Search Results (26,195)

Regulated proteolysis via autophagy is essential for cellular homeostasis, yet the specific role of autophagy-related gene 7 (ATG7) in corneal epithelial maintenance remains unclear. Using a conditional knockout mouse model (Atg7^f/f K14Cre^+/−), we investigated the impact of ATG7 deficiency on corneal epithelial autophagy, morphology, and vascular dynamics. Loss of ATG7 disrupted autophagosome formation, evidenced by increased LC3B expression but reduced LC3B-positive puncta and absence of autophagosomes ultrastructurally. Although gross corneal morphology was preserved, ATG7 deficiency led to thickened epithelium and increased peripheral lymphatic vessel sprouting, indicating a pro-inflammatory and pro-lymphangiogenic microenvironment. Proteomic analysis revealed upregulation of RAB8, TM9S3, and RETR3, suggesting activation of compensatory pathways such as exophagy, reticulophagy, and Golgiphagy. Inflammatory and angiogenic components were downregulated, suggesting a moderate loss of inhibitory capacity based on the lymphatic phenotypes observed. At the same time, while these two compensatory changes occur, other proteins that positively regulate lysosome formation are reduced, resulting in a phenotype linked to deficient autophagy. These findings demonstrate that ATG7-mediated autophagy maintains corneal epithelial homeostasis and immune privilege, with implications for understanding corneal inflammation and lymphangiogenesis in ocular surface diseases. Full article

Urban year-round green exercise (YRGE)—defined as moderate to vigorous physical activity performed regularly in natural urban settings throughout all seasons and weather conditions—has the potential to promote health, well-being, and social connectedness. This study investigates the relationship between YRGE and individuals’ perceived health status, psychological well-being, and reasons for engagement. A cross-sectional online survey was conducted with 408 adult participants engaged in urban green exercise. The findings indicate that physical activity in adverse meteorological conditions, such as rain, cold, and wind, is positively associated with perceived current health, health over the past 12 months, and well-being. Social connectedness is particularly influenced by environmental factors like sun exposure and heat. The study also reveals key motivational factors for YRGE participation, including improving health and fitness, disconnecting from everyday life, enjoying nature, and experiencing tranquility, with significant variation depending on age and individual nature connectedness. These results suggest that YRGE serves as an accessible and inclusive public health intervention with consistent benefits across socio-demographic groups. Urban planning and health promotion initiatives should prioritize the maintenance and accessibility of urban green spaces and offer guided YRGE programs to encourage sustainable participation across the population. Full article

Digital Twin (DT) models are gaining attention as promising tools for improving efficiency, sustainability, and responsiveness in textile manufacturing. This paper provides a critical review of existing DT applications and outlines seven underexplored areas where such systems could offer tangible benefits. By linking DT models with real-time data, textile producers can optimise energy usage, reduce production errors, enhance machine reliability, and accelerate decision-making processes. Moreover, DTs offer long-term opportunities for smarter waste management, personalised production with lower return rates, and better workforce training. The paper concludes with stakeholder-specific recommendations, such as integrating digital product passports for recyclability, and calls for a cross-disciplinary approach to digital transformation in the sector. These findings offer practitioners a roadmap for adopting DT technologies not only as monitoring tools but as strategic enablers for circularity, agility, and competitiveness. Full article

Because they account for realistic effects in opportunistic maintenance modeling, dependency hypotheses are extremely diverse in the literature. Despite recent reviews, a clear view of the dependency hypotheses is currently missing in the literature, especially regarding component interactions, resource constraints and human factors. In this paper, we provide a conceptual background on dependence modeling and the notion of maintenance opportunity. Then, a critical systematic literature review, following the PRISMA guidelines, is carried out, focusing on the current hypotheses in opportunistic maintenance, including component interactions, workers’ skills and resource constraints, economic dependence and optimization objectives. The different dependence types are identified and defined, and their presence in the literature is quantified. The included papers in this review ($n=91$) were selected on the basis of relevance to the research questions from the Web of Science, Scopus and Google Scholar databases. Exclusion criteria were set, related to the year of publication (from 2000) and language (limited to French or English), and inclusion criteria required the paper to cover modeling, simulating or reviewing literature related to opportunistic maintenance with dependencies. The results show that economic dependence is mostly modeled by sharing downtime or set-up costs. The objective function for optimization is mostly found to be the economic cost of maintenance, with concerningly little consideration for environmental indicators. These results are finally discussed in light of advances in predictive analytics and current challenges in the sustainability of industrial processes. Further developments should consider including the social and environmental aspects of sustainability in the dependencies, but also look into the benefits that predictive analytics can bring to opportunistic maintenance. The variety of modeling assumptions and dependences presented in the literature does not always allow comparing the results of the models. Full article

