Risk Events Research Articles

Risk Management Practices among Micro, Small, and Medium Enterprises (MSMEs): Basis for Risk Assessment Framework

Understanding the risk management practices used by MSMEs is crucial in order to ensure their long-term growth and survival. This study aims to determine the risk management practices among Micro, Small and Medium Enterprises (MSMEs) in the City of San Jose Del Monte, Bulacan which would be the basis for the risk assessment framework. One hundred MSMEs owners’ respondents were considered for this study which utilized quantitative approach and descriptive research design. A survey questionnaire determined how MSMEs deal with different risk they face and what practices they need to implement and lessen the risk that might have an impact on the achievement of their strategic goals. This study employed the percentage, mean, and analysis of variance. Market risks such as equity price, interest rate, foreign exchange, and commodity risks has been perceived by the respondents as the most likely or has a significant likelihood of encountering this challenge. The assessment of risk management practices reflected a positive outlook, with respondents generally agreeing on the effective implementation of key practices like enterprise risk management, objective setting, internal environment, monitoring, event identification, and information communication.

Improving Resilience in Water Distribution Systems: An Application of the House of Risk Method at PDAM Gowa Unit Tompobulu

PDAM Gowa, a local government-owned company, plays a vital role in supplying clean water to the Tompobulu community in Gowa District. However, ensuring efficient and reliable water distribution poses challenges at the local and regional levels. This research focuses on enhancing the resilience of the water distribution system to mitigate various risks. The study utilizes the House of Risk (HOR) method for effective risk management and developing suitable mitigation strategies within PDAM's framework. The research identifies 10 risk events and 17 risk factors that affect water distribution in PDAM. Key risks include pipe leakage and supply disruptions. To mitigate these risks, the study proposes nine actionable recommendations for PDAM Gowa Unit Tompobulu. These recommendations involve regular maintenance of distribution infrastructure and equipment to ensure system quality and reliability. Additionally, improving pipe network condition monitoring, implementing leakage control policies, and fostering collaboration with stakeholders to address water supply disruptions are crucial mitigation measures. This research significantly contributes to understanding the risk management associated with water distribution in PDAM. By implementing the recommended mitigation strategies, PDAM Gowa Unit Tompobulu can reduce water distribution risks, enhance system efficiency, and provide improved clean water services to consumers. Moreover, the study has the potential to drive scientific progress and promote the development of best practices and technologies in the drinking water industry.

Emotional differences based on comments on doctor-patient disputes with varying levels of severity.

The risks associated with negative doctor-patient relationships have seriously hindered the healthy development of medical and healthcare and aroused widespread concern in society. The number of public comments on doctor-patient relationship risk events reflects the degree to which the public pays attention to such events. To explore public emotional differences, the intensity of comments, and the positions represented at different levels of doctor-patient disputes. Thirty incidents of doctor-patient disputes were collected from Weibo and TikTok, and 3655 related comments were extracted. The number of comment sentiment words was extracted, and the comment sentiment value was calculated. The Kruskal-Wallis H test was used to compare differences between each variable group at different levels of incidence. Spearman's correlation analysis was used to examine associations between variables. Regression analysis was used to explore factors influencing scores of comments on incidents. The study results showed that public comments on media reports of doctor-patient disputes at all levels are mainly dominated by "good" and "disgust" emotional states. There was a significant difference in the comment scores and the number of partial emotion words between comments on varying levels of severity of doctor-patient disputes. The comment score was positively correlated with the number of emotion words related to positive, good, and happy) and negatively correlated with the number of emotion words related to negative, anger, disgust, fear, and sadness. The number of emotion words related to negative, anger, disgust, fear, and sadness directly influences comment scores, and the severity of the incident level indirectly influences comment scores.

A new method for measuring financial resilience

This paper proposes a new model to measure financial resilience from the perspective of external risk shocks, which composes of three submodels, namely, the dynamic factor model, the time-varying parameter vector autoregression (TVP-VAR) model, and the resilience characteristic measurement model with two dimensions: absorption intensity and absorption duration. At the theoretical level, we simulate and analyze the changing paths of financial resilience under different scenarios. At the empirical level, we apply the model to study the resilience of the UK financial market. The results indicate that the UK financial resilience fluctuations exhibit phased characteristics and there is a clear inverse relationship between absorption intensity and absorption duration. Notably, periods of low resilience often coincide with specific risk events.

