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Risk management has always been a trend in manufacturing related literature in the era of zero-defect manufacturing (ZDM). However, a gap still exists to present a holistic viewpoint of the integration for a product and its related processes involved during decision-making in manufacturing industry. The (knowledge-driven) integrated-decision support system indicates the opportunity by integrating the product design and manufacturing processes related risks in a manufacturing industry to make better decisions at the shop floor. It further proposes a direction towards development of a decision support system framework for their respective risks' identification as well as mitigation to enhance the quality, while minimizing time and cost. Over the years, risk identification has been considered well but risk mitigation has mostly been overlooked in the published literature. This paper scanned over a thousand papers from renowned journals published between 2005 and 2024. Currently, the evolution involved in the advancement of decision support tools for risk management has been reviewed by utilizing systematic literature review methodology. The study also provides a design overview, highlighting its features, pros, and cons of the existing methods which can be used for risk identification, prioritization, and mitigation in the development of a dynamic decision support system to aim (data-driven) zero-defect manufacturing (ZDM). Lastly, the paper discusses the current challenges and opportunities to lessen the manufacturing recalls in the industry, followed by phases of the proposed model. [ABSTRACT FROM AUTHOR]

Objectives: This bibliometric analysis was conducted to determine the trends of studies on cardiovascular disease risk identification and prevention in primary care from 2013 to 2024 and visualize the latest developments. Methods: The data were collected in February-March 2024 from the database "Web of Science Core Collection," the analysis was carried out using the VOSviewer program. The change in the number of publications of the published articles by year, author, country, and institution citation analyses, country, institution, and author collaboration analyses, journal and author co-citation analyses, and keyword analyses were evaluated. Results: Five hundred and ninety-two authors from 64 countries and 377 institutions contributed to 443 studies published in 80 journals between 2013 and 2024 on determining and preventing cardiovascular disease risk in primary care. "BMC Family Practice" was the journal in which most articles were published, and "Circulation" was the most cited. The first three countries that support published articles most are the United States, England, and Australia. Focusing on the topics "blood-pressure control", "coronary-artery calcium", "physician-pharmacist collaboration", "low-density lipoprotein cholesterol", "health-risk assessment", "pollution", "primary care", "coronary heart disease", "prevention", "cardiovascular disease" and "mortality" will help fill the gap in the field. Conclusions: This bibliometric analysis has shown increasing interest in studies related to cardiovascular disease risk and prevention in primary care. Primary prevention guidelines are important resources in addressing risk factors. Global collaborations and long-term studies are necessary in this field, led by developed countries with a high disease burden. [ABSTRACT FROM AUTHOR]

This study identifies the risks entailed in a service-oriented supply chain. By employing supply chain mapping, contingency, and Risk Breakdown Structure (RBS) theories to elaborate a picture of the current knowledge, we were able to summarise and categorise the various risks into three main levels of cause. Next, these literature-based risks were narrowed down into a specific industry context and were then tested and confirmed using exploratory factor and confirmatory factor analyses to form an industry-oriented RBS for the construction business. The data were collected from 195 firms who participated in a large-scale survey sponsored by the Japanese Government to promote sustainable socio-economic development for the ASEAN region. As a result, seven risk categories related to demand, supply, operations, information, finance, time (delays) and external sources (human-made or natural-related) were identified and will be a valuable reference for researchers and practitioners. Furthermore, the relationship between identified risk and supply chain performance was explored. This paper is the first to introduce the concept and characteristics of a service-oriented supply chain by using a systematic review combined with a bibliometric mapping of the literature and an empirical study to generate both generic and industry-oriented RBSs in a single study. [ABSTRACT FROM AUTHOR]

Public transit has been continuously developing because of advocacy for low-carbon living, and concerns about its safety have gained prominence. The various factors that constitute the bus operating environment are extremely complex. Although existing research on operational security is crucial, previous studies often fail to fully represent this complexity. In this study, a novel method was proposed to identify the risk factors for bus operations based on a Bayesian network. Our research was based on monitoring data from the public transit system. First, the Tabu Search algorithm was applied to identify the optimal structure of the Bayesian network with the Bayesian Information Criterion. Second, the network parameters were calculated using bus monitoring data based on Bayesian Parameter Estimation. Finally, reasoning was conducted through prediction and diagnosis in the network. Additionally, the most probable explanation of bus operation spatial risk was identified. The results indicated that factors such as speed, traffic volume, isolation measures, intersections, bus stops, and lanes had a significant effect on the spatial risk of bus operation. In conclusion, the study findings can help avert dangers and support decision-making for the operation and management of public transit in metropolitan areas to enhance daily public transit safety. [ABSTRACT FROM AUTHOR]

