Search Results (207)

To address the two fundamental contradictions in distribution network digital simulation systems: “diversified simulation requirements versus standardized configuration” and “massive simulation outputs versus sparse decision-making information”, this paper builds upon existing work on Common Information Model (CIM)-based automatic simulation model generation and dynamic voltage regulation simulation and further constructs a user-oriented intelligent simulation service layer. This layer is collaboratively composed of an Orchestration_Agent (simulation orchestration agent) and an Analysis_Agent (result analysis agent), tasked with three responsibilities: based on multi-level simulation granularity (L0–L3) and a simulation template library, leveraging a large language model (LLM) to achieve natural language requirements parsing and automatic workflow orchestration; based on a simulation knowledge graph, implementing parameter recommendation, verification, and anomaly-adaptive recovery for process control; and based on a hybrid architecture of rule templates, statistical analysis, and causal graph models, achieving automatic result analysis, root cause reasoning, and structured report generation, with case feedback driving knowledge base iteration. Validation was conducted on data from a real 10 kV feeder with 91 distribution transformer areas over 30 consecutive days (2880 time cross-sections): comprehensive requirements-parsing accuracy of 96.3%, automatic parameter configuration coverage rate of 94.7%, anomaly identification recall/precision of 94.0%/96.9%, root cause reasoning accuracy of 92.1%, and the median end-to-end time per simulation shortened from approximately 36 min under the manual mode to 4.7 min. The results demonstrate that the proposed service layer provides a viable engineering technology pathway for the evolution of distribution network simulation from tool-oriented to service-oriented. Full article

Compound spring floods in cold-continental and semi-arid interiors arise from interacting snowmelt, rain-on-snow events, intense precipitation, and frozen or saturated soils, yet these mechanisms remain poorly synthesized for Central Asia. This review develops a process-oriented framework linking preconditioning, triggers, propagation/amplification, impacts, and recovery outcomes. The synthesis shows that the most destructive spring floods occur when substantial antecedent snow storage and restricted infiltration coincide with rapid warming and rainfall, producing efficient runoff generation and widespread impacts. Evidence from Kazakhstan and comparable continental regions indicates that mechanistic understanding is relatively robust, but standardized event-level reporting of snow-water equivalent, soil wetness, precipitation phase, routing constraints, and recovery indicators remains uneven. To support post-disaster comparison, we introduce and demonstrate a Recovery Effectiveness Index (REI) combining housing resettlement, compensation, infrastructure restoration, and equity of assistance. A proof-of-concept application to the 2024 Kazakhstan floods produced a 21 June 2024 snapshot REI of 0.607–0.757 and an end-year REI of 0.850–1.000, depending on the treatment of the equity component. The framework supports compound-driver monitoring, early warning, recovery benchmarking, and more harmonized flood-risk assessment in data-sparse continental regions. Full article

The changing climate raises the level of hydroclimatic non-stationarity and export of pollutants at the event scale in agricultural, mixed-land-use, and urbanizing watersheds. In this review, there is an emphasis on nitrogen, phosphorus, and sediment; however, selective references are made to pesticides, pathogens, microplastics, and wet-weather mixed-source processes when characteristics similar to event-driven transport, threshold exceedance, and adaptive control are identified. Drawing on a structured literature search of studies published from 2000 to December 2025, this narrative review synthesizes evidence from 138 selected references on how extreme rainfall, drought–rewetting, warming, and freeze–thaw processes alter source activation, hydrological connectivity, biogeochemical processing, and receiving-water hazards. Our resilience assessment is based on resistance, recovery, robustness, and persistence, which we interpret using exposure, sensitivity, and adaptive capacity. It is shown that standard average-load and fixed-baseline measurements may not detect short pollution pulses, cross-scenario failure, and long-term drift; operational measurement must thus involve event thresholds, recovery trajectories, tail-risk measures, and propagation of uncertainty. Extrapolation, interpretability, data demand, and applicability for data-sparse basins are used to compare process-based, data-driven, and hybrid models. Adaptation options are associated with measurable triggers as part of a monitoring–trigger–action cycle with location-specific instructions for monsoon-agricultural, cold-region, semi-arid and urban systems. The novel aspect of this framework is the integration of mechanism-based evidence, quantitative resilience indicators, model uncertainty, and adaptive governance into one decision-focused workflow. This sustainability-oriented framework advances long-term watershed management by linking water-quality protection and resilient development. Full article

