Search Results (14,740)

Local threshold limits translate abstract information on hydrological drought into concrete operational guidance for individual water resources. For reservoirs, surface water intakes, and groundwater sources that are important to a region, key monitored quantities and failure conditions are identified, critical levels corresponding to loss of function or unacceptable quality are derived, and an advance time to reach them is set. From these, threshold values analogous to flood stages are calculated, possibly varying over the year with the hydrological regime and demand. Implemented through regional drought plans and displayed in the HAMR (Hydrology–Agronomy–Meteorology–Retention drought monitoring and prediction system, hamr.chmi.cz), local threshold limits complement nationwide warnings by capturing the specific behaviour of each resource and enabling timely, proportionate and locally accepted drought management actions. Full article

Residential customers are frequently exposed to electricity supply interruptions caused by system failures, natural hazards, or human-related events. Community microgrids have emerged as a promising solution to improve supply reliability. Therefore, this study quantifies the additional cost of guaranteeing different levels of energy self-sufficiency through the optimal design of reliability-constrained community microgrids capable of maintaining electricity supply during outages regardless of when they occur throughout the year. To account for the inherent diversity of residential demand, hundreds of optimization problems were solved, resulting in the design of hundreds of community microgrids. The results indicate that guaranteeing 2 h of self-sufficiency increases annual costs by 14.1% for communities of 20 households. Furthermore, the impact of demand response (DR) on community microgrid planning is also investigated. The findings indicate that the economic benefits of residential DR are limited, not exceeding 4.4% of the total microgrid cost. Full article

Deep Neural Networks (DNNs) are increasingly being used in the design of industrial safety-critical autonomous applications such as autonomous vehicles, industrial robotics, and medical instrumentation and control systems. Ensuring reliable and robust operation of the DNN-based safety-critical systems is challenging because of the complex structure of DNN hardware accelerators utilized for inference that are susceptible to the effects of multi-faults, common-cause fault models, data uncertainties, and unpredictable erroneous behavior. Additionally, transient, permanent, and timing faults affect the accelerator design of processing elements, memory arrays, and datapaths, propagate through DNN computations, and potentially can cause catastrophic failures at the system level. The objective of this survey paper is to systematically evaluate the state-of-the-art fault-tolerant DNN accelerator architectures with particular emphasis on their applicability to safety-critical autonomous systems in industry. The survey investigates architectural perspective, fault modeling, and platform-level trade-offs, runtime resilience, validation practices, and certification readiness, following a PRISMA methodology with evidence-driven synthesis and unbiased study selection. Database searches across IEEE Xplore, Scopus, and Web of Science identified 200 records, of which 82 studies were included based on predefined inclusion and exclusion criteria emphasizing industrial safety-critical relevance, fault modeling at the hardware level, and the implementation at the architectural level. The results indicate that there was a clear shift from traditional redundancy-based approaches to cross-layer and adaptive approaches that provide better trade-offs between performance, reliability, and hardware overhead. The current studies presented are based on simplified fault models, incomplete validation- procedures, and limited consideration of system-level and certification needs, which often do not consider critical failure modes such as Silent Data Corruption (SDC). This has resulted in a significant gap between research-level solutions and industrial deployment requirements. This survey underscores the need for scalable, integrated, and certification-aware design approaches to help connect fault modeling, architectural resilience, validation, and safety assurance to develop reliable and deployable DNN accelerator systems for next-generation industrial safety-critical autonomous applications. Full article

