Risk Assessment System Research Articles

An investigation of the molecular characterization of the tripartite motif (TRIM) family and primary validation of TRIM31 in gastric cancer

Most TRIM family members characterized by the E3-ubiquitin ligases, participate in ubiquitination and tumorigenesis. While there is a dearth of a comprehensive investigation for the entire family in gastric cancer (GC). By combining the TCGA and GEO databases, common TRIM family members (TRIMs) were obtained to investigate gene expression, gene mutations, and clinical prognosis. On the basis of TRIMs, a consensus clustering analysis was conducted, and a risk assessment system and prognostic model were developed. Particularly, TRIM31 with clinical prognostic and diagnostic value was chosen for single-gene bioinformatics analysis, in vitro experimental validation, and immunohistochemical analysis of clinical tissue microarrays. The combined dataset consisted of 66 TRIMs, of which 52 were differentially expressed and 43 were differentially prognostic. Significant survival differences existed between the gene clusters obtained by consensus clustering analysis. Using 4 differentially expressed genes identified by multivariate Cox regression and LASSO regression, a risk scoring system was developed. Higher risk scores were associated with a poorer prognosis, suppressive immune cell infiltration, and drug resistance. Transcriptomic data and clinical sample tissue microarrays confirmed that TRIM31 was highly expressed in GC and associated with a poor prognosis. Pathway enrichment analysis, cell migration and colony formation assay, EdU assay, reactive oxygen species (ROS) assay, and mitochondrial membrane potential assay revealed that TRIM31 may be implicated in cell cycle regulation and oxidative stress-related pathways, contribute to gastric carcinogenesis. This study investigated the whole functional and expression profile and a risk score system based on the TRIM family in GC. Further investigation centered around TRIM31 offers insight into the underlying mechanisms of action exhibited by other members of its family in the context of GC.

Review on Risk Assessment of Power System under Rainstorm-Flood and Disaster Control Improvement Strategies

Abstract The global warming and El Nino effects are becoming more and more obvious now. These result in frequent rainstorms, especially in coastal areas. It is necessary to deeply study evolution mechanism of rainstorm-flood disasters, assess disaster risk of power grid, and formulate efficient power grid flood control strategies. Firstly, the research progress of disaster modeling is summarized according to the disaster evolution mechanism. The differences between hydrological models and machine learning models are compared, and the possibility of combining them is discussed. Secondly, current disaster risk assessment methods of power grid and their application in rainstorm-flood disasters to quantify flood prevention capacity are reviewed. Thirdly, the power grid flood control measures are studied from the perspective of pre-disaster grid reinforcement and post-disaster repair to improve resilience. Finally, in view of the above content, the shortcomings of current researches and feasible research directions in the future are proposed.

Cloud-based machine learning algorithms for anomalies detection

Gradient boosting machines harnesses the inherent capabilities of decision trees and meticulously corrects their errors in a sequential fashion, culminating in remarkably precise predictions. Word2Vec, a prominent word embedding technique, occupies a pivotal role in natural language processing (NLP) tasks. Its proficiency lies in capturing intricate semantic relationships among words, thereby facilitating applications such as sentiment analysis, document classification, and machine translation to discern subtle nuances present in textual data. Bayesian networks introduce probabilistic modeling capabilities, predominantly in contexts marked by uncertainty. Their versatile applications encompass risk assessment, fault diagnosis, and recommendation systems. Gated recurrent units (GRU), a variant of recurrent neural networks, emerges as a formidable asset in modeling sequential data. Both training and testing are crucial to the success of an intrusion detection system (IDS). During the training phase, several models are created, each of which can recognize typical from anomalous patterns within a given dataset. To acquire passwords and credit card details, "phishing" usually entails impersonating a trusted company. Predictions of student performance on academic tasks are improved by hyper parameter optimization of the gradient boosting regression tree using the grid search approach.

