Search Results (250)

Early identification of depression risk from social media text can support large-scale screening and timely follow-up. However, posts are often emotionally complex and linguistically ambiguous, which makes robust detection challenging. This paper proposes RST-DS (Rhetorical Structure Theory-based Depression Scanning), a discourse-aware framework that integrates Rhetorical Structure Theory (RST) signals with lexical evidence for classifying posts as Depressed/Non-Depressed (D/ND). Using Reddit posts, we compute an RST-derived score capturing rhetorical relationships and coherence and fuse it with lexical features represented via Term Frequency–Inverse Document Frequency using two strategies: feature addition (+) and feature concatenation ($\left|\right|$). We evaluate K-Nearest Neighbors (KNN), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP), along with a soft-voting ensemble model named MLPBoostReg (an ensemble of LR, XGBoost, and MLP). Using a 5-fold cross validation, the concatenation strategy consistently outperforms the addition strategy across accuracy, precision, recall, and F1-score. The best-performing configuration, MLPBoostReg$\left|\right|$ achieves the highest accuracy, precision, recall, and F1-score of 0.970, indicating a strong balance between identifying depression-related content and limiting false alarms. These findings suggest that combining rhetorical structure with lexical evidence improves depression detection from social media text, while noting that social-media labels do not substitute for clinical diagnosis. Full article

Background: Entrustable professional activities (EPAs) are increasingly used in competency-based assessment, but evidence remains limited for nursing workplace-based assessment (WBA) systems that can capture learning trajectories while providing defensible reliability for programmatic decisions. Objective: The objective of this study is to develop a respiratory nursing EPA framework and clinically validate core EPAs using longitudinal WBA data, including performance progression, attainment patterns, and reliability requirements based on generalizability (G) theory and decision (D) studies. Methods: This study included two phases: (1) EPA framework development using evidence synthesis and a two-round Delphi consultation and (2) longitudinal clinical validation using WBA data. In the validation phase, 30 respiratory nurses were assessed by five trained raters over 12 weeks on four core EPAs (EPA1, EPA2, EPA5, and EPA11) using a 1–5 entrustment scale (attainment defined as a score ≥ 4). Learning trajectories were modeled using a negative exponential function (learning speed indexed by β0). Reliability was evaluated using a crossed-facet G-study framework (person × rater × occasion) with mixed-effects modeling, and D-studies were used to identify minimal rater–occasion configurations meeting EP² thresholds. Results: In the Delphi phase, 21 experts completed both rounds (100% response rate), with high authority coefficients (Cr = 0.841 and 0.843) and significant inter-expert agreement (Kendall’s W = 0.504 and 0.532; both p < 0.01), yielding a final 13-item respiratory nursing EPA framework. In the validation phase, 1392 core EPA assessments were analyzed, and event-level attainment ranged from 72.5% to 89.1%. Learning speeds were heterogeneous (β0 = 0.038–0.156), with the fastest improvement in EPA11 and the slowest in EPA2. In the G-study, residual variance accounted for 65.05% of the total variance, followed by person (18.21%) and occasion/week (10.13%) effects, and the main effect on rater was small (0.14%). Under the observed protocol (five raters and approximately four ratings/week), reliability reached acceptable levels (G = 0.792; EP² = 0.712). The minimum D-study configurations were one rater × 11 ratings/week for EP² ≥ 0.70 (EP² = 0.703) and two raters × 12 ratings/week for EP² ≥ 0.80 (EP² = 0.812). Conclusions: The respiratory nursing EPA framework showed strong expert-consensus support and promising longitudinal clinical validation evidence. A core EPA-based WBA approach demonstrated developmental sensitivity and acceptable dependability under feasible sampling plans, supporting its use for structured competency assessment and progression monitoring in respiratory nursing training. Full article

