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The development of multiple attack methods by external attackers in recent years poses a huge challenge to the security and efficient operation of software‐defined networks (SDN), which are the core of operational controllers and data storage. Therefore, it is critical to ensure that the normal process of network interaction between SDN servers and users is protected from external attacks. In this paper, we propose a risk‐aware SDN defense framework based on safe reinforcement learning (SRL) to counter multiple attack actions. Specifically, the defender uses SRL to maximize the utility by choosing to provide a honeypot service or pseudo‐honeypot service within predefined security constraints, while the external attacker maximizes the utility by choosing an anti‐honeypot attack or masquerade attack. To describe the system risk in detail, we introduce the risk level function to model the simultaneous dynamic attack and defense processes. Simulation results demonstrate that our proposed risk‐aware scheme improves the defense utility by 17.5% and 142.4% and reduces the system risk by 42.7% and 59.6% compared to the QLearning scheme and the Random scheme, respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: Trichinella spp. is a nematode parasite that causes Trichinellosis worldwide, a foodborne zoonotic disease. To ensure high standards in food safety, a Quality Management System (QMS) was implemented in Satellite Laboratories (SLs) performing meat inspection of Trichinella spp. in southern Spain. The study analyzed how deviations from standard procedures may affect risk levels using Canonical Discriminant Analysis (CDA). Data were collected from audits of 18 SLs in Cordoba and Seville over six years. It was found that technical deviations, such as incorrect tests or calculations and failure to follow procedures, significantly increased risk levels. These deviations caused up to a 1.150-fold increase in risk levels, indicating their critical impact on risk determination. However, deviations related to records and documents, such as incomplete or erroneous data, did not significantly influence risk levels. The results emphasize the need to improve Trichinella spp. control strategies by addressing critical deviations in technique, trial information, and quality assurance to mitigate associated risks. Trichinellosis is a major foodborne zoonotic disease responsible for 41 human cases, according to the European Union One Health Zoonoses Report. In southern Spain, a quality management system (QMS) was applied to satellite laboratories (SLs) that conduct meat inspections of Trichinella spp. ensuring excellence practices. This study aimed to determine how eventual deviations from standard procedures may influence risk levels using Canonical Discriminant Analysis (CDA). Data were collected during slaughterhouses and game handling establishments' official audits in 18 SLs located in the provinces of Cordoba and Seville during a 6-year period. Technical requirement deviations regarding technique and trial information, such as performing tests or calculations incorrectly or not following technical procedures, significantly increased risk level differences. Imminent risk levels were detected if the above-mentioned deviations arose. Quality assurance compromising deviations were responsible for 1150 times risk level differences, suggesting finding such may be critical for risk determination. A lack of significant influence of records and documents compromising deviations (incomplete forms or missing-erroneous or illegible data) was found. These results strengthen Trichinella spp. control strategies by pinpointing crucial aspects within QMS that require improvement, particularly in addressing deviations related to technique, trial information, and quality assurance procedures to mitigate associated risks effectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southeast University (English Edition) is the property of Journal of Southeast University 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.)

Driver's risk perception ability is of great significance to traffic safety. Driver's risk perception ability is reflected in the risk handling in traffic situation and has a great impact on driving decisions, especially speed decisions. However, the mechanism of the influence of risk perception on driving decisions under risk situations is still unclear. According to standardized risk perception test, we divided drivers into high risk perception ability (H-RPA) group and low risk perception ability (L-RPA) group. Participants were shown traffic pictures with different levels of risk and asked to make decisions about whether to slow down, while their electrical brain responses were recorded. All participants were interviewed after the experiment. The results showed that in the low-risk traffic situation, compared to the drivers with H-RPA, the drivers with L-RPA made less deceleration decisions and evoked larger N1 and smaller P1 amplitude. 93.75% of the drivers with L-RPA stated in interviews that they did not notice the traffic risks on the road. The results show that risk perception affects the allocation of attention to risks in traffic situations, leading to a decrease in the safety of driving decisions. Our research results provide a reference for the training of safe driving skills for drivers. [ABSTRACT FROM AUTHOR]

Risk perception and use of personal protective equipment (PPE) in pesticide use were studied in 109 farmers of northern Greece. Farmers differed in the levels of pesticide risk perception, with 26.6% showing very low and low levels, 34.9% showing moderate levels, and 38.5% showing high and very high levels. Pesticide risk perception was positively correlated with education and large cultivated area, but negatively with age and non-farming as a main profession. Most farmers (82.6%) showed adequate use of PPE (mostly face mask and gloves). The use of PPE was positively correlated with education and risk perception, but negatively with age and non-farming as a main profession. Farmers with high levels of risk perception used 1.3 and 2.6 times more PPE items than those with moderate and low levels of risk perception, respectively. High education was a positive predictor, whereas main profession other than farming was a negative predictor of PPE use. [ABSTRACT FROM AUTHOR]