Analysis and Improvement of the Risk Assessment System Based on the Risk Priority Number

Analysis and Improvement of the Risk Assessment System Based on the Risk Priority Number

Development of an Integrated Hybrid Risk Assessment System for Construction Disputes during the Preconstruction Phase Using the Delphi Method

Development of an Integrated Hybrid Risk Assessment System for Construction Disputes during the Preconstruction Phase Using the Delphi Method

STRIDE-Based Cybersecurity Threat Modeling, Risk Assessment and Treatment of an In-Vehicle Infotainment System

In modern automobiles, the infotainment system is crucial for enhancing driver and passenger capabilities, offering advanced features such as music, navigation, communication, and entertainment. Leveraging Wi-Fi, cellular networks, NFC, and Bluetooth, the system ensures continuous internet connectivity, providing seamless access to information. However, the increasing complexity of IT connectivity in vehicles raises significant cybersecurity concerns, including potential data breaches and exposure of sensitive information. To enhance security in infotainment systems, this study applied component-level threat modeling to a proposed infotainment system using the Microsoft STRIDE model. This approach illustrates potential component-level security issues impacting privacy and security concerns. The study also assessed these impacts using SAHARA and DREAD risk assessment methodologies. The threat modeling process identified 34 potential security threats, each accompanied by detailed information. Moreover, a comparative analysis is performed to compute risk values for prioritizing treatment, followed by recommending mitigation strategies for each identified threat. These identified threats and associated risks require careful consideration to prevent potential cyberattacks before deploying the infotainment system in automotive vehicles.

Hydrological Drought Risk Assessment and Its Spatial Transmission Based on the Three-Dimensional Copula Function in the Yellow River Basin

Administrative strategies to cope with drought are steadily changing, from emergency procedures to day-to-day monitoring. More consideration must be paid to long-term and preventive drought control measures in the future. This paper discusses the risk of hydrological drought in the Yellow River Basin. The standardized runoff index (SRI) was used to characterize hydrological drought, and the run theory was used to identify drought states and quantify drought characteristic variables. Based on the drought severity and duration, a drought development plan was proposed and a three-dimensional copula function was constructed to obtain the joint distribution function of three-dimensional drought characteristic variables. A drought risk assessment system based on the loss × probability risk theory was constructed to explore the spatial and temporal characteristics of hydrological drought risk in the Yellow River Basin. Finally, according to the risk assessment results, the risk level was divided into low, medium and high risk, and a Bayesian network was used to explore the probability of hydrological drought. The main results are as follows: (1) From 1960 to 2018, the severity of hydrological drought in the Yellow River Basin increased, the duration lengthened, and the development speed accelerated. (2) The hydrological drought risk in the Yellow River Basin showed an overall upward trend, with the fastest increase in the HJ region of 0.041/10a. The highest annual average drought risk in the TDG region is 0.598. (3) The spatial transmission of hydrological drought risk is divided into three types: constant, enhanced and mitigation types, of which the constant type is the most common. The transmission probabilities of low, medium and high risk of hydrological drought from the HYK region to the low, medium and high risk of hydrological drought in the LJ region are 0.68, 0.66 and 0.78, respectively.

A novel web-based risk calculator for predicting surgical site infection in HIV-positive facture patients: a multicenter cohort study in China.

Surgical site infection (SSI) is a common complication in HIV-positive fracture patients undergoing surgery, leading to increased morbidity, mortality, and healthcare costs. Accurate prediction of SSI risk can help guide clinical decision-making and improve patient outcomes. However, there is a lack of user-friendly, Web-based calculator for predicting SSI risk in this patient population. This study aimed to develop and validate a novel web-based risk calculator for predicting SSI in HIV-positive fracture patients undergoing surgery in China. A multicenter retrospective cohort study was conducted using data from HIV-positive fracture patients who underwent surgery in three tertiary hospitals in China between May 2011 and September 2023. We used patients from Beijing Ditan Hospital as the training cohort and patients from Chengdu Public Health and Changsha First Hospital as the external validation cohort. Univariate, multivariate logistic regression analyses and SVM-RFE were performed to identify independent risk factors for SSIs. A web-based calculator was developed using the identified risk factors and validated using an external validation cohort. The performance of the nomogram was evaluated using the area under the receiver operating characteristic (AUC) curves, calibration plots, and decision curve analysis (DCA). A total of 338 HIV-positive patients were included in the study, with 216 patients in the training cohort and 122 patients in the validation cohort. The overall SSI incidence was 10.7%. The web-based risk calculator (https://sydtliubo.shinyapps.io/DynNom_for_SSI/) incorporated six risk factors: HBV/HCV co-infection, HIV RNA load, CD4+ T-cell count, Neu and Lym level. The nomogram demonstrated good discrimination, with an AUC of 0.890 in the training cohort and 0.853 in the validation cohort. The calibration plot showed good agreement between predicted and observed SSI probabilities. The DCA indicated that the nomogram had clinical utility across a wide range of threshold probabilities. Our study developed and validated a novel web-based risk calculator for predicting SSI risk in HIV-positive fracture patients undergoing surgery in China. The nomogram demonstrated good discrimination, calibration, and clinical utility, and can serve as a valuable tool for risk stratification and clinical decision-making in this patient population. Future studies should focus on integrating this nomogram into hospital information systems for real-time risk assessment and management.

