Free sample articles newly available from International Journal of Economic Policy in Emerging Economies

The following sample articles from the International Journal of Economic Policy in Emerging Economies are now available here for free:

• Working capital as a determinant of firm performance
• A decade of competition laws in Arab economies: a de jure and de facto assessment
• Understanding the actual buying behaviour of organic food users in India: a PLS-SEM approach
• Impact of central bank's COVID-19 policy measures on banks: evidence from India
• The impact of external debt on economic growth in emerging economies: investigating the role of capital formation

Free Open Access article available: "3D image reconstruction using an improved BEV model and global convolutional attention fusion"

The following paper, "3D image reconstruction using an improved BEV model and global convolutional attention fusion" (International Journal of Information and Communication Technology 26(6) 2025), is freely available for download as an open access article.

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

Free Open Access article available: "Electric vehicle charging station planning based on the development of distribution networks and coupled charging demand"

The following paper, "Electric vehicle charging station planning based on the development of distribution networks and coupled charging demand" (International Journal of Information and Communication Technology 26(6) 2025), is freely available for download as an open access article.

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

Research pick: Digital safeguarding – screen time, safe time - "Fuzzy expert system for access control of children to the internet"

As digital devices become more integrated into children’s lives, concerns about their impact on physical and mental health continue to grow. In modern households, smartphones, tablets, and computers are now commonplace, leading to increased exposure to online content. This shift has raised important questions about how much screen time is appropriate and what effects it has on children’s well-being.

The issue of screen time has been widely debated, with research pointing to both potential risks and benefits. Excessive screen use has been linked to physical issues such as eye strain, headaches, and sleep disruption. There are also concerns about the relationship between increased screen time and physical inactivity, as children who spend more time on devices might be less engaged in outdoor play and exercise, both essential for their physical development.

On the other hand, the online world offers numerous opportunities for learning, creativity, and socialization. Educational apps, online learning platforms, and digital games can stimulate intellectual growth, promote critical thinking, and even foster social connections with peers across the globe. The challenge is finding a balance that maximizes the benefits of digital engagement while mitigating the potential negative effects on health and well-being.

Beyond physical health, the psychological effects of digital media are also a growing concern. Research indicates that extended use of devices, particularly those providing access to social media, can influence children’s emotional well-being, intellectual development, and sense of identity. While some cases have linked excessive screen time to negative outcomes, the full psychological impact of digital media remains an area of ongoing research. It is important to also acknowledge the positive effects, such as improved cognitive skills and the opportunity for global social connections.

Given these concerns, researchers are exploring more personalized methods of regulating screen time, such as the use of fuzzy logic inference systems. These systems, a type of artificial intelligence, can evaluate complex and imprecise data, making them ideal for tailoring screen time recommendations and restrictions based on a child’s unique characteristics.

Parents, guardians, or teachers could input data about a child’s age, health, and psychological profile into the system, which would then use this information to determine appropriate screen time and content limits. Unlike generic restrictions, which may be difficult to enforce or inappropriate for all young users, fuzzy logic systems offer a more customized and flexible approach to managing screen use.

While there are existing tools that restrict screen time and block content, an adaptive approach, could be key to managing both the quantity and quality of screen time. Younger, more vulnerable users would have stricter controls and limits, while older, more mature children could access a wider range of appropriate resources, all based on their individual developmental profiles.

Alguliyev, R.M., Abdullayeva, F.J. and Ojagverdiyeva, S.S. (2024) ‘Fuzzy expert system for access control of children to the internet’, Int. J. Reasoning-based Intelligent Systems, Vol. 16, No. 6, pp.455–462.

Free Open Access article available: "Pseudo-coordinates graph convolutional generative adversarial network for art style transfer"

The following paper, "Pseudo-coordinates graph convolutional generative adversarial network for art style transfer" (International Journal of Information and Communication Technology 26(6) 2025), is freely available for download as an open access article.

