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Found 6374 publications
The Impact of Terrain Sampling Density on 5G NR-V2X Downlink Channel Modeling Using Various Propagation Models at the 3.6 GHz Band
Publication Name: Radioengineering
Publication Date: 2025-12-01
Volume: 34
Issue: 4
Page Range: 603-623
Description:
This study investigates the sensitivity of radio wave propagation models to terrain sampling density in a 5G New Radio Vehicle-to-Everything downlink scenario at 3.6 GHz. Four widely used models are analysed: the empirical ITU-R P.1546-6, the deterministic Parabolic Equation Method, and the hybrid ITU-R P.1812-6 and ITU-R P.452-16. Real terrain profiles from Hungary are considered at multiple resolutions, allowing a systematic assessment of how accuracy degrades as the representation of terrain becomes coarser. The analysis reveals a consistent ranking across environments: the empirical model is the least affected by resolution changes, while deterministic and hybrid methods are significantly more sensitive. To interpret these differences, the study introduces a spectral complexity measure of terrain profiles and establishes its strong relationship with error growth through regression analysis. This provides a novel framework for explaining and quantifying the impact of terrain detail on model behaviour. The findings highlight both the methodological contribution of linking spectral complexity to propagation accuracy and the practical implications for optimising the trade-off between computational efficiency and prediction reliability in vehicular network planning.
Open Access: Yes
Identifying Consumer Segments for Advanced Driver Assistance Systems (ADAS): A Cluster Analysis of Driver Behavior and Preferences
Publication Name: Future Transportation
Publication Date: 2025-12-01
Volume: 5
Issue: 4
Page Range: Unknown
Description:
The rapid advancement of Advanced Driver Assistance Systems (ADAS) is reshaping the future of mobility by offering potential improvements in safety, efficiency, and driving experience, yet consumer acceptance remains uneven across regions. This study addresses the gap in knowledge and trust by examining how Hungarian drivers, as part of the Central and Eastern European context, perceive and adopt ADAS technologies. To achieve this, we conducted two expert in-depth interviews to refine the research instrument, followed by an online survey of 179 drivers. Using k-means cluster analysis, we identified three distinct consumer segments: Conservative Controllers, who demonstrate low levels of trust and willingness to adopt ADAS; Cautious Adopters, who weigh costs and benefits carefully; and Pragmatic Innovators, who are open to experimentation and display the highest acceptance and willingness to pay. The results reveal that awareness and familiarity strongly influence acceptance, highlighting the role of consumer education and transparent communication in shaping adoption. The findings suggest that manufacturers, driving schools, and policymakers can accelerate the diffusion of ADAS by developing targeted strategies tailored to different consumer groups. Strengthening knowledge and trust in these systems will not only support their market success but also contribute to safer, more sustainable transportation.
Open Access: Yes
A novel numerical investigation of fiber Bragg gratings with dispersive reflectivity having polynomial law of nonlinearity
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Fiber Bragg gratings represent a pivotal advancement in the field of photonics and optical fiber technology. The numerical modeling of fiber Bragg gratings is essential for understanding their optical behavior and optimizing their performance for specific applications. In this paper, numerical solutions for the revered optical fiber Bragg gratings that are considered with a cubic-quintic-septic form of nonlinear medium are constructed first time by using an iterative technique named as residual power series technique (RPST) via conformable derivative. The competency of the technique is examined by several numerical examples. By considering the suitable values of parameters, the power series solutions are illustrated by sketching 2D, 3D, and contour profiles. The results obtained by employing the RPST are compared with exact solutions to reveal that the method is easy to implement, straightforward and convenient to handle a wide range of fractional order systems in fiber Bragg gratings. The obtained solutions can provide help to visualize how light propagates or deforms due to dispersion or nonlinearity.
Open Access: Yes
ESG disclosure topics and reporting frameworks: exploratory research across automotive, construction, and energy industries
Publication Name: Discover Sustainability
Publication Date: 2025-12-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
Environmental, Social, and Governance (ESG) reporting and proper measurement of greenhouse gas emissions are becoming increasingly important for industries with substantial environmental impact. This research aims to assess the current state of ESG reporting practices and highlight areas for improvement across the automotive, construction and energy industries operating in the Central Eastern European (CEE) region. To achieve this aim, a multi-industry sustainability disclosure database was created and analyzed through a Python-based text-mining methodology, using term frequency-inverse document frequency and keyword-in-context analysis. The process involved extracting and preprocessing text from 60 sustainability reports for the year 2021, followed by constructing a custom dictionary of key ESG terms aligned with the European Sustainability Reporting Standards. The findings reveal considerable variance in the focus of qualitative disclosures across industries, particularly regarding climate change and biodiversity. The investigation underscores the need for enhanced transparency, consistent metrics, and rigorous validation in ESG reporting. The study also provides new insights into the technical possibilities of automated text analysis for sustainability reporting in the CEE region, and highlights key areas where improvement appears necessary.