A neuro-fuzzy security risk assessment system for software development life cycle

This study aims to protect software development by creating a Software Risk Assessment (SRA) model for each phase of the Software Development Life Cycle (SDLC) using an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. Software developers discovered and validated the risk variables affecting each SDLC phase, following which relevant data about risk factors and associated SRA for each SDLC phase were collected. To create the SRA model for SDLC phases, risk factors were used as inputs, and SRA was used as an output. The formulated model was simulated using 70 % and 80 % of the data for training, while 30 % and 20 % were used for testing the model. The performance of the SRA models using the test datasets was evaluated based on accuracy. According to the study findings, many risk variables were discovered and confirmed for the requirement, design, implementation, integration, and operation phases of SDLC 11, 8, 9, 4, and 6, respectively. The SRA model was formulated using the risk factors using 2048, 256, 512, 16, and 64 inference rules for the requirement, design, implementation, integration, and operation phases, respectively. The study concluded that using the SRA model to assess security risk at each SDLC phase provided a secured software development process.

Intelligent Systems Integrating BIM and VR for Urban Subway Microenvironmental Health Risks Management

With the rapid development and construction of urban subways, various risks associated with human health and wellbeing within subway microenvironments have seriously increased. However, only a few intelligent systems have been validated as suitable for facilitating the management of subway environments. Field tests can be time-consuming and inefficient, and questionnaires often lack true intuitiveness for participants. Therefore, to enhance subway environment management, this study proposed intelligent systems that integrate building information modeling (BIM) and virtual reality (VR) for managing health risks in urban subway microenvironments. The systems were developed using Revit 2021, Navisworks 2020, Unity 2019, MYSQL 8.0, and Visual Studio 2019. Additionally, they were applied in scenarios for environmental assessment and passenger coping capability enhancement, differentiated into an expert visual-based health risk assessment system and a gamified simulation system for passenger risk prevention. The feasibility of the approach was validated with the case of Xinzhuang subway station of Line 3 in Nanjing, China. The findings revealed that the assessment system enabled experts to have a better and straight understanding of subway microenvironmental health risks and the gamification simulation system significantly enhanced the passengers’ coping capacity. The integration of BIM and VR, with its design features such as visibility, optimization, and simulation, compensated for BIM’s lack of providing an immersive experience for systems. The intelligent systems introduced in this study present novel models for environmental assessment and passenger training, catering to both subway operators and researchers. The innovative systems serve as a cornerstone in guaranteeing the health and safety of public transportation operations.

A graph embedding‐based approach for automatic cyber‐physical power system risk assessment to prevent and mitigate threats at scale

AbstractPower systems are facing an increasing number of cyber incidents, potentially leading to damaging consequences to both physical and cyber aspects. However, the development of analytical methods for the study of large‐scale power infrastructures as cyber‐physical systems is still in its early stages. Drawing inspiration from machine‐learning techniques, the authors introduce a method inspired by the principles of graph embedding that is tailored for quantitative risk assessment and the exploration of possible mitigation strategies of large‐scale cyber‐physical power systems. The primary advantage of the graph embedding approach lies in its ability to generate numerous random walks on a graph, simulating potential access paths. Meanwhile, it enables capturing high‐dimensional structures in low‐dimensional spaces, facilitating advanced machine‐learning applications, and ensuring scalability and adaptability for comprehensive network analysis. By employing this graph embedding‐based approach, the authors present a structured and methodical framework for risk assessment in cyber‐physical systems. The proposed graph embedding‐based risk analysis framework aims to provide a more insightful perspective on cyber‐physical risk assessment and situation awareness for power systems. To validate and demonstrate its applicability, the method has been tested on two cyber‐physical power system models: the Western System Coordinating Council (WSCC) 9‐Bus System and the Illinois 200‐Bus System, thereby showing its advantages in enhancing the accuracy of risk analysis and comprehensiveness of situational awareness.

