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Found 6374 publications
Integration of data-driven T-spherical fuzzy mathematical models for evaluation of electric vehicles: Response to electric vehicle market demands
Publication Name: Renewable and Sustainable Energy Reviews
Publication Date: 2025-11-01
Volume: 223
Issue: Unknown
Page Range: Unknown
Description:
The rapid growth of the electric vehicle (EV) market necessitates advanced multi-criteria decision-making (MCDM) frameworks capable of integrating diverse quantitative and qualitative factors under uncertainty. Traditional MCDM approaches often struggle to capture the complexity and imprecision inherent in EV evaluations, particularly in dynamic contexts like India. To address this gap, this study proposes the T-Spherical Fuzzy (T-SF) MARCOS and T-SF MOORA methods, which utilize T-Spherical Fuzzy Numbers (T-SFNs) to enhance decision precision. T-SFNs extend conventional fuzzy models by independently incorporating degrees of membership, non-membership, and hesitation, enabling a more granular and realistic modeling of expert judgments. In the methodological construction, numerical criteria (e.g., battery capacity, charging time) are directly incorporated, while qualitative criteria (e.g., safety, comfort) are initially evaluated by four domain experts through linguistic assessments, subsequently transformed into T-SFNs for integrated evaluation and accurate criteria weighting. The developed models are then employed to rank ten EV alternatives across 21 comprehensive technical and consumer-centric criteria. Comparative analysis shows that T-SF MARCOS and T-SF MOORA achieve superior ranking accuracy, with a high mutual Pearson correlation of 0.71, while traditional SF methods like SF-WSM and SF-WASPAS exhibit negative correlations of −0.43 and −0.42, respectively. Sensitivity analyses—covering variations in criteria weights and additional criteria integration—confirm the robustness and stability of the frameworks, with rank reversal rates remaining below 10 % across all scenarios. This study presents a technically resilient, uncertainty-aware evaluation framework, offering strategic insights for advancing consumer-centric EV development.
Open Access: Yes
Research progress in small-molecule donor-polymer acceptor organic photovoltaic cells
Publication Name: Organic Electronics
Publication Date: 2025-11-01
Volume: 146
Issue: Unknown
Page Range: Unknown
Description:
Organic solar cells (OSCs), characterized by their lightweight, flexibility, solution-processability for large-area fabrication, and low cost, exhibit significant complementary advantages to silicon-based photovoltaics, positioning them as a cutting-edge research frontier in clean energy. Among emerging architectures, small-molecule donor/polymer acceptor (SDPA)-based OSCs have attracted considerable attention due to their unique active layer stability, particularly their ability to maintain optimized phase-separated morphology under high-temperature conditions (>85 °C), offering potential to overcome the stability bottleneck in organic photovoltaic industrialization. However, the current record power conversion efficiency (PCE) of SDPA-OSCs remains at 12.1 %, significantly lagging behind mainstream bulk heterojunction systems (PCE >20 %). To advance the efficiency of SDPA-OSCs, extensive efforts have been devoted to optimizing materials, device engineering, and processing techniques. This review systematically summarizes recent progress in SDPA-OSCs from the perspectives of device architecture and active layer processing. Key focus areas include the impact of device structure engineering (conventional vs. inverted configurations) and active layer fabrication strategies (bulk heterojunction solution-coating and layer-by-layer deposition techniques) on charge carrier transport and device performance. By establishing robust “material structure–morphology–device performance” correlations, this work provides critical insights and technical references for developing high-efficiency SDPA-OSCs. Furthermore, future research directions and challenges in material innovation, morphology control, and scalable manufacturing are discussed to guide the advancement of SDPA-based organic photovoltaics.
Open Access: Yes
Approaches to studying wheat and maize drought stress responses
Publication Name: Plant and Soil
Publication Date: 2025-11-01
Volume: 516
Issue: 1
Page Range: 115-132
Description:
Introduction: Drought stress remains a critical challenge to sustainable agriculture worldwide, threatening crop productivity and food security. Understanding the physiological processes and defense mechanisms that crops employ under water-limited conditions is essential for developing strategies to enhance drought resilience. Plant responses to drought vary widely depending on species, genotype, developmental stage, and the severity and duration of stress. Beyond annual rainfall totals, yield is also shaped by seasonal precipitation patterns and related environmental factors, which influence the choice of cultivars and crop performance. Finding: This review examines the drought responses of maize and wheat, two globally important cereals, across morphological, physiological, biochemical, and molecular dimensions. Key responses include enhanced root development, reduced leaf area, stomatal regulation, decreased photosynthetic activity and water potential, and elevated proline and abscisic acid levels. Although varietal differences are noted, they are discussed only briefly. Water stress is commonly quantified via water potential and measured using tools such as the Scholander pressure chamber or the increasingly adopted ZIM probe, which allows non-destructive, continuous monitoring. While conventional breeding efforts have targeted drought tolerance, progress is constrained by these traits' polygenic and environmentally sensitive nature. Recently, biostimulants such as seaweed extracts and microalgae-based products have emerged as promising tools for enhancing stress tolerance. Conclusion: To meet the demands of a changing climate, future research should prioritize the integration of genetic, physiological, and biochemical strategies to develop crops with robust and durable drought resistance.
