Comprehensive Risk Assessment Framework Research Articles

Advancing Sustainable Healthcare Technology Management: Developing a Comprehensive Risk Assessment Framework with a Fuzzy Analytical Hierarchy Process, Integrating External and Internal Factors in the Gulf Region

Advancing Sustainable Healthcare Technology Management: Developing a Comprehensive Risk Assessment Framework with a Fuzzy Analytical Hierarchy Process, Integrating External and Internal Factors in the Gulf Region

Risk assessment of patients with underlying health conditions who present for IVF treatment.

The increasing number of women with underlying health conditions seeking in vitro fertilization (IVF) necessitates a comprehensive risk assessment framework tailored to their needs. This review aims to provide a practical framework for IVF specialists to systematically assess and manage the risks associated with medically complex patients. The GRASP mnemonic (Genetics, Retrieval, Anaesthetics, Stimulation, Pregnancy) is introduced to facilitate a thorough risk assessment process. The review emphasizes the importance of obtaining detailed medical histories, corroborating information from other healthcare providers, and involving maternal medicine specialists in pre-pregnancy counselling. Furthermore, it highlights the ethical considerations of informed consent, advocating for fertility specialists to take a central role in coordinating care and ensuring patient understanding of risks. Continuous health evaluation throughout the IVF process and clear communication of risks are crucial for improving patient outcomes.

Towards a comprehensive framework for risk assessment of organizational development project portfolios

The benefits of risk management in the context of project portfolios have been widely recognized in the literature. However, approaches that assess the risk of organizational development project portfolios from the perspective of how the portfolio delivers value to the parent organization remain largely unexplored. To address this gap, our research takes a constructivist approach and an organizational perspective on project portfolios. We conducted twenty-eight semi-structured interviews and used thematic analysis to identify and relate four themes of a comprehensive project portfolio risk assessment (PPRA) framework: "project portfolio as the organizational unit for PPRA"; "organizational capabilities as portfolio outcomes in which PPR can be assessed"; "project portfolio levels as sources of risk factors in PPRA"; and "balance between project portfolio attributes complexity". Within the framework of organizational development project portfolios, this study contributes to our understanding of PPRA by providing two propositions: (1) The capabilities to be generated by the project portfolio can be used as the portfolio primary results on which PPRA can assess the risk of the project portfolio, establishing the impact of PPR on the project portfolio value delivering to the parent organization, and (2) The risk factors that impact the project portfolio expected results can be represented into PPRA as ‘output-related’ risk factors and ‘outcome-related’ risk factors.

Optimizing risk management in oil and gas trading: A comprehensive analysis

The oil and gas trading sector is inherently volatile, influenced by geopolitical events, market dynamics, and regulatory changes. Effective risk management is crucial for mitigating these uncertainties and ensuring operational stability and profitability. This paper presents a comprehensive analysis of optimizing risk management in oil and gas trading, highlighting strategies, tools, and best practices to manage and mitigate risks. Risk management in oil and gas trading encompasses a broad spectrum of activities, including market risk, credit risk, operational risk, and compliance risk. To optimize risk management, firms must employ a multifaceted approach that integrates advanced analytics, robust risk assessment frameworks, and strategic decision-making processes. Key strategies include the use of financial instruments such as futures, options, and swaps to hedge against price volatility. These instruments enable traders to lock in prices and margins, thereby reducing exposure to market fluctuations. Advanced analytics and data-driven decision-making are essential components of effective risk management. By leveraging big data, machine learning, and artificial intelligence, traders can gain deeper insights into market trends, forecast price movements, and identify potential risks. Predictive analytics allows for proactive risk management, enabling firms to anticipate and respond to market changes swiftly. A comprehensive risk assessment framework is also critical. This involves regular monitoring and evaluation of risk exposure, stress testing, and scenario analysis. Such practices help identify vulnerabilities and ensure that risk mitigation measures are in place. Additionally, fostering a risk-aware culture within the organization is vital. This includes training and educating employees on risk management practices and encouraging transparent communication and reporting of risks. Compliance risk, driven by regulatory changes, is another significant aspect of risk management in oil and gas trading. Staying abreast of regulatory developments and ensuring compliance with international and local regulations is essential to avoid legal penalties and reputational damage. Implementing robust compliance programs and leveraging technology to monitor regulatory changes can help firms stay compliant. In conclusion, optimizing risk management in oil and gas trading requires a holistic approach that combines financial hedging, advanced analytics, comprehensive risk assessment, and compliance management. By adopting these strategies, firms can effectively mitigate risks, enhance decision-making, and achieve sustainable growth in a highly volatile industry. Keywords: Optimizing, Risk Management, Oil and Gas, Trading, Analysis.

