Model Assessment Research Articles

The role of agriculture for achieving renewable energy-centered sustainable development objectives in rural Africa

Multi-dimensional and overlapping barriers to wellbeing severely affect many areas in rural sub-Saharan Africa. In the region, more than 90% of cropland is rainfed, less than one third of households have electricity, almost 60% of the population reports food insecurity, and more than 35% of the population lives below the international poverty line. Climate change impacts on vulnerable systems with limited adaptive capacity and strong population growth are increasing the magnitude of these challenges, slowing and potentially reversing development. Thus, there is a strong need for multi-sector interventions across multiple levels, from national policies, to regional and river catchment-scale planning, to local planning and investment. To implement such actions, it is key not only to assess technological solutions and their investment needs, but also to appraise their feasibility and implementation potential (from both a policy and a financial point of view). Here, we implement a modelling platform (RE4AFAGRI platform), which soft-links bottom-up process-based water and energy demand and techno-economic infrastructure assessment models (WaterCROP, M-LED, OnSSET) into a multi-node, national Nexus-extended Integrated Assessment Model (MESSAGEix-Nexus) for supply and investment assessment. The results of our analysis shed light on the role of water and energy demand in the agricultural sector for jointly affecting infrastructure and investment requirements for achieving rural sustainable development objectives. We find that scenarios with increased ambition in expanding irrigation and agricultural productivity result in improved diffusion and economic feasibility of infrastructure to provide universal energy access while supporting productive uses of energy. Moreover, we conduct business model analysis to appraise the framework conditions and micro and macro determinants that can ensure feasibility of investment and uptake of small-scale infrastructure, crucial for rural development. Altogether, our research demonstrates how integrated modelling with an explicit focus on Nexus interlinkages can represent the enabling role and the business conditions for renewable energy input in agriculture to become a leverage of rural sustainable development. In turn, important policy and investment-relevant insights can be derived.

Food Web Bioaccumulation Model for Ecological Risk Assessment of Emerging Organic Pollutants in Marine Ecosystems: Principles, Advances and Challenges

The bioaccumulation and trophic transfer of pollutants in marine ecosystem members determine their ultimate ecological risks. Food web bioaccumulation models are widely used in scientific and regulatory programs to assess the bioaccumulation and ecological risks of pollutants at the ecosystem scale. The food web models are mainly established through concentration- and fugacity-based modeling approaches and include some chemical, food web-related, physiological and environmental factors. The models applied in the “forward approach” predict bioaccumulation and conduct internal exposure level-based ecological risk assessment (IEL-ERA), whereas those in the “reverse approach” are used to back-calculate the IEL-based predicted no-effect concentrations (PNECs) or environmental criteria. However, some challenges still exist in the application of food web model integrated risk assessment, including the lack of standardized/generalized frameworks, the lack of chemical- and species-specific toxicokinetic data and internal exposure (or tissue residue)-based toxicity data, and the lack of uncertainty-control methods in model estimation and parameterization. There are urgent requirements to improve models, integrate methods and update study designs in the assessment and prediction of “system-scale risks” of marine emerging organic pollutants.

Multi-Label Feature Aware XGBoost Model For Student Performance Assessment Using Behavior Data in Online Learning Environment

In light of recent outbreaks like COVID19, the use of online-based learning streams (i.e., e-Learning systems) has increased significantly. Institutional efforts to boost student achievement have made precise predictions of academic success a priority. To analyze student sessions-streams and anticipate academic success, e-Learning platforms are starting to combine Datamining (DM) with Machine-Learning (ML) techniques. Recent research highlights the difficulties that ML-based methods have while dealing with unbalanced data. In tackling ensemble-learning, we combine several ML algorithms to select the most appropriate approach for the given data. Current ensemble-based approaches for predicting student achievement, nevertheless, don't do exceptionally well, particularly when it comes to multi-label classification, because they don't factor the relevance of features into their approaches. This study presents multi-label feature aware XGBoost (MLFA-XGB) method that improves upon the previously used ensemble-learning technique. The MLFA-XGB makes use of a robust cross-validation approach for gaining a deeper understanding of feature relationships. The experimental results demonstrate that in comparison with the state-of-the-art ensemble-based student achievement predictive approach, this suggested MLFA-XGB based approach provides much higher accuracy for prediction.