The human immune system consists of two main components: innate and adaptive immunity. To date, research on the pathogenesis of autoimmune neurological diseases has primarily focused on the role of adaptive immunity. However, growing evidence highlights the significant contribution of innate immune mechanisms in the development of neurological disorders. The aim of this article is to present the current state of knowledge regarding the involvement of innate immunity in the pathogenesis and treatment of selected autoimmune neurological diseases: multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), MOG antibody-associated disease (MOGAD), myasthenia gravis (MG), and chronic inflammatory demyelinating polyneuropathy (CIDP). A literature review was conducted, including both experimental and clinical data on the activity of innate immune effector cells—such as dendritic cells, macrophages, microglia, and natural killer (NK) cells—as well as plasma proteins, including the complement system. Relevant clinical and preclinical studies on targeted therapies affecting these components were also identified. All analyzed diseases demonstrate the involvement of innate immune elements in the initiation and maintenance of the inflammatory process. Furthermore, it has been shown that therapies targeting these components may offer clinical benefits. Full article

Background/Objectives: General anesthesia is occasionally required for cesarean delivery (CD). Propofol target-controlled infusion (TCI) enables dosing based on pharmacokinetic modeling. During the transition from induction to maintenance, infusion pauses. This simulation study assessed propofol from induction to delivery and the proportion of deliveries estimated during this pause. Methods: Surgical data from women undergoing CD were compiled, and the demographics were entered into a TCI pump using the Schnider model. Effect-site targets (6 and 8 mcg/mL) were simulated for induction, followed by 2.5 mcg/mL for maintenance. Outcomes were estimated propofol dose from induction to delivery and timing of delivery relative to infusion pause. Results: Among 50 women, the estimated mean propofol dose from induction to delivery was 19 ± 22 mg (0.2 ± 0.3 mg/kg) at 6 mcg/mL and 13 ± 17 mg (0.2 ± 0.2 mg/kg) at 8 mcg/mL. Delivery occurred during the infusion pause in 40% and 50% of cases, and it was more often in emergency than elective procedures. Emergency status, but not age or body mass index, predicted delivery during the pause. Conclusions: Standardized TCI with reduced effect-site targets for maintenance resulted in modest propofol administration between induction and delivery. These findings require confirmation in clinical studies, where dosing should be guided by depth-of-anesthesia monitoring. Full article

In underground mine roadways, enlarged cross-sections have led to escalating surrounding rock stress, resulting in frequent support failures, elevated accident risk, and increased maintenance costs. However, the potential of fiber reinforcement to improve shotcrete under these high-stress conditions remains under-investigated. To address these issues, this study developed a novel fiber-reinforced cement-based composite using field construction-grade washed sand. The effects of binder-to-material ratios, fiber types (polyvinyl alcohol (PVA), polypropylene (PP), and basalt (BF)), and fiber dosages (1%, 2%, and 3%) were systematically investigated under uniaxial tension, uniaxial compression, and variable-angle shear. Based on the experimental results, an optimal mix formulation was determined via orthogonal experimental design to meet mining operational requirements. The findings demonstrate that fiber incorporation significantly enhances mechanical performance. Notably, PP fiber reinforcement increased the tensile strength by up to 675%, while BF fibers improved compressive strength by up to 198.5%, relative to unreinforced shotcrete. This study provides a theoretical foundation for optimizing fiber-reinforced shotcrete mix designs for mining and offers technical insights for field applications. Full article

Cracks are the initial manifestation of various diseases on highways. Preventive maintenance of cracks can delay the degree of pavement damage and effectively extend the service life of highways. However, existing crack detection methods have poor performance in identifying small cracks and are unable to calculate crack width, leading to unsatisfactory preventive maintenance results. This article proposes an integrated method for crack detection, segmentation, and width calculation based on digital image processing technology. Firstly, based on convolutional neural network, a optimized crack detection network called CFSSE is proposed by fusing the fast spatial pyramid pooling structure with the squeeze-and-excitation attention mechanism, with an average detection accuracy of 97.10%, average recall rate of 98.00%, and average detection precision at 0.5 threshold of 98.90%; it outperforms the YOLOv5-mobileone network and YOLOv5-s network. Secondly, based on the U-Net network, an optimized crack segmentation network called CBU_Net is proposed by using the CNN-block structure in the encoder module and a bicubic interpolation algorithm in the decoder module, with an average segmentation accuracy of 99.10%, average intersection over union of 88.62%, and average pixel accuracy of 93.56%; it outperforms the U_Net network, DeepLab v3+ network, and optimized DeepLab v3 network. Finally, a laser spot center positioning method based on information entropy combination is proposed to provide an accurate benchmark for crack width calculation based on parallel lasers, with an average error in crack width calculation of less than 2.56%. Full article