The transition to a circular economy (CE) is central to achieving SDG 12, yet institutional transformation varies significantly across countries. This study conducts a comparative institutional analysis of CE transitions in China and Russia to quantify Russia’s institutional lag and assess the maturity of both systems in the context of SDG indicator 12.5.1 (Circular Material Use Rate—CMUR). Drawing on neo-institutional theory, multi-level governance, and institutional trap theory, we develop an original methodology that computes institutional lag through three key milestones, an integrated maturity index across eight institutional components, and CMUR estimates based on official statistics, legislative acts (1998–2025), and industry reports. Our results show that Russia’s average institutional lag relative to China is 15.3 years: Russia’s 2024 municipal solid waste recycling rate (13.9%) matched China’s 2009–2010 level, while China achieved Russia’s 25% target for 2030 in 2018. The integrated maturity index is 8.25 for China versus 5.25 for Russia, with the largest gaps in industrial symbiosis, R&D and human capital, and international integration. Russia’s CMUR stands at 5–7%, compared to 25–30% in China. We also demonstrate that Russia’s high recycling growth is driven by a low-base effect and is not evidence of institutional efficiency; the compound annual growth rate (CAGR) of Russian recycling volume (33.0%) significantly exceeds China’s (13.1%), but this reflects the much lower starting point rather than systemic maturity. We conclude that without systemic institutional reforms—including eco-industrial parks, green finance, and competence centres—Russia will remain at an early CE stage, failing to substantially contribute to SDG 12 by 2030. Full article

Pump stations are critical water-management assets in arid irrigation systems, but maintenance planning often separates operational importance from physical condition. This study presents a sequential two-stage Analytic Hierarchy Process (AHP) and Dempster–Shafer (D-S) evidence theory framework for screening and aging assessment of pump stations in Fayoum City, Egypt. In the first stage, the AHP results from a previous priority-ranking study are used to identify operationally important pump stations. In the second stage, D-S evidence theory evaluates the physical aging condition of selected high-priority stations under uncertain expert judgment. The Fayoum dataset includes 72 pump stations, distributed as 25 stations under the East Fayoum Irrigation Department, 17 stations under the West Fayoum Irrigation Department, and 30 stations under the Electro Mechanical Department. The D-S stage was applied to five A-ranked stations from the East Fayoum Irrigation Department because field access, expert familiarity, and comparable expert-rating data were available for these stations. Seven experts assessed the stations using six condition-based criteria: comprehensive management, mechanical and electrical equipment, buildings and civil works, overall pump station condition, pipe condition, and gate condition. Expert ratings were transformed into D-S belief masses, combined using Dempster’s rule, and synthesized using normalized criterion weights. The final D-S scores were 50.28 for Al-Tahooun, 50.42 for Ysar Wahba left side, 55.52 for Al-Fallahah, 68.01 for Losato, and 80.44 for Abu Dinqash. Al-Tahooun and Ysar Wahba left side formed the highest-priority intervention group, while Abu Dinqash showed the best condition profile among the assessed stations. The results show that AHP screening followed by D-S condition assessment can support maintenance planning in data-limited irrigation systems, but the conclusions are limited to the five selected East Fayoum stations assessed in the D-S stage. Full article

The credibility of corporate environmental claims is fundamental to achieving China’s dual-carbon goals, which aim to peak emissions by 2030 and achieve neutrality by 2060, yet widespread ESG decoupling, a phenomenon where disclosed ESG performance deviates from actual actions, directly undermines policy effectiveness. Guided by the Fraud Triangle Theory, this paper examines whether and how AI adoption curbs decoupling practices. Using data from Chinese A-share listed firms from 2009 to 2024 and mediation analysis, we find that AI adoption significantly reduces ESG decoupling. Specifically, we identify three distinct mediating pathways through which AI exerts its inhibitory effect: it reduces environmental uncertainty and ambiguity, thereby compressing opportunities for managers to engage in decoupling; it heightens media scrutiny and analyst attention, increasing deterrence pressure on firms; and it improves disclosure quality and transparency, undermining the rationalization of decoupling. Heterogeneity analyses reveal stronger effects in firms with R&D-experienced executives, higher reputation, and stronger governance, as well as in highly competitive industries, suggesting that complementary capabilities amplify AI’s governance. These findings provide novel evidence on how AI governs environmental information fraud and offer policy implications for using digital technologies to enhance ESG authenticity. Full article

