Risk Situations Research Articles

Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure.

The COVID-19 pandemic has had a profound impact on various aspects of our lives, affecting personal, occupational, economic, and social spheres. Much has been learned since the early 2020s, which will be very useful when the next pandemic emerges. In general, mobility and virus spread are strongly related. However, most studies analyze the impact of COVID-19 on mobility, but not much research has focused on analyzing the impact of mobility on virus transmission, especially from the point of view of monitoring virus incidence, which is extremely important for making sound decisions to control any epidemiological threat to public health. As a result of a thorough analysis of COVID-19 and mobility data, this work introduces a novel measure, the Infection Ratio (IR), which is not sensitive to underestimation of positive cases and is very effective in monitoring the pandemic's upward or downward evolution when it appears to be more stable, thus anticipating possible risk situations. For a bounded spatial context, we can infer that there is a significant threshold in the restriction of mobility that determines a change of trend in the number of infections that, if maintained for a minimum period, would notably increase the chances of keeping the spread of disease under control. Results show that IR is a reliable indicator of the intensity of infection, and an effective measure for early monitoring and decision making in smart cities.

Analysis of Systemic Risk on the Financial Performance during the COVID-19 Pandemic: The Case of the Colombian Banking Industry

This study seeks to analyze the financial performance of the Colombian banking industry during the COVID-19 pandemic. The frame of reference is based on the concept of systemic risk; concerning this, the pandemic is conceived as an external shock, which impacted the dynamics of the banking industry. To conduct this study, a descriptive-correlational scope is proposed, from which an analysis of different accounting items related to the banking business is made and validated by expert judgment. The analysis horizon covers six years, but the focus is placed on March 2020, when COVID-19 was declared a pandemic by the World Health Organization (WHO). For this purpose, a longitudinal design is proposed, which analyzes the time series describing the behavior of some relevant items in the management of the banking business, such as operating revenue, provisions, interest on deposits and drawings, valuation of trading derivatives, and technology-related expenses. In addition, these items’ correlation with banking establishments’ performance is analyzed. The results of the study show that during the pandemic period, there was a significant increase in the level of volatility in the foreign exchange market, which impacted the operating revenue of banking establishments. It is concluded that, although exchange rate volatility affected the results of the banking industry, the main factor related to the financial performance of Colombian banks is their business itself, i.e., revenue from the loan portfolio and the quality of the loan portfolio. Therefore, systemic risk must be addressed regarding its implications on banks’ main profit drivers, such as portfolio revenue, cost of deposits, and provisions. Based on the above, it is recommended that Colombian banking establishments make greater efforts to diversify their sources of income to reduce their exposure to systemic risk situations.

Mortalité maternelle par thromboembolie veineuse en France 2016–2018

Pregnancy and the post-partum period represent a thromboembolic risk situation, with pulmonary embolism (PE) remaining one of the leading causes of direct maternal deaths in developed countries. Between 2016 and 2018 in France, twenty maternal deaths were caused by venous thromboembolic complications (VTE), yielding a Maternal Mortality Ratio (MMR) of 0.9 per 100,000 live births (95%CI 0.6–1.3), with no change compared to the periods 2013–2015 or 2010–2012. Among these 20 deaths, 1 death was related to cerebral thrombophlebitis, and the remaining 19 were due to PE. Regarding the timing of death, 2 deaths occurred after an early termination of pregnancy, 40% (8/20) during an ongoing pregnancy, and 50% (10/20) in the post-partum period. Among the 20 VTE deaths, 20% (4/20) occurred outside of a healthcare facility (at home or in a public place). Among the nineteen cases with documented BMI, seven women had obesity (37%), three times more than in the population of parturients in France (11.8%, ENP 2016). Among the nineteen PE deaths and the case of cerebral thrombophlebitis, eleven were considered preventable, six possibly preventable (35%), two probably preventable (12%), and three preventability undetermined. The identified preventability factors were inadequate care and the patient's failure to interact with the healthcare system. From the case analysis, areas for improvement were identified, including insufficient consideration of major and minor risk factors, the early initiation of appropriate prophylactic treatment, and the absence of fibrinolysis in cases of s refractory cardiac arrest due to suspected PE.