Risk assessment of gas pipeline using an integrated Bayesian belief network and GIS: Using Bayesian neural networks for external pitting corrosion modelling

AbstractCorrosion poses a great risk to the integrity of oil and gas pipelines, leading to substantial investments in corrosion control and management. Several studies have been conducted on accurately estimating the maximum pitting depth in oil and gas pipelines using available field data. Some of the frequently employed machine learning techniques include artificial neural networks, random forests, fuzzy logic, Bayesian belief networks, and support vector machines. Despite the ability of machine learning methods to address a variety of problems, traditional machine learning methods have evident limitations, such as overfitting, which can diminish the model's generalization capabilities. Additionally, traditional machine learning models that provide point estimations are incapable of addressing uncertainties. In the current study, a Bayesian neural network is proposed to include uncertainty in estimating the corrosion defect of a pipeline exposed to external pitting corrosion. The results are then incorporated into a Bayesian belief network for evaluating the probability of failure and its corresponding consequences in terms of social impact, thus forming a comprehensive risk assessment framework. The results of the Bayesian neural network are validated using field data and achieved a testing accuracy of 90%. The framework of the study offers a powerful decision‐making tool for the integrity management of pipelines against external corrosion.

A comprehensive risk assessment framework for occupational health and safety in pharmaceutical warehouses using Pythagorean fuzzy Bayesian networks

Risk assessment plays a crucial role in managing occupational health and safety in various industries, including pharmaceutical warehouses. Bayesian Networks (BN) have been widely employed for risk assessment due to their ability to handle uncertainty and quantify risks. However, the traditional BN approach has limitations in dealing with ambiguity and continuous variables. To address this, the fuzzy BN technique, combining fuzzy logic with BN, has emerged as an effective method for risk assessment. In this study, a fuzzy BN model using Pythagorean fuzzy sets is proposed for risk assessment in a pharmaceutical warehouse. The model incorporates 24 identified risk factors, and survey data is used to determine the conditional probabilities of these factors. The novelty of the study lies in the application of Pythagorean fuzzy sets and the development of risk assessment criteria specifically for pharmaceutical warehouses. The results of a comprehensive literature review and the proposed methodology are presented. A real case analysis is conducted, followed by validation and sensitivity analysis. The results provide valuable insights into enhancing occupational health and safety practices in pharmaceutical warehouses. The study contributes to enhancing occupational health and safety practices in pharmaceutical warehouses and provides a framework for future research.

Assessing risks for the use of drones for wind turbine inspections

This paper presents a comprehensive risk assessment framework for integrating drones into wind turbine inspections, addressing technical, operational, human factors, environmental, and regulatory risks. Utilizing a multidimensional approach based on Failure Modes Effects and Criticality Analysis (FMECA), the study evaluates potential risks in drone-assisted wind turbine inspections, considering various factors like communication reliability, sensor functionality, battery performance, and environmental impacts. The research emphasizes the importance of systematic risk identification, evaluation, and prioritization in enhancing the safety and reliability of drone operations in the renewable energy sector. The framework’s practical application is demonstrated through a case study involving real-world offshore wind farms, engaging industry experts and employing structured data collection methods. The study identifies, assesses, and offers mitigation strategies for critical risks, underlining the significant potential of drones in optimizing maintenance processes and advancing sustainable energy production. Future research directions include refining risk assessment methodologies, advancing drone technology, and fostering international collaboration for broader adoption of drone-based inspections in renewable energy.