Assessment of joint distributions of wave heights and periods

Short-term joint distributions of wave heights and periods provide information about the sea surface that is needed for engineering applications. In recent years much effort has been invested in predicting and validating the (univariate) distributions of wave heights; however, the same cannot be said about the joint probability of wave heights and periods. Comparisons of the joint distribution models with field observations are quite limited in terms of both the duration of the data and the number of locations considered. In addition, the often-contradicting demands for longer sea-state records and stationarity in the datasets have presented researchers with a bit of a conundrum. It is likely that some previous studies that aggregated data from several non-stationary sea states might have yielded improper inferences. The current study attempts a robust assessment of the available theoretical distribution models utilizing an extensive dataset of wave measurements around the UK coast. The results indicate that the Zhang and Guedes Soares (2106) model generally performs the best, and is perhaps a notable improvement over the somewhat bleak outlook laid out for these types of models in earlier years. The results also suggest that the discrepancy between the models and the data arises primarily from the poor ability of the marginal period distributions to capture reality.

Accuracy assessment of recent global ocean tide models in coastal waters of the European North West Shelf

The accuracy of global ocean tide models is assessed in coastal waters of the European North West Shelf to ascertain where higher resolution local (forecast) models are most needed for geophysical and navigational applications, and which global models are most suitable for providing boundary conditions for regional and local tide models. Five recent global ocean tide models (FES2014b, EOT20, TPXO9-atlas-v5, GOT4.10c, and DTU16) are considered, with the models first compared by interpolating them onto common grids and computing the mean absolute deviation at each grid point. Coastline tide gauge and offshore bottom pressure sensor data were collated from several sources to give a total of 279 observation sites for evaluating model accuracy, including observational values from 137 locations that have not previously been released and have therefore not been assimilated into any of the global models tested. The residual errors between each model’s predicted phasor and the corresponding observed phasor were calculated at each observation location, and quantified using the root mean square (RMS) and median absolute residual (MAR) for the eight tidal constituents M2, S2, N2, O1, K1, K2, P1, and Q1. To avoid RMS values being biased by observation point density, a Voronoi-weighted RMS based on the water area of the Voronoi polygon about each observation location was also developed and used. Four zones were defined based on ocean depth to gauge model performance, and model inaccuracy is again demonstrated in near-shore regions. Seven further zones were defined based on geographical areas, which reveals inhomogeneity among the global models. The smallest overall root sum square (RSS) RMS error across all eight constituents arises with FES2014b, although TPXO9-atlas-v5 has the best performance when using the MAR and Voronoi-weighted RMS metrics. Using only the 137 observation sites that have not been assimilated by any model and therefore provide an independent accuracy assessment, FES2014b exhibits the smallest errors at the coastline, with an RSS RMS of 24.46 cm. All models exhibit larger errors with the 137 independent observation sites than with all 279 observation sites, with an average overall increase in RSS RMS error of 12%, and an increase of 30% for coastline tide gauges, highlighting the need for local model development in these areas.

Applicability assessment of equivalent circuit-thermal coupling models on LiFePO4 batteries operated under wide-temperature and high-rate pulse discharge conditions

In military scenario, high-power lithium iron phosphate (LFP) batteries are frequently used under wide-temperature and high-rate pulse discharge conditions. An accurate electro-thermal coupling model (ETCM) is crucial for the safe operations. Equivalent circuit-thermal coupling model (ECTCM), which combines equivalent circuit model (ECM) and lumped thermal model, is the most widely used type of ETCM in applications. However, the applicability of ECTCM under wide-temperature and high-rate pulse discharge conditions is not clear. To assess the applicability of ECTCM under these special conditions, this study establishes six typical ECTCMs and accurately identify their corresponding model parameters. Then, these models are tested under high-rate pulse discharge conditions from -40oC to 50oC. The results indicate that ECTCMs are effective for pulse discharge at ambient and high temperatures, but not suitable for low-temperature conditions below 0oC. When the temperature is below 0oC, the pulse discharge voltage of the batteries can not be accurately simulated by ECTCMs. This work provides guidance for electro-thermal coupling modeling under high-rate pulse discharge conditions, and also points out the direction for the development of high-precision ETCM capable of handling wide-temperature and high-rate pulse discharge in the future.

