The evaluation and ranking of educational institutions are of paramount importance to a wide range of stakeholders, including students, faculty members, funding organizations, and the institutions themselves. Traditional ranking systems, such as those provided by QS, ARWU, and THE, have offered valuable insights into university performance by employing a variety of indicators to reflect institutional excellence across research, teaching, international outlook, and more. However, these linear rankings may not fully capture the multifaceted nature of university performance. This study introduces a novel clustering analysis that complements existing rankings by grouping universities with similar characteristics, providing a multidimensional perspective on global higher education landscapes. Utilizing a range of clustering algorithms—K-Means, GMM, Agglomerative, and Fuzzy C-Means—and incorporating both traditional and unique indicators, our approach seeks to highlight the commonalities and shared strengths within clusters of universities. This analysis does not aim to supplant existing ranking systems but to augment them by offering stakeholders an alternative lens through which to view and assess university performance. By focusing on group similarities rather than ordinal positions, our method encourages a more nuanced understanding of institutional excellence and facilitates peer learning among universities with similar profiles. While acknowledging the limitations inherent in any methodological approach, including the selection of indicators and clustering algorithms, this study underscores the value of complementary analyses in enriching our understanding of higher educational institutions’ performance.

Recent advances in green and smart mobility aim to reduce congestion and foster greener, cheaper and with less delay transportation. The reduction of fuel consumption and CO2 emissions have worked on light-duty vehicles. However, the reduction of emissions and consumables without sacrificing on emission standards is an important challenge for heavy-duty vehicles. The paper introduces a big data system architecture that provides an on-demand route optimization service reducing NOx emissions of heavy-duty vehicles. The system utilizes the information provided by the navigation systems, big data analytics such as predictive traffic and weather conditions, road topography and road network and information about vehicle payload, vehicle configuration and transport mission to develop a strategy for the best route and the best velocity profile. The system was proven efficient during the performance evaluation phase, since the cumulative engine-out NOx has been decreased more than 10%.

The deployment of wireless sensor networks in many application areas like environment control, target tracking in battlefields, requires an optimization to the communication among the sensors so as to serve data in short latency and with minimal energy consumption. Cooperative data caching has been proposed as an effective and efficient technique to achieve these goals concurrently. The design of protocols for such networks depends mainly on the selection of the sensors which will take special roles in coordinating the procedure of caching and take forwarding decisions. This article introduces a new metric to aid in the selection of such nodes. Based on this metric, we propose a new energy efficient cooperative caching protocol, which is compared against the state-of-the-art competing protocol. The simulation results attest the superiority of the proposed protocol.

Combining an Internet of Things-driven approach with machine learning algorithms holds great promise in discerning pure gases across various applications. Interconnecting gas sensors within a network allows for continuous monitoring and real-time environmental analysis, producing valuable data for machine learning models. Utilizing supervised learning algorithms, like random forests, enables the creation of accurate classification models that can effectively distinguish between different pure gases based on their distinct features, such as spectral signatures or sensor responses. This groundbreaking integration of the Internet of Things and Machine Learning fosters the development of robust, automated gas detection systems, ensuring high accuracy and minimal delay in recognizing pure gases. Consequently, it opens avenues for enhanced safety, efficiency, and environmental sustainability in numerous industrial and commercial scenarios.

In recent years the number of people who exercise every day has increased dramatically. More precisely, due to COVID period many people have become recreational runners. Recreational running is a regular way to keep active and healthy at any age. Additionally, running is a popular physical exercise that offers numerous health advantages. However, recreational runners report a high incidence of musculoskeletal injuries due to running. The healthcare industry has been compelled to use information technology due to the quick rate of growth and developments in electronic systems, the internet, and telecommunications. Our proposed intelligent system uses data mining algorithms for the rehabilitation guidance of recreational runners with musculoskeletal discomfort. The system classifies recreational runners based on a questionnaire that has been built according to the severity, irritability, nature, stage, and stability model and advise them on the appropriate treatment plan/exercises to follow. Through rigorous testing across various case studies, our method has yielded highly promising results, underscoring its potential to significantly contribute to the well-being and rehabilitation of recreational runners facing musculoskeletal challenges.

This study examines how emotions and motivation shape computer science (CS) pathways in Greek upper secondary education at a time when AI skills are increasingly essential. Using the control–value theory of achievement emotions and the Science Motivation Questionnaire II, data were collected from 132 students across two curricular tracks: applied ICT in A class and algorithmic programming in B class. Findings show that while enjoyment decreased during CS tasks, pride increased and shame declined, indicating that engagement fostered accomplishment despite initial frustration. Emotional and motivational profiles were largely similar across curricula, with A class students reporting higher grade-related motivation. Gender differences were modest but underscore the importance of equitable curriculum design. The results highlight the need for CS curricula that balance cognitive challenge with positive emotional experiences, thereby strengthening resilience, self-efficacy, and motivation to pursue CS pathways in an AI-driven era.