Aims: We assessed care in cancer patients with cachexia across leading health systems (LHSs). Patients & methods: Qualitative interviews and quantitative surveys were conducted with LHSs executives and frontline health care personnel, representing 46 total respondents and 42 unique LHSs and including oncology service line leaders, supportive care services, dietitians and surgical oncologists. Results: Cachexia was not considered a top priority, and formal diagnoses were rare. Participants highlighted the importance of addressing barriers to increase clinical trial enrollment and support frontline health care personnel and patients in early detection of cachexia. Conclusion: Cachexia prioritization needs to be elevated across LHSs executives to obtain capital and strategic imperatives to advance related care. Article Highlights Despite its widespread prevalence and its impact on the care of patients with cancer, cachexia is underdiagnosed and is often untreated or undertreated. We report findings from an assessment of cachexia care in cancer patients with cachexia across leading health systems (LHSs) through an analysis of LHSs C-Suite executives, enterprise-wide service line leaders, and frontline healthcare personnel (HCP) perspectives. We also assessed perspectives from oncology service line leaders, supportive care services, dietitians and surgical oncologists. We conducted qualitative interviews and administered a quantitative survey to LHSs executives and frontline HCP, representing 46 total respondents and 42 unique LHSs. Cachexia was not considered a top priority, and formal diagnoses were rare. Participants highlighted the importance of addressing barriers to increase clinical trials enrollment, supporting frontline HCP and patients in early detection of cachexia. The prioritization of cachexia needs to be elevated across C-Suite executives. To elevate cachexia as a priority among C-Suite executives, it is crucial that service line leaders demonstrate the value of investing in cachexia care and resources to LHSs. [ABSTRACT FROM AUTHOR]

Simple Summary: Spatial epidemiology, integrating traditional epidemiology, geography, statistics, environmental science, and ecology, provides a comprehensive framework for analyzing the spatial dimensions of health and disease. This interdisciplinary approach enhances the development of effective public health strategies and interventions. However, its multifaceted nature can bring complexities in practical application. Using the case of spatial epidemiology in swine viral diseases (SVDs), we illustrate the objectives, methodologies, and essential considerations for the application of spatial epidemiology, which we hope to offer as a comprehensive reference for researchers in this field. Spatial epidemiology offers a comprehensive framework for analyzing the spatial distribution and transmission of diseases, leveraging advanced technical tools and software, including Geographic Information Systems (GISs), remote sensing technology, statistical and mathematical software, and spatial analysis tools. Despite its increasing application to swine viral diseases (SVDs), certain challenges arise from its interdisciplinary nature. To support novices, frontline veterinarians, and public health policymakers in navigating its complexities, we provide a comprehensive overview of the common applications of spatial epidemiology in SVD. These applications are classified into four categories based on their objectives: visualizing and elucidating spatiotemporal distribution patterns, identifying risk factors, risk mapping, and tracing the spatiotemporal evolution of pathogens. We further elucidate the technical methods, software, and considerations necessary to accomplish these objectives. Additionally, we address critical issues such as the ecological fallacy and hypothesis generation in geographic correlation analysis. Finally, we explore the future prospects of spatial epidemiology in SVD within the One Health framework, offering a valuable reference for researchers engaged in the spatial analysis of SVD and other epidemics. [ABSTRACT FROM AUTHOR]

To mitigate accidents in submarine cable construction within the rapidly expanding offshore wind power sector, this study employed the analytic hierarchy process (AHP) and risk matrix method (LS) to assess the risks associated with identified factors. Based on project research and expert consultations, five primary and twenty-two secondary risk factors were identified. AHP was utilized to rank the primary risk factors by severity, probability, and detection difficulty, with the highest risk being the environmental impact, followed by third-party destruction and worker error. LS was applied to rank the secondary risk factors by likelihood and severity, with the highest risks being complex submarine topography, low underwater visibility, and fishing operations. The study proposes risk reduction measures based on these evaluations and offers methodological guidance for improving construction safety in similar enterprises. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To improve the safety level of pile foundation construction for offshore wind power, in this study, the risk indicators of pile foundation construction were evaluated using the analytic hierarchy process (AHP) and comprehensive evaluation methods. The pile foundation construction operation process for offshore wind power mainly includes four phases: preparation for construction, pile sinking, end of construction, and foundation scour protection construction. Pile foundation construction risk indicators are systematically identified as human factors, material factors, management factors, and environmental factors. The most important indicators for pile foundation construction for offshore wind power were evaluated using AHP and comprehensive evaluation methods, which included five indicators: piling equipment, protective equipment, special skills, safety awareness, and emergency management. The four more important indicators are workplace environment, lifting equipment, fire protection systems, and operations. According to the results of our evaluation of the pile foundation construction safety indicators presented herein, corresponding recommendations are made that consider four aspects—human factors, material factors, management factors, and environmental factors. The construction industry should focus on improving the safety measures related to aspects with greater risk indicators. Pile foundation construction for offshore wind power can be evaluated using the method discussed in this paper, allowing industry stakeholders to prioritize and focus on improving safety measures related to aspects with greater risk indicators. [ABSTRACT FROM AUTHOR]

Introduction Sport and tactical populations are often impacted by musculoskeletal injury. Many publications have highlighted that risk is correlated with multiple variables. There do not appear to be existing studies that have evaluated a predetermined combination of risk factors that provide a pragmatic model for application in tactical and/or sports settings. Purpose To develop and test the predictive capability of multivariable risk models of lower extremity musculoskeletal injury during cadet basic training at the U.S.Military Academy. Materials and Methods Cadets from the class of 2022 served as the study population. Sex and injury history were collected by questionnaire. Body Mass Index (BMI) and aerobic fitness were calculated during testing in the first week of training. Movement screening was performed using the Landing Error Scoring System during week 1 and cadence was collected using an accelerometer worn throughout initial training. Kaplan–Meier survival curves estimated group differences in time to the first musculoskeletal injury during training. Cox regression was used to estimate hazard ratios (HRs) and Akaike Information Criterion (AIC) was used to compare model fit. Results Cox modeling using HRs indicated that the following variables were associated with injury risk : Sex, history of injury, Landing Error Scoring System Score Category, and Physical Fitness Test (PT) Run Score. When controlling for sex and history of injury, amodel including aerobic fitness and BMI outperformed the model including movement screening risk and cadence (AIC: 1068.56 vs. 1074.11) and a model containing all variables that were significant in the univariable analysis was the most precise (AIC: 1063.68). Conclusions In addition to variables typically collected in this tactical setting (Injury History, BMI, and aerobic fitness), the inclusion of kinematic testing appears to enhance the precision of the risk identification model and will likely continue to be included in screening cadets at greater risk. [ABSTRACT FROM AUTHOR]

