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Found 5152 publications
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
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
Robustness of a flux-intensifying permanent magnet-assisted synchronous reluctance machine focusing on shifted surface-inset ferrite magnets
Publication Name: Computers and Structures
Publication Date: 2025-09-01
Volume: 316
Issue: Unknown
Page Range: Unknown
Description:
Flux-intensifying permanent magnet-assisted synchronous reluctance machines use relatively small amounts of non-rare earth permanent magnets, making them viable alternatives for remanufacturing older machines, aligning with EU directives and circular economy principles. The asymmetric rotor topology is particularly suited for micromobility applications, which benefit from shifting inset magnets, as reverse motoring is rarely required. However, this design could be more sensitive to manufacturing and positioning errors of the magnets. To investigate the effects of the uncertainties of the shifted surface inset magnets, first, an optimal topology is selected based on average torque, torque ripple, and cogging torque using the NSGA-II optimisation method. The effects of the magnet shifting and its robustness are analysed using the Taguchi and ANOVA methods, validated by Full Factorial calculations. Results indicate a 31.25 % reduction in permanent magnet volume without compromising torque output with magnet shifting. The machine's average and cogging torque remain within a 5 % robustness threshold for a ±0.06 mm discrete manufacturing tolerance. Torque ripple may exceed this limit up to 14.77 %. However, the likelihood of exceeding the threshold is only 12.10 %. The reduced magnet volume and maintained performance make this topology a promising option for remanufactured machines in micromobility applications, supporting circular economy goals.
Open Access: Yes
Genetic Strategies for Improving Pig Robustness: Reducing Antibiotic Use Through Enhanced Resilience and Disease Resistance
Publication Name: Animals
Publication Date: 2025-09-01
Volume: 15
Issue: 18
Page Range: Unknown
Description:
This review investigates genetic strategies aimed at improving robustness in pigs to enhance disease resistance and reduce reliance on antibiotics. Robustness refers to a pig’s ability to stay healthy and productive under stressful or challenging conditions. The review outlines current breeding practices focused on key traits such as maternal ability, growth, immune function, and survival, and highlights that these robustness-related traits show measurable heritability, making them suitable for genetic improvement. Special attention is given to resistance against porcine reproductive and respiratory syndrome (PRRS), a major disease in swine. We also evaluate breed-specific differences, environmental influences, and immune response profiles, emphasizing their impact on breeding outcomes. The development of robust pig lines emerges as a sustainable strategy to reduce antibiotic dependence and enhance herd health. A distinctive contribution of this work is the integration of genetic robustness and resilience strategies with antibiotic stewardship objectives. We link genomic selection, advanced phenotyping, and targeted management interventions within a One Health framework to outline actionable, system-level pathways for reducing antimicrobial inputs. To our knowledge, this combined genetic and public health perspective has not been comprehensively addressed previously.
Open Access: Yes
DOI: 10.3390/ani15182753
Unveiling the mechanisms and implications: how artificial intelligence drives green growth in China’s Huaihe River Ecological Economic Belt under the carbon neutrality agenda
Publication Name: Carbon Footprints
Publication Date: 2025-09-01
Volume: 4
Issue: 3
Page Range: Unknown
Description:
Amidst the backdrop of global climate warming and China’s proactive chase of its carbon peak and carbon neutrality goals, the Huaihe River Basin (HRB), a region of significant strategic importance in the heartland and eastern expanse of the nation is confronted with formidable challenges, including high energy consumption and severe environmental pollution. Despite its substantial contributions to economic development, the traditional development model of the HRB conflicts with the principles of green development, necessitating the urgent exploration of innovative pathways to sustainable progress. Through a comprehensive review of scholarly literature and rigorous theoretical analysis, this study demonstrates that artificial intelligence (AI) can significantly drive green development by enhancing eco-innovation and optimizing industrial structures. Using a panel dataset from 27 cities in the Huaihe River Ecological Economic Belt (HEB) from 2010 to 2022, this study employs a bidirectional fixed-effects model to analyze the repercussions of AI on green development. The baseline regression results show that for every one-unit increase in AI development level (AIDL), HEB’s urban green development level significantly increases by 0.087. This positive influence is further confirmed through robustness tests. We found that AI can indirectly influence the mechanism and pathway of green development through intermediate variables. AI drives green development indirectly through two pathways: green technology innovation and the rationalization of the industrial structure, with a total explanatory power of 56.7% (R2 = 0.812). Based on these findings, we propose vigorously promoting the green effects of AI, refining industrial structures, and leveraging mediating effects to foster sustainable regional development. These insights offer novel perspectives for the green development of the HRB but also provide valuable references for the green transformation of other areas with similar challenges.
