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Found 6278 publications
Surface Waviness of EV Gears and NVH Effects—A Comprehensive Review
Publication Name: World Electric Vehicle Journal
Publication Date: 2025-09-01
Volume: 16
Issue: 9
Page Range: Unknown
Description:
Electric vehicle (EV) drivetrains operate at high rotational speeds, which makes the noise, vibration, and harshness (NVH) performance of gear transmissions a critical design factor. Without the masking effect of an internal combustion engine, gear whine can become a prominent source of passenger discomfort. This paper provides the first comprehensive review focused specifically on gear tooth surface waviness, a subtle manufacturing-induced deviation that can excite tonal noise. Periodic, micron-scale undulations caused by finishing processes such as grinding may generate non-meshing frequency “ghost orders,” leading to tonal complaints even in high-quality gears. The article compares finishing technologies including honing and superfinishing, showing their influence on waviness and acoustic behavior. It also summarizes modern waviness detection techniques, from single-flank rolling tests to optical scanning systems, and highlights data-driven predictive approaches using machine learning. Industrial case studies illustrate the practical challenges of managing waviness, while recent proposals such as controlled surface texturing are also discussed. The review identifies gaps in current research: (i) the lack of standardized waviness metrics for consistent comparison across studies; (ii) the limited validation of digital twin approaches against measured data; and (iii) the insufficient integration of machine learning with physics-based models. Addressing these gaps will be essential for linking surface finish specifications with NVH performance, reducing development costs, and improving passenger comfort in EV transmissions.
Open Access: Yes
DOI: 10.3390/wevj16090540
QUANTITATIVE ANALYSIS AND OPTIMIZATION OF ENERGY EFFICIENCY IN ELECTRIC MULTIPLE UNITS
Publication Name: Facta Universitatis Series Mechanical Engineering
Publication Date: 2025-08-01
Volume: 23
Issue: 2
Page Range: 351-375
Description:
The increasing urgency for sustainable transportation solutions necessitates a thorough examination of energy efficiency within railway systems. This study investigates the energy performance of Siemens Ventus (i.e., Siemens Desiro ML type) electric multiple units on Austria's Raaberbahn network, focusing on route-specific energy consumption and the optimization of regenerative braking. Utilizing data collected from January to May 2023, the research employs a robust methodology that integrates statistical analysis, curve-fitting, and geospatial modeling to assess energy trends along routes connecting Vienna, Bratislava, and Deutschkreutz. The findings reveal that terrain, operational practices, and external environmental factors significantly contribute to energy inefficiencies. Specifically, hotspots of energy overconsumption were identified, leading to the development of tailored optimization models for each route. The analysis also produced heatmaps that illustrate critical spatial and temporal patterns, which are essential for implementing targeted interventions aimed at enhancing energy efficiency.
Open Access: Yes
Reliability-based topology optimization of imperfect structures considering uncertainty of load position
Publication Name: Structures
Publication Date: 2024-11-01
Volume: 69
Issue: Unknown
Page Range: Unknown
Description:
In this paper, a novel optimization technique is implemented to explore the effects of considering uncertain load positions. Therefore, the integration of reliability-based design into structural topology optimization, while considering imperfect geometrically nonlinear analysis, is proposed. By comparing the results obtained from perfect and imperfect geometrically and materially nonlinear analyses, this study examines the impact of nonlinearity on probabilistic and deterministic analyses. Concerning probabilistic analysis, the originality of this research lies in its incorporation of the position of the applied load as a stochastic variable. This distinctive approach complements the consideration of other relevant parameters, including volume fraction, material properties, and geometrical imperfections, with the overarching goal of capturing the variability arising from real-world conditions. For the assessment of uncertainties, normal distribution is assumed for all these parameters. Normal distributions are chosen due to their advantages in terms of simplicity, ease of implementation, and computational efficiency. These characteristics are particularly beneficial when dealing with complex optimization algorithms and extensive analyses, as is the case in our research. The proposed algorithm is validated according to the results of benchmark problems. Structural examples like cantilever beam, pinned-shell, and L-shaped beam problems are further explored within the context of imperfect geometrically nonlinear reliability-based topology optimization, with specific regard to the probabilistic aspect of the location of the externally applied loads. Moreover, the results of the suggested approach suggest that the inclusion of a probabilistic design strategy has influenced topology optimization. The reliability index acts as a controlling constraint for the resulting optimized configurations, including the mean stress values associated with the resulting topologies.
