Search Results (44,495)

To address the challenges of rapid voltage fluctuations and operational economy in new power systems with high wind power integration under sandstorm weather, this paper proposes a multi-timescale cooperative voltage control strategy for new power systems. On the second-level timescale, a discrete state-space model of wind turbines and reactive power devices is established considering sudden wind speed changes. Model predictive control (MPC) is then used to rapidly calculate the optimal reactive power references for wind turbines, static var generator (SVG), and on-load tap changer (OLTC), thereby effectively ensuring rapid stabilization of the grid-connection point voltage. On the minute-level timescale, a two-stage topology reconfiguration method is adopted. A feasible radial network is first generated through a sequential switch opening strategy, followed by iterative optimization via a switch exchange strategy. This approach rapidly identifies the optimal switch configuration to minimize network losses and improve operational economy. Simulation results demonstrate the effectiveness of the proposed strategy in voltage regulation and loss reduction, highlighting its capability to enhance the robustness of new power systems under sandstorm weather. Full article

Background/Objectives: Precise genetic access to the zebrafish habenula remains limited by a scarcity of compact, sequence-defined cis-regulatory elements (CREs). Here, we integrated developmental expression mapping, deep-learning predictions on long-range sequences, and in vivo reporter assays to identify compact regulatory sequences driving habenular expression. Methods: Using a transgenic zebrafish line enriched for habenular reporter expression, we isolated GFP-positive cells from larval brains and profiled their transcriptomes via microarray. A subset of candidate genes enriched in this dataset was validated using whole-mount in situ hybridization across two developmental stages. This analysis identified genes with highly reproducible habenular expression, leading to the selection of the gng8 and ano2 loci for subsequent CRE characterization. We developed ZEN-former (Zebrafish EN-former), an Enformer-based sequence-to-function model trained on neuronal subclass chromatin accessibility profiles from the adult mouse brain. Results: The model demonstrated strong correlation between predicted and experimentally measured signals across held-out genomic regions. To prioritize regulatory candidates, we integrated ZEN-former predictions with available zebrafish ATAC-seq data, RepeatMasker annotations, and gene models, identifying two ~600 bp intervals at each gene locus. These selected intervals were combined to generate ~1.2 kb reporter constructs for gng8 and ano2 loci, which were then evaluated using Tol2 transposon mediated transgenesis assays in zebrafish. In transiently injected larvae, both constructs successfully drove reporter expression in the habenular region. Furthermore, the resulting stable transgenic lines displayed highly specific and reproducible habenular expression. Quantitative confocal analysis showed mean habenular labeling completeness values of 84.5% and 96.2% for the gng8- and ano2-derived lines, respectively. Conclusions: Together, these findings provide a proof of concept that sequence features learned from mammalian chromatin accessibility datasets can effectively guide the prioritization of functional regulatory elements across species in zebrafish. The compact regulatory constructs and stable transgenic lines generated here offer robust genetic tools for investigating habenular circuitry. Full article

Agricultural machinery operates under variable loads, impacts, dust, and changing soil–crop interactions, allowing faults to propagate through energy, material, information, and control pathways. This qualitative review synthesizes 195 research publications across fault formation, trustworthy diagnosis, prognostics and proactive risk control, maintenance and recovery, and case-based evidence assessment. Empirical findings are reported separately from review-derived recommendations, the authors’ conceptual requirements, and mandatory provisions of applicable standards or law. The literature most consistently supports controlled-fault identification, selected single-machine field monitoring, and localized operational compensation. Evidence is weaker for transfer across machines and seasons, calibrated prognostics, safety authorization, post-repair verification, and fleet-scale deployment. On this basis, the review recommends mission profile-specific fault boundaries, traceable diagnostic outputs, explicit uncertainty and abstention, and risk decisions linked to remaining work and resources. It also proposes a diagnostic passport, risk evolution trajectory, repair-effectiveness label, case-evidence matrix, and closed-loop repair workflow as review-level organizing constructs. No included study continuously followed the same machine or fleet through diagnosis, prognosis, authorization, maintenance, acceptance, and feedback; the framework therefore connects evidence-supported stages theoretically rather than claiming end-to-end validation. Full article

