Risk Assessment System Research Articles

Truck Driving Assessment for Chinese Logistics and Transportation Companies Based on a Safety Climate Test System

By applying the “safety atmosphere” measurement theory to Chinese management companies engaged in road transportation, a corporate and individual safety risk assessment system was established that is consistent with the management and cultural climate in China, thereby reducing the driving safety risk of truck drivers. The system realizes the safety risk assessment of enterprises, fleets and individuals in the form of test scales by constructing a structural model of the enterprise safety atmosphere, including the management, communication, and supervision of enterprises, fleets, and individuals. The safety climate was modeled using a two-level framework, at the organizational level and fleet level, and three dimensions of test items for each level were obtained by exploratory factor analysis. The three dimensions of safety management, safety supervision, and safety priority at the organizational level, and the three dimensions of positive communication, safety awareness, and self-discipline at the fleet level, respectively, passed a valid factorial test (p < 0.01). Finally, the validity of the system evaluation results was verified by relying on the actual in-vehicle monitoring data and accident records of the corporate transportation fleet. The results show that the total test scores at the organizational level and the fleet level are significantly correlated with their driving risk behaviors, and both are linearly and negatively correlated with the number of accidents per thousand kilometers. This indicates a high degree of consistency between the system’s test results and actual risky accidents.

Formation of functional, extended bile canaliculi, and increased bile acid production in sandwich-cultured human cryopreserved hepatocytes using commercially available culture medium

Drug-induced cholestasis results in drug discontinuation and market withdrawal, and the prediction of cholestasis risk is critical in the early stages of drug development. Animal tests and membrane vesicle assay are currently being conducted to assess the risk of cholestasis in the preclinical stage. However, these methods have drawbacks, such as species differences with humans and difficulties in evaluating the effects of drug metabolism and other transporters, implying the need for a cholestasis risk assessment system using human hepatocytes. However, human hepatocytes hardly form functional, extended bile canaliculi, a requirement for cholestasis risk assessment. We previously established a culture protocol for functional, extended bile canaliculi formation in human iPSC-derived hepatocytes. In this study, we modified this culture protocol to support the formation of functional, extended bile canaliculi in human cryopreserved hepatocytes (cryoheps). The production of bile acids, which induces bile canaliculi extension, increased time-dependently during bile canaliculi formation using this protocol, suggesting that increased bile acid production may be involved in the extended bile canaliculi formation. We have also shown that our culture protocol can be applied to cryoheps from multiple donors and that bile canaliculi can be formed stably among different culture batches. Furthermore, this protocol enables long-term maintenance of bile canaliculi and scaling down to culture in 96-well plates. We expect our culture protocol to be a breakthrough for in vitro cholestasis risk assessment.

Application of Research on Risk Assessment of Roadway Roof Falls Based on Combined Weight Matter Element Extension Model

Roof falls in coal mine roadways are the main causes of many casualties, shutdowns and production plan delays. To understand the relationship between the influencing factors of roadway roof fall accidents and the importance ranking of the accidents, we will reduce safety accidents in coal mines. To enable the timely prediction and control of roadway roof fall risks, based on the investigation of many roadway roof fall risk factors, 12 evaluation indexes such as the roadway roof rock thickness, geological conditions and roadway section shape were selected. An evaluation index system of roadway roof fall risks is constructed. A risk degree standard of roadway roof falls is proposed. The risk evaluation model of roadway roof falls was established by using the combination weight of the analytic hierarchy process (AHP), entropy weight method (EW) and matter element extension theory. According to the principle of the maximum membership degree, the risk degree of roadway roof falls is determined. Based on Java Web, a risk assessment system for roadway roof falls was developed. We name the system Multiple Weight-Material Element Web (MW-MEW). The MW-MEW system was used to evaluate the risk degree of roof falls in the C9 return airway of the Xingu Coal Mine. Compared with the evaluation results of the AHP matter element extension model, it is found that the evaluation results of the MW-MEW system are more in line with the actual engineering conditions. The successful application of the MW-MEW system will provide new avenues for the quantitative evaluation of roof fall risks in coal mine roadways.

