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Drawing parallels to the theory of heart zone training for exercise, this essay advocates for slowing down when making the majority of decisions in the workplace. In making the case for slower decision making, the author shares theories from change management and practices targeted toward enhancing inclusivity. While there are situations when decisions should be made quickly or delegated to others, the author maintains that leaders should take their time when making decisions. [ABSTRACT FROM AUTHOR]

Abstract In South Korea, the snowy season spans from October to April, and the annual average snowfall varies significantly depending on specific regions, latitudes, and elevations, ranging from 0 to 260 cm. The average annual snowfall in South Korea is 25.1 cm. Despite of the relatively shallow snowfall depth, over the past decade, South Korea has experienced approximately 120 million dollars in damages attributed to snow-related incidents. In this study, the DPSIR (Driver-Pressure-State-Impact-Response) framework was employed to consider the meteorological and socioeconomic factors to calculate the snow damage vulnerability. A total of 17 indicators were taken into account to comprehend meteorological conditions, socioeconomic factors, and historical damage records from 1994 to 2020. However, due to the limited availability of meteorological observatories and changes in greenhouse design standards, accurately estimating the snow damage amount poses challenges. Therefore, based on the vulnerability, the risk levels were classified into four categories and estimated snow damage generated by the categorized models was compared with those of the model constructed using the entire dataset. The categorized models offer improved estimation results, as the meteorological and socioeconomic characteristics within each category differ and should be addressed separately in modeling. Among the categorized models, the Green zone exhibited the best results, primarily because it did not include outlier snow damage incidents. The developed model in this study could be utilized to mitigate the impact of heavy snowfall and prioritize snow removal regions.

In South Korea, the snowy season spans from October to April, and the annual average snowfall varies significantly depending on specific regions, latitudes, and elevations, ranging from 0 to 260 cm. The average annual snowfall in South Korea is 25.1 cm. Despite of the relatively shallow snowfall depth, over the past decade, South Korea has experienced approximately 120 million dollars in damages attributed to snow-related incidents. In this study, the DPSIR (Driver-Pressure-State-Impact-Response) framework was employed to consider the meteorological and socioeconomic factors to calculate the snow damage vulnerability. A total of 17 indicators were taken into account to comprehend meteorological conditions, socioeconomic factors, and historical damage records from 1994 to 2020. However, due to the limited availability of meteorological observatories and changes in greenhouse design standards, accurately estimating the snow damage amount poses challenges. Therefore, based on the vulnerability, the risk levels were classified into four categories and estimated snow damage generated by the categorized models was compared with those of the model constructed using the entire dataset. The categorized models offer improved estimation results, as the meteorological and socioeconomic characteristics within each category differ and should be addressed separately in modeling. Among the categorized models, the Green zone exhibited the best results, primarily because it did not include outlier snow damage incidents. The developed model in this study could be utilized to mitigate the impact of heavy snowfall and prioritize snow removal regions. [ABSTRACT FROM AUTHOR]

Coal spontaneous combustion is the main disaster affecting the safety production of mines, and efficient early warning is the key to controlling coal spontaneous combustion disasters. In order to construct an early warning system for spontaneous combustion disasters of coal with different degrees of metamorphism, the programmed-temperature control experiment was conducted to obtain the change curve of the volume fraction of the index gases of the low, medium and high degrees of metamorphosis, and then the Logistic function is used to fit the volume fraction and temperature of the index gases, and the mathematical model of the index gas and temperature of the index gases with different degrees of metamorphism is obtained, and the inflection point temperature is determined, and finally the spontaneous combustion hazard level of the coal is divided in combination with the initial temperature of the index gases. The results show that the changes of coal sample index gases and characteristic temperatures with different degrees of metamorphism are different; the initial appearance temperature of CO is about 30°C, while the initial appearance temperature of C2H4 and C2H2 is positively correlated with the degree of metamorphism, and the initial appearance temperature is 117°C (low), 123°C (medium) and 137°C (high) for C2H4, 291°C (low), 297°C (medium) and 297°C (high) for C2H2; For the same marker gas, the inflection point temperature increases with the degree of metamorphism, indicating that the higher the degree of metamorphosis, the less likely it is to oxidize at the beginning; According to the variation characteristics of coal spontaneous combustion, based on the initial emergence temperature of CO and C2H4 and CO inflection point temperature, coal spontaneous combustion is divided into five states: safe, low risk, medium risk, high risk and catastrophic state, and the energy level transition map between each state indicates that the energy required to transition from safe state to low risk and high risk state is the largest. Once the high-risk state is crossed, it does not require higher energy to maintain the continuous development of spontaneous combustion of coal and evolve into a catastrophic state. The classification of coal spontaneous combustion hazard levels with different degrees of metamorphism can provide prior data and form effective support for intelligent early warning of coal spontaneous combustion. [ABSTRACT FROM AUTHOR]