Comparative life cycle assessment and route optimization modeling of smart versus conventional municipal waste collection: Environmental impact analysis in an urban context

Comparative life cycle assessment and route optimization modeling of smart versus conventional municipal waste collection: Environmental impact analysis in an urban context

Explainable machine learning models for predicting the acute toxicity of pesticides to sheepshead minnow (Cyprinodon variegatus).

A quantitative structure-activity relationship (QSAR) study was conducted on 313 pesticides to predict their acute toxicity to Sheepshead minnow (Cyprinodon variegatus) by using DRAGON descriptors. Essentials accounting for a reliable model were all considered carefully, giving full consideration to the OECD (Organization for Economic Co-operation and Development) principles for QSAR acceptability in regulation during the model construction and assessment process. Nine variables were selected through the forward stepwise regression method and used as inputs to construct both linear and nonlinear models. The obtained models were validated internally and externally. Generally, machine learning-based methods, namely support vector machine (SVM), random forest (RF), and projection pursuit regression (PPR), perform better than the multiple linear regression (MLR) model. The statistical results (R2 = 0.682-0.933, Q2LOO = 0.604-0.659, Q2F1 = 0.740-0.796, CCC = 0.861-0.882) of the developed models show that they are robust, reliable, reproducible, accurate and predictive. Comparatively, the RF model performs best, giving predictive correlation coefficient Q2 of 0.814, root mean squared error (RMSE) of 0.658 and mean absolute error (MAE) of 0.534 for the test set, respectively. The RF model (as well as SVM and PPR models) was visualized and explained by using the SHapley Additive explanation (SHAP) analysis to enhance its transparency and credibility. In addition, the applicability domain (AD) range of the RF model was characterized by the Williams plot and the tree manifold approximation and projection (TMAP) technology was utilized to illustrate similarity and diversity of the entire data space, to assist in the analysis of the outliers. Activity cliff detection was investigated by using Arithmetic Residuals in K-groups Analysis (ARKA) descriptors. It was found that none of the pesticides was identified as an activity cliff in the training set or a potential prediction cliff in the test set. Therefore, the RF model fulfills each OECD principle in regulation for QSAR models. The research in this work will aid in the in silico QSAR prediction of the acute toxicity to Sheepshead minnow (Cyprinodon variegatus) for untested and new toxic pesticides and can also be extended to other studies.

Hydrological drought assessment of the Yellow River Basin based on non-stationary model

Study Regionthe Yellow River Basin Study FocusNon-stationary hydrological conditions are becoming increasingly common due to climate change and human activities, and pose a novel challenge to the management of water resources and related risks, especially for large basins. However, prevalent research on drought assessment often ignores the non-stationary characteristics of hydrological processes. In this study, we investigated the stationarity of the runoff of the Yellow River Basin (YRB), the second-longest basin in China. We used an approach for assessing non-stationary droughts based on the generalized additive model for location, scale, and shape to establish a standardized runoff index containing covariates (SRI_cov) to identify hydrological droughts in the basin from 1986 to 2015. New Hydrological Insights for the RegionThe results show that the runoff was non-stationary in the YRB. Based on SRI_cov, hydrological drought predominantly occurred in the entire YRB in spring. From 1986, the number of months in which droughts occurred in the YRB exhibited a general trend of increase and peaked around 2002. After that, the total number of droughts significantly decreased but extreme droughts had become more prominent since 2005. The drought was more severe in the middle reaches of the Yellow River, and was characterized by a high frequency, intensity, and severity. Our analysis enhances the understanding of hydrological modeling and drought assessment under non-stationary conditions.