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

PurposeIn order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.Design/methodology/approachFirst, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.FindingsThe cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/valueThis study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

Developing a smart green supplier risk assessment system integrating natural language processing and life cycle assessment based on AHP framework: An empirical study

Global carbon reduction drives the transformations of corporations, emphasizing carbon and environmental impact within supply chains. A critical challenge in risk management is converting unstructured data into quantifiable information, and ruining the conversion often leads to ineffective risk assessments. While artificial intelligence is increasingly used in supply chain risk management, previous studies seldom integrate text analysis for risk extraction and lack accurate risk assessment methods. This study pioneers an intelligent green supplier risk assessment system integrating natural language processing (NLP) and life cycle assessment (LCA) based on an analytic hierarchy process (AHP) framework. The proposed approach utilizes NLP keyword extraction to identify and assess risk and uses LCA to uncover environmental risks, minimizing negative impacts. The text classification model attains 81.7 % accuracy, ensuring a trustworthy risk assessment system. The proposed method could significantly improve risk analysis efficiency, foster sustainable procurement, and elevate enterprise competitiveness in sustainable business practices.

A Study of Insurance Company Underwriting Risk Based on EWM and TOPSIS Methods

This study proposes a risk assessment model called CPSF, which aims to assess insurance risk in areas where extreme weather is frequent. The model adopts a multidimensional and comprehensive assessment system, considering climate change, demographic and socio-economic, and financial conditions as the main factors, and constructs corresponding assessment indicators. The study establishes a scientific and reliable risk assessment system through the steps of data source, data pre-processing, indicator establishment, weight determination, and comprehensive assessment. By applying the model to two regions, namely, Southeast China and Southeast United States, where extreme weather often occurs, the insurance risk situation of these regions is analyzed and the future trend is predicted, which provides an important reference for insurance companies and policy making.

Artificial Intelligence for safety and reliability: A descriptive, bibliometric and interpretative review on machine learning

This research provides a structured review of studies that utilize Artificial Intelligence for safety and reliability. In particular, it focuses on Machine Learning techniques to perform fault detection and diagnosis, anomaly detection, system prognosis, reliability analysis, and risk assessment of engineering-related systems across the industry. Relevant studies were identified through clear research questions, screened, and assessed for eligibility. Explicit inclusion and exclusion criteria were defined to verify the suitability of the records. The analysis encompasses a descriptive, bibliometric, and interpretative review. The descriptive analysis details how different ML approaches are adapted and implemented across various domains of safety and reliability. The bibliometric analysis provides in-depth and comprehensive statistics, covering aspects such as data types used, preprocessing steps undertaken, and categories of ML algorithms employed. The interpretative analysis offers a critical and forward-looking perspective on the current state of the field. A total of 308 papers were analyzed, mostly adopting supervised learning frameworks (81%) and addressing fault detection and diagnosis (57%) in more than 16 different industrial fields. The outcome of this study reflects the rapid development of this cross-cutting and interdisciplinary research field and highlights the potential for future improvement in the data-driven operational safety of industrial plants. Challenges and limitations have been highlighted and discussed, including data availability and label scarcity, data quality, trust and explainability, and interdisciplinary collaboration. Additionally, suggestions on potential solutions to overcome existing limitations and outline future directions are provided.