Road testing can accelerate the development and validation of autonomous vehicles (AVs). AV road testing can come with high safety risks, particularly in a complex road traffic environment, due to the immaturity of AV technology. A priori safety risk assessments of the road traffic environment before AV road testing are of great importance, allow the quantifying of risk levels in different road scenarios, and provide guidelines for AV road testing in low to high-risk environments. This study proposes a framework, namely Safety Risk Assessment for AV road testing (SRAAV), based on the probability and severity of five categories of potential AV accidents. Four groups of influencing factors are considered comprehensively in assessing AV safety risk, and their impacts are quantified using impact coefficients derived from a Bayesian network and empirical AV road testing data. The safety risk is assessed on a road section level, based on which an overall risk level is defined for a corridor and a region. Afterwards, the quantified safety risk is classified into four levels according to expert experience and knowledge, through a questionnaire survey. Applications of the proposed SRAAV framework are conducted for urban roads in Shanghai, and expressways in Shanghai and Gothenburg. The assessment results are validated using disengagement data from AV road testing. The results show that the SRAAV framework and its models could estimate the safety risk levels of road traffic environments for AV road testing in a sound way and have the flexibility for further extensions to be made. The framework and assessment results can provide technical support for determining where and when to grant permission for public roads to be used for AV road testing, and how to choose public roads from a low to a high risk level, guaranteeing the safety of AV public road testing. [ABSTRACT FROM AUTHOR]

Abstract Background Chronic conditions are one of the main determinants of frailty, functional disability, loss of quality of life and the number one cause of death worldwide. This study aimed to describe the survival of patients with chronic conditions who were followed up in primary care according to the level of risk by adjusted morbidity groups and to analyse the effects of sex, age, clinician and care factors on survival. Methods This was a longitudinal observational study of a retrospective cohort of patients with chronic conditions identified by the adjusted morbidity group stratifier of the electronic medical records in a primary health centre of the Region of Madrid, which has an assigned population of 18,107 inhabitants. The follow-up period was from June 2015 to June 2018. A description of survival according to the Kaplan–Meier method and Cox proportional hazards multivariate regression model was used to analyse the effects of sex, age, clinician and care factors. Results A total of 9,866 patients with chronic conditions were identified; 77.4% (7,638) had a low risk, 18.1% (1,784) had a medium risk, and 4.5% (444) had a high risk according to the adjusted morbidity groups. A total of 477 patients with chronic conditions died (4.8%). The median survival was 36 months. The factors associated with lower survival were age over 65 years (hazard ratio [HR] = 1.3; 95% confidence interval [CI] = 1.1–1.6), receiving palliative care (HR = 3.4; 95% CI = 2.6–4.5), high versus low risk level (HR = 2.4; 95% CI = 1.60–3.7), five chronic conditions or more (HR = 1.5; 95% CI = 1.2-2), complexity index (HR = 1.01; 95% CI = 1.02–1.04) and polymedication (HR = 2.6; 95% CI = 2.0-3.3). Conclusions There was a gradual and significant decrease in the survival of patients with chronic conditions according to their level of risk as defined by adjusted morbidity groups. Other factors, such as older age, receiving palliative care, high number of chronic conditions, complexity, and polymedication, had a negative effect on survival. The adjusted morbidity groups are useful in explaining survival outcomes and may be valuable for clinical practice, resource planning and public health research.

