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Energy markets are crucial for integrating Distributed Energy Resources (DER) into modern power grids. However, this integration presents challenges due to the inherent variability and decentralized nature of DERs, as well as poorly adapted regulatory environments. This paper proposes a medium-term decision-making approach based on a comprehensive suite of computational tools for integrating DERs into Colombian energy markets. The proposed framework consists of modular tools that are aligned with the operation of a Commercial Virtual Power Plant (CVPP). The tools aim to optimize participation in bilateral contracts and short-term energy markets. They use forecasting, uncertainty management, and decision-making modules to create an optimal portfolio of DER assets. The suite's effectiveness and applicability are demonstrated and analyzed through its implementation with heterogeneous DER assets across various operational scenarios. [ABSTRACT FROM AUTHOR]

The objective of the article was to describe the importance of a good technically and economically planned process for purchasing a rail vehicle. Compliance with this process with safety standards and energy efficiency is crucial regarding effectiveness in the long-life cycle cost of a rail vehicle. Methods that were used focused on audit and document analysis. In the result based on a specific audit of a railway company, some non-compliances were found, and it was methodically proven that they are significant risk factors in terms of performing such processes in the future. Major conclusions regarded the importance of fulfilling legal requirements of SMS for purchase purposes, involvement of a safety engineer post in this process, and usage of technical feedback regarding previous operation of railway vehicles and operational findings from the past. The transport policy of the EU and other developed countries sets ambitious goals for reducing energy consumption in transportation activities, which is linked to the aim of reducing environmental burdens. [ABSTRACT FROM AUTHOR]

Background: Clinical judgment, also known as gestalt or gut feeling, can predict deterioration and can be easily and rapidly obtained. To date, it is unknown what clinical judgement precisely entails. The aim of this study was to elucidate which features define the clinical impression of health care professionals in the ED. Method: A nominal group technique (NGT) was used to develop a consensus-based instrument to measure the clinical impression score (CIS, scale 1–10) and to identify features associated with either a more severe or less severe estimated disease severity. This single-center observational cohort study included 517 medical patients visiting the ED. The instrument was prospectively validated.. The predictive value of each feature for the clinical impression was assessed using multivariate linear regression analyses to adjust for potential confounders and validated in the infection group. Results: The CIS at the ED was associated with ICU admission (OR 1.67 [1.37–2.03], p < 0.001), in-hospital mortality (OR 2.25 [1.33–3.81], p < 0.001), and 28-day mortality (OR 1.33 [1.07–1.65], <0.001). Dry mucous membranes, eye glance, red flags during physical examination, results of arterial blood gas analysis, heart and respiratory rate, oxygen modality, triage urgency, and increased age were associated with a higher estimated disease severity (CIS). On the other hand, behavior of family, self-estimation of the patient, systolic blood pressure, and Glascow Coma Scale were associated with a lower estimated disease severity (CIS). Conclusion: We identified several features that were associated with the clinical impression of health care professionals in the ED. Translating the subjective features and objective measurements into quantifiable parameters may aid the development of a novel triage tool to identify patients at risk of deterioration in the ED. [ABSTRACT FROM AUTHOR]

The five independent countries of Central Asia, namely Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, contain one of the richest areas in the world for the specific and intraspecific diversity of temperate fruit and nut tree species. Research was carried out via the collaboration of national research and education institutes with local community-based agencies and farmer communities. Raw data (2014 observations) for almond, apple, apricot, cherry plum, currant, grapevine, pear, pomegranate, and walnut were collected at the household (HH) level across the five countries: Uzbekistan, Kazakhstan, Tajikistan, Turkmenistan, and Kyrgyzstan. A set of models was used, including household variety richness as the dependent variable, to understand the influence of socio-ecological variables on the amount and distribution of crop varietal diversity in the farmers' production systems. Four variables were included as explanatory variables of variety richness (fixed factors): ecoregion, ethno-linguistic group, management, and abiotic stress. The results show clear evidence that abiotic stress determines a higher richness of intra-specific diversity in the form of local varieties grown by farmers living in climatically unfavorable areas. The results for the studied ecoregions follow the same trend, with ecoregions with harsher conditions displaying a higher positive correlation with diversity. Mild environments such as the Central Asian riparian woodlands show an unexpectedly lower diversity than other harsher ecoregions. Ethno-linguistic groups also have an effect on the level of varietal diversity used, related to both historic nomadic practices and a culture of harvesting wild fruit and nuts in mountainous areas. The home garden management system hosts a higher diversity compared to larger production systems such as orchards. In Central Asia, encouraging the cultivation of local varieties of fruit and nut trees provides a key productive and resilient livelihood strategy for farmers living under the harsh environmental conditions of the region while providing a unique opportunity to conserve a genetic heritage of global importance. [ABSTRACT FROM AUTHOR]

The safety and reliability of undivided two-way highway–rail grade crossings (HRGCs) are of paramount importance in transportation systems. Utilizing crash data from the Federal Railroad Administration between 2020 and 2021, this study aims to predict crash injury severity outcomes and investigate various factors influencing injury severities. The χ2 test was first used to select variables that were significantly associated with injury outcomes. By employing the eXtreme Gradient Boosting (XGBoost) model and interpretable SHapley Additive exPlanations (SHAP), a cross-category safety assessment that offers an evidence-based hierarchy and statistical inference of risk factors associated with crashes, crossings, vehicles, drivers, and environment was provided for killed, injured, and uninjured outcomes. Some significant predictors overlapped between the killed and injured models, such as old driver, driver was in vehicle, main track, went around the gate, adverse crossing surface, and truck, while the other different significant factors revealed that the model could distinguish between different severity levels. Additionally, the results suggested that the model has varying performances in predicting different injury severities, with the killed model having the highest accuracy of 93.36%. The SHAP dependency plots for the top three features also ensure reliable predictions and inform potential interventions aimed at strengthening traffic safety and risk management practices, such as enhanced warning systems and targeted educational campaigns for older drivers. [ABSTRACT FROM AUTHOR]

The paper examines the valuation and hedging of life insurance obligations in the presence of mortality risk using the local risk-minimizing hedging approach. Roughly speaking, it is assumed that the lifetime of policyholders in an insurance portfolio is modeled by a point process whose stochastic intensity is controlled by a diffusion process. The stock price process is assumed to be a regime-switching Lévy process with non-zero regime-switching drift, where the parameters are assumed to depend on the economic states. Using the Föllmer–Schweizer decomposition, the main valuation and hedging results for a conditional payment process are determined. Some specific situations have been considered in which the local risk-minimizing strategies for a stream of liability payments or a unit-linked contract are presented. [ABSTRACT FROM AUTHOR]

In today's technology-centric business environment, where organizations encounter numerous cyber threats, effective IT risk management is crucial. An objective risk assessment—based on information relating to business requirements, human elements, and the security culture within an organisation—can provide a sound basis for informed decision making, effective risk prioritisation, and the implementation of suitable security measures. This paper focuses on asset valuation, supply chain risk, and enhanced objectivity—via a "segregation of duties" approach—to extend and apply the capabilities of an established security culture framework. The resultant system design aims at mitigating subjectivity in IT risk assessments, thereby diminishing personal biases and presumptions to provide a more transparent and accurate understanding of the real risks involved. Survey responses from 16 practitioners working in the private and public sectors confirmed the validity of the approach but suggest it may be more workable in larger organisations where resources allow dedicated risk professionals to operate. This research contributes to the literature on IT and cyber risk management and provides new perspectives on the need to improve objectivity in asset valuation and risk assessment. [ABSTRACT FROM AUTHOR]

