Model Of Factors Research Articles

Spatio-temporal evaluation of ecological security of cultivated land in China based on DPSIR-entropy weight TOPSIS model and analysis of obstacle factors

Cultivated land’s ecological security ensures human well-being and is critical to global food security. However, the unchecked expansion and excessive exploitation of agricultural resources have led to considerable land degradation issues in China, including soil erosion, contamination, and environmental degradation, raising concerns over the cultivated land ecological security (CLES). Current research on cultivated land (CLES is relatively limited, particularly at the national level across different provinces. In addition, a unified and comprehensive CLES evaluation index system has yet to be formed. To fill this research gap, we construct a CLES evaluation system with 35 indicators from three dimensions of “society-natural-economy” based on the DPSIR model framework and entropy weight TOPSIS model. Simultaneously, we conducted a spatial–temporal analysis of CLES in 31 provinces of China from 2012 to 2021 and applied the obstacle degree model to analyze the main factors impeding the CLES in China. The results showed that: (1) During the study period, the CLES and its subsystems showed a good development trend. Spatially, the CLES in China presents a regional difference of “high in the east and low in the west” and a significant regional difference among provinces. (2) The spatial CLES in different provinces of China presents a significant positive correlation, and the spatial clustering effect of CLES is gradually strengthening. The “High-High” clusters are mainly distributed in the northeast and eastern regions, while the “Low-Low” clusters are mainly located in the western region. (3) From the perspective of the criterion level, the state subsystem and the impact subsystem are the main obstacles hindering the improvement of CLES in China. At the indicator level, the common obstacle factors affecting the enhancement of CLES across provinces in China include soil erosion control intensity, number of green food certifications, per capita agricultural loans, per capita cultivated land area, and per capita grain production. Our research results can provide practical policy reference for further improving the ecological protection of cultivated land.

Menelusuri Penyebab Ketidakaktifan BUMDesa di Provinsi Jambi

The purpose of this study is to formulate a model of the inactive BUMDesa factors in Jambi Province. This type of research is a type of qualitative research research with a phenomenological type. This qualitative research analysis will be assisted by using the Nvivo qualitative research software. Based on the research results, it can be concluded that the main factors causing the inactivity of BUMDes in Jambi province can be divided into 3, these are: BUMDes management factors, village officials and the community.

Machine Learning-Enabled Fuhrman Grade in Clear-cell Renal Carcinoma Prediction Using Two-dimensional Ultrasound Images

ObjectiveAccurate assessment of Fuhrman grade is crucial for optimal clinical management and personalized treatment strategies in patients with clear cell renal cell carcinoma (CCRCC). In this study, we developed a predictive model using ultrasound (US) images to accurately predict the Fuhrman grade. MethodsBetween March 2013 and July 2023, a retrospective analysis was conducted on the US imaging and clinical data of 235 patients with pathologically confirmed CCRCC, including 67 with Fuhrman grades Ⅲ and Ⅳ. This study included 201 patients from Hospital A who were divided into training set (n = 161) and an internal validation set (n = 40) in an 8:2 ratio. Additionally, 34 patients from Hospital B were included for external validation. US images were delineated using ITK software, and radiomics features were extracted using PyRadiomics software. Subsequently, separate models for clinical factors, radiomics features, and their combinations were constructed. The model's performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA). ResultsIn total, 235 patients diagnosed with CCRCC, comprising 168 low-grade and 67 high-grade tumors, were included in this study. A comparison of the predictive performances of different models revealed that the logistic regression model exhibited relatively good stability and robustness. The AUC of the combined model for the training, internal validation and external validation sets were 0.871, 0.785 and 0.826, respectively, which were higher than those of the clinical and imaging histology models. Furthermore, the calibration curve demonstrated excellent concordance between the predicted Fuhrman grade probability of CCRCC using the combined model and the observed values in both the training and validation sets. Additionally, within the threshold range of 0–0.93, the combined model demonstrated substantial clinical utility, as evidenced by DCA. ConclusionThe application of US radiomics techniques enabled objective prediction of Fuhrman grading in patients with CCRCC. Nevertheless, certain clinical indicators remain indispensable, underscoring the pressing need for their integrated use in clinical practice. Advances in knowledgePrevious studies have predominantly focused on using computed tomography or magnetic resonance imaging modalities to predict the Fuhrman grade of CCRCC. Our findings demonstrate that a prediction model based on US images is more cost-effective, easily accessible and exhibits commendable performance. Consequently, this study offers a promising approach to maximizing the use of US examinations in future research.

