Search Results (36,461)

This article introduces an intelligent framework using deep learning to recognize and classify different faults through the real-time detection of multiple faults in power distribution systems. A collection of data representing normal operating conditions, alongside various fault scenarios including line-to-ground (LG), line-to-line (LL), double line-to-ground (LLG), and three-phase line (LLL) faults, was created using three phase current signals obtained from the Real-Time Digital Simulator (RTDS) microgrid test system. To properly model the system dynamics, a feature extraction method that integrates phase currents, differential currents, summation currents and magnitude results was developed. The temporal features of the fault signals were identified by using a sliding window approach to fit the data. A one-dimensional convolutional neural network (CNN) was developed to identify different types of faults. This model performed well, obtaining nearly 96.15% accuracy while testing. In order to evaluate the feasibility of the approach, the trained model was loaded on Raspberry Pi 5, NodeMCU, ESP32 and existing sensing devices. The fault classification performed in real-time was time-sensitive. The proposed intelligent framework is applicable to low-scale operation for smart grid fault monitoring and protection and it is an economically viable solution. Full article

During the late Ming and early Qing dynasties, the introduction of Western scientific knowledge to China, facilitated by Western missionaries, included logic as a critical element of Western philosophy and scientific culture. This concept was translated, interpreted, and disseminated, carrying both academic contribution and a historical mission of cultural integration and intellectual enlightenment. The development of the Chinese conceptualization of logic mirrors the intricate process of cultural negotiation and conceptual accommodation between Chinese and Western intellectual traditions. This process went beyond simple terminology translation, representing a significant epistemological shift that introduced into traditional Chinese thought a mode of systematic reasoning previously underdeveloped in the indigenous scholarly tradition. Unlike the systematic formalization of logic in the Western tradition, logical reflection in classical Chinese culture took different forms without coalescing into a comparable systematic field. This paper finds that the introduction of Western logic, with its emphasis on formal deduction and systematic reasoning, constituted an early but significant encounter that contributed to the longer-term transformation of Chinese philosophical discourse in three aspects: it introduced a cognition-centered methodological framework that offered an alternative to the ethically oriented traditional Chinese concepts; it provided intellectual resources that encouraged a gradual shift from purely moral speculation toward incorporating empirical investigation and logical demonstration; and it laid the essential conceptual groundwork for the eventual establishment of logic as a modern academic discipline in China. Collectively, these translated texts and concepts introduced new conceptual possibilities into the Chinese intellectual landscape, contributing over time to a gradual shift from prioritizing moral introspection and analogical reasoning toward increasingly valuing empirical investigation, formal demonstration, and systematic argumentation. Ultimately, the translation of logic was not a passive reception but an active intellectual engagement that introduced new conceptual possibilities into Chinese philosophical discourse, contributing over time to a broader reorientation toward rationality and systematicity. Full article

Background/Objective: The identification of novel non-invasive diagnostic and prognostic biomarkers is urgently needed in pleural mesothelioma (PM). While soluble mesothelin-related peptides (SMRP) are the most established circulating biomarker, their prognostic value is limited. A wide range of microRNAs (miRs) play diverse roles in regulating gene expression in PM. MiR-21 has been shown to be upregulated in mesothelioma tissue; nevertheless, the diagnostic and prognostic utility of miR-21 in the circulation and its association with survival in PM have not been extensively investigated to date. The objective of the current study was to evaluate miR-21 as a potential blood-based diagnostic and prognostic biomarker in PM. Methods: Plasma samples from PM patients (n = 94) were collected at the time of diagnosis, prior to treatment. Sex- and age-matched healthy individuals (n = 30) served as controls. MiR-21 levels were measured using quantitative RT-PCR and normalized to miR-16, and potential correlations with clinicopathological data were analyzed. Serum SMRP levels were measured in matched patients (n = 84), and a direct comparative analysis of miR-21 and SMRP was conducted. In situ hybridization (ISH) was used to confirm the presence of miR-21 in tumor cells. Results: Plasma miR-21 levels were significantly elevated in PM patients compared to healthy controls (p < 0.001), demonstrating good diagnostic performance (AUC 0.81). The localization of miR-21 in PM cells was confirmed by ISH. High miR-21 levels were associated with significantly shorter median overall survival (12.4 vs. 24.3 months, p < 0.001). Elevated SMRP levels were also associated with reduced survival (12.4 vs. 19.5 months, p = 0.032); however, SMRP did not retain independent prognostic significance in multivariable analysis. In contrast, high-circulating miR-21 was confirmed as an independent predictor for poor survival (HR 3.12, p < 0.001). Conclusions: Our findings highlight that circulating miR-21 is a potential non-invasive biomarker with both diagnostic and independent prognostic value in pleural mesothelioma and outperforms SMRP in multivariable survival analysis. Further research is warranted to validate its role in the biology of this disease and to assess its correlation with outcome and treatment responses. Full article