Athletes carry an increased risk of cardiovascular (CV) conditions. Due to the relatively high loads and intensity of the training process, athletes' CV systems undergo various adaptations, which can combine in the future and provoke unexpected outcomes. Most CV screening protocols have several successive steps. The aim of our study was to perform a cardiological functional assessment of the National Olympic Team of Kazakhstan via several noninvasive protocols to close the gaps between the approaches and collect solid data for the prevention of sudden cardiac death (SCD) incidence among Kazakhstani athletes. Methods: The methods used in this study were 12-lead resting electrocardiography (ECG), echocardiography, cardiointervalography, cardiopulmonary exercise testing (CPET), and HyperQ stress testing. Results: One case was detected via 12-lead resting ECG. Another case of the slowdown of the heart rate (HR) recovery was detected via cardiointervalography with no clinical signs and normal ECG. The HyperQ stress testing of the women's basketball team detected a positive result in four leads in one athlete. Conclusion: Our results demonstrate that the CV systems of athletes require the implementation of several diagnostic methods in rest and stress conditions for more precise evaluation, with each of the methods fulfilling the whole picture for the prevention of such tragic events as sudden cardiac death and sudden cardiac arrest. [ABSTRACT FROM AUTHOR]

Background. Hemoglobinopathies, among which thalassemic syndromes (transfusion-dependent and non-transfusion dependent thalassemias) and sickle cell disease (SCD), are the most widespread monogenic diseases worldwide. Hemoglobinopathies are endemic and spread-out all-over Italy, as result of internal and external migration flows. Nowadays, the increase therapeutic options associated to the general aging of patients with hemoglobinopathies related to the improvement in clinical management, contribute to the abnormalities in kidney function going from blood and urine test alterations to chronic kidney disease and end stage renal disease. Methods. Here, we carried out a revision of the literature as panel of recognized experts in hemoglobinopathies with the consultancy and the revision of two nephrologists on kidney alteration and kidney disease in patients with TDT, NTDT and SCD. This is part of the action of the Italian society for the study of thalassemia and hemoglobinopties (SITE). The purpose of this "good practice (GP)" is to provide recommendations for follow-up and therapy for the management of kidney alterations in patients with TDT, NTDT and SCD. The literature review covers the period 1.1.2016 to 31.12.2022. In consideration of the rarity of these diseases, the analysis was extended from 5 to 7 years. Moreover, in the absence of relevant scientific papers in the identified time frame, we referred to pivotal or population studies, when available. Finally, in the absence of evidence-based data from prospective and randomized trials, the authors had to refer to expert opinion (expert consensus) for many topics. Results. We generated question and answer boxes to offer a friendly consultation, using color code strategy and focused answers. Conclusions. The present GP will help in improving the clinical management, and the quality of care of patients with hemoglobinopathies. [ABSTRACT FROM AUTHOR]

Simple Summary: Aerosol insecticides are widely used in food processing and warehouse facilities to help control insect contamination of grain and packaged products. These tests were conducted in the experimental flour mill at a regional university. One insecticide, Turbocide®, was applied from a high-pressure cylinder. The nozzle pressure was ~800 psi, and the spray plumes were very fine (VMD ~18 µm). The other insecticide, Pyrocide®, was applied using a hand-held fogging system, which delivered a larger distribution of droplets (VMD ~50 µm). These insecticides were dispensed from varied locations on the milling floor. The spray applications were monitored with droplet counting instruments and bioassay dishes with either adult or larvae flour beetles. Droplet data were summarized into two measurements: mass concentration index (MCI) and deposition index (Dep.Idx). Contour plots of Dep.Idx values were developed and were useful to show and compute the amount of floor area that was poorly treated. For Turbocide, adult insect efficacy increased with increasing MCI and Dep.Idx values, but larval insect efficacy was high at all MCI and Dep.Idx values. In contrast, with Pyrocide, there was no apparent relationship between aerosol MCI or Dep.Idx and adult insect efficacy. However, with Pyrocide, there was a positive relationship between the larval efficacy and the Dep.Idx. These results are useful in developing more effective application strategies for optimum insect control. Aerosol insecticides are widely used in stored product insect management programs in food facilities. Previous research has shown spatial variation in aerosol efficacy within facilities, but information on how spatial patterns of aerosol droplet concentration, size distribution, dispersal, and deposition contribute to this variation in efficacy is limited. This study involved two aerosol application systems: a high-pressure cylinder containing TurboCide Py-75® with pyriproxyfen IGR (ChemTech Ltd., Des Moines, IA, USA) and a hand-held fogger containing Pyrocide 100® (MGK, Minneapolis, MN, USA) with Diacon II which contains methoprene IGR (Wellmark, Schaumburg, IL, USA). These systems were used at single or multiple application locations. The spray trials were conducted in a small-scale flour mill, Hall Ross Flour Mill (Kansas State University, Manhattan, KS, USA). The droplet size distributions were monitored at multiple positions within the room using nine aerodynamic particle sizing (APS, TSI Incorp, Shoreview, MN, USA) instruments. The APS data collected over the treatment period were summarized into a mass concentration index (MCI), which ranged from 155 to 2549 mg/m3 for Turbocide and 235–5658 mg/m3 for Pyrocide. A second parameter called the Deposition Index (Dep.Idx) was derived to estimate potential insecticide depositions on the floor and has units of g/m2. The Dep.Idx was below 5.3 g/m2 for most Turbocide applications, while the Dep.Idx was below 8.4 g/m2 for most Pyrocide applications. The MCI and Dep.Idx values varied with APS position and spray application location, with proximity to the aerosol application location and degree of obstruction between the release point and APS position contributing to this variation. We assessed the relationship between aerosol droplet parameters and insect efficacy using Tribolium confusum Jacqueline DuVal, the confused flour beetle. The adults were treated directly, while the larvae were treated two weeks later during the residual test (previously published). For Turbocide, efficacy against adults increased with MCI and Dep.Idx values, but for residual efficacy of the IGR, efficacy was high at all aerosol droplet values, so no relationship was apparent. In contrast, the relationship between Pyrocide deposition and adult insect efficacy was highly variable. But with larval insect efficacy, residual larvae control was directly related to increases in Pyrocide MCI and Dep.Idx. Contour plots of Dep.Idx values were developed, which could be used to predict areas of the mill that are not receiving an adequate application rate, and this could be used to develop more effective application strategies for aerosol insecticides in food facilities. [ABSTRACT FROM AUTHOR]

