Search Results (36,286)

This study proposes a multi-level performance margin modeling and belief reliability framework for redundant systems. Starting from system performance, a “performance–margin–reliability” linkage is established by defining the performance and margin of multi-level redundant systems and deriving performance, margin, and metric equations that account for failures. For complex redundant systems, a hierarchical Behavior Interaction Priority (BIP) modeling approach is developed to explicitly represent the normal and failure states of atomic component models. The effects of redundant components on the overall system are transformed into variations of performance parameters, enabling quantitative analysis of redundancy mechanisms. This paper proposes a boundary search algorithm for pruning optimization, which breaks through the computational bottleneck of non-analytic threshold sets in high-dimensional topological spaces. A case study on a power supply system with multi-level structural redundancy is conducted. Based on the proposed method, a performance-margin model of the redundant power supply system is constructed, critical states are analyzed, and system reliability is calculated. The results verify the effectiveness of the proposed margin-equation formulation and solution algorithm, offering practical guidance for reliability design of redundant systems. Full article

Background: The COVID-19 pandemic exposed major deficiencies in global health governance, including fragmented authority, inequitable resource distribution, and weak compliance mechanisms. Although the WHO Pandemic Agreement (2025) addresses several of these gaps, significant operational and institutional challenges remain. This study aims to develop and empirically validate a Global Governance for Health (GGFH) Framework that strengthens leadership, financing, equity, and legal accountability across global, regional, and national levels. Methods: A three-round Delphi study was conducted. Thirty-one experts from diverse sectors, including public health, international law, economics, environment, and diplomacy, evaluated 32 structured governance statements across seven domains. Experts rated all statements using a 7-point Likert scale. Consensus was determined using a strict threshold median ≥ 6; SD ≤ 1.35; ≥75% agreement. Open-text comments were systematically reviewed through thematic analysis. All statements were systematically mapped to the WHO Pandemic Agreement articles to identify areas lacking operational clarity or enforceability. Results: All seven governance domains achieved consensus by Round 3. High agreement emerged on strengthening WHO leadership, implementing sustainable and equitable financing mechanisms, embedding LMIC representation, establishing legal preparedness and capacity-building, and integrating independent accountability tools. Correlation and interdependence analyses demonstrated that governance goals form an integrated, mutually reinforcing system, with financing, equity, and legal frameworks identified as core enablers of effective treaty implementation. Conclusions: The Delphi process validated a comprehensive and operational Global Governance for Health Framework. The GGFH complements the WHO Pandemic Agreement by addressing its unresolved governance, financing, and equity limitations and offers a structured roadmap to guide global pandemic preparedness and treaty implementation. Full article

Background: Vasicine (Vas) is a quinazoline alkaloid derived from Adhatoda vasica Nees, which has good anti-allergic asthma and anti-inflammatory effects. However, its specific functional mechanism on allergic asthma is unclear. This study aims to investigate the protective effect of Vas on allergic asthma and its underlying mechanisms. Methods: Initially, the therapeutic effects of Vas were assessed in ovalbumin-sensitized BALB/c mice using airway hyperresponsiveness (AHR), histopathological examinations, immunohistochemistry, and enzyme-linked immunosorbent assays (ELISA). Subsequently, a non-targeted metabolomic analysis was performed to examine the influence of Vas on lung metabolites, while molecular docking was utilized to clarify the mechanisms by which Vas intervenes in allergic asthma. Lastly, RBL-2H3 cells were employed in vitro to validate the metabolomic findings by measuring intracellular Ca²⁺ concentrations, in addition to conducting ELISA and Western blot analyses. Results: In vivo, Vas alleviates AHR in mice with allergic asthma, enhances histopathological conditions, and reduces inflammatory factors. Non-targeted metabolomics analyses indicate that the primary pathway implicated in its intervention in allergic asthma may be the FcεRI pathway. Furthermore, molecular docking techniques were utilized to evaluate the binding affinity between Vas and proteins associated with this pathway. In vitro, Vas effectively inhibits degranulation in RBL-2H3 cells and diminishes the release of inflammatory factors by modulating the FcεRI/Lyn + Syk/MAPK pathway. Conclusions: These findings indicate that Vas may effectively alleviate allergic asthma by reducing inflammatory responses, decreasing AHR, and improving histopathological features. Furthermore, Vas seems to inhibit mast cell degranulation and modulate the FcεRI/Lyn + Syk/MAPK pathway. Full article