Predictive models of post-traumatic stress disorder, complex post-traumatic stress disorder, depression, and anxiety in children and adolescents following a single-event trauma.

This study examined the power of theory-derived models to account for the development of PTSD, Complex PTSD (CPTSD), depression, and anxiety in children and adolescents who had experienced a single-event trauma. Children (n = 234, aged 8-17 years) recruited from local Emergency Departments were assessed at two and nine weeks post-trauma. Data obtained from self-report questionnaires completed by the child, telephone interviews with parents, and hospital data were used to develop four predictive models of risk factors for PTSD, CPTSD, depression, and Generalized Anxiety Disorder (GAD). ICD-11 proposed diagnostic criteria were used to generate measures for CPTSD and PTSD to assess for risk factors and identify the sample prevalence of these disorders. At nine weeks post-trauma, 64% did not meet criteria for any disorder, 23.5% met criteria for PTSD, and 5.2% met criteria for CPTSD. 23.9% and 10.7% had developed clinically significant symptoms of depression and GAD, respectively. A cognitive model was the most powerful predictive model, a psychosocial model was weak, and subjective markers of event severity were more powerful than objective measures. Youth exposed to single-incident trauma may develop different forms of psychopathology, and PTSD and CPTSD are frequently experienced alongside other conditions. The cognitive model of PTSD shows utility in identifying predictors of PTSD, CPTSD, depression, and GAD, particularly the role of trauma-related negative appraisals. This supports the application of cognitive interventions which focus upon re-appraising trauma-related beliefs in youth.

Machine learning based prognostics and statistical optimization of the performance of biogas-biodiesel blends powered engine

In this study, waste biomass-derived biogas was employed as the main fuel while the biodiesel-diesel blend was used as pilot fuel. This paper describes the development of a Decision Tree and Response Surface methodology-based statistical framework for prediction modeling and optimization. The compression ratio, fuel injection time, fuel injection pressure, and biogas flow rate were employed as controllable inputs, and brake thermal efficiency, peak combustion pressure, and exhaust emission were selected as responses. The experimental data for model development was gathered for the development of prediction models and optimization. The decision tree-based models were robust with almost negligible mean squared errors and R2 values of more than 0.9487 for all models. Response surface methodology-based optimized engine parameters were validated with the following results compression ratio was 17.9, fuel injection pressure was 225 bar, fuel injection timing was 26.3-degree crank angle after top dead center, and the biogas flow rate was 0.85 kg/h. Validation results were within 5 % of the model-optimized results. The prognostic models for all control factors were developed with decision tree-based machine learning with high predictive efficiency and low errors.

Medium and short-term effective prestress losses considering multiple factors: Laboratory and on-site beam experiments

The coupling between the creep and shrinkage of concrete, tendon relaxation, and temperature variations makes it difficult to determine the influence of each individual factor on prestress loss. This study investigates the influence of these multiple factors on the medium and short-term prestress of beam bridges, crucial for their durability and safety. Initially, laboratory experiments were conducted on six model beams to analyze medium and short-term prestress variations. Using a pre-designed scheme, the study isolated and quantified the impacts on effective prestress, with losses calculated through superposition. The findings were compared with existing models, leading to the proposal of a refined model for multiple factors affecting prestress. Then the prestress loss experiments on ten full-sized beams, with thirty-five strands, were calibrated based on the proposed model by regression analysis. Based on the modified model of prestress loss, the mentioned influencing factors would be calculated separately, which agree with the measured data from laboratory and in-situ experiments. In the end, the study concludes that creep has the greatest impact on prestress loss, with other factors contributing to varying degrees. Through full-scale tests, parameters for the prestress loss model were verified. The proposed model in this study effectively describes prestress loss within the tested timeframe. The study further concludes that there existed a time lag in prestress tendons and ambient temperature in both laboratory model beams and in-situ full-scale beams. For practical applications in lift-off tests of effective prestress, the appropriate testing time after tensioning to yield relatively stable results is recommended.

