Don’t let the landslide bring the power down

Artificial intelligence (AI) can identify landslides and other geological changes that threaten electricity transmission towers, potentially helping operators intervene before infrastructure fails, according to research in the International Journal of Power and Energy Conversion.

The researchers focused on change detection, a remote-sensing technique that compares images of the same location taken at different times to identify disturbances in the landscape. While widely used in environmental monitoring and disaster assessment, such systems have not historically worked well with landslides, because disaster datasets are limited.

The new model analyses satellite or drone images captured before and after a disaster using a twin-network architecture, in which two linked AI systems process and compare images from different periods. It also uses a visual foundation model, a large AI system pre-trained on remote-sensing imagery, to provide broader information about terrain and landscape features.

A key component of the approach is an attention-based alignment module, which allows the AI to focus on relevant information. Here, the module filters out irrelevant differences, such as seasonal vegetation changes or lighting variations, while highlighting structural changes linked to hazards.

Tests on a real disaster dataset showed the system outperformed several recent change-detection methods.

Wu, J., Tang, H., Cen, G. and Wang, K. (2026) ‘Change detection framework for power facilities in disaster scenarios’, Int. J. Power and Energy Conversion, Vol. 17, No. 5, pp.1–20.

Free Open Access issue published by International Journal of Information and Communication Technology

The International Journal of Information and Communication Technology has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• A case study on vision-based intelligent inspection for surface defect detection in CNC machining using YOLOv7 and transfer learning
• Zero-sample accounting standards migration framework empowered by meta-learning
• Multimodal knowledge fusion and intelligent generation model in decision support systems for energy industry
• A utility-aware scheduling model for online learning tasks based on dynamic psychological cognitive load perception
• Lightweighting and end-side deployment of multi-modal large model based on cross-modal attention distillation

Research pick: Colour feels - "Evaluating the impact of colour congruence on marketing effectiveness within Instagram campaigns"

Brands seeking attention on social media outlets, such as Instagram, may benefit from keeping the colours in their posts visually consistent, according to research in the International Journal of Mobile Communications.

The research analysed 365 responses from Instagram users. It found that they were more positively engaged with a post and its associated brand when the background colours of a post matched the main visual elements of the image. The team suggests that this effect is due to processing fluency, the ease with which people understand and interpret information.

Using a controlled experiment based on Instagram-style brand content, researchers compared posts with congruent colour schemes against those with contrasting colours. Participants exposed to the former reported stronger brand attitudes and greater intentions to revisit the brand’s Instagram page and purchase its products than those seeing the more conflicting colour schemes.

The findings suggest that colour does more than attract attention. In crowded digital environments, consumers often rely on visual cues to form rapid judgements about a brand. Consistent colour schemes appeared to reduce the mental effort required to interpret a post, allowing viewers to focus more on its message.

Chen, C-C. and Chiu, Y-P. (2026) ‘Evaluating the impact of colour congruence on marketing effectiveness within Instagram campaigns’, Int. J. Mobile Communications, Vol. 27, No. 4, pp.417–434.

New Open Access article available: "A survey on skills and education needs for the industrial circular economy transition"

The following International Journal of Product Lifecycle Management article, "A survey on skills and education needs for the industrial circular economy transition", is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

Research pick: Resetting your fingerprints - "Fingerprint template protection: cancellable biometrics"

The obvious problem with biometrics is that once someone has stolen your fingerprint or iris ID, you cannot simply reset those to block their access as you might a password. Now, research in the International Journal of Computational Vision and Robotics offers a new approach to protecting biometric authentication data so that the risk associated with this kind of irreversible identity theft can be largely avoided and give users an option to reset their fingerprints and other biometrics, as it were.

Biometric authentication systems identify individuals using physiological or behavioural characteristics, such as fingerprints, facial features, or even how they type or move a computer mouse rather than passwords or physical tokens. However, such traits are essentially fixed and so compromised data cannot simply be reset like a password. To address this, the study focuses on the idea of cancellable biometrics, a technique in which biometric data is deliberately transformed from the start so that it can be revoked and replaced if stolen, while still allowing accurate identity verification.

