Risk Analysis Research Articles

PPE Education to Reduce the Risk of Workplace Accidents in The Furniture Industry

Occupational safety and health is an important aspect in the industrial world, including the furniture industry. If a company implements the Occupational Health and Safety well, it can reduce the risk of work accidents, thereby increasing work productivity. Work accidents can occur due to workers’ negligence or suboptimal implementation of the Occupational Health and Safety System. To reduce the risk of work accidents, a risk analysis is carried out, which is one part of risk management. One of the risk analyses that can be done is by using the Failure Mode and Effect Analysis (FMEA) method. Risk identification in the furniture industry is based on field reviews, which then assess the severity (S), occurrence rate (O), and detection level (D). Then the assessment results are sorted to determine the highest Risk Priority Number (RPN) value variable. In the furniture production process, there are various potential hazards, with the highest RPN found in the cutting process, where a machine incident occurred with an RPN value of 120. The severity factor is given a value of 6 because the error leads to torn injuries and equipment damage, so it must be handled immediately, and there is the repair of the damage. One effort to reduce the risk of work accidents is to provide PPE at work.

A thorough assessment of various forest management planning initiatives and development of improvement strategies towards an ecosystem-based planning

Management planning for forests in Turkey has undergone gradual evolution over the last century. Developing and implementing a comprehensive management planning framework present significant challenges. This paper evaluates the effectiveness of various forest management planning approaches implemented across the country over different time periods, with overarching planning principles. It formulates a robust planning framework and proposes improvements grounded in scientific advancements and international standards. The assessment indicated that the management plans were developed using reputable scientific methods and principles aimed at ensuring the sustainable management of forest resources with certain strengths and opportunities presented by SWOT analysis. All management plans shared a common planning concept primarily focused on maximizing wood production through the area-control harvest scheduling method (except for continuous cover forest, which employed the single tree selection method). Each management plan established its unique vision, targets, policies, objectives, and planning guidelines. Forest inventory data were gathered through a combination of ground surveys and remotely sensed data to characterize and stratify the landscape. A robust in-house management authority and governance system with appropriate technical capacity and guidelines were developed and implemented, fostering a sound common working culture and tools. However, some notable drawbacks were identified, including political pressure, biomass/carbon accounting, growth-yield modelling, economic analysis, limited characterization of the full range of ecosystem services, risk and uncertainty analysis, food security and, particularly, long-term sustainability and scenario analysis with the appropriate decision-making tools and methods. Despite a few strengths, these limitations may raise concerns about the far-sighted design and application, potentially jeopardizing the sustainable management of forest ecosystems. Proposed improvement strategies for an efficient forest ecosystem management planning system include characterization of ecosystem services, modelling their productivity, scenario analysis with a decision support system, stakeholder involvement, balancing utilization and conservation targets, conducting risk and uncertainty analysis and economic analysis of management actions.

Risk analysis and prevention in computer security in institutional servers, a systematic review of the literature

In recent years, computer attacks on the server infrastructure in organizations have been increasing, and the pandemic of covid-19 and remote work have been the main causes for this massive wave of large-scale attacks, small businesses are especially vulnerable because to optimizing resources they leave aside the cyber security in their network infrastructure. The present research is a systematic review that compiles 58 articles where policies, techniques, and infrastructure for the prevention of threats in enterprise servers have been implemented and raised, these articles have been collected from major databases such as IEEE Xplore, SAGE, Science Direct, Scopus, and IOP Publishing. The results show that one of the most effective methods in preventing communications between institutional servers is public key infrastructure/SSL-TLS encryption. Most research claims that it is the most effective method as it provides a central certifier and manages the certificates for the servers allowing each of the modules or attachments within the infrastructure to identify and validate other members and to proceed with the encryption of network traffic, Finally, a security implementation model is proposed.