Change detection in remote sensing imagery plays a vital role in land use analysis, disaster assessment, and ecological monitoring. However, existing remote sensing change detection methods often lack a structured and tightly coupled interaction paradigm to jointly reconcile multi-scale representation, bi-temporal discrimination, and fine-grained boundary modeling under practical computational constraints. To address this fundamental challenge, we propose a Multi-scale Interaction and Supervised Attention Network (MISANet). To improve the model’s ability to perceive changes at multiple scales, we design a Progressive Multi-Scale Feature Fusion Module (PMFFM), which employs a progressive fusion strategy to effectively integrate multi-granular cross-scale features. To enhance the interaction between bi-temporal features, we introduce a Difference-guided Gated Attention Interaction (DGAI) module. This component leverages difference information between the two time phases and employs a gating mechanism to retain fine-grained details, thereby improving semantic consistency. Furthermore, to guide the model’s focus on change regions, we design a Supervised Attention Decoder Module (SADM). This module utilizes a channel–spatial joint attention mechanism to reweight the feature maps. In addition, a deep supervision strategy is incorporated to direct the model’s attention toward both fine-grained texture differences and high-level semantic changes during training. Experiments conducted on the LEVIR-CD, SYSU-CD, and GZ-CD datasets demonstrate the effectiveness of our method, achieving F1-scores of 91.19%, 82.25%, and 88.35%, respectively. Compared with the state-of-the-art BASNet model, MISANet achieves performance gains of 0.50% F1 and 0.85% IoU on LEVIR-CD, 2.13% F1 and 3.02% IoU on SYSU-CD, and 1.28% F1 and 2.03% IoU on GZ-CD. The proposed method demonstrates strong generalization capabilities and is applicable to various complex change detection scenarios. Full article

Li₃PO₄ is a promising raw material for the low-cost synthesis of high-performance LiFePO₄. Reactive crystallization from low-concentration lithium-rich brine is a key process for the efficient preparation of high-quality Li₃PO₄ products. The effect of operating conditions (temperature/supersaturation/impurities/ultrasonic) on the induction time was investigated using a focused beam reflectance measurement. The evaluation of the primary nucleation, growth kinetics, and parameters for the extraction of Li₃PO₄ from low-concentration lithium-rich brine was conducted using an induction time method. The dominant mechanisms at different stages were inferred through online monitoring of the particle size distribution during the Li₃PO₄ crystallization process. Results show that induction time decreases with increasing operating conditions (temperature/supersaturation/ultrasonic frequency), indicating that their increases all promote nucleation. Impurities (NaCl/KCl) did not significantly affect the induction time, whereas Na₂SO₄ and Na₂B₄O₇ significantly increased it, with Na₂B₄O₇ showing the most notable effect. Classical nucleation theory was applied to determine kinetic parameters (nucleation activation energy/interfacial tension/contact angle/critical nucleus size/surface entropy factor). Results indicate that Li₃PO₄ mainly nucleates through heterogeneous nucleation, with a temperature increase weakening the role of heterogeneous nucleation. Fitted models indicate that Li₃PO₄ predominantly follows the secondary nucleation and spiral growth mechanism. Our findings are crucial for crystallization design and control in producing high-quality Li₃PO₄ from lithium-rich brines. Full article