The proposed system combines several computational techniques to protect biometric templates. Feature extraction is performed using Speeded-Up Robust Features (SURF), a computer vision method that detects distinctive points in images. The resulting data is then processed using a Fast Fourier Transform (FFT), a mathematical tool that converts signals into frequency components. Security is further enhanced through index-of-maximum hashing, which encodes dominant features into compact representations, and a matrix-based operation used to combine vectors securely.

The team has tested their approach on standard fingerprint datasets and found it to be comparable with existing methods but stronger than some in terms of strengthening resistance to attacks, including record multiplicity attacks, where adversaries attempt to reconstruct original data by linking multiple compromised templates.

Shaikh, A.S. and Patel, V.D. (2026) ‘Fingerprint template protection: cancellable biometrics’, Int. J. Computational Vision and Robotics, Vol. 16, No. 4, pp.399–410.

New Open Access article available: "AI and linguistics give a new meaning to the concept of semantic interoperability in electronic medical records systems"

The following International Journal of Healthcare Technology and Management article, "AI and linguistics give a new meaning to the concept of semantic interoperability in electronic medical records systems", is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

New Open Access article available: "Generative adversarial networks for simulating emotional resonance in industrial product design"

The following International Journal of Simulation and Process Modelling article, "Generative adversarial networks for simulating emotional resonance in industrial product design", is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

New Open Access article available: "Simulation modelling of fashion colour harmonisation with visual transformers"

The following International Journal of Simulation and Process Modelling article, "Simulation modelling of fashion colour harmonisation with visual transformers", is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

New Scopus and Clarivate Web of Science achievements for Inderscience journals

Scopus and Clarivate have released their latest CiteScores, impact factors and citation indicators, and Inderscience's Editorial Office is pleased to announce that several Inderscience journals have increased their Scopus CiteScores, particularly the International Journal of Hydromechatronics, International Journal of Mobile Learning and Organisation and International Journal of Environment and Health.

With regard to Clarivate, the International Journal of Hydromechatronics, International Journal of Mobile Learning and Organisation, International Journal of Bio-Inspired Computation and International Journal of Managerial and Financial Accounting continue to maintain notably high impact factors, with the International of Mobile Learning and Organisation and International Journal of Hydromechatronics performing particularly well in terms of citation indicators.

CiteScores and impact factors can be found on all indexed journals' homepages. We thank and congratulate all the editors, board members, reviewers and authors who have played their part in these latest indexing achievements.

New Open Access article available: "Endoscopic ultrasonography in differential diagnostics of benign and malignant pathology of the common bile ducts utilising fuzzy mathematical technologies"

The following International Journal of Medical Engineering and Informatics article, "Endoscopic ultrasonography in differential diagnostics of benign and malignant pathology of the common bile ducts utilising fuzzy mathematical technologies", is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

Research pick: Under the radar love for brands - "Unveiling the recall power of covert advertising: a study on stealth advertising in Indian reality shows"

A study of advertising embedded in Indian reality television examines how stealth, or subliminal, advertising, affects brand recall in a media environment saturated with promotional messages. The work, published in the International Journal of Indian Culture and Business Management, looks at how audiences exposed to thousands of brand impressions daily are affected by this approach to advertising. The findings have implications for marketing departments, which are increasingly using subtle product placement to familiarise potential and known customers with brands.

The research used within-subject experiments where participants viewed 12-minute clips from five Indian reality programmes containing embedded brand appearances. Recall was measured using standardised questionnaires assessing brand memory after exposure. Across 20 brands, most achieved moderate recall through this form of advertising.

The team found that two factors most influenced brand recall: whether the placement was spoken or visual and whether it meshed with the premise or arc of the segment. The results showed that verbal references were more effective than visual placements, but higher congruity between brand and the storyline of the segment improved recall.

Perhaps paradoxically, visual prominence of a product did not affect brand recall significantly. This challenges the marketing assumption that visibility drives effectiveness. The researchers concluded that embedded advertising depends largely on more subtle cues than straightforward visibility in the segment. Product placement is on the rise across TV, streaming platforms, and in digital content. Marketing departments hoping to familiarise consumers with their products need to understand these findings to benefit the most.