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

Background: Chronic conditions are one of the main determinants of frailty, functional disability, loss of quality of life and the number one cause of death worldwide. This study aimed to describe the survival of patients with chronic conditions who were followed up in primary care according to the level of risk by adjusted morbidity groups and to analyse the effects of sex, age, clinician and care factors on survival. Methods: This was a longitudinal observational study of a retrospective cohort of patients with chronic conditions identified by the adjusted morbidity group stratifier of the electronic medical records in a primary health centre of the Region of Madrid, which has an assigned population of 18,107 inhabitants. The follow-up period was from June 2015 to June 2018. A description of survival according to the Kaplan–Meier method and Cox proportional hazards multivariate regression model was used to analyse the effects of sex, age, clinician and care factors. Results: A total of 9,866 patients with chronic conditions were identified; 77.4% (7,638) had a low risk, 18.1% (1,784) had a medium risk, and 4.5% (444) had a high risk according to the adjusted morbidity groups. A total of 477 patients with chronic conditions died (4.8%). The median survival was 36 months. The factors associated with lower survival were age over 65 years (hazard ratio [HR] = 1.3; 95% confidence interval [CI] = 1.1–1.6), receiving palliative care (HR = 3.4; 95% CI = 2.6–4.5), high versus low risk level (HR = 2.4; 95% CI = 1.60–3.7), five chronic conditions or more (HR = 1.5; 95% CI = 1.2-2), complexity index (HR = 1.01; 95% CI = 1.02–1.04) and polymedication (HR = 2.6; 95% CI = 2.0-3.3). Conclusions: There was a gradual and significant decrease in the survival of patients with chronic conditions according to their level of risk as defined by adjusted morbidity groups. Other factors, such as older age, receiving palliative care, high number of chronic conditions, complexity, and polymedication, had a negative effect on survival. The adjusted morbidity groups are useful in explaining survival outcomes and may be valuable for clinical practice, resource planning and public health research. [ABSTRACT FROM AUTHOR]

Abstract Background Patients with chronic diseases have increased needs for assistance and care. The objective of this study was to describe the characteristics and use of primary care (PC) and hospital care (HC) health services by chronic patients according to risk level based on adjusted morbidity groups (AMG) and to analyze the associated factors. Methods Cross-sectional descriptive observational study. Patients from a basic health area classified as chronically ill by the AMG classification system of the Madrid PC electronic medical record were included. Sociodemographic, clinical-care characteristics (classified as predisposing factors or need factors) and service utilization variables were collected. Univariate, bivariate and simple linear regression analyses were performed. Results The sample consisted of 9866 chronic patients and 8332 (84.4%) used health services. Of these service users, 63% were women, mean age was 55.7 (SD = 20.8), 439 (5.3%) were high risk, 1746 (21.2%) were medium risk, and 6041(73.4%) were low risk. A total of 8226 (98.7%) were PC users, and 4284 (51.4%) were HC users. The average number of annual contacts with PC was 13.9 (SD = 15); the average number of contacts with HC was 4.8 (SD = 6.2). Predisposing factors associated with services utilization at both care levels were: age (B coefficient [BC] = 0.03 and 0.018, 95% CI = 0.017–0.052 and 0.008–0.028, respectively, for PC and HC) and Spanish origin (BC = 0.962 and 3.396, 95% CI = 0.198–1.726 and 2.722–4.070); need factors included: palliative care (BC = 10,492 and 5047; 95% CI = 6457–14,526 and 3098-6995), high risk (BC = 4631 and 2730, 95% CI = 3022–6241 and 1.949–3.512), number of chronic diseases (BC = 1.291 and 0.222, 95% CI = 1.068–1.51 and 0.103–0.341) and neoplasms (BC = 2.989 and 4.309, 95% CI = 1.659–4.319 and 3.629–4.989). Conclusions The characteristics and PC and HC service utilization of chronic patients were different and varied according to their AMG risk level. There was greater use of PC services than HC services, although utilization of both levels of care was high. Service use was related to predisposing factors such as age and country of origin and, above all, to need factors such as immobility, high risk, and number and type of chronic diseases that require follow-up and palliative care.

