Search Results (18,641)

Accurate above-ground biomass (AGB) quantification is confounded by signal saturation and data fusion challenges, particularly in structurally complex ecosystems like bamboo forests. To address these gaps, this study developed a two-stage framework to map the AGB of Dendrocalamus giganteus in a subtropical mountain environment. This study first employed Empirical Bayesian Kriging Regression Prediction (EBKRP) to spatialize sparse GEDI and ICESat-2 LiDAR metrics using Sentinel-2 and topographic covariates. Subsequently, a stacked ensemble model, integrating four machine learning algorithms, predicted AGB from the full suite of continuous variables. The stacking model achieved high predictive accuracy (R² = 0.84, RMSE = 11.07 Mg ha⁻¹) and substantially mitigated the common bias of underestimating high AGB, improving the predicted observed regression slope from a base model average of 0.63 to 0.81. Furthermore, SHAP analysis provided mechanistic insights, identifying the canopy photon rate as the dominant predictor and quantifying the ecological thresholds governing AGB distribution. The mean AGB density was 71.8 ± 21.9 Mg ha⁻¹, with its spatial pattern influenced by elevation and human settlements. This research provides a robust framework for synergizing multi-source remote sensing data to improve AGB estimation, offering a refined methodological pathway for large-scale carbon stock assessments. Full article

Background and Objectives: Multiple sclerosis (MS) is a chronic immune-mediated neurological disorder that primarily affects young adults and is frequently accompanied by psychiatric comorbidities such as depression and anxiety, both of which significantly diminish patients’ quality of life (QoL). This study investigated the effect of two oral disease-modifying therapies (DMTs), fingolimod and cladribine, on mental health and QoL in patients with relapsing-remitting MS (RRMS). The aim of the study was to compare levels of depression, anxiety, and health-related quality of life (HRQoL) in RRMS patients treated with fingolimod or cladribine, and to evaluate their associations with clinical and radiological parameters. Materials and Methods: Eighty RRMS patients aged 18 to 50 years with Expanded Disability Status Scale (EDSS) scores of 3.0 or less, no recent disease relapse, and no history of antidepressant use were enrolled. Forty patients were treated with fingolimod and forty with cladribine. Depression and anxiety were assessed using the Hamilton Depression Rating Scale (HDRS) and the Hamilton Anxiety Rating Scale (HARS). QoL was evaluated using the Multiple Sclerosis QoL-54 (MSQOL-54) instrument. Additional clinical data, including MRI-based lesion burden, EDSS scores, age, disease duration, and occupational status, were collected. Results: No statistically significant differences were observed between the two groups regarding HDRS and HARS scores (p > 0.05). However, patients treated with fingolimod had significantly higher scores in the Energy/Fatigue subdomain (7.55 ± 2.02 vs. 6.56 ± 2.57, p = 0.046) and Composite Mental Health (CMH) score (64.73 ± 15.01 vs. 56.00 ± 18.93, p = 0.029) compared to those treated with cladribine. No significant differences were found in the independent items of the MSQOL-54. A negative correlation was identified between total lesion load and QoL scores. Conclusions: Although fingolimod and cladribine exert comparable effects on depression and anxiety levels, fingolimod may be associated with better mental health outcomes and reduced fatigue in RRMS patients. Furthermore, lesion burden and clinical parameters such as age and EDSS score may independently influence QoL, regardless of the DMT used. Full article

With the large-scale integration of high-penetration distributed photovoltaic (DPV) into distribution networks, its output volatility and reverse power flow characteristics are prone to causing voltage violations, necessitating the accurate identification of weak nodes to enhance operational reliability. This paper investigates the definition, quantification criteria, and multi-indicator comprehensive determination methods for weak nodes in distribution networks. A multi-criteria assessment method integrating voltage deviation rate, sensitivity analysis, and power margin has been proposed. This method quantifies the node disturbance resistance and comprehensively evaluates the vulnerability of voltage stability. Simulation validation based on the IEEE 33-node system demonstrates that the proposed method can effectively identify the distribution patterns of weak nodes under different penetration levels (20~80%) and varying numbers of DPV access points (single-point to multi-point distributed access scenarios). The study reveals the impact of increased penetration and dispersed access locations on the migration characteristics of weak nodes. The research findings provide a theoretical basis for the planning of distribution networks with high-penetration DPV, offering valuable insights for optimizing the siting of volatile loads such as electric vehicle (EV) charging stations while considering both grid safety and the demand for distributed energy accommodation. Full article

