and green finance to drive sustainable performance of SMEs of Pakistan’, Int. J. Business Innovation and Research, Vol. 38, No. 6, pp.30–54.
The following International Journal of Business Innovation and Research article, "Digital financial literacy and resilience in MSMEs: a bibliometric systematic literature review", is freely available for download as an open access article.
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The following International Journal of Business Innovation and Research article, "Impacts of geographic region on knowledge spillover effects and innovation performance in healthcare", is freely available for download as an open access article.
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The following International Journal of Continuing Engineering Education and Life-Long Learning article, "Evaluation method of Chinese grammar interactive teaching based on sentiment classification", is freely available for download as an open access article.
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Artificial intelligence (AI) might be used to improve the early detection of chronic obstructive pulmonary disease (COPD), according to research in the International Journal of Innovative Computing and Applications. COPD is a serious and ultimately terminal condition of the lungs.
COPD is a long-term, progressive lung condition common in smokers and those exposed to noxious volatile substances, although it can affect non-smokers too. It is an umbrella term enshrouds chronic bronchitis and emphysema, both of which cause narrowing and damage to the airways and lead to a persistent cough, excess mucus, shortness of breath, and frequent respiratory infections. The disease gradually reduces the lungs’ ability to move air in and out, and although incurable, early diagnosis allows for better management with medication, pulmonary rehabilitation and lifestyle changes.
The new approach to diagnosis uses machine-learning techniques to analyse digital recordings of lung sounds could help recognise a large number of COPD cases that remain undiagnosed worldwide.
The researchers trained algorithms to differentiate between the sounds of air being inhaled and exhaled by healthy individuals and by patients with confirmed COPD, and other conditions such as asthma, pneumonia, respiratory tract infection, and bronchiolitis. This multifarious training reflects what clinicians routinely face: patients with symptoms that overlap with other respiratory conditions and problems. The algorithmic approach would assist the auscultation approach commonly used by clinician, whereby they listen to the patient breathing using a stethoscope and interpret what they hear.
The algorithms thus-developed can identify the subtle acoustic cues linked to respiratory disease through their prior exposure to the large, diverse datasets. The system achieves 95 per cent accuracy, which make it a useful addition to the diagnostic approaches available, and could be used to triage at-risk patients where clinician numbers and specialist resources are limited. Given that COPD is a leading cause of death and disability, and often progresses unnoticed until lung function is severely impaired, the approach could improve outcomes and quality of life for many putative patients.
Amose, J., Manimegalai, P., Amritha, M. and George, S.T. (2025) ‘Acoustic analysis of chronic obstructive pulmonary disorder using transfer learning: a three-class problem’, Int. J. Innovative Computing and Applications, Vol. 15, No. 3, pp.135–144.
The following International Journal of Continuing Engineering Education and Life-Long Learning article, "How intelligent semi-supervised learning illuminates influencing factors in college students' employment psychology", is freely available for download as an open access article.
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The following International Journal of Continuing Engineering Education and Life-Long Learning article, "Multi-objective construction of English online autonomous learning based on mobile intelligent information system", is freely available for download as an open access article.
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The following International Journal of Continuing Engineering Education and Life-Long Learning article, "Creation and application of education and management intelligence software based on artificial intelligence strategy", is freely available for download as an open access article.
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The following International Journal of Continuing Engineering Education and Life-Long Learning article, "Innovation of college English listening and speaking teaching model based on multimedia technology", is freely available for download as an open access article.
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A new approach to sentiment analysis could reduce one of the field’s most stubborn sources of error: the misinterpretation of sarcasm. The system is discussed in the International Journal of Intelligent Engineering Informatics. It shows that machine-learning models can be trained to recognise when language means the opposite of what it appears to say. This is an important advance in language processing with implications for businesses, policymakers, and analysts who rely on automated readings of public opinion.
Sentiment analysis aims to classify text as positive, negative or neutral, but often comes unstuck when analysing remarks that a human reader would immediately recognise as sarcasm. Sarcastic remarks often invert the literal meaning, and so conventional algorithms can misread the tone, distorting everything from political polling to consumer-behaviour forecasts. With online communication proliferating across social media and forums, the cost to society of such errors is on the increase.
The new framework system combines two distinct techniques to handle this problem. First, it uses BERT, Bidirectional Encoder Representations from Transformers. This is a language model that reads text in two ways and can identify subtle cues that signal irony, a contradiction or a tonal shift. These contextual embeddings are essentially numerical representations of meaning. They are passed to a so-called random forest algorithm for classification to improve reliability. Random forests are well suited to spotting complex, non-linear patterns in data, making them a natural complement to BERT’s linguistic sensitivity.
The researchers trained their new system on a bespoke dataset rich in realistic, fine-grained examples of sarcastic speech. When they tested the trained model against established sentiment-analysis models, including lexicon-based, statistical, and deep learning systems, they were able to spot sarcasm with 85 per cent accuracy. The same system could also identify neutral sentiment, which is an area where existing tools often struggle because sarcasm can the true intent of what is being said.
The researchers emphasise how more accurate detection of sarcasm could yield more trustworthy analytics across sectors that depend on understanding public mood.
Davidson, G.P., Ravindran, D. and Pratheeba, R.A. (2025) ‘CASD on enhancing sentiment analysis using context-aware sarcasm detection on social media’, Int. J. Intelligent Engineering Informatics, Vol. 13, No. 3, pp.267–296.
A new algorithmic framework that can predict flooding could help us save lives and reduce the devastation as climate change drives more intense and unpredictable rainfall. The model described in the International Journal of Information and Communication Technology uses the Multi-Scale Adaptive Neuro-Fuzzy Inference System (MS-ANFIS) and combines deep learning with a form of fuzzy logic that quantifies uncertainty. Features that were missing from earlier data-driven flood models.
Flood prediction usually focuses on hydrological models that simulate how rainfall moves across landscapes and into rivers. These are grounded in environmental science but depend on detailed land-surface information and can be computationally expensive, limiting their usefulness for rapid or large-scale forecasting. Attempts to reduce the computing demands as well as speed up predictions using statistical and early machine-learning approaches have proved useful but still struggle to cope with diverse data sources or respond to highly localised events. Even cutting-edge deep-learning models, which can spot patterns in vast datasets, treat river systems as deterministic in behaviour and do not take into account the inherent variability that arises because of extreme weather.
