Optimal Model Research Articles

Analysis of Rewetting Characteristics and Mechanical Properties in Leaf Blades and Main Veins of Cured Tobacco Leaves

The moisture content and mechanical properties are crucial factors influencing the processing quality of cured tobacco leaves. Understanding the rewetting characteristics and mechanical properties of leaf blades and main veins is essential to for optimizing tobacco processing. This study measured the rewetting behavior and mechanical properties of leaf blades and main veins under varying temperature (20–40 °C) and relative humidity (RH, 50%–90%) conditions. Rewetting curves were obtained and dynamic models were constructed. Low-field nuclear magnetic resonance (LF-NMR) analysis was utilized to obtain information of relaxation time (T2), and correlation models between moisture state and mechanical properties were established. The results indicated that the rewetting rate of leaf blades and main veins decreased gradually with increasing moisture content, while higher temperature and RH enhanced the rewetting process. The rewetting time of leaf blades at 30 °C with 90% RH was reduced by 71.43% compared to that at 30 °C with 70% RH. For main veins, the rewetting time at 30 °C with 90% RH was shortened by 50.00% compared to 30 °C with 80% RH. The Peleg model was determined as the optimal dynamic model for describing the rewetting characters for leaf blades and main veins. Two moisture states were observed: strongly bound moisture and weakly bound moisture. The peak area of strongly bound moisture increased gradually, while the peak area of weakly bound moisture initially increased and then decreased. At the moisture content of 0.15 g/g, the strongly bound moisture content of leaf blades decreased by 7.75% compared to main veins, whereas the weakly bound moisture content of leaf blades increased by 3.04 times. However, the proportion of weakly bound moisture decreased despite the increasing proportion of strongly bound moisture. Furthermore, the relaxation time of leaf blades and main veins increased during rewetting. The mechanical properties analysis revealed a correlation between breaking force, strain, and elastic modulus with the content of strongly bound moisture. This study provides a theoretical basis for the development of improved rewetting processes in tobacco production.

Research on multi-apparent defects detection of concrete bridges based on YOLOR

To enhance the accuracy of detecting apparent defects in concrete bridges, this study proposes an improved model based on the YOLOR. Initially, a concrete bridges defect (CBD) dataset comprising 7009 images was established, encompassing nine distinct types of defects: pockmark, spalling, cavity, whitening, reticular crack, single crack, rust expansion, exposed rebar and leakage. Subsequently, using the CBD dataset, performance comparison experiments involving YOLOv3, YOLOv4, YOLOR-640, and YOLOR-1280 were conducted to select the optimal model. Based on the characteristics of the apparent defects in bridges, the anchor boxes and localization loss function were optimized respectively, thereby proposing an improved model named YOLOR-BDIM. The results indicate that the mAP of YOLOR-BDIM increased by 2.7 % compared with initial model. Furthermore, YOLOR-BDIM achieved a detection accuracy of 97.5 % during the random tests, showing excellent detection performance even in complex environments.

An optimal model of agricultural real estate transactions in the light of Professor Aleksander Lichorowicz’s research

An optimal model of agricultural real estate transactions in the light of Professor Aleksander Lichorowicz’s research

Construction of Remote Sensing Quantitative Model for Biomass of Deciduous Broad-Leaved Forest in Mazongling Nature Reserve Based on Machine Learning

AbstractAs an important forest type, deciduous broad-leaved forest is crucial for estimating forest carbon sequestration capacity and evaluating forest carbon balance. This study focuses on the natural deciduous broad-leaved forest of Mazongling Nature Reserve in Jinzhai County of China. WorldView-2 images were selected as data source. 36 candidate factors including vegetation indices, texture features, and topographic factors were used for modelling. Three machine learning algorithms (i.e., random forest, k-nearest neighbor, and artificial neural network) were used to establish the optimal quantitative retrieval model for natural deciduous broad-leaved biomass. Results showed that the ANN model was the best predictor with R2 = 0.69 and RMSE = 31.53 (Mg·ha−1). Combining the ANN model with the complete spatial coverage of remote sensing data, we developed a distribution map of natural deciduous broad-leaved biomass in the Mazongling forest farm. The estimated average biomass of the study area was 90.34 ± 47.96 Mg·ha−1. In addition, the influence of light saturation on model accuracy is also discussed. This study confirms that remote sensing data in temporal and spatial space can improve the model estimation accuracy.

Research on the visual location method for strawberry picking points under complex conditions based on composite models.