Event Identification for Supply Chain Risk Management Through News Analysis by Using Large Language Models

AbstractEvent identification is important in many areas of the business world. In the supply chain risk management domain, the timely identification of risk events is vital to ensure the success of supply chain operations. One of the important sources of real-time information from across the world is news sources. However, the analysis of large amounts of daily news cannot be done manually by humans. On the other hand, extracting related news depends on the query or the keyword used in the search engine and the news content. Recent advancements in artificial intelligence have opened up opportunities to leverage intelligent techniques to automate this analysis. This paper introduces the LUEI framework, a lightweight framework that, with only the event’s name as input, can autonomously learn all the related phrases associated with that event. It then employs these phrases to search for relevant news and presents the search engine results with a label indicating their relevance. Hence, by conducting this analysis, the LUEI framework is able to identify the occurrence of the event in the real world. The framework’s novel contribution lies in its ability to identify those events (termed as the Contributing Events (CEs)) that contribute to the occurrence of a risk event, offering a proactive approach to risk management in supply chains. Pinpointing CEs from vast news data gives supply chain managers actionable insights to mitigate risks before they escalate.

Dynamic risk early warning system for tunnel construction based on two-dimensional cloud model

Due to the uncertainty of geological conditions and construction status during tunnel construction, the risk of tunnel construction will vary with updates to environmental information. The current risk management practice for tunnel construction projects in China is static and relies on the subjective judgment of experts and practitioners, so it is necessary to propose a risk assessment methodology that takes into account dynamically updated risk information and uncertainty. This paper proposes a dynamic risk warning system for tunnel construction that integrates knowledge-driven consequence datasets and data-driven likelihood datasets of risk events, using a probability algorithm based on a two-dimensional cloud model for risk decision-making. The algorithm quantifies the risk probabilities by randomly simulating the risk discrimination results under the influence of uncertainty multiple times and statistically analyzing the distribution pattern of a large number of simulation results within the constructed quantitative risk matrix. Decisions on risk level and warning state are made based on risk probabilities. Moreover, this study conducts extensive simulation tests to determine the optimal number of simulated cloud droplets and develops a software toolbox to facilitate rapid feedback on tunnel construction risk. The proposed algorithm is applied to the construction of Guiyang Metro Line 3. The results show that the standard deviation of the simulation results is less than 0.025 and the algorithm has good stability. The two-dimensional cloud model offers a superior reflection of risk uncertainty compared with its one-dimensional counterpart, making it more appropriate for assessing the risks associated with tunnel construction—a complex scenario characterized by intricate index relationships and significant uncertainty.

Hydrogen leakage risk analysis of hydrogen emergency power supply vehicles using the AET-RM method

In recent years, research and application of hydrogen fuel emergency power supply vehicle technologies have been steadily advancing. However, due to the highly flammable and explosive nature of hydrogen gas, the safety risk of hydrogen leakage has been taken seriously. Addressing the current inadequacies in the understanding of hydrogen leakage risks, unclear sources of risk, incomplete coverage of risk scenarios, and insufficiencies in risk assessment methods associated with hydrogen fuel emergency power supply vehicles, this study firstly establishes a multi-scenario and multi-factor coupled hydrogen leakage risk event database and an accident risk system. This effort focuses on the diversity of application scenarios, types of hydrogen leakage hazards and complexity of interfering factors. Subsequently, by refining existing risk assessment methods, a more balanced subjective-objective evaluation and more granular risk ratings approach, termed AET-RM, is proposed, which ensures finer risk grading. Finally, through this method, the types of hydrogen leakage hazards and their harm boundaries are clarified, achieving a systematic and precise graded quantitative assessment of risk sources, probabilities, and consequences. The study results indicate that the established event database contains no risk events rated as I (acceptable) or V (unacceptable). There are 37 events classified as low risk (rating II), accounting for 17.54% of the total. Medium-risk events (rating III) comprise 142 incidents, making up 67.30%, while high-risk events (rating IV) include 32 cases, representing 15.17%. The highest risk score, determined by the AET-RM method, pertains to a risk event in the power generation scenario, Contingencies: metal corrosion within the battery.