High-resolution 3D hydrodynamic modeling and comprehensive assessment of tidal current energy resources

As a clean and renewable resource, tidal current holds significant development potential. However, accurately assessing tidal current energy resources has been a persistent challenge. This study proposes a comprehensive assessment method using a small-scale, high-precision, multi-criteria three-dimensional hydrodynamic model to assess resource reserves applied to the Zhairuoshan Waterway. The hydrodynamic model was employed to simulate critical parameters such as tidal level, flow velocity, and flow direction. Multiple statistical indicators were used to evaluate the model's accuracy, showing consistency. The study analysed the flow speed distribution over a complete cycle, revealing an average flow speed of 1.51 m/s, with a maximum speed reaching 3.83 m/s. The generally high flow speeds within the waterway, coupled with the stability of the tidal current, indicate the significant resource potential of the study area. A quantitative analysis of the spatial distribution of tidal current energy was conducted using energy flux density, and the results showed that high energy density areas are widely distributed. Furthermore, the theoretical tidal energy resources of the waterway were calculated, showing that the maximum resource reserves could reach 9.98 MW at the selected cross-sections. Coupled with the analysis of unit-width energy flux, the high-potential areas within the waterway were further identified.

Impacts of LULC changes on runoff from rivers through a coupled SWAT and BiLSTM model: A case study in Zhanghe River Basin, China

Changes in river runoff have a significant impact on the sustainable use of water resources in a watershed, and these changes are closely linked to variations in land use/land cover (LULC). This research explores an innovative approach in the Zhang River Basin (ZRB), China, by coupling a concept-based hydrological model, the Soil and Water Assessment Tool (SWAT), with a deep-learning model, the Bidirectional Long Short-Term Memory Network (Bi-LSTM), to improve the accuracy of river runoff simulations. By analyzing LULC changes in 2002, 2012, and 2022, this study developed three SWAT models and three coupled SWAT-BiLSTM models to quantitatively assess the impacts of these changes on river runoff through eight LULC scenarios. The findings revealed significant LULC changes from 2002 to 2022, with cropland and grassland areas decreasing while forest and urban land areas increased. The total area of grassland, forest, and cropland made up over 93 % of the basin, indicating active land type conversions. Calibration and validation results demonstrated that the SWAT-BiLSTM model outperformed the conventional SWAT model, yielding higher accuracy in runoff simulations. Specifically, the SWAT-BiLSTM model achieved R2 values of 0.89 and 0.90 during calibration and validation, compared to the SWAT model's R2 values of 0.76 and 0.79. Scenario analyses indicated that expansions in farmland, grassland, and urban areas were correlated with increased river runoff, while an expansion in forested areas led to reduced runoff. Notably, urban land changes had the most pronounced impact on runoff, emphasizing the need for careful runoff management and flood risk mitigation in urban planning. By combining SWAT and Bi-LSTM models, this study provides an innovative assessment of the impact of LULC changes on water resources in the ZRB. The results offer valuable insights for water resource management, LULC optimization, and flood risk management, highlighting the potential application of deep learning techniques in hydrological simulation. This research serves as a scientific basis for policy-making and sustainable land use planning in the ZRB and similar regions.

Dynamic model and performance assessment of inter-story isolation system with supplement inerter (IISI)

