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Proper and systematic management of food industry failures can improve the quality of products and save a lot on the costs of organizations and people's health. One of the conventional methods for risk assessment is the Failure Modes and Effects Analysis (FMEA) which is often performed in a phase or stage. Compared to the combined methods, this method is less accurate due to similar priorities of failure in the evaluation and the lack of consideration of the interaction between risks. The current research has applied an integrated approach based on two techniques, FMEA and Fuzzy Cognitive Map (FCM), in a multi-stage manner to increase assessment accuracy and ranking of failures. By considering the risks of an industry in an uncertain environment and the causal relationships between failures, this approach can evaluate the industry's risks better than conventional methods. In the research method, the initial prioritization of failures by the FMEA method is used as the input of the multi-stage FCM. The cause-and-effect relationship between the failures is determined by experts and the functional records of the processes, and the FCM is prepared. Since no research evaluates the risks of the malting industry step by step and considers the causal relationships between the risks, the present study has improved risk evaluation in the malting industry by using a multi-stage FCM. The ranking results with the proposed hybrid approach and its comparison with the conventional methods showed that the rating became more accurate, and the multiple priorities were improved. Managers of the malt beverage industry can make effective investment decisions to reduce or better control the risks of this industry by using the results of applying the proposed approach. [ABSTRACT FROM AUTHOR]

Subsea immersed tunnels, have large construction scale, multiple structural types, high environmental requirements, and difficult construction management. There are many safety risks throughout the entire construction phase of the project. Taking the entire construction process of the Dalian Bay Immersed Tunnel Project as the research background, based on the characteristics of project construction, the HSE management method for engineering construction based on coupled matrix decomposition system was adopted to establish a risk assessment model for subsea immersed tunnel construction. The construction risk sources and factors were analyzed in detail and depth. From the perspective of system, a comprehensive evaluation index system for HSE risks in immersed tunnel engineering project was constructed, and the fuzzy comprehensive evaluation method was put forward by combining with fuzzy mathematics, which can comprehensively evaluate the HSE risk of immersed tunnel engineering project. Further explore the causes of HSE risks of subsea immersed tunnel engineering projects from the root, and gain a deeper understanding and grasp of the connotation of HSE risks in construction of subsea immersed tunnel engineering projects. So as to improve the construction management level of subsea tunnel projects. [ABSTRACT FROM AUTHOR]

With the continuous promotion of China's " going out " strategy and the Belt and Road Initiative, the demand for overseas geopolitical risk research is also increasing. Southeast Asia is a key region for the Belt and Road Initiative, with a complicated geographical environment and frequent geopolitical incidents. Based on geopolitical events, this paper proposes a geopolitical risk measurement method, taking Southeast Asia as an example to measure and analyze its spatio-temporal characteristics. Some conclusions can be drawn as follows: (1) Geopolitical risks in Southeast Asia are generally stable and controllable, but the spatio-temporal differences are large. It is relatively high in the Philippines and Myanmar, low in Laos, Singapore, Timor-Leste and Vietnam, and fluctuating in other countries. (2) The geopolitical risks of Indonesia, Cambodia, Thailand and Malaysia are more affected by internal events than those of the other five countries. Internal problems need to be solved urgently. Brunei, Laos, Timor-Leste, Vietnam and Singapore have high geopolitical risks affected by external events and relatively stable internal environments. (3) The geopolitical risks of South Asian countries are much more affected by external events than internal events, among which external events mainly come from countries outside the region such as the United States, China and Australia, while countries within the region account for a relatively low proportion, mainly from Thailand and Malaysia. The results of this study provide scientific support for grasping the geopolitical risks in Southeast Asia and ensuring the construction of the Belt and Road Initiative. [ABSTRACT FROM AUTHOR]

Artisanal and Small-scale Gold Mining (ASGM) is a source of supply in many areas of the world, especially in developing countries. This is often carried out illegally using toxic substances such as mercury. Mercury, due to its chemical–physical properties and the transport factors involved between the different environmental matrices, can percolate through soil and from surface water to groundwater. The objective of this study was to conduct a human health risk assessment. For this purpose, a screening of mercury concentrations was carried out, collecting 67 water samples at selected points, and a risk assessment was performed applying both a deterministic and a probabilistic approach. A deterministic approach is a specific analysis based on determining the values of the risk quotient (HQ) and the risk index (HI) for each receptor category (adults and children) and scenario (residential and recreational) considered; a probabilistic approach is based on stochastic simulation techniques and the evaluation of the statistical quantities. There was found to be a discrepancy between the results provided by the two approaches, with the deterministic approach suggesting a more worrisome picture. However, in general, the results showed a greater exposure in the provinces of El Oro and Esmeraldas, and a greater vulnerability of child receptors. [ABSTRACT FROM AUTHOR]

Background: Aortic intramural hematoma (IMH) is one of the typical entities of acute aortic syndrome and probably accounts for 5–25% of all cases. The ulcer-like projections (ULP), which are described as a focal, blood-filled pouch protruding into the hematoma of the aortic wall, are regarded as one of the high-risk imaging features of IMH and may cause initial medical treatment failure and death. Case presentation: We present a case report of an acute type B IMH patient with impaired renal function and newly developed ULP in the acute phase. The 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MR) was performed to evaluate the condition of aortic hematoma. The 18F-FDG focal uptake along the aortic wall of the hematoma was normal compared to the background (SUVmax 2.17; SUVSVC 1.6; TBR 1.35). We considered the IMH stable in such cases and opted for medical treatment and watchful observation. Six months after discharge, the patient's recovery was satisfactory, and aortic remodeling was ideal. Conclusions: The 18F-FDG PET/MR is a novel tool to evaluate the risk of IMH patients and thus provides information for therapy selection. [ABSTRACT FROM AUTHOR]