High-dimensional change point detection is a fundamental problem in modern statistical learning, particularly when distributional changes are driven by a small and unknown subset of variables. In heterogeneous settings, uniform aggregation across coordinates may suffer from signal dilution, because stable or noisy variables can mask the evidence carried by structurally unstable coordinates. Moreover, many existing procedures primarily focus on temporal localization and provide limited information about the variables responsible for a detected structural break. To address these challenges, we propose WAVE, a weighted adaptive variable selection procedure for interpretable change point detection. WAVE constructs variance-standardized global CUSUM evidence and locally standardized exponentially weighted evidence for each coordinate and then adaptively maps intervalwise coordinate evidence into a continuous weight vector. The learned weights strengthen coordinates with persistent or local evidence of change while downweighting nuisance coordinates with weak evidence. The resulting weighted scan statistic is calibrated by a residual moving block bootstrap that preserves temporal and cross-sectional dependence and re-applies the weighting rule within bootstrap samples to account for data-adaptive aggregation. Detected change points are further equipped with coordinate-level attribution through a multi-criteria fusion rule combining adaptive weights, local standardized effect sizes, and marginal testing evidence. Simulation studies show that WAVE achieves accurate localization and reliable support recovery in both single and multiple change point settings, particularly under sparse and heterogeneous alternatives. An empirical analysis of S&P 100 stock returns in 2020 further demonstrates that WAVE identifies economically meaningful market regime shifts with interpretable coordinate-level attribution. Full article

Post-disaster recovery is a multi-year, multi-dimensional process, yet most remote-sensing assessments rely on single indicators and are hard to apply in data-sparse regions—limiting their value for sustainable, evidence-based reconstruction. We develop a Google Earth Engine (GEE)-based multi-sensor Recovery-Activity Index (RAI), built entirely from free satellite data, and apply it to the 2023 Al Haouz earthquake (Mw 6.8) in the High Atlas, Morocco. The index is framed explicitly as an observed recovery-activity monitoring proxy, not a direct measure of welfare or resilience capacity. Monthly VIIRS nighttime-light (NTL) anomalies, Dynamic World built-up probability, and precipitation-corrected Sentinel-2 NDVI were extracted for a 30 km rural core zone (January 2022–May 2026), deseasonalized, standardized, and integrated. NTL anomalies rose after the earthquake (post-event mean +18%) and appeared to precede built-up anomalies by about two months; because monthly series are short and autocorrelated, we tested this lead with block-bootstrap and block-permutation methods and report it as a reproducible but modest early-activity lead (r = 0.65, p = 0.02; p = 0.14 after correction) that is not an artefact of optical data gaps. NDVI was governed mainly by precipitation (R² = 0.61) with negligible earthquake-attributable change, so vegetation signals do not confound the index. The integrated RAI peaked in December 2024 and proved robust to indicator weighting (pairwise r ≥ 0.97), baseline choice (r = 0.88), and spatial domain (<9% variation), with a genuinely multi-sensor peak (NTL 62%, built-up 43%). Province-level analysis revealed an uneven recovery hierarchy (Chichaoua > Al Haouz > Taroudannt) driven by differences in physical-rebuilding signal rather than baseline luminosity. Running in minutes server-side at no cost, the RAI offers data- and resource-limited administrations a scalable, reproducible tool to flag where reconstruction activity lags and to prioritize targeted ground verification—supporting more equitable, sustainability-oriented recovery governance—rather than serving as a stand-alone, validated recovery measure. Full article