The risk control model of the power supply chain system is established. A fault information identification method based on fuzzy clustering is proposed. This method fully considers the power grid's characteristics and uses terrible data. A risk assessment model based on fuzzy set theory is established by the COWA operator weight method and grey cluster evaluation method. The security risk identification model of power grid enterprises uses insufficient data. The security risk identification data are normalized and classified. Empirical analysis determines various risk factors that may appear in power projects. The applicability and feasibility of the index system and evaluation model are verified. [ABSTRACT FROM AUTHOR]

BACKGROUND: Safety design covers proactive actions as it analyzes accident risks early in the enterprise life cycle, and considers the designer acting on accident prevention as a member of the construction team. OBJECTIVE: This paper proposes an accident investigation to establish links between accident causes and design to support Prevention through Design (PtD) tools. METHODS: This article analyzed more than a thousand severe and fatal accident cases in the construction sector. A systematic analysis method was structured based on descriptions of accident causes and measures that could be taken to avoid accidents. RESULTS: Analyzing the severe and fatal accidents, the safety measures implemented in the project design could avoid at least 23.6% of the events. As a result, the architectural and structural designs were more effective in accident prevention. The reference percentages and the design types that are more effective in preventing accidents are analyzed through a representative sample of the analysis of the accident. CONCLUSIONS: This research contributes to applying safety guidelines in design projects, directly assisting in project and construction management. [ABSTRACT FROM AUTHOR]

Enterprise risk management as a key element of the internal control system has become important over the years. The changes in the conditions of companies, especially banks, have increased the scope and complexity of their risk, and this has led to an increase in the demand for using enterprise risk management as an enterprise risk management framework. As the most important financial institution in the money market, the bank faces various risks. The implementation of this system in the bank makes the process of identifying, evaluating and monitoring risks with a comprehensive approach. The purpose of this study is to develop a comprehensive risk management implementation framework in the banking industry. Despite the growing importance of enterprise risk management, its implementation in Iran has received little attention and is facing challenges, and no study has been conducted to investigate its implementation conditions, effective factors, necessary foundations, and existing obstacles. Therefore, the current research has developed a framework for implementing enterprise risk management in the banking industry by using qualitative approach and systematic review and multiple case study methods by examining two banks. The statistical population of this research includes the board of directors, senior managers, members of specialized committees and experienced experts. The results of the research show that the extracted framework of enterprise risk management is not fully implemented in Iranian banks, and the challenges of its implementation include the lack of information technology infrastructure, the lack of sufficient knowledge and government restrictions. The current research can be considered as a suitable model for the banking industry.

Abstract Ensuring workplace safety for healthcare workers is vital considering the important role they play in various societies which is to save life. Healthcare workers face different risks when performing tasks in various departments within hospitals, hence there is a need to assess work safety analysis procedures among healthcare workers. As a result, this study aims to assess the effectiveness of work safety analysis procedures among healthcare workers at Muvonde and Driefontein Sanatorium rural hospitals in Chirumanzu district. The research applied the descriptive cross-sectional design, combining quantitative and qualitative data collection methods. A questionnaire with both closed and open ended questionnaire was used for data collection among 109 healthcare workers at Muvonde hospital and 68 healthcare workers at Driefontein Sanatorium hospital. Secondary data sources, observations and interviews were also included as data collection methods. Quantitative data collected during the study was analysed using SPSS version 25. Braun and Clarke (2006)’s six phase framework was applied for qualitative data analysis. Ethical approval form was obtained from the District Medical Officer and Midlands State University. Findings of the study indicated that risks identified at Muvonde and Driefontein Sanatorium rural hospitals are classified as ergonomic, physical, chemical, psychosocial and biological risks. Respondents specified that these risks occur as a result of inadequate equipment, poor training, negative safety behaviour, poor management and pressure due to high workload. Safety inspection, safety workshops and monitoring of worker’s safety behaviour were mentioned as measures to manage risks. However, the strengths and weaknesses of the current safety procedures need to be assessed to highlight areas for improvement to reduce occurrence of risks within the hospitals.