In predictive maintenance frameworks, risk curves are used as interpretable, real-time indicators of equipment degradation. However, existing approaches generally assume a monotonically increasing trend and neglect the corrective effect of maintenance, resulting in unrealistic or overly conservative risk estimations. This paper addresses this limitation by introducing a novel method that dynamically corrects risk curves through a quantitative measure of maintenance effectiveness. The method adjusts the evolution of risk to reflect the actual impact of preventive and corrective interventions, providing a more realistic and traceable representation of asset condition. The approach is validated with case studies on critical feedwater pumps in a combined-cycle power plant. First, individual maintenance actions are analyzed for a single failure mode to assess their direct effectiveness. Second, the cross-mode impact of a corrective intervention is evaluated, revealing both direct and indirect effects. Third, corrected risk curves are compared across two redundant pumps to benchmark maintenance performance, showing similar behavior until 2023, after which one unit accumulated uncontrolled risk while the other remained stable near zero, reflected in their overall performance indicators (0.67 vs. 0.88). These findings demonstrate that maintenance-corrected risk curves enhance diagnostic accuracy, enable benchmarking between comparable assets, and provide a missing piece for the development of realistic, risk-informed predictive maintenance strategies. Full article

Timely detection of leaks is essential for the safe and reliable operation of pressure vessels used in superconducting systems, aerospace, and medical equipment. To address the lack of efficient online leak detection methods for such vessels, this paper proposes a quasi-distributed fiber Bragg grating (FBG) sensing network combined with theoretical stress analysis to diagnose vessel conditions. We analyze the stress–strain distributions of vacuum vessels under varying pressures and examine stress concentration effects induced by small holes; these analyses guided the design and placement of quasi-distributed FBG sensors around the vacuum valve for online leakage monitoring. To improve measurement accuracy, we introduce a vibration correction algorithm that mitigates pump-induced vibration interference. Comparative tests under three leakage scenarios demonstrate that when leakage occurs during vacuum extraction, the proposed system can reliably detect the approximate leak location. The results indicate that combining an FBG sensing network with stress concentration analysis enables initial localization and assessment of leak severity, providing valuable support for the safe operation and rapid maintenance of vacuum pressure vessels. Full article

Students with autism spectrum disorder (ASD) often face difficulties in solving arithmetic word problems, particularly in transitioning from informal counting strategies to more efficient methods based on number facts and formal operations. This study examined the development of problem-solving strategies in a single student with ASD and intellectual disability across two sequential single-case experiments using multiple baseline designs. Study 1 (age 13 years 9 months; 17 sessions) employed Modified Schema-Based Instruction (MSBI) to teach addition and subtraction change problems, while Study 2 (age 14 years 10 months; 18 sessions) utilized the Conceptual Model-based Problem Solving (COMPS) approach for multiplication and division equal-group problems. Success was defined as both correctness of the response and correctly identifying the required operation. Results indicated that the student’s performance improved in all problem types in both studies, with maintenance observed 8 weeks after Study 1 and 5 weeks after Study 2. Instruction effects generalized to two-step addition and subtraction problems in Study 1, and to two-step addition and multiplication problems in Study 2. The findings indicate that both MSBI and COMPS facilitated the student’s shift from informal strategies to efficient operation-based problem solving. Implications for practice include the need for individualized reinforcements, careful adaptation of instruction, and providing teachers with a variety of problems and knowledge of these teaching methods to support students with ASD in developing advanced problem-solving skills. Full article

In traditional scheduling operations, dispatchers mainly rely on SCADA/EMS systems or personal experience. However, with access to a large number of new energy sources, the scale of the distribution network continues to expand, and its topology becomes increasingly complex, leading to potential security risks in scheduling operations. Therefore, it is very important to carry out risk assessments before scheduling operations. In this paper, risk theory is introduced into the field of distribution network scheduling operations, and a new risk assessment method is proposed considering various uncertain factors in the distribution network. In order to comprehensively analyze the influence of uncertainty factors in the operational process of a new distribution network, the output probability models of wind power, photovoltaic power, and load are first constructed in this study. Then, the improved Latin hypercube sampling method is used to extract the operating state of the distribution network system from the probability model, and the node voltage over-limit and line power flow overload are used as indicators to measure the severity of the consequences so as to establish a quantitative scheduling operation risk assessment system and analyze its framework in detail. Finally, simulation analysis is carried out in the improved IEEE-RTS79 test system: taking 15–25 lines from the operation state to the maintenance state as an example, this paper analyzes the influence of different locations and capacities of wind and solar access on the scheduling operation risk of distribution networks. The results can provide a reference for dispatchers to prevent risks before operation. Full article