Open Access: Yes
Application of Psychoacoustic Metrics in the Noise Assessment of Geared Drives
Publication Name: World Electric Vehicle Journal
Publication Date: 2025-11-01
Volume: 16
Issue: 11
Page Range: Unknown
Description:
Psychoacoustic metrics offer a valuable complement to traditional noise evaluation methods for gear transmissions, as they account for the human perception of sound quality rather than relying solely on physical measurements. While parameters such as overall sound pressure level (SPL) and spectral content quantify noise intensity and frequency distribution, they often fail to reflect subjective annoyance caused by tonal or high-frequency components common in gear systems. This review provides a structured overview of how psychoacoustic metrics—including loudness, sharpness, roughness, fluctuation strength, and tonality—are applied in the analysis of gear transmission noise. Relevant studies were identified through a comprehensive search across multiple scientific databases, with 54 meeting the inclusion criteria. The findings highlight both the benefits and limitations of these metrics, and present examples of their industrial application in automotive and mechanical engineering contexts. The review also identifies gaps in current research, particularly in integrating psychoacoustic evaluation with predictive modelling and machine learning, and suggests directions for future work.
Open Access: Yes
DOI: 10.3390/wevj16110611
Environmental Impacts of Synthetic Fuels †
Publication Name: Engineering Proceedings
Publication Date: 2025-11-01
Volume: 113
Issue: 1
Page Range: Unknown
Description:
In 2024, synthetic fuels regained attention as potential low-emission alternatives for internal combustion engines (ICEs), particularly in sectors where electrification remains challenging. This paper compares the estimated CO2 emission factors of fossil-based fuels and synthetic fuels blended with 20% bioethanol under standardized usage conditions. A key finding is that the emission factor of synthetic fuels is highly dependent on the carbon intensity of the electricity used to produce green hydrogen via electrolysis. Using the projected EU electricity mix for 2030, synthetic fuels show no clear advantage over fossil fuels. However, with a cleaner electricity mix expected by 2050, their emission factor becomes significantly lower. From an economic standpoint, the viability of synthetic fuel production largely depends on reducing green hydrogen costs of €1.50–2.00 per kg through technological advancements and large-scale deployment. This analysis offers a realistic perspective on when and how synthetic fuels could contribute to climate objectives and outlines the technical and economic conditions necessary for their environmental and market viability.
Open Access: Yes
A Study of the Linguistic Landscape of a Hungarian University That Is Going International
Publication Name: Education Sciences
Publication Date: 2025-11-01
Volume: 15
Issue: 11
Page Range: Unknown
Description:
The study of the linguistic landscape is a key area for mapping the linguistic and cultural characteristics of university campuses. This attention is manifest in the language choice employed in the signage on campus, which serves as a physical indicator of these institutions’ linguistic policies and practices. The following paper will present a multi-faculty study conducted at Széchenyi István University in Hungary. The objective of this research is to address the question of how internationalization is explicitly manifested in the institution. A further aim of this investigation was to determine to what extent foreign languages, especially English and German, are represented in the texts found at the university, and what functions these texts perform. Therefore, mixed-method research was conducted in the university’s central academic buildings and their immediate surroundings, during which photos of the signage were taken, analysed, and systematically categorized. This research yielded a comprehensive understanding of the university’s linguistic landscape and revealed the lack of a coherent foreign language policy at the university. The results can provide relevant information for consciously (re)designing the linguistic landscape of the university studied and can help other universities to plan their language policies.