Aquatic product safety is closely linked to human health, yet the effects of historical residual drugs in aquaculture pond sediment remain unclear. Malachite green (MG), a prohibited aquaculture drug, is rapidly converted to leucomalachite green (LMG) after illegal use and may persist in bottom sediment, raising the question of whether residue-positive sediment can cause positive detections in fish at harvest. In order to answer this question, we developed a time-dependent predictive model for the sediment–water–fish system based on a food web bioaccumulation model and a sediment–water equilibrium model. The model examines the relationship between historical MG residues in sediment and positive detections in aquatic products, and estimates LMG residue levels in fish at harvest. The reliability of this model was further validated through comparison with measured data. The results showed that when the sediment residue concentration was 227.48 μg/kg, the maximum LMG concentration in fish during the culture period reached 0.56 μg/kg, indicating a positive correlation with fish concentration. The closer the habitat water layer was to the sediment, the higher the risk of positive detection in fish; in the middle water layer, the LMG concentration in fish could reach 0.28 μg/kg, and, under ideal conditions, a two-fold relationship existed between the middle and lower water layers. Sediment organic carbon content determined the amount of LMG released into the water; when it was set to 0.0035, the initial LMG concentration in water was 0.07 μg/kg, showing a negative correlation with fish concentration. This study confirmed that a certain amount of residual malachite green in sediment could cause positive detection in mandarin fish during the culture period, which is consistent with the validation experiment. If the model predicted no positive detection at the pollutant concentration corresponding to the sediment of the positive detection pond, this would indicate that malachite green originated from other sources during the culture process; otherwise, the sediment may lead to the risk of positive detection in fish. One limitation of the model is that it may underestimate pollutant concentrations in fish during the early stages of aquaculture. In addition, its ability to predict the effects of combined pollutant exposure remains to be validated. The model developed here can be used to estimate residue levels of aquaculture drugs during the culture period and may provide regulatory authorities with a basis for distinguishing historical residues from illicit use, thereby improving the efficiency of aquatic product quality and safety supervision. Full article

Tool–prepreg friction contributes to process-induced residual stress and distortion in thermoset composite manufacturing, but its variation with the resin cure state remains insufficiently characterized. This study experimentally characterizes the friction response of untreated and post-gel pretreated T800/bismaleimide prepregs using a pull-out apparatus. The effects of slip velocity, temperature, and normal pressure are evaluated, and the measured trends are interpreted using Coulomb-type contact, viscous film shearing, and mixed-lubrication concepts. The friction coefficient of the untreated prepreg increases with the slip velocity and decreases with temperature and pressure, indicating a substantial contribution from viscous resin-film shearing. In contrast, the post-gel pretreated prepreg is nearly insensitive to the slip velocity and pressure, while its friction coefficient increases with temperature, consistent with a predominantly solid-like interfacial response. During a cure cycle, the friction increases slowly at low cure levels and more rapidly during the later stages of curing. The results provide experimental friction data for a high-temperature bismaleimide prepreg system and suggest that the gelation state may serve as a useful reference for distinguishing early- and later-stage interfacial behavior. Full article

Reliable rooftop photovoltaic planning requires design-stage energy predictions to be verified against actual system performance. The novelty of this study is the integration of a multi-year assessment of agreement with PV*SOL design estimates with an independent assessment of normalized productivity, interannual variability, seasonality, and meteorological effects for several rooftop systems operating under the same regional conditions. PV*SOL, a commercial photovoltaic simulation software used to estimate system energy production during the design stage, was evaluated using three years (2023–2025) of monitored data from three rooftop photovoltaic (PV) systems (17.60–75.40 kWp) in Rzeszów, south-eastern Poland. The analysis comprised 108 installation-month observations and included final yield, capacity factor, annual prediction errors, seasonal variability, Pearson correlations, and hierarchical regression. Mean annual final yield ranged from 907.4 to 966.2 kWh/kWp, while annual deviations from PV*SOL design estimates ranged from −0.80% to +7.41%. Monthly final yield was strongly associated with solar irradiation, and the final hierarchical regression model explained 96.4% of its variability. The results indicate that PV*SOL provides a useful annual design reference, but operational monitoring and local benchmark data remain essential for reliable performance assessment. The study supports United Nations Sustainable Development Goal 7 (Affordable and Clean Energy) by improving the evidence base for rooftop photovoltaic planning and monitoring. Full article

