Search Results (777)

Promoting the sustainable development of virtual water trade is of great significance to safeguarding China’s water resource security and balanced regional economic growth. This study analyzes the virtual water trade network among 31 Chinese provinces based on multi-regional input–output tables from 2012, 2015, and 2017, using total trade decomposition, social network analysis, and exponential random graph models. The key findings are as follows: (1) The total virtual water trade volume remains stable, with Xinjiang, Jiangsu, and Guangdong as the core regions, while remote areas such as Shaanxi and Gansu have lower trade volumes. The primary industry dominates, and it is driven by simple value chains. (2) Provinces such as Xinjiang, Heilongjiang, and Jiangsu form the network’s core. Network density and symmetry increased from 2012 to 2015 but declined slightly in 2017, with efficiency peaking and then dropping, and the clustering coefficient decreased annually. Four economic sectors exhibit distinct interactions: frequent two-way flows in Sector 1, significant inflows in Sector 2, prominent net spillovers in Sector 3, and key brokers in Sector 4. (3) The network evolved from a core-periphery structure with weak ties to a stable, heterogeneous, and resilient system. (4) Influencing factors, such asper capita water resources, economic development, and population, significantly impact trade. Similarities in economic levels, population, and water endowments promote trade, while spatial distance has a limited effect, with geographic proximity showing a significant negative impact on long-distance trade. Full article

The effects of changes in China’s final demand structure on its forestry sector and associated supply chains have not been thoroughly examined. This study aims to provide a detailed analysis of the quantitative relationships and underlying mechanisms between these interactive changes. Using China’s 153-sector input–output tables from the National Bureau of Statistics and applying a Leontief-based input–output model, we conducted scenario simulations through three distinct schemes, generating both quantitative and qualitative results. Our findings indicate that (1) For China’s forestry sector and its entire value chain to thrive, policymakers should boost consumer demand. This can better stimulate the development of forestry and the “agriculture-forestry-animal husbandry-fishery services” sector and related service industries; (2) Increased investment demand effectively stimulates the development of tertiary industries and secondary industries within the forestry supply chain and boosts the demand and production of intermediate products; (3) Changes in net exports have a significant impact on forestry and the forestry industry chain. To reduce dependence on foreign timber resources, China should strategically expand commercial plantation development; (4) Regarding intermediate product production, investment has a more pronounced effect on increasing total volume compared to consumption. Additionally, the Sino–US tariff disputes negatively impact the forestry industries of both countries. China needs to accelerate import substitution strategies for timber products, adjust international trade markets, and expand domestic consumption and investment to ensure the healthy and stable development of its forestry sector. Full article

This research experimentally and numerically evaluates the effectiveness of viscoelastic (VE) and rotational friction (RF) dampers in enhancing the seismic performance of coupled shear wall (CSW) systems. This study consists of two phases: (1) element testing to characterize the hysteretic behavior and energy dissipation capacity of VE and RF dampers, and (2) shake table testing of a large-scale CSW structure equipped with these dampers under the white noise, sinusoidal and Kokuji waves. The experimental results are validated through numerical analysis using STERA 3D (version 11.5), a nonlinear finite element software, to simulate the dynamic response of the damped CSW system. Key performance indicators, including inter-story drift, base shear, and energy dissipation, are compared between experimental and numerical results, demonstrating strong correlation. The findings reveal that VE dampers effectively control high-frequency vibrations, while RF dampers provide stable energy dissipation across varying displacement amplitudes. The validated numerical model facilitates the optimization of damper configurations for performance-based seismic design. This study provides valuable insights into the selection and implementation of supplemental damping systems for CSW structures, contributing to improved seismic resilience in buildings. Full article

Climate change is profoundly impacting human health in South Africa, aggravating existing health challenges and creating new threats, particularly in vulnerable populations. This scoping review aims to comprehensively map existing evidence of climate change and diverse human health impacts to assist in the equipping of health systems to address evolving challenges of climate change. The scoping review will inform the development of evidence-based policy, improve public health preparedness, and ensure that adaptation strategies are effectively tailored to South Africa’s socio-economic and environmental conditions. This scoping review protocol will be conducted using the Joanna Briggs Institute (JBI) methodology, following five steps: (1) defining the research question, (2) search strategy, (3) setting inclusion criteria, (4) extracting data, (5) assessing, summarizing, and presenting findings. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) tool will be used. A comprehensive peer-reviewed literature search, including PubMed, Scopus, ScienceDirect, and Google Scholar, will be conducted by two independent reviewers. The review will be conducted over eight weeks, focusing on English studies published between 2015 and 2025, and conducted within South Africa. A two-stage screening process will determine article eligibility. Disagreements will be resolved through consensus and consultation of a third reviewer. The results of this review will be presented as tables, including a narrative synthesis of the findings. Full article

