Search Results (6,955)

Background: While stable dietary patterns and hedonic responses in food preferences are associated with depression, whether neurobiological mechanisms mediate this relationship remains unknown. This study therefore investigates the food preference–depression associations and their potential neurostructural mediation. Methods: Over 140,000 participants were included in a longitudinal study and cross-sectional. We employed Cox regression models to examine the relationships between food preferences and depression. Half-longitudinal mediation analysis and cross-lagged models estimated the mediating roles of brain grey matter and prospective relationships with depressive symptoms. Results: We found protective effects of moderate salty, bitter and spicy preferences against depression, especially in overweight and obese participants. Further, brain structure demonstrated widespread positive correlations with both food preferences and depressive symptoms. The mediation analysis identified volumes of peripheral cortical grey matter, ventricular cerebrospinal fluid, and grey and white matter as potential mediators in the relationships between salty preferences and depression. Of these, cross-lagged models revealed the distinct directional relationships between the volume of grey matter in the VI cerebellum (vermis)/lateral occipital cortex inferior division (right) and depression, indicating that their mutual influences are mediated through different neural mechanisms. Conclusions: Moderate, but not extreme, liking of specific tastes (spicy, bitter, or salty) reflects healthier mental states. The structure of brain grey matter may mediate the relationship between salty preference and depression; also, the cerebellum together with lateral occipital regions may potentially serve as potential emotional correlates. Further neurobiological investigations are needed to confirm this pathway and inform novel therapeutic strategies for depressive disorders. Full article

Flood disaster risk assessment serves as an important foundation for formulating regional sustainable development strategies. This study establishes a risk assessment model for flood disasters in small and medium-sized rivers based on a theoretical framework integrating Geographic Information Systems (GIS), the Delphi method, and Bayesian networks (Delphi–BNs). An indicator system for the assessment was developed from three dimensions: hazard, vulnerability, and exposure. Hazard is represented by flood inundation area and depth; vulnerability is indicated by population distribution and economic layout; and exposure is reflected by road accessibility. By constructing a GIS-based Bayesian network and employing the Delphi method to create a probabilistic and spatially explicit model, this approach quantifies various sources of uncertainty in the assessment process, enabling a probabilistic expression of risk. Based on the risk assessment results, a stratified, phased flood emergency rescue and personnel transfer plan was established, designating extremely high-risk areas as the core zones for the first phase of personnel transfer, high-risk areas as the second-phase rescue zones, and medium-risk areas as the third-phase rescue zones, thereby providing clear operational guidance for flood emergency response in the basin. The Delphi–BN assessment framework developed in this study focuses on the core elements of flood disaster risk formation, organically integrates expert experience with spatial big data, and effectively overcomes the limitations of traditional assessment methods, such as strong subjectivity, insufficient accuracy, and poor quantification. It achieves a refined and quantitative assessment of flood risk in small and medium-sized river basins in semi-arid regions. The outcomes of this research contribute to a clearer understanding of both the driving mechanisms and the spatial patterns of regional flood risk. Furthermore, they establish a scientifically credible and operationally relevant foundation for key disaster-response decisions, encompassing timely emergency actions, phased population transfers, and the optimized deployment of limited emergency resources. Full article

This study investigates the influence of rainfall variability on runoff generation in the Upper Ing River Basin and inflow to the Mae Tum Reservoir in northern Thailand using the physically based Soil and Water Assessment Tool (SWAT) version 2012. In small mountainous watersheds, water supply reliability for irrigation and domestic use—particularly for unmonitored royal initiated projects like the Mae Tum Reservoir—has become a critical concern due to shifting climatic extremes. A SWAT model was developed using detailed spatial data on topography, land use, and soil characteristics together with long-term daily climate and streamflow records. The model performance at Station I.17 was evaluated through calibration and validation using the R², Nash–Sutcliffe Efficiency (NSE), and percent bias indices. Rainfall regimes were classified into dry, normal, and wet years based on the mean and standard deviation of 25-year gauge records to drive scenario simulations. The calibrated model reproduced seasonal runoff patterns satisfactorily (monthly NSE up to 0.685 and R² up to 0.712). The simulations demonstrated the strong sensitivity of both the runoff at Station I.17 and reservoir inflow to interannual rainfall differences, with the annual runoff ranging from 71.5 to 379.7 million m³ and the annual inflow to Mae Tum Reservoir ranging from 28.84 to 48.33 million m³. These findings demonstrate that physically based spatial modeling can effectively replace traditional empirical operating rules, providing a highly transferable framework for runoff forecasting, reservoir inflow assessment, and climate responsive water resources planning in data-scarce tropical mountainous basins. Full article