Abstract The quantitative characterization of rock mass and stress changes induced by mining activities is crucial for structural stability monitoring and disaster early warning. This paper investigates the time–space–intensity distribution of microseismic sources during the pillar-free large-area continuous extraction. Furthermore, it explores a method involving collaborative evolution patterns of the velocity field and spatial b-value to identify stress and structural changes at the panel stope. Results show that anomalous zones in wave velocities and b-values form at the intersections of extraction drifts, strike drifts, cross drifts, and connection roadways influenced by mining activities, as well as in footwall ore-rock contacts, often accompanied by the nucleation of microseismic events. The synergistic use of wave velocity fields and spatial b-value models reveals the relationship between stress migration behavior and stope structure changes due to mining disturbances. The velocity field primarily reflects macroscopic changes in the structure and stress distribution, while spatial b-values further explain stress gradients in specific areas. Additionally, we have advanced the identification of an instability disaster at the connection roadway and cross drift intersection based on increases in wave velocity and abnormal changes in b-value. This paper demonstrates the potential of risk identification using the proposed method, providing insights into predicting geotechnical engineering disasters in complex stress environments.

Aircraft manufacturers have been striving to reduce quality and safety incidents during aircraft manufacturing，operating，maintenance phases. If only using the Process Failure Mode Effects Analysis（PFMEA）method for quality risk identification and assessment，there are problems such as the analysis does not include the aircraft use and maintenance process，the failure mode recognition rate and accuracy are not high，and the recommended measures are imperfect. The quality risk identification and evaluation method proposed in this paper expands the PFMEA method to analyze the quality risks during the use and maintenance of aircraft，maps the Design Failure Mode Effects Analysis（DFMEA）results to the PFMEA analysis through a set of rules to improve the recognition rate and accuracy of failure modes，and integrates and optimizes risk control measures using the tree analysis diagram method. Using the method proposed in this paper to analyze a hydraulic pump circulating heat dissipation pipe in a hydraulic system，the results show that the method proposed in this paper can more comprehensively and accurately analyze the quality risks during aircraft manufacturing，operating，and maintenance，and obtain a more optimized set of quality risk control measures.

Ensuring workplace safety for healthcare workers is vital considering the important role they play in various societies which is to save life. Healthcare workers face different risks when performing tasks in various departments within hospitals, hence there is a need to assess work safety analysis procedures among healthcare workers. As a result, this study aims to assess the effectiveness of work safety analysis procedures among healthcare workers at Muvonde and Driefontein Sanatorium rural hospitals in Chirumanzu district. The research applied the descriptive cross-sectional design, combining quantitative and qualitative data collection methods. A questionnaire with both closed and open ended questionnaire was used for data collection among 109 healthcare workers at Muvonde hospital and 68 healthcare workers at Driefontein Sanatorium hospital. Secondary data sources, observations and interviews were also included as data collection methods. Quantitative data collected during the study was analysed using SPSS version 25. Braun and Clarke (2006)'s six phase framework was applied for qualitative data analysis. Ethical approval form was obtained from the District Medical Officer and Midlands State University. Findings of the study indicated that risks identified at Muvonde and Driefontein Sanatorium rural hospitals are classified as ergonomic, physical, chemical, psychosocial and biological risks. Respondents specified that these risks occur as a result of inadequate equipment, poor training, negative safety behaviour, poor management and pressure due to high workload. Safety inspection, safety workshops and monitoring of worker's safety behaviour were mentioned as measures to manage risks. However, the strengths and weaknesses of the current safety procedures need to be assessed to highlight areas for improvement to reduce occurrence of risks within the hospitals. [ABSTRACT FROM AUTHOR]

The quantitative characterization of rock mass and stress changes induced by mining activities is crucial for structural stability monitoring and disaster early warning. This paper investigates the time–space–intensity distribution of microseismic sources during the pillar-free large-area continuous extraction. Furthermore, it explores a method involving collaborative evolution patterns of the velocity field and spatial b-value to identify stress and structural changes at the panel stope. Results show that anomalous zones in wave velocities and b-values form at the intersections of extraction drifts, strike drifts, cross drifts, and connection roadways influenced by mining activities, as well as in footwall ore-rock contacts, often accompanied by the nucleation of microseismic events. The synergistic use of wave velocity fields and spatial b-value models reveals the relationship between stress migration behavior and stope structure changes due to mining disturbances. The velocity field primarily reflects macroscopic changes in the structure and stress distribution, while spatial b-values further explain stress gradients in specific areas. Additionally, we have advanced the identification of an instability disaster at the connection roadway and cross drift intersection based on increases in wave velocity and abnormal changes in b-value. This paper demonstrates the potential of risk identification using the proposed method, providing insights into predicting geotechnical engineering disasters in complex stress environments. [ABSTRACT FROM AUTHOR]

Against the backdrop of the gradually exposed credit bond default risk in China, how to accurately identify and efficiently warn of corporate bond default risk has become a key concern for both academia and practice. To effectively solve a series of key problems in the traditional credit risk warning model, such as insufficient warning performance, single optimization target of hyperparameters, and weak model interpretability, this study integrates machine learning algorithms such as LightGBM, NSGA- II, and SHAP to constructs a LightGBM-NSGA- II-SHAP for early warning of corporate bond default risk, and empirically analyzes and tests the warning performance of the proposed model. The research results show that the warning accuracy of the proposed model exceed 85%, and compared with traditional machine learning models, the warning performance of the proposed model in this study is more excellent. In addition, the impact of visualization of warning features on warning results is demonstrated through the SHAP algorithm, and it is found that coupon interest rate, profit margin on fixed assets, total issuance, and receivable turnover etc. are the key features for identifying corporate bond defaults. [ABSTRACT FROM AUTHOR]