Sustainable urban transportation is fundamentally linked to public health outcomes, specifically the mitigation of fatal traffic risks under environmental stress. While stressors like adverse weather affect entire cities, traditional road safety models often assume uniform risk, thereby masking the spatial inequalities inherent in the urban fabric. This study addresses this gap by investigating the geographically heterogeneous impact of environmental stressors—including rainfall, surface moisture, and lighting conditions—on the conditional probability of fatal crash outcomes in Melbourne, Australia. Analyzing 43,075 severe crashes through a multi-stage geospatial framework (Getis-Ord Gi* and Geographically Weighted Logistic Regression), this research diagnoses how varying urban development patterns mediate the lethality of these stressors. The findings unmask a critical “threshold-crossing” pattern for wet surfaces, where risk transitions from protective to hazardous based on local infrastructure form and street geometry. Significant spatial inequalities are identified: high-density inner-urban cores and adjacent coastal corridors exhibit a heightened sensitivity to visibility failures and moisture, whereas newer industrial peripheries show stronger protective “risk compensation” effects. These results reveal a systemic mismatch between historical urban form and contemporary climate-driven public health risks. By identifying localized “lethality thresholds”, this study provides a robust evidence base for integrated planning and equitable resource allocation. It enables urban planners to move beyond generalized safety warnings toward targeted structural interventions, ensuring that sustainable transportation networks prioritize safety equity for all citizens regardless of their location within the urban environment. Full article

This study examines likelihood-based estimation of the joint survival function $S(t_1,t_2)=Pr\{T^{\left(1\right)}>t_1,T^{\left(2\right)}>t_2\}$ for systems with two competing failure modes observed under right censoring. Rather than introducing a new distributional family, the study compares established dependence mechanisms within a common observed-data framework. Exponential and Weibull margins are combined with three types of dependence: Archimedean copulas, represented by the Gumbel and Clayton families; shared gamma frailty, used to model latent measurement-level heterogeneity; and Marshall–Olkin extensions, used to represent common shocks and simultaneous failures. The same observation scheme, likelihood construction, censoring design, and performance criteria are used across models. Model performance is evaluated through Monte Carlo simulation using bias, integrated mean squared error, and empirical coverage, and the workflow is illustrated with the Device G reliability data. The results show that ignoring dependence can distort joint survival estimates, especially under moderate or high censoring. They also show that copula, frailty, and Marshall–Olkin specifications can lead to different reliability assessments because they encode different stochastic mechanisms. The estimation workflow includes multi-start optimization and diagnostics for boundary solutions, Hessian stability, and irregular likelihood behavior. Full article

At present, photovoltaic (PV) systems are becoming the core of low-carbon power systems, but their large-scale integration is still limited by weather-driven intermittency, heterogeneous data, equipment failures, operational uncertainty, and life-cycle sustainability requirements. Unlike specific task reviews that only focus on photovoltaic forecasting, fault diagnosis, or general artificial intelligence applications in renewable energy, this review develops an integrated data-driven perspective for machine learning and artificial intelligence in photovoltaic power generation systems. It links data governance, feature engineering, prediction, and uncertainty quantification, fault diagnosis and predictive maintenance, energy management, market participation, and carbon-aware optimization within a framework for photovoltaic systems. This review indicates that traditional machine learning, deep learning, graph learning, reinforcement learning, generative artificial intelligence, and physics-based artificial intelligence are suitable for different photovoltaic tasks based on data structure, time range, operational constraints, and deployment maturity. The main contribution is cross-task integration, which links the output of artificial intelligence models, including scheduling, storage scheduling, maintenance planning, virtual power plant operation, and low-carbon management, with actual decision-making. The review further identified the most critical deployment barriers, such as incomplete benchmarks, weak cross-site generalization, insufficient uncertainty calibration, limited interpretability, network security risks, and computational costs. The resulting methodological approach emphasizes data management, uncertainty awareness, physical constraints, decision orientation, and sustainability-driven photovoltaic intelligence. Full article

