Search Results (1,451)

The large-scale integration of renewable energy has significantly improved the low-carbon performance of power systems, but has also increased operational uncertainty. Under extreme weather conditions, wind and solar power may experience consecutive days of simultaneous output shortfalls—referred to as “renewable energy drought”—leading to persistently high net load and severe challenges to supply guarantee. To address this issue, this paper proposes a multi-source coordinated supply-guarantee dispatch strategy for consecutive-day renewable energy drought scenarios. First, net load is defined as the total system load minus the available wind and solar output. Based on magnitude and duration thresholds, renewable energy drought events are extracted from historical data to generate representative scarcity scenarios. Second, a multi-source coordinated optimization dispatch model is constructed, incorporating wind power, solar power, thermal units, battery energy storage, and pumped-storage hydro. The objective is to minimize the total system operating cost, which includes thermal fuel cost, start-up/shut-down costs, storage cycling cost, wind/solar curtailment penalty cost, and load shedding penalty cost. The load shedding penalty coefficient is set to a magnitude much higher than conventional costs to highlight the priority of supply guarantee. The model accounts for operational constraints such as minimum up/down times, deep regulation capability, ramping limits of thermal units, and charge/discharge power limits of storage. Taking a provincial power system in China for the year 2030 as a case study, a dispatch case covering four consecutive days (96 time periods) is designed. Based on a baseline scenario, eight groups of sensitivity analyses are conducted to comprehensively investigate the impacts of key factors on the supply-guarantee strategy, including: the minimum up/down time of thermal units, deep regulation capability, load shedding penalty cost, load level, rated energy capacity and charge/discharge efficiency of battery energy storage, rated energy capacity and pumping/generating efficiency of pumped-storage hydro, thermal fuel cost coefficient, and renewable energy capacity. Simulation results show that the proposed strategy can effectively coordinate multiple resources under consecutive-day drought conditions; reducing the minimum up/down time of thermal units improves supply flexibility but increases start-up/shut-down costs; enhancing deep regulation capability optimizes storage utilization and reduces total system cost; the load shedding penalty cost directly determines the trade-off between supply guarantee and economic efficiency; and as load level decreases by 5%, 10%, and 15%, the total system operating cost reduces by approximately 6.3%, 12.5%, and 18.8%, respectively. This study provides a quantitative method and technical support for supply-guarantee dispatch decisions and resource allocation in high-renewable power systems under persistent drought conditions. Full article

Electric vehicles (EVs) utilize batteries that generate thermal energy during charging and discharging processes. Inadequate heat management can result in thermal runaway, which is marked by a rapid and uncontrolled increase in battery temperature and may lead to fire or explosion. Battery Thermal Management Systems (BTMSs) are implemented to reduce the risk of thermal runaway by maintaining battery temperature within a defined safe operating range. Hybrid BTMS configurations integrate multiple cooling methods to enhance operational effectiveness and reliability, surpassing the performance of conventional liquid coolant systems. This work investigates the enhanced efficiency of an integrated phase change material (PCM)–liquid hybrid approach evaluated under severe 3C discharge conditions over a 10,000 h operational reliability window. Our study carefully investigates the system-level reliability using a Functional Fault Tree Analysis (FTA) framework to classify top, intermediate, and basic events. Multi-physics and probabilistic evaluation results demonstrate that the hybrid system achieves a 5 °C reduction in peak cell temperature and a 3 °C improvement in spatial temperature uniformity compared to standard liquid cooling. Reliability assessment establishes a system top-event failure probability of 6% over the 10,000 h window, identifying the coolant pump as the primary failure bottleneck with an individual contribution of 27%. These quantitative insights advance our understanding of hybrid safety architectures, providing essential baseline metrics for future electric vehicle thermal management development. Full article

