Search Results (15,151)

This article uses two waves of panel data from China Land Economic Survey (CLES) in Jiangsu Province and employs a fixed-effects two-stage least squares (FE-2SLS) approach to identify pension effects on farmers’ labor allocation and land transfer decisions. In the FE-2SLS models, pension is instrumented by the average pension of other households in the same village. The results show that pension promotes land transfer-out, reduces household farm labor input, and increases household off-farm labor input. We further identify intra-household heterogeneity behind the pension effects. Specifically, pensioners in a household tend to leave farming activities without transitioning to off-farm employment, while non-pensioners shift the labor from farm to off-farm employment. We also examine heterogeneity by household budget pressure using two grouping strategies based on shortage experience and a composite budget-constraint indicator. The results show that the pension effects are more clearly observed among households without budget shortage. The estimates for households with budget shortage are less precise. These findings suggest that pension effects are complex in driving farmers’ resource allocation in their households. However, Jiangsu Province provides a substantial number of off-farm employment opportunities and features a well-developed land transfer market. The estimated pension effect in this area may not be applicable to less developed regions. Full article

Objectives: The aim of this study was to evaluate the psychometric properties of the Pain Catastrophizing Scale (PCS) in patients with carpometacarpal osteoarthritis of the thumb. Methods: In this cross-sectional register-based study of 253 patients with carpometacarpal osteoarthritis of the thumb, a two-parameter item response theory analysis was used to evaluate the items’ difficulty and discrimination parameters. Results: Of 253 patients, 245 (57%) were women. The mean age was 56.0 (SD 16.5) years. The mean total PCS score was 14.0 (SD 10.5) points. Difficulty estimates were distributed fairly evenly across the item score scale, with a slight shift towards higher scores. Discrimination of both total and subscale scores was perfect, varying from 1.91 to 2.84. Conclusions: PCS was able to discriminate well between different levels of catastrophizing. PCS performed slightly more accurately when the catastrophizing level was above average in the studied sample. PCS can be recommended for clinical use when assessing catastrophizing in patients with carpometacarpal osteoarthritis of the thumb. Full article

Structural reconstruction helps infer the spatial relationships and object layouts in a scene, which is an essential computer vision task for understanding visual content. However, it remains challenging due to the high complexity of scene structural topologies in real-world environments. To address this challenge, this paper proposes RegionGraph, a novel method for structural reconstruction of buildings from a satellite image. It utilizes a layout region graph construction and graph contraction approach, introducing a primitive (layout region) estimation network named ConPNet for detecting and estimating different structural primitives. By combining structural extraction and rendering synthesis processes, RegionGraph constructs a graph structure with layout regions as nodes and adjacency relationships as edges, and transforms the graph optimization process into a node-merging-based graph contraction problem to obtain the final structural representation. The experiments demonstrated that RegionGraph achieves a 4% improvement in average F1 scores across three types of primitives and exhibits higher regional completeness and structural coherency in the reconstructed structure. Full article

Freshwater demand for cementing operations in the Delaware Basin continues to increase with expanding unconventional development, creating a high demand for an alternative source of water. This study develops a chemistry screening and operational framework to evaluate the reusability potential in cementing operations in the Delaware Basin. A three-tier screening system for the produced-water samples was established by using the major-ion chemistry, total dissolved solids (TDS), pH, and saturation index (SI) thresholds derived from the cement literature and American Petroleum Institute (API) guidelines. The results of the geochemical screening aid in classifying the water samples into four suitability categories: Excellent/Preferred, Good/Suitable, Moderate/Marginal, and Poor/Unsuitable. The results suggest that the samples obtained from the Loving, Pecos, Reeves, Eddy and Lea counties meet the criteria for reuse in cementing operations with minimal conditioning. To assess the feasibility of operational use, a probabilistic forecasting model was developed to predict the cement water demand in 2026 for the basin. Linear regression of historical drilling trends between 2015 and 2025 showcased that approximately 3595 new wells will be drilled, with an average well depth of 21,778 ft. To evaluate whether the produced-water volumes in the basin are adequate for reuse in cementing, a Monte Carlo simulation (10,000 iterations) estimated an annual cementing water requirement centered at 6.16 MMbbl/year (P50). Produced-water availability from wells classified as Excellent/Preferred was also modeled probabilistically, using uncertainty in the water–oil ratio (WOR), estimated ultimate recovery (EUR), and forecast duration. These results demonstrate the potential for produced-water reuse to reduce freshwater demand for cementing operations in the Delaware Basin. Full article