MS-ANFIS might plug the holes in earlier approaches. It uses a feature pyramid network. This is a deep-learning architecture that extracts information at multiple scales. In doing so it can capture detailed runoff patterns and broader rainfall trends visible in satellite data. The fuzzy layer then interprets the data and expresses uncertainty in a structured, interpretable way. The result is flood prediction with a measure of confidence in the prediction built in.
The researchers have tested their system on data from five major river basins, covering markedly different weather patterns and hydrological behaviour. The model’s confidence intervals quickly captured more than 90 per cent of extreme events. Such accuracy could help emergency planners judge when to trust, or question, a forecast in real-time. And so make provision for the impact of a likely flood ahead of it happening through reservoir management and evacuation decisions.
Zhao, H. and Xia, T. (2025) ‘Flood disaster prediction using multi-scale deep learning and neuro-fuzzy inference’, Int. J. Information and Communication Technology, Vol. 26, No. 41, pp.91–106.
Airbnb guests who feel a strong emotional bond with the platform are significantly more likely to help shape its services, according to research in the European Journal of International Management. The work sheds light on how digital platforms might cultivate loyalty in the sharing economy. The research looked at London-based travellers and their “brand love” for the platform. Brand love can play a decisive role in whether a customer engages in value co-creation with a product to effectively enhance the experience to the mutual benefit of both customer and company.
The researchers found that such an emotional attachment is built less on marketing messages than on how travellers perceive the identity of the platform and the quality of its website. When users view the platform as trustworthy, visually appealing and aligned with their own values, they are more inclined to take an active role in their stay. They will more often than not communicate with their hosts to personalise arrangements and in return offer detailed feedback to help the hosts better cater to future guests.
Such co-creation is increasingly important to digital businesses, of which Airbnb is just one of many. The customer is no longer a passive recipient of goods and services, but can help shape both the product and the way in which the wider community perceives that product or service.
The work in EJIM argue that such involvement not only improves the perceived value of the service but also deepens the users’ sense of belonging. This is very much an outcome that modern digital companies in the sharing economy are keen to achieve.
The work is not only relevant to Airbnb itself, but to tourism managers and policymakers hoping to develop and foster meaningful user engagement.
Foroudi, P. and Marvi, R. (2025) ‘‘Some like it hot’: the role of identity, website, co-creation behaviour on identification and love’, European J. International Management, Vol. 27, No. 4, pp.623–670.
One of the big problems facing the use of digital systems in healthcare is the matter of security. Research in the International Journal of Medical Engineering and Informatics offers a new approach to defending against cyber-related patient-safety risks in the so-called smart hospital. The approach uses an anomaly-detection system that can analyse the full range of data generated by modern medical systems. By integrating numerical time-series analysis with image-based classification techniques it can identify irregularities that existing tools often miss.
Anomalies in this context are any unexpected deviations in a device’s behaviour or data stream, whether a sudden spike in a sensor reading, a breach of a device’s operating constraints, or an unusual pause in data transmission. While such events can indicate technical faults, they may also signal security breaches. Given that whole healthcare systems have been the subject of cyber-attack in recent years and suffered major outages as a result, there is a growing need for protection.
As hospitals begin to use more and more interconnected devices, such as monitors and wearable sensors, the vulnerabilities will only continue to grow. The researchers point out that even minor disruptions can cascade into clinical delays or expose systems to malicious interference.
The proposed system can manage the increasing complexity of electronic healthcare systems by using feature extraction to filter out the digital noise and highlight only the relevant relationships in the data.
One obstacle that is difficult to overcome is how to test and demonstrate the efficacy of the system, as there is a scarcity of real-life clinical datasets with which to work. The researchers plan to generate synthetic but representative datasets to evaluate each component of their detection architecture. They hope to develop it so that it can minimise false alarms while capturing irregularities in a timely manner. Their success will lead to security tools that could underpin digital healthcare as hospitals become ever more data-driven.
Haiba, S. and Mazri, T. (2025) ‘Anomaly detection architecture for smart hospitals based on machine learning, time series, and image recognition analysis: survey’, Int. J. Medical Engineering and Informatics, Vol. 17, No. 7, pp.1–14.
Machine learning could improve the way learners are assessed when it comes piano performance. An approach described in the International Journal of Information and Communication Technology offers a more precise understanding of rhythm than earlier methods. The approach, PianoTrans-Fusion, combines audio, video, and MIDI data to evaluate timing and beat consistency, addressing limitations of earlier automated methods of assessment.
Conventional rhythm assessment usually relies on human observation. But, there are times when a student of the piano might wish to assess their own development in this area. Basic audio analysis can assist, but is generally slow and cannot capture the subtle timing variations that distinguish a skilled pianist from someone merely tickling the ivories.
Tools based on neural networks have improved objectivity, but typically focus only on audio, ignoring other informative signals. PianoTrans-Fusion’s innovation lies in integrating multiple types of input and using a machine learning method that can detect patterns across long sequences. The system uses “self-attention” mechanisms, which allow it to weigh the relative importance of different moments in the performance, capturing fine-grained fluctuations in timing. By bring together information from sound and visual recordings of the performer, and the structured note data of a music file in the MIDI format, the new model constructs a detailed map of the performance.
In tests using the MAESTRO dataset, a large collection of professionally recorded piano performances, PianoTrans-Fusion outperformed five baseline systems. It showed improved rhythm consistency and reduced beat errors. These findings suggest the system could provide a more reliable foundation for tasks such as automated accompaniment or performance evaluation.
Future work may expand the diversity of datasets, allowing the researchers to optimize the algorithm for efficiency, and to link rhythm assessment to broader aspects of musical interpretation, such as style and emotional expression.
Deng, J. (2025) ‘Piano performance beat assessment: integrating transformer with multimodal feature learning’, Int. J. Information and Communication Technology, Vol. 26, No. 41, pp.74–90.