E커머스 시장에서 중국 기업의 성공적 진출 요인 연구: 글로벌 공급사슬 전략 측면

This paper was conducted over a period of about three months from April to June 2024, and among Chinese C-commerce companies AliExpress, TEMU, and Shein that entered the global e-commerce market and the K-commerce market in Korea, in April 2024, a year later than April 2023, we analyzed the case of TEMU, which is rapidly encroaching on the K-commerce market by securing the highest number of users of 5.81 million. The purpose of this study is to derive the success factors of C-commerce companies in the global e-commerce market through case analysis. The research methodology used domestic and foreign reports related to the research target company, market research methods such as domestic and foreign Internet sites, and Brain Storming, Delphi Method, PESTLE Methodology, and SWOT Analysis verified in consulting and research. As a result of the study, 19 factors for C-commerce company TEMU’s successful entry into the global e-commerce market were derived, and they were simplified into 13 and grouped by ranking. The first priority grouped was the marketing sector, the second priority was the investment sector, the third priority was the minimum risk sector, and the fourth priority was the customer satisfaction sector. In addition, TEMU’s ultra-low-cost e-commerce business model, which can only be found in this paper, and the ranking model of factors for successful entry into the global e-commerce market based on the efficiency of execution based on corporate resources and capabilities and the urgency to determine the fullness of business advancement were another research achievement that added great reward.

Causal model of factors influencing the fundraising in Thai sports industry: Investment ecosystem evaluation for scale-up to innovation-driven enterprises

This pioneering research aimed to verify the causal model of factors influencing fundraising in Thai sports industry. Investment ecosystem and theory of planned behavior were employed to obtain understanding of investment situations in this industry. A mixed-method research approach was also utilized. In qualitative, seven experts were interviewed to drawing the structure of the causal model. In quantitative, the top 798 sport investors responded to modified rating-scale questionnaires for confirming indicators of underlying factors in the causal model. Analysis based on grounded theory revealed in the causal model that policy, market, culture, finance, supports, and human capital are antecedent factors that induce attitude, perceived value, and behavioral intention. Fundraising behavior was directly affected by behavioral intention. Confirmatory factor analysis supported that all indicators were appropriately loaded into 10 factors. Structural equation modelling of the causal model indicated that finance, human capital, and attitudes played a vital role in fundraising. The causal model showed that policymakers and entrepreneurs in Thailand will benefit from scaling the industry to innovation-driven enterprises.

Study on Coaches’ and Athletes’ Attitude Towards the Consumption of Doping Substances

Doping continues to attract the attention of specialists in the field of Sports Science due to its frequency of occurrence and the need for scientific substantiation of educational interventions. In all the conditional factors of doping behaviour, coaches and athletes are the key factors. Coaches are often identified as a potential enabler in athletes' doping, but precisely for this reason, they continue to be identified with their status as important potential agents in doping prevention. In turn, the athlete is the product of his own development and social-educational-cultural factors. The behavior of rejection or acceptance of the use of prohibited substances is determined by internal, subjective factors and external, social-environmental factors. An extensive model of risk factors, at international level, shows that interventions are needed at different levels for the educational effect to increase. In order to identify the attitude of coaches and athletes working in the Romanian sports system, between February and April 2022, online, with the help of Google forms, two questionnaires about anti-doping knowledge and attitudes were applied, one for athletes and one for coaches. The analysis of the results found that experienced coaches are better informed and have objective views on the impact of doping in sport. On the other hand, athletes resort to doping under peer pressure, lack of time required for training and as a result of competition pressure. Knowledge of these attitudinal peculiarities allows the formulation of recommendations on educational activities in the field of anti-doping education. Keywords: coaches, athletes, anti-doping, doping substances

Modelling of Human Factors in Aviation Maintenance Using HFACS ME Human Factors Analysis and Classification System Maintenance Extension and Bayesian Network

Aircraft maintenance is a complex task involving a skilled human workforce, spare parts, and various other resources. Human factors are an inherent element of the human workforce. Human factors analysis, therefore, becomes an essential aspect of aviation maintenance. Human factors have been identified and classified using various methods in existing literature. However, there is a gap in the study of the interdependency of critical human factors including subfactors, and measuring them effectively to reduce incidents and accidents. This research work proposed a novel approach for human factors modeling using human factors analysis and classification system maintenance extension (HFACS-ME), and bayesian network (BN). Inadequate maintenance processes, inadequate documentation, inadequate supervision, Judgement decision, and attention memory were identified as some of the critical human factors in aircraft maintenance. These critical human factors were further analysed and divided into subfactors. The main contribution of the present research work is the methodology of developing a dependency model of the human factors and subfactors to analyse their measured effects on aircraft maintenance. The proposed BN model demonstrated the estimation of the probability of effective maintenance by considering the critical human factors with available facilities, and resources in an aviation maintenance setup.