State-of-charge (SOC) estimation for lithium-ion batteries in smartphones is complicated by nonlinear load variation, electro-thermal coupling, aging effects, and heterogeneous user behaviors. This study proposes a multi-physics coupled SOC estimation framework, termed the Multi-Physics Thermo-Electrochemical Coupled SOC Model (MTEC-SOC), to characterize battery behavior under representative user-load conditions within controlled ambient thermal boundaries. The model combines system-level power profiling, thermal evolution, voltage dynamics, and aging-related capacity correction within a unified framework. To support model development and validation, a dual-source dataset is established using laboratory battery characterization data and real-world smartphone behavioral data, from which users are classified into light, heavy, and mixed usage patterns. Comparative results against four benchmark models (M1–M4) show that MTEC-SOC achieves the highest overall accuracy, with average MAE, RMSE, and TTE error values of 0.0091, 0.0118, and 0.08 h, respectively. The results suggest distinct degradation tendencies across user types: calendar aging dominates under prolonged high-voltage dwell in light-use scenarios, whereas, within the tested thermal range, heavy-use scenarios exhibit stronger voltage sag, relative temperature rise, and polarization-related stress; mixed-use scenarios are characterized by transient responses induced by abrupt load switching. Sensitivity analysis further indicates that the predictive behavior of the model is strongly scenario-dependent, with higher-load operation within the calibrated range amplifying parameter perturbations. Overall, the proposed MTEC-SOC framework provides accurate SOC estimation and physically interpretable insight within the evaluated dataset and operating conditions, offering potential guidance for battery management and energy optimization in intelligent mobile terminals. Full article

Due to the high energy demand of buildings, especially educational buildings, it is crucial to improve total building energy consumption. The proposed methodology is the integration of a photovoltaic (PV) system with a smart control plan for educational buildings. The main aim is to improve energy consumption in an educational building (Electrical Engineering Department, Assiut University, Egypt) using photovoltaic integration and a smart control plan to regulate energy and boost indoor comfort without requiring a significant change in the building architecture. This study was conducted in two main phases: field measurements for annual energy consumption in Assiut University over a five-year period from 2009 to 2014, and an analysis of energy consumption for the Electrical Engineering Department. Then, integration of PV panels on the roof to generate electricity was considered, with the calculation of the shading factor and tilt angle to ensure a realistic estimation of energy yield and to improve energy efficiency using smart control plans. The findings indicate that the average annual peak consumption reached about 30 GWh in Assiut University during the academic years 2009 to 2014. The maximum energy consumption for a typical occupied day in the educational building is 47 kWh. An improvement in building energy consumption was achieved using PV, producing 33–35 MWh annually with an effective smart control plan and without installing sensor-based systems. The results of this study will help improve energy consumption for educational buildings in hot arid climates without building modifications. This study highlights that unoccupied periods—when human activity is absent in classrooms and other rooms—account for up to 40% of the scheduled energy consumption. Using PV panels will result in a shading factor of 0.562 from the total roof area. Full article

Characterizing surface runoff and the transport process of non-point source pollutants (NSPs) carried by this runoff is crucial for identifying key source areas, estimating pollution loads entering water bodies, and implementing pollution control, which is particularly important in regions dominated by smallholder farming in China. Currently, most of the commonly used NSP models originated from international countries and have shortcomings such as high data requirements, high generalization degrees, and requiring the calibration of numerous parameters in the application process. Therefore, a distributed non-point source pollution model based on the time-varying gain and stormwater runoff response was constructed, designed for application at the watershed scale. This study describes the construction of the model, introducing its principles and structure through three key modules: a rainfall–runoff module, a soil erosion module, and a pollutant migration and transformation module. The proposed model was used to simulate the rainfall–runoff, soil erosion, and nutrient migration and transformation processes at different spatiotemporal scales. Although it achieved the best performance at the monthly and annual scales, its simulation results at the daily and hourly scales still met the relevant requirements, with relative errors within 20% and Nash–Sutcliffe Efficiency (NSE) coefficients of approximately 0.7. The annual sediment delivery ratios for the Yangliu Small Watershed and the basin above the Ankang section in 2022 were determined to be 0.445 and 0.36, respectively. The pollutant processes corresponding to different runoff events in the Yangliu Small Watershed were simulated, and the average NSE for total nitrogen (TN), ammonia nitrogen (NH₃-N), nitrate nitrogen (NO₃-N), total phosphorus (TP), and soluble reactive phosphorus (SRP) were determined to be 0.69, 0.74, 0.79, 0.71, and 0.71, respectively. For the basin above the Ankang section, the NSE coefficients for the simulation of NH₃-N and TP pollutant processes were 0.78 and 0.83, respectively. The model demonstrated robust applicability across various spatial (ranging from small to large watersheds) and temporal (hourly−daily−monthly−annual) scales, and exhibited stability across different basins in a semi-humid region of China. The model is characterized by a parsimonious parameter set, ease of calibration, and strong spatiotemporal versatility, thus providing an efficient and reliable tool for non-point source pollution simulation. Full article