The following International Journal of Information and Communication Technology article, "Intelligent fault diagnosis system for railway infrastructure based on deep learning", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following nternational Journal of Information and Communication Technology article, "Flood disaster prediction using multi-scale deep learning and neuro-fuzzy inference", is freely available for download as an open access article.
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Urban Indonesian women are turning to luxury lingerie as a discreet form of self-expression. Research in the International Journal of Business Innovation and Research suggests that private, emotionally driven motivations now play a bigger role in luxury consumption than public displays of status. The study, based on survey responses from 309 women aged 20 to 45, argues that in a culture shaped by modesty and social restraint, intimate apparel has become a quiet vehicle for identity, confidence, and emotional reassurance.
The research used Structural Equation Modelling, a statistical method that tests how multiple psychological factors interact, to determine what most strongly influences women’s intentions to buy luxury lingerie. They found that attitude towards the product is the key driver of the decision to purchase, as one might expect. Self-concept, defined as an individual’s perception of who they are or aspire to be, and emotional attachment to the product also both feed into the decision. Together, they shape whether consumers feel a piece of luxury lingerie fits their sense of self and enhances their emotional well-being.
Interestingly, the research showed that brand trust did not matter much in making a purchase decision. For such items, worn privately, rather than displayed publicly, the researchers suggest, emotional resonance outweighs confidence in a brand’s reliability or reputation. Social media, however, emerged as an outlier in the decision-making process. The personal identity-associated factors worked through attitude, but digital exposure exerted a direct influence on purchase intention. The research suggests that online content, influencer, and targeted advertising, can pique interest in private luxury.
The team points out that “inconspicuous luxury consumption” perhaps sits better in Indonesia’s society, where overt indulgence might attract disapproval. Luxury lingerie allows women to navigate ambition and cultural expectations at the same time. The value of such private clothing lies less in visibility than in the feelings of refinement, femininity, and control it can afford. This understanding, of course, feeds into how luxury brands can better sell their goods in such socially conservative markets.
Mores, H. and Pradipto, Y.D. (2025) ‘Adoption barriers of luxury lingerie as an inconspicuous consumption product’, Int. J. Business Innovation and Research, Vol. 38, No. 6, pp.1–29.
The following International Journal of Information and Communication Technology article, "Intelligent Q&A model construction supported by natural language processing and knowledge graphs", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Application of distributed artificial intelligence technology in key frame extraction of film and television video", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Multi-modal similarity feature exchange and structural perception for person re-identification", is freely available for download as an open access article.
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People working alongside robots has, to a degree, been a part of the industrial landscape for many years. But researchers writing in the International Journal of Manufacturing Research, suggest that human-robot collaboration is set to transform modern manufacturing by combining the adaptability of humans with the precision and speed of robots.
Unlike conventional industrial robots, which excel at repetitive, high-accuracy tasks but struggle with variability, collaborative systems would allow humans and robots to work side-by-side in shared workspaces. This would be particularly suited to complex assembly and production environments, where flexibility and nuanced decision-making are essential.
Recent advances emphasise multimodal interaction, in which robots can interpret and respond to human speech, gestures, touch and perhaps even brain signals, allowing them to respond dynamically in real time. Voice commands are an obvious and common means of control, although they do not necessarily work well in noisy factory settings. Gesture recognition provides a non-verbal alternative, capturing hand, arm, facial, and full-body movements to convey instructions. By integrating skeletal tracking and motion capture, robots might anticipate human actions and adjust their movements safely and efficiently.
Physical interaction is also evolving through haptic technologies, which allow operators to guide robots directly using touch. Adaptive control techniques, including sensorless admittance and impedance control, translate these contact forces into precise robotic movements, enabling responsive and safe collaboration. Complementing this, digital twin simulations allow manufacturers to optimise assembly processes and predict human behaviour before applying changes on the factory floor, bridging the gap between virtual planning and real-world execution.
The integration of advanced sensing, AI-driven interpretation, and flexible control strategies might also help manufacturing evolve into a more intuitive, efficient, and adaptive enterprise. Multimodal human-robot collaboration might improve assembly efficiency and safety but might also lay the groundwork for factories where human and robotic strengths are combined, opening new possibilities for intelligent, collaborative production systems.
Liu, S., Liu, Z., Qin, Q., Wang, X.V. and Wang, L. (2025) ‘Multimodal human-robot collaboration: advancements and future directions’, Int. J. Manufacturing Research, Vol. 20, No. 5, pp.1–47.
The following International Journal of Information and Communication Technology article, "Intelligent recognition and analysis system of students' behaviour in continuing education based on classroom video", is freely available for download as an open access article.
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The growing fitness app market is being shaped less by technical features than by the power of play, according to research in the International Journal of Business and Emerging Markets that has examined what keeps users returning to digital workout platforms.
The research has focused on China, which has more than 400 million fitness app users and an annual market value exceeding USD 14billion. It could be said that China leads the world in fitness app adoption. Users frequently combine several apps with wearable devices such as smartwatches to track workouts, monitor health metrics, and follow guided exercise programmes. That said, keeping users engaged remains a challenge, as many abandon apps once the novelty wears off and their initial motivation fades or their routines change. As such, understanding what might drive sustained use has become a priority for developers and marketers alike.
The study has found that among a sample of several hundred Chinese fitness app users, gamification, the incorporation of game-like elements such as badges, challenges, leaderboards, and rewards, can play an important role in encouraging long-term engagement. The researchers examined four behavioural factors commonly associated with technology adoption: performance expectancy (the perceived usefulness of the app), social influence (the impact of peers and community), hedonic motivation (the enjoyment derived from use), and habit. Surprisingly, none of these factors directly predicted whether a user would carry on using an app, but those factors did influence how much users interacted with the gamified features and so in turn determined ongoing engagement to a degree.
The findings indicate that gamification acts as a crucial intermediary. It can convert expectations, social context, enjoyment, and habits into sustained app use. Features such as progress tracking, interactive storytelling, and peer challenges were particularly effective, the team found. They point out that in China’s collectivist society, where social cohesion and community participation are culturally emphasised, gamified peer interactions were particularly strong.
Gong, Y. and Yi, J. (2025) ‘Gamified features as a mediator of fitness app engagement: a cross-sectional study’, Int. J. Business and Emerging Markets, Vol. 17, No. 6, pp.1–29.