An improved normal wiggly hesitant fuzzy FMEA model and its application to risk assessment of electric bus systems

An improved normal wiggly hesitant fuzzy FMEA model and its application to risk assessment of electric bus systems

PBAT-modified starch blended film extract induces in vitro toxicity in L-02 cells: induction of oxidative stress, inflammation, and modulation of AMPK pathway

PBAT-modified starch blended film are thermoplastic biodegradable materials with good properties and a wide range of applications. In this study, L-02 cells were used as an in vitro toxicity evaluation system for risk assessment of PBAT-modified starch films with migration studies obtained in different food simulants. Determination of total migration and organic matter revealed that the results were in accordance with the standard except for the total organic matter under 95% (v/v) ethanol food simulant which exceeded the standard. The CCK-8 assay showed that these compounds affect the cell viability of L-02 cells. It was observed that the compounds made the cells express increased AST, ALT, TNF-α, IL-6, IL-1β, and ROS, and decreased SOD, GSH, and ATP. In addition, we explored the effect of migration in PBAT-modified starch composites on protein and gene expression levels in L-02 cells using a transcriptomic approach and found that the AMPK signaling pathway was affected. The expression of AMPK signaling pathway-related proteins was detected by Western Blot, and the expression levels of p-AMPK/AMPK were found to be upregulated, and those of p-mTOR/mTOR, SIRT1, PGC-1α, NRF1 and TFAM were downregulated. The above data suggest that the compounds migrating into the PBAT-modified starch film when exposed to food may induce oxidative stress and inflammation in hepatocytes, and may cause damage to hepatocytes through the AMPK pathway.

Cardiorespiratory Response to Workload Volume and Ergonomic Risk: Automotive Assembly Line Operators’ Adaptations

Repetitive tasks can lead to long-term cardiovascular problems due to continuous strain and inadequate recovery. The automobile operators on the assembly line are exposed to these risks when workload volume changes according to the workstation type. However, the current ergonomic assessments focus primarily on observational and, in some cases, biomechanical methods that are subjective and time-consuming, overlooking cardiorespiratory adaptations. This study aimed to analyze the cardiorespiratory response to distinct workload volumes and ergonomic risk (ER) scores for an automotive assembly line. Sixteen male operators (age = 38 ± 8 years; BMI = 25 ± 3 kg·m2) volunteered from three workstations (H1, H2, and H3) with specific work cycle duration (1, 3, and 5 min respectively). Electrocardiogram (ECG), respiratory inductance plethysmography (RIP), and accelerometer (ACC) data were collected during their shift. The results showed significant differences from the first to the last 10 min, where H3 had its SDRRi reduced (p = 0.014), H1’s phase synchrony and H2’s coordination between thoracic and abdominal movements decreased (p < 0.001, p = 0.039). In terms of ergonomic risk, the moderate-high rank showed a reduction in SDRRi (p = 0.037) and moderate-risk activities had diminished phase synchrony (p = 0.018) and correlation (p = 0.004). Thus, the explored parameters could have the potential to develop personalized workplace adaptation and risk assessment systems.

Analysis of tunnel collapse disasters during operation and exploration of disaster damage mechanisms

Highway tunnel collapse during the operation period is a serious threat to the stability of tunnel operation. Thus, it is important to investigate the disaster-causing mechanism to prevent the occurrence of disasters. In this paper, based on 155 cases of domestic and foreign highway tunnel collapses during operation, we statistically analyze the causes of the collapses and the year of occurrence, and we explore the disaster-causing mechanism of tunnel collapses through numerical simulation. Tunnel collapses in operation period are affected by geological factors, lining factors, and accidental factors. The primary causes of tunnel collapses are determined to be the post-lining cavity, lining crack expansion, and fire. A single factor cannot lead to tunnel collapse, and the probability of collapse increases under the coupling of multiple factors. Through a numerical simulation method, the evolution mechanism of tunnel collapse caused by the post-lining cavity is analyzed, which lays the foundation for establishing the tunnel collapse prediction model and qualitative risk assessment system of highway tunnel collapse.

A Study on the Introduction of Impact Assessment System for Coastal Erosion

Recently, the impact of climate change with sea levels rise, abnormal high waves, and continuous construction of artificial structures such as ports and harbors, has led to an increasing trend in coastal erosion. In this study, the scope and method of Environmental Impact Assessment, Utilization of Sea Areas, Disaster Impact Assessment, and Risk Assessment of Coastal Disasters System, which are carried out during development projects and erosion reduction projects carried out on the coast, are analyzed to identify each problem. , we proposed a plan to introduce the Impact Assessment System for Coastal Erosion, which can minimize the impact of coastal erosion by deriving improvement measures.