There has been an emerging interest by financial institutions to develop advanced systems that can help enhance their anti-money laundering (AML) programmes. In this study, we present a self-organising map (SOM) based approach to predict which bank accounts are possibly involved in money laundering cases, given their financial transaction histories. Our method takes advantage of the competitive and adaptive properties of SOM to represent the accounts in a lower-dimensional space. Subsequently, categorising the SOM and the accounts into money laundering risk levels and proposing investigative strategies enables us to measure the classification performance. Our results indicate that our framework is well capable of identifying suspicious accounts already investigated by our partner bank, using both proposed investigation strategies. We further validate our model by analysing the performance when modifying different parameters in our dataset.

Background: Patients with chronic diseases have increased needs for assistance and care. The objective of this study was to describe the characteristics and use of primary care (PC) and hospital care (HC) health services by chronic patients according to risk level based on adjusted morbidity groups (AMG) and to analyze the associated factors.Methods: Cross-sectional descriptive observational study. Patients from a basic health area classified as chronically ill by the AMG classification system of the Madrid PC electronic medical record were included. Sociodemographic, clinical-care characteristics (classified as predisposing factors or need factors) and service utilization variables were collected. Univariate, bivariate and simple linear regression analyses were performed.Results: The sample consisted of 9866 chronic patients and 8332 (84.4%) used health services. Of these service users, 63% were women, mean age was 55.7 (SD = 20.8), 439 (5.3%) were high risk, 1746 (21.2%) were medium risk, and 6041(73.4%) were low risk. A total of 8226 (98.7%) were PC users, and 4284 (51.4%) were HC users. The average number of annual contacts with PC was 13.9 (SD = 15); the average number of contacts with HC was 4.8 (SD = 6.2). Predisposing factors associated with services utilization at both care levels were: age (B coefficient [BC] = 0.03 and 0.018, 95% CI = 0.017-0.052 and 0.008-0.028, respectively, for PC and HC) and Spanish origin (BC = 0.962 and 3.396, 95% CI = 0.198-1.726 and 2.722-4.070); need factors included: palliative care (BC = 10,492 and 5047; 95% CI = 6457-14,526 and 3098-6995), high risk (BC = 4631 and 2730, 95% CI = 3022-6241 and 1.949-3.512), number of chronic diseases (BC = 1.291 and 0.222, 95% CI = 1.068-1.51 and 0.103-0.341) and neoplasms (BC = 2.989 and 4.309, 95% CI = 1.659-4.319 and 3.629-4.989).Conclusions: The characteristics and PC and HC service utilization of chronic patients were different and varied according to their AMG risk level. There was greater use of PC services than HC services, although utilization of both levels of care was high. Service use was related to predisposing factors such as age and country of origin and, above all, to need factors such as immobility, high risk, and number and type of chronic diseases that require follow-up and palliative care. [ABSTRACT FROM AUTHOR]

Compliance with new regulations in old plants remains a recurring challenge because of negative outcome of incidents. This challenge stems from uncertainties in the facilities' integrity, owing to inadequacy of existing integrity-validating technologies. Process facilities deteriorate through cyclic operations, while encroachments from expanding population characteristically raise the risk-levels, leading to need for higher grade materials to meet operational expansions. Retroactive compliance becomes a nightmare with every new regulation without a robust cost-to-benefit assurance. This paper discusses two-phased qualitative and quantitative risk modelling approach through systematic field-data-gathering, hazards identification and analysis by a twelve-man risk management engineers. The methodology successfully computed a "health-check" of the facility's compliance to new regulations, 17 high-risk-hazards were extracted from 42 potential hazards and successfully established varied individual risk levels ranging from 4.07E-06 to 1.64E-04/year. Also, risks ranged from 1.00E-04 to 5.00E-05/year of tolerable risks to the environment, society, and business were recorded across the facility while, 22 risk-mitigation actions were recommended. [ABSTRACT FROM AUTHOR]

