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Found 6374 publications
A Perspective on Artificial Intelligence for Process Manufacturing
Publication Name: Engineering
Publication Date: 2025-09-01
Volume: 52
Issue: Unknown
Page Range: 60-67
Description:
To achieve sustainable development goals and the requirements of a circular economy, a new class of intelligent computer-aided methods and tools is needed. Artificial intelligence (AI) techniques have been gaining much attention due to their ability to provide options to tackle the challenges we are currently facing. However, the successful application of AI to solve problems of current interest requires AI to be integrated with associated process systems engineering methods and tools that are already available or being developed. In this perspective paper, we highlight the use of a collection of process systems engineering methods and tools augmented by AI techniques to solve problems related to process manufacturing, with a focus on chemical product design, process synthesis and design, process control, and process safety and hazards.
Open Access: Yes
Assessing predictive validity of competency coefficient in automotive project performance
Publication Name: Tasmimgiri Va Tahqiq Dar Amaliyyat
Publication Date: 2025-09-01
Volume: 10
Issue: 3
Page Range: 469-490
Description:
Purpose: This paper aims to evaluate the predictive validity of the Competency Coefficient (K), a behavioral indicator derived from structured assessments of automotive R&D project managers, by examining its correlation with objective project performance outcomes. Methodology: The study performs a statistical analysis of K against five z-standardized Key Performance Indicators (KPIs) to identify the relationship between behavioral competencies and project performance. Predictive validity was evaluated using Pearson/Spearman correlations and Ordinary Least Squares (OLS) regression; robustness in small samples and model adequacy were assessed with 10,000-sample bootstrap intervals, Leave-One-Out Cross-Validation (LOOCV),Prediction Sum of Squares (PRESS) (PRESS/Q2), and tests for quadratic nonlinearity. Findings: Results reveal positive and statistically significant associations between the Competency Coefficient (K) and KPI-based performance indices. The linear model explained roughly 93% of the variance in project results, and cross-validation confirmed consistent out-of-sample performance (Q2 = 0.88). The restricted sample size (n = 7) and singular organizational environment limit generalizability, while contextual factors may also influence the reported outcomes. Originality/Value: The paper provides original empirical evidence that competency-based behavioral indicators can function as dependable, measurable elements of project performance assessment. The findings emphasize methodological feasibility rather than universal applicability. The contribution lies in the measurement and validation technique, which may be duplicated for verification in larger and more diverse samples.
Open Access: Yes
Role of the Transcription Factor CREB in Ethanol-Induced Endoplasmic Reticulum Stress and Apoptosis in PC12 Cells
Publication Name: Biology
Publication Date: 2025-09-01
Volume: 14
Issue: 9
Page Range: Unknown
Description:
Ethanol is a known neurotoxic agent that induces endoplasmic reticulum (ER) stress and apoptosis in nerve cells. The transcription factor CREB is crucial for cell survival under stress; however, its involvement in ethanol-induced endoplasmic reticulum (ER) stress remains poorly understood. We examined the effects of ethanol on wild-type PC12 cells and CREB-overexpressing PC12-CREB cells. Cell viability was evaluated by ATP assays, apoptosis was detected by Hoechst staining, and key proteins involved in ER stress and apoptotic signaling were analyzed by Western blot analysis. Ethanol treatment decreased cell viability and increased apoptosis in wild-type PC12 cells in a time-dependent manner. In contrast, PC12-CREB cells-maintained viability and showed significantly lower apoptotic cell numbers. Ethanol activated markers of ER stress (BiP, CHOP, ATF6) and pro-apoptotic pathways (phosphorylation of JNK and p38 MAPK) in wild-type cells. In CREB-overexpressing cells, CHOP induction and JNK activation were decreased, while the expression of the anti-apoptotic protein Mcl-1 was increased. CREB overexpression protects against ethanol-induced ER stress and apoptosis. This protective effect is mediated through modulation of unfolded protein response (UPR) signaling and regulation of pro-and anti-apoptotic gene expression. These findings underscore a potential role for CREB in attenuating ethanol-induced neurotoxicity.
