Level Of Risk Research Articles

Assessment of the Level of Air Pollution and Aerogenic Risk to the Health of the People of Novocherkassk

Assessment of the Level of Air Pollution and Aerogenic Risk to the Health of the People of Novocherkassk

Gas Fire Coupling Control of Goaf Based on Numerical Simulation

ABSTRACT In response to the problem that traditional gas control methods such as water injection and inert gas fire extinguishing are not effective and have a high level of risk in goaf, this study uses COMSOL software to establish a three-dimensional numerical simulation model of goaf, and combines air leakage intensity, oxygen concentration, and floating coal thickness to calculate the gas fire hazard area, in order to achieve effective control. The results show that the deviation between the data obtained from the numerical simulation and the actual measurement value is about 7 m, which proves that the given solution is reliable. The gas concentration of the wind lane and the upper corner is controlled below 1%, which proves that this method ensures the safety of the working environment. The above results show that the design method realizes the effective control of gas fire in the goaf, and provides reference for mine safety management and gas disaster control.

Parental multiple sclerosis, changes in family routine and mental health of young children

Introduction: Parental illness generates changes in family dynamics and in the mental health of all family members. Children of parents with chronic illnesses, such as multiple sclerosis face unique challenges, as they are forced to adapt to a new reality where parental illness limits the care and attention capabilities towards them. Methods: a systematic literature review was conducted using the PsycInfo, Scopus and Medline databases, with the equation "multiple sclerosis"; AND "family"; AND "parents"; AND "child parent relations", conducted in September 2022. Results: n = 26 studies were included. Children of people with multiple sclerosis (MS) present more adjustment problems, anxiety disorders and behavioral disturbances than controls. This distress is associated with factors such as parental physical and psychological comorbidity and changes within family roles. The level of risk varies in relation to factors such as support network, sex of the ill parent, time of exposure to the disease and the role of the healthy parent. Lack of access to information and support turns out to be a common complaint of young children. Conclusions: Most of the children living with parental illness present experiences alterations in their mental health and show a higher prevalence of psychological distress; however, this does not occur in all cases. The most predominant protective factors seem to be: parental adherence to treatment, and, above all, access to effective information in accordance with the level of understanding of the different populations involved.

A Study of Central Bank Swap Line Use: Are They a Suitable Tool for the IMF in Maintaining Central Bank Liquidity for Developing Countries?

Developing countries do not have the same access for swap line use during times of economic stress, currently used by central banks in developed countries to maintain liquidity. Biblically, economic tools should favor all and not just developed nations. As Christians we are commanded to help others by bearing the burden of others and to help the poor. Of course, biblical wisdom is necessary, specifically an understanding of how exactly swap lines may be of benefit before making any recommendations, along with and considering the reality of level of risk when working with developing nations. Swap lines are a currency exchange between two banks where an agreed upon amount is traded-- a liquidity swap between two banks. The research on swap line use is limited, especially when related to use by developing countries. Since developing nations are more vulnerable to economic stress, expanding the use of swap lines from developed countries to developing nations appears reasonable and the IMF being the lender of last resort is the recommended organization to use them. The purpose of the study is to determine if swap lines maintain or improve liquidity between central banks during times of economic stress, which in turn will be considered for feasibility as an additional financial assistance tool the IMF can use for developing countries. A pragmatic mixed approach will be used to determine if swap lines are improving liquidity conditions with the capacity to deliver U.S. dollar funding to institutions in their jurisdiction during times of market stress. The comparisons consider capital flow and foreign exchange rate differences between countries that used swap lines and countries not using swap lines during periods of economic stress. The results will be considered for use by the IMF for developing countries.

Comparative Study of Machine Learning Based Diabetes Predictive System

Diabetes is one of the most lethal diseases in the world. It is also a precursor to various other disorders such as coronary failure, blindness, and kidney diseases. Patients often need to visit diagnostic centers to get their reports after consultation, which requires a significant investment of time and money. However, with the growth of machine learning methods, we now have the ability to address this issue. Advanced systems utilizing information processing can forecast whether a patient has diabetes or not. Furthermore, early prediction of the disease can provide patients with critical interventions before it fully develops. Data mining techniques can extract hidden information from large datasets of diabetes-related information. The aim of this research is to develop a system that can predict the diabetic risk level of a patient with higher accuracy. The model development is based on classification methods such as K-Nearest Neighbors, Decision Tree, and Support Vector Machine (SVM) algorithms. For K-Nearest Neighbors, the models achieve an accuracy of 71%, 78% for SVM, and 70% for the Decision Tree algorithm. The outcomes demonstrate a significant accuracy of these methods.