The hybrid control combining the conventional seismic isolation and tuned mass damper inerter is recently gaining popularity for the seismic protective strategy. This paper presents a simplified dynamic model of the inter-story isolation system with a supplement inerter (IISI) for seismic performance assessment. The dynamic characteristics, such as modal frequencies, mode shapes, damping ratios, and participation factors, as well as the seismic response spectrum, were studied considering the importance of the equivalent linear model for seismic design. The vibration characteristic formula of the IISI was derived using a simplified 2-degree-of-freedom (2DOF) model. The influence of the calculated parameters of the model on the vibration characteristics was analyzed. To understand the isolation control mechanism, the response transfer function in the frequency domain was derived, and the effects of model parameters on the deformation amplitude amplification factor of the superstructure and substructure were analyzed. Based on equivalent linear properties, a seismic response prediction formula based on the response spectrum method is provided and verified by time history analysis (THA). The effect of soil-structure interaction (SSI) on the simplified 4-DOF model was also studied. The seismic responses and effectiveness of the IISI were evaluated using a Multi-degree-of-freedom (MDOF) model example. The research results demonstrate that the addition of an inerter changes the vibration characteristics of the traditional isolation system. The supplement inerter is effective in controlling the seismic response of the IISI, and the established analysis method can be used to analyze the seismic response of the IISI by incorporating additional inerter mounted in the isolation layer. The hybrid control strategy shows better effectiveness in reducing displacement in the isolation layer.

Network-based resilience assessment of an urban rail transit infrastructure with a multi-dimensional performance metric

With urban and public transportation development, the urban rail transit networks (URTN) become increasingly complex, evolving into multi-layer and intricate systems. The operating environment is complex, and the rising frequency of emergencies significantly impacts the entire network. Consequently, the resilient ability of URTN to deal with risk attacks has become an important research field. In this paper, a bilayer URTN model is constructed, encompassing urban and suburban rail transit, proposing the adjacency matrix and the coupling mechanism of subnetworks. Then, the performance of a URTN jointly considering network efficiency, network structure entropy, and the number of trips per unit time is addressed. By considering passenger travel alternatives under disruptions, we develop an improved Logit stochastic user equilibrium (SUE) passenger assignment model based on generalized travel time costs, introducing the failure degree of the stations, to predict passenger flow paths. A multi-dimensional resilience assessment model is proposed using the resilience change curve for attack scenarios and recovery strategies, and a comprehensive network performance system through the AHP method to determine the weight coefficients of indicators. Finally, we present a case study based on the Shanghai URT network to explore the resilience of URTN in the morning peak and simulate the network resilience under different failure scenarios. The results indicate that the proposed model can assess the resilience of the Shanghai MRTN under random and malicious failure, and the former has less comprehensive performance losses and greater resilience. Additionally, the sensitivity analysis is conducted to explore the impact of the number of failed stations, the failure degree of stations, and recovery ability on network resilience. This study provides theoretical support for urban managers and builders regarding network structure and emergency management, and offers relevant suggestions.

Life cycle assessment of the climate change impact of magnesium phosphate cements formulated with tundish deskulling waste compared to conventional cement

Ordinary Portland cement (OPC) production significantly contributes to greenhouse gas emissions due to high resource consumption and CO2 output. It is therefore imperative to investigate alternative cements, such as magnesium phosphate cement (MPC), as a potential solution. This study is based on Life Cycle Assessment (LCA) methodology, comparing OPC with alternative magnesium phosphate cements (MPC) developed at the laboratory scale. The novelty of this study considers two types of alternative cements that use two different sources of MgO: MPC-MgO, developed with pure MgO, and MPC-TUN, formulated using tundish deskulling waste from steelmaking industry.The evaluated functional units are 1 tonne of cement, 1 m3 of cement paste, and 1 m3 of mortar, all of them are designed for the same function, which is as non-structural precast elements. The study assesses climate change impacts under two future scenarios: 1) electricity decarbonisation in the background economy using projections from Integrated Assessment Models and 2) electricity decarbonisation and a fuel switch in the cement kilns.The results indicate that MPC-TUN exhibits a lower impact of climate change in terms of CO2 emissions across all functional units and scenarios compared to the other materials. In the most ambitious climate scenario, MPC-TUN mortar exhibits 42% and 56% lower climate change impacts than OPC-CEM I and MPC-MgO mortars, respectively, demonstrating its potential as a more sustainable construction material. Although further research is needed on the applicability of MPC-TUN in construction, regulatory frameworks are advised to simplify barriers to expedite the adoption of sustainable alternative cements.

European anchovy's abundance, more affected by climatic conditions than fishing activities in the northwest African waters.