COVID-19 in Brazilian Pediatric Patients: A Retrospective Cross-Sectional Study with a Predictive Model for Hospitalization.

This study was conducted to ascertain the most frequent symptoms of COVID-19 infection at first consultation in a pediatric cohort and to devise a predictive model for hospitalization. This is a retrospective cross-sectional study of 1028 Brazilian patients aged <18 years with SARS-CoV-2 infection in a single reference hospital in the first year of the pandemic. Clinical, demographic, laboratory, and disease spectrum data were analyzed via multivariate logistic regression modeling to develop a predictive model of factors linked to hospitalization. The majority of our cohort were schoolchildren and adolescents, with a homogeneous distribution concerning sex. At first consultation, most patients presented with fever (64.1%) and respiratory symptoms (63.3%). We had 204 admitted patients, including 11 with Pediatric Multisystem Inflammatory Syndrome. Increased D-dimer levels were associated with comorbidities (p = 0.018). A high viral load was observed in patients within the first two days of symptoms (p < 0.0001). Our predictive model included respiratory distress, number and type of specific comorbidities, tachycardia, seizures, and vomiting as factors for hospitalization. Most patients presented with mild conditions with outpatient treatment. However, understanding predictors for hospitalization can contribute to medical decisions at the first patient visit.

The causal relationship model of factors influencing COVID-19 preventive behaviors during the post-pandemic era and implications for health prevention strategies: a case of Bangkok City, Thailand

BackgroundThough, many countries are currently in the COVID post-pandemic era, people’s health protective behaviours are still essential to protect their health and well-being. This study aims to evaluate people’s understanding and perceptions of COVID-19 risk characteristics (i.e. threat occurrence, threat severity, perceived susceptibility and exposure), the health risk perception towards COVID-19, and health protective behaviours. The study also aims to estimate the associations among these factors by the analysis of structural equation modelling (SEM).MethodsFrom 15 October to 9 November 2022, questionnaire surveys were administrated to 521 people living in Bangkok of Thailand by using the convenience sampling technique. The analyses were carried out in three phases including descriptive statistical analyses, a measurement model assessment using a confirmatory factor analysis (CFA), and structural equation modelling (SEM) analysis.ResultsThe results of descriptive analyses demonstrated that the majority of respondents, 39.9%, had the age between 20 and 30 years old, and 61.4% of them were female. Approximately 52.1% of them had a bachelor’s degree. Upon analysing individuals’ understanding and perceptions of all risk characteristics, individuals’ understanding of COVID-19 severity did not statistically affect health risk perception towards COVID-19, whereas perceived exposure had the strongest effect and in turn influenced health protective behaviours. Perceived susceptibility and understanding of the threat occurrence also significantly affected health risk perception, and indirectly affected health protective behaviours.ConclusionsThis study implies that though the potential health impact of COVID-19 is perceived as less severe, people can still construct a perception of its risk particularly based on their perceived exposure and susceptibility. Thus, communicating people about exposure conditions and susceptibility can greatly contribute to people’ construction of risk perception towards COVID-19 which subsequently leads to the decision to perform health protective behaviours.

An Elderly-Oriented Form Design of Low-Speed New Energy Vehicles Based on Rough Set Theory and Support Vector Regression

With the current trend of social aging, the travel needs of the elderly are increasingly prominent. As a means of urban transportation, low-speed new energy vehicles (NEVs) are widely used among the elderly. Many studies are devoted to exploring the function of cars and the travel modes that meet the needs of older people. However, in addition to product performance, the Kansei needs of users also play a key role in communication between enterprises and users. Therefore, the problem of how to improve car shapes in the initial stage of design to meet the Kansei needs of elderly users remains to be solved. In order to fill this gap, the design of low-speed NEVs are selected as the study objects so as to explore the relationship between the visual perception of elderly users and car design; thus, a design method for the form of elderly-oriented cars is proposed. Firstly, using the research framework of Kansei engineering, factor analysis is used to cluster elderly-oriented Kansei factors. Second, the cars’ appearances are deconstructed by morphological analysis, and the key design features affecting elderly-oriented satisfaction are identified by a rough set attribute reduction algorithm. Finally, support vector regression is used to establish a mapping model of elderly-oriented Kansei factors and the key design features to predict the elderly-oriented form design of optimal low-speed NEVs. The research results show that selecting “Hub6”, “Headlight9”, “Car side view2”, “Rearview mirror9”, and “Front door10” in the form deconstruction table for low-speed NEVs can elicit optimal emotions in elderly users. The research results enable enterprises to more effectively understand the emotional cognition of elderly users related to the form of low-speed NEVs and improve the purchase desire and satisfaction of elderly users, providing references and guidance for the elderly-oriented design and development of intelligent transportation tools.