Impact on Long Duration of Overvoltage Due to Back-Feed Restoration of Distribution Network

In power system, power outages can be critical; if the environment and public safety within risk situation. Therefore, restoration power is needed to respond on system distribution to reduce the power restoration time and making the system reliable and efficient. Backfeeding using a nearby feeder in a power system is proposed in this research. When a power outage occurred, the faulted area is isolated from the system and searching available nearby feeders that can restore the outage area without any violation. However, analysing the effects of using a nearby feeder for back feeding is needed to avoid voltage violations. This paper analyses the voltage during the healthy and faulted condition, the impact of using a nearby feeder for backfeeding for a full load, half load and during no-load conditions are examine and also determine the maximum increment limitation of a load during back-feed if a qualified feeder is selected. Newton Raphson method is applied to calculate the power flow in the system and observe the stability of backfeed configuration. The results show the highest voltage supply occur during no load followed by half load and lastly during full load. The maximum voltage increment of load on backfeed is determined to be 0.95pu as an ideal voltage value.

Joint Effects of Perceived Hazard Risk and Contextual Situation on Responses

This study examined the relationship between risk perception and evacuation behavior, and the sociospatial contexts that affect evacuation. It is argued that risk perception and contextual conditions have a joint effect on evacuation. Data were collected from 3,756 evacuees in sixteen cities and towns affected by the 2011 Tohoku earthquake off the Pacific coast. Using geographic information systems (GIS), data from questionnaires on risk perception and activity diary surveys on behavioral response were combined with geospatial data. The main finding was that high risk perception leads to evacuation behavior, but that the difference between immediate and delayed evacuation depends on the sociospatial context. Delayed evacuation, compared with immediate evacuation, tends to occur in the following contexts: (1) the physical context (e.g., being inland at the time of the disaster impact); (2) the household context (e.g., the need to confirm the safety of household members); and (3) the social context (e.g., the need for assistance with mobility). This study extends the literature on disaster science and cognitive-behavioral geography by considering the relationship between perception and behavior in urban spaces in emergencies. The findings are relevant to the development of disaster preparedness planning to minimize human losses.

Риск-ориентированная онтологическая модель поддержки принятия управленческих решений в грузовом порту на основе многоуровневого целеполагания

In today's unstable market environment, transport logistics enterprises face the task of predicting possible risk situations in order to avoid them, as well as developing tools that allow them to form management decisions, evaluate the effectiveness of each of them and choose the most appropriate one, taking into account specific tasks and various influencing factors. Situations of failure to achieve the goals of a cargo port, as well as cases of failure by indicators to assess these goals of normative values, are considered as risks. The goals and indicators are outlined and described in the previously built Balanced Scorecard (MTSP). A set of links between goals and indicators of their assessment with risks at various levels of management is presented in the form of a developed multi-level hybrid logical-probabilistic (LV) risk model of a cargo port, as well as in detailed scenario models of this LV model. The integrated use of SSP and LV modeling technologies allowed us to build a general concept of multi-level goal setting, the main advantage of which lies in the detailed elaboration of the company's goals subordinate to the main strategic one, which allows, if necessary, to influence operational events and obtain a positive result in the longer term, in tactical and strategic plans. The ontological model contains all the information about the interaction of elements that affect the occurrence of risky events, and allows you to select options for getting out of a risky situation in accordance with various specified conditions. The results of queries to the risk-oriented ontological model are variants of management decisions aimed at reducing risks in the cargo port.