In recent years, large language models (LLMs) have shown an impressive ability in translating text to SQL queries. However, in real-world applications, standard loss functions frequently fail to capture the complexity of queries adequately. Therefore, in this study, a dynamic loss function is proposed, which assigns different weights to specific groups of tokens, such as SQL keywords or table names. The objective is to guide the model during training to facilitate the mastery of more fundamental concepts within the SQL. Our custom loss function is composed of four components: cross-entropy with sequence matching loss, focal loss, F-beta loss, and contrastive sequence loss. During the training process, the weights of each component of the loss function are dynamically adjusted to prioritize different aspects of query generation at the appropriate stage. This approach avoids computationally expensive approaches such as SQL validation or detokenization, which improves the efficiency of the learning process compared to alternative methods. We empirically tested this method on several open source LLMs with less than 2 billion parameters, using a customized real vehicle diagnostic dataset. The findings demonstrate that the employment of our dynamic loss function can enhance SQL execution accuracy by up to 20% in comparison with standard cross-entropy loss. It has been demonstrated that customized loss functions for specific tasks can improve the efficiency of LLMs without extending the model or acquiring additional labelled data. The proposed technique is also scalable and adaptable to new domains or more complex weighting schemes, highlighting the importance of custom design of loss functions in real world applications. Full article

Microplastics (MPs) have emerged as pervasive environmental contaminants due to their widespread presence across various ecosystems, including their use in single-use plastic food ware and table salt dispensers. This issue coincides with the presence of heavy metals in water sources in Doha, Qatar. Fourier Transform Infrared (FTIR) analysis revealed that the plastic plate and spoon were composed of polyolefin, with the spoon exhibiting additional peaks that indicated oxidation or the presence of additives. Thermogravimetric Analysis (TGA) revealed that the spoon exhibited higher thermal stability, retaining approximately 10% of its mass at 700 °C, than the plate, which retained 2%, indicating the presence of complex additives or contamination. MPs in food-grade salt samples were verified through filtration and Fourier Transform Infrared (FTIR) Spectroscopy, identifying polymers such as polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET). These MPs likely stem from exposure to packaging or environmental contaminants. FTIR spectra confirmed the integrity of the polymers after treatment. Inductively Coupled Plasma–Optical Emission Spectroscopy (ICP-OES) analysis revealed varying levels of heavy metals in bottled and tap water, with notable findings including detectable arsenic and lead in both, higher calcium and magnesium in bottled water, and the presence of copper present in tap water only, highlighting potential health and infrastructure-related concerns. These results highlight the possible risks associated with exposure to MPs and heavy metals from everyday products and water sources, underscoring the need for enhanced regulatory oversight and safer material choices to ensure protection. Full article

The hydrological processes of agricultural fields during the fallow season in east-central Mississippi remain poorly understood, due to the region’s unique rainfall patterns. This study utilized long-term rainfall records from 1924 to 2023 to evaluate runoff characteristics and the runoff response to various rainfall events during fallow seasons in Mississippi by applying the DRAINMOD model. The analysis revealed that the average rainfall during the fallow season was 760 mm over the past 100 years, accounting for 65% of the annual total. In dry, normal, and wet fallow seasons, the average rainfall was 528, 751, and 1010 mm, respectively, corresponding to runoff of 227, 388, and 602 mm. Runoff frequency increased with wetter weather conditions, rising from 16 events in dry seasons to 23 in normal seasons and 30 in wet seasons. Over the past century, runoff dynamics were predominantly regulated by high-intensity rainfall events during the fallow season. Very heavy rainfall events (mean frequency = 11 events) generated 215 mm of runoff and accounted for 53% of the total runoff, while extreme rainfall events (mean frequency = 2 events) contributed 135 mm of runoff, making up 34% of the total runoff. Water table depth played a critical role in shaping spring runoff dynamics. As the water table decreased from 46 mm in March to 80 mm in May, the soil pore space increased from 5 mm in March to 14 mm in May. This increased soil infiltration and water storage capacity, leading to a steady decline in runoff. The study found that the mean daily runoff frequency dropped from 13.5% in March to 7.6% in May, while monthly runoff decreased from 74 to 38 mm. Increased extreme rainfall (R95p) in April contributed over 45% of the total runoff and resulted in the highest daily mean runoff of 20 mm, compared to 18 mm in March and 16 mm in May. The results from this century-long historical weather data could be used to enhance field-scale water resource management, predict potential runoff risks, and optimize planting windows in the humid east-central Mississippi. Full article