Perspectives on Artificial Intelligence in Nursing in Asia.

Artificial intelligence (AI) is reshaping health care, including nursing, across Asia, presenting opportunities to improve patient care and outcomes. This viewpoint presents our perspective and interpretation of the current AI landscape, acknowledging its evolution driven by enhanced processing capabilities, extensive data sets, and refined algorithms. Notable applications in countries such as Singapore, South Korea, Japan, and China showcase the integration of AI-powered technologies such as chatbots, virtual assistants, data mining, and automated risk assessment systems. This paper further explores the transformative impact of AI on nursing education, emphasizing personalized learning, adaptive approaches, and AI-enriched simulation tools, and discusses the opportunities and challenges of these developments. We argue for the harmonious coexistence of traditional nursing values with AI innovations, marking a significant stride toward a promising health care future in Asia.

Reliability and risk assessment of digital twin system based on improved failure mode and effects analysis

Reliability and risk assessment of digital twin system based on improved failure mode and effects analysis

Nano- and microplastics drive the dynamic equilibrium of amoeba-associated bacteria and antibiotic resistance genes

As emerging pollutants, microplastics have become pervasive on a global scale, inflicting significant harm upon ecosystems. However, the impact of these microplastics on the symbiotic relationship between protists and bacteria remains poorly understood. In this study, we investigated the mechanisms through which nano- and microplastics of varying sizes and concentrations influence the amoeba-bacterial symbiotic system. The findings reveal that nano- and microplastics exert deleterious effects on the adaptability of the amoeba host, with the magnitude of these effects contingent upon particle size and concentration. Furthermore, nano- and microplastics disrupt the initial equilibrium in the symbiotic relationship between amoeba and bacteria, with nano-plastics demonstrating a reduced ability to colonize symbiotic bacteria within the amoeba host when compared to their microplastic counterparts. Moreover, nano- and microplastics enhance the relative abundance of antibiotic resistance genes and heavy metal resistance genes in the bacteria residing within the amoeba host, which undoubtedly increases the potential transmission risk of both human pathogens and resistance genes within the environment. In sum, the results presented herein provide a novel perspective and theoretical foundation for the study of interactions between microplastics and microbial symbiotic systems, along with the establishment of risk assessment systems for ecological environments and human health.

Leakage risk assessment on sealing efficiency of caprock during CO2 huff-n-puff for safe sequestration

CO2 geological sequestration is the most realistic and feasible technology to ensure large-scale carbon emission reduction to achieve the global carbon capping and carbon neutrality goals, and the leakage risk assessment of the sequestration system is the basis of safe sequestration. However, at present, leakage risk assessment mainly focuses on the simulation and research of ultra-long-term (more than 1000 years) storage process, and few scholars has studied the impact of development operations, such as CO2 huff-n-puff, on caprock leakage risk. In this paper, a numerical model of CO2 huff-n-puff that incorporates gas adsorption/desorption, diffusion, and hydrochemical reaction is established, and the leakage risk of caprock during CO2 huff-n-puff in tight reservoir is evaluated. The model's reliability is verified by matching with the production history and CO2 mole fraction of commercial software. Finally, the effects of parameters, including adsorption/desorption, diffusion, hydrochemical reaction, caprock permeability, caprock-to-reservoir thickness ratio, and CO2 injection rate on leakage risk of caprock during CO2 huff-n-puff, are analyzed. The results show that after 15 consecutive cycles of huff-n-puff operations, the escape ratio of CO2 in the caprock is less than 2%, and the injection rate of each cycle is 1 × 105 m3/d for 120 days. It is proved that huff-n-puff operation improves the recovery of tight gas reservoirs and has little effect on the sealing efficiency of caprock sealed for a long time in the later stage. This work provides a theoretical basis for the leakage risk assessment of CO2 geological sequestration after development operations.