Strawberry, being an important economic crop, requires a large amount of human labor for harvesting operations. Efficient and non-destructive harvesting by strawberry harvesting robots requires the precise location of the picking points. Current algorithms for locating picking points encounter significant issues with location errors and minimal effective information in complex situations. To improve the accuracy of the location of picking points, this study proposes a visual location method based on composite models. This method employs object detection and instance segmentation models to detect fruits and segment peduncles sequentially, thereby enabling the identification of picking points and inclination on the peduncle. Different object detection algorithms and instance segmentation models were validated to explore the optimal model combination, and the Convolutional Block Attention Module (CBAM) was integrated into YOLOv8s-seg to construct YOLOv8s-seg-CBAM. Test results show that the composite model built with YOLOv8s and YOLOv8s-seg-CBAM achieved a peduncle detection accuracy of 86.2%, with an inference time of 30.6 ms per image. The picking point visual location method based on YOLOv8s and YOLOv8s-seg-CBAM composite models can better balance accuracy and efficiency and can provide more accurate guidance for automated harvesting. © 2024 Society of Chemical Industry.

Leveraging machine learning to develop a postoperative predictive model for postoperative urinary retention following lumbar spine surgery

IntroductionPostoperative urinary retention (POUR) is the inability to urinate after a surgical procedure despite having a full bladder. It is a common complication following lumbar spine surgery which has been extensively linked to increased patient morbidity and hospital costs. This study hopes to development and validate a predictive model for POUR following lumbar spine surgery using patient demographics, surgical and anesthesia variables.MethodsThis is a retrospective observational cohort study of 903 patients who underwent lumbar spine surgery over the period of June 2017 to June 2019 in a tertiary academic medical center. Four hundred and nineteen variables were collected including patient demographics, ICD-10 codes, and intraoperative factors. Least absolute shrinkage and selection operation (LASSO) regression and logistic regression models were compared. A decision tree model was fitted to the optimal model to classify each patient’s risk of developing POUR as high, intermediate, or low risk. Predictive performance of POUR was assessed by area under the receiver operating characteristic curve (AUC-ROC).Results903 patients were included with average age 60 ± 15 years, body mass index of 30.5 ± 6.4 kg/m2, 476 (53%) male, 785 (87%) white, 446 (49%) involving fusions, with average 2.1 ± 2.0 levels. The incidence of POUR was 235 (26%) with 63 (7%) requiring indwelling catheter placement. A decision tree was constructed with an accuracy of 87.8%.ConclusionWe present a highly accurate and easy to implement decision tree model which predicts POUR following lumbar spine surgery using preoperative and intraoperative variables.

Machine learning enables electrical resistivity modeling of printed lines in aerosol jet 3D printing.

Among various non-contact direct ink writing techniques, aerosol jet printing (AJP) stands out due to its distinct advantages, including a more adaptable working distance (2-5mm) and higher resolution (~ 10μm). These characteristics make AJP a promising technology for the precise customization of intricate electrical functional devices. However, complex interactions among the machine, process, and materials result in low controllability over the electrical performance of printed lines. This significantly affects the functionality of printed components, thereby limiting the broad applications of AJP. Therefore, a systematic machine learning approach that integrates experimental design, geometrical features extraction, and non-parametric modeling is proposed to achieve printing quality optimization and electrical resistivity prediction for the printed lines in AJP. Specifically, three classical convolutional neural networks (CNNs) architectures are compared for extracting representative features of printed lines, and an optimal operating window is identified to effectively discriminate better line morphology from inferior printed line patterns within the design space. Subsequently, three representative non-parametric machine learning techniques are employed for resistivity modeling. Following that, the modeling performances of the adopted machine learning methods were systematically compared based on four conventional evaluation metrics. Together, these aspects contribute to optimizing the printed line morphology, while simultaneously identifying the optimal resistivity model for accurate predictions in AJP.

Unveiling the enigma of acute kidney disease: predicting prognosis, exploring interventions, and embracing a multidisciplinary approach.

