Search Results (608)

Historical mining districts are long-term sources of environmental contamination, especially where agriculture and grazing occur near mining areas. This study evaluates mercury (Hg) and other potentially toxic elements (PTEs) in soils from Solana de Peñarrubia, within the Almadén mining district (Spain). Thirty-four soil samples collected using a regular staggered grid were analyzed for total Hg and major oxides and selected trace elements. Spatial patterns were evaluated using Kriging interpolation, hierarchical clustering, PCA, and the geoaccumulation index, after standardizing variables to minimize differences in magnitude among them. Thermal desorption and microscopy were performed on three selected high-Hg samples, with thermal assignments interpreted as operational rather than definitive mineralogical identifications. Hg concentrations ranged from 10 to 994 mg kg⁻¹, with the highest values in the northern sector. The PCA separated cropland soils, associated mainly with lithological variables, from soils with anthropogenic inputs related to Hg, Pb, SO₃, and SiO₂. The geoaccumulation index indicated Hg enrichment, reaching the extremely polluted category at the maximum concentration. Thermal desorption profiles were dominated by fractions within the reference range of α-HgS, accounting for 78.27–87.44% of the estimated relative abundance, while microscopic examination revealed particles consistent with cinnabar in high-Hg samples. The Hg anomaly showed no straightforward relationship with the mapped local lithology and may partly reflect, as a working hypothesis, the possible redistribution of Hg-bearing mineralized materials associated with historical mining or metallurgical activities. Full article

Cross-domain distribution shifts severely degrade the diagnostic performance of rolling bearing models under unseen variable operating scenarios. Single-source domain generalization (SDG) builds fault diagnosis models using only single-source vibration data, which fits the practical limitations of industrial data collection. Existing contrastive learning methods adopt uniform spectral perturbations for data augmentation, which easily corrupt fault harmonic characteristics and require massive, labeled training samples. To tackle these drawbacks, this paper proposes an unsupervised contrastive learning framework named FDACL. An adaptive frequency-domain augmentation (AFA) module equipped with learnable weights is designed to separate fault-critical frequency bands from noise components. Differentiated amplitude perturbations are applied to two categories of spectral signals to generate diverse pseudo-samples while retaining intrinsic fault information. A shared encoder is trained with combined InfoNCE contrast loss and classification loss to learn domain-invariant fault representations. Validations are carried out on three datasets, namely Case Western Reserve University (CWRU), Paderborn University (PU), and the industrial CRRC Qingdao Sifang railway wheelset bearing dataset acquired from physical test benches. FDACL achieves average cross-speed diagnostic accuracies of 92.68% and 77.85% on CWRU and PU, respectively, and maintains competitive performance on the Qingdao Sifang industrial dataset. It outperforms state-of-the-art baselines by 4.23–8.71% across all SDG transfer tasks. Ablation experiments and hyperparameter analysis verify the efficacy of the AFA module and contrastive learning scheme, providing an unsupervised diagnostic approach for railway bearings under unknown working conditions. Full article

Artificial intelligence is rapidly reshaping sustainability reporting, influencing how environmental, social, and governance (ESG) information is collected, analysed, and disclosed. While AI-assisted reporting improves efficiency and analytical capability, it also raises important concerns regarding transparency, accountability, verification, and AI-enabled greenwashing, creating new challenges for the credibility of sustainability disclosures. This study adopts a doctrinal legal research design supported by qualitative analysis, comparative regulatory assessment, and a structured review of legal, regulatory, and academic sources. It examines how emerging approaches to AI governance and sustainability reporting address these challenges and identifies the governance principles required to support trustworthy AI-assisted ESG reporting. Existing regulatory initiatives strengthen important aspects of sustainability reporting, yet AI governance, ESG disclosure, and greenwashing continue to be addressed through separate regulatory instruments. To bridge this gap, the study develops an integrated governance framework that combines transparency, meaningful human oversight, AI auditing, sustainability verification, and clearly allocated accountability within a coherent governance structure. The proposed framework contributes to the literature by offering a structured governance model specifically designed for AI-assisted sustainability reporting. The framework also provides practical guidance for regulators, standard setters, organisations, and assurance providers seeking to strengthen reporting integrity and stakeholder confidence in AI-assisted ESG reporting. Full article