Interactive effects analysis of road, traffic, and weather characteristics on shared e-bike speeding risk: A data-driven approach

As electric bikes (e-bikes) rapidly develop in China, their traffic safety issues are becoming increasingly prominent. Accurately detecting risky riding behaviors and conducting mechanism analysis on the multiple risk factors are crucial in formulating and implementing precise management policies. The emergence of shared e-bikes and the advancements in interpretable machine learning present new opportunities for accurately analyzing the determinants of risky riding behaviors. The primary objective of this study is to examine and analyze the risk factors related to speeding behavior to aid urban management agencies in crafting necessary management policies. This study utilizes a large-scale dataset of shared e-bike trajectory data to establish a framework for detecting speeding behavior. Subsequently, the extreme gradient boosting (XGBoost) model is employed to identify the level of speeding risk by leveraging its excellent identification ability. Moreover, based on measuring the degree of interaction among road, traffic, and weather characteristics, the investigation of the complex interactive effects of these risk factors on high-risk speeding is conducted using bivariate partial dependence plots (PDP) by its superior parsing ability. Feature importance analysis results indicate that the top five ranked variables that significantly affect the identified results of speed risk levels are land use density, rainfall, road level, curbside parking density, and bike lane width. The interaction analysis results indicate that higher levels of road and bike lane width correspond to an increased possibility of high-risk speeding among riders. Land use density, curbside parking density, and rainfall display a nonlinear effect on high-risk speeding. Introducing road level, bike lane width, and time interval could change the patterns of nonlinear effects in land use density, curbside parking density, and rainfall. Finally, several policy recommendations are proposed to improve e-bike traffic safety by utilizing the extracted feature values associated with a higher probability of high-risk speeding.

Cybersecurity investments in supply chains with two-stage risk propagation

Cyber-attacks present a significant threat to supply chains as their nodes are directly or indirectly vulnerable to risk propagation at various stages. The risk level varies depending on the type of attack. A cybersecurity insurance offers a practical method to mitigate this risk, and it is crucial to determine optimal cybersecurity investments for all supply chain nodes. Previous studies have overlooked the joint impact of the attack type, two-stage risk propagation, and cybersecurity insurance in optimizing cybersecurity investments. This paper addresses this research gap by examining optimal investments under targeted and opportunistic attacks in a two-stage supply chain using game theory. The findings indicate that optimal investments differ based on the type of attack. For instance, retailers should invest more in cybersecurity under opportunistic attacks, while suppliers need to spend more under targeted attacks. Additionally, the results show that under opportunistic attacks, members should reduce their investments. Conversely, under targeted attacks, investments should initially increase and then stabilize. In the case of opportunistic attacks, suppliers and retailers should prioritize reconfiguring their systems over investing heavily in cybersecurity. The model presented in this paper demonstrates that not all cyber risks are worth defending against and that cybersecurity insurance for the entire supply chain can be more cost-effective than addressing cybersecurity risks individually. The paper also explores the impact of joint decisions on cybersecurity insurance when firms are unwilling to invest individually. The insights obtained enable supply chains to identify their optimal cybersecurity investment strategies effectively.

Risk assessment and categorization of terrorist attacks based on the Global Terrorism Database from 1970 to 2020

Risk assessment and categorization of terrorist attacks can assist enhance awareness of terrorism and give crucial information support for anti-terrorism efforts. This study utilizes quantitative approaches for the risk assessment and categorization of terrorist attacks. A total of 210,454 terrorist attacks that occurred worldwide from 1970 through 2020 were collected in the Global Terrorism Database, and 22 indicators related to the risk of terrorist attacks were selected. Then, the moment estimation theory and four comprehensive evaluation models were utilized to identify the top 10 riskiest terrorist attacks in the world. Furthermore, the five clustering analysis methods and three evaluation criteria were performed for the risk categorization of terrorist attacks, and the visual analysis was carried out using the kernel density estimation method. The research results have identified the top 10 riskiest global terrorist attacks, which were led by the September 11 terrorist attack event, along with their downward counterfactual events. The spatial distribution of global terrorist attack risk is primarily composed of four “turbulent cores” in the region of Central Asia, Middle East & North Africa, South Asia, and Central America & Caribbean. This study also provided insights and recommendations for anti-terrorism efforts. It has realized the risk assessment and categorization of terrorist attacks, aiding in the swift identification of its risk levels, and holds immense significance for safeguarding global national security and societal stability under new circumstances.