Acute kidney disease (AKD) is a critical transitional period between acute kidney injury and chronic kidney disease. The incidence of AKD following acute kidney injury is approximately 33.6%, and it can occur without identifiable preceding acute kidney injury. The development of AKD is associated with increased risks of chronic kidney disease, dialysis, and mortality. Biomarkers and subphenotypes are promising tools to predict prognosis in AKD. The complex clinical situations in patients with AKD necessitate a comprehensive and structured approach, termed "KAMPS" (kidney function check, advocacy, medications, pressure, sick day protocols). We introduce "MAND-MASS," an acronym devised to summarize the reconciliation of medications during episodes of acute illness, as a critical component of the sick day protocols at AKD. A multidisciplinary team care, consisting of nephrologists, pharmacists, dietitians, health educators, and nurses, is an optimal model to achieve the care bundle in KAMPS. Although the evidence for patients with AKD is still lacking, several potential pharmacological agents may improve outcomes, including but not limited to angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, mineralocorticoid receptor antagonists, sodium-glucose cotransporter 2 inhibitors, and glucagon-like peptide 1 receptor agonists. In conclusion, accurate prognosis prediction and effective treatment for AKD are critical yet unmet clinical needs. Future studies are urgently needed to improve patient care in this complex and rapidly evolving field.

Assessment of cone-beam CT technical image quality indicators and radiation dose for optimal STL model used in visual surgical planning.

The aim of this study was to identify cone-beam computed tomography (CBCT) protocols that offer an optimal balance between effective dose and 3D model for orthognathic virtual surgery planning, using CT as a reference, and to assess whether such protocols can be defined based on technical image quality metrics. Eleven CBCT (VISO G7, Planmeca Oy, Helsinki, Finland) scan protocols were selected out of 32 candidate protocols, based on effective dose and technical image quality measurements. Next, an anthropomorphic RANDO SK150 phantom was scanned using these 11 CBCT protocols and 2 CT scanners for bone quantity assessments. The resulting DICOM files were converted into STL models that were used for bone volume and area measurements in the predefined orbital region to assess the validity of each CBCT protocol for VSP. The highest CBCT bone volume and area of the STL models were obtained using normal dose protocol (F2) and ULD protocol (J13) which resulted in 48% and 96% of the mean STL bone volume and 48% and 95% of the bone area measured on CT scanners, respectively. The optimal normal dose CBCT protocol" F2" offered optimal bone area and volume balance for STL. The optimal CBCT protocol can be defined exhibited similar using CNR and MTF values that were similar with of those of the reference CT scanners'. CBCT scanner with selected protocols can offer a viable alternative to CT scanners for acquiring STL models for VSP at a lower effective dose.

A machine learning and CFD modeling hybrid approach for predicting real-time heat transfer during cokemaking processes

As the development of green energy and smart industries progresses, concerted efforts are being directed towards the transformation of coking industries. Effective management of heat transfer during the coking process is essential to fortify energy consumption management and augment the quality of coke produced. However, the process during cokemaking is a complex thermochemical conversion process where even minor deviations in operating conditions can lead to instability. Additionally, due to the environmental and chemical conditions inside the coke oven, temperature measurements during operation are difficult. As the fields of Industry 4.0 continue to evolve, numerous industries have begun to leverage intelligent strategies to enhance advanced process management. The digital twin (DG) technique stands as a pivotal technology in the realm of industrial transformation, paving the way for a smarter future. However, given the inherent difficulty in real-time data collection for process operations, the challenge of achieving synchronization becomes increasingly prevalent in the development of DG for the smart coking process. As a result, predicting changes in coal temperature during the cokemaking process is crucial. Currently, computer technology allows for the simulation of complex chemical processes. While some numerical models describe the cokemaking process, these models are limited by the time-consuming characteristic and computing resources. To address this issue, this study proposed a Machine Learning (ML) and Computational Fluid Dynamics (CFD) hybrid model for real-time prediction of heat transfer during the cokemaking process. A CFD model was built based on an industrial-scale coke oven, with cokemaking process data generated through CFD simulation serving as input for the ML models. This study used a total of nine ML models, including ensemble and deep learning models. The hyperparameters of each model were optimized by Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and JAYA algorithms to identify the optimal model structure. Both simulation and empirical experimentation using a coke oven, coupled with industrial data verification, have substantiated the superiority of the proposed model. It simulates the heat transfer during the coking process with high accuracy and efficiency, thereby demonstrating its capacity to significantly reduce energy consumption within the coking industry.

