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Found 6374 publications

Body Mass and Aerobic Capacity are Robust Predictors of the 2000m Ergometer Rowing Performance: A Laboratory Study

Ferenc Ihász Celal Bulgay Zsolt Bálint Katona Angéla Somogyi Ádám Balog Anna Pápai-Horváth Zoltán Alföldi Attila Szabo

Publication Name: International Journal of Kinesiology and Sports Science

Publication Date: 2025-01-01

Volume: 13

Issue: 2

Page Range: 78-85

Description:

Purpose: Predicting performance in sports competitions is a popular topic in research. However, only a few studies exist in rowing sports, which suggest that some anthropometric and performance indices might predict performance in various situations. Methods: This work expands past research by examining the effects of five anthropometric measures, such as body mass index (BMI), height, weight, fat, and muscle, and three performance indicators, such as aerobic capacity, maximum speed, and force, while also considering the training history of 38 elite rowers (Mage = 16.89 ± 1.85, range 14.7 to 22.6 years, 61% males) participating in a national championship. Results: Apart from BMI, all measures correlated statistically significantly with the 2000m rowing time. A bootstrapped forward multiple regression yielded the best model with only two predictors (R2 =.995), aerobic capacity and body mass, accounting for 99.5% variance in the 2000m rowing time. Conclusions: While the results support previous findings, such robust prediction has not been reported in the literature. We conjecture that the differences from other past works rest with the high-pressure 2000m performance preceding a national championship. If these findings could be replicated, their practical implication is substantial in preparatory training for rowing contests.

Open Access: Yes

DOI: 10.7575/aiac.ijkss.v.13n.2p.78

Heart disease prediction with a feature-sensitized interpretable framework for the Internet of Medical Things sensors

Dragan Pamucar Nallakaruppan Kailasanathan Rajesh Kumar Dhanaraj Gangadevi Ezhilarasan Nathan Shankar Shitharth Selvarajan Gangadevi Ezhilarasan Rajesh Kumar Dhanaraj Nallakaruppan Kailasanathan

Publication Name: Frontiers in Digital Health

Publication Date: 2025-01-01

Volume: 7

Issue: Unknown

Page Range: Unknown

Description:

Introduction: Cardiovascular health is increasingly at risk due to modern lifestyle factors such as obesity, smoking, stress, hypertension, and sedentary behavior. Post-pandemic health practices and medication side effects have further contributed to rising cases of early heart failure, particularly among individuals aged 25–40 years. This highlights the need for an automated and interpretable framework to predict heart disease at an early stage. Methods: In this study, body vitals acquired from a secondary dataset. Machine learning models including Support Vector Machine, Random Forest, Decision Tree, and Logistic Regression were employed for classification. Model performance was evaluated using accuracy, F1-score, and k-fold cross-validation. Results: Among the tested models, the Random Forest classifier demonstrated superior performance with an accuracy and F1-score of 0.955. The interpretability is enhanced with model predictions were explained using Local Interpretable Model-Agnostic Explanations (LIME) for local surrogates and SHAP values for global surrogates. SHAP decision plots provided clear insights into classification behaviour and feature contributions. Discussion/Conclusion: The proposed interpretable machine learning framework successfully predicts heart disease with high accuracy while maintaining transparency in decision-making. With the integration of sensor data with cloud-based analysis and explainable AI techniques, this study contributes to reducing the incidence of early heart failures and supports more reliable decision-making in healthcare applications.

Open Access: Yes

DOI: 10.3389/fdgth.2025.1612915

Metaheuristics in Hierarchical Nested Structure

Boldizsar Tuu-Szabo Ali Jawad Ibada Laszlo T. Koczy

Publication Name: Cinti 2025 IEEE 25th International Symposium on Computational Intelligence and Informatics Proceedings

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 547-550

Description:

Metaheuristic algorithms have become indispensable tools for solving complex combinatorial optimization problems. However, their performance often depends critically on the selection of internal parameters, which are frequently tuned in an ad hoc manner. This paper investigates the hierarchical nested structure of the metaheuristic algorithm and its impact on optimization performance, where parameters of one metaheuristic are optimized using another, resulting in a multi-level optimization framework. We demonstrate this concept using a four-tier architecture: the Genetic Algorithm (GA) optimizes the Radius (R) parameter in the Circle Group Heuristic (CGH), which in turn constructs high-quality initial populations for the Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA). The DBMEA itself is a memetic algorithm that integrates global evolutionary mechanisms from Bacterial Evolutionary Algorithm (BEA) and with local search strategies (2-OPT and 3- OPT), thus comprising two inherent levels. Together, this nesting creates a four-level metaheuristic hierarchy. The DBMEA is then applied to solve variants of NP-Hard problems such as Traveling Salesman Problem (TSP). Our experiments on benchmark datasets show that this nested structure not only improves convergence speed and solution quality but also demonstrates the potential of deeply nested metaheuristic designs for scalable, robust optimization.