The strategic balance between strength and electrical conductivity in Al-Mg-Si alloys is a critical challenge that must be overcome to enable their widespread adoption as viable alternatives to copper conductors in power transmission systems. To address this, the present study comprehensively investigates model alloys with Mg/Si ratios ranging from 1.0 to 2.0. A multi-faceted experimental approach was employed, combining tailored thermo-mechanical treatments (solution treatment, cold drawing, and isothermal annealing) with comprehensive microstructural characterization techniques, including electron backscatter diffraction (EBSD) and scanning electron microscopy (SEM). The results elucidate a fundamental competitive mechanism governing property optimization: excess Mg atoms concurrently contribute to solid-solution strengthening via the formation of Cottrell atmospheres around dislocations, while simultaneously enhancing electron scattering, which is detrimental to conductivity. A critical synergy was identified at the Mg/Si ratio of 1.75, which promotes the dense precipitation of fine β″ phase while facilitating extensive recovery of high dislocation density. Furthermore, EBSD analysis confirmed the development of a microstructure comprising 74.1% high-angle grain boundaries alongside a low dislocation density (KAM ≤ 2°). This specific microstructural configuration effectively minimizes electron scattering while providing moderate grain boundary strengthening, thereby synergistically achieving an optimal balance between strength and electrical conductivity. Consequently, this work elucidates the key quantitative relationships and competitive mechanisms among composition (Mg/Si ratio), processing parameters, microstructure evolution, and final properties within the studied Al-xMg-0.5Si alloy system. These findings establish a clear design guideline and provide a fundamental understanding for developing high-performance aluminum-based conductor alloys with tailored Mg/Si ratios. Full article

Background/Objectives: The present study investigated the local analgesic effect of a novel synthetic cyclohexanone derivative, 2,6-bis-4-(hydroxyl-3-methoxybenzilidine)-cyclohexanone, or BHMC, in a mouse model of peripheral nociception. Methods: Local administration of BHMC (0.5–60 µg/paw) intra-plantarly in the hindpaws of mice exhibited significant inhibition in carrageenan-induced paw hyperalgesia. Intra-plantar pretreatment of naloxone (non-selective opioid receptor blocker), D-Phe-Cys-Tyr-D-Trp-Orn-Thr-Pen-ThrNH2 (CTOP, selective µ-opioid receptor blocker), and nor-binaltorphimine (nor-BNI, selective κ-opioid receptor blocker), but not naltrindole hydrochloride (selective δ-opioid receptor blocker), reversed the anti-nociceptive effect of BHMC. The peripheral analgesic effect of BHMC was also reversed by intra-plantar pretreatment of methylene blue (soluble guanosyl cyclase blocker), but not N^G-nitro-L-arginine (L-NAME, nitric oxide synthase blocker). Involvement of the potassium channel in the local analgesic effect of BHMC was shown through the reversed analgesic effect by intra-plantar pretreatment of glibenclamide (ATP-sensitive potassium channel blocker), but not by charybdotoxin (large-conductance calcium-sensitive potassium channel blocker), apamin (small-conductance calcium-sensitive potassium ion channel blocker), or tetraethylammonium (voltage-sensitive potassium channel blocker). Results: Taken together, the present study demonstrated that the local administration of BHMC attenuated nociception, with possible mechanisms that may involve the desensitization of inflammatory mediators’ receptors, opioid receptor activation, and nitric oxide-independent cyclic guanosine monophosphate activation of ATP-sensitive potassium ion channel opening. Conclusion: The current findings may further support the exploration of BHMC as a new therapeutic agent for pain and inflammation, for the betterment of human health. Full article

Fire hazard events for road tunnel has correspondingly increased with battery electric vehicle (BEV) penetration rate rising. Compared with conventional internal combustion engine vehicles (ICEV), the research on damage degree of road tunnels caused by BEV fires is not mature. To this end, the temperature distribution and residual load-bearing capacity of road tunnel were studied considering the difference temperature rise curve of BEV fire and ICEV fire. By using the indirect thermal–mechanical coupling approach, the temperature field obtained from fire simulations was applied to the structural model. The assessment of mechanical properties after high-temperature exposure was conducted using the deflection limit method and concrete plastic damage theory. The results show that different heating curve conditions have significant differences in the temperature field and damage distribution of the tunnel. Although different fire effects cause different degrees of structural damage to the tunnel lining, the overall bearing capacity of the structure still has a certain surplus. The results provide a basis for the formulation of repair schemes and reinforcement measures for tunnel structures to assess the safety and normal operation of tunnel structures. Full article