Craniomaxillofacial trauma (CMFt) poses a significant burden, yet in many countries the evidence base is fragmented across single-center hospital series without specialized registry. Using Chile as a case study, we demonstrate a dual-synthesis approach to construct a national CMFt profile. Six databases were searched through February 2026 (PROSPERO: CRD420261290860). Two reviewers independently screened studies. Risk of bias was assessed with the JBI critical appraisal tool. Fracture-site proportions were pooled via random-effects meta-analysis and synthesized using GRADE. DEIS trauma discharges (2001–2024) were analyzed with negative binomial interrupted time-series. Nineteen studies were included. CMFt represented 2.6–6.1% of emergency consultations. CMFt admissions were 54.2/1000 trauma discharges; this rate dropped during 2020–2021 and rebounded post-2022. Pooled fracture-site distributions were highest for mandibular (45.3%) and zygomatic (24.2%) fractures. CMFt disproportionately affected males across both hospital series and national discharge data. According to DEIS, low-energy accidental injuries were the predominant etiology, followed by transport-related high-energy injuries and interpersonal violence, contrasting with hospital series where interpersonal violence predominated among adult surgical cohorts. Fracture admissions had longer length of stay (LOS) than soft-tissue CMFt (+0.94 days), with mean LOS ranging from 2.08 (nasal) to 8.35 days (multiple skull/facial fractures). These findings support prioritizing surgical preparedness and training in common fracture patterns, while strengthening trauma surveillance, referral pathways, and service planning in health systems without dedicated CMFt registries. Full article

Background/Objectives: Abnormal vaginal discharge (AVD) is a common complaint among women of reproductive age, often involving multiple, overlapping etiologies, most commonly bacterial vaginosis (BV), vulvovaginal candidiasis (VVC), aerobic vaginitis (AV), and sexually transmitted infections (STIs). We aimed to evaluate a syndromic diagnostic approach by developing qPCR-derived dysbiosis indices for BV, VVC, and AV, subsequently comparing their performance against established reference methods and clinician-assigned diagnoses. Methods: Vaginal swabs were collected in a case–control design from 74 symptomatic and 64 asymptomatic women at two clinics in Slovenia. Commercial qPCR assays quantified the microbial species associated with AVD. Relative abundances were integrated into novel dysbiosis indices. Diagnostic performance was validated against the Nugent scoring system (for BV), semiquantitative Candida culture with clinical symptoms (for VVC), and Hay–Ison criteria (for AV). Results: In this internally validated study, dysbiosis indices demonstrated high agreement with their respective reference tests and outperformed clinician-assigned diagnoses across all three conditions. The syndromic approach further revealed that mixed etiologies were frequent, leading to a diagnostic resolution for this patient subset. Conclusions: qPCR-based microbial dysbiosis indices offer a robust alternative to microscopy, particularly in settings where microscopy is not routinely performed. This method improves the accuracy of AVD evaluation and supports more targeted clinical management. Full article

In modern power systems, the rapid growth of renewable energy capacity, such as wind and solar photovoltaic (PV) power, has led to a decline in system equivalent inertia and primary frequency regulation margin. At the same time, net load fluctuations have intensified across multiple time scales, making it more likely for the RoCoF, frequency nadir, and quasi-steady-state frequency deviation to approach safety limits following disturbances. To achieve a balance between frequency security and economic operation, this paper proposes a two-stage stochastic frequency-security-constrained unit commitment (FSC-SUC) model tailored for scenarios with high renewable energy penetration. The day-ahead hourly dispatch stage jointly determines the on/off status and reference output of synchronous units and the reservation of slow frequency regulation capacity, as well as energy storage charging and discharging plans, SoC trajectories, and the reservation of fast frequency regulation capacity. The intraday minute-level real-time dispatch stage accommodates prediction errors through scenario-based rescheduling and ensures the deliverability of both slow and fast frequency regulation capabilities via commitment consistency constraints. To address the challenge of directly embedding frequency nadir constraints into mixed-integer optimization, this paper employs a modeling approach that combines analytical criteria with linear surrogate constraints. The RoCoF and quasi-steady-state frequency deviation are specified via aggregated analytical constraints, while the nadir is embedded into the main problem after generating samples offline using a simplified frequency response model and training a polyhedral linear surrogate for external approximation. The safety margin is then calibrated using high-quantile residuals from the validation set to ensure conservativeness. Case studies on the IEEE 33-bus system under different renewable penetration levels demonstrate that the proposed method significantly reduces the probability of frequency nadir violations and load-loss risk with only a modest cost increase while also improving coordination between fast and slow frequency regulation. Full article