The adoption of instrumented wheelsets on diagnostic trains offers the possibility of continuous monitoring of wheel–rail contact forces. The collection of large datasets can be exploited for diagnostic purposes, aiming to localize specific track defects, allowing significant improvements in terms of safety and maintenance costs. Machine learning (ML) techniques can be used to automate anomaly detection. In this work, the authors compare the application of various ML algorithms based on the identification of different frequency or time-based features of analyzed signals. To perform the activity, a significant number and variety of local defects have been included in the recorded data. From a practical point of view, the insertion of real known defects into an existing line is extremely time-consuming, expensive, and not immune to safety issues. On the other hand, the design of anomaly detection algorithms involves the usage of relatively extended datasets with different faulty conditions. The authors propose deliberately adding real contact force profiles of healthy lines to a mix of synthetic signals, which substantially reproduce the behavior and the variability of foreseen faulty conditions. The results of this work, although preliminary and still to be completed, offer a contribution to the scientific community both in terms of obtained results and adopted methodologies. Full article

Children with Autism Spectrum Disorder (ASD) frequently exhibit marked impairments in nonverbal communication, particularly in eye contact, which serves as a foundational element for social interaction and relational development. This study evaluated the effectiveness of an early intervention program utilizing interactive games supported by Augmented Reality (AR) technology to enhance eye contact behaviors, specifically initiation and maintenance, in children with autism. Using a multiple baseline across participants single-case experimental design, four boys (aged 5–7 years) diagnosed with ASD participated in an 8-week intervention at a specialized center in Saudi Arabia. The intervention featured tablet-based, gamified AR tasks incorporating real-time visual feedback, graduated difficulty levels, and reinforcement mechanisms designed to elicit social gaze and sustained eye contact. Eye contact duration and frequency were measured during structured social interactions via systematic direct observation. The results demonstrated significant improvements across all participants, with the mean duration of eye contact increasing from a baseline of 2.0 s to 5.8 s post-intervention. Visual analysis revealed robust treatment effects, further supported by substantial Tau-U effect sizes (range = 0.89–0.96; M = 0.93). Follow-up data collected three weeks post-intervention confirmed the maintenance of gains for three of the four participants. These findings suggest that AR-based interventions provide an effective and culturally responsive approach for enhancing specific nonverbal communication behaviors among children with autism in Middle Eastern contexts. Implications for clinical practice and directions for future research are discussed. Full article

Mental fatigue (MF) has been hypothesized to contribute to injury risk in athletes, but observational studies have not directly investigated this relationship. Therefore, the current study evaluates potential relationships between mental fatigue and subsequent injury occurrence in basketball. Using an observational design, we monitored fourteen male semi-professional basketball players (age: 22 ± 4 years; stature: 192.6 ± 8.8 cm; body mass: 85.5 ± 9.1 kg; Tier 3) from a single team for 21 weeks throughout the competitive season. Each week, the players participated in 5 team-based training sessions, 2–4 individual training sessions, and 1–2 official games. Subjective MF ratings were collected using 100 mm visual analogue scales twice a week (the day before and after the official game) and then averaged. Time-loss injuries were registered, noting the body location, mechanism, and context (training and games). Generalized logistic mixed models were employed to evaluate whether MF levels were associated with injury occurrence in the subsequent 1, 3, and 5 days and 1, 2, 3, and 4 weeks of basketball activity. A total of 11 injuries were registered during the study (7.40 per 1000 h of basketball activity), with an average time loss of 12 ± 19 days. There were no associations between MF and injury occurrence in the following 1, 2, 3 or 4 weeks (all p > 0.05, odds ratios: 1.00–1.28). In male semi-professional basketball settings, preliminary evidence indicates that MF might not be associated with injury occurrence. However, due to the dearth of injury events, the statistical power of this study is insufficient to detect potential small–medium effects. Therefore, the current results should be considered exploratory as opposed to a definitive rejection of the hypothesis. Future studies should evaluate the relationship between MF and injury risk in larger samples and among professional athletes. Full article