Multi-label classification of financial news is frequently affected by incomplete and noisy annotations, while obtaining expert-curated labels at scale is prohibitively expensive. This study proposes a weakly supervised classification framework that combines large language model (LLM) zero-shot annotation with a serial label-correction strategy. The framework first uses an LLM to generate initial weak labels and then refines them through a two-stage Correct→Clean procedure that recovers missing labels via centrality-weighted graph propagation before suppressing label noise. Systematic experiments on a financial subset of Reuters-21578 show that, under an extreme mixed-corruption setting with 80% missing labels and 15% noise labels, Correct→Clean increases the Micro-F1 from 0 to 0.6748. In an end-to-end evaluation, the proposed framework achieves a Micro-F1 of 0.8882 with reduced-dimensional features, recovering 88.69% of the performance gap to fully supervised learning. Additional experiments on the RCV1 Topics and AAPD datasets confirm that the advantage of Correct→Clean is consistently reproduced across domains and dataset sizes. These findings demonstrate that coupling LLM-generated annotations with ordered label correction offers an effective means of addressing the joint effects of missing and noisy labels, providing a promising approach to financial text classification when expert annotations are scarce. Full article

Class imbalance is prevalent in real-world datasets. Minority samples are far fewer than majority samples. Traditional classifier design typically assumes balanced data, which causes classifiers to favor the majority class when faced with imbalanced datasets. Thus, there are high misclassification costs for minority classes in critical fields like healthcare and finance. Most existing oversampling methods for handling imbalance problems, such as SMOTE (the synthetic minority oversampling technique), suffer from limitations like noise sensitivity, failure to consider minority-class sub-cluster structures, and poor adaptability to the heterogeneity of sample distributions. This article addresses these issues by proposing a novel oversampling algorithm: the adaptive weighting–synthetic minority oversampling technique (AW-SMOTE). It consists of three progressive stages. In the first stage, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering identifies the distribution structure of the minority samples. It identifies potential sub-clusters while removing noise interference. A clear data foundation for subsequent sampling is established. In the second stage, the most representative boundary sample in each cluster is used to evaluate the weight of each cluster. The total number of synthetic samples is allocated to different clusters according to their weights. This provides global sample enhancement support. In the third stage, adaptive sample generation is performed within each cluster. It combines the two perspectives of boundary tightness and local density. The sigmoid function is used to dynamically adjust the weight ratio. Finally, new samples are synthesized in key regions to both preserve distribution characteristics and enhance discriminability in classification. Through experiments on standard datasets from the KEEL repository, the feasibility and effectiveness of this algorithm are demonstrated. Full article

Yield losses due to water-deficit (WD) conditions, especially during the reproductive stages of plant development, pose a significant threat to global canola (Brassica napus L.) production. Therefore, it is critical to investigate traits contributing to improved productivity under increased WD conditions. Here we present phenotypic, physiological and transcriptomic changes in response to WD across contrasting canola accessions exhibiting variation in drought resistance-related traits. WD significantly reduced shoot biomass, plant height, harvest index, leaf water content, photosynthetic CO₂ assimilation rate, intrinsic water-use efficiency and carbon isotope discrimination. WD caused 49 to 100% of the seed yield reduction: the minimum seed yield reduction (49.66%) was observed in a doubled-haploid (DH) line, 06-5101.137, while the maximum yield reduction (94.1 to 100%) occurred in the late-flowering DH lines (06.5101.088, 06-5101.306). Seed yield showed a positive correlation (r = 0.35 to 0.97) with shoot biomass, plant height, harvest index, leaf water content, photosynthetic CO₂ assimilation rate, intrinsic water use efficiency and carbon isotope discrimination. However, it showed negative correlations with days to flower, specific leaf weight, root length and root biomass (r = −0.08 to −0.80) across water treatments. The leaf transcriptome analysis of the two parental lines of DH population that exhibit variation for effective water use under well-watered and water-deficient conditions revealed different categories of differentially expressed genes (DEGs): WD-responsive DEGs in BC1329 parental line (1116) and BC9102 (1205) with 754 and 853 DEGs unique to BC1329 and BC9102, respectively, WD-responsive DEGs (906), genotype-dependent DEGs (8465) and genotype × treatment interaction DEGs (353). DEG annotations revealed that the WD-treatment-affected genes were involved in stress responses and growth and development. We further located 235 DEGs within the QTL regions underlying agronomic and physiological performance. Our study provides a conceptual framework for the morphological, physiological and molecular determinants involved in water-use efficiency. Seedlings’ traits with high heritability values, such as shoot biomass, leaf weight, leaf water content and Δ¹³C, serve as proxies for trait-based selection for improved seed yield under both water-limited and non-water-limited conditions. Full article

