Search Results (37,061)

Replicating the authentic masticatory properties of conventional animal meat remains a primary technical bottleneck for sustainable plant-based analogues. To address critical textural deficiencies like structural fragmentation, this study systematically optimized plant-based patty formulations. The independent and interactive effects of textured fibrous soy protein (TFSP), water, and konjac glucomannan (KGM) were quantified using single-factor experiments and Response Surface Methodology (RSM). Single-factor experiments revealed that springiness peaked at 60 g TFSP, 15 g water, and 10 g KGM, respectively, with excessive additions of each component resulting in structural network disruption. Designating springiness as the core metric, a reliable quadratic regression model identified the optimal matrix: 63.36 g TFSP, 14.39 g water, and 8.57 g KGM. Empirical validation achieved a maximum springiness of 1.56 mm and hardness of 5.51 N, with a negligible relative error (1.27%) from theoretical predictions. Mechanistically, KGM functioned as an active polymeric filler, interacting synergistically with hydrated protein fibers via hydrogen bonding and hydrophobic associations to reinforce the structural network. Comparative Texture Profile Analysis demonstrated that the optimized PBP exhibited a tender masticatory profile with hardness and springiness approximating conventional beef patties, while presenting lower chewiness and higher adhesiveness attributable to the water-binding capacity of KGM. Ultimately, this research provides mathematically validated engineering parameters and theoretical insights into protein–polysaccharide phase behaviors to facilitate the industrial manufacturing of premium plant-based meats. Full article

This paper provides a systematic review of recent applications of NLP methods for analyzing traffic crash reports, with a focus on estimating crash severity, crash duration, and crash causation. The review covers prior research using probabilistic topic modeling methods such as LDA, STM, and hierarchical Dirichlet processes in addition to research using transformer-based language models, which include encoder-based models like BERT and PubMedBERT as well as decoder-based models like GPT, GPT2, ChatGPT, GPT-3, and LLaMA. The review starts with a systematic literature selection process with predefined inclusion criteria. We categorize the reviewed studies into the following application areas: crash severity prediction, risk factor identification in crashes, and road safety analysis. The results show several complementary advantages of using different NLP techniques to achieve different analytical goals. Topic models allow for interpretable and exploratory pattern discovery, while encoder models are well-suited for structured prediction problems. Decoder models have the additional flexibility to perform zero-shot and few-shot reasoning, which makes them useful for reasoning about under-sampled or under-reported data. Across the literature, hybrid methods that combine text and structured data outperform individual methods in terms of prediction accuracy and broad applicability. Challenges across the literature include class imbalance, lack of standardization in preprocessing and evaluation methods, and the tradeoff between prediction accuracy and interpretability of prediction models. These findings highlight the importance of aligning model selection with data availability and operational constraints, pointing toward future research directions in hybrid modeling frameworks, standardized evaluation protocols, and real-world deployment of NLP-driven traffic safety systems. Full article

The development of metro-led urban underground public spaces (UUPSs) provides urban residents with extensive pedestrian-friendly activity areas sheltered from rain, snow, strong winds, and other extreme weather conditions. Although an increasing number of people are engaging in daily commercial and leisure activities within UUPSs, problems such as inconvenient transfer, poor visibility, and a lack of natural light, which indicate poor environmental quality, have led to an uneven distribution of user behavior, thereby reducing the efficiency of space utilization. Our aim in this study was to predict UUPS utilization rates by investigating the relationship between UUPS environmental attributes and user behavior characteristics and preferences. Six typical UUPSs in Wuhan were selected as case studies. User behavior data were collected using panoramic camera recordings, on-site observations, and space syntax methods, while spatial environmental factors were quantified. The correlation between various factors and multi-dimensional user behavior characteristics was discussed, and a Random Forest model was established to predict behavioral preferences. Our results indicate that accessibility and visibility are fundamental factors influencing user behavior characteristics, while the impact of landscape elements is relatively low. Regarding behavioral preference prediction, UUPS environmental features achieved the highest prediction accuracy for leisure behaviors, whereas the predictive performance for sports activities was lower. In this study, we reveal the influence of UUPS environmental factors on user behavior characteristics and predict preference patterns of different behaviors for space types. Focusing on the behavioral needs of space users, we provide a reference for the subsequent human-centered design of UUPSs. Full article