Predictive value of inflammatory prognostic index for contrast-induced nephropathy in patients undergoing coronary angiography and/or percutaneous coronary intervention

The purpose of this study was to investigate the relationship between Inflammatory Prognostic Index (IPI) levels and Contrast-Induced Nephropathy (CIN) risk and postoperative clinical outcomes in patients undergoing coronary angiography (CAG) and/or percutaneous coronary intervention (PCI). A total of 3,340 consecutive patients who underwent CAG and/or PCI between May 2017 and December 2022 were enrolled in this study. Based on their baseline IPI levels, patients were categorized into four groups. Clinical characteristics and postoperative outcomes were compared among these groups. In-hospital outcomes focused on CIN risk, repeated revascularization, major bleeding, and major adverse cardiovascular events (MACEs), while the long-term outcome examined the all-cause readmission rate. Quartile analysis found a significant link between IPI levels and CIN risk, notably in the highest quartile (P < 0.001). Even after adjusting for baseline factors, this association remained significant, with an adjusted Odds Ratio (aOR) of 2.33 (95%CI 1.50–3.64; P = 0.001). Notably, baseline IPI level emerged as an independent predictor of severe arrhythmia, with aOR of 0.50 (95%CI 0.35–0.69; P < 0.001), particularly driven by the highest quartile. Furthermore, a significant correlation between IPI and acute myocardial infarction was observed (P < 0.001), which remained significant post-adjustment. For patients undergoing CAG and/or PCI, baseline IPI levels can independently predict clinical prognosis. As a comprehensive inflammation indicator, IPI effectively identifies high-risk patients post-procedure. This study underscores IPI's potential to assist medical professionals in making more precise clinical decisions, ultimately reducing mortality and readmission rates linked to cardiovascular disease (CVD).

The Place and Approaches of Family Medicine Doctors in the Health Management of CBRN Events: The Case of Türkiye

In this study, it was aimed to determine the level of knowledge of Family Medicine residents about Chemical, Biological, Radiological and Nuclear (CBRN) events, their risk assessments and their thoughts on the roles that CBRN events can play in healthcare services. As a multi-center and cross-sectional analytical study, 444 family medicine residents at the national level were included in the study via an online survey application method. The data form included a total of 25 questions prepared to evaluate the sociodemographic information of the participants and their evaluation, awareness, knowledge level, approach to events and general opinions regarding CBRN event risks. 91.7% of physicians evaluated their knowledge level as "very little" or "little" and 95.7% evaluated their preparation level as "very little" or "little". 77.3% of physicians think that family physicians should have a role in CBRN incidents. For a possible CBRN event in the next 5 years, physicians predict biological events as the highest risk and nuclear events as the lowest risk. The rate of physicians who stated that they had previously received CBRN training was 17.8%, and the rate of those who participated in the CBRN drill was only 0.9%. While 89.9% of the participants wanted to receive training on health management in CBRN incidents; 80.2% want the training to be included in the specialty curriculum. While the participants' level of knowledge about the symptoms that may develop after a CBRN event is high, when asked about the awareness of CBRN risks around their institutions, 79.3% of physicians stated that they were not aware. It was determined that the level of knowledge, preparation, training, practice and awareness of family physicians regarding CBRN events was low. On the other hand, their willingness to receive training and their belief that they had a role in CBRN incidents were found to be high. According to the data of our study, we evaluate that there is a need for institutional structuring against CBRN risks, including primary health care services, and we suggest that CBRN training modules should be included in specialty curricula, starting from medical faculties.