A Multi-Level Deep Neural Network-Based Tourism Supply Chain Risk Management Study

With the rapid advancement of the tourism, the capital demand of tourism enterprises has gradually risen, but the confusion of market management has increased the difficulty of risk assessment of tourism enterprises by financial institutions, which has led to difficulties in financing tourism enterprises and seriously hindered their development. On this basis, this research first analyses the risk structure of the tourism supply chain (TSC) from the financial aspect and establishes a relevant risk assessment system. After confirming the assessment indexes, the data is dativized and normalized, and finally a multi-level deep neural network (DNN) is used to construct a TSC risk prediction model to calculate the transformed indexes and assess the risk degree of the enterprise according to the results. The experimental results indicate that the model has the best performance when the H-Net hidden layer is 3 layers and the L-Net hidden layer is 4 layers, and its accuracy reaches 93.35%, sensitivity reaches 84.13%, convergence starts at 25 iterations, the final loss value is only 0.8, and the predicted and the real value error is within 2.5%. Therefore, the multi-level DNN model constructed in this experiment has certain application value in TSC risk management.

Safety risk assessment for automotive battery pack based on deviation and outlier analysis of voltage inconsistency

Safety risk assessment is essential for evaluating the health status and averting sudden battery failures in electric vehicles. This study introduces a novel safety risk assessment approach for battery systems, addressing both cell and pack levels with three key indexes. The core of the assessment lies in representing the relative deviation of cell voltages through scatter diagrams across various stages of service life. Specifically, the study quantifies voltage deviations and deviation angles within different state of charge intervals to gauge safety risks at the cell level. Leveraging clustering algorithms aids in identifying outlier values. Furthermore, the dispersion of scatter points is utilized to assess safety risks at the pack level. Validation of this proposed model is conducted through cycling tests on battery modules with a deformed cell, demonstrating its efficacy in capturing inconsistent features of mechanical deformation abuse across the three indexes, thereby triggering alarms during operation. Moreover, the method is applied to assess the safety risks of five hazardous and accident-prone vehicles, enabling comprehensive evaluation of potential faulty cells and safety risks at the pack level. This proposed approach offers a fresh perspective on the comprehensive safety risk assessment of battery systems.

Machine-learning-based models assist the prediction of pulmonary embolism in autoimmune diseases: A retrospective, multicenter study.

Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD. In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort. In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone. ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE. Chictr.org.cn : ChiCTR2200059599.

An Ergonomic Risk Assessment System Based on 3D Human Pose Estimation and Collaborative Robot

Human pose estimation focuses on methods that allow us to assess ergonomic risk in the workplace and aims to prevent work-related musculoskeletal disorders (WMSDs). The recent increase in the use of Industry 4.0 technologies has allowed advances to be made in machine learning (ML) techniques for image processing to enable automated ergonomic risk assessment. In this context, this study aimed to develop a method of calculating joint angles from digital snapshots or videos using computer vision and ML techniques to achieve a more accurate evaluation of ergonomic risk. Starting with an ergonomic analysis, this study explored the use of a semi-supervised training method to detect the skeletons of workers and to estimate the positions and angles of their joints. A criticality index, based on RULA scores and fuzzy rules, is then calculated to evaluate possible corrective actions aimed at reducing WMSDs and improving production capacity using a collaborative robot that supports workers in carrying out critical operations. This method is tested in a real industrial case in which the manual assembly of electrical components is conducted, achieving a reduction in overall ergonomic stress of 13% and an increase in production capacity of 33% during a work shift. The proposed approach can overcome the limitations of recent developments based on computer vision or wearable sensors by performing an assessment with an objective and flexible approach to postural analysis development.

Risk Assessment and Analysis of New Power System Operation Under Extreme Disasters

Abstract In recent years, new power systems have been developing continuously, and the operational risk assessment of new power systems under extreme disaster conditions has attracted much attention. This paper uses Latin hypercube sampling technology to establish a power system failure model under extreme disasters and proposes a new power system risk assessment method that applies the power system power flow model to establish a comprehensive system risk index. In addition, taking the power grid of a certain province in eastern China as an example, a case analysis is conducted on the IEEE39 node system to verify the effectiveness of the method.