Frequent mountain torrent disasters have caused significant losses to human life and wealth security and restricted the economic and social development of mountain areas. Therefore, accurate identification of mountain torrent hazards is crucial for disaster prevention and reduction. In this study, based on historical mountain torrent hazards, a mountain torrent hazard prediction model was established by using Bayesian Model Average (BMA) and three classic machine learning algorithms (gradient-boosted decision tree (GBDT), backpropagation neural network (BP), and random forest (RF)). The mountain torrent hazard condition factors used in modeling were distance to river, elevation, precipitation, slope, gross domestic product (GDP), population, and land use type. Based on the proposed BMA model, flood risk maps were produced using GIS. The results demonstrated that the BMA model significantly improved upon the accuracy and stability of single models in identifying mountain torrent hazards. The F1-values (comprehensively displays the Precision and Recall) of the BMA model under three sets of test samples at different locations were 3.31–24.61% higher than those of single models. The risk assessment results of mountain torrents found that high-risk areas were mainly concentrated in the northern border and southern valleys of Yuanyang County, China. In addition, the feature importance analysis result demonstrated that distance to river and elevation were the most important factors affecting mountain torrent hazards. The construction of projects in mountainous areas should be as far away from rivers and low-lying areas as possible. The results of this study can provide a scientific basis for improving the identification methods of mountain torrent hazards and assisting decision-makers in the implementation of appropriate measures for mountain torrent hazard prevention and reduction. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Identifying safety risks in distribution networks is of great significance for ensuring the safety of personnel and the stable operation of the distribution system. Existing research on safety risk identification in distribution network operations mainly focuses on personnel irregular dress detection and dynamic unsafe behavior identification. However, the actual operation scenario of the distribution network involves a complex process of multi-element interaction and integration of personnel, tools, and equipment machinery, where the risk of violations is often hidden within the intricate web of interactions. For this reason, this paper focuses on the problem of violation identification of human-object interaction relations in distribution network operation scenarios and proposes a violation risk identification method based on multiple interaction relations. The method firstly extracts the features of the distribution network operation image by convolutional neural network resnet101, then introduces the coding-decoding structure to re-encode the feature vectors to get the feature vectors with different interactions, and at the same time, utilizes the conditional filtering module to improve the convergence speed of the structure, and utilizes the Residual Information Exchange Module and the multi-layer mlp structure to discriminate the interaction pairs of multiple relationships, and finally takes the ladder climbing operation scenario as an example for the experimental validation. The experimental results showed that the proposed method can realize the accurate identification of human-object interaction relationships and violation risk, and has strong practical application value.

Abstract Safety incidents have always been a crucial risk in work spaces, especially industrial sites. In the last few decades, significant efforts have been dedicated to incident control measures to reduce the rate of safety incidents. Despite all these efforts, the rate of decline in serious injuries and fatalities (SIFs) has been considerably lower than the rate of decline for non-critical incidents. This observation has led to a change of risk reduction paradigm for safety incidents. Under the new paradigm, more focus has been allocated to reducing the rate of critical/SIF incidents, as opposed to reducing the count of all incidents. One of the challenges in reducing the number of SIF incidents is the proper identification of the risk prior to materialization. One of the reasons for risk identification being a challenge is that companies usually only focus on incidents where SIF did occur reactively, and incidents that did not cause SIF but had the potential to do so go unnoticed. Identifying these potentially significant incidents, referred to as potential serious injuries and fatalities (PSIF), would enable companies to work on identifying critical risk and taking steps to prevent them preemptively. However, flagging PSIF incidents requires all incident reports to be analyzed individually by experts and hence significant investment, which is often not affordable, especially for small and medium sized companies. This study is aimed at addressing this problem through machine learning powered automation. We propose a novel approach based on binary classification for the identification of such incidents involving PSIF (potential serious injuries and fatalities). This is the first work towards automatic risk identification from incident reports. Our approach combines a pre-trained transformer model with XGBoost. We utilize advanced natural language processing techniques to encode an incident record comprising heterogeneous fields into a vector representation fed to XGBoost for classification. Moreover, given the scarcity of manually labeled incident records available for training, we leverage weak labeling to augment the label coverage of the training data. We utilize the F2 metric for hyperparameter tuning using Tree-structured Parzen Estimator to prioritize the detection of PSIF records over the avoidance of non-PSIF records being mis-classified as PSIF. The proposed methods outperform several baselines from other studies on a significantly large test dataset.

Salatiga City Disperinnaker, a government agency focusing on industry and labor, has developed the Salatiga Job Vacancy Information System (SIOLGA) to streamline its operations. As the SIOLGA application has recently completed its development phase, there arises a necessity for robust risk management to anticipate potential threats and vulnerabilities. Employing ISO 31000 standards, the research aims to mitigate risks effectively. The ISO 31000 framework encompasses risk identification, analysis, evaluation, and treatment phases. Through this process, the study identified 18 potential risks within the SIOLGA application, categorized into three levels: high, medium, and low. Specifically, there are 5 high-level risks, 10 medium-level risks, and 3 low-level risks. By implementing rigorous risk management strategies, the expectation is for the SIOLGA application to operate more efficiently and optimally, fulfilling its intended objectives.