Epidemic maps, by incorporating outbreak characteristics and visually presenting the data, have become the primary means for the public to grasp spatiotemporal risk insights. This study aims to provide design strategies to enhance the readability and risk perception effectiveness of epidemic maps, optimizing their visual design to improve information acquisition efficiency and risk perception efficacy. Employing a synthesis of event-related potentials and questionnaires, we integrate participants’ subjective perceptions for six epidemic map types with EEG metrics, probing the cognitive underpinnings as they engage with diverse symbol and risk categorizations. The results reveal significant differences in participants’ subjective risk perception scores across the six types of epidemic maps, with notable variances in the amplitude and latency of P200 and P300 components triggered by these different maps. The above findings indicate: (1) Point-symbol epidemic maps exhibit a higher arousal level, more effectively capturing individual attention; (2) Area-symbol characteristics in low-risk contexts aid in enhancing information retrieval efficiency; (3) High-level risk information garners more attentional resources from individuals. [ABSTRACT FROM AUTHOR]

Pressure ulcers carry a significant risk in clinical practice. This paper proposes a practical and interpretable approach to estimate the risk levels of pressure ulcers using decision tree models. In order to address the common problem of imbalanced learning in nursing classification datasets, various oversampling configurations are analyzed to improve the data quality prior to modeling. The decision trees built are based on three easily identifiable and clinically relevant pressure ulcer risk indicators: mobility, activity, and skin moisture. Additionally, this research introduces a novel tabular visualization method to enhance the usability of the decision trees in clinical practice. Thus, the primary aim of this approach is to provide nursing professionals with valuable insights for assessing the potential risk levels of pressure ulcers, which could support their decision-making and allow, for example, the application of suitable preventive measures tailored to each patient's requirements. The interpretability of the models proposed and their performance, evaluated through stratified cross-validation, make them a helpful tool for nursing care in estimating the pressure ulcer risk level. [ABSTRACT FROM AUTHOR]

Tailings storage facility (TSF) failures have tremendous impacts on surrounding populations, communities, and ecosystems. Therefore, a three-dimension risk matrix considering failure probability, event intensity, and exposure of failure-bearing bodies is established to assess the risk of TSF failure. First, based on the established assessment indicator system, the probability level is determined by the uncertainty measurement theory combined with the extension analytic hierarchy process. Then, the event intensity level is determined by two parameters, namely the volume of released tailings and the maximum distance traveled by tailings. The exposure level is characterized by the potential loss of four failure-bearing bodies, including population, economy, environment, and society. Finally, the risk index is calculated by the Euclidean distance method and the risk level is determined. In the proposed model, the probability, intensity, exposure, and the risk of TSF failure are all divided into four levels (Level I, Level II, Level III, Level IV) from high to low. The application of the case verifies the applicability and availability of the proposed model. [ABSTRACT FROM AUTHOR]

Disasters can devastate individuals and their properties, highlighting the importance of risk assessment to promote safety. Recently, deep learning techniques have shown the potential in identifying hazardous situations during disasters. Recognizing potentially dangerous objects in indoor environments can be essential for assisting individuals in responding appropriately to emergencies. In this article, we present an indoor-risk analysis framework for disasters based on deep learning. Our framework utilizes modern deep learning techniques to calculate an indoor risk rating based on dangerous objects' sizes, enabling comprehensive risk assessment of indoor environments during disasters. To that end, we use (Mask R-CNN) to identify hazardous indoor objects in disaster situations with 94% accuracy. By incorporating object size information, our framework offers a more nuanced and detailed risk assessment than previous approaches. Our proposed system provides a valuable tool for promoting ongoing safety improvement and enhancing indoor safety during natural disasters. [ABSTRACT FROM AUTHOR]