The higher-order moments connectedness between rare earth and clean energy markets and the role of geopolitical risk:New insights from a TVP-VAR framework

Against the backdrop of escalating geopolitical risks, the interdependence between rare earth and clean energy markets are crucial for cross-market investors and regulators. However, previous research has not adequately examined the higher-order moment connections between these markets. This study addresses this gap by integrating GRACHSK and TVP-VAR methods to explore the asymmetric and tail risk connectedness between rare earth and solar, wind, nuclear, and electric vehicle markets. Further, we investigated how geopolitical risks impact these connections over time. The results reveal significant higher-order moment connectedness between the clean energy and rare earth markets, each serving as an exporter and receiver, respectively. Among these, wind energy and solar energy exhibit significant net spillovers of skewness and kurtosis to rare earths. These high-order moment spillovers are time-varying and exacerbate during major geopolitical crises, such as the COVID-19 pandemic and the Russian-Ukraine war. The portfolios with minimum connectedness are effective at hedging extreme risks, particularly in terms of kurtosis. It implies that portfolios constructed based on higher-order moments can better hedge against extreme risks. GPR exacerbates higher-order moment spillovers between rare earth and clean energy markets in the short- and medium-term. In particular, the impulse response of kurtosis spillovers to GPR shocks shows prominent and frequent fluctuations, indicating that extreme risk spillover effects with significant lag effects would rapidly diffuse across various markets when geopolitical risks occur. The research findings contribute to three aspects: First, the connectedness analysis offers a network and dynamic perspective on extreme risk management between rare earth and clean energy markets. Second, higher-order moments can enhance the returns of diversified cross-market investment portfolios. Third, the conclusions aid in understanding the evolving patterns of rare earth and clean energy markets under geopolitical risk events, facilitating timely investment and regulatory strategies.

Optimism bias and its impact on cyber risk management decisions

This study explores how optimism bias influences decision-making in cyber risk management by developing a novel model that reflects utility loss aversion, a factor previously unexplored in this context. We find that decision-makers with self-protection as reference point are less likely to invest in other cyber risk management measures, providing support for optimism bias observed in the cyber-insurance market. We also show that decision-makers with higher loss aversion tend to not invest in other cyber risk management measures. Our results help to explain the lack of demand for cyber-insurance and have important implications for corporate risk management and public policy on cyber risk. They also help better understand cyber risk events which can trigger huge systemic consequences for economies and societies.

Violencia institucional en comisarías de familia de Boyacá, Colombia

This study aimed to analyze cases of institutional violence and rights violations at Family Police Stations in the handling of gender violence cases in the provinces of Márquez and Centro, in the department of Boyacá, Colombia. The research methodology was qualitative, supported by a narrative design of topics; the information collection technique was the semi-structured interview, the analysis method was axial coding, and the information sources were selected through non-probabilistic convenience sampling. A total of 40 women in the early and middle stages of their adult life cycle participated. The results allowed for the identification that the most common violations are centered around the revictimization of the traumatic experience when coming into contact with the office professionals to report and in the articulation with entities for expert examinations, coerciveness and questioning in the description of the punishable facts, lack of taking provisional protection measures not adjusted to the case allowing the recurrence of risk events by the aggressor, negligence in the follow-up of the adopted measures, and bureaucracy in the administrative procedures that facilitate immediate action against non-compliance with protection measures and assessment to establish the risk profile. It is concluded that this is generated due to factors such as improper diligence in the attention phases and the lack of complementary training for the attention in cases of gender violence.

A method for visualizing urban road events using distributed acoustic sensing

This study presents the construction of an urban underground sensing system using distributed acoustic sensing (DAS) technology, which utilizes the existing optical fiber infrastructure around urban roads for communication. To address the challenges posed by the complexity and variability of DAS data in infrastructure monitoring environments such as urban roads, as well as the difficulty and poor effectiveness of raw data visualization, a novel method for visualizing DAS data is proposed. This method involves preprocessing the data through wavelet threshold denoising, combining it with the root-mean-square (RMS) energy index to generate a visualization, and applying the dynamic threshold method to remove and suppress abnormal data indicators. Finally, this paper tested the visualization performance to assess the effectiveness of the proposed method in improving urban road safety management. The study focused on three typical urban road safety risk events: vehicle driving, construction, and road subsurface cavity incidents. The results demonstrate the efficacy of the data visualization method, showing improved visualization of vehicle trajectory directions and numbers, construction segment behaviors, and approximate road subsurface cavity locations in the time domain compared to the original data.

The effects of regular home delivery of HIV self-testing and follow-up counselling on HIV testing and prevention outcomes in men who have sex with men who test infrequently in the United States: a pragmatic, virtual randomized controlled trial.