Open Access: Yes

DOI: 10.1109/CINTI67731.2025.11311852

Design and Implementation of a Modular Smart Home System Using ESP32 and Apple HomeKit Integration

Stefan Koprda Zoltan Balogh Gyorgy Molnar

Publication Name: Sisy 2025 IEEE 23rd International Symposium on Intelligent Systems and Informatics Proceedings

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 125-130

Description:

This paper presents a modular smart home system integrating ESP32 microcontrollers with Apple HomeKit ecosystem to address scalability and data retention challenges in residential IoT deployments. The proposed solution employs the HomeSpan library for HomeKit-Compatible accessories, enabling secure local communication while maintaining compatibility with Apple's Home application. The key scientific contribution lies in the hybrid architecture combining real-time HomeKit control with a dedicated REST API server featuring SSL encryption and API-key authentication for historical data collection. The system addresses Apple Home app's limitation of lacking data retention capabilities through automated 5-minute data transmission intervals to a secure MariaDB database. Performance evaluation demonstrates stable operation across multiple sensor types (DHT22, PIR motion sensors) and actuators with modular deployment flexibility. Comparative analysis shows improved data persistence and analytics capabilities over standard HomeKit implementations while maintaining the security benefits of local communication protocols. The implementation achieves practical IoT deployment suitable for residential environments with enhanced comfort, safety, and energy efficiency monitoring capabilities.

Open Access: Yes

DOI: 10.1109/SISY67000.2025.11205419

Digital Transformation of Public Services: The Case of the Document Management Application

Roland Zsolt Szabó Borbála Szedmák László Varga

Publication Name: International Journal of Public Administration

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

The article examines digital transformation in the public sector, focusing on its implementation and impact. A key milestone in Hungary’s municipal digitalization is analyzed through a case study on the document management system of the Application Service Provider. Based on longitudinal data from over 3,000 municipalities, findings show that digital transformation delivers significant value to citizens and shortens administrative lead times. It enhances transparency, comparability, and efficiency in public administration. The study also emphasizes that adopting new technologies, standardizing processes, and centralizing IT management are critical factors in achieving these efficiency gains and modernizing public sector operations.

Open Access: Yes

DOI: 10.1080/01900692.2025.2520522

Transition in the mining industry with green energy: Economic dynamics in mining demand

Laszlo Vasa Ashutosh Kolte Omkar Hirlekar

Publication Name: Resources Policy

Publication Date: 2025-01-01

Volume: 100

Issue: Unknown

Page Range: Unknown

Description:

This paper examines transformation of the mining industry in the Global South due to the rising demand for electric vehicles (EVs), which is a part of disruptive green technologies. South Africa & Democratic Republic of the Congo (DRC) are two important suppliers of critical minerals like cobalt, nickel, lithium, copper. This research tries to explore economic dynamics of mineral extraction and green transport. Using quantitative regression analysis, this paper tries to find the relationship between demand for EVs and its economic impact on mining industry's overall sales. The analysis has shown impact of critical minerals & mining sale and how disruptive technology like Evs are affecting mineral-rich countries sustainable mining. This paper is trying to shows some light on economic importance of critical minerals in transition of mining industry due to green vehicles or Evs. The association between the emerging green technology and the mining sector. The study focuses on nations in the Global South that have substantial control over the supply chain of essential minerals used in electric car batteries. The main objective of this study is to conduct an academic investigation of the many implications of green transport on the mining sector in the Global South.

Open Access: Yes

DOI: 10.1016/j.resourpol.2024.105409

Floristic and vegetation change on the Sphagnum-dominated mire of Egerbakta

János György NAGY Dániel TÖPLER János András DUDÁS Júlia HOBLYÁK

Publication Name: Kitaibelia

Publication Date: 2025-01-01

Volume: 30

Issue: 1

Page Range: 129-138

Description:

Since 1988, open stands of Menyanthes trifoliata have disappeared, communities of Carex rostrata have declined, and the Sphagnum-dominated willow carr has expanded. The mire’s central associations include Caricetum rostratae, Salici cinereae–Sphagnetum recurvi sphagnetosum squarrosi, and Calamagrosti–Salicetum cinereae. In the mainly nudum lagg zone, the following communities occur: Bidenti–Polygonetum hydropiperis, Bidenti–Polygonetum hydropiperis urticetosum dioicae, Caricetum acutiformis, Juncetum effusi, Glycerietum maximae, and a community dominated by Poa nemoralis. We recorded 77 vascular plant species, 62 of which were new to the site. Rare species have declined in number: Menyanthes trifoliata and Cicuta virosa are now absent, and of the former eight Sphagnum species, only Sphagnum squarrosum remains. The original Salici cinereae–Sphagnetum recurvi sphagnetosum recurvi subassociation transitioned into Salici cinereae–Sphagnetum recurvi sphagnetosum squarrosi after the mire remained completely dry and peat-moss-free for several years around 2000. Central communities reflect a cool, moderately acidic, oligotrophic environment with low pH and conductivity. In contrast, lagg vegetation indicates warmer, nutrient-rich, less acidic conditions. The mire’s most valuable zone is its central, Sphagnum-rich area, whose preservation depends on a natural water supply maintained by continuous forest cover in the catchment and stable or reduced large game populations.

Open Access: Yes

DOI: 10.17542/kit.30.068

Smart Cities and Data Enrichment: The Role of LiDAR and Point Cloud Upsampling in Sustainable Urban Management

K. Kozma Szeverin Oláh Árpád Barsi

Publication Name: Chemical Engineering Transactions

Publication Date: 2025-01-01

Volume: 121

Issue: Unknown

Page Range: 97-102

Description:

Geospatial data with high resolution and spatio-temporal accuracy can further support sustainable infrastructure and optimise urban services to improve the quality of life of city residents. LiDAR-based technologies are commonly used to produce 3D urban models and can include terrestrial laser scanning (TLS), mobile mapping systems (MMS), and airborne platforms such as photogrammetric drones. Point cloud datasets can be utilised for transportation planning and management, utility management, green infrastructure evaluation, and emergency response. Despite the utility of these point cloud datasets, the intrinsic incompleteness or sparsity due to the costs of surveying, the characteristics of the sensors, and environmental occlusion are significant limitations for effective precision modelling at the urban scale. Point cloud upsampling appears to be an innovative modelling gap for synthetically increasing point density, while preserving geometric accuracy. Deep learning–based networks demonstrably reduced the quantified improvements of the point cloud upsampling method. Previous studies have shown that reduced point-to-surface deviation from ~0.146 to ~0.140 (10-2 scale; 6.11 % improvement), and improved distribution uniformity from 0.315 to 0.219 (30.55 % improvement), and frequency-selective geometry upsampling provided up to 4.4×s less point-to-point compared to PU-Net and at 4× upsampling factors These results demonstrate that advanced point cloud upsampling methods would reasonable improve the accuracy or precision of derived products such as digital terrain models (DTMs), canopy height models (CHMs), and other ecological indices that are generally sensitive to point density. This paper reviews the latest upsampling algorithms and proposes a way of thinking and structuring data science that can scale into urban monitoring processes.

Open Access: Yes

DOI: 10.3303/CET25121017

Energy Storage System Selection for AI-Controlled Microgrids Using Complex Hesitant Fuzzy MCDM Approach Based on Dombi Operators

Dragan Pamucar Tahir Mahmood Jabbar Ahmmad Ubaid Ur Rehman Raja Zohaib Hayat

Publication Name: Contemporary Mathematics Singapore

Publication Date: 2025-01-01

Volume: 6

Issue: 3

Page Range: 3269-3300

Description:

The current definition of the Complex Hesitant Fuzzy Set (CHFS), derived from the Ramot form of complex numbers, cannot process information as in Tamir’s complex fuzzy form. We have data with uncertainty and extra information that cannot be described by any other structure than Tamir’s complex fuzzy form. Hence, in this article, we initiated the idea of CHFS based on Tamir’s complex fuzzy form and established its operational laws. Since Decision-Making (DM) theory is central to nearly all disciplines, we have proposed a novel complex hesitant fuzzy Multi-Criterion Decision-Making (MCDM) model. This method can handle all sorts of real-life MCDM problems, where the data contains uncertainty, hesitancy, and extra fuzzy information. While developing this method, we also develop and apply Dombi aggregation operators in this manuscript. After that, we discussed a case study that concerns energy storage system selection for AI-controlled microgrids and discussed how the theory we have developed can be applied to real-world challenges. Last, we conferred on how this proposed theory is superior to other theories and why it should be adopted.

Open Access: Yes

DOI: 10.37256/cm.6320256576