Daily groundwater-level prediction in arid inland basins is driven by complex meteorological–hydrological conditions, water supply, pumping, and irrigation demand. Using data from the Zhangye Basin (2018–2025), this study selected 10 representative wells from 51 candidates to build a one-day-ahead framework with a 60-day input window. Dempster–Shafer evidence theory fused five criteria (Pearson, Spearman, lagged correlation, mutual information, and tree-model importance) to screen external variables. Long short-term memory network (LSTM), temporal convolutional network (TCN), and Transformer served as first-level sequence models; extreme gradient boosting (XGBoost) as the second-level stacking learner; and SHapley Additive exPlanations (SHAP) to quantify feature contributions. Dempster–Shafer evidence theory (D-S evidence theory) results indicated that groundwater pumping proxy variable (GPV), irrigation water-demand intensity proxy variable (IWD), surface-water supply proxy variable (SWS), canal-diversion proxy variable (CDV), air temperature (AT), runoff, vapor pressure deficit (VPD), and canal irrigation supply–demand coupling intensity (CISDCI) exhibited high process-representation relevance. During the 90-day test period, Stacking achieved the lowest RMSE for six of 10 wells. Regional average RMSE, MAE, and NSE values were 0.1596 m, 0.0772 m, and 0.9326 for the Zhangye group, and 0.0185 m, 0.0133 m, and 0.9177 for the Gaotai group. SHAP showed historical groundwater-level data dominated contributions, accounting for 64.17% and 43.96% in the Zhangye and Gaotai groups, respectively, and indicating model dependence rather than direct hydrological causality. This framework provides a cautious reference for short-term groundwater forecasting and input selection under the given data conditions. Full article

The medium- and low-voltage distribution network is a critical hub connecting the transmission grid and end-users, and its power supply reliability directly determines livelihood security and socio-economic operational efficiency. Typhoon-induced strong winds and rainfall often trigger large-scale power outage risks, severely threatening power grid security and resilience. To achieve the rapid and accurate perception of outage risk areas based on building damage after typhoons, this study proposes the ResiDS-Net method, which infers distribution network outage risk levels by identifying building damage levels. An improved Dempster–Shafer evidence theory is here adopted to fuse the outputs of CM-ResNet50, Inception-V3 and DenseNet121, enhancing perception accuracy. A two-stage “coarse screening–fine judgment” framework using dual datasets is established to quickly identify large-scale suspected power outage areas from building group damage data. To address the issue that equating building damage with power outages reduces judgment accuracy, this study further develops a hierarchical building damage dataset, classifying individual buildings by damage level to achieve precise outage risk identification. Our experiments show that ResiDS-Net achieves 96.66% and 93.00% accuracy on the two datasets, 2.13% and 2.50% higher than nine comparative networks including Inception-V3. The proposed method effectively improves outage risk perception precision and provides a scientific basis for power emergency repair. It should be noted that the proposed method provides building-damage-based outage risk inference for emergency decision support, rather than the direct detection of verified actual outage status. Full article