Risk, Sensitive Analysis, and Optimization of a New Multi-generation System Activated by Solar and Biomass Energies

In recent years, the use of clean energies has become much more important due to the increase of environmental pollutants and the reduction of fossil fuel resources. Another important parameter in the establishment of energy systems is the assessment of the risks caused by the construction of the system and the provision of operational solutions to manage and reduce these risks. In this research, a new multi-generation system that is activated using solar and biomass energies has been evaluated thermodynamically and thermo-risky. This system consists of a Brayton cycle, a steam Rankine cycle, a transcritical CO2 cycle, a proton membrane electrolyzer (PEME) unit, and an internal combustion engine (ICE). A notable characteristic of the proposed system is its capability to recover waste heat from the gas turbine cycle, which is subsequently utilized to drive both a steam Rankine cycle and a transcritical CO2 cycle. The sensitivity analysis has been performed to investigate the effects of the input parameters on the system performance and finally, an optimization for the effective parameters of the system has been conducted. The results of the calculations show that the proposed system has energy and exergy efficiencies of 22.05% and 15.93%, respectively. The risk analysis was conducted by considering the potential hazards such as jet fires, jet of combusted gases, and system overpressure. The analysis indicates that the system poses a low level of risk. The overall risk, along with the risk per unit of system exergy, has been estimated at 4.247 ×10-8 injuries/year and 3.73 ×10-11 injuries/year.kW, respectively. In optimal conditions, the energy and exergy efficiency of the system reaches 25.79% and 19.08%.

S-task simulation builder framework as preventive work risk analysis

S-task simulation builder framework as preventive work risk analysis

RISK MANAGEMENT FOR COOPERATIVES OF SAVINGS AND LOANS AND SHARIA FINANCING: LESSON FROM INDONESIA

This research aims to find out and describe the risks that exist in sharia financial institutions in the form of Cooperatives of Savings and Loans and Sharia Financing (KSPPS). This research uses a qualitative descriptive approach in the form of library research, which uses secondary data from journal articles and regulations relevant to the research topic. The research results show that there are various risks that KSPPS can experience. According to Minister of Labour Decree No. 193 of 2017, risk is the potential for financial losses and worsening social performance of Cooperatives of Savings and Loans and Sharia Financing/Sharia Business Units of Savings and Loans and Sharia Financing (KSPPS/USPPS). Based on these regulations, risk consists of strategic risk, financing risk, financial risk, and operational risk. Risk management in the management of KSPPS and similar institutions is an absolute priority to pay attention to. The risk often experienced by KSPPS is financing risk. Risk management in dealing with financing consists of risk analysis steps, which can be in the form of SWOT analysis, prudential analysis, 5C analysis, and 7P analysis, as well as risk handling mechanisms, which can be in the form of persuasive efforts, rescheduling, reconditioning, restructuring, combinations, and confiscation of collateral.

Machine learning for credit risk analysis across the United States

Money-Lending financial institutions face risk, which necessitates adopting a robust framework to manage it effectively. While traditional methods have been applied across the financial industry, the advent of artificial intelligence offers organizations the opportunity to utilize advanced methods to manage credit risk. This paper focuses on the application of machine learning techniques for credit risk analysis. Secondary data on information related to borrowers was extracted from Kaggle database simulating Credit Bureau data. Two ensemble models in random forest and gradient boosting were adopted for this study. The findings showed that percentage of income for loan repayment, borrower’s income, and interest rates on loans are the most important features for determining defaulters. Furthermore, the evaluation results revealed that both the random forest and the gradient boosting algorithms performed well, with F1 scores of 92.9% and 93% respectively. It was recommended that financial institutions should priorities the verification and comprehensiveness of their data, as precise data is essential for developing resilient models.