Highly integrable silicon carbide (SiC) has emerged as a promising platform for photonic integrated circuits (PICs), offering a comprehensive set of material and optical properties that are ideal for the integration of nonlinear devices and solid-state quantum defects. However, despite significant progress in nanofabrication technology, the development of SiC on an insulator (SiCOI)-based photonics faces challenges due to fabrication-induced material optical losses and complex processing steps. An alternative approach to mitigate these fabrication challenges is the direct deposition of amorphous SiC on an insulator (a-SiCOI). However, there is a lack of systematic studies aimed at producing high optical quality a-SiC thin films, and correspondingly, on evaluating and determining their optical properties in the telecom range. To this end, we have studied a single-source precursor, 1,3,5-trisilacyclohexane (TSCH, C₃H₁₂Si₃), and chemical vapor deposition (CVD) processes for the deposition of SiC thin films in a low-temperature range (650–800 °C) on a multitude of different substrates. We have successfully demonstrated the fabrication of smooth, uniform, and stoichiometric a-SiCOI thin films of 20 nm to 600 nm with a highly controlled growth rate of ~0.5 Å/s and minimal surface roughness of ~5 Å. Spectroscopic ellipsometry and resonant micro-photoluminescence excitation spectroscopy and mapping reveal a high index of refraction (~2.7) and a minimal absorption coefficient (<200 cm⁻¹) in the telecom C-band, demonstrating the high optical quality of the films. These findings establish a strong foundation for scalable production of high-quality a-SiCOI thin films, enabling their application in advanced chip-scale telecom PIC technologies. Full article

Recovering oil by fracturing fluid imbibition has demonstrated significant potential for enhanced oil recovery (EOR) in tight oil reservoirs. White mineral oil (WMO), kerosene, or saturated alkanes with matched apparent viscosity have been widely used as “crude oil” to investigate imbibition mechanisms in light shale oil or tight oil. However, the representativeness of these simulated oils for low-maturity crude oils with higher viscosity and greater content of resins and asphaltenes requires further research. In this study, imbibition experiments were conducted and T₂ and T₁–T₂ nuclear magnetic resonance (NMR) spectra were adopted to investigate the oil recovery characteristics among resin–asphaltene-rich Jimusar shale oil and two WMOs. The overall imbibition recovery rates, pore scale recovery characteristics, mobility variations among oils with different occurrence states, as well as key factors influencing imbibition efficiency were analyzed. The results show the following: (1) WMO, kerosene, or alkanes with matched apparent viscosity may not comprehensively replicate the imbibition behavior of resin–asphaltene-rich crude oils. These simplified systems fail to capture the pore-scale occurrence characteristics of resins/asphaltenes, their influence on pore wettability alteration, and may consequently overestimate the intrinsic imbibition displacement efficiency in reservoir formations. (2) Surfactant optimization must holistically address the intrinsic coupling between interfacial tension reduction, wettability modification, and pore-scale crude oil mobilization mechanisms. The alteration of overall wettability exhibits higher priority over interfacial tension in governing displacement dynamics. (3) Imbibition displacement exhibits selective mobilization characteristics for oil phases in pores. Specifically, when the oil phase contains complex hydrocarbon components, lighter fractions in larger pores are preferentially mobilized; when the oil composition is homogeneous, oil in smaller pores is mobilized first. Full article