Harnessing the Power of AI for Enhanced Regulatory Compliance and Risk Management in Fintech

Purpose: This article analyzes how artificial intelligence (AI) is revolutionizing risk management and regulatory compliance in the fintech industry. The objective is to conduct an analysis of AI applications, highlighting how it may be used for proactive risk management, fraud prevention, real-time regulatory monitoring, and risk assessment.
 Methodology: Using a literature review methodology, the paper puts together data gathered from multiple sources to give a comprehensive knowledge of how AI is being applied to change the regulatory and risk landscape for fintech. As part of the method, significant works in the subject are reviewed and analyzed, and numerous perspectives are integrated to provide a thorough overview.
 Findings: The results highlight how AI significantly improves decision-making processes in response to complicated risk situations and dynamic regulatory contexts while also increasing efficiency and lowering costs. Fintech practices are evolving due to specific applications such as proactive risk management, precise risk assessment, fraud detection, real-time monitoring, and accurate risk management.
 Unique contribution to theory, practice and policy: The work adds additional value by combining various AI applications for risk management and regulatory compliance in finance. It provides useful insights for researchers, practitioners, and policymakers by bridging the gap between theory and practice. The article offers industry professionals useful implications in addition to educating the academic community on the complex effects of AI on fintech. It also draws attention to the necessity of flexible regulatory frameworks that can keep up with the fintech industry's rapid advancements in technology, which adds to the policy considerations in this dynamic environment. For individuals negotiating the convergence of artificial intelligence, regulatory compliance, and risk management in the fintech industry, the article is essentially a short and important resource.

Risk management in software development in the field of information protection

In the context of increasing digitalization of sea and river transport, the immersion of the entire transport infrastructure in the digital environment, the volume of information flows serving the transport process is increasing, and the requirements for information protection are increasing in conditions of an aggressive external information environment. In the digital environment, a software product must not only serve the document flow of transport chains, but also ensure its confidentiality. Automation of transport facilities (ships, platforms, berths, warehouses) management increases their potential vulnerability from unauthorized access to control systems; the latter must be taken into account in servicing programs, increasing their size (for example, there is a need to fragment coherent information blocks and their alternative routing). This, in turn, increases the risk of errors in the software products themselves and significantly complicates their structure. Risks of failures (including confidentiality violations) during the software operation that implements information exchange can entail significant material and reputational losses for the developer. If the developer is legally and functionally involved in the industry, such losses can and should be considered among the general range of risks in water transport. An important aspect of risk management, which until recently was practically not considered in a quantitative aspect within the framework of mathematical models, is the joint consideration of losses when a risk situation occurs and the developer’s costs associated with reducing the likelihood of such occurrences; the latter may entail a reduction in the total expected losses, formalized as the corresponding mathematical expectation. As a result, risk management can be formulated in terms of mathematical programming problems with different (discrete or continuous) sets of constraints and with different properties of objective functions.

Forest fire risk zoning based on fuzzy logic and analytical network process.

Forest fires have a significant impact on human life, property safety, and ecological environment. Deve-loping high-quality forest fire risk maps is beneficial for preventing forest fires, guiding resource allocation for firefighting, assisting in fire suppression efforts, and supporting decision-making. With a multi-criteria decision analysis (MCDA) method based on geographic information systems (GIS) and literature review, we assessed the main factors influencing the occurrences of forest fires in Youxi County, Fujian Province. We analyzed the importance of each fire risk factor using the analytic network process (ANP) and assigned weights, and evaluated the sub-standard weights using fuzzy logic assessment. Using ArcGIS aggregation functions, we generated a forest fire risk map and validated it with satellite fire points. The results showed that the areas classified as level 4 or higher fire risk accounted for a considerable proportion in Youxi County, and that the central and northern regions were at higher risk. The overall fire risk situation in the county was severe. The fuzzy ANP model demonstrated a high accuracy of 85.8%. The introduction of this novel MCDA method could effectively improve the accuracy of forest fire risk mapping at a small scale, providing a basis for early fire warning and the planning and allocation of firefighting resources.

