Search Results (1,433)

In the context of global efforts toward energy conservation and emission reduction, accurate short-term electric load forecasting plays a crucial role in improving energy efficiency, enabling low-carbon dispatching, and supporting sustainable power system operations. To address the growing demand for accuracy and stability in this domain, this paper proposes a novel prediction model tailored for power systems. The proposed method combines Spearman correlation analysis with modal decomposition techniques to compress redundant features while preserving key information, resulting in more informative and cleaner input representations. In terms of model architecture, this study integrates Bidirectional Gated Recurrent Units (BiGRUs) with dilated convolution. This design improves the model’s capacity to capture long-range dependencies and complex relationships. For parameter optimization, an Improved Beluga Whale Optimization (IBWO) algorithm is introduced, incorporating dynamic population initialization, adaptive Lévy flight mechanisms, and refined convergence procedures to enhance search efficiency and robustness. Experiments on real-world datasets demonstrate that the proposed model achieves excellent forecasting performance (RMSE = 26.1706, MAE = 18.5462, R² = 0.9812), combining high predictive accuracy with strong generalization. These advancements contribute to more efficient energy scheduling and reduced environmental impact, making the model well-suited for intelligent and sustainable load forecasting applications in environmentally conscious power systems. Full article

Fine-needle aspiration cytology (FNAC) is the cornerstone of thyroid nodule evaluation, standardized by the Bethesda System. However, indeterminate categories (Bethesda III–IV) remain a major challenge, often leading to unnecessary surgery or delayed molecular testing. Deep learning (DL) has recently emerged as a promising adjunct in thyroid cytopathology, with applications spanning triage support, Bethesda category classification, and integration with molecular data. Yet, routine adoption is limited by preanalytical variability (staining, slide preparation, Z-stack acquisition, scanner heterogeneity), annotation bias, and domain shift, which reduce generalizability across centers. Most studies remain retrospective and single-institution, with limited external validation. This article provides a technical overview of DL in thyroid cytology, emphasizing preanalytical sources of variability, architectural choices, and potential clinical applications. We argue that standardized datasets, multicenter prospective trials, and robust explainability frameworks are essential prerequisites for safe clinical deployment. Looking forward, DL systems are most likely to enter practice as diagnostic co-pilots, Bethesda classifiers, and multimodal risk-stratification tools. With rigorous validation and ethical oversight, these technologies may augment cytopathologists, reduce interobserver variability, and help transform thyroid cytology into a more standardized and data-driven discipline. Full article

Psychological safety (PS), knowledge management (KM), and organizational learning (OL) are increasingly recognized as critical foundations for resilient, adaptive, and innovative organizations. However, the connections among these constructs remain fragmented in the literature, making bibliometric mapping an essential step to consolidate knowledge in this domain. This study analyzes the relationships between PS, KM, and OL, identifying thematic patterns and theoretical contributions that support the integration of these constructs into organizational cultures. Drawing from empirical literature indexed in Web of Science (WoS) (2000–2025), we applied the SPIDER framework and PRISMA methodology to identify and evaluate 103 peer-reviewed articles. Using VOSviewer (version 1.6.20) and data mining techniques, we generated bibliometric networks and thematic clusters that offer a comprehensive view of the conceptual landscape. Findings reveal that PS acts as a key enabler of knowledge sharing and OL, particularly in inclusive environments with leadership support and tolerance for error. An inductively developed conceptual model illustrates how trust-driven cultures can enhance knowledge flows and reduce dysfunctional behaviors such as knowledge hiding. By mapping these intersections, the study consolidates fragmented literature and demonstrates how PS, KM, and OL contribute to sustainable learning cultures while also highlighting promising avenues for future research on collective learning and organizational resilience. Full article