The energetic valorization of digestate obtained from anaerobic digestion (AD) of the organic fraction of municipal solid waste (OFMSW) was investigated via pyrolysis in a bench-scale rotary kiln. The mass rate of dried digestate to the rotary kiln pyrolyzer was fixed at 500 gr/h. The effect of the pyrolysis temperature was investigated at 600, 700, and 800 °C. The pyrolysis products, char, oil, and gas, were quantified and chemically analyzed. It was observed that with the increase in the temperature from 600 to 800 °C, the char decreased from 60.3% to 52.2% and the gas increased from 26.5% to 35.3%. With the aim of increasing the methane production and methane concentration in syngas, the effect of CaO addition to the pyrolysis process was investigated at the same temperature, too. The mass ratio CaO/dried digestate was set at 0.2. The addition of CaO sorbent has a clear effect on the yield and composition of pyrolysis products. Under the experimental conditions, CaO was observed to act both as a CO₂ sorbent and as a catalyst, promoting cracking and reforming reactions of volatile compounds. In more detail, at the investigated temperatures, a net reduction in CO₂ concentration was observed in syngas, accompanied by an increase in CH₄ concentration. The gas yield decreased with the CaO addition because of CO₂ chemisorption. The oil yield decreased as well, probably because of the cracking and reforming effect of the CaO on the volatiles. A very promising performance of the CaO sorbent was observed at 600 °C; at this temperature, the CO₂ concentration decreased from 32.2 to 13.9 mol %, and the methane concentration increased from 16.1 to 29.4 mol %. At the same temperature, the methane production increased from 34 to 63 g/kg_digestate. Full article

Background and Clinical Significance: Neuroleptic malignant syndrome (NMS) is a life-threatening condition usually caused by the exposure to antipsychotics. This case report presents a catatonia syndrome that may have developed in the context of a moderate NMS. Case Presentation: An 18-year-old male patient presented with a treatment-resistant catatonia syndrome that debuted 2 weeks prior to the presentation (creatin kinase levels = 4908 U/lL maximum temperature = 38.9°C, white blood count = 13.20 × 10⁹/L, Bush–Francis Catatonia Rating Scale = 30 points). Possible organic causes of catatonia were ruled out, according to the negative results obtained. The patient’s condition improved under benzodiazepine treatment and he was later discharged. After discharge, the catatonia was attributed to a possible NMS with moderate severity. The diagnosis was supported by NMS Diagnosis Criteria Score = 85 points and the presence of Levenson’s triad. Conclusions: This case highlights the concomitant manifestation of both catatonia and NMS in the same patient and the difficulty of establishing a correct diagnosis involving both entities. Full article

The construction industry has been troubled by a shortage of skilled labor and safety accidents in recent years. Therefore, more and more robots are introduced to undertake dangerous and repetitive jobs, so that human workers can concentrate on higher-value and creative problem-solving tasks. Nevertheless, although human–robot collaboration (HRC) shows great potential, most existing evaluation methods still focus on the single performance of either the human or robot, and systematic indicators for a whole HRC team remain insufficient. To fill this research gap, the present study constructs a comprehensive evaluation framework for HRC team performance in construction projects. Firstly, a detailed literature review is carried out, and three theories are integrated to build 33 indicators preliminarily. Afterwards, an expert questionnaire survey (N = 15) is adopted to revise and verify the model empirically. The survey yielded a Cronbach’s alpha of 0.916, indicating excellent internal consistency. The indicators rated highest in importance were task completion time (µ = 4.53) and dynamic separation distance (µ = 4.47) on a 5-point scale. Eight indicators were excluded due to mean importance ratings falling below the 3.0 threshold. The framework is formed with five main dimensions and 25 concrete indicators. Finally, an AHP-TOPSIS method is used to evaluate the HRC team performance. The AHP analysis reveals that Safety (weight = 0.2708) is prioritized over Productivity (weight = 0.2327) by experts, establishing a safety-first principle for successful HRC deployment. The framework is demonstrated through a case study of a human–robot plastering team, whose team performance scored as fair. This shows that the framework can help practitioners find out the advantages and disadvantages of HRC team performance and provide targeted improvement strategies. Furthermore, the framework offers construction managers a scientific basis for deciding robot deployment and team assignment, thus promoting safer, more efficient, and more creative HRC in construction projects. Full article