This paper presents a conducting-channel model aimed at elucidating the generation of high-energy particles within a plasma chamber. Initially, the chamber is charged with neutral hydrogen gas at a density of approximately ~3.3 × 10²²/m³, equivalent to 1 torr at 300 K under ideal gas conditions. A Townsend discharge (dark discharge), driven by an externally imposed electric potential (500–1000 V) across the cathode and anode, is utilized to induce partial ionization of the hydrogen gas. Once a stable conducting channel with a high conductivity is established, a low electric potential (e.g., 100–500 V) is introduced to sustain the current in the conducting channel. Our investigation then delves into the impact of a high-emissivity cathode, such as lanthanum hexaboride (LaB6), on an arc discharge. We develop a theoretical model of the conducting channel that may emerge under these conditions. As the cathode surface heats, thermionic electrons form a localized layer of negative charge density outside the cathode, leading to an electric potential dip. Our multi-fluid simulations reveal the emergence of an electron-ion two-stream instability owing to the high-density electron layer, leading to the appearance of multiple potential peaks and dips, each measuring several to tens of kV. We delineate a set of conditions conducive to the formation of these potential peaks and dips within the conducting channel. Our proposed scenario furnishes a framework for elucidating electron and ion acceleration within a weakly ionized plasma chamber. Full article

The removal of recalcitrant industrial dyes from wastewater has emerged as a critical environmental challenge, particularly in the context of the accelerating decline of global freshwater reserves. Given that these contaminants originate predominantly from the effluents of textile, chemical, and related manufacturing sectors, the deployment of advanced treatment technologies prior to discharge is imperative to mitigate their ecological impact. This study investigates the photocatalytic degradation of indigo carmine using a synthesized MgAl–LDH material. LDH is shown to act as an active photocatalytic component rather than a support, with its remarkably simple synthesis offering a practical alternative to the complex catalysts dominating the current literature. The catalyst’s structural, morphological, and surface characteristics were comprehensively validated through XRD, SEM, EDX, and BET analyses. The catalyst was evaluated under varying hydrogen peroxide doses and across an initial dye concentration range of 5 × 10⁻⁵ to 5 × 10⁻⁴ M. Increasing the H₂O₂ volume (3.5–20 mL, corresponding to H₂O₂ excess ratios of 17.5–100) significantly enhanced the oxidation rate, whereas higher dye concentrations reduced efficiency due to photon competition and partial saturation of catalytic sites. These experiments provided the basis for extracting kinetic parameters and assessing the mechanistic pathways governing the photocatalytic process. The kinetic behavior of indigo carmine degradation was evaluated by fitting the experimental data to zero-order, first-order, and second-order empirical models to identify the rate law that best describes the reaction. Reusability tests showed that MgAl–LDH maintains high activity over multiple cycles, with only a moderate decline, demonstrating its stability and suitability for practical wastewater treatment applications. Full article