Clear cell renal cell carcinoma (ccRCC) is characterized by marked biological heterogeneity, which limits the prognostic accuracy of conventional clinicopathological models. Increasing attention has therefore focused on identification of biomarkers that can enhance risk stratification throughout all stages of the disease. Starting from the current state of the art, this narrative review summarizes and critically appraises the evidence published over the past decade regarding prognostic biomarkers in ccRCC. The analysis is structured into four overarching domains: (i) genomic biomarkers, covering somatic alterations and transcriptomic signatures; (ii) tissue-based biomarkers, including immunohistochemical surrogates and immune microenvironment features; (iii) circulating biomarkers, such as systemic inflammation parameters and indices; and (iv) integrated predictive models, represented by emerging multi-omic approaches. Going through the broad framework of potential prognostic biomarkers, emphasis is placed on their individual and integrative value in relation to classic clinical-pathological factors and survival parameters. At the tissue level, chromosome 3p-related alterations constitute a central molecular feature of ccRCC. Among these, BAP1 loss has emerged as one of the most consistently validated indicators of aggressive tumor behavior. Disruption of the SETD2/H3K36me3 axis and immune-related biomarkers, including PD-L1 expression, have demonstrated prognostic associations in selected settings, although with variable and context-dependent performance. In the circulating compartment, plasma KIM-1 has shown prognostic relevance following nephrectomy, while postoperative detection of circulating tumor DNA (ctDNA) may identify patients at increased risk of recurrence. However, limited analytical sensitivity and methodological heterogeneity currently restrict the broader clinical applicability of ctDNA-based strategies. Systemic inflammatory indices, such as the neutrophil-to-lymphocyte ratio, show reproducible associations with outcomes but largely reflect host inflammatory status rather than tumor-specific biology. However, no single biomarker currently supports routine prognostic implementation in ccRCC. Future progress will likely depend on integrative models combining genomic, tissue-based, immune, and circulating parameters with established clinical variables. Prospective validation and clear demonstration of incremental clinical utility will be essential before such strategies can meaningfully inform therapeutic decision-making. Full article

Objective: To identify risk factors for progression to sepsis in patients with non-ventilator hospital-acquired pneumonia (NV-HAP) and to develop a practical nomogram for individualized risk assessment in this population. Methods: We retrospectively screened 408 hospitalized patients with hospital-acquired pneumonia at Peking University Third Hospital between January 2017 and December 2021. After excluding patients with an unclear diagnosis date or missing critical variables required for SOFA score calculation, 368 eligible patients with NV-HAP were included and randomly divided into a training cohort (n = 260) and an internal validation cohort (n = 108). An independent temporal validation cohort of 68 patients admitted between January 2022 and December 2022 at the same center was further used for temporal validation. Univariable and multivariable logistic regression analyses with backward stepwise selection were performed in the training cohort to identify predictors associated with progression to sepsis. A nomogram was then constructed based on the final model and evaluated by discrimination, calibration, and decision curve analysis. Results: A total of 368 patients were included in the model development dataset. The final multivariable model retained six predictors: male sex (OR = 2.393, 95% CI: 1.333–4.296), diabetes (OR = 2.205, 95% CI: 1.126–4.319), coagulation dysfunction (OR = 3.327, 95% CI: 1.726–6.413), PaO₂/FiO₂ (OR = 0.955 per 10-unit increase, 95% CI: 0.912–1.001), platelet count (OR = 0.900 per 10 × 10⁹/L increase, 95% CI: 0.853–0.949), and bilirubin (OR = 1.176 per 1 μmol/L increase, 95% CI: 1.100–1.258). The nomogram showed acceptable performance, with an apparent C-index of 0.809 and a bootstrap-corrected C-index of 0.792 in the training cohort. The C-index was 0.750 (95% CI: 0.658–0.841) in the internal validation cohort and 0.754 (95% CI: 0.639–0.870) in the temporal validation cohort. Calibration analysis showed acceptable agreement between predicted and observed probabilities, and decision curve analysis indicated a positive net clinical benefit across clinically relevant threshold probabilities. Conclusions: In patients with NV-HAP, male sex, diabetes, coagulation dysfunction, lower PaO₂/FiO₂, lower platelet count, and higher bilirubin were associated with progression to sepsis. The developed nomogram showed acceptable discrimination, calibration, and clinical utility, and may serve as a practical tool for early individualized risk stratification in patients with NV-HAP. Full article