This study investigates the feasibility of pumice-based internal curing based on the 3D printability of engineered cementitious composites (ECCs) for water-scarce environments and arid regions. Natural river sand was partially replaced with the presoaked pumice lightweight aggregates (LWAs) at two different levels, 30% and 60% by volume, and 50% of the cement was replaced with slag to enhance sustainability. Furthermore, 2% polyethylene (PE) fibers were used to improve the mechanical characteristics and 1% methylcellulose (MC) was used to increase the rheological stability. Pumice aggregates, presoaked for 24 h, were used as an internal curing agent to assess their effect on the printability. Three ECC mixes, CT-PE2-6-10 (control), P30-PE2-6-10 (30% pumice), and P60-PE2-6-10 (60% pumice), were printed using a 3D gantry printing system. A flow table and rheometer were used to evaluate the flowability and rheological properties. Extrudability was measured in terms of dimensional consistency and the coefficient of variation (CV%) to evaluate printability, whereas buildability was determined in terms of the maximum number of layers stacked before failure. All of the mixes met the extrudability criterion (CV < 5%), with P30-PE2-6-10 demonstrating superior printing quality and buildability, having 16 layers, which was comparable with the control mix that had 18 layers. Full article

The main goal of our work was to evaluate approaches for modeling lateral outflow from shallow unconfined aquifers in a one-dimensional model of vertical variably-saturated flow. The HYDRUS-1D model was modified by implementing formulas representing lateral flow in an aquifer, with linear or quadratic drainage functions describing the relationship between groundwater head and flux. The results obtained by the modified HYDRUS-1D model were compared to the reference simulations with HydroGeoSphere (HGS), with explicit representation of 2D flow in unsaturated and saturated zones in a vertical cross-section of a strip aquifer, including evapotranspiration and plant water uptake. Four series of simulations were conducted for sand and loamy sand soil profiles with deep (6 m) and shallow (2 m) water tables. The results indicate that both linear and quadratic drainage functions can effectively capture groundwater table fluctuations and soil water dynamics. HYDRUS-1D demonstrates notable accuracy in simulating transient fluctuations but shows higher variability near the surface. The study concludes that both quadratic and linear drainage boundary conditions can effectively represent horizontal aquifer flow in 1D models, enhancing the ability of such models to simulate groundwater table fluctuations. Full article

Background: The N-methyl-d-aspartate receptor (NMDA-R) is a glutamate ionotropic receptor in the brain that is crucial for synaptic plasticity, which underlies learning and memory formation. Dysfunction of NMDA receptors is implicated in various neurological diseases due to their roles in both normal cognition and excitotoxicity. However, their dynamics are challenging to capture accurately due to their high complexity and non-linear behavior. Methods: This article presents the elaboration and calibration of experimentally constrained computational models of GluN1/GluN2A NMDA-R dynamics: (1) a nine-state kinetic model optimized to replicate experimental data and (2) a computationally efficient look-up table model capable of replicating the dynamics of the nine-state kinetic model with a highly reduced footprint. Determination of the kinetic model’s parameter values was performed using the particle swarm optimization algorithm. The optimized kinetic model was then used to generate a rich input–output dataset to train the look-up table synapse model and estimate its coefficients. Results: Optimization produced a kinetic model capable of accurately reproducing experimentally found results such as frequency-dependent potentiation and the temporal response due to synaptic release of glutamate. Furthermore, the look-up table synapse model was able to closely mimic the dynamics of the optimized kinetic model. Conclusions: The results obtained with both models indicate that they constitute accurate alternatives for faithfully reproducing the dynamics of NMDA-Rs. High computational efficiency is also achieved with the use of the look-up table synapse model, making this implementation an ideal option for inclusion in large-scale neuronal models. Full article

We propose Observer-Linked Branching (OLB), a mathematically rigorous extension of quantum theory in which an observer’s cognitive commitment actively modulates collapse dynamics at macroscopic scales. The OLB framework rests on four axioms, employing a norm-preserving nonlinear Schrödinger evolution and Lüders-type projection triggered by crossing a cognitive commitment threshold. Our expanded formalism provides five main contributions: (1) deriving Lie symmetries of the observer–environment interaction Hamiltonian; (2) embedding OLB into the Consistent Histories and path-integral formalisms; (3) multi-agent network simulations demonstrating intentional synchronisation toward shared macroscopic outcomes; (4) detailed statistical power analyses predicting measurable biases (up to ~5%) in practical experiments involving traffic delays, quantum random number generators, and financial market sentiment; and (5) examining the conceptual, ethical, and neuromorphic implications of intent-driven reality selection. Full reproducibility is ensured via the provided code notebooks and raw data tables in the appendices. While the theoretical predictions are precisely formulated, empirical validation is ongoing, and no definitive field results are claimed at this stage. OLB thus offers a rigorous, norm-preserving and falsifiable framework to empirically test whether cognitive engagement modulates macroscopic quantum outcomes in ways consistent with—but extending—standard quantum predictions. Full article