The creation of Basic Probability Assignment (BPA) still represents a basic problem in the Dempster-Shafer (D-S) theory of evidence especially when it comes to representing continuous uncertainty and class ambiguity. In order to overcome this problem, this paper suggests a BPA construction model depending on Gaussian overlap. The main principle behind the approach is the creation of focal elements based on the overlaps between conditional probability distributions of classes, allowing characterisation of uncertainty in a data driven manner. Namely, attribute level evidence is represented by Gaussian distributions, and singleton and composite focal elements are composite focal elements are generated through Gaussian product responses and normalized to obtain BPAs. Composite focal elements are further projected into singleton-level decision scores through proportional belief and plausibility transformations for decision-making and attribute-weight calculation. Moreover, to dynamically modify the role played by different attributes, a Kullback-Leibler (KL) divergence-based weighting scheme is used. These parts combine to form a full pipeline of continuous evidence modeling to BPA generation as proposed by the given method. The experimental results show that the proposed method achieves 98.00 ± 2.67% accuracy on the Iris dataset, 97.21 ± 1.76% accuracy on the Wine dataset, and 90.86 ± 1.20% accuracy on the Breast Cancer Wisconsin dataset. Compared with existing BPA generation methods, the proposed method obtains the best performance on the Iris and Wine datasets. Compared with classical machine learning models, the method also achieves the highest accuracy on the Iris dataset and remains competitive on the Wine and Breast Cancer Wisconsin datasets. Full article

Inverters are widely applied in aviation, distributed power grids, and vehicles, where their health status directly impacts the stable operation of entire systems. Existing health assessment methods suffer from poor real-time performance, require additional measurement circuits, and are prone to misjudgment, while failing to adequately address slow degradation behaviors during inverter operation. To address these challenges, this study proposes an inverter health assessment method based on an improved D-S evidence theory. First, based on the practical requirements of subway auxiliary inverters, 13 key evaluation indicators were selected. Subjective weights were obtained using the Analytic Hierarchy Process (AHP), while objective weights were derived through the Critic method, credibility, and falsity weighting. These were then fused using game theory to obtain composite weights. Next, after data normalization, a ridge-type membership function was employed to describe health state uncertainty. Finally, the improved D-S evidence theory integrates multi-source information to achieve online health status assessment. Experimental validation demonstrates that this method effectively evaluates the impact of IGBT failures, sensor malfunctions, and capacitor–inductor degradation on the inverter. It exhibits strong robustness under DC voltage fluctuations and load variations, enabling real-time output of health scores and grades to provide a reliable basis for maintenance decisions. Full article

This study integrated Item Response Theory (IRT) models with ordinal survey instruments to establish a baseline psychometric framework and identify multidimensional factors associated with academic achievement among first-semester leveling students (N = 1558 pre-test; N = 1676 post-test) at the Escuela Politécnica Nacional, Ecuador. A dual-component methodology was employed in this study. Initially, an 80-item ordinal survey was utilized to assess eight latent constructs, yielding substantial validation metrics through Confirmatory Factor Analysis (CFA). Secondly, structured diagnostic assessments in core STEM and language subjects were calibrated using three-parameter logistic (3PL) IRT models via Expected A Posteriori (EAP) estimation. Results demonstrated high internal consistency ($r = 0.93$ between IRT and raw scores), with mean IRT-scaled ability $\overline{\theta } = 10.45$ ($SD = 3.51$) on a 1–20 scale. Estimated item parameters yielded a mean discrimination of $\overline{\mathrm{a}} = 1.92$ and a centered mean difficulty of $\overline{b} = 0.05$. The Orlando–Thissen $S-X^2$ goodness-of-fit test, applied at a significance threshold of $p < 0.01$, identified 19 items (23.75%) whose observed response patterns deviated significantly from model predictions, with the majority concentrated in the physics and chemistry content domains. Factor scores and performance outcomes were statistically contrasted against 24 categorical demographic variables, revealing differential performance patterns across student subgroups. This research provides validated psychometric instruments, reproducible IRT-LMS integration protocols, and empirical evidence supporting targeted interventions to strengthen university transition. Full article