Background: Oxidative stress represents a key pathogenic factor in spondyloarthritis (SpA), yet its comprehensive assessment remains underutilized in routine clinical practice. Objectives: We evaluated oxidative stress biomarker profiles in SpA patients to determine associations with disease activity, systemic inflammation, structural damage, lifestyle factors, and therapeutic responses for practical clinical implementation. Methods: This cross-sectional study included 101 patients meeting the Assessment of SpondyloArthritis International Society (ASAS) 2009 criteria. Oxidative stress assessment utilized a validated biomarker panel: copper, zinc, glutathione peroxidase (GPx), ceruloplasmin (Cp), transferrin (TF), haptoglobin (Hp), bilirubin (BR), and uric acid (UA). Clinical, radiological, lifestyle, and therapeutic data underwent systematic analysis. Results: Glutathione peroxidase activity was elevated in 82.1% of patients, establishing it as the most sensitive oxidative stress marker. Copper levels increased in 30.7% and zinc deficiency occurred in 36.4% of cases. Oxidative stress markers correlated significantly with inflammatory parameters (erythrocyte sedimentation rate [ESR], C-reactive protein [CRP], neutrophil-to-lymphocyte ratio [NLR], platelet-to-lymphocyte ratio [PLR], neutrophil-to-monocyte ratio [NMR], systemic immune-inflammation index [SII]) and disease activity scores (Bath Ankylosing Spondylitis Disease Activity Index [BASDAI], Ankylosing Spondylitis Disease Activity Score based on CRP [ASDAS-CRP], Disease Activity Score 44 [DAS44-CRP]). Higher oxidative stress was associated with a poorer quality of life, as indicated by elevated Ankylosing Spondylitis Quality of Life (ASQoL) scores. Physical activity and adherence to a Mediterranean diet were independently associated with better antioxidant capacity. Smoking and nonsteroidal anti-inflammatory drug (NSAID) use correlated with increased oxidative burden. Anti-tumor necrosis factor alpha (anti-TNFα) therapy was associated with reduced levels of oxidative stress. Structural damage, particularly cervical spine involvement, correlated with heightened oxidative stress. Conclusions: This comprehensive evaluation reveals significant clinical correlations between oxidative stress and multiple disease domains in SpA. Modifiable lifestyle factors and therapeutic interventions have a significant impact on the redox balance. These findings establish practical targets for personalized management. The integration of oxidative stress assessment into routine practice could enhance disease monitoring and inform the development of antioxidant-based therapeutic strategies. Full article

With the increasing penetration of renewable energy and the rising demand for power quality, the dynamic performance and accuracy of grid-voltage detection have become crucial for the control of grid-following devices such as dynamic voltage restorers (DVRs). However, the conventional moving average filter (MAF) in grid-voltage detection suffers from inherent limitations in dynamic response. To address this issue, this paper proposes a voltage-detection method, which is based on an adaptive variable-parameter filtering algorithm termed MAF-MATCH-V. First, a cascaded filter model is constructed by integrating a zero-pole matcher (MATCH) with the MAF. Frequency-domain analysis demonstrates that the MATCH compensates for the mid- and high-frequency magnitude attenuation and reduces the phase delay of the MAF, thereby accelerating the dynamic response while preserving its harmonic-rejection capability. Second, the influence of the matching coefficient on the time-domain response is investigated, and a time-varying adaptive strategy is designed to balance rapid disturbance recognition with steady-state convergence. Finally, experimental results under various non-ideal grid conditions demonstrate that the proposed method achieves superior overall performance compared with conventional approaches. Specifically, MAF-MATCH-V realizes millisecond-level event recognition and zero steady-state error convergence, making it a practical solution for the real-time control of grid-following equipment in modern power systems. Full article