Dua, G.G., Vaidya, R.D. and Bapat, G.S. (2026) ‘Unveiling the recall power of covert advertising: a study on stealth advertising in Indian reality shows’, Int. J. Indian Culture and Business Management, Vol. 38, No. 1, pp.68–93.

Free Open Access special issue on "Green Supply Chain Management: Innovations in Sustainable Business Environment and Digital Transformation – Part 2" published by International Journal of Environment and Sustainable Development

The International Journal of Environment and Sustainable Development has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• Evaluation on the impact of digital transformation on the economic eco-environmental benefits of enterprises under the background of sustainable development
• Coordinated development of rural green industry planning based on ecological sustainable model and the internet of things
• From smart facilities to smart services: building a new ecosystem integrating tourism and smart cities
• Environmental variable regulation and optimisation strategy in forest seedling cultivation based on reinforcement learning
• Optimisation of landscape feature intelligent recognition algorithm based on deep learning
• AI-based green ecological construction of sustainable environment
• Landscape infrastructure optimisation design based on a BIM-GIS coupling model

New Open Access article available: "Fuelling the customer experience: an exploration of service quality in fuel stations through importance-performance analysis"

The following International Journal of Business Innovation and Research article, "Fuelling the customer experience: an exploration of service quality in fuel stations through importance-performance analysis", is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

Newly announced journal: International Journal of Technology and Digital Intelligence

 

The International Journal of Technology and Digital Intelligence proposes and fosters discussion on the convergence of emerging technology ecosystems, sector-specific digital transformation and human-centred digital intelligence, examined through a practice-to-policy lens. Rather than focusing on algorithms or computational systems, IJTDI foregrounds the sociotechnical dynamics through which intelligent digital technologies reshape industries, organisations, governance frameworks and human experience. This perspective acknowledges digital intelligence as an interface between technological innovation, organisational practice and societal governance.

Free Open Access issue published by International Journal of Reasoning-based Intelligent Systems

The International Journal of Reasoning-based Intelligent Systems has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• The stable diffusion model incorporating group feature constraints and interactive guidance, and a high-fidelity generation method for public group portraits
• Water-economic collaborative management in the Yellow River Basin by dual-channel adaptive spatio-temporal graph transformer
• Multi-feature fusion model for interactive behaviour recognition in university classrooms using convolutional neural networks and temporal attention mechanisms
• Characteristic mapping using HTTGCN in structural materials
• A multimodal adaptive deep network for emotion recognition in artistic images

Research pick: Cruise through crime prediction with AI - "Multidimensional crime prediction technique optimisation combining feature extraction and GAN"

Researchers have developed an artificial intelligence model that predicts crime more accurately than several existing approaches by combining information about where crimes occur, when they happen, and wider social patterns. They report details of the approach in the International Journal of Innovative Computing and Applications.

The model combines a graph convolutional network, which identifies relationships between locations, with a transformer, an AI architecture designed to detect patterns over time. Together, the techniques allow the system to capture both spatial and temporal trends in criminal activity. The researchers also incorporated a generative adversarial network (GAN), a system in which two AI models compete to improve performance. The GAN was enhanced using a variational autoencoder, a method that helps generate more representative data while reducing common training problems such as biased outputs and vanishing gradients, where learning slows or stops.

The system integrates several machine-learning techniques to analyse complex datasets that traditional methods often struggle to process. In tests on historical data from several US cities, including Los Angeles and Seattle, the model achieved an accuracy rate of 86.3 per cent when predicting robberies. The strongest competing systems had an accuracy of 83.2 per cent. The new system also gave strong results across other crime categories.

The researchers suggest that accurate forecasting could help law enforcement allocate resources more effectively and identify areas at higher risk of crime. However, there are limitations. The system was less accurate in areas with sparse crime data and struggled to make predictions in locations with little or no historical information. Future work will focus on adapting the model to such environments through transfer learning, which will allow knowledge gained in one setting to be applied to another.

Xie, M. (2026) ‘Multidimensional crime prediction technique optimisation combining feature extraction and GAN’, Int. J. Innovative Computing and Applications, Vol. 15, No. 5, pp.289–299.