Pre-operative pulmonary risk assessment in surgery patients

Aims: Postoperative pulmonary complications (PPC) that may develop after surgery are important causes of morbidity and mortality. PPCs cause a prolongation of hospital stays and an increase in hospitalization costs. The study aims to determine factors associated with PPCs to predict PPCs in surgical patients undergoing preoperative evaluation. Methods: A retrospective cohort study was conducted at Şişli Hamidiye Etfal Training and Research Hospital using data from 200 patients referred for preoperative pulmonary evaluation from anesthesia and surgery clinics. This study analyzed the characteristics and outcomes of patients with PPC and those without PPC. The Canet pulmonary risk scores are used for PPC in all preoperative surgery patients. The study’s primary endpoints are to determine the development of respiratory failure, bronchospasm/asthma, COPD exacerbation, atelectasis, pleural effusion, or pneumonia. The study also analyzed the effective respiratory function parameters for PPC development using a logistic regression model. Results: The total study population included 200 patients with a median age of 53.5 years (aged between 19-88), 103 (51.5%) of whom were female. PPCs were observed in 38% (n=76) of the study group. There was a statistically significant difference between the patients in terms of the development of postoperative pulmonary complications according to gender (higher in males, p=0.001) and smoking (p=0.0001). Preoperative oxygen saturation (SpO2) and FEV1/FVC ratio were significant predictors of PPC development, and complications were more frequent in low-saturated patients (p=0.0001, p=0.013 respectively). The relationship between SpO2 and PPC was confirmed via logistic regression analysis. A one-unit increase in saturation reduced the occurrence of postoperative respiratory complications by 0.645-fold. The cut-off value for the saturation value was 97.5%, with a sensitivity of 46.8% and a specificity of 71.1% [p=0.0001, 95% CI, (0.521-0.798)]. Conclusion: In this study, the Canet (ARISCAT) score, a preoperative evaluation scale validated in Turkey that predicts postoperative pulmonary complications and mortality, was used. The Canet risk score is a simple risk score with moderate discriminatory performance for predicting PPCs. It may be useful in identifying individual patients at high risk of PPC and in the design of future studies to evaluate interventions to prevent these complications. However, a customized preoperative risk assessment system is needed for each patient.

ПОЛ ПЛОДА В ОЦЕНКЕ РИСКА АНТЕНАТАЛЬНЫХ, ИНТРАНАТАЛЬНЫХ И РАННИХ НЕОНАТАЛЬНЫХ ПОТЕРЬ

Unfavorable demographic trends, combined with unstable dynamics of perinatal mortality rates, require improvement of the perinatal forecasting system. The search for new perinatal risk factors is also becoming relevant. The appearance of publications on the influence of sexual dimorphism on the course of pregnancy and childbirth indicates the need to study the possibility of using this factor in predicting perinatal losses. Objective of the study: to study the role of fetal sex as a risk factor for antepartum and intrapartum fetal death, early neonatal death of a newborn. Materials and research methods. A retrospective study was conducted with the participation of patients with antepartum (n=70) and intrapartum (n=40) fetal death, early neonatal death of a newborn (n=50) and a favorable perinatal outcome (n=50) as a control. We studied the structure of patients by fetal sex, conducted its comparative analysis in these groups, determined the prognostic value of fetal sex in antepartum, intrapartum fetal death and early neonatal death of a newborn. We used the program "Statistica 10.0" for statistical processing of the obtained data and the Bayes model of prediction with analysis by A. Wald, method of E.V. Gubler and the criterion of S. Kullback. Results. The male sex of the fetus prevailed over the female in all groups of perinatal risk. In the group with a favorable perinatal outcome, on the contrary, female fetuses were more common. We found a statistically significant difference in fetal sex between the antepartum risk and control groups: the male sex of the fetus occurred at antenatal losses 1.5 times more often than at favorable perinatal outcomes (p=0.028). We have calculated prognostic coefficients and indicators of the informativeness of fetal sex in antepartum, intrapartum and early neonatal losses. We have determined the prognostic value of fetal sex and established the nature of its effect on perinatal prognosis for each of the risk groups. Conclusion. The conducted study allows us to consider the sex of the fetus as a factor of differentiated risk of antepartum, intrapartum and early neonatal losses. The obtained results clearly demonstrate the prognostic role of fetal sex and indicate the need to include this factor in the perinatal risk assessment system at each stage of the perinatal period.