This Data Descriptor presents a georeferenced dataset of electromagnetic-field exposure measurements collected in Colombia between 2 November 2023 and 30 June 2024. The records originate from the Sistema de Monitoreo de Campos of the Agencia Nacional del Espectro (ANE), which uses isotropic probes to monitor broadband radiofrequency electromagnetic fields from 100 kHz to 8 GHz and reports six-minute averages of incident power density in W/m². The comma-separated value file contains 1,286,346 timestamped records and seven source variables, including Well-Known Text (WKT) point geometry. The principal 2024 subset comprises 995,238 records from 24 monitoring stations. We provide a reproducible workflow for timestamp and coordinate parsing, structural and numeric validation, station-level aggregation, and spatial sensitivity analysis using inverse distance weighting with the mean, median, and 95th percentile. The dataset does not provide frequency-resolved measurements, calibration certificates, or measurement-uncertainty budgets; these omissions limit the use of the public file for formal regulatory-compliance assessment. The accompanying repository includes validated station-level summaries, descriptive tables, figures, and reproducible R code. The package supports environmental monitoring, geospatial analysis, methodological comparison, and reproducible reuse of electromagnetic-field exposure data. Full article

Commiphora gileadensis (L.) C.Chr. has long been regarded as the most likely botanical source of the historical apharsemon (Balm of Gilead). Despite considerable historical interest, little is known about the chemical variability of geographically separated populations currently assigned to this taxon. In the present study, resin samples obtained from putative C. gileadensis accessions originating from Ein Gedi (Israel), Oman, and a Somalia-derived accession cultivated in Yotvata, Israel, were analyzed by GC–MS. In addition, freshly collected resin from the Somalia-derived accession was compared with the same resin sample stored under laboratory conditions for ten years. The Ein Gedi accession exhibited a monoterpene-rich profile dominated by sabinene (48.26%) and α-pinene (13.96%). In contrast, the Oman accession was characterized by copaene (17.94%), limonene (10.51%), β-elemene (8.06%), and isocembrol (6.31%). The Somalia-derived accession displayed a markedly different profile dominated by linalyl acetate (59.59%), accompanied by α-pinene (9.95%) and γ-terpinene (5.83%). After ten years of storage, linalyl acetate remained the dominant constituent (40.51%), and the characteristic pleasant aroma of the resin was largely preserved. The results demonstrate substantial phytochemical divergence among geographically separated accessions currently assigned to C. gileadensis. The Somalia-derived accession represents a distinct fragrance-rich chemotype whose unusual aroma profile and long-term stability may be of particular interest in future studies of balsam-producing Commiphora populations. Full article

Background/Objectives. Annual flaxseed (Linum usitatissimum) is the most cultivated Linum species. Perennial species (Linum austriacum, Linum lewisii, Linum perenne) show potential as alternative oilseed and fiber sources. It is critical to determine genetic variation in any collection to understand relationships and utilization within a breeding program. The research objectives were to analyze the genetic structure of the trifecta perennial flax using single-nucleotide polymorphic (SNP) markers for species differentiation and to discover potential Centers of Origin and/or diversity. Methods. We tested 70 USDA-GRIN-global wild populations (19 L. austriacum, 32 L. lewisii, 19 L. perenne; N = 850 seedling genotypes) to generate 9804 DArTseqLD SNPs (Group 1). Results. After filtering, within L. austriacum, 1199 SNPs (261 genotypes; Group 2) remained; in L. lewisii, there were 90 unique SNPs (273 genotypes; Group 3); in L. perenne, there were 2716 SNPs (309 genotypes; Group 4). Four genetic clusters were detected using STRUCTURE, consistent with principal coordinate analysis and SplitsTrees. Clear distinctions within and among each perennial species’ populations were found, separating into two distinct pure taxon groupings, along with peripheral populations or outliers. Both L. austriacum and L. lewisii potentially had two putative Centers of Origin, although L. perenne had one. Conclusions. The occurrence of sympatric Linum species in the wild could explain the occurrence of peripheral population groups within each taxon, although other explanations are also possible. This may be consistent with the potential for genetic exchange between the perennial species and the greater genetic variability available. Future research will evaluate additional Linum to further delineate the genetic structure and variation within the genus Linum. Full article