Fifty-one surface sediment samples from Dongshan Bay, China, were analyzed for heavy metals to evaluate their distribution, pollution status, and controlling factors. The enrichment factor is suggestive of the potential pollution status, ranging from minimal to moderate enrichment, for Pb, As, Zn, and Hg, with one site showing significant enrichment in As. A principal component analysis and the geochemical characteristics indicate that heavy metal concentrations are mainly influenced by clay minerals and Fe oxides, while Pb and Hg levels are also closely linked to the absorption of Mn oxides. Potential pollution is primarily from aquaculture (Cd, Zn, Cu, Pb) and industrial and domestic discharges. Approximately 270 tons of heavy metals were estimated to have been deposited in Dongshan Bay in 2021, highlighting the potential impact of human activities on coastal sediment quality. [ABSTRACT FROM AUTHOR]

Due to diminishing ice shelves in the Arctic, no doubt caused by climate change, previously unnavigable marine spaces are opening up, which in turn may provide new maritime routes. The shipping and maritime industry may capitalise on these routes more in the future as they provide shorter sailing times, and may assist with increasing oil and gas exploration and extraction in the region. However, increased maritime expansion in the Arctic comes with a number of unique risks and hazards for the insured and insurer. This opinion therefore seeks to discuss the implications of the multitude of risks that are associated with Arctic shipping from a marine insurance perspective and to highlight some of the gaps in existing regulatory frameworks. It is argued that marine insurers require improved data and risk management methodologies in order to better calculate the premiums of Hull & Machinery (H&M) and Protection & Indemnity (P&I) insurance policies for ships travelling through the Arctic. Until such time as these risks can be adequately assessed, evaluated, and managed by insurers; which of course is extremely difficult, due to the extreme environment, the unpredictability of climate change, a lack of key data, and the complexity of the various scenarios that can unfold in the Arctic environment, the practical viability of Arctic shipping remains doubtful. To date, insurers have paid out more in ship damage that has occurred in the Arctic than they have collected in premiums, which in itself is problematic. This situation may change in the future if Arctic shipping grows, and if it does the insurance market will no doubt, need to respond. [ABSTRACT FROM AUTHOR]

In order to ensure the safe operation of urban road traffic system and reduce the probability of urban traffic accidents, a risk evaluation model of urban road traffic system based on game theory combination empowerment cloud object element was proposed. Starting from the risk factors of urban road traffic system, combining with the Evaluation Indicator System of Urban Road Traffic Management, 15 risk evaluation indicators such as the proportion of trainee drivers were selected to construct the evaluation indicator system of “human-vehicle-road-ring”. Based on the cloud object element model, it solves the problem of ambiguity and randomness in the evaluation indexes, and at the same time, it uses game theory to optimize the combination of the expert scoring value processed by the C-OWA ordered weighting operator and the weights obtained by the improved entropy weighting method, and then calculates the risk level around the affiliation degree between the object element to be evaluated and the risk level. The model is applied to the road transportation system of a city A in the west of China to carry out the risk evaluation, and at the same time, the evaluation results of the model are compared with those of the C-OWA cloud object element model and the entropy weight cloud object element model. The study shows that the risk evaluation results of the indicators in the city A road transportation system obtained from the model are basically consistent with the results of the field visit research and the actual situation, and the game theory combination of empowerment cloud physical element urban road transportation system risk evaluation model is more practical and effective, which can provide a new way of thinking for the evaluation of the risk of the urban road transportation system. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The psychological factors of experts play a special role in the process of decision-making, especially in some situations that experts are not completely rational. Traditional decision-making methods always just focus on the aggregation of positive preference information, which do not take the negative attribute information into account at the same time. The probabilistic dual hesitant fuzzy set (PDHFS) is one of the latest fuzzy sets, which can depict experts' positive and negative preference information with the corresponding probability at the same time. Therefore, to manage the applications with incomplete rationality and two opposite kinds of uncertain preference information, this paper considers the influence of psychological behavior on decision-making results and introduces an interactive method based on the prospect theory. Taking the advantages of PDHFSs in group decision-making problems, we propose the distance measure of PDHFSs, based on which an improved TODIM (TOmada deDecisão Iterativa Multicritério) method under the probabilistic dual hesitant fuzzy environment is also developed. Meanwhile, we provide the specific implementation process of the proposed method. The proposed improved TODIM is applied to the risk evaluation of Arctic geopolitics. We also make a comparison with the traditional aggregation method of PDHFSs. The difference among alternatives obtained by the proposed TODIM method with prospect theory is much greater than the traditional aggregation methods without prospect theory. This paper highlights the benefits and advantages of the proposed TODIM method that is developed based on the prospect theory and probabilistic dual hesitant fuzzy distance measure. [ABSTRACT FROM AUTHOR]

Numerous countries are implementing building-integrated photovoltaic (BIPV) technology to enhance the energy performance of buildings, as new energy sources have attracted global interest. BIPV residential programs are an essential method to alleviate energy stress and promote energy transition in buildings; however, the high level of technology and capital investment required have hampered their marketization. Although certain obstacles have been examined by researchers, there remains a lack of studies concerning risk assessment in the context of the development of BIPV residential projects. Therefore, this study strives to develop a risk assessment model for the development of these projects. First, a risk evaluation index system is proposed by identifying and analyzing the risks associated with the development of BIPV residential projects, following the lines of risk identification–risk analysis–risk evaluation–risk management. Second, the DEMATEL-ANP-gray cluster analysis was utilized to construct the development risk assessment model. Finally, a case study demonstrates that the methodology proposed in this study can effectively solve the issues associated with correlating risk factors and the quantification of the magnitude of risks in the development of BIPV residential projects. This study will serve as a valuable reference for architect-urban developers and engineer contractors to formulate risk governance countermeasures for BIPV residential projects as it provides a framework for assessing the risk associated with their development. [ABSTRACT FROM AUTHOR]

The potential hazards of chlorfenapyr warrant attention owing to its widespread application on vegetables. A comprehensive investigation of the fate of chlorfenapyr in the ecosystem is imperative. This paper presents a method for detecting chlorfenapyr and tralopyril in cabbages, which exhibits good linearity (determination coefficients > 0.99) and satisfactory recoveries (82.50 %–108.03 %). Chlorfenapyr residues in cabbages demonstrate a positive correlation with its application dose and time. Tralopyril can inhibit the dissipation of chlorfenapyr, as evidenced by the half-lives of 5.67–11.14 d (chlorfenapyr) and 6.91–14.77 d (total chlorfenapyr). The results of terminal residues (