The issue of credit risk management becomes especially relevant for the banking system of Ukraine nowadays, in the context of opening up the worldwide economic space. The objective need to improve the efficiency of bank’s credit risk management requires improvement of the legal, methodological, and organizational foundations of banking activity. The carried out research discloses the nature and place of credit risk among bank risks, its specific characteristics, and the factors of influence upon. The article emphasizes that, in order to make the functioning of bank efficient, credit risk should not be perceived as the probability of a negative event or danger, but rather as the income-generating activity. The main elements of the credit risk management system have been provided. The types and levels of credit risk have been distributed by the classification attributes. The role of internal and external environment factors influencing the bank lending policy has been explored. The role of the credit risk management strategy and tactics has been emphasized. The stages of the credit risk management strategy, its main methods and tools have been disclosed. Ways to successfully implement the credit risk management strategy for contemporary banks have been determined.

It has been demonstrated that perception of objects automatically evokes potential actions to interact with those objects; this is termed as affordance. The present study investigated how the corresponding affordance effect of graspable objects was modulated by the cue about risk levels of object. Participants were presented with pictures of dangerous graspable objects or neutral graspable objects. The participants were required to perform an upright/upside down discrimination task by pressing different keys. Results showed that both the affordance effects of dangerous object and neutral object in children were enhanced when a cue preceded the object, t(35) = 3.83, p <.01, Cohen's d = 1.29. These results indicate that the prompt of risk level can improve individual's appropriate manipulation to the object. [ABSTRACT FROM AUTHOR]

The objective of this study is to develop a driving fatigue strain index using fuzzy logic to analyze the risk levels of driving activity among road users. Driving fatigue is always related to the driving activity and has been identified as one of the vital contributors to the road accidents and fatalities in Malaysia. Therefore, the present article introduces the use of fuzzy logic for the development of strain index to provide the systematic analysis and propose an appropriate solution in minimizing the number of road accidents and fatalities. The development of strain index is based on the six risk factors associated with driving fatigue; muscle activity, heart rate, hand grip pressure force, seat pressure distribution, whole‐body vibration, and driving duration. The data are collected for all the risk factors, and consequently the three conditions or risk levels are defined as 'safe', 'slightly unsafe', and 'unsafe'. A membership function is defined for each fuzzy condition. IF‐THEN rules were used to define the input and output variables, which correspond to physical measures. This index is a reliable advisory tool for providing analysis and solutions to driving fatigue problem, which constitutes the first effort toward the minimization of road accidents and fatalities. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Animals can adjust their behaviours depending on ecological context (i.e., behavioural plasticity), and an individual's response to a given context may also vary from occasion to occasion (intra‐individual variability). Recognizing the roles of both behavioural plasticity and intra‐individual variability is important in understanding how behavioural diversity is maintained within populations. However, how the ecological context itself influences the individual behavioural response and intra‐individual variability (e.g., how variable an individual is in their behavioural expression) remains largely unexplored. Here, we examine boldness expression (the duration of startle response) in a specialised spider‐eating jumping spider, Portia labiata, across three contexts following a mild disturbance: presence of a conspecific intruder (most dangerous), environmental change but no conspecific intruder, and no conspecific intruder or environmental change (safest). We found that context does not significantly influence the average boldness expression at the population level. However, each individual responded to each context differently, and the repeatability of boldness expression—the proportion of behavioural variation attributable to the between ‐individual level—is context‐dependent. We also found that in the presence of a conspecific intruder, spiders behave less predictably than in the environmental change context, but not differently from the safest context. These findings may suggest that the presence of conspecifics influences behavioural consistency in individuals, but that this may occur without influencing the population average behaviour. [ABSTRACT FROM AUTHOR]

The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system; a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical–physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support system (DSS) with predictive capacity, when fed by the actual measured data, and as a simulation bench performing the tuning of certain input values, to identify which of them led to a decrease in the degree of risk. In this way, we aimed to design different scenarios to compare different restrictive strategies and the actual expected benefits, to adopt measures sized to the actual needs, adapted to the specific areas of analysis and useful for safeguarding human health; and we compared the economic and social impacts of the choices. Although ours is a concept paper, some preliminary analyses have been shown, and two different case studies are presented, whose results have highlighted a correlation between NO2, mobility and COVID-19 data. However, given the complexity of the virus diffusion mechanism, linked to air pollutants but also to many other factors, these preliminary studies confirmed the need, on the one hand, to carry out more in-depth analyses, and on the other, to use AI algorithms to capture the hidden relationships among the huge amounts of data to process.