The performance degradation of concrete structures in underground water sumps within the Ordos mining area has become increasingly prominent due to environmental factors, particularly the sulfate-induced dry–wet cycles. These conditions lead to the development of cracks, spalling, and structural instability, which poses significant safety risks. This issue must be addressed with consideration of the regional hydrogeological characteristics and the current requirements for safe, sustainable, and environmentally responsible coal mining practices. The study investigates the concrete employed in the underground central water reservoir of Bulianta Coal Mine in the Ordos mining area. A novel approach is proposed for developing sulfate-resistant concrete capable of withstanding dry–wet cyclic conditions in underground environments through the utilization of coal gangue sourced from the same mining operation. Considering concrete performance, cost-effectiveness, and coal gangue utilization, a laboratory mix optimization study was conducted and the optimal mixture proportion was determined to be a 60% gangue content, a 30% fly ash content, a water–binder ratio of 0.38, which produced concrete with a compressive strength of 31 MPa. Sulfate resistance tests were conducted on the optimal mixture of dry–wet cycle-resistant concrete. The effect of different dry–wet cycle counts on the compressive strength of the coal gangue concrete was investigated, and the evolution patterns of the ascending segment shape coefficient a and descending segment shape coefficient b under sulfate-induced dry–wet cycling were analyzed. Combining the Guo Zhenhai concrete constitutive model, a concrete constitutive model suitable for the dry–wet cycle conditions of sulfate was established. Based on the proposed constitutive model, the uniaxial compressive mechanical behavior of coal gangue concrete subjected to sulfate attack was investigated through numerical simulations using the Abaqus (2020) software. The simulation results are basically consistent with the laboratory results, which proves the applicability of the constitutive model and confirms the performance of the optimal proportioning scheme for preparing sulfate-resistant dry–wet cycle concrete using coal gangue from underground mines. This study provides a new type of concrete for similar underground conditions in this mining area and offers a new approach for the comprehensive utilization of coal gangue. Full article

This study aims to address the pronounced stress concentration in roadway-surrounding rock under conditions of multiple thick and hard roof strata at Xiao jihan coal mine, China. The work was carried out on the 13216 working mining face as the engineering background. A systematic investigation was conducted using a combination of theoretical analysis, numerical simulation, and field experiments. Under double mining disturbance, the lower thick hard roof behaves as a cantilever beam and the upper hard roof strata form a masonry beam structure, producing strong stress transfer to the roadway. The mechanical model indicates a peak stress of 28.90 MPa, 18.34 MPa higher than the in situ stress. Hydraulic roof cutting was designed at the upper thick hard roof horizon. UDEC simulations show that the vertical stress decreases from 26.10 MPa to 13.20 MPa. Field monitoring confirms pressure relief: the non-cutting zone shows a peak of 30.75 MPa, while the roof-cutting zone drops to 22.51 MPa, a 24.62% reduction. The findings of this study provide practical guidance for lateral structure regulation under similar geological and mining conditions. Full article

Wheat (Triticum aestivum L.) is a strategic food crop for arid, hot regions such as the Arabian Peninsula, the Middle East, and North Africa. In these areas, production is limited by extreme environmental and agronomic conditions, leading to heavy dependence on imported wheat. Irrigation is often essential for successful cultivation, but available water sources are frequently saline. This study evaluated the comparative effects of irrigation salinity and genotype on agronomic performance, physiological responses, and grain quality. Nine Syrian wheat genotypes and one French bread-making cultivar, Florence Aurora, were grown in sandy soil under three irrigation salinity levels (2.6, 10, and 15 dS m⁻¹) across two seasons at the International Center for Biosaline Agriculture (Dubai, UAE). Salinity strongly negatively impacted yield, which decreased by 61% from the control to 15 dS m⁻¹, along with key yield components such as thousand grain weight and total biomass. Physiological traits, including carbon isotope composition (δ¹³C) and Na concentrations in roots, shoots and grains, increased significantly with salinity, while chlorophyll content showed a modest decline. Effects on grain quality were relatively minor: total nitrogen concentration and most mineral levels increased slightly, mainly due to a passive concentration effect associated with reduced TGW. Genotypes varied significantly in yield, biomass, TGW, physiological traits, and grain quality. The highest-yielding genotypes under control conditions (ACSAD 981 and ACSAD 1147) also performed best under saline conditions, and no trade-off was observed between yield and grain quality parameters (TGW, nitrogen, zinc, and iron concentrations). Separate analyses conducted for control and saline treatments identified different drivers of genotypic variability. Under control conditions, chlorophyll content, closely linked with δ¹³C, was the best predictor of genotypic differences and was positively correlated with yield across genotypes. Under salinity stress, grain magnesium (Mg) concentration was the strongest predictor, followed by grain δ¹³C, with both traits positively correlated with yield. These findings highlight key physiological traits linked to salinity tolerance and offer insights into the mechanisms underlying genotypic variability under both optimal and saline irrigation conditions. Full article