The Ria de Aveiro lagoon has been considered one of the most important Southern European long-term monitoring sites (LTER-site) concerning climate change and anthropogenic pollution. Nevertheless, studies concerning pesticide contamination herein are few and mostly outdated. Here, 56 pesticides dissolved in the aqueous phase (DAP) and suspended particulate matter (SPM) were examined to fill the knowledge gap, considering the hydrophobic nature of most evaluated compounds. Water samples were taken seasonally from eight sites, and both DAP and SPM fractions were analysed by gas-chromatography-mass spectrometry. The annual average concentrations of fungicides, herbicides and insecticides were ≈57 ng/L, ≈409 ng/L and ≈1058 ng/L in DAP and ≈7 μg/g, ≈28 μg/g and ≈67 μg/g in SPM. These pesticides and the excessive phosphate concentrations suggest that the lagoon's water quality may be hazardous to biota and humans. Invertebrates were identified as the most threatened trophic level using deterministic risk assessment, with chlorpyriphos and chlorfenvinphos being the primary culprits. Acute experiments with Artemia and Daphnia supported the suppositions.

The hydrogen production station system is complex and vulnerable to external attacks, causing cascading failures and serious safety accidents. The cascading failure deduction method based on event tree was adopted to determine the consequences and occurrence probability of cascading failures of hydrogen production station equipment. On this basis, a cascading fault risk evaluation index system considering safety, economy and environmental protection was established. Considering the randomness and uncertainty of serious consequences and impact, a fuzzy membership function was used to quantify the evaluation index. Finally, a risk assessment model based on evidential reasoning was established and applied to the risk evaluation of hydrogen production station equipment, to realize the risk assessment and ranking of hydrogen production station equipment. The example shows that the method is feasible and effective, which provides a support for the next step of making safety precautions for hydrogen production station equipment.

Business Processes (BPs) can use Internet of Things (IoT) devices to make decisions based on information about the environmental context and even to actuate on it. These IoT-aware BPs, when applied to critical systems, have to deal with different types of IoT device failures and their consequences. To address such scenarios, decision-makers can take advantage of risk assessment techniques to analyze the risk associated with BPs. Current proposals analyze risk at the process level without providing a more fine-grained perspective. This paper presents a risk analysis methodology that calculates the risk level of BPs following a (de)composition approach using the risk-related information of BP elements. Therefore, it supports decision-makers in identifying the groups of tasks that contribute the most to the overall BP risk. In addition, it provides information for analyzing the worst-case scenarios at different levels of BP granularity. This information allows process designers to adjust and improve BPs to meet the risk acceptance criteria.

The evaluation of auto insurance risks is a fundamental task for financial institutions, crucial for setting equitable premiums and managing risks effectively. Traditional machine learning algorithms often struggle to capture the intricate relationships necessary for accurate risk assessment. In contrast, deep learning methods, while capable of processing complex data structures, lack the ability to model feature interactions and provide interpretability, which are essential for transparent decision-making in the insurance industry. To address these challenges, we introduce the Actuarial Transformer (AT)–a pioneering model that leverages the self-attention mechanism of the Transformer architecture to meticulously map feature interactions. The AT integrates advanced residual models with tree-based methods, enhancing its predictive accuracy. Additionally, it incorporates the SHAP (SHapley Additive exPlanations) model, which uses Shapley values from cooperative game theory to ensure interpretability and transparency in its risk assessments. Our empirical analysis, conducted on a representative dataset of auto insurance risks, demonstrates the AT’s superior performance in risk prediction. The SHAP analysis further validates the model’s ability to prioritize features logically, providing clear insights into the decision-making process. The AT not only improves the precision of auto insurance risk evaluations but also enhances the interpretability of these evaluations, making it a valuable tool for both industry practitioners and clients.

The discovery of security vulnerabilities and their mitigation in networked systems managed by Software-Defined Networking (SDN) are fundamental for ensuring their normal operation. The main goal of this survey was to investigate the literature on preventing system security vulnerabilities instead of detecting ongoing cyber-attacks as quickly as possible. Thus, in our opinion, organizations should fortify their systems’ security by identifying and eliminating any new security vulnerabilities before they can be successfully exploited. We comprehensively discuss different vulnerability detection approaches based on important comparison parameters such as vulnerability assessment, the SDN controller used, automation capability, system risk indicators, passive scanning and active probing of system vulnerabilities. The paper also analyzes relevant literature considering the mitigation mechanisms for discovered vulnerabilities such as the proposed SDN controller, automation capability, solution adaptation to system operational changes, risk indicators, and the solution’s impact on network quality metrics like latency and throughput. Despite the strengths of the surveyed work, we have also identified promising open issues that need further consideration by scholars, industry participants, and policymakers. We concluded that the majority of analyzed literature contributions are largely reactive in their implementation against running network threats. This suggests a new research domain for applying SDN in the automatic detection of security vulnerabilities and their proactive mitigation before external cyber-attackers can exploit them.

Abstract To achieve an objective and reasonable judgment on slope stability, the partially ordered set (Poset) theory is applied to the slope stability evaluation against the engineering background of an open pit slope. From the four aspects of rock mass structural characteristics, slope morphology, environment conditions and engineering conditions, 24 typical evaluation indices such as cohesion, internal friction angle, elastic modulus and Poisson’s ratio are selected. Based on the Poset theory, a Poset evaluation model is established. According to the slope stability evaluation indices and the risk grade classification criteria, the slope stability risk grade is divided into four grades: stable (Grade I), generally stable (Grade II), slightly stable (Grade III) and unstable (Grade IV). The index data are normalized and the weight ranking information is used. Implicit weighting is carried out by means of accumulation. The comparison relation matrix, the Hasse matrix and the Hasse diagram are constructed for each factor layer and comprehensive index layer, respectively. This method was utilized to evaluate the risk grade of an engineering example, determine the slope risk grade, and compare with the actual situation. The results show that the slope to be evaluated is generally stable (Grade II) under the comprehensive evaluation indices of various factors, and the assessed stability condition is consistent with the actual situation. These verify the rationality and applicability of the proposed method and provide a new insight for accurately identifying the stability condition of open-pit slopes.