Accurate prediction of multivariate effluent quality is essential for achieving reliable operation and sustainable management of wastewater treatment processes (WWTPs). However, the strong nonlinearity, coupling relationships, and non-prioritized multi-input multi-output (MIMO) characteristics of WWTP pose significant challenges to conventional prediction methods. To address these issues, a vectorized evidential reasoning-based multivariate effluent quality (VER-MEQ) prediction method is proposed. First, a VER model is developed, in which the nonlinear mapping between multiple process variables and multiple effluent quality indicators is established through a vector evidence matrix (VEM), enabling simultaneous online prediction of multiple outputs within a unified inference framework. Subsequently, a structured hybrid initialization (SHI) strategy is introduced to improve the initialization quality of the genetic algorithm, and the VER inference process is incorporated into parameter optimization to enable online model parameter updating, thereby improving prediction performance. The proposed method is validated under sunny, rainy, and stormy operating scenarios. Experimental results demonstrate that VER-MEQ achieves competitive prediction accuracy, provides a transparent belief-based inference process, and maintains preliminary anti-interference performance under the tested conditions. By providing transparent and credible prediction results for effluent ammonia nitrogen (NH₃-N_e) and total nitrogen (TN_e), the proposed framework can support proactive operational decision-making, improve effluent compliance, reduce the risk of nutrient discharge, and contribute to the sustainable operation of WWTPs. Full article

In order to investigate the suppression effect of different extinguishing agents on lithium-ion battery fires in real confined spaces, a comparative experiment was conducted using aerosols, heptafluoropropane, and perfluorohexanone. In tests without any fire suppression measures, the peak heat release rate reached up to 69.09 kW, and a total of 8.05 MJ of heat was generated along with multiple deflagration events. Moreover, the heptafluoropropane and perfluorohexanone both effectively extinguished the flames with extinguishing times of 12 and 20 s, respectively. The aerosol agent caused a significant contraction of the flames, but it was unable to achieve complete extinguishment. Regarding cooling performance, the heptafluoropropane decreased the front surface temperature of the battery by 147 °C, while perfluorohexanone achieved a reduction of 230 °C. Additionally, the liquid-phase adhesion characteristics of perfluorohexanone enabled sustained cooling. A comprehensive comparison indicates that the perfluorohexanone agent exhibits outstanding performance in flame extinguishment, cooling efficiency, and the suppression of thermal propagation. Heptafluoropropane demonstrates rapid fire suppression and is suitable as a fast-response agent, whereas the aerosol requires a multi-discharge design to achieve reliable performance. Based on these findings, it is recommended that energy storage systems adopt a composite suppression strategy for fire protection. Full article

Background/Objectives: Health-related quality of life perception (HRQoL) reflects the impact of individuals’ health conditions on their physical, psychological, and social well-being, and can be compromised after an accident The general aim of this study was to analyze the effect of occupational accident (OA) outcomes on injured workers’ HRQoL. Methods: We conducted a cross-sectional study, using a convenience sample of 101 participants at the end of their recovery and professional reintegration (PR) process. They were submitted to a personal injury assessment (PIA) conducted by medico-legal specialists, and data related to injury severity (IS), permanent professional disability (PD), and PR were collected from the respective forensic reports. Subsequently, they underwent a psychological interview and filled out self-report questionnaires to measure HRQoL (SF-36) and resilience (RSA). For each variable, two groups were defined. Analyses included descriptive statistics, correlations, group comparisons, and multiple linear regression analyses. Results: Injured workers reported lower HRQoL than Portuguese norms across most domains. HRQoL scores were positively associated with resilience and PR, and negatively associated with IS and PD. In multivariable models, IS, and RSA emerged as significant independent associated variables of the physical–social HRQoL component. Conclusions: These findings highlight the importance of a biopsychosocial and multidisciplinary approach to OA victims’ professional reintegration, integrating physical treatment and psychological support with resilience-building and work rehabilitation, before medical discharge and PIA. Full article