Background: The surgical management of chronic pelvic pain (CPP), particularly in endometriosis, often focuses on lesion excision or nerve decompression. However, persistent pain frequently occurs despite “anatomical perfection,” suggesting central nervous system involvement. Neuropelveology faces a “surgical paradox” when dealing with central sensitization (CS), where peripheral interventions fail to address a systemic nociplastic state. Methods: This systematic review followed PRISMA guidelines and was registered in PROSPERO (CRD420261335008). A search across PubMed, Embase, and Cochrane (2010–2026) identified 71 relevant studies involving over 12,000 participants. Results: CS prevalence in the endometriosis population ranges from 11.3% to 58.2%, rising to 74.8% in specialized tertiary referral centers. The Central Sensitization Inventory (CSI) is a robust predictor of surgical failure; every one-point increase in preoperative CSI raises the risk of persistent pain (OR 1.02, p = 0.02). Objective markers, such as the collapse of Conditioned Pain Modulation (CPM), confirm that “high-sensitizers” (CSI ≥ 40) suffer from a systemic “software” failure of pain inhibition. Conclusions: We propose a paradigm shift in neuropelveology. In patients with high CSI scores (≥40), functional neuromodulation—specifically the LION procedure—should be prioritized over traditional nerve decompression to address the nociplastic nature of the pain. Full article

This paper develops a unified probabilistic framework for the simultaneous assessment of reliability and robustness of series structural systems. Part I formulates an axiomatic theory of structural robustness: the robustness factor F_rob is defined as the negative decadic logarithm of the ratio between the damaged-state system failure probability and a normalised target failure probability, and five formal properties are proven—normalisation, monotonicity in damage severity, exact combination bounds under independent damage scenarios, invariance under reliability-preserving transformations, and boundedness. Part II describes the computational core, the evaluation of series system failure probability: the principal approximation methods (simple bounds, the complement-product reformulation, Ditlevsen’s narrow bounds) are formally derived, and a geometric mean-bound approximation over a dominant-element subset is introduced, with four propositions establishing consistency with bounds, monotonicity, convergence, and a complete characterisation of the error direction under positive equicorrelation, including a provably conservative regime above a threshold correlation. Part III synthesises both parts into a computational framework that evaluates F_rob from first-order reliability method (FORM) outputs alone, avoiding repeated multi-dimensional numerical integration in the inner loop of design optimisation, with an explicit, computable error bound guaranteeing invariance of the robustness classification; a two-sided Slepian envelope further extends this guarantee to systems with non-equicorrelated, non-negative inter-element correlation. The framework is illustrated through a numerical example and validated against a published case study of a glued–laminated timber truss. Full article

The hallmark feature of empty follicle syndrome (EFS) is failure to retrieve oocytes from apparently mature follicles despite adequate ovarian stimulation and appropriate ovulation triggering. Although considered uncommon, with a reported prevalence ranging from 0.2% to 7%, EFS may have a profound clinical and psychological impact and can recur in assisted reproductive technology (ART) cycles. Modern classification systems divide EFS into genuine and false forms. Genuine EFS is potentially associated with intrinsic abnormalities involving luteinizing hormone/choriogonadotropin receptor (LHCGR) signaling, oocyte competence, and cumulus–oocyte interaction, whereas false EFS is primarily attributed to pharmacokinetic or pharmacodynamic factors resulting in inadequate trigger exposure. Borderline EFS represents a third phenotype characterized by incomplete or partial impairment of final oocyte maturation. This review examines the pharmacodynamics of ovulation-triggering agents, including human chorionic gonadotropin (hCG), gonadotropin-releasing hormone (GnRH) agonist protocols, and dual-trigger strategies, and their roles in regulating final oocyte maturation. The molecular aspects of periovulatory signal transduction and the mechanisms of LHCGR activation, epidermal growth factor (EGF)-like pathways, and meiotic resumption in relation to EFS etiopathogenesis will be described. The impact of patient-dependent conditions like obesity, poor ovarian reserve, polycystic ovary syndrome (PCOS), and pituitary response on trigger response will be assessed. New approaches like post-trigger monitoring of hormones and rescue treatment with gonadotropins represent a valuable method for avoiding cycle cancellation in patients at risk. Overall, EFS is increasingly regarded not as a single disorder but as a heterogeneous spectrum of periovulatory dysfunction arising from pharmacological, endocrine, and intrinsic ovarian factors that impair completion of final oocyte maturation. Full article