Simple Summary: Despite being preventable, cervical cancer remains a lead killer among women in low–middle income countries. The reasons are clear. Global inequity is reflected in limited health resources, such as limited access to HPV-vaccination, screening, and treatment, which are the key pillars for the elimination of cervical cancer. The high burden of HPV-coinfections in women living with HIV/AIDS challenges the use of sensitive HPV-testing, leading to massive over-treatment. As advised by the WHO, a "test-and-treat" strategy could be optimal. In this study, 710 under-screened South African women, about half of them living with HIV, were tested for mRNA expression restricted to the predominant HPV types of cervical cancer. The effectiveness of a test-and-treat strategy to detect and manage severe cervical abnormalities (CIN3+) by increasing the number of HPV types included in the test was modeled. A combination of 6 types (16, 18, 31, 33, 35, 45) resulted in 25% positive tests requiring treatment, with the potential to prevent up to 85% of all cervical cancers. Background: Cervical cancer prevention in regions with limited access to screening and HPV vaccination necessitates innovative approaches. This study explored the potential of a test-and-treat strategy using mRNA HPV tests to impact cervical cancer prevention in a high-prevalence HIV population. Methods: A cervical screening study was conducted at three South African hospitals involving 710 under-screened, non-pregnant women (25 to 65 years) without known cervical diseases. Cytology, HPV testing, colposcopy, and biopsies were performed concurrently. Histopathologists determined final histological diagnoses based on biopsy and LLETZ histology. mRNA-HPV-genotyping for 3 (16, 18, 45) to 8 (16, 18, 31, 33, 35, 45, 52, 58) high-risk types was performed on leftover liquid-based cytology material. The preventive potential of the test-and-treat approach was estimated based on published data, reporting the causative HPV types in cervical cancer tissue from South African women. Treatment was provided as needed. Results: The HPV positivity rate more than doubled from 3-type (15.2%; 95% CI: 12.6–17.8) to 8-type mRNA (31.5%; 95% CI: 28.8–34.9) combinations, significantly higher among HIV-positive women. CIN3+ prevalence among HIV-positive women (26.4%) was double that of HIV-negative women (12.9%) (p < 0.01). The 6-type combination showed the best balance of sensitivity, specificity and treatment group size, and effectiveness to prevent cervical cancer. A 4-type combination (16, 18, 35, 45) could potentially prevent 77.6% (95% CI: 71.2–84.0) of cervical cancer burden by treating 20% and detecting 41.1% of CIN3 cases in the study group. Similarly, a 6-type combination (16, 18, 31, 33, 35, 45), treating 25% and including 62% of CIN3 cases, might prevent 85% of cervical cancer cases (95% CI: 79.6–90.6) among HIV-positive and negative women. Conclusion: Employing mRNA HPV tests within a test-and-treat approach holds huge promise for targeted cervical cancer prevention in under-screened populations. Testing for mRNA of the 6 highest-risk HPV types in this population and treating them all is projected to effectively prevent progression from CIN3 to invasive cervical cancer while reducing overtreatment in resource-constrained settings. [ABSTRACT FROM AUTHOR]

Entropy serves as a measure of chaos in systems by representing the average rate of information loss about a phase point's position on the attractor. When dealing with a multifractal system, a single exponent cannot fully describe its dynamics, necessitating a continuous spectrum of exponents, known as the singularity spectrum. From an investor's point of view, a rise in entropy is a signal of abnormal and possibly negative returns. This means he has to expect the unexpected and prepare for it. To explore this, we analyse the New York Stock Exchange (NYSE) U.S. Index as well as its constituents. Through this examination, we assess their multifractal characteristics and identify market conditions (bearish/bullish markets) using entropy, an effective method for recognizing fluctuating fractal markets. Our findings challenge conventional beliefs by demonstrating that price declines lead to increased entropy, contrary to some studies in the literature that suggest that reduced entropy in market crises implies more determinism. Instead, we propose that bear markets are likely to exhibit higher entropy, indicating a greater chance of unexpected extreme events. Moreover, our study reveals a power-law behaviour and indicates the absence of variance. [ABSTRACT FROM AUTHOR]

This paper endeavors to enhance the prediction of volatility in financial markets by developing a novel hybrid model that integrates generalized autoregressive conditional heteroskedasticity (GARCH) models and long short-term memory (LSTM) neural networks. Using high-frequency data, we first estimate realized volatility as a robust measure of volatility. We then feed the outputs of multiple GARCH models into an LSTM network, creating a hybrid model that leverages the strengths of both approaches. The predicted volatility from the hybrid model is used to generate trading strategy signals, which are subsequently used to build an investment strategy. Empirical analysis using the China Securities Index 300 (CSI300) dataset demonstrates that the hybrid model significantly improves value-at-risk (VaR) prediction performance compared to traditional GARCH models. This study's findings have broad implications for risk management in financial markets, suggesting that hybrid models incorporating mathematical models and economic mechanisms can enhance derivative pricing, portfolio risk management, hedging transactions, and systemic risk early-warning systems. [ABSTRACT FROM AUTHOR]

The capital allocation framework presents capital allocation principles as solutions to particular optimisation problems and provides a general solution of the quadratic allocation problem via a geometric proof. However, the widely used haircut allocation principle is not reconcilable with that optimisation setting. Our study complements and generalises the unified capital allocation framework. The goal of the study is to contribute in the following two ways. First, we provide an alternative proof of the quadratic allocation problem based on the Lagrange multipliers method to reach the general solution, which complements the geometric proof. This alternative approach to solve the quadratic optimisation problem is, in our opinion, easier to follow and understand by researchers and practitioners. Second, we show that the haircut allocation principle can be accommodated by the optimisation setting with the quadratic optimisation criterion if one of the original conditions is relaxed. Two examples are provided to illustrate the accommodation of this allocation principle. [ABSTRACT FROM AUTHOR]

As renewable energy (RE) costs decrease, private non-subsidized revenue sources, such as power purchase agreements (PPA), will increase in off-grid areas. This paper's objective is to improve policymakers' and utilities' understanding of PPA's role in mitigating private investors' risks in off-grid areas to accelerate the energy transition. The paper built a PPA dataset for the largest off-grid area in the Philippines and developed novel and efficient techniques to evaluate the risk mitigation ability of a PPA. While oil-based technologies are expensive, they are low-capital-intensive, and the fuel, the bulk of the cost, is passed through to consumers and primarily funded by subsidies. In contrast, the most affordable energy source, RE, requires higher upfront capital investments, financed primarily with equity. Investors chose low-capital-intensive technologies (oil), rehabilitated power plants, and utilized old equipment, all investment decisions to mitigate residual PPA risks, i.e., distribution utilities' low creditworthiness and capital recovery uncertainty. Presenting the investment as a PPA residual mitigation tool is one of the paper's contributions to the literature. The distribution utility needs to reduce investors' uncertainty by covering reasonable investors' costs. Policymakers need to level the playing field between fossil fuels and RE by reducing subsidies and strengthening distribution utility creditworthiness. [ABSTRACT FROM AUTHOR]

The objective of this study is to evaluate assets' performance by considering the exit time within the risk measurement framework alongside Shannon entropy and, alternatively, excluding these factors, which can be used to create a portfolio aligned with short- or long-term objectives. This portfolio effectively balances the potential risks and returns, guiding investors to make decisions that are in line with their financial goals. To assess the performance, we used data envelopment analysis (DEA), whereby we utilized the risk measure as an input and the mean return as an output. The stop point probability–CVaR (SPP-CVaR) was the risk measurement used when considering the exit time. We calculated the SPP-CVaR by converting the risk-neutral density to the real-world density, calibrating the parameters, running simulations for price paths, setting the stop-profit points, determining the exit times, and calculating the SPP-CVaR for each stop-profit point. To account for negative data and to incorporate the exit time, we have proposed a model that integrates the mean return and SPP-CVaR, utilizing DEA. The resulting inefficiency scores of this model were compared with those of the mean-CVaR model, which calculates the risk across the entire time horizon and does not take the exit time and Shannon entropy into account. To accomplish this, an analysis was conducted on a portfolio that included a variety of stocks, cryptocurrencies, commodities, and precious metals. The empirical application demonstrated the enhancement of asset selection for both short-term and long-term investments through the combined use of Shannon entropy and the exit time. [ABSTRACT FROM AUTHOR]