Background/Objectives: Class II malocclusion in growing patients may be associated with altered mandibular posture, occlusal instability, and adaptive changes in masticatory muscle function. This prospective, controlled, non-randomized longitudinal study evaluated changes in masseter and anterior temporalis muscle activity during Andresen functional appliance therapy and compared them with untreated Class I and Class II controls. Methods: Seventy children with skeletal and dental Class II malocclusion were treated with a customized Andresen appliance; 50 untreated Class II children and 30 untreated children with physiological Class I occlusion served as comparison groups. All participants were in mixed dentition and at cervical stage CS3 at baseline. Standardized bilateral sEMG recordings were obtained at T0, T1 (6 months), and T2 (12 months). Longitudinal sEMG outcomes were analyzed using mixed-design repeated-measures ANOVA, with time as the within-subject factor and group as the between-subject factor; prespecified T0-to-T2 contrasts were accompanied by Cohen’s d effect sizes. Results: In the treated Class II cohort, IMPACT increased from 108.64 ± 3.40 at T0 to 111.58 ± 3.13 at T1 and 113.82 ± 3.27 at T2 (within-group time effect p < 0.001). ATTIV changed from −0.88 ± 0.42 at T0 to +0.96 ± 0.55 at T1 and +1.65 ± 0.78 at T2 (p < 0.001). In contrast, neither the untreated Class II nor the physiological Class I group showed statistically significant within-group time effects for IMPACT (p = 0.96 and p = 0.58, respectively) or ATTIV (p = 0.78 and p = 0.93, respectively). Significant T0-to-T2 differences between treated and untreated Class II groups were observed for ATTIV (p < 0.001; d = +5.28) and IMPACT (p < 0.001; d = +2.00). Conclusions: Andresen therapy was associated with statistically supported longitudinal differences in masticatory muscle recruitment compared with untreated Class II controls. The observed pattern may reflect treatment-associated neuromuscular adaptation; however, causal attribution remains limited by the non-randomized design, and the sEMG changes should be interpreted as modifications in muscle recruitment rather than as evidence of physiological normalization or clinical improvement. Full article

This study presents a systematic comparison of two dominant paradigms for improving rotation robustness in remote sensing semantic segmentation: rotation-invariant architectural modules (represented by Rotation-Invariant Channel Mapping, RICM) and random rotation data augmentation. Despite both approaches being widely adopted, a fair and side-by-side evaluation under unified experimental conditions remains lacking. Using U-Net as the baseline on the LoveDA dataset, we comprehensively evaluate six strategies: RICM alone, random rotation augmentation at probabilities $p=0.25$ and $p=0.5$, and their combinations. Performance is assessed under both discrete rotations (0°, 90°, 180°, 270°) and continuous angles (0°–180° with 15° intervals), supplemented by Rotation Consistency (RC) analysis, class-wise evaluation, and computational cost comparison. Cross-dataset validation on ISPRS Potsdam confirms generalizability. Our results demonstrate that: (1) random rotation augmentation consistently outperforms RICM, delivering substantial robustness gains with zero inference overhead; (2) augmentation probability controls a clear accuracy–robustness trade-off, with $p=0.25$ achieving the best balance (maintaining 0° mIoU at 74.8% while boosting 90° mIoU from 54.9% to 63.1%) and $p=0.5$ achieving near-invariance at the cost of reduced baseline accuracy; and (3) RICM offers only marginal benefits and becomes redundant when augmentation is applied. Analysis of the observed performance patterns suggests that early-stage RICM insertion may suppress useful orientation-specific features and that its rotation-ensemble averaging may be insufficient for dense prediction tasks. Overall, random rotation augmentation proves to be a simple, effective, and computationally efficient strategy for improving rotation robustness in remote sensing segmentation. Full article

The transition from open pit to underground mining is becoming increasingly important as near-surface ore is depleted and mines seek to extend operating life. Yet its environmental and operational effects are often discussed separately. This systematic review examines the risks reported during the transition stage and develops an integrated framework for mine planning. Following a PRISMA-guided approach, peer-reviewed literature published between 2010 and 2026 was screened, and 69 records were included in the final review, comprising 66 primary studies and three directly relevant review articles. The evidence shows that the transition redistributes rather than simply reduces environmental pressure. Water management, ground stability, underground air quality, ventilation, and energy demand may become more important as underground development advances, while some surface impacts may decline as open pit activity decreases. The review therefore treats the active overlap period as a distinct planning stage and brings the main environmental and operational risks into one transition-planning framework. Full article