New Open Access article available: "Quantifying wall effects on spherical particle settling with the Lattice Boltzmann method"

The following Progress in Computational Fluid Dynamics, An International Journal article, "Quantifying wall effects on spherical particle settling with the Lattice Boltzmann method", is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

Free Open Access special issue on "Exploring AI: Methods and Applications for Data Mining – Part 1" published by International Journal of Business Intelligence and Data Mining

The International Journal of Business Intelligence and Data Mining has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• Improved multi-objective quantum genetic algorithm for low-carbon economic dispatch in power grids
• Optimisation of nursing human resources allocation in large hospitals based on improved particle swarm optimisation algorithm
• Study on fault diagnosis and recovery for digital distribution networks: ITOT fusion
• Automatic correction of subjective sentence similarity questions based on text image recognition
• Research on personalised recommendation of tourist attractions based on improved collaborative filtering algorithm
• Research on fuzzy clustering of ideological and political MOOC resources under the background of 'Internet plus'
• A colour restoration method for multi-texture oil painting images based on colour transfer
• An operation status prediction of transformer calibration instrument using PSO-attention-LSTM algorithm
• Automatic classification and mining algorithm for massive big data based on machine learning
• An identification method of enterprise financial information transparency based on blockchain
• An optimisation of power system load forecasting driven by deep learning model

New Open Access article available: "Classroom student emotion recognition using an improved segmentation clustering and multi-feature fusion emotion recognition algorithm"

The following International Journal of Continuing Engineering Education and Life-Long Learning article, "Classroom student emotion recognition using an improved segmentation clustering and multi-feature fusion emotion recognition algorithm", is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

New Open Access article available: "Change detection framework for power facilities in disaster scenarios"

The following International Journal of Power and Energy Conversion article, "Change detection framework for power facilities in disaster scenarios", is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

Research pick: AI vital signs - "Intelligent monitoring of patient vital signs based on adaptive attention fusion spatiotemporal graph neural network"

Researchers have developed an artificial intelligence-based patient monitoring system they say can identify signs of clinical deterioration earlier and more accurately than existing approaches. The system could help hospital staff intervene before a patient’s condition becomes critical. Details are discussed in the International Journal of Ad Hoc and Ubiquitous Computing.

Traditional monitoring systems rely largely on fixed thresholds for individual measurements such as heart rate, blood pressure or oxygen levels. However, these approaches often fail to account for differences between patients and may overlook how physiological changes interact across the body.

The new approach combines three machine-learning techniques. An adaptive attention mechanism continuously adjusts the importance assigned to different physiological signals. A spatiotemporal graph neural network analyses how vital signs influence one another and evolve. The system also incorporates reinforcement learning, a method in which algorithms learn decision-making strategies through feedback, enabling it to provide active clinical decision support rather than simply issuing alarms.

Tests were carried out to see how well the system performed in predicting historical outcomes recorded in two major intensive care unit (ICU) databases, MIMIC-III and eICU. The system achieved 96.3 per cent anomaly detection accuracy, generated warnings almost 40 minutes before critical events occurred, and reduced false alarms to 6.4 per cent.

Cheng, S., Zhu, J., Guan, S., Cheng, J. and Dou, T. (2026) ‘Intelligent monitoring of patient vital signs based on adaptive attention fusion spatiotemporal graph neural network’, Int. J. Ad Hoc and Ubiquitous Computing, Vol. 52, No. 5, pp.48–62.

Free Open Access issue published by International Journal of Information and Communication Technology

The International Journal of Information and Communication Technology has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• Empirical analysis of the impact of personal income tax reform on household consumption: based on 2016-2022 microdata
• Unsupervised transfer learning for real-time motor resonance fault diagnosis on programmable logic controllers
• Psychological trait mining of juvenile offending from large language models and online behaviour analytics
• A machine learning-based intelligent evaluation and feedback system for college English speaking
• Modelling of dynamic coverage process by quadrotor swarms for sudden abnormal crowd evacuation

New Open Access article available: "Automation measures and power quality optimisation of power line carrier control based on electric spring and wavelet transform"

The following International Journal of Systems, Control and Communications article, "Automation measures and power quality optimisation of power line carrier control based on electric spring and wavelet transform", is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

New Open Access article available: "Unlocking computational thinking: immersive technologies for solving complex problems"