Harmful algal blooms (HABs) are recurring problems in New Jersey (NJ). Some HABs produce organic phycotoxins that are detrimental to both ecosystem organisms and human health. While most studies on HABs and phycotoxins focus on freshwater in NJ, comparative measurements of phycotoxins in marine coastal environments are understudied. In the summer of 2025, surface water from 11 freshwater, 3 brackish rivers, and 10 coastal sites in northern and central NJ was collected and analyzed for 12 dissolved phycotoxins using a combination of solid phase extraction and liquid chromatography-tandem mass spectrometry. Phycotoxin concentrations and distribution varied spatially among sampling sites. Total okadaic acid (OA) and total dinophysistoxin-1(DTX-1) were present with the highest concentrations, followed by brevetoxin and microcystin. Cluster analysis revealed separation of phycotoxin patterns mostly between freshwater and brackish/marine sites. Correlation analysis revealed significantly positive correlations among total OA, total DTX-1, and pectenotoxin-2 concentrations, revealing their potential common source from the same HAB species. Temperature, dissolved oxygen, and pH were mostly correlated with multiple phycotoxins, suggesting their important role in shaping HABs and phycotoxin production. This study detects phycotoxins at low levels, which can help to provide early warnings of HABs to establish water quality baselines and maintain environmental health. Full article

Background/Objectives: Psychological safety is increasingly recognized as a key factor in promoting communication, learning, and teamwork in healthcare, with implications for patient safety culture. Although psychological safety and patient safety culture have been reviewed separately, evidence on how nurse managers contribute to both constructs within nursing teams remains fragmented. This scoping review aimed to map the available evidence on the role of nurse managers in promoting psychological safety and patient safety culture in nursing teams, identify leadership behaviors, strategies, and competencies, and highlight facilitating factors and barriers. This review provides an integrated synthesis of evidence on how nurse managers contribute to fostering both psychological safety and patient safety culture within nursing teams, addressing a gap in the existing literature. Methods: A scoping review was conducted following established methodological frameworks and prospectively registered in the Open Science Framework. Systematic searches were conducted in MEDLINE (PubMed), CINAHL, Psychology and Behavioral Sciences Collection, Scopus, BVS, and WorldCat for gray literature, with the final search performed on 11 May 2026. A total of 229 records were identified, with 112 duplicates removed. The remaining 117 records were screened, resulting in 25 studies included for analysis. Data were extracted and synthesized using thematic analysis. Results: Twenty-five sources were included, comprising predominantly quantitative (mainly cross-sectional) studies, together with qualitative studies, systematic reviews, conceptual papers, expert opinion papers, and gray literature. The findings highlight a close and interdependent relationship between psychological safety and patient safety culture. Nurse managers’ relational leadership behaviors—such as visible presence, active listening, emotional support, and constructive feedback—emerged as key strategies for fostering psychologically safe environments. The reviewed evidence suggests that psychological safety may mediate the relationship between leadership practices and communication, speaking up, incident reporting, and organizational learning. Facilitators include supportive leadership and a just culture, while barriers encompass punitive environments, hierarchical structures, workload pressures, and resource constraints. Conclusions: The findings suggest that psychological safety may be an important mechanism through which nurse managers contribute to patient safety culture. However, leadership alone is insufficient, requiring alignment with organizational conditions and system-level support. These findings support strategies to strengthen safety culture and guide leadership development. Full article