With the gradual transfer of shallow coal mining to deep coal mining in China, the rock burst disasters are becoming an increasingly serious problem. In the process of rock burst mechanism cognition to rock burst prevention engineering, the primary task is to clarify the main factors of rock burst and to identify its risk level. In this paper, four kinds of objective factors i.e., coal rock impact tendency, mining depth, hard roof and geological structure, and three kinds of human factors i.e., coal pillar, goaf and mining unloading effect, were proposed. And the disaster evolution mechanism of each factor was discussed in detail. In terms of objective controlling factors, the impact tendency is the inherent attribute of coal/rock to accumulate deformation energy and induce impact failure. The mining depth is positively correlated with the deformation energy accumulated in the surrounding rock of the roadway, which is an essential condition for the occurrence of rock burst. The impact dynamic load and kinetic energy formed by large-scale hard roof periodic fracture are the 'fuse' to rock burst. The influence of geological structure on rock burst is significant. For fault structure, the two walls will relatively ‘rebound’ under the sudden unloading caused by mining disturbance. And the equivalent elastic modulus of the thinning area of the coal seam becomes larger, and the advanced abutment pressure is distributed in a 'double peak' pattern, which expands the impact influence range. In terms of subjective controlling factors, coal pillar is a high stress concentration area, and its size, dip angle and relative position will directly affect the probability and strength of rock burst. The goaf will induce a sudden release of energy accumulated in the stress concentration area, especially under large mining height and insufficient roof collapse conditions. Mining unloading will lead to the rapid “migration” of the stress concentration area and release a large amount of energy stored in the coal/rock, which is an important external inducement of rock burst. On this basis, the differences of main control factors of rock burst disaster in the main rock burst mining area, such as Xinwen, Luxi, Erdos, Binchang, Xinjiang and Gansu were compared and analyzed. The study emphasized the importance of identifying the main control factors and their influence degree of rock burst from an entire mine, a panel to a working face. Also, it constructed the engineering management path of rock burst from energy-reducing, energy-releasing, energy-damping to energy-resisting.

To accurately characterize human health hazards, human, animal, and mechanistic data must be integrated and the relevance to the research question of all three lines of evidence must be considered. Mechanistic data are often critical to the full integration of animal and human data and to characterizing relevance and uncertainty. This novel evidence integration framework (EIF) provides a method for synthesizing data from comprehensive, systematic, quality-based assessments of the epidemiological and toxicological literature, including in vivo and in vitro mechanistic studies. It organizes data according to both the observed human health effects and the mechanism of action of the chemical, providing a method to support evidence synthesis. The disease-based component uses the evidence of human health outcomes studied in the best quality epidemiological literature to organize the toxicological data according to authors' stated purpose, with the pathophysiology of the disease determining the potential relevance of the toxicological data. The mechanism-based component organizes the data based on the proposed mechanisms of effect and data supporting events leading to each endpoint, with the epidemiological data potentially providing corroborating information. The EIF includes a method to cross-classify and describe the concordance of the data, and to characterize its uncertainty. At times, the two methods of organizing the data may lead to different conclusions. This facilitates identification of knowledge gaps and shows the impact of uncertainties on the strength of causal inference. [ABSTRACT FROM AUTHOR]

Treatment and care of patients in intensive care units require the use of many medical and technological instruments. Pressure injuries occur when medical devices, which are used more in intensive care patients and are in direct or indirect contact with the skin, cause focal and localized forces on the superficial or deep tissues. In this study, it was aimed to examine the risk factors, incidence and characteristics of medical device-related pressure injuries in intensive care patients. This study has a prospective and descriptive design. The study was carried out in the adult intensive care unit of a healthcare institution located in the western Turkey. 138 intensive care patients treated in the level 3 adult intensive care unit were enrolled in the study. The first observations and evaluations of intensive care patients in terms of pressure injuries were made within the first 24 h after admission to the clinic. Observations continued daily during the hospitalization period of the patient. Data were collected with the Intensive Care Patient Information Form, Glasgow Coma Scale, Braden Pressure Ulcer Risk Assessment Scale and Identification Form for Medical device-related Pressure Ulcers. Analysis of data was performed with descriptive statistical methods, Shapiro-Wilk Test, Mann-Whitney U Test and Chi-Square analysis. Medical device-related pressure injury developed in 11.6% (n = 16) of intensive care patients. Anatomically, pressure injury occurred most frequently on the lip (37.5%) and most frequently due to the intubation tube (37.5%). Most of the developed wounds (75.0%) were found to be stage 2. Multinominal logistic regression analysis, which was performed to determine the effect of independent variables on medical device-related pressure injuries in intensive care patients, was found to be statistically significant (X2 = 37.098, p < 0.001). When the regression coefficients were examined, it was found that total hospitalization time in the intensive care unit (β = 0.948, p < 0.01) and PaCO 2 level (β = 0.923, p < 0.01) had a positive, and duration of aerobic respiration with nasal cannula or mask (β = −0.920, p < 0.01) and Braden score (β = −0.948, p < 0.01) had a negative and significant effect on medical device-related pressure injuries. In this study found that the MDRPIs development rate was lower than other studies. It was observed that pressure injuries due to medical devices developed more frequently in patients with longer hospitalization days, higher PaCO 2 levels, shorter duration of oxygenated breathing with nasal cannula or mask, and lower Braden scores. • This study contributes to the definition of the incidence and risk factors of medical device-related pressure injuries, to the reporting of commonly affected anatomical regions and commonly affecting medical devices and to the etiology and prevention of pressure injuries in intensive care patients. In addition, it emphasizes that identification of medical device-related pressure injury risk is also important besides pressure ulcer risk that has been considered more important in intensive care patients until recently. • These research findings should be taken into account when evaluating the risk factors that are effective in the development of medical device-related pressure injuries as preventable and non-preventable risk factors. [ABSTRACT FROM AUTHOR]