Soil organic matter (SOM) is a key indicator for evaluating soil fertility and ecological functions, and hyperspectral technology provides an effective means for its rapid and non-destructive estimation. However, in practical soil systems, the spectral response of SOM is often highly covariant with mineral composition, moisture conditions, and soil structural characteristics. Under small-sample conditions, hyperspectral SOM modeling results are usually highly sensitive to spectral preprocessing methods, sample perturbations, and model architecture and parameter configurations, leading to fluctuations in predictive performance across independent runs and thereby limiting model stability and practical applicability. To address these issues, this study proposes a multi-strategy collaborative deep learning modeling framework for small-sample conditions (SE-EDCNN-DA-LWGPSO). Under unified data partitioning and evaluation settings, the framework integrates spectral preprocessing, data augmentation based on sensor perturbation simulation, multi-scale dilated convolution feature extraction, an SE channel attention mechanism, and a linearly weighted generalized particle swarm optimization algorithm. Subtropical red soil samples from Guangxi were used as the study object. Samples were partitioned using the SPXY method, and multiple independent repeated experiments were conducted to evaluate the predictive performance and training consistency of the model under fixed validation conditions. The results indicate that the combination of Savitzky–Golay filtering and first-derivative transformation (SG–1DR) exhibits superior overall stability among various preprocessing schemes. In model structure comparison and ablation analysis, as dilated convolution, data augmentation, and channel attention mechanisms were progressively introduced, the fluctuations of prediction errors on the validation set gradually converged, and the performance dispersion among different independent runs was significantly reduced. Under ten independent repeated experiments, the final model achieved R² = 0.938 ± 0.010, RMSE = 2.256 ± 0.176 g·kg⁻¹, and RPD = 4.050 ± 0.305 on the validation set, demonstrating that the proposed framework has good modeling consistency and numerical stability under small-sample conditions. Full article

In the preliminary design stage of wind farms, the theoretical energy output must be adjusted by multiple reduction factors to estimate the actual grid-connected power. As renewable energy becomes increasingly integrated into electricity markets, the conventional approach using static, averaged reduction coefficients for annual yield estimation can no longer meet the market’s demand for high-resolution power time series. Addressing this gap, the novelty of this paper lies in shifting the focus from total annual estimation to hourly-level dynamic allocation. This paper proposes a time-series reduction coefficient evaluation method based on the time-varying entropy weight method (TV-EWM). Under the assumption that the total annual reduction quantity adheres to standard design specifications, this method utilizes long-term wind measurement data, integrates unique ultra-high-altitude wind resource characteristics, and constructs a scenario-based indicator system. By quantifying the coupling relationships between key meteorological variables and incorporating a dynamic weighting mechanism, the proposed approach achieves hourly refined reduction estimation for theoretical power output. Comparative analysis was conducted against the traditional static average reduction method. Results indicate that, compared to the traditional average reduction method, the TV-EWM approach significantly enhances the model’s ability to capture seasonal variability, increasing the coefficient of determination (R²) by 4.19% to 0.7061. Furthermore, it demonstrates higher stability in error control, reducing the Normalized Root Mean Square Error (NRMSE) by 4.51% to 15.45%. The TV-EWM more accurately captures the temporal evolution and coupling effects between meteorological elements and curtailed generation under various reduction scenarios, retains full-load operational features, and enhances physical interpretability and time responsiveness, providing a new analytical framework for market-oriented power generation assessment. Full article