Terrorist Attacks, Corporate Social Responsibility, and Firm Value

Synopsis Research problem According to the World Economic Forum, terrorism is one of the main managerial concerns for companies worldwide. However, there is surprisingly scant research on how terrorist attacks impact managerial decision-making. To fill this gap, we investigate how terrorist attacks influence firms’ socially oriented activities, as measured by their investments in corporate social responsibility (CSR). Motivation or theoretical reasoning In light of the crucial role of CSR in financial markets and the growing apprehension regarding terrorism risk, this study explores whether and to what extent firms’ CSR investments contribute to the resilience to terrorism risk. The motivation for our research question is grounded in the idea that terrorist attacks create a unique set of circumstances that prompt firms to reconsider their socially oriented initiatives. We argue that firms impacted by such events tend to recognize the importance of demonstrating commitment to social responsibility in the aftermath of a crisis. Overall, the theoretical underpinnings of this study revolve around the notion that CSR becomes a strategic response for firms affected by terrorism, through which they address various stakeholder concerns, maintain legitimacy, and strategically manage their public image in the aftermath of a crisis. The test hypotheses We empirically examine two key research questions in this study. First, we examine the effect of terrorist attacks on the CSR investments of publicly traded firms. Second, we examine whether and how the increase in CSR investments of publicly traded firms after terrorist attacks affects firm value. Target population Our study extends the limited research on the impact of terrorism on capital markets, which is an area of great concern to various stakeholders, including shareholders and corporate managers. The findings will also help researchers understand the determinants of CSR and the potential channels through which CSR creates value for shareholders. Adopted methodology Using a multivariate regression model and controlling for all other determinants of corporate CSR investment documented by prior studies, we test the effect of terrorist events on firms’ CSR investment. To reduce the concern that a growing awareness of the importance of CSR may explain the changes in firms’ investment in CSR over time, we use a difference-in-differences research design. Analyses Using a sample of 53 major terrorist attacks occurring in the U.S. between 1994 and 2015, this study examines the causal effect of terrorist attacks on firms’ CSR activities. Findings Our findings indicate that public firms located in close proximity to terrorist attacks (i.e., the impact firms) substantially increase their investment in CSR following those terrorist events, and that the increase in CSR investment is positively associated with firm value. We further observe a strengthened association between increased CSR and firm value when the CSR efforts of the impact firms attract greater media attention. In conclusion, our findings substantiate the hypothesis that firms exposed to higher levels of terrorism risk are more inclined to enhance their CSR investments. This inclination is driven by the dual impact of CSR efforts: not only do they contribute to bolstering stakeholders’ confidence in firms’ future performance, but they are also linked to increased returns in the aftermath of a terrorist attack.

Occurrence, Source Apportionment, and Risk Assessment of Antibiotics in Mangrove Sediments from the Lianzhou Bay, China