Water retention on the lunar surface is governed by water–mineral interactions, yet the atomic-scale transition from isolated adsorption to high-coverage water accumulation remains insufficiently understood. We perform spin-polarized first-principles calculations to investigate single- and multi-water adsorption on representative surfaces of four major lunar regolith minerals: CaAl₂Si₂O₈, MgFeSi₂O₆, FeTiO₃, and Mg₃FeSi₂O₈. Single-water adsorption reveals that H₂O preferentially anchors at exposed metal sites via O-M coordination, with Ti and Fe sites exhibiting stronger initial binding than Mg, Ca, or Al sites. The Hard–Soft Acid–Base (HSAB) principle provides a qualitative framework for this low-coverage site preference based on Lewis acidity. Specifically, the accessible d-orbitals and localized states of Ti/Fe centers introduce substantial covalent orbital coupling and interfacial polarization, which effectively reinforce the binding with the hard O-donor of water. However, as water coverage increases, the stabilization mechanism undergoes a fundamental transition. At low coverage, adsorption is localized and site-specific, governed by cation acidity. At high coverage, the formation of laterally connected hydrogen-bonded networks becomes the dominant stabilizing factor, and the overall adsorption behavior is increasingly dictated by surface topology and geometric compatibility for hydrogen-bond connectivity rather than by isolated cation acidity. This coverage-dependent evolution from electronic-driven anchoring to topology-driven network formation establishes a dual-stage cooperative mechanism for water accumulation on lunar mineral surfaces. Our findings suggest that models for volatile retention on airless bodies must account for both the electronic activity of surface cations and the structural topology of mineral surfaces. Full article

Studies of Broadband China reach different environmental conclusions, but they do not always measure the same policy implementation or environmental outcome. We test whether conclusions survive changes in implementation, outcome, denominator, weighting, and baseline city capacity. We link the 2014–2016 cohorts to prefecture-level panels, using administrative outcomes through 2019 and annual PM_2.5 through 2023. First, designation does not establish a reliable physical-network first stage: access ports rise, but the estimate fails the pretrend test, while adjusted fiber models fail overlap or balance diagnostics. Second, on the same 2427 city-years, industrial wastewater rises by 22.26%, whereas sulfur dioxide, smoke/dust, and PM_2.5 remain imprecise. On the adjusted sample, the wastewater estimate falls from 16.45% to an imprecise 7.69% after cross-fitted selection adjustment. Third, an exploratory within-sample analysis finds that a one-standard-deviation increase in baseline digital readiness is associated with an 18.76% lower treated-versus-control smoke/dust change (95% confidence interval: −30.54% to −4.98%), although mechanism tests do not survive multiplicity adjustment. These findings do not imply that broadband has no environmental effects. They show instead that any such claim must be tied to verified implementation, the environmental measure, and prepolicy city capacity. Full article

Lung cancer is the leading cause of cancer-related mortality worldwide, yet the majority of cases are diagnosed at advanced stages when a cure is rarely achievable. Low-dose computed tomography (LDCT) screening in high-risk populations reduces lung cancer mortality by 20–24% in randomised trials, but fewer than 20% of eligible adults in the United States undergo annual screening. We conducted a structured narrative review of PubMed, Embase, and the Cochrane Library for studies published between January 2002 and March 2026, supplemented by review of current clinical guidelines from the USPSTF, NCCN, ACS, CHEST, and ERS. Evidence from the National Lung Screening Trial (NLST) and the NELSON trial establishes the mortality benefit of LDCT screening, though both trials have important methodological limitations that affect generalisability. The NLST predominantly detected non-small cell lung cancer (NSCLC), particularly adenocarcinoma and squamous cell carcinoma, while small cell lung cancer (SCLC) was infrequently screen detected and showed no survival benefit from early detection. Guideline eligibility criteria have progressively broadened, and multivariable risk model-based selection using the PLCOm2012 now demonstrates superiority over categorical smoking thresholds in prospective validation cohorts, with the added benefit of reducing racial and ethnic eligibility disparities. Overdiagnosis estimates have declined substantially with extended follow-up, reaching 7% when observation exceeds five years. Implementation remains critically deficient: patient stigma, provider knowledge gaps, structural barriers, and inadequate electronic health record infrastructure collectively account for screening uptake below 20%. Integrating smoking cessation into screening encounters is evidence-based and cost-effective. Artificial intelligence tools show promising performance in nodule detection and risk prediction, but lack the prospective external validation required for routine clinical deployment. The field has established efficacy; the urgent challenge is now effectiveness at scale. Transitioning to risk model-based eligibility, expanding access to underserved populations, and mandating cessation integration represent the three highest-priority actions. A research agenda addressing never-smoker screening, personalised intervals, and robust AI validation must proceed in parallel. Full article