Biodiesel, as one of the alternatives to fossil fuels, faces significant challenges in large-scale industrial production due to its high production costs. In addition to raw material costs, catalyst costs are also a critical factor that cannot be overlooked. This review summarizes various methods for preparing biodiesel catalysts from solid waste. These methods not only enhance the utilization rate of waste but also reduce the production costs and environmental impact of biodiesel. Finally, the limitations of waste-based catalysts and future research directions are discussed. Research indicates that solid waste can serve as a catalyst carrier or active material for biodiesel production. Methods such as high-temperature calcination, impregnation, and coprecipitation facilitate structural modifications to the catalyst and the formation of active sites. The doping of metal ions not only alters the catalyst’s acid-base properties but also forms stable metal bonds with functional groups on the carrier, thereby maintaining catalyst stability. The application of microwave-assisted and ultrasound-assisted methods reduces reaction parameters, making biodiesel production more economical and sustainable. Overall, this study provides a scientific basis for the reuse of solid waste and ecological protection, emphasizes the development potential of waste-based catalysts in biodiesel production, and offers unique insights for innovation in this field, thereby accelerating the commercialization of biodiesel. Full article

Research on children’s singing development is extensive. Different ages, approaches, and variables have been taken into consideration. However, research on singing with text or a neutral syllable is scarce, and findings are inconclusive. This study investigated the influence of singing with text and a neutral syllable on children’s vocal performance. Children aged 4 to 9 (n = 135) participated in two periods of instruction and assessment. In Period One, Song 1 was taught with text and Song 2 with a neutral syllable, and in Period Two, the text was added to Song 2. In each period, children were individually audio-recorded singing both songs. Three independent raters scored the songs’ vocal performances using two researcher-designed rating scales, one for each song, which included the assessment of tonal and rhythm dimensions. Before data analysis, the validity and reliability of the rating scales used to assess vocal performance were examined and assured. The results revealed that 4-, 5-, and 7-year-olds sang Song 1 significantly better in Period One, and 4- and 5-year-olds sang Song 1 significantly better in Period Two. Thus, singing with text seems to favour younger children’s vocal performance. Findings also revealed that girls scored significantly higher than boys for Song 1 in both periods, but not for Song 2 in Period One. The implications of incorporating songs with text and neutral syllables into music programs, as well as the instruments used to assess vocal performances, are discussed. Full article

Large, high-quality patient datasets are essential for applications like economic modeling and patient simulation. However, real-world data is often inaccessible or incomplete. Synthetic patient data offers an alternative, and current methods often fail to preserve clinical plausibility, real-world correlations, and logical consistency. This study presents a patient cohort generator designed to produce realistic, statistically valid synthetic datasets. The generator uses predefined probability distributions and Cholesky decomposition to reflect real-world correlations. A dependency matrix handles variable relationships in the right order. Hard limits block unrealistic values, and binary variables are set using percentiles to match expected rates. Validation used two datasets, NHANES (2021–2023) and the Framingham Heart Study, evaluating cohort diversity (general, cardiac, low-dimensional), data sparsity (five correlation scenarios), and model performance (MSE, RMSE, R², SSE, correlation plots). Results demonstrated strong alignment with real-world data in central tendency, dispersion, and correlation structures. Scenario A (empirical correlations) performed best (R² = 86.8–99.6%, lowest SSE and MAE). Scenario B (physician-estimated correlations) also performed well, especially in a low-dimensions population (R² = 80.7%). Scenario E (no correlation) performed worst. Overall, the proposed model provides a scalable, customizable solution for generating synthetic patient cohorts, supporting reliable simulations and research when real-world data is limited. While deep learning approaches have been proposed for this task, they require access to large-scale real datasets and offer limited control over statistical dependencies or clinical logic. Our approach addresses this gap. Full article

Multimodal review helpfulness prediction (MRHP) aims to identify the most helpful reviews by leveraging both textual and visual information. However, prior studies have primarily focused on modeling interactions between these modalities, often overlooking the consistency between review content and ratings, which is a key indicator of review credibility. To address this limitation, we propose CRCNet (Content–Rating Consistency Network), a novel MRHP model that jointly captures the semantic consistency between review content and ratings while modeling the complementary characteristics of text and image modalities. CRCNet employs RoBERTa and VGG-16 to extract semantic and visual features, respectively. A co-attention mechanism is applied to capture the consistency between content and rating, and a Gated Multimodal Unit (GMU) is adopted to integrate consistency-aware representations. Experimental results on two large-scale Amazon review datasets demonstrate that CRCNet outperforms both unimodal and multimodal baselines in terms of MAE, MSE, RMSE, and MAPE. Further analysis confirms the effectiveness of content–rating consistency modeling and the superiority of the proposed fusion strategy. These findings suggest that incorporating semantic consistency into multimodal architectures can substantially improve the accuracy and trustworthiness of review helpfulness prediction. Full article