The nuclear event risk (NER) is an important and disputed factor that should be reasonably considered when planning the pathway of nuclear power development (NPD) to assess the benefits and risks of developing nuclear power more objectively. This paper aims to explore the impact of nuclear events on NPD pathway planning. The influence of nuclear events is quantified as a monetary risk component, and an optimization model that incorporates the NER in the objective function is proposed. To optimize the pathway of NPD in the lowcarbon transition course of power supply structure evolution, a simulation model is built to deduce alternative NPD pathways and corresponding power supply evolution scenarios under the constraint of an exogenously assigned carbon emission pathway (CEP); moreover, a method is proposed to describe the CEP by superimposing the maximum carbon emission space and each carbon emission reduction (CER) component, and various CER components are clustered considering the emission reduction characteristics and resource endowments of different power generation technologies. A case study is conducted to explore the impact of NER and its risk valuation uncertainty on NPD pathway planning. The method presented in this paper allows the impact of nuclear events on NPD pathway planning to be quantified and improves the level of coordinated optimization of benefits and risks.

Abstract Background Brucellosis is a zoonotic affliction instigated by bacteria belonging to the genus Brucella and is characterized by a diverse range of pervasiveness, multiple transmission routes, and serious hazards. It is imperative to amalgamate the current knowledge and identify gaps pertaining to the role of ticks in brucellosis transmission. Methods We systematically searched China National Knowledge Infrastructure (CNKI), WanFang, Google Scholar, and PubMed on the topic published until April 23, 2022. The procedure was performed in accordance with the Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. The selected articles were categorized across three major topic areas, and the potential data was extracted to describe evidence-practice gaps by two reviewers. Results The search identified 83 eligible studies for the final analyses. The results highlighted the potential capacity of ticks in brucellosis transmission as evidenced by the detection of Brucella in 16 different tick species. The pooled overall prevalence of Brucella in ticks was 33.87% (range: 0.00–87.80%). The review also revealed the capability of Brucella to circulate in parasitic ticks' different developmental stages, thus posing a potential threat to animal and human health. Empirical evidence from in vitro rodent infection experiments has revealed that ticks possess the capability to transmit Brucella to uninfected animals (range: 45.00–80.00%). Moreover, significant epidemiological associations have been found between the occurrence of brucellosis in animals and tick control in rangelands, which further suggests that ticks may serve as potential vectors for brucellosis transmission in ruminants. Notably, a mere three cases of human brucellosis resulting from potential tick bites were identified in search of global clinical case reports from 1963 to 2019. Conclusions It is imperative to improve the techniques used to identify Brucella in ticks, particularly by developing a novel, efficient, precise approach that can be applied in a field setting. Furthermore, due to the lack of adequate evidence of tick-borne brucellosis, it is essential to integrate various disciplines, including experimental animal science, epidemiology, molecular genetics, and others, to better understand the efficacy of tick-borne brucellosis. By amalgamating multiple disciplines, we can enhance our comprehension and proficiency in tackling tick-borne brucellosis.

Due to the complex hydrogeological conditions and water hazards in coal mines, there are multiple indexes, complexities, incompatibilities, and uncertainty issues in the risk evaluation process of coal-mine water hazards. To accurately evaluate the risk of coal-mine water hazards, a comprehensive evaluation method based on extension theory, game theory, and Dempster–Shafer (DS) evidence theory is proposed. Firstly, a hierarchical water-hazard risk-evaluation index system is established, and then matter-element theory in extension theory is used to establish a matter-element model for coal-mine water-hazard risk. The membership relationship between various evaluation indexes and risk grades of coal-mine water-hazard risk is quantified using correlation functions of extension set theory, and the quantitative results are normalized to obtain basic belief assignments (BBAs) of risk grades for each index. Then, the subjective weights of evaluation indexes are calculated using the order relation analysis (G1) method, and the objective weights of evaluation indexes are calculated using the entropy weight (EW) method. The improved combination weighting method of game theory (ICWMGT) is introduced to determine the combination weight of each evaluation index, which is used to correct the BBAs of risk grades for each index. Finally, the fusion of DS evidence theory based on matrix analysis is used to fuse BBAs, and the rating with the highest belief fusion result is taken as the final evaluation result. The evaluation model was applied to the water-hazard risk evaluation of Sangbei Coal Mine, the evaluation result was of II grade water-hazard risk, and it was in line with the actual engineering situation. The evaluation result was compared with the evaluation results of three methods, namely the expert scoring method, the fuzzy comprehensive evaluation method, and the extension method. The scientificity and reliability of the method adopted in this paper were verified through this method. At the same time, based on the evaluation results, in-depth data mining was conducted on the risk indexes of coal-mine water hazards, and it was mainly found that 11 secondary indexes are the focus of coal-mine water-hazard risk prevention and control, among which seven indexes are the primary starting point for coal-mine water-hazard risk prevention and control. The groundwater index in particular has the most prominent impact. These results can provide a theoretical basis and scientific guidance for the specific water-hazard prevention and control work of coal mines.