Detection of Fall Risk Behaviors in Patients with Severe Mobility Issues Using FMCW Radar: Sitting Up and Sitting on the Side of the Bed

This study aimed to detect fall risk behaviors using radar—a non-contact sensor—to prevent falling accidents, which are one of the most fatal problems faced by older adults. Hospitals and nursing homes often have patients who cannot move alone without caregivers. In this context, the process of a patient sitting up from a lying-down position shortly before standing up has been observed as a fall risk behavior. This study added movement information as a new characteristic feature to the range and velocity information used in conventional radarbased behavior recognition studies. Performance comparisons confirmed that the addition of movement information performs excellently in detecting risk situations. Furthermore, a bidirectional long short-term memory model was trained using a feature to predict risk situations. The model exhibited accuracy, recall, and precision rates of 93.84%, 88.57%, and 99.07%, respectively. Additionally, its performance in detecting fall risk behavior was verified by conducting experiments involving continuous behaviors.

Development of a mobile application to represent food intake in inpatients: dietary data systematization

BackgroundNutritional risk situations related to decreased food intake in the hospital environment hinder nutritional care and increase malnutrition in hospitalized patients and are often associated with increased morbidity and mortality. The objective of this study is to develop and test the reliability and data similarity of a mobile application as a virtual instrument to assess the acceptability and quality of hospital diets for inpatients.MethodsThis intra- and interobserver development and reliability study investigated an in-hospital food intake monitoring application based on a validated instrument for patients with infectious diseases who were treated at the Evandro Chagas National Institute of Infectious Diseases (INI/FIOCRUZ). The instrument was sequentially administered to patients 48 h after admission to INI hospital units using the printed instrument (paper) and the digital application (ARIETI) simultaneously. The tested reliability factor was the consistency of the method in the digital platform, checking whether the application provided equivalent data to the paper instrument, and finally, a statistical analysis plan was performed in the R platform version 4.2.0. This project was authorized by the FIOCRUZ/INI Research Ethics Committee.ResultsThe ARIETI was developed and tested for reliability in 70 participants, showing a similar ability to calculate caloric intake in Kcal, protein intake (g), the proportion of caloric intake and protein intake relative to the prescribed goal. These instrument comparison analyses showed statistical significance (p < 0.001). The application was superior to the paper-based instrument, accelerating the time to perform the nutritional risk diagnosis based on food intake by approximately 250 s (average time).ConclusionsThe ARIETI application has demonstrated equivalent reliability compared to the original instrument. Moreover, it optimized the time between the diagnosis of nutritional risk related to dietary intake and the nutritionist’s decision making, showing an improved ability to maintain information quality compared to the paper-based instrument.

Pretravel medical consultation

Many patients seek pretravel advice during routine consultations in a general practice so that basic knowledge of travel medicine is warranted. Using the example of trips to Bali, Peru and Tanzania, the most relevant topics of apretravel consultation for these popular destinations are depicted. These include vaccinations, malaria prevention and recommendations on exposure prophylaxis for insect bites. Furthermore, special risk situations, such as travel to high altitudes or freshwater contact are discussed. In special cases, the advice of an expert in travel medicine is needed.

A Framework for Valuation and Portfolio Optimization of Venture Capital Deals with Contractual Terms

Venture capitalists invest not only in the business aspect of a deal but also in its contractual terms. Therefore, the selection of deals and the combination of contractual terms pose challenging decisions for them. This paper consists of two main sections. The first section introduces a novel framework for the valuation of venture capital (VC) deals, including startups and their contractual terms. By taking into account risk situations, this section presents the valuation of combined contractual terms, including call options, liquidity preference, and participant rights. In the second section, a new multiobjective mathematical model for VC deals and contractual terms portfolio selection is developed using right-tail probability, strategy alignment, and a utility function. To solve the proposed model, three metaheuristic algorithms—Non-Dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Binary Harmony Search Algorithm, and Dynamic Tuning Parameter Binary Harmony Search Algorithm (DTPBHS)—are applied. Based on numerical examples, DTPBHS outperforms other algorithms in the “Mean Ideal Distance” index, but NSGA-II demonstrates the best performance in the “Rate of Achievement of two objectives simultaneously” index. Furthermore, we demonstrate that the proposed utility function is more robust than the right-tail probability function under default deals conditions.

