http://hdl.handle.net/20.500.14044/36202 Sun, 26 Jul 2026 03:29:04 GMT 2026-07-26T03:29:04Z https://https://oda.uni-obuda.hu:443/bitstream/id/27d4e132-ee0b-4179-95d4-6ae498a72d01/ http://hdl.handle.net/20.500.14044/36202 http://hdl.handle.net/20.500.14044/36451 Screen Natives Becoming Digital Orphans: The Online Challenges of Generation Alpha Módné Takács, Judit; Pogátsnik, Monika Petőné Csuka, Ildikó In the digital era, the unprecedented online presence of Generation Alpha creates novel challenges related to problematic internet use. Investigating this phenomenon is essential for understanding the cognitive and social development of this cohort and for formulating effective prevention strategies. Objective: The aim of this study is to measure Problematic Internet Use (PIU) and its influencing and moderating factors. It also investigates the relationships between the dimensions of PIU (control, obsession, and neglect) and demographic variables among representatives of Generation Alpha. Method: This study employed a quantitative, cross-sectional survey design. PIU was measured using a validated, 6-item questionnaire (Problematic Internet Use questionnaire, PIU-6) on a 5-point Likert scale. A total of N = 837 participants were recruited from educational institutions in Fejér County, Hungary. Results: Based on the results, average daily internet use time is the strongest behavioral predictor of problematic internet use; however, the strength of this relationship is significantly moderated by gender and family context. The association between online screen time and PIU is significantly stronger for girls than for boys. Furthermore, the relationship between internet use time and PIU was significant for children from families with higher levels of parental education. Conclusion: Considering family context and gender differences is essential when evaluating the effects of online screen time, as these factors can function as either protective or risk-enhancing elements in the development of problematic internet use among Generation Alpha. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/20.500.14044/36451 2025-01-01T00:00:00Z http://hdl.handle.net/20.500.14044/36433 Multifunctional power unit concept with hydrostatic drive system. Kovács, Ádám; Bráda, Csaba Petőné Csuka, Ildikó Peace and war. Two periods that cyclically replace each other in history. What they have in common is that industry must serve both. Current events, such as the Russian-Ukrainian conflict and the threat of terrorism, require preparedness. In addition, it is necessary to economically and adequately satisfy the constant demands of peacetime. Based on this line of thought, we have created an agricultural power plant concept that can also be manufactured under Hungarian conditions. The primary goal of the project is to develop a hydrostatic drive system concept that will serve such a machine. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/20.500.14044/36433 2025-01-01T00:00:00Z http://hdl.handle.net/20.500.14044/36365 Comparative Analysis of Lightweight CNNs for Multilingual Handwritten Character Recognition Bartos Ediboglu, Gaye; Ozmen Akyol, Serel Petőné Csuka, Ildikó Handwritten character recognition remains a fundamental problem in pattern recognition, with applications ranging from digital document processing to historical manuscript digitization. Multilingual handwritten character recognition introduces additional challenges due to variations in scripts, diacritics, and handwriting styles. In this work, we explore lightweight convolutional neural networks (CNNs) for recognizing handwritten characters across multiple languages using the merged T-H-E dataset, which combines visually identical upper- and lower-case characters into 54 classes. Four CNN architectures, as LeNet-5 (baseline), Simple CNN, Depthwise-CNN, and MiniVGGNet, are evaluated based on their efficiency, number of parameters, training time, and misclassification behavior. Experiments were conducted under standardized hyperparameters and using a fixed random seed to ensure a fair comparison. Our analysis shows that lightweight networks can achieve competitive performance while requiring minimal computational resources, and the study provides insights into common misclassification patterns related to visually similar characters and diacritics. These findings establish a fast and resource-efficient baseline for multilingual handwritten character recognition on small binary datasets. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/20.500.14044/36365 2025-01-01T00:00:00Z http://hdl.handle.net/20.500.14044/36363 Explainable MLP-Based Surrogate Model for Predicting Terahertz Metamaterial Absorber Performance Yalçın, Nesibe; Ünaldı, Sibel; Bartos Ediboglu, Gaye Petőné Csuka, Ildikó This study presents an explainable surrogate modeling approach for predicting the absorption behavior of broadband THz metamaterial absorbers based on vanadium dioxide (VO2) by a Multi-Layer Perceptron (MLP) network. The proposed model was trained on an electromagnetic full- wave simulation dataset, which included design parameters and frequency, with the corresponding absorption as output. The MLP-based surrogate model was evaluated using performance metrics such as the coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Additionally, explainable artificial intelligence (XAI) methods were used to understand how the input parameters affect the absorption. Thus, the proposed model offers important clues regarding the physical relationship between design parameters and absorption performance. The results obtained indicate that the MLP model achieves high prediction accuracy. This approach offers a promising alternative for the design and optimization of next-generation THz absorber devices. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/20.500.14044/36363 2025-01-01T00:00:00Z