Open Access: Yes
Reliable generative interpretable framework for efficient predictive analysis of air quality index
Publication Name: Egyptian Informatics Journal
Publication Date: 2025-09-01
Volume: 31
Issue: Unknown
Page Range: Unknown
Description:
Air quality management is one of the most important sustainability goals in the era of Industry 5.0. The magnitude of air pollution and impact of drastic pollutants increase day by day despite the significant efforts of the environmental enthusiasts and researchers. The role of Artificial Intelligence (AI) in determining the Air Quality Index (AQI) is significant with reasonable accuracy of classification achieved. The proposed model is a multi-class problem, that classifies the AQI into six different classes. Various ML models such as Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting(GB), Logistic Regression (LR). The RF provided reliable performance metrics for AQI category prediction, achieving an accuracy and Precision of 0.99. This model is selected for the implementation of Explainable AI (XAI) models such as Local Interpretable Model Agonistic Explainer (LIME) for explanation using the local surrogacy plots and SHapley Additive exPlanations (SHAP) explainer for the global surrogacy plots. The Generative Adversarial Network (GAN) can generate synthetic data, which addresses critical issues such as missing data, class imbalance, noise, and redundant data. The performance the GAN shows optimized performance in classification of the AQI data with accuracy closer to 100 %. This is mainly due to the synthetic data generated by the GAN which enhances the performance of the classification. The proposed work integrates the efforts of the GAN-AI-XAI that enhances the performance, reliability, trustworthiness and robustness of the AQI classification model.
Open Access: Yes
The ACTN3 R577X Nonsense Allele Is Underrepresented in Professional Volleyball Players and Associated with an Increased Risk of Muscle Injury in Female Players
Publication Name: Genes
Publication Date: 2025-09-01
Volume: 16
Issue: 9
Page Range: Unknown
Description:
Background: Muscle injuries pose a significant challenge in sports, leading to decreased performance and shortened career longevity. Individuals homozygous for the nonsense X allele of the ACTN3 rs1815739 (R577X) polymorphism, characterized by a complete absence of α-actinin-3, have been associated with reduced power performance and may have an increased injury risk. This study aimed to investigate the association between the ACTN3 R577X polymorphism and both volleyball player status and the risk of non-contact musculoskeletal injuries in female volleyball players. Methods: The study included 5382 Turkish and Russian subjects of European descent (187 professional volleyball players and 5195 controls), of whom 50 female players provided injury data. Sport-related injury information was obtained from medical records maintained by team physicians and physiotherapists. Results: A pooled analysis of the two cohorts demonstrated that the frequency of the ACTN3 X allele was significantly lower in volleyball players than in controls, with an odds ratio of 0.763 (95% CI: 0.61–0.95, p = 0.02). In the pre-specified recessive contrast (XX vs. RR + RX) among 50 players, exact methods indicated higher injury odds for the XX genotype (OR = 7.87, 95% CI: 0.94–374.58; p = 0.0366), which was classified as borderline/exploratory. Penalized (Firth) regression produced estimates of a similar magnitude after adjustment for age and playing position (adjusted OR = 5.92, 95% CI: 1.12–60.98), although confidence intervals remained wide. Conclusions: The ACTN3 X allele is underrepresented in professional volleyball players, and it is associated with an increased risk of muscle injury in female players.
Open Access: Yes
Data-driven deep learning for predicting ligament fatigue failure risk mechanisms
Publication Name: International Journal of Mechanical Sciences
Publication Date: 2025-09-01
Volume: 301
Issue: Unknown
Page Range: Unknown
Description:
The pathogenesis of musculoskeletal disorders is closely associated with the cumulative damage and fatigue failure behavior of fibrous connective tissues under long-term repetitive loading. However, significant technological challenges remain in real-time dynamic monitoring of ligament fatigue life, particularly the lack of efficient computational mechanics modeling frameworks and precise assessment tools adaptable to real-world movement scenarios. The multimodal integrated framework for ligament fatigue life assessment was proposed in this study. First, the high-accuracy subject-specific musculoskeletal models were developed based on individualized medical imaging data. A coupled hyperelastic-viscoelastic constitutive model was incorporated to accurately characterize the nonlinear mechanical behavior of ligamentous tissues and their fatigue damage evolution under cyclic loading. Furthermore, by integrating continuum damage mechanics theory, a time-dependent cumulative damage evolution equation was established to systematically quantify the coupling relationship between fatigue failure probability and dynamic mechanical loading. In the data-driven prediction module, an innovative deep-learning model that integrates kinematic-dynamic coupling was developed. By integrating wearable inertial measurement units, the model enables real-time inversion of ligament loading force-fatigue failure states and prediction of fatigue life. This approach effectively overcomes the limitations of traditional mechanical modeling in long-term, multi-scenario dynamic monitoring, achieving high-precision and minimally invasive fatigue life evaluation of ligaments. The proposed computational framework breaks the static-loading constraints of conventional fatigue testing, achieving the dynamic biomechanical analysis and fatigue life prediction under real movement conditions. This work not only provides novel theoretical insights into the mechanisms and modeling of ligament fatigue damage, but also provides a generalizable tool for biomechanical injury prevention, rehabilitation planning, and soft tissue fatigue analysis in the musculoskeletal system.