Past research shows that HIV self-testing (HIVST) can increase testing and facilitate more HIV diagnoses relative to clinic testing. However, in the United States, the use of HIVSTs is limited due to concerns that those who use HIVST could be less likely to be linked to care. From January 2019 to April 2022, we recruited 811 men who have sex with men (MSM) in the United States who tested infrequently using an online marketing campaign and randomized them 1:1:1 to receive one of the following every 3 months for a year: (1) text message reminders to get tested at a local clinic (control); (2) mailed HIVST kits with access to a free helpline (standard HIVST); and (3) mailed HIVST kits with counselling provided within 24 hours of opening a kit (eTest). Quarterly follow-up surveys assessed HIV testing, sexually transmitted infection (STI) testing, pre-exposure prophylaxis (PrEP) use and sexual risk behaviour. Eight participants were diagnosed with HIV, and all but one were through HIVST. Participants in either HIVST condition, standard or eTest, had significantly higher odds of any testing (OR = 7.9, 95% CI = 4.9-12.9 and OR = 6.6, 95% CI = 4.2-10.5) and repeat testing (>1 test; OR = 8.5, 95% CI = 5.7-12.6; OR = 8.9, 95% CI = 6.1-13.4) over 12 months relative to the control group. Rates of STI testing and PrEP uptake did not differ across study condition, but those in the eTest condition reported 27% fewer sexual risk events across the study period relative to other groups. HIVST vastly increased testing, encouraged more regular testing among MSM, and identified nearly all new cases, suggesting that HIVST could diagnose HIV acquisition earlier. Providing timely follow-up counselling after HIVST did not increase rates of STI testing or PrEP use, but some evidence suggested that counselling may have reduced sexual risk behaviour. To encourage more optimal testing, programmes should incorporate HIVST and ship kits directly to recipients at regular intervals.

Application of neural network technologies to assess the competence of personnel in the tasks of controlling the operational risk of a credit institution

The article is devoted to issues of controlling the operational risks of a credit institution associated with the actions of personnel. Operational risk control is an important aspect of a credit institution’s business. Despite the fact that the Bank of Russia in regulatory documents described in detail the set of actions that banks should take to control operational risks, in practice credit institutions experience serious difficulties in dealing with operational risk associated with the actions of personnel. This may be explained, first, by the difficulty of identifying and formalizing the specified risk. One of the main sources of operational risks associated with personnel actions is employees’ lack of qualifications. This can lead to reduced availability and quality of services provided by credit institutions, as well as possible financial and reputational losses. The purpose of the research conducted by the authors is to improve the system of control of operational risks in a credit institution using artificial intelligence technologies, including the development of tools for assessing in an automated mode the level of criticality of the influence of personnel competence on the occurrence of operational risk events. To achieve this goal, an artificial neural network (ANN) was developed using the high-level Keras library in Python. This paper defines a set of key indicators that have the most significant impact on the possibility of operational risk associated with the actions of the personnel in a credit institution. The article presents the results of checking the generated sets of training and test data using application software packages that implement mathematical methods to assess the consistency of the generated data sets. The paper presents graphs showing the results of training and testing of the artificial neural network that has been constructed. The results obtained are new and may allow credit institutions to significantly increase the efficiency of their work by digitalizing the solution of tasks to control the level of operational risk associated with the actions of personnel.

Coupling characteristics of construction safety risk and complicated factors of metro station: a case study of Ningbo

ABSTRACT This paper investigates the coupling relationship and degree of complex factors corresponding to the metro station construction in the Ningbo region. To this end, the interpretative structural modeling (ISM) method, a structural interpretation model, is utilized to develop the coupling connection network of construction risk factors. Moreover, the cloud model theory and coupling degree model are used to calculate the risk factor coupling degree within and between the construction safety risk subsystems. The analysis indicates that the coupling degree of risk factors within and among subsystems is notably high, falling into categories characterized by high coupling and strong coupling. It is also found that the entire coupling system is in a state of high-strength coupling. Therefore, during the construction process, the coupling impact of risk factors must be avoided, and risk factors must be prevented from further developing into risk events. The quantitative evaluation of the coupling degree between and among risk factor subsystems in this study provides a reference for project risk evaluation and facilitates the development of subsequent construction safety measures.