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

Free Open Access article available: "Research on over-the-air programming and real-name authentication technology of eSIM based on 5G communication technology"

The following paper, "Research on over-the-air programming and real-name authentication technology of eSIM based on 5G communication technology" (International Journal of Information and Communication Technology 26(6) 2025), is freely available for download as an open access article.

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

Free Open Access article available: "Real-time frequency adaptation in carrier communication algorithm based on 2sVCNet network"

The following paper, "Real-time frequency adaptation in carrier communication algorithm based on 2sVCNet network" (International Journal of Information and Communication Technology 26(6) 2025), is freely available for download as an open access article.

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

Research pick: Urbanisation, musically speaking - "Comparative analysis of the impact of urban music on students of state, private and parochial educational institutions"

Urban music, which originated in marginalized communities in The Caribbean and the USA, has found a global audience, resonating especially with young people, as is often the case with emerging music genres. Urban music has evolved into more than just a genre of entertainment, it has become a significant cultural force that shapes the identities, behaviour, and educational experiences of young people.

A study in the International Journal of Knowledge and Learning has examined the impact of urban music on secondary school students in Peru. The work sheds new light on its multifaceted role in adolescent life, which may well have wider implications. The findings suggest that urban music, encompassing styles such as hip-hop and reggaetón can serve as a platform for cultural expression and social belonging, influencing students in ways that are deeply linked to their socio-economic environments.

Agustin Angel Roberto Chumpitaz-Avila and Luis Fernando Castro-Llacsa of the National University of San Agustín of Arequipa in Arequipa, Peru, highlight how this musical genre has penetrated schools across Peru, including state, private, and religious institutions. This reflects the wide-reaching influence of urban music. While critics have long asserted that urban music might somehow promote antisocial behaviour, the research suggests that its influence on youth is not so easily categorized and indeed can have a strong positive influence.

Urban music does commonly have explicit lyrics that often feature violence, overtly sexual imagery, and drug use. Those social observers who malign it for these characteristics suggest that young listeners may internalize these messages. However, the current study found that while some students might adopt attitudes reflected in the music, the broader socio-economic and familial context plays a more significant role in determining their behaviour. Urban music, it seems, is a tool for young people to interpret their surroundings rather than an inherently harmful influence.

Chumpitaz-Avila, A.A.R. and Castro-Llacsa, L.F. (2025) ‘Comparative analysis of the impact of urban music on students of state, private and parochial educational institutions’, Int. J. Knowledge and Learning, Vol. 18, No. 2, pp.170–185.

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.

• Real-time frequency adaptation in carrier communication algorithm based on 2sVCNet network
• Research on over-the-air programming and real-name authentication technology of eSIM based on 5G communication technology
• Pseudo-coordinates graph convolutional generative adversarial network for art style transfer
• Electric vehicle charging station planning based on the development of distribution networks and coupled charging demand
• 3D image reconstruction using an improved BEV model and global convolutional attention fusion
• Sentiment analysis for tourism reviews based on dual-stream graph attention fusion network
• Construction of a CS-ELM-based assessment model for civic education within a multidimensional analysis framework

Research pick: Classical class – notes on automated music analysis - "Classification of classical music genres based on Mel-spectrogram and multi-channel learning"

As digital music libraries continue to expand, the challenge of accurately categorizing musical genres remains high on the agenda. A study in the International Journal of Information and Communication Technology introduces a deep learning model designed to improve the classification of classical music genres.

By employing multi-channel learning (MCL) and Mel-spectrogram analysis, the model, known as MC-MelNet, offers what the research suggests is a more nuanced and efficient approach to genre identification. Tests carried out by its developer, Lei Zhang of the Henan Academy of Drama Arts at Henan University in Zhengzhou, China, show that it outperforms traditional classification methods.

The ability to classify music automatically has far-reaching implications for streaming services, music recommendation algorithms, and digital archiving. Classical music, with its intricate structures and subtle variations, presents a particular challenge for automated classification. Zhang explains that MC-MelNet addresses these issues by integrating multiple layers of analysis, capturing both the tonal and temporal characteristics of a composition.