The following International Journal of Business Innovation and Research article, "Adoption barriers of luxury lingerie as an inconspicuous consumption product", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Medical Engineering and Informatics article, "Anomaly detection architecture for smart hospitals based on machine learning, time series, and image recognition analysis: survey", is freely available for download as an open access article.
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The following International Journal of Manufacturing Research article, "Multimodal human-robot collaboration: advancements and future directions", is freely available for download as an open access article.
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Research in the International Journal of Environment, Workplace and Employment has looked at the psychological factors that might affect an employee’s engagement with workplace environmental initiatives. The work shows that one of the “darker” personality traits, usually seen as a negative can influence pro-environmental behaviour in a perhaps surprising way.
The researchers studied the so-called “Dark Triad” of personality, Machiavellianism, narcissism, and psychopathy, and their association with voluntary environmentally responsible actions. These actions are known formally as organisational citizenship behaviour for the environment (OCBE). OCBE encompasses discretionary activities such as recycling, conserving energy, and reducing waste, which are not a prerequisite of environmental efforts, but can significantly improve sustainability goals in the workplace.
Using statistical methods including hierarchical regression and mediation analysis, the study found distinct patterns among the traits. Employees high in Machiavellianism, characterised by strategic calculation and goal-oriented thinking, were more likely to engage in OCBE. This suggests that individuals with a strong focus on personal gain may participate in environmental initiatives if they see a clear advantage, such as career progression or benefits to their own reputation. Psychopathy, marked by impulsiveness and a lack of empathy, however, was associated with lower levels of OCBE, indicating resistance to environmental programmes. Narcissism, defined by self-focus and a desire for admiration, showed no direct link to environmental behaviour.
When the team looked deeper into the data, they could see that egotism, altruism, and biospheric values could reflect the reasons individuals might act environmentally. Egotistical values, which prioritise personal benefit, boosted the influence of both narcissism and, unexpectedly, psychopathy on OCBE. In contrast, altruistic behaviour and biospheric values, linked to concern for others or the planet, generally did not mediate the relationship, except that biospheric values weakened engagement among those with psychopathic tendencies. In other words, environmentally driven appeals rooted in genuine ecological concern were less effective for individuals predisposed to psychopathy.
The findings highlight the complex interplay between personality, values, and sustainability in an organisational setting. The team suggests that interventions aimed at increasing employee participation in environmental initiatives might benefit from focusing on personal incentives, particularly for those with Machiavellian or egotistical characters. Conversely, strategies emphasising moral or ecological duty may not resonate with individuals high in psychopathy.
Lau, J.L., Jamaluddin, A. and Zainudin, N. (2025) ‘Do dark personality traits predict environmental citizenship at work? A mediation analysis of value orientations’, Int. J. Environment, Workplace and Employment, Vol. 9, No. 3, pp.197–215.
The following International Journal of Information and Communication Technology article, "AI-enabled association rule mining with cloud platforms for rural digital finance services", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Research on artistic style recognition and image transfer method based on deep visual feature extraction", is freely available for download as an open access article.
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An individual’s assessment of their own creativity and their assumptions about how others judge them are driven by different personality traits, according to research published in the International Journal of Business Innovation and Research. The study look at the so-called Big Five personality traits among participants and found that the perceptions of how innovative a person feels operate as a separate psychological construct in terms of how organisations identify and support creative work.
The Big Five model categorises personality into openness to experience, conscientiousness, extraversion, agreeableness and neuroticism. The researchers found that openness and extraversion were the strongest predictors of both self-perceived “innovativeness” and a person’s perception of how they feel others view their innovativeness. These two traits are commonly linked to imagination, curiosity and sociability, seem to shape a person’s confidence in generating new ideas but also their expectation when it comes to peer recognition.
Other traits had more uneven effects. Conscientiousness, defined as the tendency to be organised and dependable, and neuroticism, reflecting emotional sensitivity and susceptibility to stress, did not increase the belief in creativity. Yet, both these traits were associated with higher “meta-perception”, implying that individuals who score highly on them may be viewed as innovative by others even if they do not see themselves that way.
Agreeableness, associated with cooperation and consideration for others, gave the most complex result. Its influence on perceived innovativeness shifted depending on whether participants rated themselves or speculated about external judgements. A more detailed analysis suggests that agreeable individuals had a heightened attentiveness to social cues, and that this may widen the gap between their inner assessment of their own creativity and their expectations of how others see them.
The work thus highlights the distinction between traits that foster idea generation, openness and extraversion, and those that matter in turning ideas into workable solutions, where conscientiousness often plays a critical role. For organisations, the study points to a need for greater nuance in how a company identifies innovative potential. Relying solely on an employee’s self-confidence or on the visibility of outspoken, idea-driven personalities might risk overlooking quieter contributors whose strengths lie in implementing or refining ideas, for instance.
Jirásek, M. and Sudzina, F. (2025) ‘The association between personality traits and perceived innovativeness’, Int. J. Business Innovation and Research, Vol. 38, No. 3, pp.314–331.
The following International Journal of Information and Communication Technology article, "Optimised scheduling of network teaching resource management based on improved genetic algorithm", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Optimisation of academic gap compensation strategy based on transfer learning", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "A furniture design assistance method based on spatiotemporal graph neural networks and multi-objective optimisation", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "An intelligent recommendation system for music therapy resources based on a knowledge graph", is freely available for download as an open access article.
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Roselle seeds, an often discarded by-product of the Hibiscus sabdariffa plant, could offer a viable caffeine-free alternative to coffee, according to new research in the International Journal of Food Safety, Nutrition and Public Health. The work has looked at the nutritional value of the seeds as well as their taste and aroma. The team found that Roselle seeds roasted for half an hour could be used to brew a beverage that had a flavour, aroma and body very close to traditional coffee and better than many existing substitutes. It was also shown to contain high levels of antioxidant compounds thought to have health benefits.
There is a growing interest in substitutes for coffee, and a market for tasty drinks that do not have the stimulant effects of caffeine. Many of the “herbal” type brews that have been marketed do now replicate coffee’s depth of flavour nor and complexity and are more akin to herbal teas.