Research on short-term photovoltaic power generation forecasting model based on multi-strategy improved squirrel search algorithm and support vector machine

Solar photovoltaic (PV) power generation is susceptible to environmental factors, and redundant features can disrupt prediction accuracy. To achieve rapid and accurate online prediction, we propose a method that combines Principal Component Analysis (PCA) with a multi-strategy improved Squirrel Search Algorithm (SSA) to optimize Support Vector Machine (MISSA-SVM) for prediction. Initially, to mitigate the impact of redundant features on prediction accuracy, KPCA is employed for feature dimensionality reduction. Subsequently, SVM is suggested as the foundational algorithm for constructing the prediction model. Furthermore, to address the influence of hyperparameter selection on model performance, SSA is introduced for optimizing SVM hyperparameters, with the aim of establishing the optimal prediction model. Moreover, to enhance solution efficiency and accuracy, a multi-strategy approach termed MISSA is proposed, which integrates Population Initialization based on the Tent map, Nonlinear Predator Presence Probability, Chaotic-based Dynamic Opposition-based Learning, and Selection Strategy, to refine SSA. Finally, through case studies, the performance of MISSA optimization is assessed using challenging CEC2021 test functions, demonstrating its high optimization performance, stability, and significance. Subsequently, the performance of the prediction model is validated using two datasets, showcasing that the proposed prediction method achieves high accuracy and robust prediction stability.

Research on day-ahead and intraday scheduling strategy of distribution network based on dynamic partitioning

With the massive participation of new energy in the operation of distribution network (DN), the uncertainties arising from both load and renewable generation have brought challenges to its secure and reliable operation. In this paper, a model of day-ahead and intraday optimal scheduling based on dynamic partitioning (DP) of DN is constructed to realize the refinement of operation in both temporal and spatial dimensions, as well as to improve the reliability of system operation. For the strong fluctuation characteristics of the source-load in DN, the DP is achieved under the premise that the partition structure in each period is tight and the internal power supply has the ability to balance the power fluctuations. In order to improve the regional autonomy of DN, a day-ahead multi-objective optimal scheduling model is constructed based on the DP with the optimization objectives of weak inter-partition power coupling, lowest deficiency rate of flexibility, and lowest operating cost. And a scheme for articulation is proposed to ensure the effective guidance of day-ahead for intraday. Then the intraday dispatch model is constructed by shifting the focus to the incorporate characteristics of DP and quickly smooth the net load fluctuations. Finally, the performance and reliability of the proposed models are verified via case studies.

Research on load prediction of low-calorific fuel fired gas turbine based on data and knowledge hybrid model

The high-precision load prediction technology plays a vital role in load control and health management for gas turbines. Low-calorific fuel fired gas turbines pose an especially significant challenge for load prediction due to the frequent and wide fluctuations of gas parameters. First, an improved hybrid-dimension physical-based model is proposed, which is corrected based on segmented operation data and integrates zero-dimensional thermodynamic knowledge of gas turbines with three-dimensional fluid dynamics simulation knowledge. Secondly, an optimal data-driven model is proposed through conducting a systematic comparative study on four popular machine learning models with two types of input variables feature extraction. Then, a novel hybrid model is proposed based on the improved physical-based model, optimal data-driven model, and the principle of minimizing hybrid model errors. The average relative prediction error of the hybrid model over the complete operation range is 0.68%. Compared with the physical-based model and the data-driven model, the proposed hybrid model can improve the prediction accuracy by 63% and 29%, respectively. As the high-calorific fuel fired gas turbine system is relatively simple and has a small range of fuel fluctuations, the method established in this paper can be extended to high-calorific fuel fired gas turbines, which is of great significance for promoting the dynamic balance of loads in a new type of electric power system based on new energy.

Optimal Allocation Model for Maritime Emergency Resource Considering the Spatial Correlation between Accident Hotspots

For effective maritime traffic emergency rescue (MTER) operations in the event of maritime traffic accidents (MTAs) and to improve rescue efficiency, it is necessary to analyze the MTER synergy problem and the cooperation between port states. First, the spatial information of accidents under the geographic information system data structure is clarified from the global integrated shipping information system of the International Maritime Organization, and the density-based spatial clustering of applications with noise algorithm is used to conduct hotspot mapping analysis of MTAs to establish the clustering and classification of accident characteristics in key areas. Second, the classification characteristics of accident samples are extracted based on spatial information, and the correlation attributes between MTA hotspots are analyzed. Furthermore, by introducing complex network measurement technology, a topological model of the MTER network is established considering the correlation of accident hotspots, and this model is combined with the sample data of MTAs in Southeast Asian waters from 1990 to 2022. Third, the MTER topological network model is quantitatively analyzed under the accident space of Southeast Asia, and the degree of correlation of traffic accidents in key areas is obtained to reveal the inevitable demand for MTER between regions. The results of the analysis show that there is a network correlation between inter-regional accident hotspots, and thus the degree of correlation between accident hotspots needs to be considered for MTER in key areas. Countries in densely connected regions would set up joint rescue exercises and consider rescue assistance between port country stakeholders, thus improving protection for accident emergency responses. The method of complex network topology based on spatial correlation between accident hotspots suggests a new approach for solving the MTER problem.