The complexity and uncertainty of construction projects contribute to low efficiency in the construction industry. This research applied the Takt-time planning method to optimize the construction working process, and proposed a risk control framework based on Value at Risk (VaR) and Conditional Value at Risk (CVaR) approaches to explore and predict a project schedule and cost performance under different scenarios. This research selected a high-rise residential building project for a case study and collected 1672 productivity data samples. Arena Simulation models were established based on 90 combinations of labor assignments to assess Takt-time planning strategies' impact on project performance in four scenarios. The VaR and CVaR evaluations at 75% and 90% confidence levels were compared to balance project benefits and risks. Without any overtime or additional workers, this research found a Takt-time planning method that can reduce the project duration by 20.2% and labor costs by 2.1% at the same time, using a labor assignment of 12 bar placers, 12 carpenters, and 5 pipefitters. The findings can assist construction managers to achieve a shorter duration, reduced cost, and safer work environment, which will be very effective and beneficial to improve project overall performance. [ABSTRACT FROM AUTHOR]

Gold mining activity is a source of supply in many areas of the world, and especially in developing countries, it is practiced illegally and by applying unsafe techniques. Particularly in Ecuador, artisanal and small-scale gold mining (ASGM) is widespread, and it is based on the use of toxic substances, such as mercury (Hg), in gold recovery. Hg is a heavy metal that is water-insoluble, which, once mobilized, poses a threat to both the environment and human health. This study analyzes Hg concentrations in the six provinces of Napo, Sucumbíos, Orellana, Pastaza, Morona Santiago, and Zamora Chinchipe of the Ecuadorian Amazon region to conduct a human health risk assessment. Significant differences in Hg levels were found between provinces, but concentrations were below MPL imposed by Ecuadorian regulations everywhere. Nevertheless, a worrisome picture emerges, especially with regard to the most vulnerable receptors represented by the child population. There are multiple factors of incidence that may affect the possible future development of the phenomenon, and with reference to the social, economic, and environmental context of the region, it can be concluded that it may be appropriate to plan further investigation to arrive at a more comprehensive assessment. The results of this study can be used by decision makers to plan further investigation and to implement monitoring networks, risk mitigation strategies, and groundwater protection measures. [ABSTRACT FROM AUTHOR]

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increasing proportion of renewable energy, the power system inertia decreases, and the operation uncertainty rises. It brings concerns about the system frequency and operational reliability. However, the impacts of the power system frequency performance on the reliability parameters of generation units have not been fully investigated. This paper studies the frequency performance and the operational reliability co-evaluation for power systems considering wind turbines. Firstly, a power system frequency regulation model is established considering the regulation capability of wind turbines. Then, the cluster of equivalent wind turbines is incorporated into the frequency regulation architecture of thermal power units, which accelerates the analysis of frequency performance. Then, the frequency performance of the power system with the participation of wind turbines under the operation uncertainty and the unit random faults is quantitatively analyzed. A frequency-dependent generator reliability parameter model is derived. Next, a multi-time scale co-evaluation framework is proposed to realize the co-evaluation of frequency performance and operational reliability. Case studies are carried out on the modified IEEE RTS-79 system and a provincial power system. Results show that compared with the existing research, the proposed method can obtain the frequency performance and reliability results efficiently. [ABSTRACT FROM AUTHOR]

Monitoring and assessing coastal subsidence is crucial to mitigating potential disaster risks associated with rising sea levels. Nansha District in Guangzhou City, representing global coastal soft-soil urban areas, faces significant challenges related to ground subsidence. However, the current understanding of the status, causative factors, and risk (includes subsidence susceptibility and vulnerability) assessment of ground subsidence in Nansha District is unclear. To address this gap, we utilized the SBAS-InSAR technique, analyzing 49 Sentinel-1A images from December 2015 to June 2019, for systematic ground subsidence monitoring. Subsequently, we assessed subsidence risk using a comprehensive index method and a risk matrix. Our findings indicate that subsidence velocity primarily ranged from −40 to −5 mm/a, with a spatial pattern of increasing subsidence from inland to coastal areas. The cumulative subsidence process unfolded in four distinct stages. The genesis of land subsidence was linked to an endogenous geological context dominated by soft-soil deposition, influenced by external factors such as surface loading and groundwater extraction. High-risk zones were concentrated in key engineering development areas, transportation pipeline trunk lines, and densely populated regions, demanding special attention. This study provides a foundational resource for disaster prevention and control strategies in Nansha District and similar coastal cities. [ABSTRACT FROM AUTHOR]

Background: Brucellosis is a zoonotic affliction instigated by bacteria belonging to the genus Brucella and is characterized by a diverse range of pervasiveness, multiple transmission routes, and serious hazards. It is imperative to amalgamate the current knowledge and identify gaps pertaining to the role of ticks in brucellosis transmission. Methods: We systematically searched China National Knowledge Infrastructure (CNKI), WanFang, Google Scholar, and PubMed on the topic published until April 23, 2022. The procedure was performed in accordance with the Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. The selected articles were categorized across three major topic areas, and the potential data was extracted to describe evidence-practice gaps by two reviewers. Results: The search identified 83 eligible studies for the final analyses. The results highlighted the potential capacity of ticks in brucellosis transmission as evidenced by the detection of Brucella in 16 different tick species. The pooled overall prevalence of Brucella in ticks was 33.87% (range: 0.00–87.80%). The review also revealed the capability of Brucella to circulate in parasitic ticks' different developmental stages, thus posing a potential threat to animal and human health. Empirical evidence from in vitro rodent infection experiments has revealed that ticks possess the capability to transmit Brucella to uninfected animals (range: 45.00–80.00%). Moreover, significant epidemiological associations have been found between the occurrence of brucellosis in animals and tick control in rangelands, which further suggests that ticks may serve as potential vectors for brucellosis transmission in ruminants. Notably, a mere three cases of human brucellosis resulting from potential tick bites were identified in search of global clinical case reports from 1963 to 2019. Conclusions: It is imperative to improve the techniques used to identify Brucella in ticks, particularly by developing a novel, efficient, precise approach that can be applied in a field setting. Furthermore, due to the lack of adequate evidence of tick-borne brucellosis, it is essential to integrate various disciplines, including experimental animal science, epidemiology, molecular genetics, and others, to better understand the efficacy of tick-borne brucellosis. By amalgamating multiple disciplines, we can enhance our comprehension and proficiency in tackling tick-borne brucellosis. [ABSTRACT FROM AUTHOR]