From drought hazard to risk: A spring wheat vulnerability assessment in the Canadian Prairies

The Canadian drought monitor (CDM) provides monthly assessments of drought severity but does not provide guidance on the potential agricultural impacts. This study extends the CDM to a comprehensive risk assessment framework by assessing the vulnerability of spring wheat to drought timing and severity in the Canadian Prairies. To achieve this, a bayesian neural network (BNN) model was developed that predicts high-resolution wheat yields and uncertainties using the CDM and normalized difference vegetation index (NDVI) data from 2010 to 2021. Model performance is comparable to previous studies, with errors ranging from 13% to 18% of mean yields and R2 values from 0.43 to 0.70 that peak in early August. The model was then applied under various drought conditions to evaluate how different drought timings and severities affect yield vulnerability. The results demonstrate that drought exposure from June to August reduces wheat yields in proportion to drought severity while abnormally dry conditions may have a positive impact of yield, particularly in August. Yield uncertainty decreases substantially from June to July while yield prediction accuracy increases, suggesting that mitigation efforts are most effective prior to July. The final component of the research involved creating operational risk maps that integrate the yield vulnerability assessment with the CDM. These maps were applied in a case study to demonstrate their utility in providing actionable, real-time information for agricultural stakeholders. Overall, this study contributes to the body of knowledge regarding crop sensitivities to varying drought conditions and represents a methodological advancement by enabling high-resolution, operational agricultural risk assessments at scale. By integrating detailed vulnerability data with drought hazard monitoring from the CDM, the information provided by these risk maps can be leveraged by agricultural stakeholders in near real-time to make well-informed decisions that mitigate the impacts of drought on wheat production, enhancing the resilience of agricultural systems to climatic variability.

Responsible Integration of Large Language Models (LLMs) in Navy Operational Plan Generation

This paper outlines an approach for assessing and quantifying the risks associated with integrating Large Language Models (LLMs) in generating naval operational plans. It aims to explore the potential benefits and challenges of LLMs in this context and to suggest a methodology for a comprehensive risk assessment framework.

A Risk Assessment Framework for Cyber-Physical Security in Distribution Grids with Grid-Edge DERs

Integration of inverter-based distributed energy resources (DERs) is reshaping the landscape of distribution grids to fulfill the socioeconomic, environmental, and sustainability goals. Addressing the technological challenges of DER grid integration requires an adaptive communication layer for efficient DER management and control. This transition has given rise to a cyberphysical system (CPS) architecture within the distribution system, causing new vulnerabilities for cyberphysical attacks. To better address potential threats, this paper presents a comprehensive risk assessment framework for cyberphysical security in distribution grids with grid-edge DERs. The framework incorporates a detailed CPS model accounting for dynamic DER characteristics within the distribution grid. It identifies vulnerabilities in DER communication systems, models attack scenarios, and addresses communication latency crucial for inverter control timescales. Subsequently, the quantification of attack impacts employs an attack probability model including both the vulnerability and criticality of cyber components. The proposed risk assessment framework was validated through testing on the modified IEEE 13-node and 123-node test feeders.