At the core of MC-MelNet’s innovation is its multi-channel learning framework, which processes multiple audio features simultaneously. Conventional approaches rely primarily on Mel-spectrograms, which break down an audio signal into different frequency components in a way that mimics human hearing. However, while effective in capturing tonal elements, Mel-spectrograms alone do not fully represent the temporal dynamics of music.

MC-MelNet overcomes this limitation by incorporating additional audio features such as Mel-frequency cepstral coefficients (MFCC) and Chroma features. MFCCs capture the timbral qualities of a sound, making them useful for distinguishing between different instruments or playing styles. Chroma features, on the other hand, focus on pitch content and harmonic structure. By combining these elements, MC-MelNet creates a richer and more detailed representation of musical compositions, allowing it to distinguish between closely related classical genres with greater accuracy.

Unlike conventional classification methods, which require manual feature extraction, MC-MelNet uses an end-to-end deep learning approach. It utilizes convolutional neural networks (CNNs) to detect spatial patterns in audio data and recurrent neural networks (RNNs), specifically long short-term memory (LSTM) networks, to process sequential musical information.

MC-MelNet might have applications beyond classical music classification. It could, for instance, be adapted for real-time sound processing and audio event detection. Enhancing the model’s generalizability by training it on a more diverse dataset could make it applicable to a wider range of genres, improving automated music classification for commercial streaming platforms.

Zhang, L. (2025) ‘Classification of classical music genres based on Mel-spectrogram and multi-channel learning’, Int. J. Information and Communication Technology, Vol. 26, No. 5, pp.39–53.

Free Open Access article available: "An objective comparison of two prominent virtual actor frameworks: Proto.Actor and Orleans"

The following paper, "An objective comparison of two prominent virtual actor frameworks: Proto.Actor and Orleans" (International Journal of Communication Networks and Distributed Systems 30(3) 2024), is freely available for download as an open access article.

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

Free Open Access article available: "Both right nearby and far away: Rural Sámi entrepreneurs' engagement with spatial contexts"

The following paper, "Both right nearby and far away: Rural Sámi entrepreneurs' engagement with spatial contexts" (International Journal of Management and Enterprise Development 24(5) 2025), is freely available for download as an open access article.

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

Research pick: Going spare roadside, cutting costs and emissions - "Using the route planning for supplying spare parts to reduce distribution costs: a case study in a roadside assistance company"

A distribution model designed to streamline spare parts delivery to roadside assistance vehicles could cut costs in half, according to work in the International Journal of Shipping and Transport Logistics. The model builds a solution to the well-known Travelling Salesman Problem, a complex optimisation problem that involves finding the shortest route that visits each city once and ends at the starting point. The model was tested on real data from a roadside assistance company operating a fleet of service vehicles.

Abolfazl Shafaei, Mohammad Reza Akbari Jokar, and Majid Rafiee of Sharif University of Technology in Tehran, Iran, and Ahmad Hemmati of the University of Bergen in Bergen, Norway, explain that the major logistical challenge for roadside assistance fleets is balancing inventory space with repair capabilities. Service vehicles have limited space onboard, so they must prioritize particular spare parts and specific tools. Service vehicles usually visit a central warehouse on a regular schedule to restock on spare parts every few days. This adds to overall fuel costs, vehicle wear and tear, and lost servicing time. The new system replaces these frequent trips with a centralized delivery truck that optimizes the frequency and route of spare part deliveries.

However, drivers everywhere expect fast, efficient service from the company with which they entrust their vehicle’s roadside maintenance, They also expect it to be inexpensive and a high-quality service.

The team tested several delivery schedules, including daily and every five days, and found that the most efficient option for this roadside assistance company was an optimized cycle on the first, second, and fourth days. This approach reduced costs by 56%.

The new model reduces the need to stockpile items by ensuring regular deliveries to the service fleet out on the road. This frees up space for repair equipment that allows for a wider variety of roadside fixes.

Beyond the immediate time and cost savings to companies running roadside assistance fleets, the model also promises significant environmental benefits. With fewer vehicles returning to a central warehouse to restock, fuel consumption and carbon emissions can be greatly reduced. Indeed, for the test case, the team found that annual carbon dioxide emissions could be reduced by 75 percent.