Charging electric vehicles (EVs) and integrating renewable energy sources (RESs) are becoming key aspects of residential energy systems. However, the variability of RES generation, combined with uncontrolled EV charging, poses challenges for reliability, power quality, and supply-demand balancing within communities. The challenges only grow when considering other electrified building loads as well. Accurate forecasting of power demand and renewable generation is essential for efficient and sustainable grid operation, optimal use of RESs, and effective energy trading within communities. Deep learning (DL), including supervised, unsupervised, and reinforcement learning (RL), has emerged as a promising solution for predicting consumer demand, renewable generation, and managing energy flows in residential environments. This paper provides a comprehensive review of the development and application of these methods for forecasting and energy management in residential communities. Evaluation metrics across studies indicate that supervised learning can achieve highly accurate forecasting results, especially when integrated with unsupervised K-means clustering and data decomposition. These methods help uncover patterns and relationships within the data while reducing noise, thereby enhancing prediction accuracy. RL shows significant potential in control applications, particularly for charging strategies. Similarly to how V2G-simulators model individual EV usage and simulate large fleets to generate grid-scale predictions, RL can be applied to various aspects of EV fleet management, including vehicle dispatching, smart scheduling, and charging coordination. Traditional methods are also used across different applications and help utilities with planning. However, these methods have limitations and may not always be completely accurate. Our review suggests that integrating hybrid supervised-unsupervised learning methods with RL can significantly improve the sustainability and resilience of energy systems. This approach can improve demand and generation forecasting while enabling smart charging coordination and scheduling for scalable EV fleets integrated with building electrification measures. Furthermore, the review introduces a unifying conceptual framework that links forecasting, optimization, and policy coupling through hierarchical deep learning layers, enabling scalable coordination of EV charging, renewable generation, and building energy management. Despite methodological advances, real-world deployment of hybrid and deep learning frameworks remains constrained by data-privacy restrictions, interoperability issues, and computational demands, highlighting the need for explainable, privacy-preserving, and standardized modeling approaches. To be effective in practice, these methods require robust data acquisition, optimized forecasting and control models, and integrated consideration of transport, building, and grid domains. Furthermore, deployment must account for data privacy regulations, cybersecurity safeguards, model interpretability, and economic feasibility to ensure resilient, scalable, and socially acceptable solutions. Full article

Embankment piping and leakage are primary causes of flood control infrastructure failure, accounting for more than 90% of embankment failures worldwide and posing significant threats to public safety and economic stability. Current manual inspection methods are labor-intensive, hazardous, and inadequate for emergency flood season monitoring, while existing automated approaches using thermal infrared imaging face limitations in cost, weather dependency, and deployment flexibility. This study addresses the critical scientific challenge of developing reliable, cost-effective automated detection systems for embankment safety monitoring using Unmanned Aerial Vehicle (UAV)-based visible light imagery. The fundamental problem lies in extracting subtle textural signatures of piping and leakage from complex embankment surface patterns under varying environmental conditions. To solve this challenge, we propose the Embankment-Frequency Network (EmbFreq-Net), a frequency-enhanced deep learning framework that leverages frequency-domain analysis to amplify hazard-related features while suppressing environmental noise. The architecture integrates dynamic frequency-domain feature extraction, multi-scale attention mechanisms, and lightweight design principles to achieve real-time detection capabilities suitable for emergency deployment and edge computing applications. This approach transforms traditional post-processing workflows into an efficient real-time edge computing solution, significantly improving computational efficiency and enabling immediate on-site hazard assessment. Comprehensive evaluations on a specialized embankment hazard dataset demonstrate that EmbFreq-Net achieves 77.68% mAP@0.5, representing a 4.19 percentage point improvement over state-of-the-art methods, while reducing computational requirements by 27.0% (4.6 vs. 6.3 Giga Floating-Point Operations (GFLOPs)) and model parameters by 21.7% (2.02M vs. 2.58M). These results demonstrate the method’s potential for transforming embankment safety monitoring from reactive manual inspection to proactive automated surveillance, thereby contributing to enhanced flood risk management and infrastructure resilience. Full article