“Naloxone? Not for me!” First cross-assessment by patients and healthcare professionals of the risk of opioid overdose

BackgroundOpioid-related mortality is a rising public health concern in France, where opioids were in 2021 implicated in 75% of overdose deaths. Opioid substitution treatment (OST) was implicated in almost half of deaths related to substance and drug abuse. Although naloxone could prevent 80% of these deaths, there are a number of barriers to the distribution of take-home naloxone (THN) among opioid users in France. This study is the first one which compares patients' self-assessment of the risk of future opioid overdose with the hetero-assessment provided by healthcare professionals in a population of individuals eligible for naloxone.MethodsThis was a multicenter descriptive observational study carried out in pharmacies across the Pays de la Loire region (France) during April and May 2022. All adult patients who visited a participating pharmacy for a prescription of OST and provided oral informed consent were enrolled in the study. Retrospective data were collected through cross-sectional interviews conducted by the pharmacist with the patient, utilizing an ad hoc questionnaire. The patient’s self-assessment of overdose risk was evaluated using a Likert scale from 0 to 10. The pharmacist relied on the presence or absence of overdose risk situations defined by the French Health Authority (HAS). The need to hold THN was assessed using a composite criterion.ResultsA total of 34 patients were interviewed; near one third were aware of the existence of THN and a minority had THN in their possession. Out of the 34 participants, 29 assessed their own risk of future opioid overdose: 65.5% reported having zero risk, while 6.9% believed they had a high risk. Nevertheless, at least one risk situation of opioid overdose was identified according to HAS criteria in 73.5% of the participants (n = 25). Consequently, 55% of the participants underestimated their risk of experiencing a future opioid overdose. Yet, dispensing THN has been judged necessary for 88.2% of the participants.ConclusionThis study underscored the imperative need to inform not only healthcare professionals but also the patients and users themselves on the availability of THN and the risk situations of opioid overdose.

A PROTEÇÃO DA CRIANÇA E DO ADOLESCENTE, DEVER DE TODOS

Unfortunately, the presence of adolescents in urban crime does not lack financial resources as the only responsible aspect. We must add to this factor the low level of education, the fragility of family bonds, as well as the social violence that integrates and plagues our daily lives. Responsibility for the adolescent's physical, cognitive, social and affective development requires a lot of attention from the family and the State. Financial difficulties, as well as in social and affective relationships, which are difficult to overcome by families, can cause a situation of risk to adolescents, which, at times, end up boosting their entry into crime. And, after entering criminality, resocialization becomes almost impossible. Preventive work is urgently and extremely necessary. We must work and provide protection before they enter the world of crime, because afterwards, it is very difficult to save them.

A PROTEÇÃO DA CRIANÇA E DO ADOLESCENTE, DEVER DE TODOS

Unfortunately, the presence of adolescents in urban crime does not lack financial resources as the only responsible aspect. We must add to this factor the low level of education, the fragility of family bonds, as well as the social violence that integrates and plagues our daily lives. Responsibility for the adolescent's physical, cognitive, social and affective development requires a lot of attention from the family and the State. Financial difficulties, as well as in social and affective relationships, which are difficult to overcome by families, can cause a situation of risk to adolescents, which, at times, end up boosting their entry into crime. And, after entering criminality, resocialization becomes almost impossible. Preventive work is urgently and extremely necessary. We must work and provide protection before they enter the world of crime, because afterwards, it is very difficult to save them.