With the deep integration of AI and IoT technologies, smartwatches have become core terminals for health management. However, research on the use mechanisms among “digital native” college students remains limited. Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and selected constructs from the Health Action Process Approach (HAPA), this study uncovers the drivers and barriers of youths’ smartwatch health function adoption to propose targeted design strategies. A mixed-methods approach was employed, collecting semi-structured questionnaire data from 226 Chinese college students. Quantitative analysis was conducted (n = 106) using Partial Least Squares Structural Equation Modeling (PLS-SEM), complemented by qualitative text mining of open-ended feedback from non-users and churned users. The model demonstrated robust predictive power, supporting five hypotheses. Habit and action planning emerged as core antecedents of use intention, which significantly promoted actual use behavior. Effort expectancy acted as a baseline hygiene factor positively influencing performance expectancy. Qualitative findings confirmed that insufficient sensor accuracy and “health data anxiety” are critical psychological barriers. Validating the integrated model’s effectiveness, we propose three strategic interventions: enhancing data precision to build trust, implementing tiered pricing, and designing anxiety-alleviating visual interfaces, offering theoretical and empirical foundations for optimizing smart health products. Full article

Azo dyes demonstrate dose-dependent carcinogenic and mutagenic effects in exposed cells. Among remediation approaches, microbial adsorption is the most sustainable and environmentally friendly method for eliminating azo dyes. A novel Aspergillus terreus silica composite was developed as a sustainable adsorbent for crystal violet dye (CVD) removal. The fungal strain was isolated from dye wastewater and was genetically identified by 18S rRNA gene sequencing. Dried mycelia of A. terreus (PX920301) were combined with SiO₂ (1:1 w/w) through iterative hydration-drying cycles, yielding a composite characterized by FTIR analyses. Removal CVD %, adsorption capacity, and CVD residual were calculated, and the adsorption process was optimized using Box–Behnken design (four factors, 25 runs). The biosafety of the composite was assessed for phytotoxicity and microbial toxicity. The composite was also applied to real dyes wastewater collected from the bacteriological laboratory. Aspergillus terreus-silica composite showed the highest CVD removal percentage by 85.4%, adsorption capacity (qe) 121.1 mg/L, and lowest CVD residual by 7.26 mg/L, followed by the dried active mycelia (DA-mycelia) with CVD removal 40.23%, adsorption capacity (qe) 57.05 mg/L, and CVD residual by 29.73 mg/L. Optimization data cleared that the maximum experimental values of CVD removal (%) was 99.59% (predicted value 100%) obtained in run number (4) using initial CVD concentration (200 mg/L), pH (8), adsorbent composite weight (0.1 g), and contact time (48 h). Biosafety evaluation demonstrated negligible phytotoxicity against Triticum aestivum seedlings post-treatment, with restored germination and growth comparable to controls. Microbial toxicity assays via well-diffusion to seven microbial isolates confirmed no toxic activities against the tested bacteria, yeast, and fungi, underscoring the composite’s environmental safety. The composite could decolorize the real dye wastewater of laboratories by 95.37%. In conclusion, A. terreus mycelial-silica composite offers a cost-effective, sustainable, and eco-friendly alternative solution for dye bioremediation. Full article