Given the issues in natural orchard environments, such as large-scale variations of defective citrus fruits, weak texture boundaries, strong illumination changes, branch and leaf occlusion, and significant background interference, this paper constructs a lightweight detection model, RT-DETR-DCEA, based on RT-DETR-R18. This model is improved through four aspects: “fine-grained defective feature extraction—multi-scale feature fusion—up-sampling detail recovery—global feature interaction for noise suppression”. First, a Dynamic Hybrid Convolution Module (DIMB) is introduced into the backbone network, drawing on the ideas of Inception-style multi-branch depthwise convolution and MetaFormer residual mixing. It extracts local textures of various forms through square convolution, horizontal strip convolution, and vertical strip convolution, and utilizes dynamic branch weights to enhance the model’s adaptability to irregular defects such as lesions, mildew, and external damage. Second, a Content-Guided Attention Feature Fusion Network (CGAFN) is designed in the neck network, which achieves adaptive fusion of low-level detail features and high-level semantic features through channel attention, spatial attention, and pixel-level fusion weights. Next, a lightweight upsampling enhancement module called EUCB-SC is constructed, which introduces channel rearrangement and Shift spatial offset into the efficient upsampling convolutional structure to enhance the local spatial interaction capability of upsampled features with low parameter overhead. Finally, adaptive sparse self-attention is introduced into the AIFI module to form AIFI-ASSA, which suppresses irrelevant background interactions through a sparse attention branch and retains necessary contextual information through a dense attention branch. The experimental results demonstrate that on a dataset containing four categories of citrus images—healthy, diseased, moldy, and severely externally damaged—RT-DETR-DCEA achieves 92.1% Precision, 86.1% Recall, and 91.8% mAP@50, with a parameter count of 1.477 × 10⁷ and an inference speed of 81 FPS. Compared with the original RT-DETR-R18 and various YOLO series models, this method strikes a favorable balance among detection accuracy, recall capability, and model lightweightness. This paper also discusses limitations such as data scale, ratio of private data, single training result, and insufficient validation on edge devices, providing a basis for subsequent cross-regional data validation and real-world deployment testing. Full article

The increasing frequency of floods and the severity of their consequences for public safety, infrastructure, and the economy demand improved methods for flood hazard identification. Flood studies that include flood hazard mapping are critical tools for informing emergency response and flood recovery, as well as for land use and mitigation planning. The methodology for such flood studies has evolved, and access to more powerful computational resources and high-resolution base data has contributed to the increased use of two-dimensional hydraulic modelling, where one-dimensional modelling previously was the default. However, local-scale flood studies face real-world constraints, including sparse data, challenging hydrologic conditions, and budget limitations, which can hinder the application of advanced techniques. This study addresses these challenges through innovative, practice-driven solutions in two case studies in Alberta, Canada: a small, partly channelized prairie stream network (Wolf Creek, Lacombe) and a laterally dynamic river on a distributary delta (Swan River, Kinuso). Three core components of flood hazard studies are described: field survey data collection, regional hydrology assessment, and hydraulic modelling. Key findings include demonstrating that LiDAR-derived terrain models alone cannot capture channel conveyance, the importance of low-flow calibration in the absence of high-water marks, the selection of a modelling methodology based on bathymetric and topographic features within a study area, and the development of inflow hydrographs for unsteady-state simulation in flat floodplains. Full article