Open Access: Yes
Biological and therapeutic implications of sex hormone-related gene clustering in testicular cancer
Publication Name: Basic and Clinical Andrology
Publication Date: 2025-12-01
Volume: 35
Issue: 1
Page Range: Unknown
Description:
Background: Gonadotropin dysregulation seems to play a potential role in the carcinogenesis of testicular germ cell tumor (TGCT). The aim of this study was to explore the expression of specific genes related to sex hormone regulation, synthesis, and metabolism in TGCT and to define specific hormonal clusters. Two publicly available databases were used for this analysis (TCGA and GSE99420). By means of hard-threshold regularized KMEANS clustering, we assigned TGCT samples into four clusters defined in respect to different expression of the sex hormone-related genes. We analysed clinical data, protein and gene expression, signaling regarding hormonal clusters. Based on whole-transcriptome gene expression, prediction of anti-cancer drug response was made by RIDGE models. Results: Cluster #1 (12–16%) consisted primarily of non-seminomatous germ cell tumor (NSGCT), characterized by high expression of PRL, GNRH1, HSD17B2 and SRD5A1. Cluster #2 (42–50%) included predominantly seminomas with high expression of SRD5A3, being highly infiltrated by T and B cells. Cluster #3 (8.3–18%) comprised of NSGCT with high expression of CGA, CYP19A1, HSD17B12, HSD17B1, SHBG. Cluster #4 (23–30%), which consisted primarily of NSGCT with a small fraction of seminomas, was outlined by increased expression of STAR, POMC, CYP11A1, CYP17A1, HSD3B2 and HSD17B3. Elevated fibroblast levels and increased extracellular matrix- and growth factor signaling-related gene signature scores were described in cluster #1 and #3. In the combined model of progression-free survival, S2/S3 tumor marker status, hormonal cluster #1 or #3 and teratoma histology, were independently associated with 25–30% increase of progression risk. Based on the increased receptor tyrosine kinase and growth factor signaling, cluster #1, #3 and #4 were predicted to be sensitive to tyrosine kinase inhibitors, FGFR inhibitors or EGFR/ERBB inhibitors. Cluster #2 and #4 were responsive to compounds interfering with DNA synthesis, cytoskeleton, cell cycle and epigenetics. Response to apoptosis modulators was predicted only for cluster #2. Conclusions: Hormonal cluster #1 or #3 is an independent prognostic factor regarding poor progression-free survival. Hormonal cluster assignment also affects the predicted drug response with cluster-dependent susceptibility to specific novel therapeutic compounds.
Open Access: Yes
Effect of heat stress and feed restriction on performance, carcass traits, and meat quality of growing rabbits
Publication Name: Livestock Science
Publication Date: 2025-12-01
Volume: 302
Issue: Unknown
Page Range: Unknown
Description:
The effects of heat stress and feed restriction were evaluated on a total of 180 weaned rabbits divided into three experimental groups (60 animals/group): 2 groups were fed ad libitum and reared under different temperatures (20 °C – 20AD and 30 °C – 30AD), while a third group was housed under controlled temperature (20 °C) but pair-fed to 30AD rabbits, thus feed restricted (20FR). During the trial, both 30AD and 20FR groups exhibited reduced growth performance, including body weight and daily weight gain (both, P < 0.001), although feed conversion ratio improved (P = 0.016). The reference carcasses of 20FR and 30AD rabbits were lighter and leaner (both, P < 0.001) than that of 20AD rabbits, while the slaughter yield decreased only in 20FR rabbits (P = 0.001). Regarding meat physical traits, 20FR rabbits exhibited the highest pHu (P < 0.001) and the lowest total losses (P < 0.001), whereas the meat-to-bone ratio decreased in both 20FR and 30AD groups (P = 0.007). As for meat proximate composition, protein and lipid contents were lower (P = 0.008 and P = 0.0002, respectively) in 20FR and 30AD rabbits, while water content was greater (P < 0.001) compared to 20AD rabbits. At the lipid level, higher TBARS (P = 0.001) were found in both 20FR and 30AD groups. The 20FR and 30AD groups showed some differences in their carcass and meat quality traits, however the majority of changes induced by chronic heat stress were mostly attributed to the reduced feed intake.