Is renewable energy technology innovation an excellent strategy for reducing climate risk? The case of China

In this era of global warming the importance of climate risk cannot be understated, and finding ways to reduce climate risk is a topic worthy of study. In this paper, we innovatively examine the role of renewable energy technology innovation on climate risk in China. In addition to their linear relationship, we also investigate the asymmetric and heterogeneous impact, detect the impact channels, and find that (1) increased renewable energy technology innovation can effectively alleviate the climate risk situation. Accordingly, the development of renewable energy technology innovation can make a significant contribution to CR abatement. (2) Pronounced impacts of renewable energy technology innovation on climate risk can be observed in the higher quantiles (75th and 90th) of climate risk. (3) Renewable energy technology innovation is a powerful weapon for eradicating climate risk in provinces with a higher level of government fiscal expenditure. And (4) renewable energy technology innovation has an impact on climate risk by facilitating energy consumption efficiency, which is a necessary mediator. These conclusions inspire us to put forward several corresponding policy implications for better climate risk governance and renewable energy technology innovation development.

Flood Risk and Preventive Choices: A Framework for Studying Human Behaviors.

The topic of flood phenomena has always been of considerable importance due to the high risks it entails, both in terms of potential economic and social damage and the jeopardizing of human lives themselves. The spread of climate change is making this topic even more relevant. This work aims to contribute to evaluating the role that human factors can play in responding to critical hydrogeological phenomena. In particular, we introduce an agent-based platform for analyzing social behaviors in these critical situations. In our experiments, we simulate a population that is faced with the risk of a potentially catastrophic event. In this scenario, citizens (modeled through cognitive agents) must assess the risk they face by relying on their sources of information and mutual trust, enabling them to respond effectively. Specifically, our contributions include (1) an analysis of some behavioral profiles of citizens and authorities; (2) the identification of the "dissonance between evaluation and action" effect, wherein an individual may behave differently from what their information sources suggest, despite having full trust in them in situations of particular risk; (3) the possibility of using the social structure as a "social risk absorber", enabling support for a higher level of risk. While the results obtained at this level of abstraction are not exhaustive, they identify phenomena that can occur in real-world scenarios and can be useful in defining general guidelines.

Research and application of automatic mapping method of distribution network protection power supply based on knowledge graph and graph convolution network

Abstract This study aims to propose an automatic mapping method for distribution network protection based on knowledge graph (KG) and graph convolution network technology and applies it to power system. The relationship between physical entities in power grid is established by constructing KG, and multisource data fusion and analysis are realized by using graph convolution network technology, so as to realize one-click and automatic mapping of power diagram in power supply places. The distinctiveness of this study lies in the incorporation of KG and deep learning techniques into the field of power supply assurance for distribution networks, achieving automated and digitized generation of power supply assurance device diagrams with real-time dynamic updates capability. This innovation significantly enhances the level of digitization and intelligence in power supply assurance work, injecting new vitality into the field of power supply assurance for distribution networks. This method can provide a digital comprehensive and intuitive presentation for the power supply service and effectively improve the ability to grasp the equipment situation and risk situation awareness.

Determination of alternative forest road routes using produced landslide susceptibility maps: A case study of Tonya (Trabzon), Türkiye

Firstly, Landslide Susceptibility Maps of the study area were produced using Frequency Ratio and Modified Information Value models. Nine factors were defined and the Landslide Inventory Map was used to produce these maps. In the Landslide Susceptibility Maps obtained from the Frequency Ratio and Modified Information Value models, the total percentages of high and very high-risk areas were calculated as 10% and 15%, respectively. To determine the accuracy of the produced Landslide Susceptibility Maps, the success and the prediction rates were calculated using the receiver operating curve. The success rates of the Frequency Ratio and Modified Information Value models were 82.1% and 83.4%, respectively, and the prediction rates were 79.7% and 80.9%. In the second part of the study, the risk situations of 125 km of forest roads were examined on the map obtained by combining the Landslide Susceptibility Maps. As a result of these investigations, it was found that 4.28% (5.4 km) of the forest roads are in very high areas and 4.27% (5.3 km) in areas with high landslide risk areas. In the last part of the study, as an alternative to forest roads with high and very high landslide risk, 9 new forest road routes with a total length of 5.77 km were produced by performing costpath analysis in with geographic information systems.