The researchers point out that Roselle is cultivated for its tart, ruby-coloured calyces, the outer part or sepals of its flowers. These are used to make teas and syrups. However, the plant also contains protein-rich seeds that take on new chemical properties when roasted. The team compared unprocessed Roselle seeds with batches roasted for 10, 20, and 30 minutes. They measured bioactive components including antioxidant flavonoids and phenols, as well as tannins and saponins. They found that roasting consistently increased the concentration of these potentially beneficial molecules.
However, it was the sensory testing that was perhaps most important and revealed the true potential of the Roselle seed as a coffee substitute. Participants rated the 30-minute roast highest for flavour, taste, body, and overall acceptability. While the Roselle brews were no real competition for commercial coffee for coffee lovers, the longer roast did score close to true coffee.
If Roselle seeds can be developed into a satisfying, caffeine-free brew similar to coffee, they could add value to a crop already grown in many tropical regions, reduce agricultural waste and offer consumers a more convincing alternative to coffee in the same way that Rooibos (Aspalathus linearis) and similar products have given tea drinkers an alternative to traditional tea.
Oduntan, A.O., Olatunji, O.A., Rapheal, D.O., Oni, O.M., Mustapha, B.O., Ahmed, R.S., Fasuan, T.M. and Akinrinola, A.O. (2025) ‘Development and evaluation of coffee substitute from roselle (Hibiscus sabdariffa) seeds’, Int. J. Food Safety, Nutrition and Public Health, Vol. 6, No. 4, pp.231–250.
The following International Journal of International Journal of Information and Communication Technology article, "Vocational education course recommendation based on neighbours under the construction of knowledge graph", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Business and Emerging Markets article, "Gamified features as a mediator of fitness app engagement: a cross-sectional study", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Application of evolutionary neural model in path optimisation of simulation system", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Information and Communication Technology article, "Dynamic modelling of brand-user relationships via graph neural networks for enhanced marketing optimisation", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Artificial intelligence can now provide an accurate perspective on the sensitive issue of male fertility. An algorithmic model described in the International Journal of System of Systems Engineering offers new hope in understanding a puzzling trend in global public health, the decline of sperm quality.
The team introduces a computational framework that is accurate to over 90 percent and outperforms conventional approached used in reproductive medicine. Computer scientists worked with biomedical researchers to develop a feedforward neural network to do the job. The digital architecture of this system builds on the way in which human neurons work but also incorporates an enhanced version of the so-called tuna swarm optimisation, which seeks out solutions to problems in a way analogous to how the fish hunt their prey.
This seemingly odd inspiration from neurons and tuna allows the hybrid model to identify subtle, non-linear patterns linking semen characteristics, such as sperm count, motility, and morphology, with biological and lifestyle factors as well as environmental influences on male fertility.
To avoid the bias inherent in medical datasets, that healthy samples outnumber abnormal ones, the researchers used the Synthetic Minority Oversampling Technique. This method generates artificial examples of the rarer cases, ensuring the model learns to recognise fertility problems as effectively as it recognises normal samples. They then tested the system on publicly available semen data from the University of California, Irvine (UCI) repository. The system achieved higher sensitivity, specificity, and overall accuracy than established approaches.
For many years, research has pointed to a troubling global decline in male fertility, which has been variously attributed to a combination of environmental pollutants, endocrine-disrupting chemicals, lifestyle factors such as poor diet and smoking, and biological influences such as metabolic disorders. The interactions among these variables have not yet been determined. This new approach might help uncover insights into what is causing the problem, although, it will inevitably be a complex combination of factors.
Shanthini, C. and Silvia Priscila, S. (2025) ‘Feed forwarded neural network with learning-based tuna swarm optimisation (FFNN-LBTSO) for semen quality prediction systems’, Int. J. System of Systems Engineering, Vol. 15, No. 5, pp.471–487.
The following International Journal of Information and Communication Technology article, "Style transfer of ink wash painting based on deep convolutional neural network and feature scaling", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Information and Communication Technology article, "ST-HGCN-enhanced real-time compensation for industrial robot positioning errors", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Enhancing cross-border e-commerce English text classification using graph neural networks and transfer learning", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
A wireless, portable incubator that could transform care for premature infants is reported in the International Journal of Medical Engineering and Informatics. It offers remote monitoring and automated environmental control to reduce the demands on hospital staff.
Premature, or pre-term, babies are those born at under 37 weeks gestation. There are around 15 million pre-term babies born globally each year. They are particularly vulnerable because their bodies struggle to regulate temperature, humidity, and oxygen levels. Traditional neonatal incubators provide a controlled environment to support them, but they require constant manual oversight. Malfunctions in temperature or humidity control have been linked to serious complications and even infant death, underscoring the need for safer and more efficient solutions.
The new incubator integrates heating, a fan, humidity and temperature sensors, and an ultraviolet light for treating jaundice, all managed through an inexpensive Arduino UNO microcontroller. By using a dedicated Android app, healthcare staff can monitor and adjust conditions from up to 30 metres away, consolidating multiple functions onto a single interface. This remote operation reduces the risk of human error while allowing a single nurse to manage several critical parameters simultaneously.
The researchers have tested their system across thirty simulation runs, and statistical analysis confirmed its reliability. The wireless design also allows staff to respond quickly to abnormal fluctuations in vital conditions, addressing one of the key safety concerns in neonatal intensive care. The system’s portability and remote access could be particularly valuable in hospitals with high patient-to-staff ratios or limited resources, where traditional incubators demand continuous human supervision.
The demand for cost-effective, portable incubators is high, especially in regions with limited neonatal care infrastructure. By combining affordability, mobility, and wireless control, the device could improve outcomes for pre-term infants, reduce mortality rates, and relieve staffing pressures in intensive care units.
The next step for the research team is to develop the system to be able to manage several incubators from a single device and perhaps even over longer distances, potentially up to 100 kilometres.
Sarvat, M., Masroor, S., Shabir, J., Jabeen, Z. and Ahmad, B. (2025) ‘Remotely operated infant incubator’, Int. J. Medical Engineering and Informatics, Vol. 17, No. 5, pp.500–512.