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Safety incidents have always been a crucial risk in work spaces, especially industrial sites. In the last few decades, significant efforts have been dedicated to incident control measures to reduce the rate of safety incidents. Despite all these efforts, the rate of decline in serious injuries and fatalities (SIFs) has been considerably lower than the rate of decline for non-critical incidents. This observation has led to a change of risk reduction paradigm for safety incidents. Under the new paradigm, more focus has been allocated to reducing the rate of critical/SIF incidents, as opposed to reducing the count of all incidents. One of the challenges in reducing the number of SIF incidents is the proper identification of the risk prior to materialization. One of the reasons for risk identification being a challenge is that companies usually only focus on incidents where SIF did occur reactively, and incidents that did not cause SIF but had the potential to do so go unnoticed. Identifying these potentially significant incidents, referred to as potential serious injuries and fatalities (PSIF), would enable companies to work on identifying critical risk and taking steps to prevent them preemptively. However, flagging PSIF incidents requires all incident reports to be analyzed individually by experts and hence significant investment, which is often not affordable, especially for small and medium sized companies. This study is aimed at addressing this problem through machine learning powered automation. We propose a novel approach based on binary classification for the identification of such incidents involving PSIF (potential serious injuries and fatalities). This is the first work towards automatic risk identification from incident reports. Our approach combines a pre-trained transformer model with XGBoost. We utilize advanced natural language processing techniques to encode an incident record comprising heterogeneous fields into a vector representation fed to XGBoost for classification. Moreover, given the scarcity of manually labeled incident records available for training, we leverage weak labeling to augment the label coverage of the training data. We utilize the F2 metric for hyperparameter tuning using Tree-structured Parzen Estimator to prioritize the detection of PSIF records over the avoidance of non-PSIF records being mis-classified as PSIF. The proposed methods outperform several baselines from other studies on a significantly large test dataset. [ABSTRACT FROM AUTHOR]

Risk identification and further risk assessment of consumer product injury data are critical tools for accident prevention. However, in Japan, existing risk assessment methods face challenges due to issues such as inconsistent semantic understanding of risk-related vocabulary among Japanese analysts, posing obstacles to the effective identification and assessment of risk information. This study develops an ontology-based knowledge base (JCPRI-KB), which includes an ontology (JCPRI-Onto) and a total of 41 standardised vocabulary sets, for the risk information identification and standardization of consumer product injury data in Japan. A graph database was used to represent and store the risk knowledge of JCPRI-KB. Finally, the JCPRI-KB is applied for a case study of risk identification and further risk assessment of child product injury data. The risk assessment results of this study demonstrate that the JCPRI-KB offers a promising new approach to the field of consumer product safety in Japan, contributing to the enhancement of accuracy and efficiency in risk identification and thus promoting consumer product safety. [ABSTRACT FROM AUTHOR]

In public–private partnership (PPP) projects, governments often need to provide guarantees to investors because of the risks created by the large scale of the investments and the long tenures of the projects. Yet, research on the assessment of fiscal risk in PPP projects remains scarce. Based on the theory of project life-cycle management, this study constructed a multidimensional identification and assessment framework for PPP fiscal risk. A fiscal risk matrix model was developed, and feasibility, standardization, and sustainability were identified. These were categorized into three dimensions of PPP fiscal risk, and their indicators' hierarchical levels were evaluated and ranked. A risk model, called the fuzzy evaluation model of PPP fiscal risk, which shows the value of the comprehensive evaluation of PPP fiscal risk based on a fuzzy analytic hierarchy process (FAHP) approach, is proposed. The results demonstrate the significance of this valuation for both governments and investors, providing a clear reference when they face pressure from the fiscal risks of PPP projects. [ABSTRACT FROM AUTHOR]

Objective: This systematic review identified key components of risk assessment for people with dementia, examined attitudes toward risk identification and risk assessment, and appraised existing risk assessment tools. Methods: Systematic searches of five databases on two platforms (EBSCO, OVID) and gray literature databases (Open Grey, Base) were conducted. Studies were screened for inclusion based on predetermined eligibility criteria and quality assessed using the Mixed Methods Appraisal Tool. Findings were tabulated and synthesized using thematic synthesis. Results: Our review found people with dementia, their family carers, and healthcare professionals differed in how risk is conceptualized, with views being shaped by media perceptions, personal experiences, socio-cultural influences, dementia knowledge, and dementia severity. We found that mobilization (causing falls inside and getting lost outside) is the most frequently identified risk factor. Our findings show people with dementia are generally risk-tolerant, while healthcare professionals may adopt risk-averse approaches because of organizational requirements. We found factors that disrupt daily routines, living and caring arrangements, medication management, and unclear care pathways contribute toward adverse risk events. We discovered that most studies about risk and risk assessment scales did not consider insight of the person with dementia into risks although this is important for the impact of a risk. No risk instrument identified had sufficient evidence that it was useful. Conclusion: Accurate risk assessment and effective communication strategies that include the perspectives of people with dementia are needed to enable risk-tolerant practice. No risk instrument to date was shown to be widely acceptable and useful in practice. [ABSTRACT FROM AUTHOR]