In asset management for energy portfolios, quantitative methodologies are typically employed. In Brazil, the NEWAVE computational model is universally used to generate scenarios of hydraulic production and future prices, which result in revenue distributions. These distributions are then used to estimate the portfolio's revenue and assess its risk. Although this is a well-established analysis, it has some shortcomings that are not always considered. The validity of the revenue series constructed by NEWAVE, especially in long-term analysis, is a real problem for agents concerning the acquisition of assets such as power plants. Another issue is the disregard for other objectives that are important for the operationality of the management task and are often ignored, such as operational risk. To address these limitations, this work combines the areas of multicriteria decision making under uncertainty and risk management and presents a methodology for evaluating the acquisition of long-term energy assets, as well as a practical application of the proposed method. Investment alternatives are evaluated in multiple developed scenarios, so it is possible to measure how robust a given option is. By analyzing several scenarios simultaneously, a larger region of uncertainties can be covered, and therefore, decision making becomes more secure. The proposed methodology includes six objectives, designed to address a wider range of stakeholder needs. This approach is applied to an illustrative portfolio, producing results that allow for a more comprehensive understanding of decision attributes. Therefore, this work not only addresses the current limitations in the field but also adds an original contribution by considering simultaneously several scenarios and integrating multiple objectives in a robust and secure decision-making framework. [ABSTRACT FROM AUTHOR]

Simple Summary: Motion of the prostate may adversely affect the outcome of radiotherapy. Online tracking of the prostate during irradiation is technologically feasible but only available at select institutions. It would be beneficial to be able to identify patients at risk of particularly high prostate intrafraction motion with simpler technology. In this paper, we present a larger inner diameter of the lesser pelvis as an anatomical predictor for high prostate intrafraction motion. It can be measured with a single planning CT, which should always be available. Risk patients identified in this way could then be selected for more rigorous online motion management or benefit from increased safety margins. Prostate motion (standard deviation, range of motion, and diffusion coefficient) was calculated from 4D ultrasound data of 1791 fractions of radiation therapy in N = 100 patients. The inner diameter of the lesser pelvis was obtained from transversal slices through the pubic symphysis in planning CTs. On the lateral and craniocaudal axes, motility increases significantly (t-test, p < 0.005) with the inner diameter of the lesser pelvis. A diameter of >106 mm (ca. 6th decile) is a good predictor for high prostate intrafraction motion (ca. 9th decile). The corresponding area under the receiver operator curve (AUROC) is 80% in the lateral direction, 68% to 80% in the craniocaudal direction, and 62% to 70% in the vertical direction. On the lateral x-axis, the proposed test is 100% sensitive and has a 100% negative predictive value for all three characteristics (standard deviation, range of motion, and diffusion coefficient). On the craniocaudal z-axis, the proposed test is 79% to 100% sensitive and reaches 95% to 100% negative predictive value. On the vertical axis, the proposed test still delivers 98% negative predictive value but is not particularly sensitive. Overall, the proposed predictor is able to help identify patients at risk of high prostate motion based on a single planning CT. [ABSTRACT FROM AUTHOR]

Avocado is one of the most commercialized and profitable fruits in the international market. Its cultivation and production are centered in countries characterized by tropical and subtropical climatic conditions, many of them with emerging economies. Moreover, the use of technology is key to agricultural production improvement strategies. Using avocado crop data to forecast the potential impacts of biotic and abiotic factors, combined with smart farming technologies, growers can apply measures during a single production phase to reduce the risks caused by pests and weather variations. Therefore, this paper aims to distinguish the most relevant variables related to agroclimatic and phytosanitary events in avocado crops, their incidence on production and risk management, as well as the emerging technologies used for the identification and analysis of pests and diseases in avocados. A scientific literature search was performed, and the first search found 608 studies, and once the screening process was applied, 37 papers were included in this review. In the results, three research questions were answered that described the pests and diseases with high impact on avocado production, along with the data sources and the principal enabling technologies used in the identification of agroclimatic and phytosanitary events in avocados. Some challenges and trends in the parameterization of the technology in field conditions for data collection are also highlighted. [ABSTRACT FROM AUTHOR]

Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheless, this approach does not take into consideration the potential risk associated with the actions taken, which may be critical in certain applications. To address that issue, the present research work introduces a novel methodology based on distributional RL to derive sequential decision-making policies that are sensitive to the risk, the latter being modelled by the tail of the return probability distribution. The core idea is to replace the Q function generally standing at the core of learning schemes in RL by another function, taking into account both the expected return and the risk. Named the risk-based utility function U, it can be extracted from the random return distribution Z naturally learnt by any distributional RL algorithm. This enables the spanning of the complete potential trade-off between risk minimisation and expected return maximisation, in contrast to fully risk-averse methodologies. Fundamentally, this research yields a truly practical and accessible solution for learning risk-sensitive policies with minimal modification to the distributional RL algorithm, with an emphasis on the interpretability of the resulting decision-making process. [ABSTRACT FROM AUTHOR]

This study examined the Security and Exchange Commission (SEC) annual reports of selected logistics firms over the period from 2006 through 2021 for risk management terms. The purpose was to identify which risks are considered most important in supply chain logistics operations. Section 1A of the SEC reports includes risk factors. The COVID-19 pandemic has had a heavy impact on global supply chains. We also know that trucking firms have long had difficulties recruiting drivers. Fuel price has always been a major risk for airlines but also can impact shipping, trucking, and railroads. We were especially interested in pandemic, personnel, and fuel risks. We applied topic modeling, enabling us to identify some of the capabilities of unsupervised text mining as applied to SEC reports. We demonstrate the identification of terms, the time dimension, and correlation across topics by the topic model. Our analysis confirmed expectations about COVID-19's impact, personnel shortages, and fuel. It also revealed common themes regarding the risks involved in international trade and perceived regulatory risks. We conclude with the supply chain management risks identified and discuss means of mitigation. [ABSTRACT FROM AUTHOR]

A clinical risk is an inherent risk in healthcare processes, including skin biopsy procedures, and may lead to misdiagnoses, increased healthcare costs and potential harm to patients. Indeed, clinical and histopathological data must be integrated if we are to reduce clinical risks and improve diagnostic accuracy in the diagnosis of dermatologic diseases. Although dermopathology services used to be part of a dermatologist's duty, the recent centralization of these laboratories has caused a loss of expertise and increased both complexity and safety issues. Some countries have implemented clinical-pathological correlation programs aimed at facilitating communication between clinicians and dermatopathologists. However, Italy has regulatory and cultural barriers that make the implementation of these programs difficult. Therefore, an internal analysis was carried out to assess the efficacy and impact that skin biopsy procedures for inflammatory and neoplastic conditions have on the quality of care in our dermatology department. As the analysis evidenced a high number of descriptive pathologic reports and discordant diagnoses, a multidisciplinary group of four dermatologists, four general pathologists and one dermatopathologist was set up. Herein, we present the results of this analysis and project and describe the structure of the multidisciplinary group. We also discuss the pros and cons, possibilities and limitations of our project, including the regulatory barriers of the Italian National Health System. [ABSTRACT FROM AUTHOR]

Along the roadmap to a Sustainable Real Estate-Scape, energy retrofit campaigns on wide city compartments represent a pivotal task, where the importance of the collaboration between the public and private sectors is crucial. Energy retrofit programs on building assets are subject to multiple uncertainty factors (e.g., climate, energy-economy forecasts, etc.) that act as a primary barrier to investment in this field. This paper aims to discuss risk management techniques to understand better how to deal with this kind of uncertainty. The research specifically addresses the techniques of sensitivity analysis and Monte Carlo simulation, focusing first on the phase of variables selection and their probability definition, including climatic, environmental, energy, economic, financial, and stochastic parameters. In this article, it is suggested to include correlation coefficients in the input variables of risk analysis, preferring the two-dimension Monte Carlo simulation to its simple version, since the results are more reliable when separating aleatory from epistemic uncertainty; thus, the results are more reliable. Moreover, it is seen how a sensitivity analysis based on percentile variations of the inputs provides a more accurate representation of risk if compared to the most common sensitivity analysis based on percentage deviations of the inputs. Conducting a sensitivity analysis using percentile variations gives realistic and reliable results, reflecting the tailored definition of uncertainty around the inputs on the basis of specific market analyses or historical series. [ABSTRACT FROM AUTHOR]