With the intensive development of oil and gas fields, multi-well layouts with reduced inter-well spacing are increasingly adopted to improve production efficiency. Such configurations, however, may significantly enhance seismic interaction among adjacent wells. In this study, a nonlinear three-dimensional finite element model incorporating soil–structure interaction is developed using GTS NX to investigate the seismic dynamic response of closely spaced oil well casings. A representative dual-well system is analyzed under horizontal earthquake ground motion. The influence of inter-well spacing on displacement response characteristics is systematically examined. Numerical simulations are conducted for three center-to-center spacing distances (5 m, 7.5 m, and 10 m). The spatial distribution of displacement responses in both the casings and the surrounding soil is analyzed at different depths and monitoring sections. The results indicate that reduced well spacing significantly amplifies dynamic coupling effects, leading to increased displacement responses in the casing–soil system. These findings provide quantitative insight into spacing-dependent seismic interaction mechanisms and offer theoretical support for seismic design and spatial optimization of multi-well systems. Full article

Introduction: Massage therapy delivers structured mechanosensory input that can influence brain function, yet the central mechanisms and potential for neuroplastic change have not been synthesized across neuroimaging modalities. This mechanistic review integrates evidence from electroencephalography (EEG), functional MRI (fMRI), and functional near-infrared spectroscopy (fNIRS) to map how massage alters human brain activity acutely and over time and to identify signals of longitudinal adaptation. Materials and Methods: We conducted a scoping, mechanistic review informed by PRISMA/PRISMA-ScR principles. PubMed/MEDLINE, Cochrane Library, Google Scholar, and ResearchGate were queried for English-language human trials (January 1990–July 2025) that (1) delivered a practitioner-applied manual massage (e.g., Swedish, Thai, shiatsu, tuina, reflexology, myofascial techniques) and (2) measured brain activity with EEG, fMRI, or fNIRS pre/post or between groups. Non-manual stimulation, structural-only imaging, protocols, and non-English reports were excluded. Two reviewers independently screened and extracted study, intervention, and neuroimaging details; heterogeneity precluded meta-analysis, so results were narratively synthesized by modality and linked to putative mechanisms and longitudinal effects. Results: Forty-seven studies met the criteria: 30 EEG, 12 fMRI, and 5 fNIRS. Results: Regarding EEG, massage commonly increased alpha across single sessions with reductions in beta/gamma, alongside pressure-dependent autonomic shifts; moderate pressure favored a parasympathetic/relaxation profile. Connectivity effects were state- and modality-specific (e.g., reduced inter-occipital alpha coherence after facial massage, preserved or reorganized coupling with hands-on vs. mechanical delivery). Frontal alpha asymmetry frequently shifted leftward (approach/positive affect). Pain cohorts showed decreased cortical entropy and a shift toward slower rhythms, which tracked analgesia. Somatotopy emerged during unilateral treatments (contralateral central beta suppression). Adjuncts (e.g., binaural beats) enhanced anti-fatigue indices. Longitudinally, repeated programs showed attenuation of acute EEG/cortisol responses yet improvements in stress and performance; in one program, BDNF increased across weeks. In preterm infants, twice-daily massage accelerated EEG maturation (higher alpha/beta, lower delta) in a dose-responsive fashion; the EEG background was more continuous. In fMRI studies, in-scanner touch and reflexology engaged the insula, anterior cingulate, striatum, and periaqueductal gray; somatotopic specificity was observed for mapped foot areas. Resting-state studies in chronic pain reported normalization of regional homogeneity and/or connectivity within default-mode and salience/interoceptive networks after multi-session tuina or osteopathic interventions, paralleling symptom improvement; some task-based effects persisted at delayed follow-up. fNIRS studies generally showed increased prefrontal oxygenation during/after massage; in motor-impaired cohorts, acupressure/massage enhanced lateralized sensorimotor activation, consistent with use-dependent plasticity. Some reports paired hemodynamic changes with oxytocin and autonomic markers. Conclusions: Across modalities, massage reliably modulates central activity acutely and shows convergent signals of neuroplastic adaptation with repeated dosing and in developmental windows. Evidence supports (i) rapid induction of relaxed/analgesic states (alpha increases, network rebalancing) and (ii) longer-horizon changes—network normalization in chronic pain, EEG maturation in preterm infants, and neurotrophic up-shifts—consistent with trait-level recalibration of stress, interoception, and pain circuits. These findings justify integrating massage into rehabilitation, pain management, mental health, and neonatal care and motivate larger, standardized, multimodal longitudinal trials to define dose–response relationships, durability, and mechanistic mediators (e.g., connectivity targets, neuropeptides). Full article