Lung cancer has a substantially worse five-year survival rate than many other malignancies and is the most common cause of cancer-related deaths in both men and women worldwide. For better disease detection and medical management, accurate survival analysis is urgently required. In the literature, few works are reviewed for survival analysis, but it fails to achieve optimal outcomes. Hence, in this paper, Cox Proportional Hazard Analysis Based Deep Learning Model (CPHA-DLM) is developed for risk identification in lung cancer detection. The proposed method is proceeding with two stages such as lung cancer detection and risk identification of patients with the basis of survival rate. At first, the databases are collected from the SEER program. The main motive of the research is survival analysis which is achieved by considering Cox Proportional Hazard Analysis. Initially, lung cancer is detected by considering the deep learning model. The databases are sent to the deep learning model of the Hybrid Convolutional Neural Network (HCNN). The deep learning model is a grouping of a Convolutional Neural Network (CNN) and Cat and Mouse based Optimizer (CMO). In CNN, the hyperparameter is optimized with the consideration of the CMO. After that, the survival rate of the patients is analyzed with hazard analysis. To compute the predictive power of the survival model, two measures are considered as concordance index and Kaplan Meier Estimate. The proposed method is validated by considering the conventional approaches. According to this study, the patient has a low risk after 20 years. The patient has a medium risk at 8 years and a high risk after 5 years, respectively. Experimental results show that the proposed method attained the maximum Precision of 96.29%, recall of 96.10%, and F-Measure of 96.16%. [ABSTRACT FROM AUTHOR]

Background: In 2016, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use updated its efficacy guideline for good clinical practice and introduced predefined quality tolerance limits (QTLs) as a quality control in clinical trials. QTLs are complementary to Quality by Design (QbD) principles (ICH-E8) and are one of the components of the risk-based clinical trial quality management system. Methods: Currently the framework for QTLs process is well established, extensively describing the operational aspects of Defining, Monitoring and Reporting, but a single source of commonly used methods to establish QTLs and secondary limits is lacking. This paper will primarily focus on closing this gap and include applications of statistical process control and Bayesian methods on commonly used study level quality parameters such as premature treatment discontinuation, study discontinuation and significant protocol deviations as examples. Conclusions: Application of quality tolerance limits to parameters that correspond to critical to quality factors help identify systematic errors. Some situations pose special challenges to implementing QTLs and not all methods are optimal in every scenario. Early warning signals, in addition to QTL, are necessary to trigger actions to further minimize the possibility of an end-of-study excursion. [ABSTRACT FROM AUTHOR]

Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order. Amidst the challenges posed by intricate and unpredictable risk factors, knowledge graph technology is effectively driving risk management, leveraging its ability to associate and infer knowledge from diverse sources. This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios. Firstly, employing bibliometric methods, the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge graphs. In the succeeding section, systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge graphs. Objectively comparing and summarizing the strengths and weaknesses of each method, we provide recommendations for addressing the existing challenges in the construction process. Subsequently, categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards, and furnishing a detailed exposition on the applicability of technical routes and methods. Finally, the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed, and relevant improvement suggestions were proposed. Practitioners and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation. [ABSTRACT FROM AUTHOR]

Risk management is a dynamic process that involves taking all necessary steps to identify and address risks that impact the organization's goals. The objective of this paper is to investigate the impact of risk management practices, specifically risk identification, on business performance in insurance companies in Palestine. The study employed an explanatory research design, and data were collected from a random sample of 140 employees from insurance companies in Palestine using a well-structured questionnaire. After confirming the normal distribution of responses and validating the tool's reliability and validity, descriptive statistics was conducted, while inferential statistics was examined using Pearson product-moment correlation. The data were analyzed using statistical analysis with SPSS. The results of this paper revealed a positive relationship between risk identification and business performance. As the levels of risk identification increase, business performance also tends to improve. Therefore, it is recommended that insurance companies take cost-effective measures to identify risks promptly, effectively mitigate risks, and promote a culture of risk management throughout the organization. Continuous evaluation of risk management practices and risk identification is essential to identify areas where resources are exposed to risks, ensuring the company's ability to thrive in a challenging work environment. [ABSTRACT FROM AUTHOR]

The technology called carbon capture, utilization, and storage (CCUS) is important for capturing CO2 emissions before they enter the air. Because everyone wants to stop global warming by reducing CO2 emissions, CCUS is an important and emerging technology that can help slow down climate change, lower emissions in many areas, and support the move toward a sustainable and carbon-neutral future. As CCUS technology and its adaptation increases, it is very important to pay attention to the CCUS risks from a supply chain (SC) point of view. The goal of this study was to identify CCUS supply chain risks and develop a conceptual framework (CF) that provides a structured approach to ensure safe and reliable CCUS supply chain operations. Therefore, this study analyzed the literature related to the SCs of different sectors and identified the SC risks, which was the foundation for CCUS SC risk identification. This study demonstrates that there is no research article that provides a comprehensive CCUS SC risk management framework that connects with risk management strategies. The conceptual framework that is proposed in this study connects CCUS SC functions, risks, and risk management strategies to construct a complete CCUS supply chain risk management system. Moreover, the CF provides guidelines for future research, which will enrich the CCUS supply chain risk management system as well as fight climate change. [ABSTRACT FROM AUTHOR]