Background/Objectives: Diabetic retinopathy (DR) segmentation faces critical challenges from domain shift and false positives caused by heterogeneous retinal backgrounds. Recent transformer-based studies have shown that existing approaches do not comprehensively integrate the anatomical context, particularly training datasets combining blood vessels with DR lesions. Methods: These limitations were addressed by deploying a DeepLabV3+ framework enhanced with more comprehensive anatomical contexts, rather than more complex architectures. The approach produced the first training dataset that systematically integrates DR lesions with complete retinal anatomical structures (optic disc, fovea, blood vessels, retinal boundaries) as contextual background classes. An innovative illumination-based data augmentation simulated diverse camera characteristics using color constancy principles. Two-stage training (cross-entropy and Tversky loss) managed class imbalance effectively. Results: An extensive evaluation of the IDRiD, DDR, and TJDR datasets demonstrated significant improvements. The model achieved competitive performances (AUC-PR: 0.7715, IoU: 0.6651, F1: 0.7930) compared with state-of-the-art methods, including transformer approaches, while showing promising generalization on some unseen datasets, though performance varied across different domains. False-positive returns were reduced through anatomical context awareness. Conclusions: The framework demonstrates that comprehensive anatomical context integration is more critical than architectural complexity for DR segmentation. By combining systematic anatomical annotation with effective data augmentation, conventional network performances can be improved while maintaining computational efficiency and clinical interpretability, establishing a new paradigm for medical image segmentation. Full article

Lameness remains a significant welfare and productivity challenge in dairy farming, often underdiagnosed due to the limitations of conventional detection methods. Unlike most previous approaches to lameness detection that rely on a single-sensor or gait-based measurement, this study integrates four complementary data domains—behavioural, physiological, biochemical, and milk composition parameters—collected from 272 dairy cows during early lactation to enhance diagnostic accuracy and biological interpretability. The main objective of this study was to evaluate and compare the diagnostic classification performance of multiple machine learning (ML) algorithms trained on multimodal data collected at the time of clinical lameness diagnosis during early lactation, and to identify the most influential physiological and biochemical traits contributing to classification accuracy. Specifically, six algorithms—random forest (RF), neural network (NN), Ensemble, support vector machine (SVM), k-nearest neighbors (KNN), and logistic regression (LR)—were assessed. The input dataset integrated physiological parameters (e.g., water intake, body temperature), behavioural indicators (rumination time, activity), blood biochemical biomarkers (non-esterified fatty acids (NEFA), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transferase (GGT)), and milk quality traits (fat, protein, lactose, temperature). Among all models, RF achieved the highest validation accuracy (97.04%), perfect validation specificity (100%), and the highest normalized Matthews correlation coefficient (nMCC = 0.94), as determined through Monte Carlo cross-validation on independent validation sets. Lame cows showed significantly elevated NEFA and body temperatures, reflecting enhanced lipid mobilization and inflammatory stress, alongside reduced water intake, milk protein, and lactose content, indicative of systemic energy imbalance and impaired mammary function. These physiological and biochemical deviations emphasize the multifactorial nature of lameness. Linear models like LR underperformed, likely due to their inability to capture the non-linear and interactive relationships among physiological, biochemical, and milk composition features, which were better represented by tree-based and neural models. Overall, the study demonstrates that combining sensor data with blood biomarkers and milk traits using advanced ML models provides a powerful, objective tool for the clinical classification of lameness, offering practical applications for precision livestock management by supporting early, data-driven decision-making to improve welfare and productivity on dairy farms. Full article