Polish accession to the European Union allowed Polish municipalities to use co-financing of development projects of local infrastructure, environmental protection and improvement of competitiveness of municipalities. The process of transition is sometimes referred to as bridging the development gaps between countries of "old fifteen" and the new member countries. The desire of municipalities to maximize the use of EU funds led to a rapid increase in the debt level of municipalities and changes in levels of risk of long-term liabilities of municipalities. The aim of this article is to empirically confirm thesis that the change in the long-term financial liabilities associated with the prospect of the European Union budget for 2007-2013 is causing a variation of the operating risks of these liabilities. A tool used to verify this hypothesis is the risk model, which is a random vector with components identified with control variables of management process in the handling of long-term financial liabilities. The design of the components of this vector is based on the basic characteristics of the probability distribution of the vector as the ex-pected value and the variance and their ideal and anti-ideal levels.

Introduction: Patients with symptomatic heart failure (HF) require additive therapies and have a poor prognosis. However, patient characteristics and clinical outcome between HF patients treated in the outpatient setting vs. those who are hospitalized remain scarce.Methods and Results: The BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF) included 2516 patients with symptoms and/or signs of HF: 1694 as inpatients and 822 as outpatients. Compared to ambulatory HF patients, inpatients had higher heart rate, urea, N-terminal pro-brain natriuretic peptide, lower blood pressure, lower estimated glomerular filtration rate, sodium, potassium, high-density lipoprotein cholesterol, had more often peripheral oedema, diabetes, anaemia, and were less often treated with beta-blockers and angiotensin-converting enzyme inhibitors (ACEi). Outpatients had a more frequent history of HF hospitalization and received more frequently beta-blockers and/or ACEi/angiotensin receptor blockers up-titrated to target doses (P < 0.001). Inpatients had higher rates of the primary outcome of death or HF hospitalization: incidence rate per 100 person-years of 33.4 [95% confidence interval (CI) 31.1-35.9] for inpatients vs. 18.5 (95% CI 16.4-21.0) for outpatients; adjusted hazard ratio 1.24 (95% CI 1.07-1.43). Subdividing patients into low, intermediate and high-risk categories, the primary outcome event rates were 14.3 (95% CI 12.3-16.7), 36.6 (95% CI 32.2-41.5), and 71.3 (95% CI 64.4-79.0) for inpatients vs. 8.4 (95% CI 6.6-10.6), 29.8 (95% CI 24.5-36.2), and 43.3 (95% CI 34.7-54.0) for outpatients, respectively. These findings were externally replicated.Conclusions: Marked differences were observed between inpatients and outpatients with HF. Overall, inpatients were sicker and had higher event rates. However, a substantial proportion of outpatients had similar or higher event rates compared to inpatients. These findings suggest that HF outpatients also have poor prognosis and may be the focus of future trials. [ABSTRACT FROM AUTHOR]

Portuguese freshwater reservoirs are important socio-economic resources, namely for recreational use. National legislation concerning bathing waters does not include mandatory levels or guidelines for cyanobacteria and cyanotoxins. This is an issue of concern since cyanotoxin-based evidence is insufficient to change the law, and the collection of scientific evidence has been hampered by the lack of regulatory levels for cyanotoxins in bathing waters. In this work, we evaluate the profile of cyanobacteria and microcystins (MC) in eight freshwater reservoirs from the center of Portugal, used for bathing/recreation, in order to determine the risk levels concerning toxic cyanobacteria occurrence. Three of the reservoirs did not pose a risk of MC contamination. However, two reservoirs presented a high risk in 7% of the samples according to the World Health Organization (WHO) guidelines for MC in bathing waters (above 20 µg/L). In the remaining three reservoirs, the risk concerning microcystins occurrence was low. However, they exhibited recurrent blooms and persistent contamination with MC up to 4 µg/L. Thus, the risk of exposure to MC and potential acute and/or chronic health outcomes should not be disregarded in these reservoirs. These results contribute to characterize the cyanobacterial blooms profile and to map the risk of toxic cyanobacteria and microcystins occurrence in Portuguese inland waters. [ABSTRACT FROM AUTHOR]