Childhood obesity increases chronic disease risk, but no comprehensive synthesis has evaluated the impact of school-based combined nutrition education and physical activity interventions on cardiometabolic biomarkers in children aged 3 to 12 years. This systematic review was conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD420251085194). Five databases were systematically searched through June 2025. Twelve randomized controlled trials involving 18,231 children were included and assessed using the PEDro scale. Ten trials demonstrated significant improvements in at least one cardiometabolic biomarker. Blood pressure (8 studies) outcomes showed systolic reductions of 1.41–6.0 mmHg in six studies. Glucose metabolism (5 studies) improved in two studies with reductions of 0.20–0.22 mmol/L. Lipid profiles (7 studies) improved in three studies, including total cholesterol (−0.32 mmol/L). Insulin levels (5 studies) decreased significantly in two investigations. Anthropometric improvements included BMI and body fat. Physical activity increased by >45 min/week and dietary habits improved significantly. Programs with daily implementation (90-min sessions 4x/week), longer duration (≥12 months), family involvement (parent education), and curriculum integration (classroom lessons) showed superior effectiveness. Interventions targeting children with overweight/obesity demonstrated higher changes compared to the general population. However, methodological limitations included a lack of assessor blinding, absence of subject/therapist blinding, and inadequate retention rates. School-based interventions combining nutrition and physical activity can produce significant improvements in cardiometabolic biomarkers, supporting comprehensive, sustained multicomponent programs for early chronic disease prevention. Full article

Aqueous organic redox flow batteries (AORFBs) represent an advancing class of electrochemical energy storage systems showing considerable promise for large-scale grid integration due to their unique aqueous organic chemistry. However, the use of small-molecule active materials in AORFBs is significantly limited by the issue of stability and crossover. To address these challenges, we designed a high-water-solubility polymer cathode material, P-T-S, which features a polyvinylimidazole backbone functionalized with 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) and sulfonate groups. P-T-S exhibits a solubility of 34 Ah L⁻¹ in water and 31 Ah L⁻¹ in 1.0 M NaCl aqueous solution (NaCl_aq). When paired with methyl viologen to assemble a pH-neutral AORFB with a theoretical capacity of 15 Ah L⁻¹, the system exhibits a material utilization rate of 92.0%, an average capacity retention rate of 99.74% per cycle (99.74% per hour), and an average Coulombic efficiency of 98.69% over 300 consecutive cycles at 30 mA cm⁻². This work provides a new design strategy for polymer materials for high-performance AORFBs. Full article

Compared to traditional real-valued neural networks, which process only amplitude information, complex-valued neural networks handle both amplitude and phase information, leading to superior performance in polarimetric synthetic aperture radar (PolSAR) image classification tasks. This paper proposes a multi-scale feature extraction (MSFE) method based on a 3D complex-valued network to improve classification accuracy by fully leveraging multi-scale features, including phase information. We first designed a complex-valued three-dimensional network framework combining complex-valued 3D convolution (CV-3DConv) with complex-valued squeeze-and-excitation (CV-SE) modules. This framework is capable of simultaneously capturing spatial and polarimetric features, including both amplitude and phase information, from PolSAR images. Furthermore, to address robustness degradation from limited labeled samples, we introduced a multi-scale learning strategy that jointly models global and local features. Specifically, global features extract overall semantic information, while local features help the network capture region-specific semantics. This strategy enhances information utilization by integrating multi-scale receptive fields, complementing feature advantages. Extensive experiments on four benchmark datasets demonstrated that the proposed method outperforms various comparison methods, maintaining high classification accuracy across different sampling rates, thus validating its effectiveness and robustness. Full article