In recent years, the widespread application of antibiotics has raised global concerns, posing a severe threat to ecological health. In this study, the occurrence, source, and ecological risks of 39 antibiotics belonging to 5 classes in mangrove sediments from Lianzhou Bay, China, were assessed. The total concentrations of the antibiotics (∑39 antibiotics) ranged from 65.45 to 202.24 ng/g dry weight (dw), with an average of 142.73 ± 36.76 ng/g dw. The concentrations of these five classes of antibiotics were as follows: Sulfonamides (SAs) > Tetracyclines (TCs) > Fluoroquinolones (QUs) > Penicillin (PCs) > Macrolides (MLs). The spatial distribution of antibiotics varied as high tidal zone > middle tidal zone > low tidal zone. The total organic carbon (TOC), pH, nitrate (NO3−-N), and nitrite (NO2−-N) of the sediment significantly influenced the distribution of antibiotics (p < 0.05). A source analysis identified untreated sewage from aquaculture as the primary source of antibiotics in the local mangrove. A risk assessment revealed that ciprofloxacin, norfloxacin, ofloxacin of QUs, and tetracycline of TCs exhibited medium risks to algae in certain sampling sites, while other antibiotics exhibited low or no risks to all organisms. Nevertheless, the total risk of all the detected antibiotics to algae was medium in 95% of the sites. The overall ecological risk level of antibiotics in the middle tidal zone was slightly lower than in the high tidal zone and the lowest in the low tidal zone. In summary, the experimental results provided insights into the fate and transport behaviors of antibiotics in mangrove sediments from Lianzhou Bay.

Fingerprinting of emerging contaminants in L'Albufera natural park (Valencia, Spain): Implications for wetland ecosystem health

Wetlands are crucial ecosystems that are increasingly threatened by anthropogenic activities. L'Albufera Natural Park, the second-largest coastal wetland in Spain, faces significant pressures from surrounding agricultural lands, industrial activities, human settlements, and associated infrastructures, including treated wastewater inputs. This study aimed at (i) establishing pathways of emerging pollutants entering the natural wetland using both target and non-target screening (NTS) for management purposes, (ii) distinguishing specific contamination hotspots through Geographic Information System (GIS) and (iii) performing basic ecological risk assessment to evaluate ecosystem health. Two sampling campaigns were conducted in the spring and summer of 2019, coinciding with the start and end of the rice cultivation season, the region's primary agricultural activity. Each campaign involved the collection of 51 samples. High-resolution mass spectrometry (HRMS) was employed, using a simultaneous NTS approach with optimized gradients for pesticides and moderately polar compounds, along with complementary NTS methods for polar compounds, to identify additional contaminants of emerging concern (CECs). Quantitative analysis revealed that fungicides comprised a substantial portion of detected CECs, constituting approximately 50% of the total quantified pesticides. Tebuconazole emerged as the predominant fungicide, with the highest mean concentration (>16.9 μg L−1), followed by azoxystrobin and tricyclazole. NTS tentatively identified 16 pesticides, 43 pharmaceuticals and personal care products (PPCPs), 24 industrial compounds, and 12 other CECs with high confidence levels. Spatial distribution analysis demonstrated significant contamination predominantly in the southwestern region of the park, gradually diminishing towards the north-eastern outlet. The composition of contaminants varied between water and sediment samples, with pharmaceuticals predominating in water and industrial compounds in sediments. Risk assessment, evaluated through risk quotient calculations based on parent compound concentrations, revealed a decreasing trend towards the outlet, suggesting wetland degradation capacity. However, significant risk levels persist throughout much of the Natural Park, highlighting the urgent need for mitigation measures to safeguard the integrity of this vital ecosystem.

Multi-fidelity deep neural network with Monte Carlo dropout technique for uncertainty-aware risk recognition of backward erosion piping in dikes

Backward erosion piping (BEP) is an increasingly critical failure mechanism in dike systems, often triggered by floods resulting from extreme rainfall events, which are exacerbated by the ongoing shifts in climate patterns. This study embarks on a nuanced examination of BEP, a phenomenon that substantially undermines flood control and emergency management endeavors. Addressing the complexities of BEP incidents and the consequential variable impact on dike infrastructure, we have incorporated Monte Carlo dropout techniques within a multi-fidelity deep learning framework to enhance predictive accuracy and provide an uncertainty-aware assessment of BEP risk. Drawing on 16 sets of physical model experiments emulating dike structures from the lower Yangtze River basin in China, we conducted a detailed study of the BEP evolution mechanism under various hydrological scenarios intensified by climate change. These experiments allowed us to observe the initiation and progression of BEP in different conditions. The development of a structural equation model quantifies the effects of several critical factors—including those exacerbated by climatic variability—on BEP dynamics. Seven key factors were identified as influential to BEP risk levels, integrating distinct BEP traits, hydrological attributes, and dike engineering conditions. A Monte Carlo dropout-enhanced multi-fidelity deep neural network (MFDNN) was crafted, synthesizing low-fidelity experimental data with high-fidelity field case studies, to construct an advanced model for BEP risk level identification. Compared against four sophisticated machine learning models, our MFDNN demonstrated superior performance, effectively blending experimental insights with real-world occurrences. The proposed model emerges as a scientifically robust and pragmatic tool for delineating BEP risk levels in dike systems, providing vital guidance for categorizing BEP incidents and forging targeted, climate-informed emergency response strategies.