Background: Pneumonia of unknown etiology (PUE), defined as pneumonia cases without an identified pathogen at the time of clinical presentation, represents a critical clinical warning signal for emerging infectious disease (EID) outbreaks with pandemic potential. Yet, conventional pathogen-centric surveillance systems suffer from an inherent blind spot: they cannot detect early clustering signals before the causative agent is identified, creating a window of vulnerability during novel pathogen emergence. To address this gap, this study aims to develop a deep learning model that leverages unstructured chest imaging text—a routinely available clinical data stream—to enable real-time, automated screening of PUE cases and early warning of EID clusters, independent of prior pathogen knowledge, within an integrated multi-pathogen surveillance framework. Methods: We retrospectively collected data from 8860 patients with respiratory illnesses at a tertiary hospital in Beijing, China, including 980 PUE cases (11.1%) and 7880 known-etiology pneumonia cases. A deep learning model (RoBERTa with attention enhancement) was developed using unstructured chest imaging reports. The Matthews correlation coefficient (MCC) curve was employed to determine the optimal decision threshold. Model performance was assessed for PUE case identification and clustering signal detection on a test set. Results: The model achieved an area under the receiver operating characteristic curve of 0.986 (95% CI: 0.981–0.991). At the optimal threshold of 0.08, selected by maximizing the Matthews correlation coefficient (MCC)—a balanced metric that accounts for all four confusion matrix outcomes—sensitivity was 89.8%, and specificity was 97.0% for identifying PUE cases. In a simulated surveillance exercise, the model showed a high correlation between the predicted and actual case counts (Pearson’s r = 0.901), suggesting its potential to detect abnormal clustering signals prior to pathogen identification. Conclusions: The developed model demonstrates potential to detect clustering signals of PUE caused by unknown pathogens and can be integrated with hospital information systems, providing a feasible, low-cost tool for integrated surveillance of pathogens with pandemic potential. This approach enables earlier outbreak detection and supports public health decision-making during the critical window before pathogen identification. Full article

To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article

Insulin-like growth factor 2 (IGF2) plays a pivotal role in regulating growth and development; however, its functional involvement in skeletal muscle satellite cells (SMSCs) remains incompletely understood. To elucidate the regulatory role of IGF2, goose SMSCs were engineered to overexpress IGF2 via lentiviral transduction, followed by comprehensive transcriptomic profiling. Comparative analysis revealed 2802 differentially expressed genes (DEGs) in IGF2-overexpressing cells relative to controls, comprising 1202 upregulated and 1600 downregulated genes. IGF2 overexpression markedly activated fibrogenic programs, as evidenced by the upregulation of AP-1 complex components (FOS, JUN), extracellular matrix-related genes (COL1A1, COL5A3), and Wnt signaling receptors (FZD1, FZD7). In contrast, genes involved in myogenic differentiation and contractile function were broadly suppressed, including key myogenic transcription factors (MEF2C, MEF2D), sarcomeric structural proteins (MYBPC1, ACTN2, MYOM3), and metabolic enzymes. Through the construction of protein–protein interaction networks coupled with functional enrichment analysis, we observed a concerted suppression of myogenic regulatory networks critical for myofiber formation. Quantitative real-time PCR validation further confirmed the reliability of the transcriptomic data. Collectively, these findings suggest that overexpression of IGF2 induces a phenotypic shift from myoblasts toward a fibroblast-like state, uncoupling proliferation from differentiation while enhancing fibrogenic identity. This study provides novel insights into IGF2-mediated regulatory mechanisms underlying skeletal muscle development and fibrotic processes. Full article

Due to the highly complex geological environment of the Tibetan Plateau, landslides occur frequently, and signs of ancient landslide reactivation are widespread, posing significant threats to major infrastructure and local communities. Taking the Lumei landslide in Cuomei County as a case study, detailed field investigations were conducted, and Sentinel-1A SAR data (84 scenes from January 2017 to December 2023) were collected to characterize surface deformation. Both PS-InSAR and SBAS-InSAR methods were applied for long-term time-series monitoring, and the results of the two techniques were comparatively analyzed. Furthermore, the influencing factors of landslide deformation were explored on the basis of analyzing the deformation characteristics. The findings reveal that the surface deformation rate exhibits significant spatial heterogeneity, with deformation values decreasing progressively outward from the central region. The surface deformation rates obtained from PS-InSAR and SBAS-InSAR range from −36.55 to −21.81 mm/yr and from −30 to −10 mm/yr, respectively. Both methods indicate a general subsidence trend along the line-of-sight (LOS) direction and show strong spatial consistency and high correlation. By combining the high-precision point results obtained from PS-InSAR and the spatially continuous surface results derived from SBAS-InSAR, the fine spatial deformation characteristics of the Lumei landslide are revealed. The research results can provide an important reference for landslide monitoring, disaster prevention and mitigation in this region. Full article