Open Access: Yes
Dictionary-based assessment of European Sustainability Reporting Standard (ESRS) disclosure topics
Publication Name: Discover Sustainability
Publication Date: 2025-12-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
As the imperative for sustainable business practices and corporate responsibility has grown, the analysis and integration of Environmental, Social, and Governance (ESG) issues into corporate strategies has become a crucial aspect of business strategy. The paper assesses the ESG reporting preparedness of companies in the Central and Eastern European (CEE) region by analyzing their compliance with the European Sustainability Reporting Standards (ESRS). The study assesses the variability in disclosures across ESG pillars and examines their relationship with financial metrics using a test of independence and bootstrapped multiple regression. By employing an automated text analysis methodology on sustainability reports from top-performing companies, including Hungary, the Czech Republic, Poland, Austria, Slovenia, and Romania, the research identifies significant differences in reporting scores across various ESG disclosure topics. The results indicate that Climate Change (E1) scores are higher than those of other topics, suggesting an uneven emphasis on different aspects of sustainability. Furthermore, the analysis reveals that larger companies tend to achieve higher ESG scores, reflecting their greater resources for comprehensive and transparent reporting practices. The research contributes to the understanding of ESG reporting practices in the CEE region and highlights the importance of improvement in sustainability reporting to foster greater transparency and comparability. The findings suggest policy initiatives to encourage balanced reporting across all ESG topics and that companies, particularly smaller ones, could benefit from capacity-building efforts to enhance their reporting capabilities.
Open Access: Yes
Advancing decision-making frameworks: Generalized distance measures in complex fuzzy set environments for enhanced precision and robustness
Publication Name: Systems and Soft Computing
Publication Date: 2025-12-01
Volume: 7
Issue: Unknown
Page Range: Unknown
Description:
A complex fuzzy distance measure (CFDM) is a way to quantify the dissimilarity or similarity between two complex fuzzy sets (CFSs). This measure often considers both the membership values and the degree of overlap between sets to compute the distance. However, CFDMs are not capable of capturing the hesitancy or uncertainty inherent in real-life problems. To overcome this difficulty, we present generalized notions of some existing distance measures (DMs), such as Zhang DM and Zeeshan DM within the framework of CFSs. The newly defined DMs are said to be complex fuzzy generalized Zhang Hesitance DM (CFGZHDM), complex fuzzy generalized weighted Zhang Hesitance DM (CFGWZHDM), complex fuzzy generalized Zeeshan Hesitance DM (CFGZHDM), and complex fuzzy generalized weighted Zeeshan Hesitance DM (CFGWZHDM). Several new set-theoretic operations and fundamental mathematical results are formally defined and developed. These are built upon the framework of the proposed decision-making models to strengthen their applicability and theoretical foundation. We utilized the proposed generalized CFDMs in applications to decision-making problems. We proposed a new decision-making algorithm that offers a flexible and nuanced approach to selecting exemplary students by considering the fuzzy and overlapping nature of attributes and allowing for uncertainty in the selection process. Furthermore, a comparative analysis is conducted between the proposed models, evaluating their performance and effectiveness about several existing fuzzy models. This comparison aims to highlight the strengths, differences, and potential advantages of the newly proposed models over conventional methods. Moreover, the newly defined decision-making approaches illustrate clear improvements over existing techniques. While traditional techniques fail to provide meaningful ranking values, our proposed approaches produce non-zero scores such as 0.13, 0.30, and 0.10, leading to a valid ordering of alternatives. When weighted information is considered, the effectiveness is further enhanced, yielding higher score values (0.80, 0.85, and 0.81) and more stable rankings.
Open Access: Yes
Bio-signal induced emotion monitoring and detection of anxiety: A sensor-driven approach with regression based random forest
Publication Name: Methodsx
Publication Date: 2025-12-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
The present study addresses the rising importance of mental health by devel oping a novel healthcare plan. We integrate physiological data from sensors, such as Heart Rate (HR) and Galvanic Skin Response (GSR), to predict and manage anxiety. These sensors provide non-invasive insights into the com plex relationship between physiological reactions and mental well-being. To analyze the collected data, we developed a novel algorithm, Regression Based Random Forest (RBRF). Using a large-scale dataset, we empirically validated the effectiveness of our approach, achieving an impressive 95 % accuracy in identifying anxiety. Our findings demonstrate the potential of sensor-based technologies and advanced algorithms to empower individuals to proactively monitor and manage their mental health. This approach holds significant promise for improving the precision and effectiveness of mental health care. • The study aims to improve mental healthcare by incorporating physiological data (Heart Rate and Galvanic Skin Response) to detect and potentially treat anxiety. • Employs a novel algorithm, Regression Based Random Forest (RBRF), to analyze the collected data and identify anxiety. • Achieved high accuracy (95 %) in identifying anxiety using the RBRF algorithm on a large dataset.
Open Access: Yes