Underactuated and strongly coupled Quadrotor Unmanned Aerial Vehicle (QUAV) systems often face challenges in formation control due to actuator failures, external unknown disturbances, and limited communication resources. To address these issues, this paper proposes a periodic adaptive event-triggered fixed-time fault-tolerant control method based on a disturbance observer. First, a dynamic estimation and compensation scheme for actuator faults is developed by combining boundary layer theory with adaptive control techniques. Next, a fixed-time disturbance observer is designed to accurately estimate and compensate for external unknown disturbances. Furthermore, considering the communication burden imposed by real-time position updates, a Non-Monitoring Periodic Adaptive Event-Triggered Control (NM-PAETC) mechanism is proposed to reduce communication resource consumption, while ensuring that the formation system maintains the desired attitude angles under the influence of actuator faults and external disturbances. The proposed method enables fixed-time formation control under limited communication resources, and the system’s convergence time is independent of the initial state. Simulation results validate the effectiveness of the proposed method. Full article

Skeletal muscle dysfunction (SMD) is a critical extrapulmonary comorbidity in chronic obstructive pulmonary disease (COPD), contributing to exercise intolerance, poor quality of life, and increased mortality. Building upon and extending the disuse model, this review synthesizes evidence establishing COPD-induced SMD as a distinct myopathy with intrinsic disease drivers. Its pathophysiology is driven by a self-reinforcing network: mitochondrial energetic crisis featuring bioenergetic failure and dysregulated dynamics, chronic oxidative stress and inflammation fueling catabolic drive via ubiquitin–proteasome system activation, and epigenetic dysregulation through alterations in key histone deacetylases (HDACs) and microRNA expression, which collectively orchestrate a pro-atrophic phenotype. We further explore how these molecular insights are translating into novel diagnostic tools, including circulating biomarkers like myomiRs and C-terminal agrin fragment, and imaging techniques such as shear wave elastography. Although exercise training remains the cornerstone of management, its limited efficacy underscores the need for adjunctive and targeted therapies. We discuss promising strategies from pharmacological and nutritional support to emerging agents targeting specific pathways, including the IL-36 receptor, lipoprotein-associated phospholipase A2, aryl hydrocarbon receptor, and mitsugumin 53. Effective management of COPD-related SMD will hinge on a precision medicine framework, leveraging biomarker-guided stratification to deploy personalized combinatorial interventions aimed at preserving muscle mass and function. Full article

Accurate assessment of pipeline defect integrity and proactive risk warning are essential for the safe, reliable, and economical transportation of oil and gas. Existing approaches are largely based on static assessment models, such as the Det Norske Veritas Recommended Practice for corroded pipelines (DNV-RP-F101), and often produce conservative failure-pressure predictions because time-dependent defect evolution and operational pressure fluctuations are not considered. To address this limitation, this study develops a dynamic defect-growth–failure-pressure coupling model that integrates a long short-term memory (LSTM) network with an enhanced DNV-RP-F101 framework. Time-varying axial and circumferential correction coefficients are introduced to update the bulging factor dynamically, thereby supporting defect-growth prediction and time-variant failure-pressure calculation. The model is validated against four established standards using public pipeline datasets. For single defects, the proposed model achieves the lowest mean square error (MSE) of 0.81 MPa and an average error of 1.18 MPa among the compared methods. For defect clusters, the prediction error remains within 8.64%. A five-level dynamic risk-warning system is further established by integrating Monte Carlo simulation with API 579 standards, enabling quantification of failure probability and prediction of remaining service life. Engineering case studies show that the proposed method can identify the time points at which pipelines enter hazardous failure-probability stages. This capability supports more precise early warning and provides a technical basis for intelligent pipeline integrity management and predictive maintenance. Full article