As automated vehicles move from controlled environments to unpredictable real-world roads, scenario-based testing has become the cornerstone of safety validation. In recent years, substantial progress has been made in scenario representation standards and generation methodologies. However, integrating scenario generation, standards-aligned packaging, validation, curation, and structured querying into a reproducible end-to-end lifecycle remains challenging in practice. This work presents a reproducible reference architecture for Scenario Databases (SCDBs) that treats scenario collections as lifecycle-governed data systems rather than static repositories. The proposed architecture unifies the scenario lifecycle within a single workflow. It integrates scenario generation and ingestion, validation and curation, immutable storage, semantic and value-based querying, and reproducible export. Scenario semantics are represented using ASAM OpenX formats (OpenDRIVE and OpenSCENARIO), together with ASAM OpenLABEL metadata, enabling standards-aligned interoperability. Querying is performed over categorical and value-carrying metadata without requiring inspection of raw scenario artifacts at query time. The reference implementation is deployed using Infrastructure-as-Code, supporting reproducibility and low operational overhead. Execution-based metric enrichment is supported as an optional extension, enabling scenarios to be augmented with execution-derived measurements and trace metadata. The contribution is not a centralized database, but a reference architecture and deployment blueprint that supports interoperable and federated scenario ecosystems. By framing SCDBs as reproducible lifecycle systems, this work supports scalable scenario reuse and more transparent safety validation workflows. Full article

This study evaluated contamination by polychlorinated dibenzodioxins and dibenzofurans (PCDDs/Fs) and polychlorinated biphenyls (PCBs) in 390 feeds and 1756 food samples collected in Latium and Tuscany (Italy, 2016–2025) using HRGC-HRMS. PCDDs/Fs and dioxin-like PCBs (dl-PCBs) are expressed as WHO 2005 toxic equivalents (WHO₀₅-TEQ). Non-dioxin-like PCBs (ndl-PCBs) lack dioxin-like toxicity mechanisms due to their non-coplanar structure and are not assigned a toxic equivalence factor. Feed results were normalised to 12% moisture content. Median levels of WHO₀₅-PCDDs/Fs+dl-PCBs TEQ at the upper limit in feed were 10–100 times lower than those reported in European monitoring data (EFSA, 2002–2010) for comparable categories, including additives, premixtures, raw materials and compound feed, with plant and animal feed materials below 0.03 ng/kg and aquaculture feed at 0.24 ng/kg. Food contamination was generally low, with the median WHO₀₅-PCDDs/Fs+dl-PCBs TEQ 2–4 times lower than Italian national data (2013–2016), considering comparable categories such as meat, fish, milk, eggs, oils, baby foods, marine oils, animal fats and liver. Higher levels were observed in game meat, sheep products and fermented milk than in pork and poultry. The contamination remained stable over time. These results indicate an improvement in food safety thanks to national and EU regulations, although continued surveillance of high-risk and undersampled categories remains essential. Full article

Population aging in middle-income economies produces macroeconomic and distributional consequences that aggregate frameworks cannot detect. This paper develops a multi-household CGE model calibrated to a 26-sector Social Accounting Matrix for Thailand (2024) and traces the labor, saving, and fiscal channels of aging across eleven counterfactual scenarios. Three findings emerge. First, aging’s primary macroeconomic cost operates through capital accumulation, not output contraction: investment falls seven times faster than the GDP under a savings-driven closure, because middle-aged households—the economy’s dominant net savers—compress lifecycle saving in response to aging. The saving channel alone amplifies the labor supply shock four-fold (range: 3.5–4.5). Second, aging can raise elderly welfare. When elderly households retain labor market attachment, wage gains from tighter factor markets outweigh declining capital returns—a welfare reversal invisible to representative agent and OLG frameworks by construction. The critical labor income threshold is ${\alpha }_L^{\ast }=35.5\%$ (range: 34.8–36.2%), confirmed across all participation increments tested (elderly welfare gain: THB 341–521 million). Third, no single instrument satisfies efficiency and equity simultaneously. Pension transfers crowd out investment nonlinearly above 12 percent of tax revenue (range: 10–14%); health demand expansion is the decisive complement that converts redistribution into a near-Pareto improvement. Policy complementarity is an empirical necessity, not a theoretical refinement. Collectively, these results reframe demographic aging as a factor price redistribution mechanism whose welfare incidence is determined by the cohort-level income composition—with direct implications for aging policy in middle-income economies facing rapid demographic transitions under tighter fiscal constraints than for advanced economies encountered at equivalent demographic stages. Full article