The nucleation and growth mechanisms of copper electrodeposition from Cu(I)-containing-reline, a deep eutectic solvent, were investigated through a combination of electrochemical techniques and surface characterization. Cyclic voltammetry revealed the characteristic nucleation loop associated with an overpotential-driven electrocrystallization process, from which the equilibrium potential of the Cu(I)/Cu(0) redox couple was determined to be −0.35 V vs. a Ag quasi-reference electrode. Experimental potentiostatic current density transients were analyzed using nucleation models capable of accounting for both adsorption and three-dimensional (3D) diffusion-controlled growth, thereby allowing deconvolution of the individual contributions to the overall current response. The kinetic parameters, including the nucleation frequency and the number density of active sites, exhibited an exponential dependence on the applied overpotential, thus indicating enhanced nucleation kinetics at greater driving forces, while determining a Cu(I) diffusion coefficient of (3.39 + 0.09) × 10⁻⁷ cm² s⁻¹. Thermodynamic analysis showed that the Gibbs free energy of the formation of the critical nucleus decreases with increasing overpotential and follows the expected dependence on the inverse square of the overpotential, in agreement with classical nucleation theory. The estimated critical nucleus size was found to be smaller than one atom, suggesting that nucleation occurs at highly active surface sites. Furthermore, an exchange current density of (3 ± 1) μA cm⁻² was estimated for the Cu(I) electrochemical reduction. Scanning electron microscopy revealed a high density of copper nanoparticles (~20 nm) distributed across the electrode surface, along with larger aggregates (~100 nm) formed by coalescence and growth, consistent with a progressive nucleation mechanism. X-ray photoelectron spectroscopy confirmed that the deposits consist exclusively of metallic copper, with no evidence of oxidized species. These results demonstrate that copper electrodeposition in reline is governed by a complex interplay between the thermodynamic driving force, the interfacial kinetics, and mass transport, comprehensively providing fundamental insight into the electrocrystallization processes in deep eutectic solvents. Full article

This study addresses the limitation of single-factor environmental assessment in autonomous sweet potato farming under open-field conditions. An IoT-based sweet potato growth environment monitoring and comprehensive evaluation system was developed by integrating an STM32-based acquisition terminal, multi-sensor data collection, Narrow Band Internet of Things (NB-IoT) transmission, and cloud-based visualization. Five key environmental variables, namely soil temperature, soil moisture, soil available nitrogen, photosynthetically active radiation (PAR), and CO₂, were continuously monitored. To improve the evaluation of heterogeneous and uncertain environmental information, a multi-factor environmental quality assessment method combining fuzzy membership functions and an improved D-S evidence theory was proposed. Field experiments were conducted in Danzhou, Hainan, China, and 600 valid synchronized samples were obtained for analysis. The results showed that most samples were classified as Suitable (63.5%), followed by Normal (30.8%) and Poor (5.7%), with a mean comprehensive environmental score of 0.802. Among the monitored variables, PAR and soil temperature showed relatively high adaptive weights, indicating their important roles in environmental quality discrimination. Furthermore, the comprehensive environmental evaluation result exhibited a significant positive correlation with sweet potato yield (r = 0.6501, p = 2.3724 × 10⁻⁷³), demonstrating good explanatory ability for yield variation. The proposed system provides an effective technical framework for real-time environmental monitoring, quantitative suitability evaluation, and precision management in autonomous sweet potato farming. Full article

This study comparatively evaluates the effectiveness of remarketing strategies under digital transformation in Uzbekistan’s service (tourism and hospitality) and manufacturing (textile) sectors, grounded in the Resource-Based View (RBV) and the Technology Acceptance Model (TAM). Using a sequential explanatory mixed-methods design, 280 enterprises (140 per sector) from four regions of Uzbekistan were surveyed, integrating quantitative analysis (OLS regression, t-test, χ²-test, PLS-SEM) and Monte Carlo simulation (20,000 iterations) with qualitative in-depth interviews (n = 32). The textile sector exhibited higher but more volatile returns (ROI = 82.1%; CV = 0.18), whereas the tourism sector achieved more stable yet lower returns (ROI = 48.3%; CV = 0.11) (t(278) = −22.84; p < 0.001; Cohen’s d = 2.73). AI-based personalization was positively associated with ROI (β = 0.28, p < 0.001) and with reduced revenue volatility through an indirect pathway (indirect effect = 5.04, 95% CI [4.10, 6.00]), with significantly stronger associations in the textile sector (Δ = 1.64, p < 0.05). This study contributes to digital marketing theory by demonstrating sector-specific heterogeneity in AI personalization mechanisms, providing empirical evidence of the infrastructure–ROI variability relationship in a transition economy, and demonstrating the value of integrating Monte Carlo–based uncertainty analysis with mixed-methods evidence as a robustness device. The findings carry direct implications for sustainable economic development in transition economies: by demonstrating how sector-specific digital marketing strategies are linked to and can enhance the long-term viability and resource efficiency of enterprises, this study contributes to advancing Sustainable Development Goal 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production). Full article