The rapid expansion of renewable energy has raised the demand for accurate, long-term wind power forecasting. However, wind power series are strongly affected by meteorological factors and exhibit pronounced volatility, making long-term prediction challenging. To model these characteristics more comprehensively, we propose STFNet, a dual-branch neural architecture that integrates time-domain and frequency-domain modeling. STFNet contains two key modules: (1) an MLFE module, which explicitly captures lag effects and non-stationary transitions through parallel multi-scale convolutions and a difference-convolution branch and further enhances multivariate dependency learning via cross-variable interaction modeling, and (2) an FGFE module, which applies DCT to capture long-cycle trends and uses a learnable low-pass filter for noise suppression. Experiments on two real-world wind farm datasets (LY and HG) show that STFNet consistently outperforms strong baselines, achieving average MSE reductions of 15.9–26.6% while maintaining a high computational efficiency. Ablation studies further confirm the effectiveness of each module, indicating the strong practical potential of STFNet for wind farm operation and management. Full article

The disposal of waste tyres presents a significant environmental challenge, necessitating sustainable, high-value recycling solutions. This study explores the incorporation of waste tyre metal fibre (WTMF) into hot mix asphalt (HMA) to enhance mechanical performance while reducing its electrical resistivity as well as the landfill burden. The primary goal of this research is to apply response surface methodology (RSM) to experimental data for modelling and optimizing WTMF-modified HMA mixes by capturing the coupled effects of fibre reinforcement and binder content on mechanical and functional performance. The microstructural characteristics of WTMF were examined using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD). WTMF-modified mixes containing five WTMF dosages (from 0% to 1.50%) and bitumen contents from 4% to 6% were prepared and tested in the laboratory. The resulting dataset was used for RSM modelling, with WTMF and bitumen contents as input factors and Marshall stability, flow, porosity, and electrical resistivity as response variables. The central composite design (CCD) technique was employed to quantify interaction effects and to identify statistically significant trends. The developed models were validated using statistical indicators, and optimal mixture compositions were determined and experimentally verified. Microstructural analysis revealed WTMF’s irregular, rough surface with microcracks and pits, aiding crack-bridging and stress transfer. RSM results indicated 0.71% WTMF and 5.1% bitumen as an optimal combination of factors. Furthermore, high R² (>0.80) and adequate precision (>4.0) values from analysis of variance (ANOVA) underscore the significance of the proposed models, revealing a robust correlation between experimental and predicted data. This study demonstrated WTMF’s potential to be used in conventional HMA mixes, offering a sustainable recycling pathway for waste tyres. Full article

Opisthorchis viverrini infection remains a significant public health concern in Southeast Asia, particularly in rural communities of Northeast Thailand, where persistent environmental and behavioral factors sustain transmission. A quasi-experimental study aimed to identify environmental and behavioral risk factors for infection and to evaluate the effectiveness of a community-based intervention program. The intervention program study was conducted over 10 months and comprised three phases: baseline survey‚ health education intervention program implementation‚ and follow-up evaluation. The results were analyzed for the prevalence of parasitic infections, and multivariable logistic regression was performed to identify associated factors. The majority of study participants were female (67.94%)‚ aged 55 to 64 years (48.09%)‚ and farmers (89.31%). Parasitic infections‚ especially O. viverrini‚ substantially decreased during the follow-up period‚ and independent risk factors predicting infection included lower education‚ previous infection‚ raw fish consumption‚ and pesticide use‚ according to multivariable logistic regression analysis. This intervention considerably improved knowledge; mean knowledge score increased by 6.29 points (p < 0.001). Analysis of fecal sludge after treatment with the sand-drying system identified S. stercoralis larvae (20 eggs/L) and Taenia spp. eggs (12.4 eggs/g). These findings indicated that, despite treatment, integrated behavioral and environmental interventions can be effective in interrupting parasite transmission in rural endemic settings. Full article