Accuracy of ultrasound in predicting thyroid malignancy: a comparative analysis of the ACR TI-RADS and ATA risk stratification systems.

Thyroid nodules are very common in clinical practice, and ultrasound has long been used as a screening tool for their evaluation. Several risk assessment systems based on ultrasonography have been developed to stratify the risk of malignancy and determine the need for fine-needle aspiration in thyroid nodules, including the American Thyroid Association (ATA) system and the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS). The aim of this study was to compare the performance of the ATA and ACR TI-RADS systems in predicting malignancy in thyroid nodules based on the nodules' final histopathology reports. We performed a retrospective review of medical records to identify patients who underwent thyroid surgery at King Abdulaziz University from 2017 to 2022. The ultrasound features of the nodules with confirmed histopathology (benign versus malignant) were evaluated. Both ATA and ACR TI-RADS scores were documented. The analysis included 191 patients who underwent thyroid surgery and fulfilled the inclusion criteria. Hemithyroidectomy was performed in 22.5% of the patients, and total thyroidectomy was performed in 77.0% of them. In all, 91 patients (47.6%) were found to have malignant nodules on histopathology. We then compared the histopathology reports with the preoperative ultrasonographic risk scores. The estimated sensitivity and specificity in identifying malignant nodules were, respectively, 52% and 80% with the ATA system and 51.6% and 90% with the ACR TI-RADS system. Both ATA and ACR TI-RADS risk stratification systems are valuable tools for assessing the malignancy risk in thyroid nodules. In our study, the ACR TI-RADS system had superior specificity compared with the ATA system in predicting malignancy among high-risk lesions.

Predict the Heart Attack Possibilities Using Machine Learning

Heart disease remains one of the leading causes of mortality worldwide, making early detection and prevention crucial. Machine learning techniques offer promising avenues for predicting heart attack possibilities by analyzing patient data and identifying risk factors. This study explores the development of a predictive model using machine learning algorithms to assess the likelihood of a heart attack based on individual patient characteristics and medical history. The dataset comprises a comprehensive range of features including demographic information, lifestyle factors, medical history, and results from diagnostic tests such as electrocardiograms (ECG), cholesterol levels, and blood pressure readings. Preprocessing techniques such as data cleaning, normalization, and feature engineering are applied to prepare the dataset for analysis. Looking ahead, the article identifies promising avenues for future research, including the integration of multimodal data sources, real-time risk assessment systems, and collaborative efforts to develop standardized benchmarks and evaluation protocols. By synthesizing the collective knowledge gleaned from decades of research, this historical review aims to inform and inspire ongoing endeavors in leveraging machine learning for proactive cardiovascular health management and prevention strategies.