Accurate streamflow prediction in small catchments remains challenging due to their rapid response times, threshold-driven behaviors, and high spatial heterogeneity. This study develops and evaluates a novel modeling approach combining physics-informed feature selection with machine learning algorithms. Overall, 1825 model configurations were tested across fifteen algorithms (including Random Forest, XGBoost, LightGBM, CatBoost, Support Vector Machines, and deep learning methods) using multiple physics-informed input structures based on classical rainfall–runoff theory and mass balance conservation. Models were evaluated for predicting minimum, average, and maximum daily water levels and discharge. Results demonstrate that models structured around Green-Ampt infiltration assumptions consistently outperformed alternative configurations, with Random Forest achieving good performance for water level predictions. Causal models outperformed autoregressive approaches while the residuals analysis showed limitations in predicting extreme values. Feature importance analysis revealed that channel and catchment morphology and initial soil moisture conditions were dominant predictors, aligning with hydrological process understanding. Full article

Background: Immune mechanisms are increasingly implicated in the heterogeneity of schizophrenia spectrum disorders. Cytomegalovirus (CMV), a latent immunomodulatory herpesvirus, is linked to cognitive and immunological alterations, but its electrophysiological correlates remain largely unexplored. This study investigates the relationships among CMV serostatus, EEG features, inflammatory markers, and clinical–cognitive variables. Methods: In this prospective cross-sectional study, 123 patients with schizophrenia spectrum disorders underwent integrated clinical, cognitive, laboratory, and qualitative visual EEG assessments. CMV exposure was determined via IgG serology. Results: Global electroencephalographic EEG organization did not differ by CMV serostatus. However, a descriptive increase in resting-state sharp-wave discharges was observed in CMV-seronegative patients, independent of baseline cortical rhythms. Immunologically, CMV-seropositive individuals exhibited significantly higher total leukocyte counts, consistent with latent viral immune remodeling rather than overt systemic inflammation. Clinically, CMV-seropositive patients demonstrated descriptively higher scores on the disorganization dimension derived from the PANSS (Positive and Negative Syndrome Scale) five-factor consensus model. While these variations did not retain statistical significance after multiple testing correction, separate dimensional analyses revealed that patients exhibiting sharp waves demonstrated better overall cognitive functioning and superior performance within a memory-related item grouping. Notably, the presence of sharp-wave activity was independent of both peripheral inflammatory profiles and treatment-resistant status, underscoring a distinct electrophysiological phenotype. Conclusions: CMV exposure represents a modulating biological background associated with corrected leukocyte elevations and subtle electrophysiological variability, rather than a direct determinant of global clinical severity. The nominal EEG variations and their independent link to better-preserved memory performance highlight non-linear neuroimmune interactions. Given the cross-sectional design, these exploratory patterns warrant a non-causal interpretation but outline a foundation for future longitudinal investigations. Full article

Background: Hyperglycemia after aneurysmal subarachnoid hemorrhage (aSAH) is associated with poor outcome, but admission glucose may not reflect dynamic metabolic stress during neurocritical care. Unlike previous studies focused primarily on admission measurements, we evaluated longitudinal glycemic trajectories and repeated glucose–potassium ratio (GPR) assessment across multiple observation windows in relation to clinical outcomes after aSAH. Methods: This retrospective single-center cohort study included 199 consecutive adults with aSAH treated between 2014 and 2025. Serial glucose and potassium measurements obtained during intensive care unit (ICU) stay were used to calculate admission values, longitudinal means across predefined observation windows, glycemic variability, hyperglycemia burden, and GPR. Primary outcomes were 30-day mortality and poor functional outcome at discharge (modified Rankin Scale ≥ 3). Secondary outcomes included delayed cerebral ischemia (DCI), delayed neurological deterioration (DND), transcranial Doppler (TCD) vasospasm, neurological deficit at ICU discharge, and length of stay. Results: Thirty-day mortality occurred in 35 patients (17.6%). Longitudinal metabolic markers demonstrated stronger associations with outcomes than admission values. Mean 30-day GPR was independently associated with mortality (OR 2.56, 95% CI 1.66–4.16; p < 0.001) and poor functional outcome (OR 2.90, 95% CI 1.80–5.03; p < 0.001). Hyperglycemia burden was associated with mortality (OR 1.10 per additional hyperglycemic day, 95% CI 1.02–1.20; p = 0.020) and poor functional outcome (OR 1.39, 95% CI 1.19–1.71; p < 0.001). Early GPR during the early brain injury period was associated with DCI (OR 1.40, 95% CI 1.01–1.93; p = 0.043), whereas 30-day GPR was associated with DND (OR 1.47, 95% CI 1.08–2.07; p = 0.019). ICU-specific GPR was associated with neurological deficit at ICU discharge (OR 2.06, 95% CI 1.29–3.50; p = 0.004), but not with TCD-defined vasospasm. Addition of GPR improved mortality prediction compared with the clinical model alone (AUC 0.86 vs. 0.77; p = 0.002). Conclusions: Longitudinal metabolic dysregulation after aSAH is strongly associated with mortality and neurological outcomes. Persistent hyperglycemia and repeated GPR assessment provide prognostic information beyond admission glucose, with early abnormalities associated with DCI and sustained disturbances linked to mortality and disability. Full article