The dynamics of aboveground biomass (AGB) in rubber plantations (RPs) provide an important basis for evaluating carbon stocks and environmental adaptability in tropical plantations. However, continuous monitoring of AGB of RPs at the regional scale is lacking, and its nonlinear responses to hydrothermal conditions remain insufficiently understood. This study focused on RPs in western Hainan Island (WHI), including Danzhou, Baisha, Lingao, and Chengmai, and integrated field plot data with multi-source remote sensing datasets. A framework for mapping RPs combining rule-based constraints and phenology-based random forest (RF) classification was developed. After key variable screening, extreme gradient boosting (XGBoost), Shapley additive explanations (SHAP), and generalized additive model (GAM) were used for AGB estimation and identification of climatic responses. The results showed that mapping of RPs achieved an overall accuracy of 92.89% and a Kappa coefficient of 0.854. The XGBoost-derived estimates showed that AGB of RPs in the study area increased by approximately 1.43 × 10⁶ Mg from 2017 to 2025, with growth areas mainly concentrated in the Danzhou–Baisha and western Chengmai. AGB exhibited significant nonlinear responses to climatic factors. Specifically, the effect of precipitation (PRE) shifted to negative after approximately 1945 mm yr⁻¹, whereas annual mean maximum temperature (TMAX) shifted to a positive effect after about 29.72 °C, although this effect gradually weakened as temperature continued to rise. Combinations such as PRE × annual mean temperature (PRE × TMP), PRE × TMAX, and PRE × potential evapotranspiration (PRE × PET) exhibited significant nonlinear interactions, indicating that the direction and magnitude of the effect of PRE shifted with changes in temperature and PET levels. These findings link the spatiotemporal changes in AGB of RPs in WHI with hydrothermal thresholds and their interacting effects, deepening our understanding of the climatic response characteristics of AGB in RPs in this region. They also provide a scientific basis for RP monitoring, carbon stock assessment, and climate-adaptive management in WHI. Full article

Municipal sewage sludge contains considerable amounts of protein, making protein recovery a viable route for sludge valorization. In this work, sludge disintegration was achieved by cyclone cutting coupled with ozone oxidation, and the mixed liquor of foam standing liquid and supernatant was used as the feedstock for protein recovery via isoelectric point precipitation. Pretreatment tests showed that under 60 mg/L ozone concentration, 10 °C and 60 min, alkaline conditions enhanced sludge lysis; the mixed liquor suspended solids (MLSS) removal rate reached 87.65% at pH 9, and the protein concentration in the foam layer reached 1530.14 mg/L at pH 11, yielding a protein-rich feedstock suitable for subsequent extraction. In the isoelectric point precipitation stage, single-factor and L9(3⁴) orthogonal experiments were conducted to examine the effects of pH, temperature and centrifugal speed on extraction rate, and four ensemble learning algorithms (GBR, RF, XGBoost and CatBoost) were employed to build prediction models. The results showed that the factor influence order was pH > centrifugal speed > temperature, with pH being extremely significant (p < 0.01). Under leave-one-out cross-validation, the XGBoost model performed best (R² = 0.9243, MAE = 2.78%, RMSE = 3.52%). Response surface analysis determined the optimal parameters as pH 4.0, 5 °C and 3500 r/min, with both predicted and measured precipitation-stage extraction rates of 86.19%. Amino acid analysis indicated that essential amino acids accounted for 39.9% of the extracted protein, with good rehydration and foaming stability. Ensemble learning algorithms can reveal the multi-factor nonlinear coupling in isoelectric point precipitation, providing data support for process optimization of sludge protein recovery. Full article