Risk analysis Erosive Tooth Wear (ETW): checklist for a quick assessment of the risk factors for ETW in the dental practice

Erosive Tooth Wear (ETW) is the loss of tooth structure without bacterial involvement. As the resulting loss of tooth structure is irreversible, an early evaluation of the multifactorial etiology, accurate diagnosis and regular follow-up are essential. The ETW is dynamic and its progression should be continuously monitored. A risk analysis table was developed to systematically record risk factors for ETW (Fig. 2). This allows ETW management to be integrated more efficiently into clinical practice and the progression to be documented over the years. If ETW is assumed, the risk analysis should be performed and updated at least every two years. In the assessment, risk-promoting and risk-inhibiting factors are marked, added together and compared. If the negative factors pre-dominate, measures should be taken to minimize the risk for ETW. The main purpose of the risk analysis is therefore to evaluate the individual etiology, coun-teract negative factors, promote positive factors and prevent progression.

The regulation of alien species in South Africa

A key global change challenge is to significantly reduce the risks of alien taxa causing harmful impacts without compromising the rights of citizens. As part of efforts to address this challenge, South Africa promulgated comprehensive regulations and lists of alien taxa in 2014. In this paper, we review how the lists developed, changed over time, and how they have been implemented. As of March 2021, 560 taxa were listed under four broad regulatory categories, and between 2014 and 2020, almost 3000 permits were issued to regulate the continued use of listed taxa. The full lists of regulated taxa, permits issued, and corresponding regulations are available in the Supplementary material. A proposed standardised, transparent, and science-informed process to revise the regulatory lists is also presented – as of 30 April 2024, risk analyses have been developed for 140 taxa using the Risk Analysis for Alien Taxa (RAAT) framework and reviewed by an independent scientific body [the Alien Species Risk Analysis Review Panel (ASRARP)] with input from taxon-specific experts. These recommendations are being considered by an interdepartmental governmental decision-making body established in March 2023 [the Risk Analysis Review Committee (RARC)]. Finally, key issues with the listing of alien taxa in South Africa that remain to be resolved are presented. As South Africa’s regulatory framework continues to develop, the process of listing and regulating alien taxa will, we believe, become more transparent, consistent, and acceptable to stakeholders, and ultimately facilitate efforts to reduce the harmful impacts of alien taxa.

Deformation Characteristics and Activation Dynamics of the Xiaomojiu Landslide in the Upper Jinsha River Basin Revealed by Multi-Track InSAR Analysis

The upper Jinsha River, located in a high-mountain gorge with complex geological features, is highly prone to large-scale landslides, which could result in the formation of dammed lakes. Analyzing the movement characteristics of the typical Xiaomojiu landslide in this area contributes to a better understanding of the dynamics of landslides in the region, which is of great significance for landslide risk prediction and analysis. True displacement data on the surface of landslides are crucial for understanding the morphological changes in landslides, providing fundamental parameters for dynamic analysis and risk assessment. This study proposes a method for calculating the actual deformation of landslide bodies based on multi-track Interferometric Synthetic Aperture Radar (InSAR) deformation data. It iteratively solves for the optimal true deformation vector of the landslide on a per-pixel basis under a least-squares constraint based on the assumption of consistent displacement direction among adjacent points on the landslide surface. Using multi-track Sentinel data from 2017 to 2023, the line of sight (LOS) accumulative de-formation of the Xiaomojiu landslide was obtained, with a maximum LOS deformation of −126 mm/year. The true surface displacement of the Xiaomojiu landslide after activation was calculated using LOS deformation. The development of two rotational sub-slipping zones on the landslide body is inferred based on the distribution of actual displacements along the central profile line. Analysis of temporal changes in water body area data revealed that the Xiaomojiu landslide was activated after a barrier lake event and continuously moved due to the influence of higher water levels’ in the river channel. In conclusion, the proposed method can be applied to calculate the true surface displacement of landslides with complex mechanisms for analyzing the movement status of landslide bodies. Furthermore, the spatiotemporal analysis of the Xiaomojiu landslide characteristics can support analyzing the mechanisms of similar landslides in the Jinsha River Basin.

Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation

IntroductionExercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to predict health care costs is not well recognized. Since machine learning (ML) applications are rapidly giving new opportunities to assist health care professionals’ work, we used selected ML tools to assess the predictive value of defined risk factors for health care costs during 12-month ECR in patients with CAD.MethodsThe data for analysis was available from a total of 71 patients referred to Oulu University Hospital, Finland, due to an acute coronary syndrome (ACS) event (75% men, age 61 ± 12 years, BMI 27 ± 4 kg/m2, ejection fraction 62 ± 8, 89% have beta-blocker medication). Risk factors were assessed at the hospital immediately after the cardiac event, and health care costs for all reasons were collected from patient registers over a year. ECR was programmed in accordance with international guidelines. Risk analysis algorithms (cross-decomposition algorithms) were employed to rank risk factors based on variances in their effects. Regression analysis was used to determine the accounting value of risk factors by entering first the risk factor with the highest degree of explanation into the model. After that, the next most potent risk factor explaining costs was added to the model one by one (13 forecast models in total).ResultsThe ECR group used health care services during the year at an average of 1,624 ± 2,139€ per patient. Diabetes exhibited the strongest correlation with health care expenses (r = 0.406), accounting for 16% of the total costs (p < 0.001). When the next two ranked markers (body mass index; r = 0.171 and systolic blood pressure; r = − 0.162, respectively) were added to the model, the predictive value was 18% for the costs (p = 0.004). The depression scale had the weakest independent explanation rate of all 13 risk factors (explanation value 0.1%, r = 0.029, p = 0.811).DiscussionPresence of diabetes is the primary reason forecasting health care costs in 12-month ECR intervention among ACS patients. The ML tools may help decision-making when planning the optimal allocation of health care resources.

Complex network-based risk analysis for maritime heavy casualties in China during 2012–2021

A collection of accident investigation reports for the 21 maritime heavy casualties that occurred in China during 2012–2021 is studied statistically and analytically in the present study. The basic accident factors are first extracted from these investigation reports to present the distribution features of these heavy casualties. Then, the accident factors in terms of crew, environment, ship equipment and management in each individual investigation report are logically linked to develop a causative chain. As a result, 21 chains developed from the 21 accident investigation reports are interweaved into a complex network by sharing the same factors. The accident factors involved in the complex network are quantitatively assessed in terms of activeness, connection strength and criticality based on the network topology parameters and the improved K-shell decomposition algorithm. The topology of the developed complex network implies that the occurrence of maritime heavy casualties is under control, and the results show that the seaworthiness of crew, ship equipment and cargo is critical to maintain safe operation on board; meanwhile, supervising ship operators and managers to ensure that they fulfil their responsibility is an effective way to maintain the maritime safety level.

Balance Analysis of Operational Risk Through the Aggregate Method in the Loss Distribution Approach

Balance Analysis of Operational Risk Through the Aggregate Method in the Loss Distribution Approach

A Safety Risk Analysis of a Steel-Structure Building Using an Improved Controlled Interval and Memory Model

A Safety Risk Analysis of a Steel-Structure Building Using an Improved Controlled Interval and Memory Model

Protecting oil storage tanks against floods: Natech risk assessment with imprecise probabilities

AbstractNatechs are technological accidents that are triggered by natural disasters. The increase in the frequency and severity of climatic natural disasters along with the growth of industrialization has accelerated the demand for development of dedicated methodologies for risk assessment and management of Natechs. Due to a lack of accurate and sufficient data, risk assessment of Natechs has largely been based on subjective assumptions and imprecise probabilities, making the assessed risks and the subsequent risk management strategies deficient in terms of cost‐effectiveness. In the present study, evidence theory, as an effective technique for dealing with imprecise probabilities, and Bayesian network, as an effective tool for reasoning under uncertainty, are combined to develop a methodology for risk analysis of Natechs based on imprecise probabilities with no attempt to increase the precision of the input data but the accuracy and cost‐effectiveness of the outcomes. Flotation of oil tanks during floods has been considered to exemplify the methodology. The methodology is demonstrated to outperform conventional approaches where average probabilities or generic probability distributions are used instead of interval probabilities for risk assessment and management.