Predictive maintenance (PdM) of biomedical equipment is increasingly recognized as a strategic lever to enhance reliability and ensure continuity of care. Yet, in resource-limited hospitals, implementation is hindered by fragmented data sources, non-standardized codification, and weak interoperability. Few studies have demonstrated the feasibility of structuring PdM data from real hospital interventions in middle-income countries. This work presents a prototype data structuring pipeline applied to six public hospitals in the Casablanca–Settat region of Morocco. The pipeline consolidates 6816 validated maintenance interventions from 780 devices across 30 departments and integrates normalized reliability indicators (Failure Rate, MTBF, MTTR corrected with IQR, and Downtime Hours). It ensures semantic harmonization, auditability, and reproducibility, resulting in a structured and interoperable dataset that constitutes a regional first in the Moroccan hospital context. To illustrate predictive potential, a proof-of-concept Random Forest model was evaluated. It achieved AUROC = 0.65 on the full imbalanced dataset and AUROC = 0.82 on a balanced 2000-intervention subset, confirming the dataset’s discriminative value while reflecting real-world challenges. This work bridges the gap between conceptual PdM frameworks and operational hospital realities, and establishes a replicable foundation for AI-driven predictive maintenance in low-resource healthcare environments. Full article

Predictive maintenance of semi-autogenous grinding (SAG) mills reduces unplanned downtime and improves throughput. This study develops a data-driven prognostic model for production SAG mill using four years of operational data (temperature, voltage, current, motor speed, etc.). We follow a MATLAB (R2025a)-based prognostics and health management (PHM) workflow: data cleaning and synchronization; feature engineering in time and frequency domains (statistical moments, spectral power, bandwidth); normalization and clustering to separate operating regimes; and labeling of run-to-failure sequences for a recurring electrical failure mode. A health indicator is derived by scoring candidate features for monotonicity, trendability, and prognosability and fusing them into a condition index. Using MATLAB Predictive Maintenance Toolbox, we train and validate multiple Remaining Useful Life (RUL) learners including similarity-based, regression, and survival models on run-to-failure histories, selecting the best via cross-validated error and prediction stability. On held-out sets, the selected model forecasts RUL consistent with observed failure dates, providing actionable lead time for maintenance planning. The results highlight the practicality of deploying a PHM pipeline for SAG mills using existing plant data and commercial toolchains. Full article

Sport performance results from the interaction of tactical, technical, physiological and psychological factors, but psychological aspects are often minimized or analyzed in a decontextualized manner. This exploratory pilot study aimed to contribute to the development of a diagnostic framework that links individual behaviors during football attack–defense transition moments (ADT) with psychological attributes. Twenty male U14 players were assessed across five official matches regarding their ADT performance indicators. The Athletic Coping Skills Inventory (ACSI-28) and the Resilience Scale (RS) were applied during the competition. Statistical analyses included correlation tests and Bayesian analysis. Players showed a significant tendency to sustain ball recovery behaviors after possession loss (p = 0.004). Psychological resilience and athletic coping skills varied substantially between individuals without positional differences, as well as RS scores were significantly below the high-resilience threshold (147; p = 0.013). A moderate positive correlation emerged between RS Factor 1 and the ACSI-28 subscale “Coping with Adversity” (r = 0.574, p = 0.008). Posterior distributions provide exploratory signals suggesting possible positive associations for two psychological constructs considering ADT individual behaviors: “Concentration” in relation to the maintenance of recovery actions (Mode = 0.439; 95% CI [0.030, 0.721]) and “Goal Setting” in relation to the rapid initiation of recovery actions (Mode = 0.465; 95% CI [0.059, 0.734]). Nevertheless, Bayes Factors favored the null model overall, indicating that these signals are weak and require replication. By contrast, most psychological constructs, including resilience, showed no reliable evidence of correlation with recovery-related actions. The findings highlight the need to further research the integration of psychological assessment into football performance diagnostics, while also indicating that psychological factors alone are insufficient to fully explain youth players’ individual ADT behaviors. Full article