Agricultural soils in arid regions require assessment approaches that separate local element enrichment from actual ecological and human health relevance. Here, a site-prioritization framework is applied to potentially toxic elements (PTEs) in agricultural soils from Onaizah, central Saudi Arabia. The approach combines contamination indices, ecological-risk screening, deterministic health-risk estimates, Monte Carlo resampling, and relative ranking of management priorities. A total of 33 surface-soil samples collected from cultivated farms were examined for As, Co, Cr, Cu, Mn, Ni, Pb, V, and Zn. The measured concentration ranges (mg/kg) were 1–5 (As), 1–12 (Co), 10–53 (Cr), 4–38 (Cu), 107–541 (Mn), 6–54 (Ni), 2–23 (Pb), 8–47 (V), and 11–168 (Zn). Based on their mean concentrations, the investigated elements decreased in the following sequence: Mn > Zn > Cr > Ni > V > Cu > Pb > Co > As. The P_N values ranged from 0.127 to 1.339, indicating 27 safe sites, 2 warning-line sites, and 4 slightly polluted sites, mainly controlled by localized Zn enrichment and, in one case, Pb. In contrast, mCd values of 0.099–0.676 indicated nil to very low contamination, while RI values of 2.743–15.701 confirmed low ecological risk across all samples. Non-carcinogenic risk was generally below the threshold of concern, with HI values of 0.204–1.018 for children and 0.024–0.119 for adults. Only one site showed a marginal child HI exceedance, emphasizing localized rather than widespread health concern. Children showed approximately 8.6-fold higher non-carcinogenic risk than adults, with Mn, Cr, As, and V as the main contributors. The total LCR values for As, Cr, and Pb ranged from 7.79 × 10⁻⁶ to 4.07 × 10⁻⁵ for children and from 3.48 × 10⁻⁶ to 1.82 × 10⁻⁵ for adults, within the commonly tolerable range of 1 × 10⁻⁶ to 1 × 10⁻⁴. Chromium was the dominant contributor to LCR. Monte Carlo resampling supported the deterministic risk classification, with only a 3.1% probability of child HI exceeding 1 and no simulated exceedance of the LCR threshold for either children or adults. From the standpoint of sustainable soil management, site 7 should undergo further health-risk assessment, while sites 30 and 33 require source verification and periodic monitoring before any remediation action is considered. Full article

Generative named entity recognition (NER) systems must identify clinical concepts and map them to exact source spans. We investigated how span-reference design affects this mapping and whether failures arise from recognition or source localisation. Four representation-and-grounding pipelines—inline XML, mention-list JSON, a tab-separated mention list, and direct-offset JSON—were compared for disease, procedure, and symptom recognition in a public Spanish clinical case-report collection. Two instruction-tuned 3-billion-parameter models were fine-tuned for each task, and outputs were evaluated for syntactic validity, parsing, grounding, and official strict-span performance. Inline XML, mention-list JSON, and the tab-separated format achieved F₁ scores of 0.637–0.736, whereas direct-offset JSON achieved 0.001–0.005. With the same unadapted checkpoints, zero-shot F₁ was at most 0.131 and three-shot F₁ at most 0.386; three-shot prompting improved the mention-list outputs, while inline XML and direct offsets remained near zero. Direct-offset outputs usually contained relevant mention text but incorrect character positions. On a separate 75-document confirmation partition, grounding the emitted strings recovered F₁ of 0.603–0.701, while replacing absolute offsets with mention-occurrence numbers achieved 0.652–0.740. Without further training, the external CARMEN-I evaluation on 458 hospital-record sections yielded F₁ of 0.610–0.659 for string-grounded or occurrence-index outputs, compared with 0.113–0.177 for inline XML and at most 0.002 for direct offsets. These results show that output validity alone does not establish usable span extraction. Separating recognition from source localisation identifies where an otherwise valid generation fails, while occurrence-based references provide a more reliable alternative to absolute character offsets under exact matching in the tested settings. Full article

Recent advances in numerical modelling, remote sensing, and artificial intelligence are bringing a transformation in our ability to assess sediment transport. Nevertheless, climate change is fundamentally altering sediment production, transport, and deposition through intensifying hydrological extremes, sea-level rise, changing storm regimes, cryosphere degradation, and increasing human modification of sediment pathways. These interacting drivers challenge conventional sediment transport assessment, which has largely evolved within separate fluvial, estuarine, coastal, and marine disciplines and often lacks an integrated perspective capable of representing source-to-sink sediment connectivity under non-stationary environmental conditions. Although significant advances have been made in process-based numerical modelling, Earth observation, and artificial intelligence (AI), these approaches are commonly reviewed independently, limiting their collective application to regional climate-responsive sediment assessment. This review examines state-of-the-art process-based numerical models, observational tools, and machine-learning approaches for sediment transport from source-to-sink. A transparent benchmarking scheme is used to compare leading modelling systems (e.g., AdH, SRH-2D, FLO-2D, HEC-RAS, TELEMAC, Delft3D, EFDC, SCHISM, XBeach, ROMS), highlighting differences in dimensionality, sediment-process representation, computational demands, and climate-scenario readiness. Remote sensing (optical, SAR, LiDAR, UAV) and AI/ML/DL methods (e.g., random forests) are reviewed as complementary tools that enhance model parametrization, improve validation, and address uncertainty in data-limited regions. A reproducible bibliometric synthesis based on Dimensions.ai records (2000–2026) reveals accelerating growth in sediment-transport research, with strong recent expansion in coastal, estuarine, and data-driven modelling applications. Major challenges include cohesive sediment physics, cross-environment coupling, limited long-term validation datasets, and the need for scalable workflows compatible with climate-model forcing. In this manuscript, we analyse and propose a future roadmap for near-term integration of satellite–field data streams, medium-term development of hybrid physics–AI models, and long-term coupling of sediment modules within Earth-system and regional climate frameworks. Collectively, this review provides a foundation for next-generation, climate-responsive sediment transport assessment supporting sustainable river basin and coastal management. Full article