This study aims to identify and evaluate the risks of the manufacturing sector in the health supply chain of the food industries. For this purpose, first, the risks related to food industry units have been identified using the fuzzy Delphi approach and then, applying the fuzzy best-worst model (F BWM) and the fuzzy decision-making trial and evaluation laboratory (F DEMATEL) model, the weights and internal relationships of criteria have been determined, respectively. For the final prioritization of the risks, a hybrid method based on F BWM and F DEMATEL has been used. To demonstrate the applicability of the proposed approach, a real case of food industry units is presented. The data are both quantitative and qualitative, and both library and field methods have been used to collect them. The results showed that among the identified factors, the biological factor had the highest priority, and the health factor had the lowest one. [ABSTRACT FROM AUTHOR]

In analytical chemistry laboratories, the basic principles of the study of the chemical composition of a material or sample are verified experimentally, through different methods of analysis, for which reason chemical and biological agents are handled daily, to which workers are exposed; hence the need to assess the risk of exposure. The objective of this work was to evaluate the risks in an analytical chemical laboratory using the semi-quantitative method of William Fine, for which a multidisciplinary commission was created made up of specialists and experts with experience in the subject. It was determined that there is a direct relationship between the chemical, fire and explosion risks, the emanation of vapors of volatile substances, due to very high temperatures, or close to sources of ignition that can cause a fire, and generate a chain of events that, when reacting with other substances, can lead to explosions. The chemical and fire risks were evaluated as critical risks, which require immediate stoppage of the activity, and the mechanical, environmental, explosion, and biological risks were evaluated as notable risks, which require an urgent necessary correction. [ABSTRACT FROM AUTHOR]

Organic pollutants were widely detected in marine fish in global oceans, and long-term consumption can pose certain health risks to humans. To study the pollution characteristics, 50 fish were collected in Xisha, China, in which Organochlorine pesticides (OCPs), polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) were determined by GC-MS. It was found that OCPs, PAHs and PCBs concentrations in fish ranged from 2.23-91.57ng/g ww, 2.11-31.70ng/g ww and 1.55-54.04ng/g ww, respectively. The ratios of DDTs/(DDE+DDD)＞ 1 in 50% of fish. The ratio of ANT/(ANT+PHE)＞ 0.1 in 88% of fish and the ratio of FL/(FL+PYR)＞0.5 in 100% of fish. Using the ILCR model to evaluate the ecological risk of fish, the ILCR values of organic pollutants in most fish were below 1×10−6. [ABSTRACT FROM AUTHOR]

The high profit of regional investment is often accompanied by high risks, and the prior assessment of investment risks is conducive to avoid investment risks. However, the traditional evaluation methods usually only focus on individual risk factors, and it is difficult to evaluate and manage risks on the whole. Therefore, the study introduces deep learning algorithm, first build regional investment risk evaluation index system, then according to the characteristics of risk evaluation, design based on deep learning regional investment risk evaluation model, the final use parameter based migration learning algorithm and composite correlation coefficient to improve the evaluation model, solve the problem of insufficient training samples. The test results showed that the randomly selected 50 test samples with two different risk assessment models were 0.80 and 0.86, the deep learning algorithm tested 0.84, and the transfer learning improved model tested was 0.92, with the highest accuracy. This shows that the deep learning regional investment risk evaluation model improved by transfer learning effectively solves the problem of insufficient training data and improves the accuracy of prediction evaluation. In the field of venture capital, the model can help investors to evaluate and predict investment risks more accurately and improve the effect of investment decisions. [ABSTRACT FROM AUTHOR]

Among the first 20 high-priority chemical substances selected by USEPA to undergo risk evaluation as part of the Toxic Substances Control Act, as amended by the Frank R. Lautenberg Chemical Safety for the 21st Century Act of 2016 is 1,3-butadiene (1,3-BD). Because much of the literature related to occupational exposure to 1,3-BD is associated with the use of the substance in synthetic rubber production and few data have been published for exposures to 1,3-BD manufacturing workers, existing industrial hygiene data collected at facilities where the substance is manufactured or processed as a reactant were compiled and analyzed. The dataset was comprised of personal air samples collected between 2010 and 2019 at facilities located throughout the United States and was compiled into a single database using a uniform data collection template. Data designated by the companies as full-shift were stratified by job group and one of three operational conditions of the workplace: routine, turnaround, and non-routine. Data designated by the companies as short-term and task-level were stratified by task description, sample duration, and operational condition. The final aggregated database contained a total of 5,676 full-shift personal samples. Mean concentrations of 1,3-BD for the job groups ranged from 0.012 ppm to 0.16 ppm. High-end estimates of 1,3-BD air concentrations for the job groups under routine operations ranged from 0.014 ppm to 0.23 ppm. The aggregated database also included 1,063 short-term and task-level personal samples. For short-term samples (< =15 min), mean concentrations ranged from 0.49 ppm to 3.9 ppm, with the highest concentrations observed for the cleaning and maintaining equipment tasks. For task samples with durations greater than 15 min, mean concentrations ranged from 0.49 to 3.6 ppm, with the highest concentrations observed for the unloading and loading task. In addition to the personal air sampling records, information on the use of PPE during various tasks was compiled and analyzed. This data set provides robust quantitative air concentration data and exposure control information for which occupational exposures to 1,3-BD in the Manufacturing and Processing as a Reactant condition of use can be assessed. [ABSTRACT FROM AUTHOR]

Elevators have become an integral part of modern buildings, and technological advances have enabled the monitoring of their operational status through sensor technology. In response to the development of the elevator industry and the need for practical elevator operation risk evaluation, this paper proposes an elevator risk evaluation method based on fuzzy theory and machine learning methods. The method begins by establishing an elevator operation risk evaluation index system. The traditional fuzzy comprehensive evaluation method is then employed to evaluate the risk levels of the 50 elevators studied. The collected index data and labels (fuzzy comprehensive evaluation results) are used as inputs to train the support vector machine (SVM) model. To optimize the SVM model, the maximum information coefficient method, enhanced by the correlation-based feature selection (MIC-CFS) method, is employed to select features for the index input to the SVM model. The improved gray wolf algorithm (IGWO) method optimizes the SVM. Finally, the model's performance is verified using new index data. The experimental results demonstrate that introducing machine learning methods for elevator risk evaluation saves time and effort while providing good accuracy compared to the traditional expert evaluation method. The optimization of the SVM model by IGWO and feature selection by the MIC-CFS method results in a more concise SVM model that converges faster during training, exhibits better stability, and achieves higher accuracy. [ABSTRACT FROM AUTHOR]