Risk Analysis of Shallot Farming Production in Malang City

Risk Analysis of Shallot Farming Production in Malang City

A Comprehensive Analysis of Fall Risk and Prevention

Fall prevention is a crucial component of healthcare, particularly for older adults and individuals with specific medical conditions. Understanding the significance of fall prevention and recognizing the various risk factors associated with falls are essential in developing effective strategies to mitigate this public health concern. This comprehensive review aims to explore the multidimensional aspects of fall prevention, from assessing individual fall risks to implementing interventions and evaluating the efficacy of fall prevention programs. By delving into the latest trends, innovative technologies, and collaborative approaches in fall prevention, this article seeks to provide valuable insights for healthcare professionals, caregivers, and policymakers in enhancing the safety and well-being of individuals at risk of falls.

AI-Related Risk: An Epistemological Approach

Risks connected with AI systems have become a recurrent topic in public and academic debates, and the European proposal for the AI Act explicitly adopts a risk-based tiered approach that associates different levels of regulation with different levels of risk. However, a comprehensive and general framework to think about AI-related risk is still lacking. In this work, we aim to provide an epistemological analysis of such risk building upon the existing literature on disaster risk analysis and reduction. We show how a multi-component analysis of risk, that distinguishes between the dimensions of hazard, exposure, and vulnerability, allows us to better understand the sources of AI-related risks and effectively intervene to mitigate them. This multi-component analysis also turns out to be particularly useful in the case of general-purpose and experimental AI systems, for which it is often hard to perform both ex-ante and ex-post risk analyses.

Predictability of Hurricane Storm Surge: An Ensemble Forecasting Approach Using Global Atmospheric Model Data

Providing storm surge risk information at multi-day lead times is critical for hurricane evacuation decisions, but predictability of storm surge inundation at these lead times is limited. This study develops a method to parameterize and adjust tropical cyclones derived from global atmospheric model data, for use in storm surge research and prediction. We implement the method to generate storm tide (surge + tide) ensemble forecasts for Hurricane Michael (2018) at five initialization times, using archived operational ECMWF ensemble forecasts and the dynamical storm surge model ADCIRC. The results elucidate the potential for extending hurricane storm surge prediction to several-day lead times, along with the challenges of predicting the details of storm surge inundation even 18 h before landfall. They also indicate that accurately predicting Hurricane Michael’s rapid intensification was not needed to predict the storm surge risk. In addition, the analysis illustrates how this approach can help identify situationally and physically realistic scenarios that pose greater storm surge risk. From a practical perspective, the study suggests potential approaches for improving real-time probabilistic storm surge prediction. The method can also be useful for other applications of atmospheric model data in storm surge research, forecasting, and risk analysis, across weather and climate time scales.

HIPAA: A Demand to Modernize Health Legislation

In the 21st-century digital age, health data privacy remains a crucial concern. This paper evaluates the effectiveness of the Health Insurance Portability and Accountability Act, known as HIPAA. More specifically, it demonstrates a need for a unified federal framework in the U.S. that aligns with General Data Protection Regulation’s protections to address modern-day cybersecurity threats better. This article argues that in an era of increased globalization, the United States should confront the task of reforming its healthcare data protection law to align with current cybersecurity risks.  We begin by examining landmark legislation across American states to reveal inconsistencies between state and federal protective rulings. Later, we uncover the reactive nature of HIPAA, in contrast to GDPR’s proactive and citizen-centric approach. Through evaluating past lawsuits related to patient protection noncompliance, this paper depicts significant differences in the purpose, coverage, and execution of data protection laws between the United States and the European Union. It highlights GDPR’s effectiveness in granting individuals greater control over their data. Furthermore, this article proposes the adoption of newfound systems for standardized risk analysis and enhanced security across healthcare providers. As healthcare becomes more accessible to the American public, the amount of data in this system increases. This nationwide surge in data underscores the critical need to assess whether privacy laws established in the 1990s remain sufficient. Therefore, updates to healthcare legislation are essential to establishing stringent patient protections in response to the significant rise in data breach incidents within the healthcare network.