Machine unlearning (MU) is increasingly studied across artificial intelligence systems in which target information may remain in model weights, adapters, retrieval indexes, caches, federated states, and temporally derived representations. However, existing reviews often examine methods, benchmarks, or deployment settings separately. Therefore, this scoping review collected and synthesized 159 sources to compare unlearning evidence across these connected system components. The evidence map separates 42 core MU sources—26 primary empirical or theoretical studies, 12 benchmark or evaluation frameworks, and 4 secondary evidence syntheses—from 117 contextual sources. First, a three-dimensional taxonomy distinguishes guarantee or reference type, update mechanism, and deployment or memory context. This structure prevents partitioning from being treated as a guarantee and influence estimation from being treated as an outcome. Next, the quantitative map identifies 13 centralized or general sources, 16 LLM- or benchmark-focused sources, 3 graph sources, 3 federated sources, 2 temporal sources, 2 quantized-network sources, and 3 cross-domain reviews. These results indicate a stronger reference-based foundation for centralized MU and a benchmark-rich but transformation-sensitive evidence base for large language models. In contrast, graph, federated, and temporal unlearning remain less mature because they are supported by smaller core evidence sets. Moreover, recent studies show that apparent forgetting may fail after 4-bit quantization, probabilistic decoding, alternative reference selection, recovery testing, benign query changes, or overlap between forget and retain knowledge. Accordingly, we present an audit-oriented deletion lifecycle that connects forgetting evidence, retained-utility testing, threat-model-specific attacks, post-transformation verification, and redeployment decisions. Calibration, citation grounding, provenance, and human review are treated as supplementary decision-readiness checks rather than direct proof of unlearning. Finally, the lifecycle is presented as a structured synthesis and reporting framework that still requires prospective validation in real systems and independent practitioner assessment. Full article

Reliable fruit quality assessment is crucial for ensuring food safety and value in modern agriculture. However, many current approaches still rely heavily on visual cues, making it difficult to assess internal quality indicators such as sweetness or internal decay. To address this limitation, we propose HSAF-Net, a heterogeneous multimodal fusion framework integrating spiking neural networks (SNNs) and artificial neural networks (ANNs) for comprehensive, non-destructive fruit quality assessment. Specifically, the SNN encodes near-infrared (NIR) spectral signals to extract internal sugar-related features, whereas the ANN-based TH-YOLOv8 model detects external surface defects from high-resolution RGB images. A microsecond-level synchronous acquisition scheme is implemented to ensure precise alignment between the NIR and RGB modalities. To effectively combine heterogeneous features, we design a Heterogeneous Modality Attention (HMA) mechanism that dynamically fuses multi-source information based on task-specific relevance. Compared with image-only detection, the proposed framework explicitly separates internal biochemical sensing from external defect localization and then integrates their complementary decisions in a unified grading pipeline. Experimental results on 616 pear samples demonstrate that the HSAF-Net achieves 95.2% classification accuracy, 95.1% mAP95, and an internal defect miss rate as low as 7.5%, outperforming conventional single-modality and early-fusion baselines by a notable margin. The system maintains a real-time inference speed of 55 ms per sample on the Ascend Atlas 200DK A2 edge platform, validating its deployment potential. The current evaluation is based on crisp pear samples collected under controlled acquisition conditions; therefore, broader cross-variety and cross-season validation remains necessary before large-scale commercial deployment. This study presents an end-to-end multimodal SNN-ANN fusion architecture tailored for fruit grading and provides a scalable, high-precision solution for post-harvest quality assessment with broad applicability to other agricultural products. Full article