Determination of contamination, source, and risk of potentially toxic metals in fine road dust in a karst region of Southwest China.

Understanding the pollution situation of potentially toxic metals (PTMs) in fine road dust (FRD) in emerging industrialized cities and identifying priority control factors is crucial for urban environmental management, resident health protection, and pollution control. This study conducted a comprehensive investigation on PTMs pollution in FRD in Zunyi, a representative emerging industrialized city in the karst region of southwestern China. The average contents of Ni, Cr, Mn, Cu, Zn, Ba, Pb, V, and Co in the FRD were 43.2, 127.0, 1232.1, 134.4, 506.6, 597.8, 76.1, 86.8, and 16.2mgkg-1, respectively, which were obviously higher than the corresponding background levels of the local soil except for V and Co. The comprehensive pollution level of the determined PTMs in the FRD was very high, primarily caused by Zn and Cu. The sources of PTMs in Zunyi FRD were traffic, industrial, construction, and natural sources, accounting for 38.0, 23.7, 21.9, and 16.4% of the total PTMs content, respectively. The PTMs in Zunyi FRD exhibited a low to moderate overall ecological risk level, mainly contributed by Cu and traffic source. The cancer risks of PTMs in Zunyi FRD were high for all populations. The non-carcinogenic risk of PTMs in Zunyi FRD was acceptable for adults, but cannot be ignored for children. According to the source-specific probabilistic health risk estimation results, the priority control source is industrial source and the priority control PTM is Cr. Local governments need to give more attention to the carcinogenic risks and health hazards posed by PTMs in the FRD.

Enterprise Credit Risk Assessment Based on Hybrid Fuzzy Synthetic Evaluation Model

This article uses a hybrid fuzzy synthetic evaluation model to evaluate the credit risk of Shouguang vegetable enterprises, and designs four risk levels: impact degree, occurrence probability, risk manageability and government support level. The model uses average score technology to calculate the scores of secondary indicators, primary indicators and evaluation targets at each level, uses sorting technology to sort secondary indicators and primary indicators at each level, and uses fuzzy synthetic evaluation technology to construct Shouguang enterprise credit risk evaluation. The model uses the geometric mean method to calculate the comprehensive score, and summarizes the comprehensive score results to make decisions. Among them, the data in this article comes from a questionnaire, which was completed by a total of 41 professionals who have a better understanding of the credit risks of Shouguang vegetable companies Through calculation, the comprehensive credit risk score of Shouguang Vegetable Enterprise is 2.8585. The risk is medium risk. Banks can lend based on the operation of specific enterprises. The calculation results also show that the risk assessment results at different levels are not completely consistent. The main first-level risk indicators at the first three levels are "enterprise technological innovation" and "enterprise financial status", and the main first-level risk indicators at the last level are "Enterprise management level" and "enterprise development plan". Indicators related to corporate technological innovation and financial status are the main influencing indicators of the credit risk of Shouguang vegetable companies.

Different concepts of neighborhood safety and child internalizing and externalizing behaviors.

Neighborhood safety is defined inconsistently across epidemiologic studies - a conceptual problem that results in incomparable measurements, hampering the design of health interventions. Using child behavior problems (measured via the Child Behavior Checklist) as the outcome of interest, this study directly compared four measures of neighborhood safety: two of experienced safety and two of perceived safety, with each one measured at family and community levels. These included children's direct experience of harm, parental perceptions, community crime statistics, and community perceptions. In a sample of 3291 ten-year-olds from the Generation R cohort (living in municipal Rotterdam, Netherlands, 2013), all four measures were correlated (χ2 ≥ 9.2, P < 0.002 in pairwise chi-square comparisons), but ultimately identified different levels of risk for behavioral health. Direct experiences of harm, parental perceptions, and community crime statistics were all associated with increased child internalizing behaviors (β = 3.12, β = 2.10, and β = 1.77, respectively), while only experiences of harm and parental perceptions were associated with increased externalizing behaviors (β = 2.75 and β = 1.31, respectively). These results provide novel evidence that the conceptual distinctions underlying different measures of neighborhood safety are meaningful for child mental health and should be considered in intervention design.