This research aims to study the source, spatial distribution characteristics and ecological risks of diuron in the coastal waters of the Yangtze River Estuary and Shanghai, water samples from 24 stations in the coastal waters of the Yangtze River Estuary, Hengsha Island （inland） waters and the waters near the Yangtze River Estuary Chinese sturgeon reserve were collected from October to November 2021, and the content of diuron in the surface water samples of each station was analyzed by solid phase extraction and high performance liquid chromatography tandem mass spectrometry. According to the test results, the mass concentration of diuron was detected in the water samples of 24 stations in three different regions. Among them, the detection range of diuron mass concentration at 10 stations along the Yangtze River Estuary in Shanghai was 182.43-439.38 ng/L, and the detection rate was 100%. The detection range of diuron mass concentration at 9 stations in Hengsha Island （inland） water area is 83.48-369.25 ng/L, and the detection rate is 100%. The detection range of diuron mass concentration at 5 stations near the Chinese sturgeon reserve is 1.97-2.85 ng/L, and the detection rate is 100%. The spatial distribution of diuron is mainly affected by factors like water transport, ship maintenance, agricultural activities, tides and seawater dynamics. Besides, the ecological risk assessment shows that the coastal waters along the Yangtze River Estuary in Shanghai are potentially at medium risk. Farming areas in Hengsha inland waters are at medium risk, and the nearby waters of the Nature Reserve are at low risk. The results show that the environment in the coastal waters of the Yangtze River Estuary in Shanghai is being threatened by the diuron, especially in the major ports and docks along the Yangtze River Estuary in Shanghai as well as the agricultural areas and river intersections in the Hengsha Island area. Although it will not threaten the Chinese sturgeon reserve in the short run, it still deserves attention. [ABSTRACT FROM AUTHOR]

Risk management in today's organizations is a part of the strategic approach to achieve the results of the projects. Paying no attention to the risk in projects can cause threats in the implementation of them and affect on time, cost and quality dimensions. On the other hand, military organizations, as the main defense elements, are obliged to protect the country from foreign risks and threats, so they must identify and analyze potential risks, plan for proper management and avoid unexpected decisions. Considering the importance of military equipment projects and referring to the lack of complete transfer of manufacturing technologies and lack of reconstruction from the manufacturing countries, as well as the sanctions, this study aims to identify and rank the risks affecting the reconstruction of tracked armament. The risks of this project were identified by reviewing articles and the Delphi method, and the Multimoora method was also used to rank them. In order to weight the indicators, the best-worst method was used. Data collected through a researchermade questionnaire for implementation of the Multimoora method. This questionnaire was designed and completed through the Likert method with the opinion of a number of activists in the discussed field. The results indicated that the quality factor was more important than other indicators in the military fields. Also, the lack of manufacturing technologies and the lack of specialists were the most important, and the lack of tools and the lack of recognition of existing capabilities were the most worthless risks. This study can help to develop and create opportunities for military organizations to carry out such projects. [ABSTRACT FROM AUTHOR]

The study used various indices of pollution to assess the level of soil contamination from Irasa Abattoir in Ado-Ekiti, Southwestern Nigeria. Samples of soil were collected at the surface, 15cm and 30cm depths from effluent, roasting and bone waste areas, so as to evaluate the influence of abattoir activities on the soil quality. The geo-accumulation index (Igeo) indicates uncontaminated to moderate contamination for the studied heavy metals, while the PLI > 1 revealed continuous deterioration of the studied sites. The potential ecological risk factor and risk index (RI) of the metals revealed low risk level. However, the study exposed the effect of wastes and other activities on the status of the soils. Therefore, there is need to monitor the deterioration level of the abattoir site. [ABSTRACT FROM AUTHOR]