Heavy and extra-heavy oils represent a large and growing share of recoverable hydrocarbon resources, yet their extreme viscosity, high heteroatom content, and non-Newtonian behavior routinely defeat empirical correlations developed for conventional crude. Machine learning has emerged as a candidate response to this modeling gap, but existing reviews largely catalog applications without asking whether the technology is actually ready for industrial deployment. This critical review synthesizes machine learning applications across five thematic domains of the heavy-oil value chain: physicochemical property prediction, enhanced oil recovery, flow assurance, reactive recovery, and downstream upgrading. Studies are read through a three-phase historical lens, tracing the field’s progression from empirical-correlation replacement to methodological diversification to physics-informed and closed-loop integration, and evaluated against a Technology Readiness Level (TRL) scale adapted specifically for heavy-oil machine learning. The multilayer perceptron anchors more of the primary corpus than any other architecture, a pattern that, in our interpretation, reflects small-sample, low-dimensional regression needs rather than any demonstrated advantage over other architectures. Enhanced oil recovery is the only cluster to reach organizational-scale deployment, anchored by a single multi-decade operator program, Chevron’s San Joaquin Valley i-field; the remaining clusters are constrained less by modeling sophistication than by single-basin datasets and undisclosed uncertainty. Measured against three falsifiable deployability criteria, fidelity preservation below 10° API, operator-grade interpretability, and demonstrated laboratory-to-field transferability, no study in the reviewed corpus is documented to satisfy all three simultaneously; because industrial implementations are frequently proprietary, this is a statement about the published record identified by this search, not a claim that the capability does not exist. Federated learning, physics-informed architectures, and sequence-aware models emerge as the directions most likely to close this gap. Full article

Polypropylene (PP) fiber-reinforced soil slopes undergo progressive resistance degradation under wet–dry cycling (WDC), while stochastic seismic loading introduces additional uncertainty, challenging deterministic seismic assessment. This study develops a time-dependent seismic reliability framework integrating the probability density evolution method and the equivalent extreme value event method. The cohesion and internal friction angle of unreinforced soil measured at different WDC states are represented as cross-correlated lognormal random fields and combined with random fiber configurations and weighted nonstationary stochastic ground motions in a nonlinear dynamic model. The main contribution is a unified uncertainty-propagation scheme that incorporates experimentally characterized WDC degradation and multiple uncertainty sources into the evolution of response probability and multilevel first-passage reliability. With increasing WDC number and PGA, the extreme displacement distributions shift toward larger values, accompanied by increased response dispersion, tail risk, and reliability loss. The reliability evolution exhibits three stages, namely initial stability, rapid degradation, and residual convergence, during the 70 s excitation. PP fiber reinforcement improves reliability, although the marginal gain becomes limited when the fiber content exceeds 0.15% under the present numerical conditions. The proposed framework provides a probabilistic basis for the seismic assessment and deformation control of PP fiber-reinforced soil slopes at different WDC degradation states. Full article

Machine learning (ML) integrated with metabolomics has emerged as a promising strategy to advance precision pharmacotherapy, enabling data-driven prediction of drug response. This review provides an overview of commonly applied ML methodologies in metabolomics-based pharmacological studies, including supervised models (Random Forest, Extreme Gradient Boosting, Support Vector Machine, Logistic Regression, K-Nearest Neighbors), unsupervised models (K-Means Clustering, Principal Component Analysis), and deep learning approaches. We summarize recent progress in the application of metabolomics-driven ML to personalized medication, with a focus on drug dosage optimization, therapeutic efficacy prediction, and adverse drug reaction assessment. Despite these advances, significant challenges remain, including limited explainability, insufficient prospective clinical validation, lack of standardization and reproducibility, and data dimensionality and quality issues. Addressing these issues will be essential for the clinical translation of ML-metabolomics integration. Looking ahead, continued methodological innovation, large-scale multi-center prospective validation, and integration with other omics platforms will be key to unlocking the full potential of metabolomics combined with ML in precision healthcare. Full article

Employing a space–time perspective, this study develops a CA-SHIRS model of corporate resonance diffusion of food safety risk, drawing on complex network theory and cellular automata theory. The study then examines the mechanisms and spatial–temporal evolution characteristics of this diffusion, considering the interplay among food firm heterogeneity, media communication strategy, and government regulatory strategy. The study reaches the following conclusions: (1) Higher probabilities of infection, conversion, and immune failure speed up risk transmission within the spatial–temporal association network of food firms. By contrast, raising the immune probability and direct immune probability helps contain the scale of risk spread. (2) The intensity of corporate resonance diffusion is positively correlated with corporate influence and media influence, and negatively correlated with corporate social responsibility, media information disclosure intensity, government penalty intensity, and government regulatory information transparency. It exhibits an inverted U-shaped relationship with corporate risk preference and a positive U-shaped relationship with media reporting preference. (3) Both corporate influence and media influence reinforce corporate resonance diffusion, while government regulation effectively mitigates it. Firms with moderate risk preference are most significantly affected by extreme media coverage; corporate social responsibility and media information disclosure intensity can jointly suppress diffusion at these nodes. Government regulation exerts a stronger inhibitory effect on corporate resonance diffusion than the amplifying effect exerted by media communication. Full article