Enhancing Patient Empowerment and Health Literacy: Integrating Knowledge Graphs with Language Models for Personalized Health Content Delivery.

Health literacy empowers people to access, understand and apply health information to effectively manage their own health and to be an active participant in healthcare decisions. In this paper we propose a conceptual model for cognitive factors affecting health literacy and related socioeconomic aspects. Then we develop the HEALIE Knowledge Graph to represent the model, drawing from various medical ontologies, resources, and insights from domain experts. Finally, we combine the Knowledge Graph with a Large Language Model to generate personalised medical content and showcase the results through an example.

Anterior Hyperfunction Syndrome: Literature Review and Conceptual Model.

Combined Kelly syndrome, also known as anterior hyperfunction syndrome, is a complex pathological condition of the stomatognathic system, originally established by five characteristics but primarily triggered by edentulism, specifically, the absence of the upper and posterior mandibular teeth. This condition is characterized by a series of clinical features, such as bone loss, tuberosity growth, enamel wear, periodontal damage, muscle fatigue, pain, and temporomandibular joint issues. However, these features are not unique and rather reflect an oral hyperfunction state. There is a lack of consensus on the best way to assess and diagnose this condition, which is proposed to be understood as an "oral hyperfunction state" rather than a syndrome. This study aims to conduct a literature review to analyze the available information on anterior hyperfunction syndrome in dentistry, with the goal of proposing a conceptual model of the etiological risk factors that contribute to early diagnosis and the prevention of complications. This approach has important clinical implications, as it would allow for the early identification and management of risk factors, thus improving the quality of life of patients and preventing malpractice that could compromise their oral health.

Exploring and Improving the Potential Factors of Preschool Teacher Resignation Using the Stratified Bayesian Best-Worst Method

The high turnover rate of preschool teachers in the educational sector threatens the ability to retain competent teaching staff and affects the quality of teaching and the development of early education in many countries. These ramifications are especially poignant given their alignment with the United Nations’ sustainable development goals (SDGs) - notably “quality education,” “good health and well-being,” and “decent work and economic growth.” Reducing the turnover rate of educators, especially at the preschool level, has become an urgent issue for the education community. Therefore, this paper develops a multiple criteria decision-making (MCDM) model based on expert knowledge to construct an evaluation model of the main factors for the resignation of preschool teachers. These factors comprise three main dimensions: personal factors, internal organizational culture, and external environment. First, potential resignation factors were identified through a literature survey and interviews with several experts. Then, a stratified Bayesian best-worst method (SB-BWM) was used to evaluate the importance of these potential factors. The method generated the optimal group weights of the resignation factors and ranked them. The results of this study reveal that the top five resignation factors are high work stress, work-induced emotional exhaustion/job burnout, poor work atmosphere, high workload, and low collaborative relationships with partners. The results can help preschool education operators and education ministries responsible for decision-making to understand the factors leading to the resignation of preschool teachers so that they can develop strategies to improve retention. Management implications are given to improve operational performance and effectively reduce the teacher turnover rate to promote stability in the learning environment so important to early childhood development, which will lead to quality preschool education development.

Exploration of the Influencing Factors of Farmers' Willingness and Behavior in New Media Marketing under the Background of “Digital Commerce and Rural Development”

Under the background of “Digital Commerce and Rural Development”, utilizing new media for marketing has become the main way to expand the sales of agricultural products.This paper takes farmer survey data as a sample, based on the theory of planned behavior, explores the influencing factors of the “Digital Commerce and Rural Development” policy, and establishes a model of the influencing factors of farmer’ new media marketing willingness and behavior in the new media environment.Research has found that subjective norms have become the most important factor influencing farmers’ attitudes towards using new media for marketing agricultural products. The policy of Digital Commerce and Rural Development’ has become the most important factor affecting farmers’ intention to use new media for marketing agricultural products. Perceived behavioral control does not have a significant impact on farmers’ attitudes and intentions towards using new media for marketing agricultural products.From the aspects of strengthening the concept of new media marketing, carrying out training of new media marketing, building a public service support system, and cultivating and introducing professional and technical personnel, this paper provides policy suggestions for enhancing farmers’ willingness to carry out new media marketing.