Suspended shellfish aquaculture, utilizing longlines and buoys fixed on the sea surface, is a crucial source of global seafood. However, typhoons, as the most common disasters in coastal regions, can generate massive waves and pose a threat to the safety of suspended aquaculture facilities. Therefore, it is essential to investigate the risk levels associated with suspended aquaculture farms during typhoon waves. The example of a large-scale suspended mussel farm in the East China sea was examined in this study. The wave conditions under extreme wind conditions (25m/s) and different typhoon paths were predicted using the SWAN model and modeled results compared well with the observed wave and wind data. The results indicated that when the center of the east-side typhoon was located at the same latitude as the farm, it will face the highest risk level. However, for the west-side and intermediate sweeping typhoons, the risk level reached highest in the farm, when the typhoon center is lower than the latitude of it. Under the combined effects of wind fetch and topography, the risk level in the southeast farming area of the island is the highest under different wind conditions. The farms to the northwest of the island face a higher risk level during the passage of east-side sweeping typhoons, while the southern farms of the island experience an elevated risk level during the west-side and intermediate sweeping typhoons. It is suggested that farming regions with lower risk levels are more conducive to the cultivation of species with longer culture cycles. Conversely, regions considered at higher risk are more suitable for species with shorter culture cycles. The study affords a reference for the optimization and insurance of suspended aquaculture farms. [ABSTRACT FROM AUTHOR]

Background and purpose: Prostate cancer is the most common male urogenital malignancy in many regions of the world, which has been increasing in recent years all over the world, including Asian countries, as well as in Iran. The relative risk of prostate cancer for those who have a family history of this disease in close relatives is almost 2 to 3 times, and the incidence rate increases with the increase in the number of affected relatives. Although family history of prostate cancer is a proven risk factor for this disease, few studies have investigated the relationship between the presence of family history of prostate cancer and the degree of prostate cancer risk. The purpose of this study is to investigate the relationship between the existence of a family history of prostate cancer and the degree of prostate cancer risk at the time of diagnosis. Materials and methods: This case-control study was conducted on 322 patients available prostate cancer patients referred to medical centers affiliated with Babol University of Medical Sciences. Patients were divided into two groups: case (family history of prostate cancer) and control (no family history of prostate cancer). Information about family history, Gleason score, PSA, and clinical stage was extracted from all patients. It was extracted through the file and phone call. A positive family history of prostate cancer is defined as follows: patients who have at least two of their first-degree relatives with prostate cancer at any age. Also, patients who have one first-degree relative and at least two second-degree relatives at any age with prostate cancer. In hereditary prostate cancer, at least 3 first-degree relatives have prostate cancer, or prostate cancer occurs in three consecutive generations of the same family (paternal or maternal). At least two first- or second-degree relatives had early onset prostate cancer under the age of 55. The relationship between the existence of a family history of prostate cancer first-degree relatives include: father, brother, and child, and second-degree relatives include: grandfather, uncle and cousin. the degree of prostate cancer in three groups (Low risk with PSA20 or Gleason score>7) at the time of diagnosis was investigated. Results: The average age of patients when diagnosed with prostate cancer was 67.23±8.97 years, who were in the age range of 43-93 years. 23.3% of the studied patients had a family history of prostate cancer and 1.9% had a hereditary family history of prostate cancer. With the increasing age of diagnosis, the degree of risk of prostate cancer increased, which is statistically significant. Thus, 63.9% of patients who were diagnosed with prostate cancer at the age of 71 years had high-risk prostate cancer, which shows that the risk level of prostate cancer should increase as the age of prostate cancer diagnosis increases (P=0.001). Between the ages of 61 and 70 years, 62 people (44.9%) were at a high risk level, while at the age of 60 years, 33 people were at a high risk level (P

The western part of South Sumatra Province is situated near Bukit Barisan. Therefore, the topography is characterized by hills and mountains. This condition makes the area vulnerable to ground movement. Information on the risk level and type of ground movement is very important for the region's development. This paper presents the mapping of risk level and type of ground movement along the national road system in South Sumatra Province. The map was developed based on slope condition data collected and tabulated in the Slope Management System developed by the Ministry of Public Works based on several parameters. These parameters were processed and analyzed using ArcGIS software. The risk level is classified as low, medium, high, and very high. The type of ground movement is categorized as debris flow, rock fall, slope failure, rock mass failure, embankment failure, rotation failure, and translation failure. The study uses data from 2018 and 2021. Results show that most ground movement on road segments 36, 37, 38, 18, 19, and 21 is slope failure. The map suggests an increase in the number and level of risk of slope failures from 28 in 2018 to 45 in 2021. The map could be used as preliminary information but not as a replacement for slope surveys and investigations.