Open Access: Yes
THE IMPACT OF TRANSPORT ROUTES ON KAZAKHSTAN’S AGRO-INDUSTRIAL COMPLEX CONSIDERING ESG APPROACHES
Publication Name: Problems and Perspectives in Management
Publication Date: 2025-01-01
Volume: 23
Issue: 1
Page Range: 656-672
Description:
This study aims to investigate the relationship between environmental sustainability, social development, and governance within Kazakhstan’s agro-industrial complex. The paper applies econometric modeling and statistical analysis to assess these relationships and provide strategic recommendations for sustainable development. A dataset from 2013 to 2023, sourced from the Bureau of National Statistics of the Republic of Kazakhstan, was utilized to assess the influence of transit routes and agriculture on ESG performance. Principal component analysis (PCA) and regression modeling identified three key components – environmental (84.3%), social (98.4%), and governance (88.33%) – as significant contributors to ESG variability. The results demonstrate that transit flows positively affect environmental and governance indicators (β = 0.266, p = 0.050), while agro-industrial activity has mixed effects: improved social sustainability but increased environmental pressure. The combined impact of transit corridors and the agro-industrial complex provides a more comprehensive explanation of ESG variability (R2 = 0.998), reinforcing the need for integrated policy approaches. The findings highlight the strategic importance of aligning transit infrastructure and agro-industrial development with ESG frameworks. This paper contributes to the discourse on sustainable development by offering practical insights for policymakers on optimizing logistics and agricultural strategies to promote ESG adoption, particularly in agriculture-dependent economies.
Open Access: Yes
Quality at the Core: A Multifaceted Analysis of Higher Education’s Impact on the Knowledge Economy
Publication Name: Journal of the Knowledge Economy
Publication Date: 2025-11-01
Volume: 16
Issue: 5
Page Range: 16637-16669
Description:
In a globalized, knowledge-driven economy, the quality of higher education is a pivotal contributor to socio-economic advancement, yet its assessment remains complex due to its inherent subjectivity and multifaceted nature. This study presents an innovative methodological approach for evaluating the quality of higher education within the knowledge economy framework, utilizing the context-input-process-output (CIPO) model, exploratory factor analysis, and stochastic frontier analysis. The input indicators include financial resources (government spending per student, direct public funding for a student, share of capital/current expenditures, compensation to the teaching/nonteaching staff), human resources (student–teacher ratio, share of enrollment in higher education, number of teachers), and expected duration of higher education. The output indicators include the general level of graduation from first-degree programs and level of education, at least completed short-cycle higher education. Indicators of economic (GDP per capita) and social (employment rate and Gini index) development of the country were chosen as context parameters. Conducting a comparative analysis across 36 European countries from 2001 to 2017 available data, the authors identified integrated factors for input and output parameters, as well as context parameters characterizing the quality of higher education. Then we categorize national higher education systems into five distinct quality levels: very low, low, satisfactory, high, and very high. This classification enables us to dissect and understand the challenges faced by countries at the lower end of the quality spectrum and propose strategic solutions informed by the best practices of the leading nations. Our findings offer critical insights into optimizing higher education quality to enhance competitive advantages for educational institutions, improve employment prospects and living standards for students, secure a more qualified workforce for employers, and spur economic growth and productivity at the national level. This comprehensive assessment underscores the role of quality education as a cornerstone of the knowledge economy, driving innovation, economic development, and societal progress.
Open Access: Yes
One-year update on physical activity and smartphone addiction in university students: A systematic review of novel research
Publication Name: Preventive Medicine Reports
Publication Date: 2025-09-01
Volume: 57
Issue: Unknown
Page Range: Unknown
Description:
Objectives: Smartphone addiction is a growing health concern, especially among university students. This updated review expands our 2024 review by synthesizing recent empirical findings on the relationship between physical activity and smartphone addiction among university students. Methods: Following PRISMA guidelines, a thorough search was conducted in PubMed, SSRN, Oxford Research Archive, JSTOR, and Google Scholar. The quality of studies was evaluated with the Mixed Methods Appraisal Tool. Results: Sixteen studies published between January 1, 2024, and March 31, 2025, met the inclusion criteria: 14 cross-sectional, one randomized controlled trial, and one longitudinal. All emerged from Asia. Most found an inverse relationship between physical activity and smartphone addiction. Experimental and longitudinal studies indicated that structured physical activity could reduce smartphone addiction symptoms over time. However, the predominantly correlational designs warrant cautious interpretation. Still, the directionally consistent average effect size was moderate to large (Cohen's d = ≈ −0.62), highlighting physical activity's protective role. Conclusions: Regular physical activity may prevent or reduce smartphone addiction in university students. However, future studies should employ longitudinal designs, use objective measures, and incorporate qualitative validation. This review reinforces our earlier findings and supports the considerable inverse relationship between physical activity and smartphone addiction.