Introduction: Breast cancer is the most common malignancy in women of reproductive age, and treatment advances have heightened the importance of fertility preservation (FP) for young patients. Despite heterogeneity across subtypes—hormone receptor-positive (HR+), HER2-positive, triple-negative (TNBC), and BRCA1/2-associated—existing guidelines lack subtype-specific FP guidance. Methods: This systematic review compared FP strategies across subtypes, identified subtype-specific challenges, and proposed pathways toward precision oncofertility care. PubMed, Scopus, and Web of Science were searched following PRISMA guidelines for English-language studies from 2004 to 2024. Results: After screening 1837 records, 19 studies met eligibility criteria (2 RCTs, 17 cohort studies); 11 of 17 non-randomized studies were at low overall risk of bias, and 6 at moderate risk due to confounding. Discussion: COS using letrozole- or tamoxifen-modified protocols was feasible, yielding 8–14 mature oocytes per cycle with reduced estradiol exposure suitable for HR+ disease. Evidence was strongest for HR+ patients; TNBC and HER2+ data were more limited, with some studies noting reduced ovarian reserve. GnRH agonists during chemotherapy reduced ovarian failure rates and improved post-treatment recovery, most consistently in hormone receptor-negative disease. BRCA1/2 carriers showed broadly comparable FP outcomes to non-carriers, though BRCA1-positive patients had modestly reduced oocyte yields in some studies with inconsistent results. Conclusions: Among studies with medium-term follow-up (3–5.5 years), no significant increase in recurrence or mortality attributable to FP was identified; long-term data beyond 5 years remain sparse. Substantial heterogeneity precluded meta-analysis; all synthesis is narrative. Standardized outco reporting and larger prospective subtype-stratified studies are required to establish precision oncofertility recommendations. Full article

Agricultural fertilizer reduction depends on farmers’ responses to policy incentives, but such responses are often observed only at a few subsidy levels and under hypothetical conditions. Using survey-based stated-preference data from 15 counties in China, this study examines whether large language model (LLM)-based methods can reconstruct fertilizer-reduction response intervals under alternative subsidy scenarios. Three LLM-based inference strategies were designed and compared with 14 conventional methods within an exploratory evaluation framework covering interval recovery, extrapolation behavior, and curve-shape plausibility. LLM-based methods were competitive in this sparse-anchor reconstruction task. The incremental inference strategy, which reconstructs target intervals through local changes between subsidy anchors, produced the most stable results. DeepSeek V3.2 Increment obtained the highest IO (0.528) and a high EIO (0.602), while Qwen3-8B Increment achieved the lowest MAME (1.291) and the highest EIO (0.636). SHAP analysis showed that reconstruction difficulty was mainly associated with fertilizer bags per mu (0.2414), annual fertilizer cost (0.1808), and fertilization training (0.1473). Overall, this study explores the potential of LLM-based inference as a flexible approach for fertilizer-reduction policy-response analysis from limited stated-preference data. Full article

Lake depth is a fundamental parameter for estimating lake storage, analyzing basin morphology, and understanding the evolution of plateau lakes. Compared with typical shallow lakes, Tibetan Plateau lakes are characterized by high elevation, strong radiation, pronounced inter-lake and inter-annual variability, and in some cases considerable basin depth, which limits the accuracy, stability, and generalization ability of existing bathymetric inversion methods based on single-source optical imagery. Meanwhile, although ICESat-2 can provide sparse but high-precision along-track bathymetric constraints, a unified framework suitable for plateau-lake scenarios is still lacking. To address this issue, this study proposes TabKAN, a bathymetric inversion framework for Tibetan Plateau lakes under joint constraints from hyperspectral imagery and ICESat-2 data. TabKAN constructs tabular input features from hyperspectral reflectance, water indices, imaging geometry, and environmental variables; employs TabNet for feature selection and encoding; and introduces a KAN regression head to enhance nonlinear bathymetric mapping. A joint-supervision and bias-correction mechanism is further designed to incorporate ICESat-2 samples, thereby improving model robustness across lakes and acquisition dates. To enhance the temporal coverage of training samples, multi-year sample expansion based on stereo-mapping data is introduced, and a stripe-aware self-supervised learning strategy is developed for hyperspectral image restoration and pretraining. Experiments on five Tibetan Plateau lakes, including Anglaren Co, Caiduo Chaka, Cuoe, Geren Co, and Qixiang Co, show that the proposed method outperforms benchmark methods in both overall accuracy and depth-stratified evaluation, while providing more stable recovery of basin morphology and depth gradients. These results demonstrate that combining hyperspectral information, ICESat-2 laser constraints, and stripe-aware pretraining can effectively improve the accuracy and robustness of bathymetric inversion for Tibetan Plateau lakes and provide a new technical route for storage estimation and change monitoring of cold inland lakes. Full article