Classification of BIM objects is critical for enhancing information interoperability and standardization within construction projects; however, research on automated BIM object classification based on standardized classification systems remains limited. Therefore, this study proposes an automated method to classify BIM objects using IFC data under the Uniclass system, aiming to enhance standardization, semantic clarity, and practical applicability. The proposed method first assigns Uniclass codes to 8715 BIM objects, then extracts 13 types of IFC-derived feature variables—including semantic, spatial, and dimensional information, and uses 2 categories of Uniclass coding information (EF and Ss tables) as classification labels, each comprising 11 and 17 classes, respectively. A Random Forest model with 100 decision trees and 10-fold cross-validation is then employed to perform automatic classification. Experimental results demonstrate that the proposed method achieves classification accuracies of 1.00 and 0.99 for BIM objects under the Elements/Functions and Systems classification tasks. This study demonstrates that accurate and fine-grained classification of BIM objects can be achieved using only low-LOD IFC data, thereby contributing to standardized information structuring and facilitating intelligent model management during the early design phase. Full article

Background/Objectives: Effective diabetes self-management is critical for glycemic management and well-being, yet Latino youth face unique cultural and socioeconomic barriers that are insufficiently explored in the literature. This review mapped existing evidence on diabetes self-management for Latino youth. Methods: Searches were conducted in PubMed, CINAHL, SCOPUS, Web of Science, LILACS, ERIC, and The Cochrane Library, using the gray literature and reference lists, in September 2024, following JBI guidelines. The included studies were qualitative, quantitative, and mixed-methods studies and reviews on diabetes self-management for Latinos aged 0–30 with type 1 or 2 diabetes. Studies including participants over 30 or with gestational diabetes were excluded. Two reviewers independently extracted data using a standardized table and analyzed findings using the Association of Diabetes Care & Education Specialists framework (ADCES7) for self-care behaviors: healthy eating, being active, monitoring, taking medication, problem-solving, reducing risks, and healthy coping. Results: Forty-five studies (forty from the United States) were included from 860 citations. The findings highlighted challenges in adopting diabetes-friendly diets, including cultural preferences, food insecurity, and limited resources. Physical activity improved glycemic control but was hindered by family and school obligations. Continuous glucose monitoring (CGM) enhanced outcomes, though economic barriers limited access. Family-centered education improved medication adherence, while family support strengthened problem-solving. CGMs and insulin pumps reduced complications, and culturally adapted psychological support enhanced emotional well-being and glycemic management. Conclusions: This review underscores persistent disparities in diabetes self-management among Latino youth. While the study designs and settings were heterogeneous, the findings highlight the need for culturally tailored, family-centered interventions that address structural barriers and psychosocial needs to improve care. Full article

Light non-aqueous phase liquids (LNAPLs) are significant groundwater contaminants whose migration in aquifers is governed by dynamic groundwater level fluctuations. This study establishes a multiphase flow coupling model integrating hydraulic, gaseous, LNAPL, and chemical fields, utilizing continuous multi-point water level data to quantify water table orientation variations. Key findings demonstrate that (1) LNAPL migration exhibits directional dependence on water table orientation: flatter gradients reduce migration rates, while steeper gradients accelerate movement. (2) Saturation dynamics correlate with gradient steepness, showing minimal variation under flattened gradients but significant fluctuations under steeper conditions. (3) Water table reorientation induces vertical mixing, homogenizing temperature distributions near the interface. (4) Dissolution and volatilization rates of LNAPLs decrease progressively with water table fluctuations. These results elucidate the critical role of hydraulic gradient dynamics in controlling multiphase transport mechanisms at LNAPL-contaminated sites, providing insights for predictive modeling and remediation strategies. Full article

The stochastic fluctuation characteristics of wind speed can significantly affect the control performance of train suspension systems. To enhance the running quality of trains in non-stationary wind fields, this paper proposes a semi-active control method for trains based on fuzzy rules of non-stationary wind fields. Firstly, a dynamic model of the train and suspension system was established based on the CRH2 (China Railway High-Speed 2) high-speed train and magnetorheological dampers. Then, using frequency–time transformation technology, the non-stationary wind load excitation and train response patterns under 36 common operating conditions were calculated. Finally, by analyzing the response patterns of the train under different operating conditions, a comprehensive control rule table for the semi-active suspension system of the train under non-stationary wind fields was established, and a fuzzy controller suitable for non-stationary wind fields was designed. To verify the effectiveness of the proposed method, the running smoothness of the train was analyzed using a train-semi-active suspension system co-simulation model based on real wind speed data from the Lanzhou–Xinjiang railway line. The results demonstrate that the proposed method significantly improves the running quality of the train. Specifically, when the wind speed reaches 20 m/s and the train speed reaches 200 km/h, the lateral Sperling index is increased by 46.4% compared to the optimal standard index, and the vertical Sperling index is increased by 71.6% compared to the optimal standard index. Full article