Open Access: Yes
Complex Building’s Decision Support Method Based on Fuzzy Signatures †
Publication Name: Buildings
Publication Date: 2024-06-01
Volume: 14
Issue: 6
Page Range: Unknown
Description:
In the inner areas of large cities, many residential buildings built at the turn of the 19th and 20th centuries remain standing. The maintenance and renovation of these buildings have emerged as critical priorities over recent decades. E.g., in Budapest during the socialist era, the majority of these buildings were not renovated, and maintenance was largely neglected. In the subsequent 10–15 years following the end of socialism, financial resources for renovations were scarce due to the extensive transfer of properties from state to private ownership. It is only in the last decade or so that renovations have begun to be systematically addressed. Consequently, a significant portion of the building stock is still pending renovation. Given the current economic conditions, sustainable maintenance and necessary conversion are of paramount importance. Unfortunately, few standardized condition assessment methods are implemented in industrial practice, and the literature on this topic is limited. To address these challenges, we have developed an algorithm and model for condition assessment and decision support, which we refer to as the Complex Building’s Decision Support System based on Fuzzy Signatures (CBDF system). Our model employs a fuzzy signature-based approach to account for uncertainties, errors, and potentially missing data that may arise during the assessment process. The primary aim of this model is to equip professionals involved in building condition assessment with a tool that enables them to make consistent and objective decisions while minimizing errors. This paper provides a brief overview of the CBDF system and presents test results from the assessment of a selected structural component of a building, demonstrating the system’s functionality.
Open Access: Yes
Investigating the Impact of Varying Sand Content on the Physical Characteristics of Expansive Clay Soils from Syria
Publication Name: Geotechnical and Geological Engineering
Publication Date: 2024-06-01
Volume: 42
Issue: 4
Page Range: 2675-2691
Description:
Expansive clayey soils often pose challenges for construction projects due to their low bearing capacity, swelling, and shrinkage properties. While previous research has explored additives to enhance these soils’ properties, the potential of sand remains underexplored. This study investigates the impact of varying sand percentages on expansive clayey soils’ consistency, compaction, and permeability. This study examines how adding different percentages of sand influences the physical properties of expansive clayey soils. Laboratory tests involved systematic testing of texture, compaction, and permeability. Findings reveal a notable improvement in the physical properties of the soil with the addition of sand. Results from the laboratory tests provided data for empirical equations that facilitate the prediction of soil properties based on the sand content. The enhancement in soil properties underscores the potential of sand as an additive for expansive clayey soils. The empirical equations presented here provide practical benefits to geotechnical engineers and practitioners engaged in construction projects involving these soils, offering them valuable insights into the benefits of sand additives to improve physical characteristics. The insights gained from this research hold promising prospects for improving construction practices and addressing the challenges associated with these soils.
Open Access: Yes
Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
Publication Name: Applied Sciences Switzerland
Publication Date: 2024-06-01
Volume: 14
Issue: 11
Page Range: Unknown
Description:
In agriculture, as precision farming increasingly employs robots to monitor crops, the use of weeding and harvesting robots is expanding the need for computer vision. Currently, most researchers and companies address these computer vision tasks with CNN-based deep learning. This technology requires large datasets of plant and weed images labeled by experts, as well as substantial computational resources. However, traditional feature-based approaches to computer vision can extract meaningful parameters and achieve comparably good classification results with only a tenth of the dataset size. This study presents these methods and seeks to determine the minimum number of training images required to achieve reliable classification. We tested the classification results with 5, 10, 20, 40, 80, and 160 images per weed type in a four-class classification system. We extracted shape features, distance transformation features, color histograms, and texture features. Each type of feature was tested individually and in various combinations to determine the best results. Using six types of classifiers, we achieved a 94.56% recall rate with 160 images per weed. Better results were obtained with more training images and a greater variety of features.
Open Access: Yes
DOI: 10.3390/app14114839