The marine waters off the coast of northwest Africa are known for being highly productive upwelling regions in the Eastern Atlantic Ocean. The present study evaluated the combined effects of climate change and sustainable fishing levels on the long-term sustainability of European anchovy (Engraulis encrasicolus), a target pelagic species found along the West African coastal waters. The present study used survey biomass time series from survey vessels and species catch time series from commercial fisheries operating in the region. Hence, we investigated the relationship between fishing dynamics and environmental factors. The status of anchovy stock was determined by analyzing changes in different initial depletion rates using a state-space Bayesian surplus production model. We used Principal component analysis to identify the essential environmental data set, while Generalized Additive Models (GAM) were employed to determine the influence of the environment on fisheries. The stock assessment model revealed that fishing intensity did not affect the abundance of anchovy in the region even when tested under different biomass depletion scenarios. However, by employing GAMs, the research assessed the impact of environmental variables on fish biomass, indicating that sea surface temperature (SST) significantly influences anchovy abundance in the region. The latitudinal gradient also had a significant influence on this species' abundance. These findings underscore the vulnerability of the small-scale fisheries sector in the tropical upwelling zone to climate change. The study concludes that even in the face of potential negative impacts from environmental factors, optimizing fishing methods can contribute to the preservation of pelagic resources in the region. It emphasizes the importance of managing fishery resources in a sustainable, economically viable, and socially acceptable way.

3D-PSSIM: Projective Structural Similarity for 3D Mesh Quality Assessment Robust to Topological Irregularities.

Despite acceleration in the use of 3D meshes, it is difficult to find effective mesh quality assessment algorithms that can produce predictions highly correlated with human subjective opinions. Defining mesh quality features is challenging due to the irregular topology of meshes, which are defined on vertices and triangles. To address this, we propose a novel 3D projective structural similarity index ( 3D- PSSIM) for meshes that is robust to differences in mesh topology. We address topological differences between meshes by introducing multi-view and multi-layer projections that can densely represent the mesh textures and geometrical shapes irrespective of mesh topology. It also addresses occlusion problems that occur during projection. We propose visual sensitivity weights that capture the perceptual sensitivity to the degree of mesh surface curvature. 3D- PSSIM computes perceptual quality predictions by aggregating quality-aware features that are computed in multiple projective spaces onto the mesh domain, rather than on 2D spaces. This allows 3D- PSSIM to determine which parts of a mesh surface are distorted by geometric or color impairments. Experimental results show that 3D- PSSIM can predict mesh quality with high correlation against human subjective judgments, across the presence of noise, even when there are large topological differences, outperforming existing mesh quality assessment models.

Stochastic assessment of voltage sags and techno-economic optimization

Power quality has become an essential concern for industries and domestic energy consumption with the development of sophisticated equipment like micro-electronic devices. When a power quality event happens, such as voltage sag, financial losses for industries increase tremendously. Therefore, voltage sag evaluation becomes crucial for predicting the number of events and severity of voltage sag events of the electrical network. Industries can determine the mitigation investment needed for optimal operation using the knowledge obtained through voltage sag analysis method. Thus, the first part of the study addressed the development of a stochastic assessment method for voltage sag at an interested bus in the power system network, which was an initial objective of the study. The second part of the research investigates dynamic voltage restorer (DVR) control techniques, as well as the necessary capacitance and energy storage dimensions. The studies also recommend a technique for economic evaluation that emphasizes the expense of voltage sag events as well as the cost of investment in DVR with and without voltage sag events. The size of DVR and economic assessment model established in these studies enables industries to rate the cost of voltage sag and the total investment in mitigation devices.

