Search in Publications
Found 6374 publications
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
Musculoskeletal modelling sequentially integrated with stress simulation reveals asymmetrical knee loading and ligament stress during long-distance running
Publication Name: BMC Sports Science Medicine and Rehabilitation
Publication Date: 2025-12-01
Volume: 17
Issue: 1
Page Range: Unknown
Description:
Background: Understanding the internal load characteristics of the knee joint is essential for investigating unilateral knee injuries associated with running. This study examined the differences in the location and magnitude of von Mises stress in the internal structures of bilateral knee joints during the stance phase of gait following 10 km running at submaximal speeds. Methods: A healthy male recreational runner participated in this study. We employed a synergistic approach, integrating subject-specific knee joint angles, reaction forces, and moments derived from musculoskeletal modeling to inform and drive the finite element (FE) modeling of running. This methodology ensured a detailed and accurate representation of knee joint mechanics. The peak stresses of the bilateral knee menisci, tibial cartilage, and five main ligaments were quantified using a FE model during the stance phase. Results: The meniscus, tibial cartilage, anterior (ACL), posterior cruciate ligament (PCL), medial (MCL), lateral collateral ligament (LCL) and experienced larger loads in the non-dominant limb across most phases of stance. Additionally, fatigue elevated the peak loading on the non-dominant limb’s ACL, PCL, and LCL during the gait stance phase but diminished the load on these ligaments in the dominant knee joint. For Patellar ligament (PL), the non-dominant side showed maximal stress at initial contact, while the dominant side dominated during the remaining stance phases. Conclusions: This proof-of-concept substantially enhances our understanding of the impact of running-induced fatigue on bilateral knee loading. It provides a detailed analysis of factors leading to unilateral knee overload during extended running. These insights are essential in formulating targeted strategies to reduce injury risks.
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
Reliable power management and predictive analysis of domestic appliances with insights of XAI
Publication Name: Energy Reports
Publication Date: 2025-12-01
Volume: 14
Issue: Unknown
Page Range: 3704-3718
Description:
The unanimous focus of the sustainable technological development is energy conservation and environmental friendly production. Power management is an essential aspect of sustainable development. It not only support energy production and conservation, but also increases the life time of domestic appliances and thereby reducing the global electronic wastage. The existing systems involving Artificial Intelligence (AI) were mere prediction models, without the evidence on the detailing behind the prediction. Traditional AI systems have focused on predictive analysis but often lack transparency in decision-making and limiting consumer trust. This study proposes a solution combining remote power monitoring with the ZigBee module and Explainable Artificial Intelligence (XAI) to offer both predictive accuracy and interpretability. XAI models are more consumer oriented in every area of application, similar to the problem discussed, which tells about the impact of various parameters in power management in domestic appliances. Local Interpretable Model Agonistic Explainer(LIME) and SHAP explainer are used in the proposed work, providing explainability in the local and global surrogates. The proposed work applies various regression models such as Decision Tree (DT), Random Forest(RF), Support Vector Regressor (SVR), Gradient Boost Regressor (GBR) and Extreme Graident Boost Regressor (XGBR). The RF provides the best R2-Score of 94.71% , which is 1.5%–3.0% more than the rest of the models, and also with variance score of 68.82% , had been chosen for explainability. This study demonstrates how XAI can improve transparency and reliability in AI-powered domestic energy systems, offering actionable insights for more sustainable power consumption.
Open Access: Yes
Integrating geophysical techniques and UAV mapping for the detection and temporal analysis of soil pipes and pipe collapses in agricultural loess landscapes
Publication Name: Geoenvironmental Disasters
Publication Date: 2025-12-01
Volume: 12
Issue: 1
Page Range: Unknown
Description:
Piping erosion represents a persistent and often concealed threat to soil and water resources in agricultural loess landscapes, particularly under semi-arid conditions. Accurate detection and temporal monitoring of soil pipes and pipe collapses (PCs) are essential for effective land management. This study investigates the spatial and temporal dynamics of piping erosion in the Aqchatal catchment, eastern Golestan Province, NE Iran, through an integrated approach combining geophysical surveys and unmanned aerial vehicle (UAV) mapping. The objectives were: (i) to inventory and characterize soil pipes using ground penetrating radar (GPR) and electrical resistivity tomography (ERT); and (ii) to monitor the evolution of PCs from 2018 to 2023 using sequential UAV imagery. Three sets of UAV-derived orthomosaics, together with eight two-dimensional ERT profiles and thirteen GPR profiles, enabled the identification of areas at high risk of piping erosion. UAV analysis revealed recurrent PCs in close proximity to previously documented locations, frequently linked to anthropogenic activities such as agricultural machinery operations. GPR anomalies and ERT measurements indicated the continued susceptibility of areas with previously filled PCs, with possible soil pipes detected at depths of 1–7 m in both geophysical datasets. ERT results further identified resistivity values of 20–30 Ω⋅m for dry clay and silt layers, while values exceeding 300 Ω⋅m corresponded to potential pipe features. The integrated methodology demonstrated high efficacy in detecting and characterizing soil pipes in loess terrain. Based on these findings, targeted management strategies—including the establishment of buffer zones and the installation of informational boards—are recommended to mitigate piping erosion risk. Further assessment of the practical implementation and acceptance of these measures among landowners is warranted.
Open Access: Yes
Improvement of the cantilever test geometry deflection measurement method to assess residual stresses in laser powder bed fusion
Publication Name: Production Engineering Archives
Publication Date: 2025-12-01
Volume: 31
Issue: 4
Page Range: 474-480
Description:
Laser powder bed fusion (L-PBF) processes allow for the creation of advanced 3D parts that are difficult to achieve through conventional manufacturing. Defects such as part deformation are a critical issue in L-PBF and seriously affect the industrial adoption of metal additive manufacturing (AM) processes for critical applications. The research community has widely accepted the deformation measurement of known geometries, such as cantilevers or bridges, for the assessment of residual stresses. In this study, quantitative measurements are obtained using a 3D optical scanner on cantilevers with different geometries, in order to study the effect of bar thickness on deformation. Furthermore, this experimental study aims to investigate cantilever deflection measurement methods and compare the obtained deviation parameters. The findings reveal that, in case of cantilever specimens, flatness and curvature attributes measured on surface are more reliable than maximum vertical deflection. The part height plays a primordial role on the deflection, the distortion changes in inverse proportion to the cantilever’s bar thickness.
Open Access: Yes
Editorial: Blockchain-driven business models
Publication Name: Digital Business
Publication Date: 2025-12-01
Volume: 5
Issue: 2
Page Range: Unknown
Description:
In this editorial, we introduce the Special Issue on Blockchain-Driven Business Models. Over the last decade, academics have proposed numerous ideas on how to integrate the unique properties of blockchain, such as immutability, traceability, and programmability, into existing business models or to utilize them for developing startups from scratch. Practitioners have tried out countless disruptive business ideas, some of which have failed, while others have already begun to transform entire industries. This Special Issue explores the concept of blockchain-based business models from various angles, offering different theoretical perspectives and paving the way for future research in this groundbreaking area.
Open Access: Yes