Accurate multivariate Gaussian simulation is critical for resource assessment and mine planning, especially in polymetallic deposits where strong trends, data bias, and multivariate outliers introduce complexity. In this scenario, standard workflows applied to non-stationary domains may result in undesirable data statistics reproduction, especially the multivariate relationships between variables. This study proposes an enhanced simulation framework that integrates data standardization, multivariate outlier detection, trend modeling and removal, and a dual application of the Projection Pursuit Multivariate Transform (PPMT). The approach is demonstrated within a high-grade mineralized breccia domain of the Peñasquito deposit, utilizing data from diamond core and reverse circulation (RC) drill holes, including Au, Ag, Pb, and Zn. Bias in RC data was corrected using data standardization, and multivariate outliers were identified through the application of a robust Mahalanobis distance. Trend modeling was performed using a moving window average and was removed using the Gaussian Mixture Model and Stepwise Conditional Transform. PPMT was applied both before and after trend modeling in order to improve decorrelation and simulation performance. Results show improved data reproduction through histograms, variograms, and complex relationships, as well as correlation coefficients. Cross-validation confirms reduced bias and improved accuracy. This research highlights the importance of treating multivariate outliers and applying PPMT both before and after trend modeling. The study demonstrates that applying PPMT twice is more effective for managing persistent non-stationary features, especially in high-grade domains. Full article

Cardiometabolic diseases (CVDs) are the leading cause of premature mortality and disability worldwide, arising from of cardiovascular and metabolic dysregulation. This review focuses on six critical therapeutic targets established in cardiometabolic regulation: GLP-1R, GIPR, FGFR1/β-Klotho, PCSK9, NF-κB, and the NLRP3 inflammasome. Drawing on curated structural datasets, we analyze the mechanisms of action and map key binding domain features that govern ligand efficacy and specificity. Dual GLP-1R/GIPR agonists, such as tirzepatide, demonstrate superior outcomes in glycemic control and weight reduction. Concurrently, inhibiting PCSK9, NF-κB, and NLRP3 helps to lower cholesterol and reduce harmful inflammation, offering cardioprotection. Structural analysis across these targets reveals complementary motifs (aromatic, hydrophobic, and polar residues). These insights guide the rational design of next-generation multi-target ligands (molecules capable of modulating two or more biological targets involved in related disease pathways, producing integrated therapeutic effects). Such integrated agents are promising for providing combined cardiovascular and metabolic benefits, thus reducing the risks associated with complex therapeutic drug combinations. Full article

The characterization of lithology within Quaternary aquifers holds significant geological importance for the protection, management, and utilization of groundwater resources, yet it continues to present considerable challenges. Indicator Kriging (IK) is a non-parametric, probability-based method of spatial interpolation. It considers the correlation and variability between data points, and its popularity stems from its alignment with geological experts’ principles. However, it still encounters issues in complex geological conditions. To address the limited capacity of conventional IK in reproducing geological variables within heterogeneous geological settings, this study develops an ordered IK method incorporating field variogram function parameters. This framework dynamically extends IK applications by integrating stratigraphic extension trends, requiring experts to formalize spatial variation trends into geological knowledge data, subsequently transformed into constraint parameters for interpolation. Estimation paths are determined via Euclidean distances between points-to-be-estimated and valid data, executing ordered IK following near-to-far and bottom-to-top principles. Results directly depict QLS formation spatial distributions or undergo expert modification for quantitative analysis, demonstrating superior integration of geological knowledge compared to empirical variogram fitting and partitioned IK estimation. The method reduces deviation from expert-interpreted spatial distributions while maintaining computational efficiency and multi-factor integration, with three case analyses confirming enhanced accuracy in lithology distribution reproduction and improved geostructural congruence in complex geological reconstruction. This approach revitalizes Kriging applications in complex geological research, synergizing domain cognition with computational efficacy to advance precision in geological characterization and support government decision-making. Full article