Hydrogen sulfide (H2S) and Carbon monoxide (CO) are the undesired products of petroleum refineries. Herein, the chemical and physical properties, some statistical values about the H2S and CO levels measured from the different sites of the Earth are compiled from the literature. Furthermore, health issues and the concentration risk levels were compiled from literature and reported. Finally, following this detailed description, health risks were evaluated and conclusions were drawn. [ABSTRACT FROM AUTHOR]

The strategic management of commercial companies in times of crisis places a strong emphasis on the management and control of the risk. There is no business activity in the world that is not associated with various types of risk. In a highly competitive and turbulent environment, the clear linking of the business strategy of the firm and its risk management makes it possible to identify and manage the risk of the surrounding environment and the on-going movements within it. Strategic risk management creates a protection value that ensures a sustainable internal environment. This is a continuous process that is embedded in the strategy. [ABSTRACT FROM AUTHOR]

The phenomenon of drought is serious in many landscapes with continental patterns of climate. In fact, drought risk is usually assessed in terms of prevailing issue (meteorological, hydrological, agronomical, etc.) and not in terms of complex landscape features. A procedure for detailed geographical drought risk modelling has been developed using recent meteorological data of dry period and prior precipitations, as well as a digital elevation model and geographic data layers of natural landscape features and land cover. The current version of the procedure starts with meteorological data (temperature and precipitation) processing followed by the use of soil and geological data and land cover, the national CORINE LC 2006 CZ database, for assessing the impact of the local natural features on drought risk. The methodology is based on GIS tools, geodata of the geological structure of the area (water holding capacity of the substrate, the horizontal and vertical water conductivity), soil cover (in agricultural and forested areas, soil types and kinds), landscape cover (land use), relief (digital elevation model and its derivatives), temperature and precipitation data from neighbouring representative meteorological and climate stations. The procedure uses regression equation for temperature and precipitation risk modelling, fuzzy standardization for estimation of different water retention within land cover categories and expert estimation for risk categories within rocks and soils. The final calculation is based on spatial decision-making techniques, especially the weighted sum method with a natural breaks reclassification algorithm. Combining geodata of soils, the geological environment and the active surface with their computed humidity conditions, it is possible to identify areas with a graded risk of geographic drought. The final results do not represent partial values, but identify five risk classes in the study area illustrating a possible level of geographical drought risk. [ABSTRACT FROM AUTHOR]

This paper investigates the power of the computer guided clinical interview (CI) and new curriculum based measurement (CBM) measures to identify and help children at risk of low mathematics achievement. We use data from large numbers of children in Kindergarten through Grade 3 to investigate the construct validity of CBM risk categories. The basic issue is whether the CBM measures assign children correctly to the basic risk categories of At Risk, Emerging, and On Track. The data show that the CBM can be useful for identifying children at risk, but that the CI offers insight into the cognitive processes underlying student difficulties. The CI is particularly useful for identifying children whose underlying mathematical reasoning is more competent than their overt performance. Information of this type is more valuable for teachers than simply learning that a student is at risk. [ABSTRACT FROM AUTHOR]