Trichinellosis is a major foodborne zoonotic disease responsible for 41 human cases, according to the European Union One Health Zoonoses Report. In southern Spain, a quality management system (QMS) was applied to satellite laboratories (SLs) that conduct meat inspections of Trichinella spp. ensuring excellence practices. This study aimed to determine how eventual deviations from standard procedures may influence risk levels using Canonical Discriminant Analysis (CDA). Data were collected during slaughterhouses and game handling establishments’ official audits in 18 SLs located in the provinces of Cordoba and Seville during a 6-year period. Technical requirement deviations regarding technique and trial information, such as performing tests or calculations incorrectly or not following technical procedures, significantly increased risk level differences. Imminent risk levels were detected if the above-mentioned deviations arose. Quality assurance compromising deviations were responsible for 1150 times risk level differences, suggesting finding such may be critical for risk determination. A lack of significant influence of records and documents compromising deviations (incomplete forms or missing-erroneous or illegible data) was found. These results strengthen Trichinella spp. control strategies by pinpointing crucial aspects within QMS that require improvement, particularly in addressing deviations related to technique, trial information, and quality assurance procedures to mitigate associated risks effectively.

Background and purpose: Prostate cancer is the most common male urogenital malignancy in many regions of the world, which has been increasing in recent years all over the world, including Asian countries, as well as in Iran. The relative risk of prostate cancer for those who have a family history of this disease in close relatives is almost 2 to 3 times, and the incidence rate increases with the increase in the number of affected relatives. Although family history of prostate cancer is a proven risk factor for this disease, few studies have investigated the relationship between the presence of family history of prostate cancer and the degree of prostate cancer risk. The purpose of this study is to investigate the relationship between the existence of a family history of prostate cancer and the degree of prostate cancer risk at the time of diagnosis. Materials and methods: This case-control study was conducted on 322 patients available prostate cancer patients referred to medical centers affiliated with Babol University of Medical Sciences. Patients were divided into two groups: case (family history of prostate cancer) and control (no family history of prostate cancer). Information about family history, Gleason score, PSA, and clinical stage was extracted from all patients. It was extracted through the file and phone call. A positive family history of prostate cancer is defined as follows: patients who have at least two of their first-degree relatives with prostate cancer at any age. Also, patients who have one first-degree relative and at least two second-degree relatives at any age with prostate cancer. In hereditary prostate cancer, at least 3 firstdegree relatives have prostate cancer, or prostate cancer occurs in three consecutive generations of the same family (paternal or maternal). At least two first- or second-degree relatives had early onset prostate cancer under the age of 55. The relationship between the existence of a family history of prostate cancer first-degree relatives include: father, brother, and child, and second-degree relatives include: grandfather, uncle and cousin. the degree of prostate cancer in three groups (Low risk with PSA<10, Gleason score<7, intermediate risk with PSA between 10 and 20 or Gleason score 7, and high risk with PSA>20 or Gleason score>7) at the time of diagnosis was investigated. Results: The average age of patients when diagnosed with prostate cancer was 67.23±8.97 years, who were in the age range of 43-93 years. 23.3% of the studied patients had a family history of prostate cancer and 1.9% had a hereditary family history of prostate cancer. With the increasing age of diagnosis, the degree of risk of prostate cancer increased, which is statistically significant. Thus, 63.9% of patients who were diagnosed with prostate cancer at the age of 71 years had high-risk prostate cancer, which shows that the risk level of prostate cancer should increase as the age of prostate cancer diagnosis increases (P=0.001). Between the ages of 61 and 70 years, 62 people (44.9%) were at a high risk level, while at the age of 60 years, 33 people were at a high risk level (P<0.001). Also, there was a statistically significant relationship between the positive family history of prostate cancer and the degree of risk (75 positive cases (23.3%) versus 247 negative cases (76.7%) (P<0.05). Conclusion: The findings of the study showed that there is a positive relationship between the family history of prostate cancer and the degree of prostate cancer risk, which indicates the importance of prostate cancer screening with PSA and digital rectal examination from the appropriate age in these patients. [ABSTRACT FROM AUTHOR]

With the intensification of global climate change, the frequency of wildfires has markedly increased, presenting an urgent challenge in assessing tripping failures for power systems. This paper proposes an innovative method to evaluate the spatial wildfire-induced tripping risk of transmission lines based on a flame combustion model. Firstly, Bayes theory is employed to assess the spatial probability of wildfire occurrence. Subsequently, Wang Zhengfei's flame combustion model is utilized to estimate the potential flame height of wildfires along the transmission corridor. Thirdly, the insulation breakdown risk of the transmission line is calculated based on the relative height difference between the flame and the transmission line. Finally, the spatial wildfire-induced tripping risk of the transmission line is then determined by combining the wildfire occurrence probability and the insulation breakdown risk. A case study conducted in Guizhou province, China validates the accuracy of the proposed model. Utilizing ArcGIS, the wildfire occurrence probability distribution in Guizhou is visualized to enhance the efficiency of operation and maintenance. The results indicate that over 80% of wildfire incidents occurred in areas with occurrence probabilities exceeding 50%. [ABSTRACT FROM AUTHOR]