The following International Journal of Information and Communication Technology article, "Vocational schools in the use of information technology by educational profiles", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
A systematic review of academic research in the International Journal of Web Based Communities has looked at the relationship between the leading online video content sites and its recommendation system and how this might affect the circulation of polarised or misleading content. The review analysed 56 studies investigating how the platform’s algorithms interact with material including political disinformation, health-related misinformation, and extremist content.
The research indicates that algorithms optimised to increase user engagement can, in some cases, correlate with patterns in which viewers are exposed predominantly to content that aligns with their existing beliefs. This phenomenon is often referred to as the “echo chamber effect.” Some experimental studies cited in the review suggest that sequences of recommended videos may influence attitudes within specific demographic groups.
Political content was the most frequently examined domain for polarisation, though other types of potentially harmful material were also included. The review highlighted diversity in research objectives and method. Approximately half of the studies focused on misinformation, while smaller numbers addressed non-political radicalisation or online toxicity. Several studies have looked at how to model these dynamics in order to find potential strategies for mitigation.
The review identified several gaps in the literature. For instance, few studies have considered the role of monetisation or financial incentives in shaping recommended content. Multi-platform analyses are increasingly common, reflecting recognition that content originating on this major video platform can be shared across other social media and messaging platforms, extending its visibility beyond the platform itself.
Researchers emphasise the distinction between polarisation, where opinions may become more extreme, and misinformation, where inaccurate or misleading claims are shared. They also note the importance of considering algorithmic design, user behaviour, and economic factors together when assessing the broader societal implications of recommendation systems.
The platform in question has implemented measures, including policy updates and fact-checking initiatives, intended to address problematic content, though the review notes that challenges remain.
Almeida, L.G., Garcia, A.C.B. and Simões, J.E. (2025) ‘Polarisation, filter bubbles and radicalisation on YouTube: a systematic literature review’, Int. J. Web Based Communities, Vol. 21, No. 4, pp.324–347.
There is increasing evidence that routine lung-function tests could play a role in diagnosing obstructive sleep apnoea (OSA), a common but frequently undetected sleep disorder. OSA occurs when the upper airway repeatedly collapses during sleep, interrupting breathing, lowering blood oxygen levels, and fragmenting sleep. Left untreated, OSA has been linked to raised blood pressure, metabolic problems, and in some cases, sudden death during sleep.
A review of existing research in the International Journal of Medical Engineering and Informatics highlights how flow-volume curves, a standard output from pulmonary function tests (PFTs), may provide a simpler and more accessible route to identifying the condition. The current diagnostic standard, polysomnography, involves overnight monitoring in a specialist laboratory and requires sophisticated equipment and trained personnel, limiting access and creating long waiting lists. Whereas the more modern approach is much simpler and far more accessible.
Indeed, lung-function tests are non-invasive, widely available, and quick to perform. They measure how much air a person can inhale and exhale, and at what speed, with the flow-volume curve providing a graphical representation of airflow against lung volume during forced breathing. Research indicates that patients with OSA often show distinctive features on these curves, such as reduced lung volumes, increased airway resistance, and altered ratios of forced expiratory volume (FEV1) to forced vital capacity (FVC). These changes are thought to reflect underlying airway collapse and compromised respiratory mechanics.
Despite the potential, relatively few studies have examined the diagnostic value of flow-volume curves systematically. Most focus on conventional spirometric measures or the “saw-tooth” pattern sometimes observed in OSA, employing standard statistical methods rather than more sophisticated computational analyses. There has been limited exploration of whether more detailed waveform features or derived biomarkers could more reliably distinguish OSA from normal respiratory function.
The current review highlights an opportunity to harness modern data science in this area. By applying machine learning to detailed flow-volume data, researchers could identify subtle respiratory signatures that are indicative of OSA. Automated, point-of-care screening, might then be possible by embedding this algorithm into a PFT device. This would allow faster, lower-cost diagnosis and early intervention for at-risk patients.
Eris, S.B., Bilgin, C., Eris, Ö. and Bozkurt, M.R. (2025) ‘A review of the relationship between flow-volume curve and obstructive sleep apnoea‘, Int. J. Medical Engineering and Informatics, Vol. 17, No. 5, pp.476-486.
The following International Journal of Shipping and Transport Logistics article, "The impacts of relocating screening scanners on efficiency of transshipment container ports: policy implications for the maritime industry", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The term artificial intelligence (AI) is becoming ubiquitous, yet many of the concepts underlying these disparate technologies have been around for decades. Techniques such as neural networks, which loosely imitate how the brain processes information, or genetic algorithms, which borrow from evolution to find better solutions, have been researched and discussed for many years. Even machine learning, something of a buzzword, still follows the basic principles of using a computer to understand patterns in data and find similar patterns in new data.
The big change is not in the theoretical principles but rather the scale. Larger datasets, faster hardware, and more sophisticated engineering have made existing methods far more capable than they ever were.
This growth in scale has created new opportunities, but has not yet overcome some of the long-standing weaknesses. Most systems remain very good at narrow tasks, spotting faces in photos, translating or summarising text, for instance, but cannot necessarily cope with bigger problems that require broad reasoning, intuition, or a detailed understanding of context. Moreover, AI tools can make mistakes and even “hallucinate”, offering answers and responses that do not mesh with reality or facts, and sometimes more worrying reflect inherent biases in their training data. There is a popular belief that AI is fast approaching human-like intelligence, but have to assume that such sophistication is still some way off.
Writing in the International Journal of Information and Operations Management Education, a UK team has looked at AI in the context of technological history, and it seems to follow a similar path to that taken by transport, communication, and other areas. Development and uptake generally follow an S-shaped curve as they progress. They start off flat and slow, but then there is a sudden, rapid burst of progress, following by a levelling out on to a plateau as key design principles settle.
At the moment, commercial priorities are pushing companies in almost every sector to adopt AI tools in order to chase profits and efficiency rather than long-term social benefit. Public enthusiasm is split among those who consider it a positive evolution in computers to those who see it as demeaning and degrading human creativity and activity. Many people in both camps have unrealistic expectations, assuming the best or the worst, whereas the truth is logically fuzzy, one might say.