Background: Recent research has highlighted the potential of postbiotics for addressing obesity and associated metabolic disorders. In this randomized, double-blind clinical trial, the efficacy of a postbiotic product in managing overweight and associated parameters was assessed. Methods: Sixty individuals were randomized into two groups: one group (n = 30) received the Postbiotic (heat-killed L. fermentum strain K8-Lb1) and the other (n = 30) a Placebo control. Body weight, waist circumference, body composition, vital signs, blood biomarkers and questionnaires for quality of life, eating behavior, eating control and gastrointestinal symptoms were assessed. Results: After a 12-week intervention, body fat mass (primary parameter) was significantly (p = 0.016) reduced in the Postbiotic group (98.15 ± 3.32% of baseline) compared to the Placebo group (100.41 ± 3.39%). In line with this, body weight (p = 0.047) and waist circumference (p = 0.034) were significantly reduced and visceral fat tended to be reduced (p = 0.053). Accordingly, the Postbiotic group tended (p = 0.066) to feel more in control of their body weight. Despite weight loss, muscle mass tended (p = 0.062) to increase. ALT, AST and GGT tended to be reduced, which may indicate an improvement in liver steatosis. Estimated average glucose (eAG) differed significantly between the groups in individuals with normal fasting glucose levels. The ability to concentrate significantly (p = 0.014) improved. Conclusions: Under an ad libitum diet, the postbiotic L. fermentum strain K8-Lb1 reduced body fat mass, body weight, and waist circumference, improved the ability to concentrate, and showed a trend towards an increase in muscle mass. The results of this pilot trial need confirmation by a pivotal trial. Full article

Background: Adolescent insomnia is a public health concern associated with affective disturbances, poor academic performance, and cardiometabolic risk. In Peru, nighttime screen use, psychosocial stressors, and social inequalities intensify its impact; however, brief, validated screening instruments remain limited. Objective: To translate, culturally adapt, and evaluate the psychometric properties of the Youth Self-rated Insomnia Scale (YSIS) in Peruvian adolescents, examining its internal structure, reliability, and invariance across sex. Methods: An instrumental study was conducted with 300 students aged 13 to 17 years (M = 15.02; SD = 1.07). Descriptive statistics were calculated, and confirmatory factor analysis (CFA) was performed using a robust estimator. Reliability was assessed through Cronbach’s alpha (α), McDonald’s omega (ω), and average variance extracted (AVE). Factorial invariance by sex was examined at the configural, metric, scalar, and strict levels. Results: The unidimensional model demonstrated adequate fit (χ² = 44.55, df = 18, p < 0.001; CFI = 0.97; TLI = 0.95; RMSEA = 0.07; SRMR = 0.04), with factor loadings ranging from 0.47 to 0.76, high internal consistency (α = 0.86; ω = 0.81), and AVE = 0.51. Although the two-factor model showed acceptable global fit indices, it revealed insufficient AVE in one factor (AVE = 0.40) and a high inter-factor correlation (r = 0.93), suggesting a lack of discriminant validity. Factorial invariance across sex was supported at all evaluated levels. Conclusions: The Spanish version of the YSIS demonstrates a unidimensional structure, adequate internal consistency, and factorial invariance across sex, supporting its use as a brief screening tool in clinical and school settings, as well as in epidemiological studies among Peruvian and Latin American adolescents. Full article