Risk assessment of water inrush accident during tunnel construction based on FAHP-I-TOPSIS

This study introduces an innovative risk assessment method, namely FAHP-I-TOPSIS, for evaluating water inrush risks during tunnel construction. Its primary objective is to address the challenges posed by various fuzzy and uncertain factors in risk assessment, aiming to enhance the reliability of on-site risk evaluation. The method combines the Fuzzy Analytic Hierarchy Process for weight calculation and the Ideal Solution Similarity Ranking Preference Interval Technique for risk interval computation. Firstly, a comprehensive risk assessment framework is established, categorizing factors influencing tunnel water inrush into geological factors (Ni), construction technology and management (Mi), and the inherent vulnerability of the tunnel itself (Vi). These three indicator layers are further subdivided based on corresponding risk factors. The method is applied to the risk analysis of water inrush in the Shijingshan Tunnel in Zhuhai, analysing the risk weights of 16 influencing factors. It is observed that cracks and fault zones have the most significant impact on the occurrence of the incident. Through a comparison with traditional TOPSIS and VIKOR methods, which calculate a single risk value, the study demonstrates that FAHP-I-TOPSIS effectively integrates various fuzzy and uncertain factors, providing a reasonable level of risk assessment. It enhances adaptability to on-site errors, significantly improving the robustness of water inrush risk assessment during tunnel construction and sustainable management.

Comprehensive Risk Assessment Framework for Flash Floods in China

Accurately assessing the risk of flash floods is a fundamental prerequisite for defending against flash flood disasters. The existing methods for assessing flash flood risk are constrained by unclear key factors and challenges in elucidating disaster mechanisms, resulting in less-than-ideal early warning effectiveness. This article is based on official statistics of flash flood disaster data from 2017 to 2021. It selects eight categories of driving factors influencing flash floods, such as rainfall, underlying surface conditions, and human activities. Subsequently, a geographical detector is utilized to analyze the explanatory power of each driving factor in flash flood disasters, quantifying the contribution of each factor to the initiation of flash flood; the flash flood potential index (FFPI) was introduced to assess the risk of flash flood disasters in China, leading to the construction of a comprehensive assessment framework for flash flood risk. The results indicate that (1) Flash floods are generally triggered by multiple factors, with rainfall being the most influential factor, directly causing flash floods. Soil type is the second most influential factor, and the combined effects of multiple factors intensify the risk of flash floods. (2) The southeastern, southern, and southwestern regions of China are considered high-risk areas for flash floods, with a high danger level, whereas the northwestern, northern, and northeastern plain regions exhibit a lower danger level. The above research results provide reference and guidance for the prevention and control of flash flood disasters.

Sustainable supply chain control strategies for the natural stone market

The natural stone market is an important component of the modern economy, with increasing demand driven by a growing population and urbanization. However, the industry faces a range of challenges related to supply chain risk management, including sourcing raw materials, transportation, and quality control. Effective risk management is crucial for the success of any business in this sector, as it helps to minimize losses, maintain quality, and ensure timely delivery of products to customers. This paper proposes the use of a flexible decision-making approach to classify major risks and identify Effective mitigation strategies for order management in the natural stone market. The proposed method considers multiple criteria, including financial, operational, and mitigation factors, to develop a comprehensive risk assessment framework that can be used to optimize control processes. By implementing the proposed framework, stakeholders in the natural stone market can improve their risk management capabilities and enhance their competitive advantage in the global market. The framework can help to identify the most critical risks and prioritize mitigation strategies, resulting in a more efficient and Effective supply chain management system. This paper presents a real case study of a Brazilian company, which illustrates the practical application of the proposed methodological framework in the natural stone market, highlighting its Effectiveness in identifying and mitigating risks related to stone slabs transportation network. The insights gained from this case study have potential for application across various industries facing analogous challenges in managing risks within their supply chains.

An intelligent Fusion Framework for Risk Assessment of Autonomous Ship through Functional Mapping Criterion Sub-Intervals into Single Interval Method

Nowadays, intelligent information technology can implement high-level information processing and decision-making activities that can support risk assessment of autonomous. Risk assessment is a critical process for deploying autonomous ships, ensuring these innovative vessels' safe and efficient operation. There is a need to identify, analyze, and mitigate potential risks associated with system reliability, collision avoidance, cybersecurity, environmental conditions, human interaction, regulatory compliance, sensor performance, data integrity, emergency response, and testing and validation. This work provides an overview of the essential considerations and objectives of risk assessment in autonomous boats. We used the multi-criteria decision-making model to deal with various criteria. The Ranking of Alternatives through Functional Mapping Criterion Sub-Intervals into Single Interval (RAFSI) method is applied to rank the alternatives. We used the ten criteria and twenty options in this study. The results show that the proposed framework can provide a comprehensive risk assessment framework that can enable stakeholders to gain insights into potential hazards and vulnerabilities unique to autonomous ships.