Smart meters are the core terminals for distribution network data acquisition in smart cities, yet their collected data commonly suffer from quality issues caused by harsh operating environments, communication failures, hardware degradation, and human factors. This paper presents a systematic review of anomaly detection and data repair methods for smart meter data based on a critical analysis of many publications. First, we characterize five typical anomalies—sudden jumps, reading stagnation, reverse readings, pulse spikes, and gradual drifts—from physical root causes to data manifestations and provide unified mathematical definitions with explicit traceability to the existing literature. Additional anomaly types including meter replacement jumps, data duplication from retransmission, complete missing segments, and timestamp errors are also discussed to present a more complete picture of operational data quality challenges. Second, existing anomaly detection methods are systematically reviewed and classified into four categories—statistical, machine learning, deep learning, and dedicated time-series methods—with representative studies, quantitative performance metrics, and scenario-specific applicability examined for each. Third, data repair approaches are reviewed across four categories—traditional interpolation, matrix completion, generative models, and time-series prediction—with systematic comparison of their accuracy and limitations across different anomaly types and durations. Based on the synthesized evidence, we identify three cross-cutting structural limitations that persist across method categories: the performance ceiling of data-only detection without physical constraint embedding, the open-loop architecture that separates detection from repair and allows error propagation, and the exclusive reliance on statistical error metrics that fails to distinguish physically plausible repairs from those violating conservation laws. To address these gaps, we discuss a physics-guided integrated framework incorporating physical constraint embedding, joint anomaly diagnosis, scenario-adaptive repair, and posterior verification as a promising forward-looking direction. Finally, open challenges and future research directions are outlined, including parameter adaptation in unlabeled scenarios, multi-source data fusion for physical disambiguation, new power system extensions, explainable AI integration, edge-computing deployment, and standardized benchmark development. This review provides a comprehensive theoretical reference and technical roadmap for smart meter data quality research in the context of smart city energy systems. Full article

With the rapid development of Industrial Internet-oriented smart energy systems, hydro-turbine generator units are increasingly monitored through networked sensors, industrial communication infrastructures, and edge/cloud-based condition-monitoring platforms. These Internet-connected monitoring environments provide abundant vibration data for intelligent operation and maintenance (O&M) but also introduce a challenging unsupervised cross-scenario diagnosis problem. Specifically, diagnostic models trained on labeled historical data may suffer severe performance degradation when deployed to unlabeled online data collected under different hydraulic conditions, rotational speeds, or operating conditions. Furthermore, existing domain adaptation methods, in their pursuit of distribution alignment, frequently overlook a critical bottleneck that limits generalization performance: inter-class entanglement. Specifically, under intense hydraulic background noise and cross-condition distribution shifts, features belonging to distinct fault types are highly susceptible to aliasing within the feature space. To overcome these issues, this paper proposes a Conditional Wasserstein Adversarial Network with Bi-level Prototype Disentanglement Regularization (CWAN-BPDR). First, a Conditional Wasserstein Adversarial Network (CWAN) is constructed by combining the smooth-gradient property of Wasserstein distance with conditional adversarial alignment, thereby achieving stable and fine-grained category-level domain adaptation. Furthermore, to alleviate the inter-class entanglement problem that may arise during cross-domain alignment, a Bi-level Prototype Disentanglement Regularization (BPDR) term is designed. By jointly implementing source–target prototype alignment and prototype–feature bidirectional alignment, BPDR explicitly suppresses inter-class confusion and enhances intra-class compactness and inter-class separability in the feature space. Experimental results on the JNU and NEFU datasets demonstrate that CWAN-BPDR achieves average diagnostic accuracies of 97.82% and 98.99%, respectively, while significantly mitigating label entanglement in challenging cross-operating-condition tasks. These results indicate that the proposed method can effectively transfer diagnostic knowledge acquired from labeled historical operating conditions to unlabeled online monitoring data. It can therefore serve as an offline-trained diagnostic module for Industrial Internet of Things-based condition-monitoring platforms in hydropower systems. Full article