This paper presents a computationally efficient Multivariate Mixture Model Thresholding (MMMT) technique for sparse signal denoising and recovery, with the goal of improving data quality in modern sensing and biomedical systems. The proposed method extends classical thresholding approaches by modeling nonzero signal coefficients using a multivariate Gaussian mixture prior, thereby capturing cross-channel and intercomponent dependencies commonly observed in multi-sensor and physiological signals. The thresholding rule is analytically derived through maximum a posteriori (MAP) estimation within a majorization–minimization (MM) optimization framework, while the associated model parameters are adaptively estimated using an expectation–maximization (EM) algorithm. Experimental results on noisy sinusoidal signals and synthetic ECG data demonstrate that MMMT consistently achieves higher correlation with ground-truth signals and improved preservation of pulse amplitude and morphological characteristics compared with benchmark methods, including the ${\mathcal{l}}_1$-fused lasso and convex–non-convex (CNC) fused lasso. Quantitative evaluations based on correlation metrics, signal-to-noise ratio (SNR), and peak signal-to-noise ratio (PSNR) further confirm the effectiveness of the proposed approach. Owing to its scalability, robustness, and strong statistical interpretability, MMMT provides a promising framework for real-time ECG signal enhancement. Although the proposed framework is general and can be adapted to other biomedical modalities such as EEG, CT, and MRI, experimental validation in this study is limited to ECG signals. Full article

In the reconstruction problem of compressed sensing, to address the challenge of adapting common sparse constraints to diverse data, we propose a data-driven inverse-weighted regularization for adaptive data matching to enhance the ability of sparse constraints. Specifically, we formulate a weighted regularization term based on the data transform domain, positing that higher values in the sparsity-promoting transform domain correspond to a greater probability of effective signals. Therefore, when solving sparse optimization problems, we inversely weight this portion based on the inverse relationship with the coefficient magnitude, thereby reducing its impact and mitigating damage to effective signals. However, recognizing that noise and other irrelevant signals are sparse and approximately uniformly distributed in the transform domain, we can increase the weight of this portion to boost the sparsity constraint in the transform domain, thereby enhancing noise suppression. Consequently, we presented the corresponding solution algorithm and convergence proof for inverse-weighted sparse regularization, along with an application example in the context of the Radon transform. Experimental data tests indicate that inverse-weighted sparse regularization enhances the capability of sparse constraints, protects effective signals, suppresses noise, and improves the recovery accuracy of compressive sensing algorithms, as demonstrated in natural image enhancement and seismic multiple suppression. Full article

Orbit determination from optical observations remains a challenging problem due to the absence of direct range measurements and the presence of sparse, noisy, and irregularly sampled data. This work presents TRACER (Tracking, Recognition, and Analysis for Celestial Ephemerides Retrieval), a robust and fully automated framework for angles-only orbit determination. The proposed approach integrates probabilistic and deterministic strategies within a unified, decision-driven architecture. In particular, statistical ranging is employed for short-arc regimes to explore admissible solutions, while deterministic methods, including modified Gauss and Väisälä techniques, are used for longer arcs and refinement. Candidate solutions are evaluated through a unified scoring function that combines observational consistency with physically motivated penalties. A key contribution of TRACER is the introduction of a randomized subset-selection outer loop, which repeatedly solves the orbit determination problem on different observation subsets and validates solutions against the full dataset, enhancing robustness in challenging scenarios. Additional mechanisms for adaptive subarc selection, recovery from failure, and progressive data assimilation further improve reliability. The resulting framework enables fully autonomous orbit determination without manual intervention, bridging the gap between individual algorithms and operational pipelines for real-world astrometric data processing. Full article