Background: Epidural procedures benefit from a pre-procedural informed estimation of epidural depth, as anticipating the approximate distance can support safer needle placement and reduce technical difficulties during analgesia or anesthesia procedures. The influence of ethnicity has been established across different populations worldwide; however, there is a lack of Saudi-specific MRI data on epidural depth among the adult population. Aim of this Study: To measure the skin to epidural space distance (SED) at the lumbar interspaces L3–L4 and L4–L5 in the Saudi adult population using magnetic resonance imaging (MRI) and to examine its correlations with age, sex, height, weight, and body mass index (BMI). Methods: In this retrospective cross-sectional study, sagittal T1-weighted lumbar MRI images of the spine of 169 adult Saudi patients were studied. The age group ranged from 20 to 70 years, with an equal distribution of males and females. The skin to epidural space distance (SED) was measured at the L3–L4 and L4–L5 interspaces, and its correlations with age, sex, height, weight, and BMI were analyzed. Results: The average measurement of skin to epidural space distance (SED) was 59.08 mm in L3–L4, and 63.21 in L4–L5. BMI and weight showed strong positive correlations with SED across both levels. Female sex was associated with longer SED values at L4–L5. There was no significant correlation between SED and age or height of the patients. Conclusions: MRI-based assessment of SED revealed strong correlations with weight and BMI, but no correlation with height, age, and sex. These findings support the individualized estimation of epidural depth and needle length selection to enhance procedural safety and reduce complications. Full article

In Time Division Duplex (TDD) Direct-Sequence Code Division Multiple Access (DS/CDMA) architectures, Pre-Rake filtering serves as a powerful transmitter-side strategy to alleviate receiver hardware constraints by leveraging channel reciprocity. Nevertheless, rapid channel fluctuations induced by high Doppler spreads critically undermine this reciprocity assumption. This failure is primarily driven by the unavoidable latency between uplink reception and downlink transmission, leading to severe performance deterioration. To address these challenges and enhance system robustness in modern high-speed scenarios, we propose an improved hybrid transceiver architecture. This scheme integrates multiplexed Pre-Rake processing with a Matched Filter-based Rake receiver and employs a Minimum Mean Square Error (MMSE) equalizer to suppress the severe Inter-Symbol Interference (ISI) and Multi-User Interference (MUI). Furthermore, we conduct a comparative analysis of channel estimation methods tailored for a 10 Mbps high-speed transmission environment.Our investigation reveals that while complex quadratic interpolation is often prioritized in low-data-rate studies, simple averaging is sufficient and even superior in high-speed communications. This is because the shortened slot duration allows simple averaging to effectively track channel variations while avoiding the noise overfitting associated with higher-order interpolation. The simulation results demonstrate that the proposed MMSE-optimized architecture achieves superior Bit Error Rate (BER) performance, providing a practical and computationally efficient solution for next-generation mobile networks. Full article

Saudi Arabia experienced rapid convergence in women’s financial inclusion between 2014 and 2024, a period marked by the 2018–2019 reforms expanding women’s economic rights and the accelerated deployment of digital payment infrastructure. Using four waves of Global Findex microdata (2014, 2017, 2021, and 2024), this study estimates probability-weighted logit models with average marginal effects and decomposes gender gaps using nonlinear Kitagawa and Blinder–Oaxaca methods. Reform-era dynamics are examined by tracing changes in the gender gap across survey waves. The findings indicate that aggregate gender gaps in account ownership and digital payment usage narrowed substantially by 2024, with conditional gaps among employed adults no longer statistically significant, while sizable disparities persist among individuals outside the workforce. Decomposition results highlight increased female labor force participation as a key correlate of convergence, consistent with labor market integration playing a central role in women’s financial inclusion during the reform era. Full article

Emergency evacuation in complex and dynamic building environments requires robust and adaptive routing strategies capable of responding to evolving hazards, blocked passages, and changing crowd behaviour. Most existing evacuation planners rely on static geometric representations and lack semantic awareness of the environment, limiting their ability to perform informed re-planning and backtracking when routes become unsafe. This paper proposes a neuro-symbolic evacuation planning framework that integrates Lifelong Planning A* (LPA*) with ontology-driven semantic reasoning and a Bidirectional Long Short-Term Memory (BiLSTM) prediction model. The building’s spatial and semantic knowledge is represented using the Web Ontology Language (OWL) and Resource Description Framework (RDF), enabling automated inference of implicit connections and enforcement of safety policies. The BiLSTM model learns temporal patterns from ontology-consistent evacuation trajectories and provides guidance for remaining-cost estimation and early prediction of routes likely to require backtracking, which is combined with a bounded semantic heuristic to preserve admissibility and optimality guarantees. Simulation results in a multi-floor academic building show that the proposed BiLSTM-guided semantic LPA* framework reduces average evacuation time by up to 9.6%, decreases node expansions by up to 32%, and increases evacuation success rates to 96.2% compared with a purely semantic baseline. The BiLSTM model also achieves strong predictive performance, with a test AUC of 0.92 for backtracking prediction and a next-state accuracy of 87.1%. The proposed framework is designed to support explainable, policy-compliant, and incrementally adaptable evacuation guidance under rapidly evolving emergency conditions. Full article