Increasingly, the best results come from hybrid approaches, where we can use AI to support expert judgement in medical diagnostics and engineering, for instance, rather than allowing it to generate answers to problems without the requisite checks and balances. The value of AI will reveal itself in how well it serves people. Progress will come through practical advances in tools that help humans make better decisions, rather than machines that claim to think for us.
AI is far more than a generator of text, images, or music. In fact, generative AI, by some definitions, is not “intelligence” at all, but mimicry based on statistics. True AI, as the technology currently stands, lies in pattern recognition, prediction, optimization, and problem-solving. It is the technology that detects disease in medical scans, forecasts supply and demand, fine-tunes transport logistics, and supports critical decision-making across countless domains. Its true promise will be realised not in imitation of human creativity, but in offering up clues and insights that allow people to think, act, and innovate more effectively.
Rugg, G. and Skillen, J.D. (2025) ‘The limits to growth for AI’, Int. J. Information and Operations Management Education, Vol. 8, No. 1, pp.61–73.
What counts as offensive is subjective: something one person finds harmless can upset another, depending on their background or experiences. Online, this is even more noticeable because people from all over the world interact instantaneously. Debates about political correctness and so-called cancel culture reflect our attempt to balance free speech with responsibility. Being aware and empathic, woke, does not mean avoiding all offence, but it does mean recognising how words and actions might affect different communities or people differently depending on context. Improved social intelligence can lead to better conversations, reduce unnecessary conflict, and build stronger ties between us.
Research in the International Journal of Computational Science and Engineering has looked at how we might automate the detection of offensive content on social media, presenting a method capable of working across more than sixty languages without requiring extensive pre-labelled datasets. The research aims to help platforms manage posts that are truly harmful or represent harassment or abuse, and so improve trust and safety for all users.
The work builds on a multilingual system that can represent text using concepts drawn from Wikipedia articles, allowing posts to be categorised based on meaning rather than language alone. This technique, known as randomized explicit semantic analysis, can then create a vector of weighted concepts for each message, enabling a single annotated dataset in one language to support classification across dozens of others.
To improve accuracy, the researchers introduced a hybrid meta-heuristic algorithm, a type of trial and error approach, that combines a statistical approach known as an adaptive Markov chain Monte Carlo tree search with an optimisation method called the enhanced eagle Aquila optimiser. This combined effort identifies the most effective configurations for categorising content. In tests, it consistently matched or even surpassed current methods when presented with publicly available datasets of offensive social media posts.
The approach also hooked into content-based signals, such as specific words or phrases, behavioural cues, such as posting patterns and metadata, as well account information and timestamps to categorise content more effectively. With such a system in place, social media platforms might be able to refine their moderation systems and focus resources more effectively on tackling content that is broadly deemed as abusive or likely to lead to greater conflict between users.
Aarthi, B. and Chelliah, B.J. (2025) ‘Multilingual language classification model for offensive comments categorisation in social media using HAMMC tree search with enhanced optimisation technique’, Int. J. Computational Science and Engineering, Vol. 28, No. 5, pp.498–514.
The following International Journal of Oil, Gas and Coal Technology article, "Study on the law of disaster caused by multi-field coupling of coal and gas near fault tectonic zone", is freely available for download as an open access article.
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Research in the International Journal of Heavy Vehicle Systems has looked at the design of the front of city trams, also known in North America as streetcars, to see whether changes to their geometry and height might be made to reduce the risk of serious injury or even death to pedestrians following a collision. This safety issue is becoming increasingly important as cities worldwide expand their tram networks in the driver towards cleaner and lower-carbon transport.
Trams are generally seen as a safe and sustainable alternative to private vehicles, their operation within mixed urban traffic means that when collisions do occur, they tend to cause severe or fatal injuries. Yet, unlike cars, trams have no established design standards focused on pedestrian protection.
The research used computer modelling to simulate what happens when a pedestrians is hit by a moving tram. They found that even relatively small adjustments to the shape of the front of a tram and the clearance height from rail to underside might reduce the number of serious injuries and deaths. In the first instance, changes in geometry could reduce the forces experienced by the pedestrian but also push them sideways out of the path of the tram. Secondly, lowering the clearance to less than 185mm would reduce the risk of a toppled pedestrian being run over by the vehicle.
A finite element method (FEM) was used to divide up the complex structure of the front end of a tram into small, simulated components that could be analysed for their behaviour in a collision. On the converse of this, the model tracked the motion of the human body and the forces on the head, chest, and limbs when someone is hit by a moving tram. In this way, they showed that avoiding convex or concave surfaces, which tend to concentrate force on the body, could reduce the severity of injury. Similarly, having an inclination of more than 15 degrees horizontally across roughly a quarter of the tram’s front width improved the likelihood, by 75 percent, of pushing a pedestrian sideways rather than forward. Vertically, a gentle slope of 5 to 10 degrees balanced lower impact forces to the head and chest and avoids secondary collision with the tram’s windscreen.
The findings regarding tram design could be incorporated into international safety standards. This would save lives but also strengthen public confidence in urban tram systems and so support the broader transition to sustainable city transport.
Zhou, H., Liu, W. and Wang, W. (2025) ‘Improvements of a tram shape for pedestrian protection’, Int. J. Heavy Vehicle Systems, Vol. 32, No. 4, pp.561–573.
China’s drive towards a low-carbon economy is showing clear signs of slowing, according to research in the International Journal of Energy Technology and Policy. The study has tracked the country’s progress on replacing fossil fuels with renewable energy sources. While there is still a shift in gear towards cleaner growth, the pace has almost skidded to a halt, from a 92 per cent rise in 2010 to just 17 per cent in 2023. The research thus raises concerns regarding the way in which early gains from heavy state investment have lost their impetus.
The researchers analysed data from 2014 to 2020 and projected trends to 2023, they then developed a detailed framework to assess how effectively renewable energy is supporting China’s economic transformation. By considering four main factors, renewable energy utilisation, ecological environment quality, economic development, and the quality of life of the population, they determined that carbon emission intensity carried the greatest weight in evaluating low-carbon performance, reflecting its importance as a direct measure of climate impact.