FrameGraph: A Scalable Performance Evaluation Method for Frame Structure Designs Using Graph Neural Network

Abstract Multilayer perceptron (MLP) and convolutional neural network (CNN) encounter a critical scalability issue when applied to the performance evaluation task for frame structure designs. Specifically, a model of MLP or CNN is limited to structures of a particular topology type and fails immediately when applied to other topology types. In order to tackle this challenge, we propose a scalable performance evaluation method (called FrameGraph) for frame structure designs using graph neural network (GNN), offering applicability to a wide range of topology types simultaneously. FrameGraph consists of two main parts: (1) Components and their connections in a frame structure are denoted as edges and vertices in a graph, respectively. Subsequently, a graph dataset for frame structure designs with different topologies is constructed. (2) A well-defined GNN design space is established with a general GNN layer, and a controlled random search approach is employed to derive the optimal GNN model for this performance evaluation task. In numerical experiments of car door frames and car body frames, FrameGraph achieved the highest prediction precisions (96.28% and 97.87%) across all structural topologies compared to a series of classical GNN algorithms. Furthermore, the comparison with MLP and FEM highlighted FrameGraph's significant efficiency advantage. This verifies the feasibility and optimality of FrameGraph for the performance evaluation task of frame structures with different topologies.

Understanding and addressing symptoms for those with kidney failure managed conservatively, without dialysis: considerations and models of care.

For those who have kidney failure and are managed conservatively without dialysis, symptoms are often prevalent, multiple, and troublesome. They interfere with quality of life, reduce wellbeing, and can affect family carers too. Symptoms can sometimes be difficult to manage, and-for professionals-they are often hard to assess and not always amenable to management with medications appropriate for use in kidney failure. Fatigue is one of the most common symptoms; alongside a general overview of symptoms in this population, we include a more detailed discussion of this often-neglected symptom. The solutions to the main symptoms experienced by those with kidney failure managed conservatively without dialysis lie in detailed assessment and monitoring of symptoms, working as a multi-disciplinary team to the maximum to draw on the full range of skills and expertise, and use of non-pharmacological, as well as pharmacological, approaches. Both nephrology and palliative care skills and expertise are important to optimise the recognition, assessment, and management of symptoms. There are few published descriptions of models of conservative kidney management (CKM) or supportive kidney care and there is a lack of evidence to suggest which model is most effective. We therefore consider the evidence on optimal models of CKM and make suggestions for best practice.

An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue disease

BackgroundThere is no individualized prediction model for intensive care unit (ICU) admission on patients with community-acquired pneumonia (CAP) and connective tissue disease (CTD) so far. In this study, we aimed to establish a machine learning-based model for predicting the need for ICU admission among those patients.MethodsThis was a retrospective study on patients admitted into a University Hospital in China between November 2008 and November 2021. Patients were included if they were diagnosed with CAP and CTD during admission and hospitalization. Data related to demographics, CTD types, comorbidities, vital signs and laboratory results during the first 24 h of hospitalization were collected. The baseline variables were screened to identify potential predictors via three methods, including univariate analysis, least absolute shrinkage and selection operator (Lasso) regression and Boruta algorithm. Nine supervised machine learning algorithms were used to build prediction models. We evaluated the performances of differentiation, calibration, and clinical utility of all models to determine the optimal model. The Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) techniques were performed to interpret the optimal model.ResultsThe included patients were randomly divided into the training set (1070 patients) and the testing set (459 patients) at a ratio of 70:30. The intersection results of three feature selection approaches yielded 16 predictors. The eXtreme gradient boosting (XGBoost) model achieved the highest area under the receiver operating characteristic curve (AUC) (0.941) and accuracy (0.913) among various models. The calibration curve and decision curve analysis (DCA) both suggested that the XGBoost model outperformed other models. The SHAP summary plots illustrated the top 6 features with the greatest importance, including higher N-terminal pro-B-type natriuretic peptide (NT-proBNP) and C-reactive protein (CRP), lower level of CD4 + T cell, lymphocyte and serum sodium, and positive serum (1,3)-β-D-glucan test (G test).ConclusionWe successfully developed, evaluated and explained a machine learning-based model for predicting ICU admission in patients with CAP and CTD. The XGBoost model could be clinical referenced after external validation and improvement.