Open Access: Yes
DOI: 10.20517/cf.2025.9
Structural Brain Abnormalities, Diagnostic Approaches, and Treatment Strategies in Vertigo: A Case-Control Study
Publication Name: Neurology International
Publication Date: 2025-09-01
Volume: 17
Issue: 9
Page Range: Unknown
Description:
Background/Objectives: Dizziness is a frequent medical complaint with neurological, otolaryngological, and psychological origins. Imaging studies such as CT (Computer Tomography), cervical X-rays, and ultrasound aid diagnosis, while MRI (Magnetic Resonance Imaging) is crucial for detecting brain abnormalities. Our purpose is to identify structural brain changes associated with vertigo, assess pre-MRI diagnostic approaches, and evaluate treatment strategies. Methods: A case-control study of 232 vertigo patients and 232 controls analyzed MRI findings, pre-MRI examinations, symptoms, and treatments. Statistical comparisons were performed using chi-square and t-tests (p < 0.05). Results: White matter lesions, lacunar infarcts, Circle of Willis variations, and sinusitis were significantly more frequent in vertigo patients (p < 0.05). Pre-MRI diagnostics frequently identified atherosclerosis (ultrasound) and spondylosis (X-ray). Common symptoms included headache, imbalance, and visual disturbances. The most frequent post-MRI diagnosis was Benign Paroxysmal Positional Vertigo (BPPV). Treatments included lifestyle modifications, physical therapy (e.g., Epley maneuver), and pharmacological therapies such as betahistine. Conclusions: MRI revealed structural brain changes linked to vertigo. Pre-MRI assessments are essential for ruling out vascular and musculoskeletal causes. A multidisciplinary treatment approach is recommended. Trial Registration: This study was registered in ClinicalTrials.gov with the trial registration number NCT06848712 on 22 February 2025.
Open Access: Yes
Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
Publication Name: Telecom
Publication Date: 2025-09-01
Volume: 6
Issue: 3
Page Range: Unknown
Description:
Free-space optical (FSO) communications have emerged as a promising complement to conventional radio-frequency (RF) systems due to their high bandwidth, low interference, and license-free spectrum. Visible-light FSO communication, using laser diodes or LEDs, offers potential for short-range data links, but performance is highly wavelength-dependent under varying atmospheric conditions. This study presents an experimental evaluation of three visible laser diodes at 650 nm (red), 532 nm (green), and 405 nm (violet), focusing on their optical output power, quantum efficiency, and modulation behavior across a range of driving currents and frequencies. A custom laboratory testbed was developed using an Atmega328p microcontroller and a Visual Basic control interface, allowing precise control of current and modulation frequency. A silicon photovoltaic cell was employed as the optical receiver and energy harvester. The results demonstrate that the 650 nm red laser consistently delivers the highest quantum efficiency and optical output, with stable performance across electrical and modulation parameters. These findings support the selection of 650 nm as the most energy-efficient and versatile wavelength for short-range, cost-effective visible-light FSO communication. This work provides experimentally grounded insights to guide wavelength selection in the development of energy-efficient optical wireless systems.
Open Access: Yes
Psychological Capital, Workplace Stress, and Mobbing in the Context of Workers’ Mental Health
Publication Name: Societies
Publication Date: 2025-09-01
Volume: 15
Issue: 9
Page Range: Unknown
Description:
This study examines how employees’ psychological capital relates to workplace stress and mobbing (also known as workplace bullying) across three European countries. Stress has become an increasingly dominant issue globally since the second half of the 20th century, moving from clinical contexts into public awareness. It is now recognized as a significant health risk factor, particularly in work environments. While positive forms of stress (eustress) can enhance performance, chronic workplace stress is linked to serious mental and physical health problems. This study investigates the relationship between psychological capital (PsyCap), workplace stress, and mobbing among employees in Germany, Austria, and Hungary. Based on a cross-sectional survey (N = 89), the research applied validated instruments (PCQ, PSS-10, COPSOQ II) to measure PsyCap, perceived workplace stress, and experiences of mobbing. Results show a high average PsyCap level (M = 4.64, SD = 0.70) and a moderate perceived workplace stress level (M = 2.73, SD = 0.62) across the sample. A strong negative correlation was identified between PsyCap and workplace stress (r = −0.573, p < 0.001), while a moderate positive correlation was found between workplace stress and mobbing experiences (r = 0.323, p = 0.002). Although PsyCap moderated stress levels, it did not significantly moderate the relationship between mobbing and perceived stress. These findings emphasize the role of PsyCap in reducing workplace stress and underline the necessity of organizational interventions in promoting psychological resilience and mobbing prevention. The results also indicate a need to further examine the causal relationship between mobbing, stress, and PsyCap.
Open Access: Yes
DOI: 10.3390/soc15090244
Integrating generative and parametric design with BIM: A literature review of challenges and research gaps in construction design
Publication Name: Applications in Engineering Science
Publication Date: 2025-09-01
Volume: 23
Issue: Unknown
Page Range: Unknown
Description:
Parametric Design (PD), Generative Design (GD), and Building Information Modelling (BIM) have emerged as transformative tools in the construction industry, offering significant potential for design optimisation, interdisciplinary collaboration, and data-driven decision making. This paper presents a comprehensive literature review to evaluate the current state of PD, GD, and BIM integration, highlighting practical applications and identifying research gaps. In addition to mapping the academic discourse, the review also highlights selected practical implementations from existing literature to illustrate how these technologies are being translated into applied workflows. Furthermore, the methodology section critically reflects on the limitations of the keyword-based search strategy and suggests future directions to mitigate potential literature gaps. While many studies demonstrate efficiency gains in early design phases, the integration of these technologies across the full building lifecycle remains limited. Key challenges include insufficient interoperability between platforms, lack of standardisation, and minimal adoption of GD-BIM combinations in construction and logistics. Furthermore, few studies address the regulatory compliance and real-world scalability of AI-assisted generative models. The review concludes that although these digital methods can accelerate innovation and sustainability, their practical implementation requires further research in construction management, code-based automation, and human-in-the-loop design workflows.
Open Access: Yes