Background: The translocation of diet-derived antigens from the maternal intestine to breast milk represents a primary gateway for neonatal immune priming, yet the structural basis for why certain proteins survive this transit while others do not remains poorly understood. This review introduces the “Survivor Peptide” hypothesis, proposing that specific food allergens possess intrinsic “stability architectures” that enable them to resist maternal digestion and navigate the gut–mammary axis to reach the infant in an immunologically active form. Methods: We analyzed the current literature regarding the detection and structural characteristics of food allergens in human milk. Integrating evidence from 26 major sources, we performed an in silico structural analysis of five representative “survivor” proteins: Gal d 1 (egg white), Bos d 5 (cow’s milk), Gal d 6 (egg yolk), Tri a 19 (wheat), and tropomyosin (Der p 10-mite/shellfish). High-resolution 3D models were retrieved from the Protein Data Bank and AlphaFold2, and then visualized in UCSF ChimeraX to map stability anchors, including disulfide bonds and hydrophobic clusters, against solvent-accessible IgE-binding epitopes. Results: We identified and categorized allergens into distinct Molecular Resilience Architectures: the “Covalent Cage” (Gal d 1), defined by dense disulfide stapling, the “Glycoprotein Shield” (Gal d 6), utilizing yolk-matrix structural anchors, the “Topological Shield” (Bos d 5), characterized by a stable β-barrel, and “Coiled-Coil Rigidity” (Der p 10). These frameworks protect large, immunogenic fragments that maintain the spatial arrangement required for IgE cross-linking. Conclusions: Allergen persistence in the gut–mammary axis is dictated by a protein’s intrinsic structural architecture. Identifying these stability fingerprints provides a unified theory for allergen persistence and offers a path for refining component-resolved diagnostics and neonatal oral tolerance strategies. Full article

Catenary support components (CSCs) are essential for the safe and efficient operation of electrified railway systems. However, detecting CSCs in images presents significant challenges due to the scarcity of labeled data, the presence of complex and diverse backgrounds, and the difficulties associated with multi-scale variations. To tackle these issues, this paper introduces a novel detection framework designed explicitly for CSCs. First, a Siamese-based self-supervised learning framework is designed as a pre-training strategy to reduce the reliance on labeled data, effectively leveraging unlabeled images and significantly lowering annotation costs. This pre-training approach enables the model to focus on identifying and extracting relevant features from prior knowledge, honing its ability to discern key patterns and structures within the data. Second, the Vision Attention-based Intrascale Feature Interaction (Vision-AIFI) and Relation Vision Module (RVM) are proposed to enhance the model, which can strengthen its ability to extract multi-scale features and effectively address challenges posed by complex backgrounds and scale variations. Third, a Dempster–Shafer (DS) evidence theory-based detection head is inserted to improve classification confidence and localization precision, ensuring accurate detection results in complex inspection scenarios. Finally, a UAV-based dataset for CSCs is constructed and validation experiments are performed. To evaluate the model, we used several standard COCO metrics, including mAP (77.84), APs (67.84), APm (70.31), and APl (90.04). In addition, the framework is further evaluated for Domain Generalization, which can demonstrate its strong adaptability and high detection accuracy for real-world CSC detection tasks. Full article