Risk assessment and regulation between the supply and demand of ecological products: A comprehensive framework and case study

Humans are facing increasing supply–demand risks of ecological products (EPs) due to the uncertainty of the interaction between ecosystems and social systems. To address this issue, a comprehensive framework for risk assessment and regulation is urgently need to identify areas and levels of risk that threaten human well-being. In this study, we constructed a framework for retrospective assessment and optimal regulation of supply–demand risks of EPs, which covers four indicators: the supply–demand index (SDI), the changing trend of SDI, the changing trend of supply, and trade-offs and synergies. Taking Qinghai Province in the northeast of the Qinghai-Tibet Plateau in China as an example, we conducted a case study on five types of EPs: food production, water conservation, carbon sequestration, soil retention, and outdoor recreation. Our results showed that various types of EPs were basically in the state of supply exceeds demand, with significant spatial differences in supply–demand risk levels. Areas with high risk levels were mainly distributed in urban concentrated development areas in the eastern part of the research area, and the spatial matching of supply and demand in these areas often present high supply and high demand. In addition, for the supply–demand risk of different levels of EPs, we proposed intra-regional and cross-regional optimization regulation strategies, combining the spatial matching status of supply and demand of EPs. It can help decision-makers improve the efficiency of the allocation of EPs supply and demand and the scientific nature of policy formulation at a regional scale.

A risk assessment framework using neutrosophic theory for the halal supply chain under an uncertain environment

PurposeThe complex network structure causes several disruptions in the supply chain that make risk management essential for supply chain management including halal supply chain (HSM). During risk management, several challenges are associated with the risk assessment phase, such as incomplete and uncertain information about the system. To cater this, the authors propose a risk assessment framework that addresses the issues of uncertainty using neutrosophic theory and demonstrated the applicability of the proposed framework through the case of halal supply chain management (HSCM).Design/methodology/approachThe proposed framework is using the capabilities of the neutrosophic number which can handle uncertain, vague and incomplete information. Initially, the risk related to the HSC is identified through a literature review and expert’s input. Further, the probability and impact of each HSM-related risk are assessed using experts’ input through linguistic terms. These linguistic values are transformed into single-value trapezoidal neutrosophic numbers (SVTNNs). Finally, the severity of each HSM-related risk is determined through the multiplication of the probability and impact of each risk and prioritised the risks based on their severity.FindingsA comprehensive risk assessment framework is developed that could be used under uncertainty. Initially, 16 risks are identified related to the HSM. Further, the identified risks are prioritised using the severity of the risks. The high-priority risk is “raw material status”, “raw material wholesomeness” and “origin of raw material” while “information integrity” and “people integrity” are low-priority risks.Practical implicationsHSM risk can be effectively assessed through the proposed framework. The proposed framework applied neutrosophic numbers to represent real-life situations, and it could be used for other supply chains as well.Originality/valueThe proposed method is effectively addressing the issue of linguistic subjectivity, inconsistent information and uncertainty in the expert’s opinion. A case study of the HSC is adopted to illustrate the efficiency and applicability of the proposed risk framework.