Shafaei, A., Akbari Jokar, M.R., Rafiee, M. and Hemmati, A. (2025) ‘Using the route planning for supplying spare parts to reduce distribution costs: a case study in a roadside assistance company‘, Int. J. Shipping and Transport Logistics, Vol. 20, No. 1, pp.131-158.

Free Open Access article available: "Piano teaching-assisted beat recognition based on spatio-temporal two-branch attention"

The following paper, "Piano teaching-assisted beat recognition based on spatio-temporal two-branch attention" (International Journal of Information and Communication Technology 26(5) 2025), is freely available for download as an open access article.

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

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.

• Implementation of gesture recognition technology optimised by neural networks in OpenMV
• Ideological opinion clustering identification based on Gibbs sampling in social new media environment
• Classification of classical music genres based on Mel-spectrogram and multi-channel learning
• An alternative method for generating fractal art patterns based on the balanced optimiser algorithm
• Knowledge creation in vocational education using multi-source data fusion under big data environment
• Intelligent fault diagnosis of mechanical equipment based on industrial big data
• Piano teaching-assisted beat recognition based on spatio-temporal two-branch attention

Free sample articles newly available from International Journal of Learning Technology

The following sample articles from the International Journal of Learning Technology are now available here for free:

• A framework for co-designing effective LADs supporting sensemaking and decision making
• An analysis of technological resources to encourage self-regulated learning behaviour in virtual learning environments in the last decade
• Modelling e-learning quality, self-efficacy and students' behaviour
• Advances in personalised recommendation of learning objects based on the set covering problem using ontology
• Demographic differences in China's higher education students' interactions and experiences with online learning during the COVID-19 pandemic

Free Open Access article available: "Intelligent fault diagnosis of mechanical equipment based on industrial big data"

The following paper, "Intelligent fault diagnosis of mechanical equipment based on industrial big data" (International Journal of Information and Communication Technology 26(5) 2025), is freely available for download as an open access article.

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

Research pick: Robots get a grip on objects with a twist - "Rotation-invariant 3D convolutional neural networks for 6D object pose estimation"

Recent work in 6D object pose estimation holds significant promise for advancing robotics, augmented reality (AR), virtual reality (VR), as well as autonomous navigation. The research, published in the International Journal of Computational Science and Engineering, introduces a method that enhances the accuracy, generalization, and efficiency of determining an object’s rotation and translation from a single image. This could significantly improve robots’ ability to interact with objects, especially in dynamic or obstructed environments.

In robotics, 6D object pose estimation refers to determining both the orientation (rotation) and position (translation) of an object in three-dimensional space. “6D” describes six degrees of freedom: three for translation (X, Y, Z axes) and three for rotation (around those axes). Accurate pose estimation is critical for autonomous systems, including robots and AR/VR systems.

Challenges arise due to variations in object shapes, viewpoints, and computational demands. Current methods rely on deep-learning techniques using large datasets of objects viewed from various angles. These models struggle with unseen objects or those with shapes different from training data.

The new technique discussed by Zhizhong Chen, Zhihang Wang, Xue Hui Xing, and Tao Kuai of the Northwest Institute of Mechanical and Electrical Engineering in Xianyang City, China, addresses the various challenges by incorporating rotation-invariant features into an artificial intelligence system known as a 3D convolutional network. This allows the system to process an object’s 3D point cloud, regardless of its orientation, leading to more accurate pose predictions even when the object is rotated or seen from unfamiliar angles. The network uses a consistent set of coordinates, known as canonical coordinates, which represent the object in a frame of reference unaffected by rotation. This innovation improves the system’s ability to generalize to new poses, overcoming a limitation of conventional methods.

Not only is the new approach more accurate, it is more efficient and so needs less training data and less computer power, making it more suited for real-time, real-world applications.

Chen, Z., Wang, Z., Xing, X.H. and Kuai, T. (2025) ‘Rotation-invariant 3D convolutional neural networks for 6D object pose estimation’, Int. J. Computational Science and Engineering, Vol. 28, No. 8, pp.1–9.