Drug-induced erectile dysfunction: a real-world pharmacovigilance study using the FDA adverse event reporting system database.

The comprehensive quantitative and comparative risk data of drug-induced erectile dysfunction (ED) are still lacking, and this study aims to supplement this information. We reviewed all the ED reports in the FDA Adverse Event Reporting System (FAERS) database from 2004 to 2023 and summarized a potential ED culprit-drug list and its corresponding reporting frequency. The reporting odds ratio (ROR) method was used to conduct disproportionality analysis. A total of 20,098 ED reports were retrieved from the FAERS database, which recorded 734 different ED culprit-drugs, involving 74 drug classes. Finasteride was the drug with the highest reporting frequency, and urologicals was the drug class with the highest reporting frequency. In disproportionality analysis, 209 drugs with positive signals showed a close relationship with ED occurrence, among which finasteride was the drug with the highest signal strength. Among 209 drugs with positive signals, 27 were compound preparations, and the risk level of compound preparations was usually higher than their single active ingredient. Our study integrated quantitative and comparative ED risk data of 734 drugs by using the FAERS database, which can provide reference information for regulators, medical personnel, and others involved in drug management and use.

Estimating erosion vulnerability within agricultural fields by downscaling the Daily Erosion Project (DEP): the OFEtool

AbstractAgriculture continues to be one of the most important sources of nonpoint source pollution to surface water bodies. Consequently, it is critical to identify and prioritize high‐contributing agricultural fields and sub‐field areas for reducing soil erosion and sediment delivery by implementing best management practices (BMPs). Current erosion risk assessment tools are either complex modelling approaches or rely on a simplified reality and generalized assumption. The Daily Erosion Project (DEP) is a daily estimator of precipitation, hillslope runoff, detachment and soil loss covering ~630 000 km2 across the Midwest United States. These estimations are reported daily and publicly at the hydrologic unit code 12 watershed resolution (approximately 100 km2). The main objective of this study was to develop a new tool (named Overland Flow Element tool [OFEtool]) that downscales the watershed scale of DEP to estimate average runoff and soil displacement within a field, helping to locate erosive hotspots at multiple scales. We also demonstrated the applicability of OFEtool in Bennet Creek‐Sugar Creek in East Central Iowa (the United States) and compared its results with other erosion vulnerability tools such as the Soil Vulnerability Index for Cultivated Cropland (SVI‐cc) and a GIS‐based Revised Universal Soil Loss Equation (RUSLE). The same erosion risk classes and ranges (low, moderate, moderately high and high) were implemented for all indexes. The advantages of the OFEtool compared to the SVI‐cc and RUSLE models are related to the use of an event‐based modelling approach, such as DEP, with updated soil loss estimates based on temporal changes in climate inputs and land use and management. The OFEtool uses a 6‐year time frame and a more up‐to‐date field inputs, while RUSLE provides a long‐term average and SVI‐cc only considers soil and topographical factors for risk assessment. Results indicated that the spatial distribution of vulnerable fields (and parts of the fields) followed a similar trend as other tested indices. However, the risk level associated with each tool differed (SVI‐cc &gt; RUSLE &gt; OFEtool). These differences could arise from intrinsic disparities within the tools (inputs, timing, processes considered, assumptions). While currently limited to the DEP domain and relying on the DEP random sampling scheme, further research is warranted to validate the tool at other Midwest locations and ensure it captures the watershed's landscape variability (combination of terrain, soil, land use and management) required to identifying critical erosion hotspots.