An electronic health record (EHR)-based risk calculator can predict fractures comparably to FRAX: a proof-of-concept study.

Fracture risk calculators, such as the Fracture Risk Assessment Tool, or FRAX, typically lie outside the clinician workflow. Conversely, the electronic health record (EHR) is at the center of the clinical workflow, and many variables in the EHR could predict fractures without having to verbally ascertain FRAX risk factors. We sought to evaluate the utility of EHR variables to predict fractures and, as a proof of concept, to create an EHR-based fracture risk model. Routine clinical data from 24,189 subjects presenting to primary care from 2010 to 2018 was utilized. Major osteoporotic fractures (MOFs) were captured by physician diagnosis codes. Data was split into training (n = 18,141) and test sets (n = 6048). We fit Cox regression models for candidate risk factors in the training set, and then created a global model using a backward stepwise approach. We then applied the model to the test set and compared the discrimination and calibration to FRAX. We found variables related to vital signs, utilization, diagnoses, medications, and laboratory values to be associated with incident MOF. Our final model included 19 variables, including age, BMI, Parkinson's disease, chronic kidney disease, and albumin levels. When applied to the test set, we found the discrimination (AUC 0.73 vs. 0.70, p = 0.08) and calibration were comparable to FRAX. Routinely collected data in EHR systems can generate adequate fracture predictions without the need to verbally ascertain fracture risk factors. In the future, this could allow for automated fracture prediction at the point of care to improve osteoporosis screening and treatment rates.

Predictive model of risk factors for 28-day mortality in patients with sepsis or sepsis-associated delirium based on the MIMIC-IV database

Research on the severity and prognosis of sepsis with or without progressive delirium is relatively insufficient. We constructed a prediction model of the risk factors for 28-day mortality in patients who developed sepsis or sepsis-associated delirium. The modeling group of patients diagnosed with Sepsis-3 and patients with progressive delirium of related indicators were selected from the MIMIC-IV database. Relevant independent risk factors were determined and integrated into the prediction model. Receiver operating characteristic (ROC) curves and the Hosmer–Lemeshow (HL) test were used to evaluate the prediction accuracy and goodness-of-fit of the model. Relevant indicators of patients with sepsis or progressive delirium admitted to the intensive care unit (ICU) of a 3A hospital in Xinjiang were collected and included in the verification group for comparative analysis and clinical validation of the prediction model. The total length of stay in the ICU, hemoglobin levels, albumin levels, activated partial thrombin time, and total bilirubin level were the five independent risk factors in constructing a prediction model. The area under the ROC curve of the predictive model (0.904) and the HL test result (χ2 = 8.518) indicate a good fit. This model is valuable for clinical diagnosis and treatment and auxiliary clinical decision-making.

Model for prediction of pesticide residues in soybean oil using partial least squares regression with molecular descriptors

We developed a partial least squares regression (PLSR) model based on competitive adaptive reweighted sampling (CARS) to predict the processing factors of 54 pesticides during soybean oil processing. Characteristic variables were selected to improve the performance of the model. Four calculators were used to compute the molecular descriptors used in the model, and the model based on values computed with ChemoPy produced the best results: Rc ​= ​0.94, RMSEc ​= ​0.67, Rp ​= ​0.91, and RMSEp ​= ​0.54 for hot-pressed oil and Rc ​= ​0.93, RMSEc ​= ​0.73, Rp ​= ​0.93, and RMSEp ​= ​0.59 for cold-pressed oil. A rapid and quantitative model of processing factors was established to predict the behaviour and distribution of pesticide residues during food processing. The model was further validated using data from field-grown soybeans; it demonstrated a high correlation coefficient between predicted and measured residue concentrations (Rp ​> ​0.93, RMSEp ​< ​0.72) and successfully predicted the distribution and behaviour of pesticide residues. Our model provides a reference for assessing safety risk and determining the maximum residue limits for pesticides in processed products.