Floods remain among the most devastating natural disasters globally, disproportionately impacting low-resource regions where real-time flood forecasting is constrained by limited computational infrastructure and the scarcity of fine-resolution predictive models. Although state-of-the-art global climate models achieve high predictive accuracy, their scale and computational complexity restrict their applicability in localized and resource-constrained settings. This study proposes a deep climate model distillation framework that transfers knowledge from a high-capacity Fourier Neural Operator (FNO)-based global climate model inspired by FourCastNet into lightweight, regionally adaptive student networks suitable for edge deployment. The framework combines climate variables, satellite observations, and hydrological measurements to improve localized flood prediction. Knowledge transfer is achieved through a multi-objective distillation strategy that combines supervised learning, soft-target alignment, and intermediate feature matching. Experimental evaluation across multiple flood-prone regions in Sub-Saharan Africa and South Asia shows that the distilled student model achieves an average classification accuracy of 0.89, an AUC of 0.91, and an F1-score of 0.88, retaining approximately 96.7% of the teacher model’s predictive performance. In continuous discharge estimation, the model attains a mean absolute error of 0.17, RMSE of 0.24, and an R² score of 0.85. The proposed distillation approach yields an 8× reduction in inference latency and over a 20× reduction in model size, enabling real-time execution on low-power edge devices such as the Raspberry Pi 4 and NVIDIA Jetson Nano. The student model further demonstrates robust regional and temporal generalization, with limited performance degradation in unseen geographic areas and during extreme flood years. Full article

Partial discharge (PD) source-type classification is essential for condition-based maintenance of high-voltage apparatus. Existing approaches based on grid discretizations of phase-resolved partial discharge (PRPD) patterns suffer from performance degradation under stochastic interference and multi-source conditions. This paper proposes a graph-based framework that integrates the morphological characterization of raw high-frequency PD waveforms with the phase-amplitude position of individual discharge events to enable multi-label classification, identifying multiple PD sources coexisting within a single test. The framework operates through three stages: a multi-task neural network extracts per-pulse embeddings and confidence scores; a construction procedure establishes selective graph connectivity based on spatial proximity and morphological similarity; and an edge-conditioned graph neural network performs classification via message passing weighted by multimodal edge attributes. Experimental evaluation on PD measurements acquired in accordance with IEC 60270 shows that the proposed framework achieves a Matthews correlation coefficient (MCC) of 0.98 and an exact match ratio of 0.97 across single-source, noisy, and multi-source conditions, substantially outperforming histogram- and set-based baselines. The framework maintains an MCC of 0.97 in multi-source scenarios, where its advantage over existing methods is most pronounced. Cross-domain evaluation on an independent dataset acquired with different laboratory equipment confirms the approach’s robustness, achieving an MCC of 0.93 without retraining. Finally, an ablation study demonstrates that the joint removal of morphological similarity filtering and confidence-based node filtering and edge gating reduces the MCC by 0.25, confirming the critical role of the waveform-informed relational structure. Full article