Open Access: Yes
On critical pounding mechanism of base-isolated buildings using an optimized multi-hazard method
Publication Name: Results in Engineering
Publication Date: 2025-09-01
Volume: 27
Issue: Unknown
Page Range: Unknown
Description:
In many studies on the effect of pounding on isolated structures, the failure to consider all potential pounding scenarios, including floor-to-floor (FF), floor-to-column (FC), and pounding with a moat wall, can introduce uncertainty into the obtained results. Therefore, this study investigates the critical pounding scenarios in isolated structures subjected to seismic excitations. Three primary types of pounding are examined: FF, FC, and MW, under both two-sided and one-sided limitations. Additionally, the study investigates the effects of varying gap sizes and structural heights on the response of structures subjected to each pounding type. In the FF and FC scenarios, six-story and nine-story base-isolated buildings are analyzed in relation to adjacent six-story fixed-base structures. The endurance time method is employed to obtain the seismic responses of the structures. The results indicate that FC pounding consistently induced the highest shear forces in the columns and represented the most critical failure mode. The base-isolated structures that are significantly taller than adjacent fixed-base structures (e.g. 9.6 m) are more susceptible to damage compared to those with similar heights to their neighbors. Furthermore, increasing the gap size can lead to a 100 % rise in inter-story drift under two-sided FF pounding and a 126 % increase in column shear force under two-sided FC pounding.
Open Access: Yes
A state-of-the-art review on machine learning techniques for driving behavior analysis: clustering and classification approaches
Publication Name: Complex and Intelligent Systems
Publication Date: 2025-09-01
Volume: 11
Issue: 9
Page Range: Unknown
Description:
Smart mobility has ushered in advanced sensing technologies. These, together with high‑level data analytics, are revolutionizing how we analyze driving behavior. Excellent performance in dealing with real-world, high-technology complexities for machine learning has made wide enthusiasm to utilize them to study driver behavior. This article gives a thorough overview of the important machine learning methods—especially clustering and classification techniques—that help analyze complex driving behaviors, predict fuel and energy use, and improve vehicle safety systems. The review specifically explains unsupervised methods like fuzzy c-means, k-means, and density-based spatial clustering of applications with noise, as well as supervised techniques such as artificial neural networks, k-nearest neighbors, and support vector machines. Also, this review discusses the integration of clustering and classification techniques with hybrid deep learning models, and examines their applications in eco-driving, energy forecasting, and intelligent transport systems while offering novel findings that contribute to more sustainable mobility. Emphasis is placed on how these methods transform vast, heterogeneous driving data into actionable insights that support real-time monitoring and personalized feedback for eco-driving and smart transportation applications. Finally, current benefits and barriers, and future research opportunities and challenges in integrating machine learning into intelligent transportation systems are reviewed. The potential to advance to safer, better, and more sustainable forms of mobility is emphasized.
Open Access: Yes
Future of Agrivoltaic projects: A review from the technological forecasting perspective
Publication Name: Cleaner Engineering and Technology
Publication Date: 2025-09-01
Volume: 28
Issue: Unknown
Page Range: Unknown
Description:
Agrivoltaic systems integrate photovoltaic (PV) energy generation with agricultural production, creating synergies that enhance land-use efficiency and environmental sustainability. This article reviews agrivoltaic technologies to identify key trends and the most promising future research and development directions. The method applied involves selecting and analysing relevant literature sources and filtering them with regard to the essential questions that need to be answered for the climates of Central Europe and China. These include global development, current applications, and technological progress. The analysis reveals growing attention to system design, performance optimisation, and crop compatibility. Innovations such as bifacial and spectrally selective PV modules boost energy yields while maintaining suitable conditions for shade-tolerant crops like leafy greens and berries. The analysis confirmed the high potential of sustainability benefits (societal, economic, and environmental) and revealed the need for systematic investigations of significant performance factors, including location and system design. A relatively underinvestigated factor is the protection of crops from excessive sunlight, which has become increasingly important. The modelling and optimisation of system operation is also necessary to provide decision-makers with robust tools for project assessment. A roadmap is proposed to guide future research and development.
Open Access: Yes