Lithium metal batteries are considered one of the most promising technological routes for next-generation energy storage systems with high energy density. However, when paired with high-voltage cathodes such as NCM811, conventional lithium bis(trifluoromethanesulfonyl)imide (LiTFSI)-based electrolytes face severe corrosion of the aluminum current collector when the operating voltage exceeds 3.8 V vs. Li⁺/Li, leading to rapid capacity decay and even cell failure. In this work, we designed and synthesized a cyano-containing lithium salt, lithium cyano(trifluoromethanesulfonyl)imide (LiCTFSI), to address this issue. The electrochemical performance of 1 M LiCTFSI and 1 M LiTFSI in the same carbonate solvent was systematically compared in NCM811/Li cells. The results demonstrate that LiCTFSI effectively suppresses aluminum corrosion at high potentials and forms a thinner and more compact cathode electrolyte interphase to protect NCM811 cathodes. With the LiCTFSI electrolyte, NCM811/Li cells (mass loading = 19.55 mg cm⁻²) achieve a capacity retention of 81.7% after 200 cycles at a high cutoff voltage of 4.6 V vs. Li⁺/Li. This work provides a new strategy for developing advanced electrolyte salts for high-voltage, high-energy-density lithium metal batteries. Full article

This study proposes a multi-criteria decision-making (MCDM) framework for the prioritization of railway switch maintenance in urban rail systems. Railway switches are critical infrastructure components subject to complex operational and structural conditions, making maintenance planning a challenging task. To address this problem, the proposed approach integrates the Analytic Hierarchy Process (AHP) for criteria weighting with TOPSIS and PROMETHEE methods for ranking alternatives. The methodology is applied to real-world data obtained from the urban rail system of Istanbul, Türkiye, including maintenance and failure records of railway switches over the period 2018–2023. Failure frequency is used as a performance indicator to evaluate the consistency of model-based rankings. The results indicate a high level of consistency between model-based and failure-based rankings, with most switches exhibiting only minor ranking deviations across methods. The proposed framework successfully identifies high-priority switches that correspond to those with the highest observed failure frequencies. Observed discrepancies are limited and can be attributed to external operational factors and data limitations. The findings demonstrate that the proposed framework provides a structured and data-informed approach for maintenance prioritization. By integrating multiple criteria with real operational data, the approach offers a practical alternative to conventional time-based maintenance strategies and supports more efficient resource allocation in urban rail systems. Full article

With the expansion of metro networks and the increasing integration of underground retail and transit facilities, metro-connected underground commercial spaces have become a common yet safety-sensitive urban form. In fire scenarios, evacuation in such environments is constrained not only by enclosure and limited egress capacity, but also by the interaction between smoke spread and strongly coupled pedestrian flows across connected zones. Existing studies have examined smoke propagation or evacuation performance in underground spaces, but fewer have explicitly addressed how smoke constraints reshape node-level safety and the relative effectiveness of different intervention strategies in metro-connected commercial environments. This study investigates smoke-constrained evacuation dynamics in a representative metro-connected underground commercial space in Nanjing, China. A coupled simulation framework integrating PyroSim and Pathfinder is employed to examine multiple fire-source scenarios. Available safe egress time (ASET) at critical evacuation nodes is assessed using tenability criteria including visibility, temperature, and CO concentration, and is then compared with evacuation performance to diagnose hazardous routes and node-level failures. On this basis, three intervention strategies—corridor widening, stair widening, and pedestrian diversion—are comparatively evaluated. The results show that, within the modeled case, visibility most frequently becomes the controlling tenability criterion, and stairway nodes tend to lose safety margins earlier than final exits. This indicates that smoke constraints in connected underground commercial environments can trigger an early node-failure process before overall exit capacity is exhausted. The comparison further shows that behavior-oriented pedestrian diversion is more effective than geometric enlargement alone in reducing critical-node pressure and improving system-level evacuation performance under the modeled conditions. Rather than proposing universally transferable design rules, this study provides case-grounded evidence on how smoke propagation and pedestrian convergence jointly shape evacuation vulnerability in metro-connected underground commercial spaces, and offers a structured basis for critical-node diagnosis and intervention comparison in similarly configured environments. Full article