A clinical observation of a previously asymptomatic native knee developing a relevant coronal alignment change after total hip arthroplasty (THA) motivated this research. This raised the question of whether the native knee morphology could influence the response to biomechanical changes elsewhere in the lower limb. First, we developed a simplified finite element model incorporating the Coronal Plane Alignment of the Knee (CPAK) classification. We developed a simplified model of the knee, where we represented the lateral distal femoral angle (LDFA) by physically rotating the femur, and to avoid remodeling the tibia coronal plane for each simulation, we represented the medial proximal tibial angle (MPTA) by adjusting the neutral angle of the joint (the angle at which the joint generates no reaction force). This allowed for quick parametric iteration over the CPAK input space, showing which type of knees were more vulnerable to extreme valgus (CPAK III) and varus (CPAK VII) due to their morphology. The simplicity of this model will also enable users with less technical expertise to quickly obtain insights into specific knee morphologies, without requiring deep knowledge about finite element modeling. This existing model lays the foundation for later work, with a deeper look into how changes to hip morphology build on the pre-existing tendencies to varus and valgus. Full article

Rapid leakage prediction during pressure-reducing valve manoeuvres requires models that reproduce inertial hydraulic effects at low computational cost. This study proposes a reproducible surrogate-modelling framework in which transient leakage responses are generated with an extended rigid water column model incorporating time-dependent valve resistance and then used to train machine learning regressors. Twenty-eight regression models were evaluated using four SCADA-oriented predictors: time, inlet flow, upstream pressure head, and valve position. Model selection followed an accuracy–cost–predictive-stability assessment that considered predictive error, training time, inference speed, model size, and behaviour under near-domain, boundary-unseen, and extreme extrapolation scenarios. Gaussian process regression achieved the lowest in-domain errors, with test root mean square error values of 0.0017–0.0019 L/s, but required training times above 21,000 s and inference speeds below 700 observations/s. Bagged Trees provided the most balanced option for PRV-operation screening within the represented hydraulic domain, combining low prediction error, high inference capacity, and stable behaviour within the evaluated near-domain and boundary-unseen range. The P3 extrapolation test showed that prediction beyond the represented hydraulic envelope requires scenario-library expansion and model reassessment. The framework supports rapid valve-operation screening and numerical assessment of RWCM-generated transient leakage responses, while field or SCADA-supported use requires local calibration and validation. Full article

This study evaluated clinical characteristics, treatment outcomes, and CNS relapse patterns in patients with breast-involved diffuse large B-cell lymphoma (DLBCL), a rare extranodal presentation with limited real-world data. We conducted a retrospective study on 33 consecutive patients with newly diagnosed breast-involved DLBCL treated from 2019 to 2024. All patients received R-CHOP. Baseline CNS screening—including neurological examination, fundoscopy, brain magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF) analysis—was routinely performed. High-dose methotrexate (HD-MTX) was offered as CNS prophylaxis after completion of systemic therapy based on multidisciplinary team evaluation and clinician–patient shared decision-making according to institutional treatment protocols. Median age was 52.6 years; 84.8% had ECOG 0. Non-GCB subtype predominated (84.8%), and Ki-67 >70% was present in 69.7%. The overall response rate was 90.9%, with 84.8% complete responses. At a median follow-up of 44 months, 5-year Overall Survival (OS) and Progression-Free Survival (PFS) were 84.8% and 66.7%. CNS relapse occurred in 4 of 6 patients (66.7%) without prophylaxis, all within 5–11 months after R-CHOP, whereas no CNS relapses were observed among prophylaxis recipients (p < 0.001), although this observation should be interpreted with extreme caution given the very small non-prophylaxis subgroup (n = 6), limited statistical power, and non-randomized treatment allocation. Exploratory analyses suggested that bulky disease was associated with inferior OS. R-CHOP achieved high response rates and favorable long-term outcomes in breast-involved DLBCL. The absence of CNS relapse among HD-MTX prophylaxis recipients, contrasted with a high relapse rate in those without prophylaxis, provides only a hypothesis-generating observation that requires confirmation in larger prospective studies; this warrants further investigation of the role of systemic CNS prophylaxis. Full article