Background: Intrahepatic cholangiocarcinoma (ihCC) is a rare aggressive liver malignancy with rising incidence. For resectable cases, surgery is the only curative approach, but recurrence rates remain high. These challenges highlight the need for personalized, evidence-based clinical decision-making to improve patient outcomes. To address this, we developed the Multiphasic Evidential Decision-making Matrix (MedMax) to support systematic, individualized therapeutic strategies for ihCC. Methods: In this retrospective single-center study, between 2010 and 2020, we assessed the ability of the MedMax matrix to make treatment decisions in 489 consecutive patients with ihCC or suspected ihCC. Patients were divided into two cohorts depending on whether their tumor was operable (surgical cohort, n = 335) or non-operable (non-surgical cohort, n = 154). We assessed the accuracy of diagnostic confirmation and treatment allocation by MedMax and evaluated how the model’s recommendations corresponded to those made by the tumor board. Results: In the surgical cohort, MedMax achieved 100% accuracy in diagnostic confirmation and 97.9% accuracy in treatment allocation. There was 74.3% concordance between the resection type proposed by MedMax and actual extent of resection. This discrepancy was caused by deviations from the preoperative plan based on intraoperative findings, which could not have been predicted preoperatively. In the non-surgical cohort, MedMax again achieved 100% accuracy in diagnostic confirmation and 98.7% accuracy in treatment allocation. All discrepancies between the decisions made by MedMax and those made by the tumor board were attributed complex, high-risk patient profiles. MedMax reliably identified risk factors (such as advanced comorbidities and multifocal disease) in both cohorts. Conclusions: The MedMax matrix can make accurate, reliable and transparent decisions about the diagnosis and treatment of patients with ihCC thanks to its modular, evidence-based approach. It can also stratify and document risks in both surgical and non-surgical settings. Full article

Electrohydrodynamic effects can significantly alter transport processes in reacting flows, even when the plasma is weakly ionized. However, predictive modeling of such plasma–flame interactions remains challenging due to the multiscale coupling among charge transport, fluid motion, and chemical kinetics. This study presents a charge-transport closure model to investigate electrohydrodynamic influences on laminar non-premixed flames. A two-dimensional computational framework in cylindrical coordinates is used to simulate plasma-assisted methane–air diffusion flames under weak electric-field conditions representative of practical combustion environments. To represent plasma–flow coupling in a computationally feasible yet physically consistent manner, a charge-transport formulation based on the drift–diffusion approximation is employed. The model solves transport equations for representative positive and negative charge carriers coupled with Poisson’s equation for the electric potential to obtain a self-consistent electric field. This formulation assumes a weakly ionized regime for low-temperature plasma-assisted combustion, in which neutral species dominate the mass and momentum transport, while ionization chemistry is simplified and charge transport primarily influences the flow through electrohydrodynamic body forces and Joule heating. Assuming a weak electric field, the steady flamelet model is applied, in which plasma effects primarily influence scalar transport and local thermal balance rather than inducing significant bulk ionization dynamics. The governing equations are discretized using a high-order compact finite-difference scheme that provides improved resolution of steep gradients in temperature, species concentration, and space-charge density near thin reaction zones. The canonical laminar flame model configuration was validated using the established laminar methane–air diffusion flame benchmark, and steady-state spatial profiles of key transport properties were evaluated. Two-dimensional analysis identified the discharge coupling location as an important factor. The application of discharge in the fuel-air mixing region leads to a clear restructuring of the flame. When the discharge is activated, electrohydrodynamic forcing and ion-driven momentum transfer produce a highly localized, columnar flame with sharp gradients and a confined reaction zone. Compared with the baseline case, the plasma-assisted flame localizes the OH-rich reaction zone, confines the high-temperature region into a narrow column, and enhances downstream H₂O formation. Full article