Copyright of Geology & Exploration is the property of Geology & Exploration Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The interrelationship between human, animal and environmental sectors leads to the spread of antibiotic resistance due to selective pressures, evolutionary traits and genomic evolution. In particular, the frequent use of antibiotics in livestock inevitably influences the emergence of specific resistance determinants in human strains, associated with reduced treatment options in clinical therapy. In this study, ESBL-producing Klebsiella pneumoniae strains isolated from chicken meat samples were evaluated for public health implications in Türkiye. Whole-genome sequencing was used for genetic dissection and phylogenetic comparison of their genomes. The isolates were assigned to four MLST types (ST147, ST37, ST2747 and ST219); two of them were found to represent the ST147 clone associated with severe human infections worldwide. In addition to cephalosporins, high resistance levels to quinolones/fluoroquinolones were identified phenotypically, caused by acquired resistance genes and chromosomal point variations. One isolate was also found to carry the qacE∆1 efflux transporter gene, which confers tolerance to quaternary ammonium compounds. The detection of virulence genes (i.e., that coding for enterobactin) associated with the pathogenicity of K. pneumoniae suggests a public health impact. Thus, comprehensive information on the occurrence and impact of K. pneumoniae from livestock is needed to derive appropriate management strategies for consumer protection. In this study, it was shown that poultry meat serves as a reservoir of clinically emerging multidrug-resistant high-risk clones. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents an analysis of the risk of neck injury in vehicle occupants as a consequence of an impact. A review of the formulation of indexes that are used in the assessment and investigation of neck injury risk is discussed with the aim of providing a new, more appropriate index using suitable sensorized equipment. An experimental analysis is proposed with a new driver monitoring device using low-cost sensors. The system consists of wearable units for the head, neck, and torso where inertial measurement sensors (IMU) are installed to record data concerning the occupant's head, neck, and torso accelerations while the vehicle moves. Two laser infrared distance sensors are also installed on the vehicle's steering wheel to record the position data of the head and neck, as well as an additional IMU for vehicle acceleration values. To validate both the device and the new index, experiments are designed in which different sensorized volunteers reproduce an emergency braking maneuver with an instrumented vehicle at speeds of 10, 20, and 30 km/h before the beginning of any braking action. The neck is particularly sensitive to sudden changes in acceleration, so a sudden braking maneuver is enough to constitute a potential risk of cervical spine injury. During the experiments, large accelerations and displacements were recorded as the test speed increased. The largest accelerations were obtained in the experimental test at a speed of 30 km/h with values of 19.17, 9.57, 9.28, and 5.09 m/s2 in the head, torso, neck, and vehicle, respectively. In the same experiment, the largest displacement of the head was 0.33 m and that of the neck was 0.27 m. Experimental results have verified that the designed device can be effectively used to characterize the biomechanical response of the neck in car impacts. The new index is also able to quantify a neck injury risk by taking into account the dynamics of a vehicle and the kinematics of the occupant's head, neck, and torso. The numerical value of the new index is inversely proportional to the acceleration experienced by the vehicle occupant, so that small values indicate risky conditions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tunnel collapse is a serious hazard that poses a substantial threat to worker safety and construction expenses. The results of risk evaluations can provide valuable information for collapse prevention and risk management. For this purpose, an improved unascertained measure function for collapse risk evaluation was proposed. Firstly, a risk evaluation index system was established using major indexes, including geological conditions, design and construction factors. Secondly, a combination method of the analytic hierarchy process (AHP) and the anti-entropy weight (AEW) based on the maximum deviation (MD) concept was proposed. The subjective and objective weights were linearly optimized to assign the index weights. Finally, the conventional linear unascertained measure function was improved by normalizing. By combining the weights, the values of the evaluation indexes were regarded as intervals for calculating the measure values. Confidence recognition analysis was used to determine the collapse risk level. Jinzhupa Tunnel was evaluated to have a high collapse risk and experienced a small-scale collapse during excavation, verifying the feasibility of the improved unascertained measure model. This method considers the uncertainty of the evaluation index and can effectively predict the risk level of tunnel collapse. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Advances in Engineering & Pure Sciences is the property of Marmara University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To steadily promote the reform of sales electricity prices, China has innovatively proposed to carry out grid enterprises’ agency power purchase business (GEAPPB) as a transitional scheme for industrial and commercial users to enter the power market in the future. However, because the GEAPPB covers a wide range of users and involves complex business processes, the risks throughout the process of the GEAPPB will affect the organization and operation of the business. This paper proposes a GEAPPB risk assessment technique. First, a risk evaluation index system containing 10 indicators is designed based on the characteristics and development process of GEAPPB. Second, a risk assessment model based on the fuzzy full consistency method (FUCOM-F) and gray relational analysis (GRA)-improved MARCOS is constructed. Finally, seven grid enterprises in China are taken as examples to verify the validity and scientificity of the model. The results show that improving the accuracy of power forecasting, optimizing the agency power purchase strategy, and strengthening demand response management are the key schemes to reduce the risk of GEAPPB. Compared with the other three evaluation models, the proposed model has good applicability and effectiveness for the risk evaluation of GEAPPB.

Background. When designing and developing a web application that allows you to get stroke risk factors on a scale of data, you have to solve a number of tasks, namely: analysis and development of a portable software package based on mobile devices, with API support for client mobile devices, experimental research of the developed methodology of portable solutions, models and technologies of interaction, implementation of a portable system with API support. Materials and methods. The methodological basis of the research is system analysis, methods of information processing and optimization of models, methods of operational calculus, automatic control theory. The research was carried out using mathematical modeling and using software, including the WebStorm programming environment and MySQL information processing software. Results. A software package has been developed and a new approach related to the use of standard mobile devices for organizing data transmission has been proposed. This method, unlike the considered analogues, uses a mobile device as a server, and works autonomously and without additional network devices. Conclusions. In modern conditions, an important factor in the convenience and efficiency of using software solutions is orientation to mobile devices. For this reason, it was decided to implement the evaluation scale in the form of a web application. Using the example of the CHA2DS2VASc scale, a technique for creating web-oriented software tools was tested. The advantages of web applications are their versatility and the ability to use them on mobile devices.

Abstract With the change and complexity of the tourism market environment, the financial risks faced by tourism enterprises are increasingly diversified. Effectively evaluating and controlling these financial risks has become the key to the development of tourism enterprises. Therefore, this study builds an accurate and real-time enterprise financial risk assessment and control model with the help of genetic algorithm. The results show that compared with other models, the maximum error value of the research model is only 0.12, and the maximum mean square error is only 0.09. The high reliability of the model is verified by simulating the data of selected tourism enterprises. After increasing the number of samples, the accuracy of the model continues to improve, and the predicted financial indicators are more in line with the actual situation. The model achieves the best results in average fitness, and the required error value is reached within 10 iterations. In the goodness of fit comparison, the goodness of fit of the training set, the test set and the verification set of the model are all over 0.7. In the empirical analysis, the ACC of the research model reached 97.4%, the accuracy rate reached 97.1%, the F1 index of the research reached 98.6%, and the other three research models were all lower than 98%. The above shows that the research model has significant advantages and can effectively evaluate and control the financial risk of tourism enterprises.

The fluctuation of renewable energy will threaten the reliable operation of the composite transmission and generation power system, so the risk level of the composite system with renewable energy attached must be studied. However, the accuracy of the renewable energy model has typically been overlooked in current risk assessment approaches. Therefore, this study provides an accurate hybrid wind/solar power output model that takes the wind farm’s seasonal and spatial characteristics, as well as the correlation between wind and solar PV power into account. For the first, build the seasonal Weibull model of the wind speed and correct the speed captured by each wind turbine considering different spatial characteristics of the wind farm. Secondly, build the probability distribution function of the solar power based on the kernel density estimation method. In the next, construct the joint probability distribution function considering the correlation between wind and solar PV power based on copula theory. Then, using the suggested accurate renewable energy model, the sequential Cross-entropy (SCE) technique is utilized to evaluate the risk indices of the composite system with renewable energy. To validate the models and technique, the modified IEEE-RTS79 is studied. The findings indicate that the accuracy of the renewable energy model influences the risk assessment outcomes.