Free sample articles newly available from International Journal of Monetary Economics and Finance

The following sample articles from the International Journal of Monetary Economics and Finance are now available here for free:

• The impact of corporate social responsibility disclosure and board characteristics on firm performance: evidence from Vietnam-listed firms
• ESG performance and cost of capital: what do we know? Evidence from the US
• Indian stock market sensitivity to macroeconomic and non-macroeconomic factors: an industry-level analysis
• Are machine learning models more effective than logistic regressions in predicting bank credit risk? An assessment of the Brazilian financial markets
• Does FinTech adoption improve bank performance?

Free Open Access article available: "An alternative method for generating fractal art patterns based on the balanced optimiser algorithm"

The following paper, "An alternative method for generating fractal art patterns based on the balanced optimiser algorithm" (International Journal of Information and Communication Technology 26(5) 2025), is freely available for download as an open access article.

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

Free Open Access article available: "Knowledge creation in vocational education using multi-source data fusion under big data environment"

The following paper, "Knowledge creation in vocational education using multi-source data fusion under big data environment" (International Journal of Information and Communication Technology 26(5) 2025), is freely available for download as an open access article.

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

Research pick: Hitting the right notes in vocal separation - "Separating voice and background music based on 2DFT transform"

Separating the human voice from the music in an audio recording has long been a challenge in signal processing. There are numerous so-called artificial intelligence (AI) tools around that can do this now with varying degrees of accuracy. The task is difficult due to the complexity of music, which involves multiple overlapping sources across the audible frequency spectrum. There is a need to increase the resolution and clarity of systems that can separate a vocal from the instrumental for a wide range of applications, such as post-production remixes of music, singing instruction and rehearsing, .

A new method is reported in the International Journal of Reasoning-based Intelligent Systems. The researchers, Maoyuan Yin and Li Pan of the School of Music and Dance at Mudanjiang Normal University in Mudanjiang, China, have, they say, improved upon existing techniques by combining several advanced signal processing techniques. Their starting point is the use of a virtual microphone array. This virtual setup helps them localize the human voice within the overall sound and isolate it from the background.

The virtual microphone array creates a spatial representation of the sound, the team explains. To further improve on the results, the team also used near-field and far-field models to simulate the propagation of sound from sources at different distances. This gives them even more precision in localising the vocal within the sound.

Once the voice is accurately located, the system constructs a time-frequency spectrum for both the human voice and the background music. The time-frequency spectrum tracks how the energy of sound signals shifts along the frequency axis over time. The system can then analyse these changes and distinguish between vocal and instrumental, isolating them from one another.

The process is further refined by the use of a sophisticated algorithmic technique – the Hamming window function, which improves the efficiency of the requisite two-dimensional fast Fourier transform (2DFT) processing of the data. This step reduces the number of dimensions of the various extracted sound signals, simplifying the final extraction of vocal from music.

Test results demonstrate the effectiveness of this new approach with a localization error of just 0.50%. For background music, the feature extraction error is reduced to 0.05%. Overall, the team could reach almost 99 percent accuracy in separating vocal from instrumental. The same approach should also work in isolating a human voice from non-musical background noise. It could thus be used to improve automated spoken-word transcription services and help in the development of better hearing aids.

Yin, M. and Pan, L. (2025) ‘Separating voice and background music based on 2DFT transform’, Int. J. Reasoning-based Intelligent Systems, Vol. 17, No. 1, pp.50–57.

Free sample articles newly available from International Journal of Advanced Operations Management

The following sample articles from the International Journal of Advanced Operations Management are now available here for free:

• Exploring the factors causing delay in export by containerised multimodal transportation
• Application of lexicographic goal programming technique to tackle production planning problem in the dairy manufacturing sector
• Healthcare quality: applying a SERVUSE model
• Optimising production scheduling decisions in flowshop manufacturing cells for a sportswear manufacturing case
• Sustainability 4.0 in the fashion industry: a systematic literature review