Optimized the Microgrid Scheduling with Ice-Storage Air-Conditioning for New Energy Consumption

In the face of the stochastic, fluctuating, and intermittent nature of the new energy output, which brings significant challenges to the safe and stable operation of the power system, it is proposed to use the ice-storage air-conditioning to participate in the microgrid optimal scheduling to improve wind and light dissipation. This paper constructs an optimal scheduling model for the ice-storage air-conditioning to participate in the microgrid, analyzes the regulation advantages of the ice-storage air-conditioning’s cold storage and cold release process and its participation in the scheduling process; secondly, based on the scenario method, the scenario modeling of the microgrid distributed wind and light output uncertainty and cold load uncertainty is carried out; lastly, an optimal scheduling model is constructed to minimize the operation and maintenance cost of each unit and the storage cost of the ice-storage air-conditioning, the highest rate of clean energy generation and the lowest cost of electricity consumption of the air-conditioning users as the objective function. The example simulations show that the proposed optimal scheduling model can promote the new energy consumption rate of the microgrid, proving that the ice-storage air-conditioning is more economical compared with ordinary air-conditioning and that the operating cost within the optimized microgrid is reduced by about 10.5%. The cost of the air-conditioning users’ electricity consumption has been reduced by about 11.7% after responding to the regulation.

Weakened continental lithosphere beneath the northern Red Sea inferred from elastic thickness

The northern Red Sea (NRS) is considered an extended continental region that has resulted in a rift system. Gravity and bathymetry data were used to estimate the Moho depth and the elastic thickness Te of the lithosphere beneath the NRS region to characterize its flexural rigidity and understand its mechanical behavior. Focusing on the Mabahiss Deep in NRS, we analyzed the lithosphere's flexural rigidity. The observed long-wavelength positive Bouguer anomaly is attributed to crustal thinning and lithospheric mantle uplift. The crustal thickness varies from 28 km in coastal areas to 24 km beneath the axial rift, supporting a regional compensation model over the Airy model. Forward modeling suggests that the optimal model explaining the regional Bouguer anomaly is a flexural model with Te equal to 7 km, indicating a weak and irregular continental crust. The primary factor contributing to this weakness is heating activity. Given the weakened state of the crust and the ongoing extension in the region, the NRS rift could evolve into a rupture, potentially leading to the formation of oceanic crust.

Perfluoroalkyl compounds in groundwater alter the spatial pattern of health risk in an arsenic‑cadmium contaminated region

Integrated health risk assessment strategies for emerging organic pollutants and heavy metals that coexist in water/soil media are lacking. Contents of perfluoroalkyl compounds and potentially toxic elements in multiple media were determined by investigating a county where a landfill and a tungsten mine coexist. The spatial characteristics and sources of contaminants were predicted by Geostatistics-based and multivariate statistical analysis, and their comprehensive health risks were assessed. The average contents of perfluorooctane acid, perfluorooctanesulfonic acid, arsenic, and cadmium in groundwater were 3.21, 0.77, 1.69, and 0.14 μg L−1, respectively; the maximum content of cadmium in soils and rice highly reached 2.12 and 1.52 mg kg−1, respectively. In soils, the contribution of mine lag to cadmium was 99 %, and fertilizer and pesticide to arsenic was 59.4 %. While in groundwater, arsenic, cadmium and perfluoroalkyl compounds near the landfill mainly came from leachate leakage. Significant correlations were found between arsenic in groundwater and arsenic and cadmium in soils, as well as perfluoroalkyl compounds in groundwater and pH and sulfate. Based on these correlations, the geographically optimal similarity model predicted high-level arsenic in groundwater near the tungsten mine and cadmium/perfluoroalkyl compounds around the landfill. The combination of analytic network process, entropy weighting method and game theory-based trade-off method with risk assessment model can assess the comprehensive risks of multiple pollutants. Using this approach, a high health-risk zone located around the landfill, which was mainly attributed to the presence of arsenic, cadmium and perfluorooctanesulfonic acid, was found. Overall, perfluoroalkyl compounds in groundwater altered the spatial pattern of health risks in an arsenic‑cadmium contaminated area.