We present a robust, data-efficient framework for early outbreak assessment using multiscale analysis and Computational Singular Perturbation (CSP). This framework overcomes the shortcomings of the standard compartmental epidemiological models, which often struggle with parameter identifiability during the early stages of a pandemic, limiting their predictive utility considerably when data is sparse. Rather than relying on curve-fitting population profiles, which are sensitive to uncertainty, our approach isolates the dominant “explosive time scale that characterizes the outbreak’s intensity and duration. Using a calibrated SEIRD model, CSP allows for the identification of the paths that drive the process during the outbreak phase and the critical transition from accelerating to decelerating growth, which serves as a reliable precursor to the epidemic peak. This framework is assessed against the 4th, 5th, and 6th waves of the COVID-19 pandemic in Greece during 2021, covering periods dominated by the Delta and Omicron variants. Using only early-stage data from short calibration windows, CSP diagnostic tools revealed distinct dynamical drivers for each wave; e.g., a transition from the 4th wave that was driven by transmission intensity (Delta variant dominance) to the 6th wave that was driven by rapid exposure-to-infection turnover and reduced opposition from recovery mechanisms (Omicron variant dominance). Furthermore, it is demonstrated that the timing of the outbreak’s weakening can be accurately predicted, demonstrating robustness with results obtained from longer observation windows. These findings position multiscale analysis as a powerful, pathogen-agnostic early-warning system, capable of disentangling complex epidemic mechanisms and assessing intervention efficacy in real-time. Full article

This systematic review maps how Artificial Intelligence (AI) operationalises Material and Digital Product Passports (MP/DPP) for circular construction (January 2020–March 2026). However, most existing AI-to-passport implementations lack standardised reporting metrics, interoperability frameworks, and benchmarks for end-of-life decision support, leaving a critical operational gap that this review systematically addresses. The review follows the PRISMA 2020 guidelines to ensure methodological transparency and reproducibility. Of 2810 records identified across Web of Science, ScienceDirect, and Scopus, 49 peer-reviewed studies met the inclusion criteria for explicitly linking AI to MP/DPP under a circular economy lens. Evidence is synthesised across AI task families (computer vision, NLP including large language models, machine learning, knowledge graphs, digital twin/IoT), lifecycle phases (design, construction, operation, end-of-life), and circularity functions (traceability, lifecycle data enrichment, reuse/recycling readiness, recovery/EoL planning). The literature concentrates on traceability and lifecycle enrichment, while decision support for reuse and end-of-life remains sparse. Methodological weaknesses include narrow field validation, limited reporting of passport-level service metrics, and weak interoperability between AI pipelines and passport schemas. The regulatory landscape has intensified: Regulation (EU) 2024/3110 (Construction Products Regulation) entered into force in January 2025, and Regulation (EU) 2024/1781 (ESPR) launched its first product groups in April 2025, transforming AI-enabled MP/DPP from a prospective research topic into an immediate operational requirement. This review also notes the emergence of Large Language Models and blockchain as pivotal technologies for NLP-based field extraction and governed trust in twin-to-passport pipelines, respectively. Three framework elements are contributed and formalised as the Digital Circularity AI Framework (DCAF): (i) a minimum reporting bundle for AI-to-passport pipelines; (ii) a governance pack for twin-to-passport updates covering provenance, versioning, latency, and blockchain-trust; and (iii) open benchmark definitions for reuse grading, deconstruction sequencing, and residual value estimation. Together, these elements aim to shift MP/DPP from identification-oriented tools toward actionable decision support for circular recovery. Full article