The following International Journal of Technology Enhanced Learning article, "Unlocking computational thinking: immersive technologies for solving complex problems", is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

Free Open Access issue published by International Journal of Information and Communication Technology

The International Journal of Information and Communication Technology has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• A real-time financial risk detection model for enterprises based on distributed reinforcement learning
• Core talent loss early warning algorithm integrating temporal collaborative filtering and the Prophet model
• Style era switching and spatial-temporal evolution simulation of revolutionary cultural relics by integrating semantic segmentation and GIS spatial analysis
• Dynamic employee turnover risk prediction model based on GraphSAGE-LSTM
• Machine learning modelling and algorithm optimisation for identifying financial risks in listed companies

Research pick: Transformers: AI in disguise detects heart disease - "Heart disease detection using 1D transformer network: case of ECG signals and clinical data"

A machine-learning model based on Transformer architecture, a form of artificial intelligence originally developed for language processing, can be used to detect heart disease from electrocardiogram (ECG), according to research in the International Journal of Medical Engineering and Informatics. Tests show it works well with data from several well-known medical datasets.

Heart disease is a major healthcare problem, with almost 18 million dying prematurely each year because of it. The challenge if finding ways to detect cardiovascular disease early enough to make treatment effective. An ECG is the standard way to record the heart’s electrical activity and is thus a common diagnostic tool. Interpretation of the trace needs significant expertise, is time-consuming and is not without the risk of misinterpretation.

The researchers discusses a one-dimensional (1D) Transformer model that can analyse ECG signals and in parallel with other clinical data. In tests, it was up to 94.2 per cent accurate in spotting the early stages of heart disease. Such precision coupled with expert clinical assessment can suffice to give the healthcare team more reliable options in taking a patient on to the next step in diagnosis and potential treatment.

The researchers suggest that their approach needs further development and validation with independent clinical datasets before it can be tested in a live clinical setting.

Miloud Aouidate, A. (2026) ‘Heart disease detection using 1D transformer network: case of ECG signals and clinical data’, Int. J. Medical Engineering and Informatics, Vol. 18, No. 5, pp.1–18.

Free Open Access special issue on "Generative AI and Integrated Sensing Communication for Enhanced Ubiquitous Computing" published by International Journal of Ad Hoc and Ubiquitous Computing

The International Journal of Ad Hoc and Ubiquitous Computing has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• Dynamic load balancing model for data centres based on bidirectional network edge detection algorithm
• Intelligent automotive trade market forecasting and decision-making system: multi-source data fusion under meta-learning
• Design and optimisation of multi-party secure computing protocols supporting dynamic participant switching
• Integrating multi-modal emotion recognition with strategy generation: a transformer approach to sustainable marketing
• Intelligent monitoring of patient vital signs based on adaptive attention fusion spatiotemporal graph neural network
• Adaptive deep reinforcement learning-based error analysis model for strength training
• Personalised service and sentiment analysis for intelligent customer relationship management systems based on natural language processing
• Modelling and simulation for biomechanical responses of acupuncture points based on multi-physics field coupling
• An optimisation framework for audit decision-making based on deep convolutional neural networks and reinforcement learning

Free sample articles newly available from International Journal of Knowledge-Based Development

The following sample articles from the International Journal of Knowledge-Based Development are now available here for free:

• Cultural industries 4.0: how digitalisation is reshaping the sector: insights from a systematic literature review
• Knowledge sharing, occupational self-efficacy, and transformational leadership: drivers of innovative work behaviour
• Role of green transformational leadership, knowledge management and green innovation in driving corporate sustainable development
• Quality evaluation of software engineering professional talent training under the background of new engineering
• The quality evaluation model of open education learning support services under the background of digital transformation

Free Open Access issue published by International Journal of Information and Communication Technology

The International Journal of Information and Communication Technology has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• Modelling news dissemination networks using a community-based graph traversal algorithm and its performance evaluation
• Reasoning application of knowledge graph fusion-enhanced graph neural networks in English reading comprehension
• An attention-based multi-engine architecture enhances the ability of English translation to resolve ambiguity
• Generative AI and multimodal learning spaces for perceiving and regulating English learning anxiety
• Measuring echo chamber effects in youth online communities with temporal clustering