Objective Patients with endometrioid endometrial cancer are stratified as high risk and low risk for extrauterine disease by surgical staging. Since patients with low-grade, minimally invasive disease do not benefit from comprehensive staging, pre-surgery stratification into a risk category may prevent unnecessary surgical staging in low risk patients. Our objective was to develop a predictive model to identify risk levels using somatic mutations that could be used preoperatively. Methods We classified endometrioid endometrial cancer patients in The Cancer Genome Atlas (TCGA) dataset into high risk and low risk categories: high risk patients presented with stage II, III or IV disease or stage I with high-intermediate risk features, whereas low risk patients consisted of the remaining stage I patients with either no myometrial invasion or low-intermediate risk features. Three strategies were used to build the prediction model: 1) mutational status for each gene; 2) number of somatic mutations for each gene; and 3) variant allele frequencies for each somatic mutation for each gene. Results Each prediction strategy had a good performance, with an area under the curve (or AUC) between 61% and 80%. Analysis of variant allele frequency produced a superior prediction model for risk levels of endometrial cancer as compared to the other two strategies, with an AUC = 91%. Lasso and Ridge methods identified 53 mutations that together had the highest predictability for high risk endometrioid endometrial cancer. Conclusions This prediction model will assist future retrospective and prospective studies to categorize endometrial cancer patients into high risk and low risk in the preoperative setting. [ABSTRACT FROM AUTHOR]

In this study, using four wave longitudinal data, we examined problem gambling severity trajectories in a sample of young adults. Using latent growth curve modeling, we examined how initial level of problem gambling severity and the rate of change were affected by 11 time-invariant predictors: gender, age of onset of gambling, experiencing a big win early in gambling career, experiencing a big loss early in gambling career, alcohol dependence, drug dependence, anxiety, depression, perceived social support, illusion of control, and impulsiveness. Five of the eleven predictors affected initial levels of problem gambling severity; however only impulsiveness affected the rate of change across time. The mean trajectory was negative (lessening of problem gambling risk severity across time), but there was significant inter-individual variation in trajectories and initial levels of problem gambling severity. The main finding of problem gambling risk diminishing over time challenges the conventional picture of problem gambling as an inevitable "downward spiral," at least among young adults, and suggests that targeted prevention campaigns may be a cost-effective alternative for reaching treatment resistant youth. [ABSTRACT FROM AUTHOR]

In general, it is important to understand drivers of safety performance. The issue of hydrocarbon leaks on offshore platforms is one of the most important safety performance indicators based on risk related to major accidents. In this study, a longitudinal design was used to investigate the relationships between work climate and hydrocarbon leaks from a broader organizational perspective. Confirmatory factor analyses tested on a sample of offshore workers ( N = 3320), supported the validity of the Scandinavian Work Climate Instrument (SWCI). Complementary results also supported the measurement model. Pearson’s correlations indicated significant associations between work climate dimensions and hydrocarbon leaks occurring in the post survey period. All correlations were negative, supporting the proposed hypothesis. Generally, the results supported the assumption and model that guided the overall investigation. Specifically, work climate was associated with safety performance. The findings indicated that several work climate factors could function as leading indicators, implying that the work climate approach has been underestimated in safety research. Correlations with hydrocarbon leaks indicate that the organizational factors explored in this study can predict different levels of leaks. This suggests that the work climate approach used in this study represents an important contribution to achieving a holistic understanding of safety performance. Future research should replicate the study design in other settings and investigate the possibility of exploring relationships with other types of safety performance indicators using SWCI. [ABSTRACT FROM AUTHOR]

This paper proposes a practical method for dynamic maintenance planning based on Dynamic Risk Assessment (DRA). This is founded on the interpretation, in terms of risk levels evolution, the available information of monitoring events and maintenance activities integrated in and that conform the condition-based maintenance (CBM) processes. DRA proposal is supported by ISO 31000 risk management framework in order to better understanding and results integration within other risk management approaches. Proposed method analyzes CBM results (monitoring events and maintenance activities) regarding their impact on failure risk level, and how to program and manage maintenance decision making (maintenance planning) regarding with dynamic risk evolution. This strategy not only helps maintenance management optimization but also facilitates the link of intelligent maintenance with global risk management within the organization, which is lined with modern Asset Management principles. To illustrate the method, an example of a real use case is presented where it is applied to the dynamic maintenance planning of a critical component in a high-speed train, and which integrates monitoring, predictive analytics and inspection data. • Build the planning proposal as a DRA tool. • Connect the results of monitoring and prognosis. • Control the status of the failure modes. • The ease of scheduling maintenance actions. • For the operator, the information to compose the level of risk is transparent. [ABSTRACT FROM AUTHOR]