A quantity-distribution synthesized framework for risk assessment of algal blooms

Algal blooms threaten water safety and quality, particularly in lakes with vulnerable environment and advanced urbanization. Due to low water exchange rate and multiple human activity influences, algal blooms in inland lakes display highly dynamic spatio-temporal distributions. This paper proposes a comprehensive framework for risk assessment of algal bloom coupling quantity and distribution based on remote sensing observations. The framework comprises algal bloom recognition, spatial zonation, quantity-distribution multivariate joint distribution construction, and risk assessment. The framework was applied to China's giant shallow and algae-prone lake, Chaohu Lake, to assess the annual algal bloom risk from 2000 to 2021. The results showed that this proposed method could achieve a more comprehensive and objective risk assessment by coupling the quantity and distribution of algal blooms. Furthermore, the internal dependence of algal bloom fluctuation in different zones was explicitly presented. This framework promotes systematic integration of algal bloom risk assessment, taking into account its highly spatio-temporal dynamic feature. It creates a quantitative approach to gain insights into the evolution of quantity and distribution in various environmental assessments.

A Brief Survey on Employing Machine Learning to Assess Risk Elements and Predict Lifespan in Heart Failure Patients

Heart failure continues to be a major global Wellness challenge due to its increased mortality rate and substantial financial consequences. In response to this urgent issue, the research paper investigates the methodologies, specifically the Survival Neural Network (Survival NN) and the Cox Proportional Hazards (Cox PH) model, to scrutinize risk factors along with predicting the life expectancy of individuals with heart failure. By applying strict feature selection and engineering approaches, we determine the most critical criteria that impact the prognosis of heart failure and pave the way for the creation of a comprehensive risk assessment framework. The Cox Proportional Hazards model facilitates the development of a trustworthy risk assessment instrument that takes the time-to-event component of heart failure outcomes into account. Furthermore, predictive abilities are enhanced by the Survival Neural Network (Survival NN) model integration by recognizing intricate, nonlinear patterns in the dataset. Combining the Survival Neural Network (Survival NN) with the Cox Proportional Hazards model allows for the development of real-time alerting and monitoring systems, which may reduce hospital readmission and mortality rates and help medical professionals make well-informed decisions. Our goal is to transform the way heart failure is treated, which will enhance patient care and the efficiency of the healthcare system.

Quantifying Arctic oil spilling event risk by integrating an analytic network process and a fuzzy comprehensive evaluation model

The development of economic globalization and increasing ice melting in Arctic area enables Arctic shipping activities more convenient. Exploring impact of extreme events (i.e., oil spilling) occurring in the Arctic region attracts increasing interest from the shipping industry and maritime traffic community. For the sustainable Arctic development and marine environment protection, it is in need to develop a comprehensive risk assessment framework to suppress potential loss caused by Arctic oil spill event. To that aim, the study quantifies the Arctic oil spill event impact with an ensemble framework supported by the analytic network process (ANP) and fuzzy comprehensive evaluation model. More specifically, we quantify varied factors (e.g., environment, economy, social influence, recovery mode) and establish oil spilling risk level via the help of fuzzy comprehensive evaluation model in the proposed framework. Experimental results suggest that our proposed framework can accurately quantify influence of an oil spilling event in Arctic area, and thus the potential risk level can be accurately identified. The proposed framework provides a novel and reasonable decision-making tool, which can help Arctic related communities conduct varied activities in a more reasonable yet lower-risk manner.

Reliable, robust, and comprehensive risk assessment framework for urban autonomous driving

Abstract Urban autonomous driving is both complex and dangerous. A multitude of road types, road users, and potential traffic rule violations all make it challenging for an autonomous vehicle to safely navigate an urban environment. To tackle this issue, in this paper, we propose a risk assessment framework capable of reliably predicting and assessing the possibility of collision in urban environments involving the concurrent occurrence of various road types, road users, and traffic rule violations. The proposed framework reliably predicts and assesses the possibility of collision by means of long-term motion prediction–based risk identification, unified risk measures, and probabilistic risk reasoning under a distributed reasoning architecture. The framework is tested and evaluated through real-world testing conducted not only on private urban test roads under a great variety of driving scenarios but also on public urban roads under real traffic conditions. Experimental results show the performance of the framework to be sufficiently reliable for urban autonomous driving in terms of risk assessment.