Review text in recommender systems provides rich insights into user preferences and experiences that cannot be fully captured by numerical ratings alone. While recent studies have increasingly leveraged review text to enhance recommendation accuracy, most have primarily focused on improving model performance, with limited attention to quantitatively examining how specific textual elements influence rating prediction. To address this gap, this study empirically investigates the impact of review text characteristics on prediction performance in review-based recommender systems. Specifically, we employ the Unstructured Context-Aware Model (UCAM), where contextual information is replaced with review text embedded using a pre-trained BERT model. Three key textual factors are examined: review length, aspect, and emotion type. Review length is divided into quartiles, and results show that removing shorter reviews significantly degrades performance, indicating their critical role. For analysis, reviews are categorized into food, service, price, atmosphere, and location, with service and food contributing most to performance improvements, while location shows relatively low influence. Emotion types are classified based on Plutchik’s framework, revealing that removing joy, trust, and anticipation reduces performance, whereas excluding sadness slightly improves it. Overall, this study highlights the differential importance of textual features and demonstrates their potential for enhancing recommender system design. Full article

To improve the harvesting efficiency of mechanized cabbage harvesting and reduce damage, the structural configuration of a cabbage harvester was designed based on the cabbage cultivation pattern, physical morphological parameters, and mechanical harvesting characteristics. The harvester consists of a crawler power chassis, pulling device, crop guiding device, clamping and conveying device, profiling device, root-cutting device, and leaf-stripping and collecting device, which enables simultaneous pulling, conveying, root cutting, outer leaf separation, and collection for two rows of cabbages in a single pass, thereby enhancing harvesting efficiency. The sources of cabbage damage during the harvesting process were analyzed, and dynamic analyses of the key components were performed to determine their structural parameters. Through single-factor experiments and response surface methodology optimization tests, the effects of forward speed, pulling roller rotational speed, clamping and conveying speed, and cutter rotational speed on the harvest qualification rate were evaluated. The optimal working parameter combination of these factors was determined and validated through field harvesting performance tests. The results showed that, under the operating conditions of forward speed 0.4 m/s, pulling roller rotational speed 114 r/min, clamping and conveying speed 0.51 m/s, and cutter rotational speed 338 r/min, the average harvest qualification rate reached 96.4%, and the average damage rate was 3.6%, which is close to the maximum theoretical harvest qualification rate of 96.78% predicted by the optimization model. The field validation tests demonstrated good performance, with all indicators meeting the design requirements and relevant standards, providing theoretical support and reference for the development and improvement of cabbage harvesting machinery. Full article

In Mexico, the high prevalence and mortality rates associated with breast cancer (BC) constitute a critical public health challenge that demands context-specific preventive measures. This study proposes an integrative framework for predicting BC risk based on a biopsychosocial model. We hypothesize that emotional suppression and repression act as key neuroendocrine disruptors and predisposing factors within the Mexican female population. To test this, we systematically compared the predictive performance of various machine learning classification models using the clinical, psychological, and combined profiles of 110 women. These models were evaluated with and without the application of a robust evolutionary algorithm: Differential Evolutionary Linear Discriminant Analysis for Feature Extraction $(DE-LD{A}_{FE}$). The results demonstrated that integrating clinical and psychological data into a combined latent space significantly improved the performance of the classification algorithms. The Artificial Neural Network achieved the highest metrics (0.9975 Precision; 0.9976 F1-score). However, due to the inherent “black-box” nature of these models (limited clinical interpretability), the Decision Tree emerged as the optimal practical alternative, providing highly competitive (0.8874 Precision; 0.8853 F1-score) and interpretable results. These findings provide empirical evidence that psychological factors, rather than being mere incidental comorbidities, could be associated with the etiology of breast cancer and be used as risk factors in predicting the disease. Ultimately, this AI-driven biopsychosocial screening model offers a scalable, low-cost, and context-adapted risk assessment tool for early BC diagnosis in Mexican women. Full article