Satellite remote sensing has become essential for water quality assessment across inland and coastal environments, with rapid improvements in recent years. Significant advances have been made in detecting optically active parameters (such as chlorophyll-a, suspended matter, and turbidity), showing consistently strong performance across multiple studies. Specifically, the median validation performance (R²) derived from the quantitative synthesis indicates R² = 0.82 for chlorophyll-a (interquartile range—IQR: 0.75–0.90), R² = 0.80 for total suspended matter (IQR: 0.78–0.85), and R² = 0.88 for turbidity (IQR: 0.85–0.90). Conversely, the retrieval of optically inactive parameters (such as nutrients like total phosphorus and total nitrogen) remains more context dependent. It exhibits moderate, more variable results, with median R² = 0.68 (IQR: 0.64–0.74) for total phosphorus and R² = 0.75 (IQR: 0.70–0.80) for total nitrogen. These findings clearly illustrate the varying success of retrievals of optically active and inactive parameters and underscore the inherent difficulties of indirect estimation methods. However, high reported accuracy has yet to translate into transferable, uncertainty-informed, and operational monitoring systems. This gap stems from structural issues in validation design, physics integration, uncertainty management, and multi-sensor compatibility rather than data limitations alone. We present a PRISMA-guided, distribution-aware quantitative synthesis of 152 peer-reviewed studies (1980–2025), based on a systematic search protocol, to evaluate satellite-based retrievals of both optically active and inactive parameters. Instead of simply averaging performance, we analyse the empirical distributions of validation metrics, considering the validation protocol, sensor type, parameter category, degree of physics integration, and uncertainty quantification. The synthesis demonstrates that validation strategy often influences reported results more than the algorithm class itself, with accuracy inflated under non-independent cross-validation methods and notable variability between studies concealed by mean-based reports. Across four decades, four persistent structural challenges remain: limited transferability across sites and sensors beyond calibration areas; weak or implicit physical integration in many data-driven models; lack of or inconsistency in uncertainty quantification; and fragmented multi-sensor harmonisation that restricts operational scalability. To address these issues, we introduce two evidence-based coding frameworks: a physics-integration taxonomy (P0–P4) and an uncertainty-quantification hierarchy (U0–U4). Applying these frameworks shows that most studies remain focused on low-to-moderate levels of physics integration and primarily consider uncertainty at the prediction stage, with limited attention to upstream sources throughout the observation and inference process. Building on this structured synthesis, we propose a transferable, physics-informed, and uncertainty-aware conceptual framework that links model architecture, validation robustness, and probabilistic uncertainty to well-founded design principles. By shifting satellite water quality modelling from isolated algorithm demonstrations towards integrated, evidence-based system design, this study promotes scalable, decision-grade environmental monitoring amid the accelerating impacts of climate change. Full article

Accurate assessment of the State of Health (SOH) is critical for battery management systems in aviation. As a step towards this goal, this study presents a proof-of-concept for a novel SOH estimation method based on an Improved Particle Swarm Optimization-Extreme Learning Machine (IPSO-ELM) model, validated under controlled laboratory cycling conditions. Although traditional Extreme Learning Machines (ELM) are widely used due to their fast computation and good generalization, their random parameter initialization often leads to unstable convergence and limited accuracy. To address these limitations, this paper proposes a novel SOH estimation method based on an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the key parameters of ELM. Three health indicators (HI)—constant-current charging time, equal-voltage-drop discharge time, and average discharge voltage—were extracted from charge–discharge curves as model inputs. The IPSO algorithm dynamically adjusts the inertia weight, introduces a constriction factor and a termination counter to enhance global search capability and avoid local optima. Experimental results on open-source datasets (B005, B007, B0018) and laboratory datasets (A001, A002) demonstrate that the proposed IPSO-ELM model achieves a Root-Mean-Square Error (RMSE) below 0.7% and a Mean Absolute Percentage Error (MAPE) below 0.5%. Compared with standard ELM and PSO-ELM models, it significantly outperforms them in accuracy (e.g., for B0018, RMSE is reduced to 0.21% and MAPE to 0.14%), convergence speed, and robustness, establishing a foundation for future development of aviation-ready SOH estimators. Full article