The rapid rise of bio-hybrid robots and hybrid human–AI systems has triggered an explosion of terminology that inhibits clarity and progress. To investigate how terms are defined, we conduct a narrative scoping review and concept analysis. We extract 60 verbatim definitions spanning engineering, human–computer interaction, human factors, biomimetics, philosophy, and policy. Entries are coded on three axes: agency locus (human, shared, machine), integration depth (loose, moderate, high), and normative valence (negative, neutral, positive), and then clustered. Four categories emerged from the analysis: (i) machine-led, low-integration architectures such as neuro-symbolic or “Hybrid-AI” models; (ii) shared, moderately integrated systems like mixed-initiative cobots; (iii) human-led, medium-coupling decision aids; and (iv) human-centric, low-integration frameworks that focus on user agency. Most definitions adopt a generally positive valence, suggesting a gap with risk-heavy popular narratives. We show that, for researchers investigating where living meets machine, terminological precision is more than semantics and it can shape design, accountability, and public trust. This narrative review contributes a comparative taxonomy and a shared lexicon for reporting hybrid systems. Researchers are encouraged to clarify which sense of Hybrid-AI is intended (algorithmic fusion vs. human–AI ensemble), to specify agency locus and integration depth, and to adopt measures consistent with these conceptualizations. Such practices can reduce construct confusion, enhance cross-study comparability, and align design, safety, and regulatory expectations across domains. Full article

Background: Acute Respiratory Distress Syndrome (ARDS) was first described in 1967 by Ashbaugh et al. as a severe acute hypoxemic respiratory failure with reduced lung compliance, representing a common end-path of severe pulmonary endothelial inflammation from diverse etiologies. Since then, several definitions for the adult syndrome have been proposed, culminating in the 2024 “New Global Definition” (Berlin 2.0). In pediatrics, dedicated criteria (pediatric ARDS, PARDS) have been established over the past decade, with the most recent update published by the Second Pediatric Acute Lung Injury Consensus Conference (PALICC-2) in 2023. Methods: We performed a narrative literature review of consensus statements and key studies defining ARDS in adult and pediatric (non-neonatal) populations. Primary sources included the full Berlin 2.0 and PALICC-2 documents, supplemented by PubMed, Embase, and society guidelines. Definitions were compared across major diagnostic domains: timing of onset, imaging requirements, oxygenation thresholds, inclusion of patients with chronic comorbidities, ventilatory support modalities, and applicability in resource-limited settings. Results: Both definitions show convergence in incorporating non-invasive oxygenation indices and adaptability to resource-limited contexts. Key distinctions include the use of the Oxygenation Index (OI) or Oxygen Saturation Index (OSI) in invasively ventilated pediatric patients—metrics that integrate mean airway pressure and correlate more strongly than PaO₂/FIO₂ with short-term outcomes—and PALICC-2’s explicit inclusion of patients with chronic lung disease or cyanotic congenital heart disease when acute deterioration is documented. Imaging criteria differ, with Berlin 2.0 requiring bilateral opacities (and permitting lung ultrasound) versus PALICC-2’s acceptance of unilateral findings. Conclusions: Berlin 2.0 and PALICC-2 represent substantial progress toward globally applicable ARDS definitions, but physiologic and structural differences remain. These distinctions have prognostic and research implications, and harmonization will be critical to improve cross-age comparability, optimize clinical trial design, and ultimately enhance patient outcomes. Full article

Event-Sourced Systems (ESSs) that adopt Domain-Driven Design (DDD) are becoming more popular because of their intuitive business process modeling and improved auditability, scalability, and flexibility. However, ensuring the correctness of domain models—particularly event-sourced aggregates (ESAs)—remains challenging. To address this, we propose root contracting, a novel, constrained, and lightweight adaptation of Design by Contract (DbC), specifying the precondition, postcondition, and class invariant exclusively at aggregate roots. Root contracting simplifies correctness enforcement by leveraging DDD principles aligned with DbC and the standardized ESA code structure. We offer uContract, a Java open-source utility that realizes root contracting, enabling automated verification of ESAs with configurable runtime overhead. Through performance evaluation and methodological discussion, we demonstrate that root contracting effectively bridges formal correctness with practical domain modeling. Our approach provides developers with a tool to streamline development workflows, potentially reducing testing overhead and supporting integration with methodologies like Behavior-Driven Development (BDD). Full article