Agriculture accounts for the largest share of anthropogenic methane emissions. Rice paddy fields emit a significant amount of methane gas worldwide. Changing paddy water management practices has an enormous potential to reduce greenhouse gases. The clean development mechanism (CDM) project uses a market mechanism to reduce methane through private participation. There are various risks associated with private investment in CDM projects, although carbon credits as an economic incentive assist in mitigating some of these risks. Farmer participation plays a key role in the success of paddy water management projects in rural areas; however, despite the significant potential to reduce global methane emissions, very few projects have been implemented. When designing a Sustainable Development Mechanism (SDM) system, it is crucial to understand why the market mechanism in the existing CDM projects has failed. This study identifies and categorizes the risks and barriers to paddy water management in CDM projects and analyzes risk management options in CDM projects in India, Indonesia, and Mozambique. The results of this study showed that aside from economic risks, barriers to the application of technology in the field pose critical risks. The lack of knowledge and implementation experiences in rural areas increases barriers to practice. This in turn causes risk of difficulties in technology transfer which can be alleviated by improving awareness and introducing new knowledge through education and training in rural project implementation. Additionally, we highlight the importance of international efforts to build governance between the private and public sectors and promote technology transfers through multi-stakeholder engagement. This study provides specific information to encourage methane reduction worldwide and vitalize rice paddy water management in carbon reduction projects. [ABSTRACT FROM AUTHOR]

The Federal Highway Administration (FHWA) mandates biannual bridge inspections to assess the condition of all bridges in the United States. These inspections are recorded in the National Bridge Inventory (NBI) and the respective state's databases to manage, study, and analyze the data. As FHWA specifications become more complex, inspections require more training and field time. Recently, element-level inspections were added, assigning a condition state to each minor element in the bridge. To address this new requirement, a machine-aided bridge inspection method was developed using artificial intelligence (AI) to assist inspectors. The proposed method focuses on the condition state assessment of cracking in reinforced concrete bridge deck elements. The deep learning-based workflow integrated with image classification and semantic segmentation methods is utilized to extract information from images and evaluate the condition state of cracks according to FHWA specifications. The new workflow uses a deep neural network to extract information required by the bridge inspection manual, enabling the determination of the condition state of cracks in the deck. The results of experimentation demonstrate the effectiveness of this workflow for this application. The method also balances the costs and risks associated with increasing levels of AI involvement, enabling inspectors to better manage their resources. This AI-based method can be implemented by asset owners, such as Departments of Transportation, to better serve communities. [ABSTRACT FROM AUTHOR]

Failure mode and effects analysis (FMEA) is a proactive risk management approach. Risk management under uncertainty with the FMEA method has attracted a lot of attention. The Dempster–Shafer (D-S) evidence theory is a popular approximate reasoning theory for addressing uncertain information and it can be adopted in FMEA for uncertain information processing because of its flexibility and superiority in coping with uncertain and subjective assessments. The assessments coming from FMEA experts may include highly conflicting evidence for information fusion in the framework of D-S evidence theory. Therefore, in this paper, we propose an improved FMEA method based on the Gaussian model and D-S evidence theory to handle the subjective assessments of FMEA experts and apply it to deal with FMEA in the air system of an aero turbofan engine. First, we define three kinds of generalized scaling by Gaussian distribution characteristics to deal with potential highly conflicting evidence in the assessments. Then, we fuse expert assessments with the Dempster combination rule. Finally, we obtain the risk priority number to rank the risk level of the FMEA items. The experimental results show that the method is effective and reasonable in dealing with risk analysis in the air system of an aero turbofan engine. [ABSTRACT FROM AUTHOR]

Peptide receptor radionuclide therapy (PRRT) using Lutetium-177 (177Lu) based radiopharmaceuticals has emerged as a therapeutic area in the field of nuclear medicine and oncology, allowing for personalized medicine. Since the first market authorization in 2018 of [¹⁷⁷Lu]Lu-DOTATATE (Lutathera®) targeting somatostatin receptor type 2 in the treatment of gastroenteropancreatic neuroendocrine tumors, intensive research has led to transfer innovative 177Lu containing pharmaceuticals to the clinic. Recently, a second market authorization in the field was obtained for [¹⁷⁷Lu]Lu-PSMA-617 (Pluvicto®) in the treatment of prostate cancer. The efficacy of 177Lu radiopharmaceuticals are now quite well-reported and data on the safety and management of patients are needed. This review will focus on several clinically tested and reported tailored approaches to enhance the risk–benefit trade-off of radioligand therapy. The aim is to help clinicians and nuclear medicine staff set up safe and optimized procedures using the approved 177Lu based radiopharmaceuticals. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has presented an unprecedented challenge to the healthcare system. Identifying the genomics and clinical biomarkers for effective patient stratification and management is critical to controlling the spread of the disease. Omics datasets provide a wealth of information that can aid in understanding the underlying molecular mechanisms of COVID-19 and identifying potential biomarkers for patient stratification. Artificial intelligence (AI) and machine learning (ML) algorithms have been increasingly used to analyze large-scale omics and clinical datasets for patient stratification. In this manuscript, we demonstrate the recent advances and predictive accuracies in AI- and ML-based patient stratification modeling linking omics and clinical biomarker datasets, focusing on COVID-19 patients. Our ML model not only demonstrates that clinical features are enough of an indicator of COVID-19 severity and survival, but also infers what clinical features are more impactful, which makes our approach a useful guide for clinicians for prioritization best-fit therapeutics for a given cohort of patients. Moreover, with weighted gene network analysis, we are able to provide insights into gene networks that have a significant association with COVID-19 severity and clinical features. Finally, we have demonstrated the importance of clinical biomarkers in identifying high-risk patients and predicting disease progression. [ABSTRACT FROM AUTHOR]

Ancient villages are precious architectural treasures that have been protected from fire hazards for centuries through traditional fire prevention strategies. However, research on traditional fire response strategies is limited, with existing studies mainly focusing on climate response strategies, conservation, and renewal. No prior research has revealed the quantitative fire response strategies used for ancient buildings. This paper takes the first ancient village in western Hunan, High-Chair village, as an example, and it (1) assesses the fire risk of High-Chair village; (2) determines the traditional fire response strategies of the ancient village, including fire prevention culture, residential layout, wall forms, and fire resistant materials; and (3) uses CFD simulation to reveal and verify the science and rationale of the traditional patio layout and hill wall forms. The study suggests utilizing CFD simulation to quantitatively assess and validate fire response strategies. Such knowledge of fire prevention can provide fire mitigation solutions for rural construction. [ABSTRACT FROM AUTHOR]