Quantitative assessment and dynamic characteristic measurement of regional resilience: from the perspective of post-earthquakes effects

Strong geological disasters have caused persistent losses in society, economy, and ecological environments. Given the unique geographical settings of the stricken areas, their resilience is prone to damage or even loss. Comprehensive risk assessment of natural disasters is the core content and important foundation for building regional resilience. Therefore, conducting dynamic characteristics analysis of resilience in mountainous disaster areas impacted by strong earthquake geological disasters is vital for ensuring the region's high-quality and sustainable development. This article takes the 51 stricken areas of Wenchuan earthquake as the research object. To this end, social, economic and ecological environmental data from 2008 to 2020 was hereby collected. Initially, a regional resilience assessment system based on "socio-economic-ecological environment" was established, considering the long-term and spatial heterogeneity of geological disasters. Secondly, the regional resilience assessment model was constructed using Spectral clustering-genetic algorithm-improved entropy weight method. Following that, the dynamic characteristics of regional resilience were quantitatively analyzed from two aspects, including change velocity state and change rate trend. Finally, based on the regional resilience characteristics, differentiated resilience enhancement strategies were proposed. Collectively, the results revealed that: (1) From a geological disaster standpoint, the risk in post-earthquake disaster areas exhibited a strikingly rapid decline, with the spatial distribution of geological disaster risk being notably higher in the central areas and diminishing towards the peripheries. (2) Overall, the regional resilience of the 51 stricken areas showed a "V-shaped" trend, with a significant upturn since 2012. (3) From the perspective of dynamic characteristics, more counties (cities) presented an upward trend. (4) The 51 stricken areas were hereby divided into the "benchmarking type", the "declination type", the "backward type", and the "potential type". In conclusion, the current study enhances the technical framework for evaluating regional resilience and provides technical support for the construction of resilient cities.

Preventative and curative treatment of venous thromboembolic disease in cancer patients

Cancer-associated venous thromboembolism (CAT) is common in patients with cancer and associated with significant morbidity and mortality. The incidence of CAT continues to rise, complicating patient care and burdening healthcare systems. Patients with cancer experiencing VTE face poorer prognoses, making prevention and effective management imperative. This narrative review synthesizes evidence on thromboprophylaxis in ambulatory patients with cancer receiving systemic therapy and acute treatment strategies for CAT. Risk assessment models (e.g., Khorana score) aid in identifying high-risk patients who may benefit from thromboprophylaxis. Pharmacological thromboprophylaxis with low molecular weight heparins (LMWHs) direct oral anticoagulants (DOACs) has been shown to reduce the risk of CAT without significantly increasing the risk of bleeding complications. However, implementation of risk-based strategies remains limited in clinical practice. For acute CAT management, LMWHs have been the standard of care, but DOACs are increasingly favored due to their convenience and efficacy. However, challenges persist, including bleeding risks and drug interactions. Emerging therapies targeting Factor XI inhibitors present promising alternatives, potentially addressing current limitations in anticoagulation management for CAT.

Chemical Characteristics and Water Quality Assessment of Groundwater in the Dongjiang-Hanjiang River Basin, China

Groundwater plays an important role in the rapid economic development of the Dongjiang-Hanjiang River Basin. This paper uses the Gibbs model, ionic ratio coefficient analysis, factor analysis, water quality index method and human health risk assessment model to estimate the chemical properties and quality of groundwater in the Dongjiang-Hanjiang Basin. The ion concentration and proportion of SO42- water in the Hanjiang River Basin are higher than those in the Dongjiang River Basin. The groundwater ions come mainly from the dissolution of carbonate rocks, silicate rocks and saline rocks, some of which come from human activities such as industry and mining. Groundwater composition is controlled by rock dissolution, alternating cation adsorption, and human activities. The proportion of rock dissolution is the greatest, but the influence of human activities is obvious. The intensity of human activities such as industry, mining and agriculture is greater in the Hanjiang River Basin than in the Dongjiang River Basin. The overall groundwater quality is poor, and the Dongjiang River Basin is better than the Hanjiang River Basin. The above-standard factors are mainly pH, I−, NO3−, NH4+, As and Pb2+. Nitrate pollution in groundwater is harmful to health and the health risk of children from nitrate is higher than that of adults. The health risk in the Hanjiang River Basin is higher than that in the Dongjiang River Basin. This can serve as a vital reference for similar areas worldwide, offering guidance for groundwater management, pollution prevention and control.