Predicting Bank Loan Eligibility Using Machine Learning

With the increasing needs of people, the demand for bank loans also rises. Each day, banks receive numerous loan applications from various customers and individuals, however, not all applicants are approved. Banks typically assess an applicant's eligibility before processing a loan application, a process that can be time-consuming and complex. In evaluating loan applications and making credit decisions, banks often rely on their credit scoring and risk assessment systems. Nevertheless, there are still cases where some applicants default on their payments annually, resulting in significant financial losses for financial institutions. In this research, we utilized machine learning algorithms to analyze a dataset of approved loans and predict which applicants are most deserving of a loan. The study incorporates customers' historical data, including factors such as age, income type, loan annuity, last credit bureau report, organization type, and length of employment. Various machine learning methods like Random Forest, XGBoost, Adaboost, Lightgbm, Decision tree, and K-Nearest Neighbor were utilized to identify the most influential features that impact prediction outcomes. These algorithms were then compared using standard metrics, with Logistic Regression achieving the highest accuracy rate of 92%. Key Words: Bank Loans, Logistic Regression, Credit Report, Risk Assessment

Weight‐Reconfigurable Neuromorphic Computing Systems for Analog Signal Integration

AbstractOwing to the necessity of high computation amounts has emerged, interest in a neuromorphic computing system has significantly increased as a compelling alternative to conventional CMOS technology. This paper presents a neuromorphic hardware algorithm to finely reconfigure multi‐input signal processing, which can be implemented as an advanced processor for diverse external information, and the hydrogen explosion risk assessment system is demonstrated as a proof of concept. Hydrogen concentration and temperature are used as sensory inputs for the signal integration and the precise values of them are determined by offsetting the effect of temperature on the electrical signal from the hydrogen sensor through a sensor circuit. Each signal is then updated by the weight control circuit and converted into a postsynaptic current to represent the hydrogen explosion risk using a multi‐input artificial synapse. This simplicity of the circuitry renders the fabrication of all components and circuits compatible with simple inkjet printing methods, enabling cost‐effective and high‐throughput manufacturing. Additionally, the real‐time demonstration of the neuromorphic computing system is successfully conducted, offering insights into the practical application of neuromorphic computing.

Disparities in Maternal Health Visits Between Rural and Urban Communities in the United States, 2016-2018.

The objective was to estimate the rural-urban differences in the receipt of prepregnancy, prenatal, and postpartum services. The authors conducted a cross-sectional data analysis using data from the Pregnancy Risk Assessment and Monitoring System from 2016 to 2018 to analyze rural-urban differences in the receipt of medical visits and care content delivery during the prepregnancy year, as well as the prenatal and postpartum periods among birthing people in the US, using survey-weighted multivariable logistic regression models. Rural-dwelling birthing people were significantly less likely to attend a medical visit in the prepregnancy year or postpartum period, even when controlled for sociodemographic and clinical characteristics. Compared to their urban counterparts, they were also less likely to receive comprehensive screening and counseling in the prepregnancy and postpartum maternity phases. Efforts to ameliorate rural-urban differences in maternal care access and quality should explicitly adopt multilevel, systemic approaches to policy and program implementation and evaluation. Policymakers and practitioners should consider telehealth as a potential complementary tool to minimize gaps in quality of care which disproportionately impact rural-dwelling birthing people.

Predicting maritime accident risk using Automated Machine Learning

Machine learning (ML), particularly, Automated machine learning (AutoML) offers a range of possibilities for analysing large volumes of historical maritime accidents data with advanced algorithms for integrating predictive analytics in operational and policy decision-making for improving maritime safety. This study explores historical data of maritime accidents in Norwegian waters over 40 years. The data has been utilised for analysing five major maritime accident categories: grounding, contact damage, fire or explosion, collision, and heavy weather damage. A total of 29 classification ML algorithms were trained, and the Light Gradient Boosted Trees Classifier was found to be the best-performing with the highest predictive accuracy. The three most impactful factors for accident risk are the category of navigation waters, phase of operation, and gross tonnage of the vessel. Based on the feature effect results, vessels sailing in narrow coastal waters, in the along-the-way operational phase, and fishing vessels are highly vulnerable to grounding relative to other types of accidents. The results can be used as input for the entire procedure of risk analysis, from hazard identification to quantification of accident consequences, and the best-performing ML algorithm can be utilized in developing a decision support system for real-time maritime accident risk assessment.