Adverse drug reactions (ADRs) are responsible for almost 5% of hospital admissions, making it necessary to implement different pharmacovigilance strategies. The additional monitoring (AM) concept has been highlighted and intended to increase the number of suspected ADRs reported, namely in medicines with limited safety data. A prospective, descriptive study of active pharmacovigilance (AP) was conducted between 2019 and 2021 in the Local Health Unit of Matosinhos (LHUM) (Porto, Portugal). A model of AP for medicines under AM, namely oral antineoplastic agents, was designed. Follow-up consultations were performed, and adverse events (AEs) data were collected. The overall response to the treatment was evaluated through the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria. A total of 52 patients were included in the study, and 14 antineoplastic drugs under AM were analyzed. Of the total number of patients included, only 29 developed at least one type of toxicity. Hematological disorders were the most reported suspected ADR. However, only four patients interrupted their treatment due to toxicity. After 12 months of treatment, most patients had disease progression, which was the main reason for therapy discontinuation. This AP model played an important role in the early detection of AEs and, consequently, contributed to better management of them. Increasing the number of suspected ADR reports is crucial for drugs with limited safety data. [ABSTRACT FROM AUTHOR]

The Black–Scholes model assumes that volatility is constant, and the Heston model assumes that volatility is stochastic, while the rough Bergomi (rBergomi) model, which allows rough volatility, can perform better with high-frequency data. However, classical calibration and hedging techniques are difficult to apply under the rBergomi model due to the high cost caused by its non-Markovianity. This paper proposes a gated recurrent unit neural network (GRU-NN) architecture for hedging with different-regularity volatility. One advantage is that the gating network signals embedded in our architecture can control how the present input and previous memory update the current activation. These gates are updated adaptively in the learning process and thus outperform conventional deep learning techniques in a non-Markovian environment. Our numerical results also prove that the rBergomi model outperforms the other two models in hedging. [ABSTRACT FROM AUTHOR]

Listeria monocytogenes is an ubiquitously occurring foodborne bacterial pathogen known to contaminate foods during the production processes. To assess the presence and persistence of L. monocytogenes in Danish ready-to-eat (RTE) food production companies in response to a Listeria awareness campaign, the production environment of selected companies were sampled in 2016 and in 2020. Whole genome sequencing (WGS) was performed to characterize the isolates (n = 50, plus 35 isolates obtained from the routine surveillance during 2016–2020), including investigation of the presence of virulence, persistence and resistance genes. The number of companies that tested positive by culture was 17/39 (43.6%) in 2016 and 11/34 (32.4%) in 2020, indicating a limited effect of the campaign. WGS analyses of the 85 isolates showed that the most common sequence types (STs) were ST8 and ST121. The single nucleotide polymorphism (SNP) analysis showed that isolates coming from the same company and belonging to the same ST exhibited <10 SNP differences regardless of the sampling year and whether the samples came from the environment or products, indicating the persistence of the specific STs. Several prevalent STs were found in clinical cases concurrently, including genetically similar isolates. This highlights the issue of persistent L. monocytogenes in the food production environment and the need for improved risk communication and mitigation strategies. [ABSTRACT FROM AUTHOR]

Sovereign default has myriad economic and political consequences. Existing research, however, has not explored the human costs of sovereign default, though some link the fiscal flexibility afforded by sovereign creditworthiness to improved human rights performance. But what are the consequences when sovereigns lose all creditworthiness and default on their debt obligations? I argue that while the average effect of default is negative for respect for physical integrity rights, a conditional effect exists. When states devote more of their resources to debt service and default, they are likely to see a short term increase in respect for physical integrity rights. I find robust support for these arguments using panel data on over 90 developing countries from 1981–2010. [ABSTRACT FROM AUTHOR]

This study aims to provide a comprehensive overview of risk management research developments in the energy sector by using bibliometric analysis techniques. We apply the SciMAT bibliometric analysis software to understand how the intellectual base of this topic has evolved over time and what the major themes are that have contributed to this evolution. We analyse 679 publications referenced in the Web of Science Core Collection and Scopus to map the content of publications on risk management research in the energy sector over a period of 30 years (1993–2022), following the methodical rigour of PRISMA (Preferred Reporting Items for Systematic and Meta-Analyses). Our results identify and support the evolution of risk management research in the energy industry, its interactions, its stability, and changes in its research network. Our work contributes to the current debate on identifying trends and enhancing understanding of the evolution in the energy sector from the perspective of risk management research. It can also be a reference point for those interested in deepening their knowledge in this field. [ABSTRACT FROM AUTHOR]

This paper is devoted to studying the influence of the structure of a complex system on its integral risk. When solving risk management problems, it often becomes necessary to take into account structural effects, which most often include risk transfer and failure propagation. This study discusses the influence of the position of the elements of a protected system inside a fixed structure of the "star" type on its integral risk. The authors demonstrate that the problem of the optimal placement of elements in such a structure from the point of view of minimizing the risk cannot be precisely solved by analytical methods and propose an algorithm for solving it with bounded errors. For the case of equal expected damages in case of a successful attack of a system element, the authors calculate upper estimates for the relative error of solving the placement problem using the proposed algorithm and also propose a methodology for rapid risk assessment for systems with a "star" type structure. Finally, for the particular case when the risks of elements are in a certain ratio, the authors have found an exact solution to the problem of the optimal placement of elements. [ABSTRACT FROM AUTHOR]

The workplace is evolving towards scenarios where humans are acquiring a more active and dynamic role alongside increasingly intelligent machines. Moreover, the active population is ageing and consequently emerging risks could appear due to health disorders of workers, which requires intelligent intervention both for production management and workers' support. In this sense, the innovative and smart systems oriented towards monitoring and regulating workers' well-being will become essential. This work presents HUMANISE, a novel proposal of an intelligent system for risk management, oriented to workers suffering from disease conditions. The developed support system is based on Computer Vision, Machine Learning and Intelligent Agents. Results: The system was applied to a two-arm Cobot scenario during a Learning from Demonstration task for collaborative parts transportation, where risk management is critical. In this environment with a worker suffering from a mental disorder, safety is successfully controlled by means of human/robot coordination, and risk levels are managed through the integration of human/robot behaviour models and worker's models based on the workplace model of the World Health Organization. The results show a promising real-time support tool to coordinate and monitoring these scenarios by integrating workers' health information towards a successful risk management strategy for safe industrial Cobot environments. [ABSTRACT FROM AUTHOR]

The Brazilian Power System is mainly composed of renewable generation from hydroelectric and wind. Hence, spot and forward electricity prices tend to represent the inherently stochastic nature of these resources, while risk management is a measure taken by agents, especially hydro power plants (HPPs) to hedge against deep financial losses. A HPP goal is to maximize its profit considering uncertainties in forward electricity prices, spot prices, and generation scaling factor (GSF) for years ahead. Therefore, the objective of this work is to simulate the real decision-making process of a HPP, where they need to have a perspective of the forward market and future spot price assessment to negotiate forward electricity contracts. To do so, the present work models the uncertainty in electricity forward prices via two-stage stochastic programming, assessing the benefits of the stochastic solution in comparison to the deterministic one. In addition, different risk aversion levels are assessed using conditional value at risk (CVaR). An important conclusion is that the results show that the greater the HPP risk aversion is, the greater the energy selling via electricity forward contracts. Moreover, the proposed model has benefits in comparison to a deterministic approach. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 in Beijing has been sporadic since the beginning of 2022 and has become increasingly severe since October. In China's policy of insisting on dynamic clearance, fine-grained management has become the focus of current epidemic prevention and control. In this paper, we conduct a refined COVID-19 risk prediction and identification of its influencing factors in Beijing based on neighborhood-scale spatial statistical units. We obtained geographic coordinate data of COVID-19 cases in Beijing and quantified them into risk indices of each statistical unit. Additionally, spatial autocorrelation was used to analyze the epidemic risk clustering characteristics. With the multi-source data, 20 influencing elements were constructed, and their spatial heterogeneity was explored by screening 8 for Multiscale Geographically weighted regression (MGWR) model analysis. Finally, a neural network classification model was used to predict the risk of COVID-19 within the sixth ring of Beijing. The MGWR model and the neural network classification model showed good performance: the R2 of the MGWR model was 0.770, and the accuracy of the neural network classification model was 0.852. The results of this study show that: (1) COVID-19 risk is uneven, with the highest clustering within the Fifth Ring Road of Beijing; (2) The results of the MGWR model show that population structure, population density, road density, residential area density, and living service facility density have significant spatial heterogeneity on COVID-19 risk; and (3) The prediction results show a high COVID-19 risk, with the most severe risk being in the eastern, southeastern and southern regions. It should be noted that the prediction results are highly consistent with the current epidemic situation in Shijingshan District, Beijing, and can provide a strong reference for fine-grained epidemic prevention and control in Beijing. [ABSTRACT FROM AUTHOR]