Open Access: Yes
Uncovering the dynamics of human-AI hybrid performance: A qualitative meta-analysis of empirical studies
Publication Name: International Journal of Human Computer Studies
Publication Date: 2025-11-01
Volume: 205
Issue: Unknown
Page Range: Unknown
Description:
Human-AI collaboration is an increasingly important area of research as AI systems are integrated into everyday workflows and moving beyond mere automation and augmentation to more collaborative roles. However, existing research often overlooks the dynamics and performance aspects of this interaction. Our study addresses this gap through a review of empirical AI studies from 2018–2024, focusing on the key factors influencing human-AI collaboration outcomes within the spectrum of Human-Centered Artificial Intelligence (HCAI). We identify 24 critical performance factors that influence hybrid performance, grouped into four categories using thematic analysis. Then, we uncover and analyze the complex, non-linear interdependencies between these factors. We present these relationships in a factor dependency graph, highlighting the most influential nodes. The graph and specific factor interactions supported by the papers reveal a quite complex web, an interconnectedness of factors. As opposed to being an easy-to-predict combination of inputs, human-AI collaboration in a given context likely leads to a dynamic, evolving system with often non-linear effects on its hybrid performance. Our findings and the previous research on automation technologies suggest that the application of AI tools in collaborative scenarios would benefit from a comprehensive performance framework. Our study intends to contribute to this future line of research with this initial framework.
Open Access: Yes
Comparative Analysis of Driving Performance and Visual and Physiological Responses Between Professional and Civilian Drivers in Simulated Environments
Publication Name: Applied Sciences Switzerland
Publication Date: 2025-11-01
Volume: 15
Issue: 22
Page Range: Unknown
Description:
Current research and development in understanding road users’ driving behaviors play a key role in improving traffic safety. Recently, several driving simulators have been employed as a suitable approach to investigate several drivers’ responses in challenging traffic scenarios. Although professional drivers represent a particular category among driving populations, the body of literature about their comparative behavioral and psychological characteristics remains limited. This study examined the differences in driving performance and visual and physiological responses between civilian and professional drivers in a simulated environment. A total of 30 drivers, with an equal split between professional and civilian categories, took part in a series of driving simulations. The simulations incorporated various infrastructure types, including four cone avoidance tasks and a high-speed motorway task. This study collected comprehensive data on performance metrics, hand usage, heart rate, and eye movements. Eye-tracking technology was used to measure visual attention. The findings revealed that during cone avoidance scenarios, civilian drivers exhibited a similar performance, visual behavior, and physiological response, except for the speed, experiment duration, and throttle, to professional drivers. In the motorway scenario, all metrics showed no significant difference between the two driver groups. These results highlight the need for cautious interpretation, particularly given the limitations of the sample. Revalidation is needed in larger studies, especially for understanding the differences between drivers’ metrics, which is crucial to elevate drivers’ safety, and assessing training programs in Hungary.
Open Access: Yes
DOI: 10.3390/app152212024
SHEAB: A Novel Automated Benchmarking Framework for Edge AI
Publication Name: Technologies
Publication Date: 2025-11-01
Volume: 13
Issue: 11
Page Range: Unknown
Description:
Edge computing is characterized by heterogeneous hardware, distributed deployment, and a need for on-site processing, which makes performance benchmarking challenging. This paper presents SHEAB (Scalable Heterogeneous Edge Automation Benchmarking), a novel framework designed to securely automate the benchmarking of Edge AI devices at scale. The proposed framework enables concurrent performance evaluation of multiple edge nodes, drastically reducing the time-to-deploy (TTD) for benchmarking tasks compared to traditional sequential methods. SHEAB’s architecture leverages containerized microservices for orchestration and result aggregation, integrated with multi-layer security (firewalls, VPN tunneling, and SSH) to ensure safe operation in untrusted network environments. We provide a detailed system design and workflow, including algorithmic pseudocode for the SHEAB process. A comprehensive comparative review of related work highlights how SHEAB advances the state-of-the-art in edge benchmarking through its combination of secure automation and scalability. We detail a real-world implementation on eleven heterogeneous edge devices, using a centralized 48-core server to coordinate benchmarks. Statistical analysis of the experimental results demonstrates a 43.74% reduction in total benchmarking time and a 1.78× speedup in benchmarking throughput using SHEAB, relative to conventional one-by-one benchmarking. We also present mathematical formulations for performance gain and discuss the implications of our results. The framework’s effectiveness is validated through the concurrent execution of standard benchmarking workloads on distributed edge nodes, with results stored in a central database for analysis. SHEAB thus represents a significant step toward efficient and reproducible Edge AI performance evaluation. Future work will extend the framework to broader workloads and further improve parallel efficiency.
Open Access: Yes