The short-term intermittency evaluation of distributed photovoltaic power

Uncertainty of distributed photovoltaic(PV) power brings great challenges to the safe and stable operation of power system, in which the intermittency problem is more challenging than the fluctuation. This paper focuses on the intermittency problem of distributed PV power, firstly, an intermittency evaluation method based on generalized extreme value(GEV) theory is proposed to solve the problem of the lack of "small-sample big-gap" events. Secondly, focusing on high risk events such as high-frequency and warning mutation events, probabilistic evaluation indexes were established, including T-hour-period mutation intensity and T-hour-period mutation frequency. Finally, a two-dimensional joint probability distribution of intermittency elements is established, and the concepts of return period and return level are introduced into the photovoltaic field to provide data support for the configuration of energy storage capacity and duration, and to provide auxiliary decision-making for the execution time and development cycle of interactive projects. The proposed method provides a solution for intermittency evaluation in small sample status quo with strong data compatibility and engineering replicability.

MITIGASI RESIKO RANTAI PASOK PADA PASOKAN PENGGUNAAN BBM DI DEPO LOKOMOTIF PT KAI SIDOTOPO

Risk is a factor that always exists in every sector of life incluidng suppky usage in PT KAI. Based on the results and discussion of this research, the conclusion is that there are 20 risk agents and 20 risk events, of which 4 priority risk agents are lack of control over fuel hoses (A13), inaccurate measuring instruments (A7), Over limit Policy (A2) and lack of clear and measurable coordination (A1). Second, there are 6 mitigation actions, namely Modification of the queue and filling system (P1), Improvement of the train supervision and monitoring system with (P2), Training and coordination staff ( P3), Training of machinists and system machinists and managemen update (P4), Modification of fuel control data with by internet (P5), and Maintenance engginer properly and scheduled ( P6). Where the most effective solutions based on HOR Phase 2 are respectively PA 2, PA 3, PA 4 PA 6, PA 5 and PA 1. Meanwhile based on AHP they are PA 2, PA 1, PA 5, PA 6, PA 3 and PA 4. So the alternative solutions developed are PA 2, PA 3 and PA 4.

Overview of dyslipidemia and metabolic syndrome in myeloproliferative neoplasms.

Myeloproliferative neoplasms (MPNs) occur due to the abnormal proliferation of one or more terminal myeloid cell lines in peripheral blood. Subjects suffering from MPNs display a high burden of cardiovascular risk factors, and thrombotic events are often the cause of death in this population of patients. Herein, we provide a brief overview of dyslipidemia and metabolic syndrome and their epidemiology in MPNs and examine the common molecular mechanisms between dyslipidemia, metabolic syndrome, and MPNs, with a special focus on cardiovascular risk, atherosclerosis, and thrombotic events. Furthermore, we investigate the impact of dyslipidemia and metabolic syndrome on the occurrence and survival of thrombosis in MPN patients, as well as the management of dyslipidemia in MPNs, and the impact of MPN treatment on serum lipid concentrations, particularly as side/adverse effects reported in the context of clinical trials.

Forecasting Crude Oil Volatility Using the Deep Learning‐Based Hybrid Models With Common Factors

ABSTRACTBased on empirical evidence of the Chinese commodity futures volatility dynamics, we propose a novel and flexible hybrid model, denoted as SAE‐HAR‐DL, which combines a supervised autoencoder (AE) with the deep learning‐based HAR model framework to capture essential common factor information and uses the reconstruction error of the AE component as a regularizer to enhance the generalization ability of the testing subsample. The empirical findings strongly support the effectiveness of this model in accurately forecasting crude oil futures volatility in the post‐COVID‐19 era, compared to the HAR, HAR‐PCA, and HAR‐DL models. Moreover, a robustness check also demonstrates the positive contribution of common factors to the volatility prediction of other commodity futures. Notably, we establish that these common factors act as effective regularizers, mitigating prediction losses within the HAR model in extreme risk events such as the COVID‐19 pandemic and the Russia–Ukraine conflict.