People spend most of their time indoors, highlighting the importance of indoor environmental quality for health and wellbeing. While previous studies have shown that exposure to nature can benefit wellbeing, much of this research has focused on outdoor environments, and less is known about how indoor plants and their spatial characteristics influence human perceptions and experiences. This paper reports on a survey study exploring how perceived health and wellbeing are influenced by indoor plants and human preferences for their characteristics, spatial arrangement, and other features within indoor environments. Indoor plants serve as visual and multisensory environmental stimuli. By examining the relationship between indoor plants, preferences, perceptions, visual comfort, multisensory experiences, and wellbeing, the study aims to understand these influences. The questionnaires include multiple-choice questions, yes-no questions, and open-ended questions, allowing the collection of both quantitative and qualitative data. The survey findings highlight the unique benefits of indoor plants, emphasising their potential to enhance wellbeing in ways that outdoor nature may not fully replicate in indoor settings. One significant finding of this study is that scattering indoor plants throughout a space can enhance the connection to nature through three-dimensional spatial interaction, potentially improving wellbeing. This arrangement may serve as a bridge to the outdoors, providing a psychological link to the natural environment. Crucial preference factors also include the complexity and coherence of indoor plants’ appearance, such as colour, shape, and size. The results further indicate that students prefer indoor plants over other elements such as cut flowers, fake plants, or artificial plant representations. The findings indicate that caring for indoor plants may foster emotional engagement, a sense of fulfilment, and place attachment through everyday interaction. In public spaces, plants may also enhance feelings of refuge and perceived security. These findings provide practical recommendations for designing indoor environments that enhance student wellbeing and human–environment interaction. Full article

Hybrid electric mining trucks operating in open-pit environments encounter highly variable gradients and payload conditions that standard energy management strategies fail to address adequately. Existing approaches are predominantly calibrated for full-load scenarios and neglect the accelerated battery degradation resulting from sustained high-power cycling, undermining long-term operational viability. This study presents a multi-objective energy management framework that couples real-time driving condition recognition with dynamic programming (DP) optimization for a 130-tonne hybrid mining truck. Field data collected from an open-pit mine in Heilongjiang Province were used to construct six physically representative driving conditions via principal component analysis and K-means clustering. A Bidirectional Gated Recurrent Unit (Bi-GRU) network (2 layers, 128 hidden units per direction) was trained on a route-based temporal split, attaining 95.8% classification accuracy across all six conditions. Condition-specific powertrain modes were subsequently defined, and a DP formulation with a weighted-sum cost function was solved to jointly minimize diesel consumption and battery capacity fade—quantified through a semi-empirical effective electric quantity metric. A marginal rate of substitution (MRS) analysis was conducted to identify the optimal trade-off between fuel economy and battery life preservation. In the DP cost function, the weight coefficient μ (ranging from 0 to 1) governs the relative emphasis placed on battery degradation minimization versus fuel consumption minimization: μ = 0 corresponds to pure fuel minimization, whereas μ = 1 corresponds to pure battery degradation minimization. The MRS analysis identified μ = 0.1 as the knee point of the Pareto trade-off: relative to pure fuel minimization (μ = 0), this setting reduces effective electric quantity by 6.1% while increasing fuel consumption by only 1.4% (MRS = 4.36). Against a rule-based baseline, the proposed strategy improves fuel economy by 12.3% and extends battery service life by 15.7%. Co-simulation results were validated against onboard fuel-flow measurements; absolute simulated and measured fuel consumption values are reported route-by-route, with deviations within 4.5%. A three-layer BP neural network (3 inputs, two hidden layers of 20 and 10 neurons, 1 output) trained on the DP solution reproduces near-optimal performance—with fuel consumption and effective electric quantity increases below 1.0% and 1.1%, respectively—while reducing computation time by over 96% (from approximately 52,860 s to 1836 s for the 1800 s driving cycle), demonstrating practical feasibility for real-time deployment. Full article