The work also shows that China’s transition to a low-carbon economy remains uneven across regions. Southern provinces, with stronger renewable infrastructure and more advanced industries, are leading the shift. In contrast, the industrial north continues to depend heavily on coal and other fossil fuels, which has led to much slower progress and greater environmental strain. This regional imbalance highlights the challenge of aligning national energy goals with local economic realities.
As the world’s largest energy consumer and carbon emitter, China’s experience is seen as a test case for how large developing economies can move towards sustainability without undermining development. Beyond China, the research offers perspective on the many difficulties facing countries attempting to reconcile rapid growth with carbon reduction.
As advances in renewable technologies and energy storage continue, the researchers suggest that future assessments should incorporate social and market factors such as consumer behaviour, pricing mechanisms and public acceptance. Only by combining technological innovation with structural reform, they argue, can China regain the momentum in its push to low-carbon.
Li, C. (2025) ‘Construction of a multiscale renewable energy economic evaluation system considering low-carbon economy and energy storage integration’, Int. J. Energy Technology and Policy, Vol. 20, No. 6, pp.3–16.
The following International Journal of System of Systems Engineering article, "Agile design framework for offshore wind turbine manufacturing", is freely available for download as an open access article.
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The following International Journal of Energy Technology and Policy article, "Methods of realising grid frequency modulation by using adiabatic electromagnetic compressed-air energy storage", is freely available for download as an open access article.
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The following International Journal of Energy Technology and Policy article, "Construction of urban sewage treatment environment model based on energy and ecological restoration concept", is freely available for download as an open access article.
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The following International Journal of Energy Technology and Policy article, "Intelligent monitoring method for variable working conditions in intelligent manufacturing systems under digital twin", is freely available for download as an open access article.
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The following International Journal of Energy Technology and Policy article, "Application of an infrared sensor based on edge computing in power electronics technology", is freely available for download as an open access article.
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The following International Journal of Energy Technology and Policy article, "Construction of a multiscale renewable energy economic evaluation system considering low-carbon economy and energy storage integration", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
A new approach to controlling robotic arms that could make industrial and collaborative robots more precise, adaptable, and efficient is discussed in the International Journal of Systems, Control and Communications. The work uses a decentralized adaptive fuzzy sliding mode control (AFSMC) that controls the robotic arm with voltage-based commands rather than traditional torque-based methods. This, the researchers, explain, simplifies the control system while allowing the equipment to maintain normal function even with uncertainties and external disturbances.
Conventional torque-based controllers rely on highly accurate models of the robot’s dynamics. This makes them computationally intensive and impractical for some real-time applications. By controlling the motor voltages directly, the AFSMC method sidesteps this issue by allowing it to handle fuzzy, or imprecise and approximate information within a sliding mode control framework. Sliding mode controllers are prone to rapid oscillations so the researchers have added a hyperbolic tangent function to their model to producing smoother and more reliable motion.
The AFSMC operates in the workspace, which also allows for more precise and flexible motion. Its decentralized design means that each joint of the robotic arm can be controlled independently while still working in coordination with the others. The team’s simulations with a three-degrees-of-freedom robotic arm show that the approach achieves high tracking accuracy and strong resistance to disturbances. The reduced computational demands compared with standard methods such as proportional-integral-derivative or proportional-derivative controllers make the approach more efficient and effective overall.
Robotic arms are increasingly tasked with high-speed, high-precision operations, from assembling electronics to handling delicate laboratory samples. By reducing the need for exact dynamic models and velocity feedback, the AFSMC could cut costs and make advanced control techniques available in embedded robotic systems. By combining fuzzy logic and sliding mode control, the researchers offer a flexible and theoretically grounded framework capable of managing the complex, non-linear behaviour typical of robotic manipulators operating under uncertain conditions.
Wang, L. (2025) ‘Decentralised adaptive fuzzy sliding mode control for robotic arms using a voltage control approach in workspace’, Int. J. Systems, Control and Communications, Vol. 16, No. 6, pp.1–20.
The following International Journal of Internet Manufacturing and Services article, "Utilising a Gaussian process classifier integrating with meta-heuristic optimisers to predict and classify performance systems", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Systems, Control and Communications article, "Decentralised adaptive fuzzy sliding mode control for robotic arms using a voltage control approach in workspace", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Economics and Business Research article, "Psychosocial risks and relational distress in the Salvadoran workforce", is freely available for download as an open access article.
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A highly precise fibre-based sensing system that can monitor and protect critical infrastructure such as power plants, borders, and military installations is described in the International Journal of Energy Technology and Policy. The system uses advanced laser technology to detect minute disturbances along optical fibres, offering a secure, real-time means of surveillance and fault detection over vast distances.
The research achieves this high precision by using high-power, ultra-narrow linewidth single-mode fibre lasers. These devices can emit light at an extremely stable and well-defined wavelength. This stability allows the system to interpret subtle back-scattered signals within the fibre with exceptional precision, using a method known as optical time-domain reflectometry (OTDR). OTDR works by sending light pulses down a fibre and measuring the light that is scattered back, revealing changes in temperature, strain, or vibration along the length of the fibre.
Laboratory tests demonstrated remarkable performance: fluctuations in transmission and central wavelength were kept below a critical level, while repeated measurements deviated by a tiny amount. This level of consistency, the paper suggests, confirms both the sensitivity and reliability of the design, combining low operational cost with the inherent safety of optical systems. The nature of ultra-narrow linewidth lasers means the signal-to-noise ratio is kept sufficiently high that accurate detection and localisation of events across extended distances can be achieved.
Conventional sensor networks rely on exposed components and electrical wiring, but this fibre-based system can be embedded directly into the ground, integrated with fences, or coiled around pipelines. This makes them resistant to tampering and environmental interference, including electromagnetic noise, extreme temperatures, and corrosion. The approach might be used to detect strain or temperature shifts that precede equipment failures or leaks in pipelines or failing integrity of bridges or tunnels. The approach is particularly suited to remote or hazardous environments, from nuclear facilities to long, unguarded borders.
Li, L., Liu, M., Wu, Q., Zhang, X., Liu, Z. and Zhang, Y. (2025) ‘Optical fibre distributed sensing system based on high-power ultra-narrow linewidth laser’, Int. J. Energy Technology and Policy, Vol. 20, No. 6, pp.17–32.