The Kelly criterion determines optimal bet sizes that maximize long-term growth. While growth is definitely an important consideration, the focus on growth alone can lead to significant drawdowns, leading to psychological discomfort for a risk-taker. Path-dependent risk measures, such as drawdown risk, provide a means to assess the risk of significant portfolio retracements. In this paper, we provide a flexible framework for assessing path dependent risk for a trading or investment operation. Given a certain set of profitable trading characteristics, a risk-taker who maximizes expected growth can still be faced with significant drawdowns to the point where a strategy becomes unsustainable. We demonstrate, through a series of experiments, the importance of path dependent risks in the case of outcomes subject to various return distributions. Based on Monte Carlo simulation, we analyze the medium-term behavior of different cumulative return paths and study the impact of different return outcome distributions. We show that in the case of heavier tailed outcomes, extra care is needed, and optimal might not be so optimal in the end. [ABSTRACT FROM AUTHOR]

This paper mainly discusses the demand bidding and risk management of user aggregators by considering profit and risk. The covariance matrix of demand price was used to analyze the risk model under an uncertain demand price. By considering revenue and cost, the demand bidding strategy of user aggregators was derived to obtain the maximum profit. By using a risk-tolerance parameter β, a new demand bidding model for the user aggregators that takes both risk and profit into consideration was formulated. We simulated the risk posed by fluctuating demand prices for user aggregators using this model. Finally, this paper proposes Feasible Particle Swarm Optimization (FPSO) to solve the demand bidding model of user aggregators. Through the dynamic adjustment of control factor parameters in the FPSO, we changed the behavioral characteristics of various types of particles, improved the search efficiency and stability of particles in high-dimensional space, and sought the optimal solution for the system as a whole. This paper provides a parameter adjustment mechanism, improves the capability of algorithm implementation, and increases the probability of finding the optimal solution. The simulation results suggest that a tradeoff between profit and risk needs to be considered in the search process. By doing so, enterprises' abilities in terms of operation and management control can be enhanced, and effective demand management can be achieved. [ABSTRACT FROM AUTHOR]

Heavy mobile machinery cabs and their equipment are still not well adjusted to operators, so it is not surprising that we are still witnessing huge consequences of accidents at sites where they operate. The checklist with 39 questions, based on the previous research, is formed, and its' measurement structure has been tested on the sample of 102 transport, construction, and mining machines, including cranes, excavators, bucket wheel excavators, bulldozers, loaders, graders, backhoe loaders, trenchers, dump trucks, and scrapers by correlation analysis, Cronbach's alpha, Spearman-Brown and Kendall's W coefficient, exploratory factor analysis, and confirmatory factor analysis. The results validate the measurement structure of a checklist with only 17 items and five constructs. The results show that special attention should be put to the design of armrests and working conditions/exhaust gases, which are negatively correlated to cab interior space and task visibility. All other correlations between seat characteristics, the characteristics of armrests, whole-body vibration influences, reaching commands, the characteristics of cab interior space and environments, and interpersonal relationships are positive, which means that improvements to one area lead to improvements in another. Accordingly, the proposed model should be used for the fast, efficient, and cost-effective evaluation of ergonomics risks and as a guideline for further cab design improvements. [ABSTRACT FROM AUTHOR]

The automotive domain is moving away from simple isolated vehicles to interconnected networks of heterogeneous systems forming a complex transportation infrastructure. The additional means of communication result in increased attack surfaces which can be exploited by physical as well as remote attackers if not secured thoroughly. Thus, the automotive sector is exposed to new cyber risk factors. Consequently, joint approaches targeting securing vehicles and infrastructure by identifying and mitigating potential threats for the automotive domain have been developed in several research projects. This paper builds on developments originating from these projects and correlated standards and regulations. Moreover, the extension of an existing threat modeling tool—THREATGET—with a novel automated approach toward attack propagation will be introduced. Therefore, we will conduct an analysis of a real-world example from the automotive domain. Furthermore, we will identify and analyze potential threats and discuss their accumulation to automatically generate an attack tree. [ABSTRACT FROM AUTHOR]

The dyslipidemia guidelines of the three major societies have been revised recently in light of new evidence. LDL-C is the primary target in the ESC, AHA/ACC/Multisociety and Canadian Cardiovascular Society (CCS) guidelines. These guidelines uniformly recommend intensifying lipid-lowering treatment with increased risk; however, the risk estimation systems are different across the guidelines. The ESC guidelines have LDL-C goals which have become more stringent over the years and advocate the use of statin and, if necessary, non-statin therapies to obtain these goals. AHA/ACC/Multisociety guidelines have LDL-C thresholds and advocate combination therapy less liberally and for selected patients. All three guidelines acknowledge the importance of shared decision making. Despite some divergent approaches and recommendations, the main principles and messages are the same across the guidelines. To combat the epidemic of cardiovascular disease, our focus should be not on the differences but on implementing the guidelines in our region. [ABSTRACT FROM AUTHOR]

In the past two decades, clean energy such as hydro, wind, and solar power has achieved significant development under the "green recovery" global goal, and it may become the key method for countries to realize a low-carbon energy system. Here, the development of renewable energy power generation, the typical hydro-wind-photovoltaic complementary practical project, is summarized, and some key problems in complementary systems such as the description and prediction of the power generation law in large-scale stations, risk management, and coordinated operation are analyzed. In terms of these problems, this paper systematically summarizes the research methods and characteristics of a hydro-wind-solar hybrid system and expounds upon the technical realization process from the prediction and description of wind and solar power station cluster output, the risks brought about by large-scale renewable energy grid-connected operation, and the long-term and short-term coordination modeling and resolution thoughts on the hydro-wind-solar hybrid system in cluster mode. Finally, based on the aforementioned analysis, the existing research gaps are discussed from the standpoints of generation forecast, risk management, and cluster scheduling, and the future work outlook is presented accordingly. A hybrid system that combines hydro, wind, and solar energy is emerging as a way to make up for each other's shortcomings and will be a fruitful area of study in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: In order to mitigate the destructive effects of the occurrence of toxic conditions on mussel farming, an automated early prevention system against such conditions was installed. The analysis in this paper demonstrates that the investment is highly profitable and can contribute to achieving broader socioeconomic benefits at the local and regional level. This paper examines the financial viability and potential socioeconomic effects of introducing and operating an automated, remote-controlled management system for mussel farms which uses probes of temperature, dissolved oxygen, and conductivity associated with prediction software to demonstrate the potential need for mussel movement between marine areas. This system provides an early warning to farmers regarding the presence of toxins in aquatic ecosystems, thus contributing to saving mussel production and avoidikng significant economic losses. The analysis combines two established methodological tools in agricultural economics (linear programming and cost-benefit analysis) and provides estimates of the Net Present Value of the investment under two scenarios—one reflecting the existing situation and one a possible future situation where the mussel production system is expanded. The results of the analysis reveal the mid- and long-term effects of using the automated system, both of which demonstrate that the system is economically viable even if it contributes to saving mussel production from toxicity occurrence for only one year during its period of operation. The annual gross margin in the first scenario was €386,069 but almost tripled in the second scenario (€1,154,649). In addition, the future development and expansion of the mussel sector will likely be based on larger farms with an entrepreneurial and exporting orientation where risk mitigation systems, such as the one appraised in this paper, can play an important role. [ABSTRACT FROM AUTHOR]