Peatlands, critical global carbon sinks, are facing increasing threats from climate change-driven heatwaves and droughts. These threats can cause a midday depression in carbon uptake through photosynthetic inhibition. Using high-temporal-resolution solar-induced chlorophyll fluorescence (SIF; ~30 s) and chamber-based CO₂ flux measurements, we investigated the coupling between SIF and gross primary production (GPP) during extreme events (air temperature > 25 °C and vapour pressure deficit > 15 hPa) in a northern peatland. Our results show that SIF tracks GPP closely under non-stress conditions (daily R² = 0.86–0.96). However, during combined heat and drought stress, midday correlations collapsed (Case A: R² = 0.04 on 27 June; Case B: R² = 0.15 and 0.01 on 29 and 30 June, respectively), indicating severe decoupling. Importantly, we discovered legacy effects from multi-day heat exposure: on 26 June, vegetation with prior cumulative stress (Case A) showed weak morning coupling (R² = 0.07), while vegetation without prior stress history (Case B) maintained strong coupling (R² = 0.93). This suggests that cumulative stress alters baseline physiology and can exacerbate midday mismatches; therefore, not just current condition controls photosynthetic regulation. These findings highlight limitations of SIF-based GPP estimation at sub-daily timescales during stress, particularly in heterogeneous peatland systems where canopy composition and physiological responses could vary among plant functional types. Full article

To address the insufficient flexibility in power systems caused by renewable energy output uncertainty during extreme weather events, a coordinated source–network–load–storage (SNLS) dispatch method that combines a flexibility supply–demand balance approach with a partitioned grid framework is proposed to achieve the effective enhancement of operational resilience. Firstly, a convolution method is employed to aggregate net load forecast error distributions, and expected flexibility demand metrics are introduced to construct a probabilistic model of compound weather impacts, thereby improving flexibility requirement quantification. Secondly, uncertainties arising from extreme meteorological conditions are considered, and an integrated economic dispatch model for the partitioned grid is established based on chance-constrained reserves and regulation capability envelopes, in order to co-optimize generation costs, demand response, and expected flexibility insufficiency penalties. Then, inter-zone power exchange and spatiotemporal unit commitment dynamics are introduced to optimally redistribute spatial generation surpluses and load deficits, so that a system-wide flexibility supply–demand balance is enabled. Finally, simulations are conducted on the real-world Guangdong 500 kV transmission network under typhoon, heatwave, and rainstorm scenarios, and the results demonstrate the effectiveness of the proposed method in eliminating flexibility deficits, reducing total dispatch costs, and capturing distinct weather-adaptive operational patterns. Full article

This study presents a systematic investigation into the mechanical responses of two tungsten-based alloys—84W–11.2Ni–4.8Fe and 88W–8.4Ni–3.6Fe—fabricated via selective laser melting (SLM). Quasi-static compression tests using a universal testing machine and dynamic impact experiments employing a split Hopkinson pressure bar (SHPB) were conducted over a temperature range of 298–598 K and strain rates spanning from 1 × 10⁻³ s⁻¹ to 2.3 × 10³ s⁻¹. Both alloys exhibited significant strain-rate hardening and thermal softening effects. Based on the experimental data, a Johnson–Cook (J–C) constitutive model was established. The fidelity of the calibrated model for the 84W alloy was rigorously validated through pulsed X-ray radiography and static armor penetration tests. The SLM-fabricated 84W-shaped charge liner produced a well-collimated jet with a tip velocity of 5101.5 m/s and achieved a penetration depth of 87 mm into rolled homogeneous armor (RHA)-equivalent steel targets. Numerical simulations using the developed J–C model showed close agreement with experimental measurements, with a maximum discrepancy of only 9.19%, thereby confirming the predictive capability of the constitutive model. These results demonstrate that the proposed J–C model can reliably characterize the large-deformation behavior of SLM-processed 84W and 88W liners under the extreme thermomechanical conditions characteristic of shaped charge jet formation—namely high temperature, high pressure, and ultra-high strain rate. Collectively, this work establishes a foundational framework for the application of SLM technology to shaped charge liner design and provides a critical basis for further research into jet formation physics and penetration mechanics of tungsten-based alloys. Full article