A two-step optimization model for virtual power plant participating in spot market based on energy storage power distribution considering comprehensive forecasting error of renewable energy output

As a complement to the medium and long-term market, the spot market plays an important role in maintaining the security and stability of the power grid. However, as spot trading is more proximate to the actual operation of the power system, the virtual power plant (VPP) is exposed to greater volatility in renewable energy output as well as market prices. To enhance its market competitiveness, this paper constructs a two-step optimization model for VPP participation in the spot market. Based on long-term transaction, the energy storage power is distributed based on the comprehensive forecasting error of renewable energy output. This makes the VPP obtain the maximum profit in the day-ahead market. The trading deviation penalty is reduced by adjusting the energy storage operation plan in the real-time market. The conditional value at risk is used to measure the risk of a trading strategy. The profit of VPP participation in the spot market is analyzed under different risk levels, thus providing a basis for decision makers with different risk appetites. The results show that: (1) The sensitivity of the growth rates of EtVaRαand EtCVaRα to risk level shows an opposite trend. And there is an excess of arbitrage power at low risk level. (2) Although the total cost decreases as the risk level increases, the actual profit in the spot market tends to increase and then decrease. The risk level of the VPP should be set at about 0.4 to make full use of energy storage and obtain the maximum market benefit. (3) The energy storage power can be distributed more accurately according to the forecasting error of renewable energy. This not only enhances its arbitrage ability, but also ensures its regulation ability, thus improving the overall benefit of VPP at different risk levels. (4) The sensitivity of the trend in the unit profit of traded quantity in the spot market to the trading proportion in the medium and long-term market is not monotonic.

Financial Risk Management in Healthcare in the Provision of High-Tech Medical Assistance for Sustainable Development: Evidence from Russia

The research determines the level of financial risk in the Russian healthcare system and identifies prospects for improving the current Russian practice of financial risk management in healthcare when providing high-tech medical care for sustainable development (using Russia as an example). The author summarizes the advanced experience of the top 20 largest healthcare organizations in Russia by revenue in 2022. Based on this experience, the author developed an SEM model of the financial risks in healthcare during the provision of high-tech medical care in Russia from a sustainable development perspective. The theoretical significance of the developed model lies in uncovering the previously unknown causal relationships between the implementation of the ICT, sustainable development support, and financial risks in healthcare. The model reveals a new market dimension of financial risks for healthcare organizations in Russia. The main conclusion is that implementing the ICT and support for sustainable development helps to reduce the financial risks in healthcare. The identified potential for reducing financial risks in providing high-tech medical care in Russia until 2026 is practically significant. This prospect can be practically applied as a roadmap for the digital modernization and sustainable development of healthcare until 2026, enhancing the state healthcare policy in Russia. The established systemic relationship between ICT implementation, sustainable development support, and financial risks in healthcare is of managerial importance because it will increase the predictability of the financial risks in the market dimension of healthcare in Russia. The newly developed approach to risk management in healthcare during the provision of high-tech medical care in Russia has expanded the instrumental framework of risk management for healthcare organizations in Russia and revealed further opportunities for improving its efficiency.

Recurrence Risk Perception, Fear of Progression, and Health Behaviors Among Patients With Ischemic Stroke.

The relationship between recurrence risk perception, fear of progression, and health behaviors in patients with ischemic stroke is unclear. To explore the effect of accuracy and level of recurrence risk perception on health behaviors and the mediating role of fear of progression. We conducted a 2-wave survey. Patients with ischemic stroke (N = 261) were recruited from 2 hospitals in Guangzhou, China. Before discharge, demographic information, objective recurrence risk, perceived recurrence risk, fear of progression, and health behaviors were investigated. After 1 month, the patient's health behaviors were followed up. The median (quartiles 1-3) scores for recurrence risk perception and fear of progression were 43.0 (39.0-46.0) and 22.0 (18.0-28.0), respectively. Only 22.2% of the patients correctly perceived the risk of recurrence, 23.0% underestimated the risk, and 30.7% overestimated the risk. Patients who overestimated the risk of recurrence (β = 0.421, P = .002) or had a higher perceived level of recurrence risk (β = 0.446, P < .001) had a higher fear of progression, which contributed to better health behaviors at 1 month (β = 0.197, P = .001). Fear of progression played a partial and full mediating role, respectively. Patients who underestimated the recurrence risk had worse health behaviors than those who accurately perceived it (β = -0.296, P = .033). Both accuracy and level of recurrence risk perception were independent predictors of future health behaviors, and fear of progression was mediating. Health care professionals should develop individualized risk education programs to help stroke patients properly understand and cope with the risk of recurrence.