In terms of climate related security risks, the region of South-Eastern Europe (SEE) can be identified as one of the world's hot spots. As weather-related hazards continue to increase in numbers and spatial distribution, risk perception in the tourism industry becomes even more important. Additionally, people's perception of natural hazards is one of the key elements in their decision-making process when choosing a travel destination. Although a vast number of studies have examined aspects of risk perception, an integrated approach which considers both objective and subjective factors related to the tourism industry and hydro-meteorological hazards remains relatively scarce. This pioneering study inspects the causality between objective perceived risks, as well as subjective risk factors. A methodological approach and the obtained results present a certain novelty since the previous conceptualized Psychological Preparedness for Disaster Threat Scale (PPDTS) was applied for the first time in the tourism industry. The obtained results reveal the presence of a statistically significant relationship between objective risks and certain subjective risk factors (gender, age, education, prior experience, anticipation, and awareness). Therefore, this study may offer a conceptual platform for both theoretical and practical implications for enhanced approaches oriented toward more qualitative risk management at a given travel destination, in regions prone to hydro-meteorological hazards. [ABSTRACT FROM AUTHOR]

Automated deep learning and data mining algorithms can provide accurate detection, frequency patterns, and predictions of dangerous goods passing through motorways and tunnels. This paper presents a post-processing image detection application and a three-stage deep learning detection algorithm that identifies and records dangerous goods' passage through motorways and tunnels. This tool receives low-resolution input from toll camera images and offers timely information on vehicles carrying dangerous goods. According to the authors' experimentation, the mean accuracy achieved by stage 2 of the proposed algorithm in identifying the ADR plates is close to 96% and 92% of both stages 1 and 2 of the algorithm. In addition, for the successful optical character recognition of the ADR numbers, the algorithm's stage 3 mean accuracy is between 90 and 97%, and overall successful detection and Optical Character Recognition accuracy are close to 94%. Regarding execution time, the proposed algorithm can achieve real-time detection capabilities by processing one image in less than 2.69 s. [ABSTRACT FROM AUTHOR]

The supply chain of the tourism industry, including air transportation, travel agencies, souvenirs, and hotel services, is almost at a breaking point, causing a rise in unemployment with huge losses during the COVID-19 pandemic period. In order to overcome these losses, we propose that luxury hotels should consider offering budget hotels at a lower cost but with satisfactory accommodation in order to create some turn-arounds in the post-pandemic era. However, budget hotels that branch off from luxury hotels cannot post the same room rates because there are some uncertain factors that affect the traveler experience when staying in budget hotels. In this study, we define four types of risk factors for the self-selection of the consumer model, and then find that the optimal room price appears to be independent of the performance risk for the service quality, brand image, and shuttle buses, but is dependent on physical risk in terms of priority number risk, the financial risk of refund rates, and the privacy risk of investment in the system. Finally, we discuss how government subsidies can encourage branched budget hotels by describing three sensitivity scenarios. The results show that subsidies that go towards staff training and higher-frequency shuttle buses will cause consumers to book more stays in budget hotels and, thereby, contribute to a higher profit. By lobbying the policy on government subsidies, budget hotels that branch off from luxury hotels are a profitable business model for a reduction in the huge losses occurred during the period of the spread of COVID-19. [ABSTRACT FROM AUTHOR]

Simple Summary: We use maximum entropy (MaxEnt) model, modelling to predict habitat change and inform on potential prevention and control of locust disasters. Our research work is based on three kinds of migratory locusts in a niche overlap and the geographic distribution of preliminary forecast of the locust, the geographical distribution of typical quantitative habitat suitability change, understanding the China–Kazakhstan border areas, the concentrated distribution area of typical locusts and its future expansion area. The scope of the initial assessment of typical locusts confirms the emphases of the locust control between the two countries in the region, to provide a basis for future monitoring work. Large-scale locust monitoring measures are difficult to implement in practice; therefore, this study will provide a regional monitoring focus for locust control efforts. Our study complements this by assessing priority areas for prevention. Climate change, especially climate extremes, can increase the uncertainty of locust outbreaks. The Italian locust (Calliptamus italicus (Linnaeus, 1758)), Asian migratory locust (Locusta migratoria migratoria Linnaeus, 1758), and Siberian locust (Gomphocerus sibiricus (Linnaeus, 1767)) are common pests widely distributed in the semidesert grasslands of Central Asia and its surrounding regions. Predicting the geographic distribution changes and future habitats of locusts in the context of climate warming is essential to effectively prevent large and sudden locust outbreaks. In this study, the optimized maximum entropy (MaxEnt) model, employing a combination of climatic, soil, and topographic factors, was used to predict the potential fitness areas of typical locusts in the 2030s and 2050s, assuming four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) in the CMIP6 model. Modeling results showed that the mean area under the curve (AUC) and true statistical skill (TSS) of the MaxEnt model reached 0.933 and 0.7651, respectively, indicating that the model exhibited good prediction performance. Our results showed that soil surface sand content, slope, mean precipitation during the hottest season, and precipitation seasonality were the key environmental variables affecting locust distribution in the region. The three locust species were mainly distributed in the upstream region of the Irtysh River, the Alatao Mountain region, the northern slopes of the Tianshan Mountains, around Sayram Lake, the eastern part of the Alakol Lake region, the Tekes River region, the western part of Ulungur Lake, the Ili River, and the upstream region of the Tarim River. According to several climate projections, the area of potential habitat for the three most common locust species will decrease by 3.9 × 104–4.6 × 104 km2 by the 2030s and by 6.4 × 104–10.6 × 104 km2 by the 2050s. As the climate becomes more extreme, the suitable area will shrink, but the highly suitable area will expand; thus, the risk of infestation should be taken seriously. Our study present a timely investigation to add to extensive literature currently appearing regarding the myriad ways climate change may affect species. While this naturally details a limited range of taxa, methods and potential impacts may be more broadly applicable to other locust species. [ABSTRACT FROM AUTHOR]

We evaluate the environment, society, and corporate governance rating (ESG rating) contribution from a new perspective; the highest ESG rating mitigates the impact of unexpected change in the implied volatility on the systemic stock market risk. For this purpose, we use exchange-traded funds (ETF) classified by their ESG rating into quartiles to estimate the synchronization as a proxy by systemic risk. Then, for each ETF quartile, we study the effect of the implied volatility over the synchronization. Our study is the first to model sustainable ETFs' synchronization by combining econometric modeling and network methods, including 100 ETFs representing 80% of the global ETF market size between 2013 and 2021. First, we find that a higher ESG rating mitigates the effect of implied volatility over ETF synchronization. Surprisingly, the effect is the opposite in the case of ETFs with lower ESG ratings, where an increase in the volatility expectation increases the synchronization. Our study depicts the effect of sustainable ETFs on lessening the systemic risk due to returns synchronization, this being a novel contribution of this asset class. Finally, this paper offers extensions to deepen the contribution of other asset classes of ETFs in terms of their synchronization behavior and impact on risk management and financial performance. [ABSTRACT FROM AUTHOR]