As the complexity of banking technology systems increases, the prevention of technological risk becomes an endless battle. Currently, most banks rely on the experience and subjective judgement of experts and employees to allocate resources for technological risk management, which does not effectively reduce the frequency of technology-related incidents. Through an analysis of mainstream risk management models, this study proposes a technology-based risk assessment system based on machine learning. It first identifies risk factors in bank IT, preprocesses the sample data, and uses different regression prediction models to train the processed data to build an intelligent assessment model. The experimental results indicated that the Genetic Algorithm–Backpropagation Neural Network model achieved the best performance. Based on assessment indicators, indicator weight values, and risk levels, commercial banks can develop targeted prevention and control measures by applying limited resources to the most critical corrective actions, thereby effectively reducing the frequency of technology-related incidents. [ABSTRACT FROM AUTHOR]

BACKGROUND: Work-related low back pain (LBP) increases the workforce disability and healthcare costs. This study evaluated the LBD risk level associated with handling the ACGIH TLVs in lifting tasks corresponding to various horizontal and vertical zones. OBJECTIVE: The aim of this study was to compare the low-risk ACGIH TLV to risk outcomes from various validated lifting assessment methods, including the OSU LBD Risk Model, NIOSH Lifting Equation, and LiFFT. METHODS: Twenty-four subjects were recruited for this study to perform various lifting conditions. The various ergonomic assessment methods were then used to obtain the risk assessment outcomes. RESULTS: The selected assessment methods showed that the ACGIH-defined TLVs are associated with less than high-risk for LBD for all the assessed tasks. The findings showed a moderate agreement (Kendall's W = 0.477) among the various assessment methods risk outcomes. The highest correlation (ρ= 0.886) was observed between the NIOSH Lifting Equation and LiFFT methods risk assessment outcomes. CONCLUSION: The findings showed that ACGIH-defined TLVs possesses less than high-risk for LBD. The outcomes of the selected ergonomic assessment methods moderately agree to each other. [ABSTRACT FROM AUTHOR]

Objectives: The objectives of this study were to explore the rate and types of inappropriate referrals to the wellness services and to assess the impact of multi-level interventions on the rate of inappropriate referrals at Rawdat Al-Khail health center in Qatar. Methods: This study employed a retrospective analysis of all referrals data to Rawdat Al-Khail wellness services extracted from the Electronic Health Records (EHRs) between July 2022 and August 2023. The monthly rates and types of inappropriate referrals were calculated. In this study, pre-post analyses were performed to evaluate the impact of two sets of interventions on reducing inappropriate referral rates. The first set involved the development and distribution of e-referral pathways training manual in September 2022. The second set, implemented in April 2023, included close monitoring the rate and types of referrals, and the initiation of feedback communication between wellness services supervisors and referring physicians for advice and corrective actions. Results: A total of 966 referrals were received during the study period, with 1:5 male-to-female ratio. Of all referrals, 34.9 % were classified as inappropriate, exhibiting considerable variations among different referring health centers. The most common reason for inappropriate referrals was due to the lack of “exercise wellness gym assessment form” (23.8 %). While interventions aimed at enhancing the referral process, they did not result in a significant overall reduction in inappropriate referral rates. However, there was a noteworthy reduction in the inappropriate referrals caused by the lack of “exercise wellness gym assessment form” observed from March to August 2023 (41 %–18 %). Conclusion: This study sheds light on the complexities of wellness services referrals, revealing a high rate of inappropriate referrals that require closer scrutiny. Despite interventions not significantly reducing the rate of these referrals, it emphasizes the need for ongoing improvement strategies. Structured, periodic interventions at higher levels are recommended to enhance referral appropriateness.

Purpose – The smoothness of the high-speed railway (HSR) on the bridge may exceed the allowable standard when an earthquake causes vibrations for HSR bridges, which may threaten the safety of running trains. Indeed, few studies have evaluated the exceeding probability of rail displacement exceeding the allowable standard. The purposes of this article are to provide a method for investigating the exceeding probability of the rail displacement of HSRs under seismic excitation and to calculate the exceeding probability. Design/methodology/approach – In order to investigate the exceeding probability of the rail displacement under different seismic excitations, the workflow of analyzing the smoothness of the rail based on incremental dynamic analysis (IDA) is proposed, and the intensity measure and limit state for the exceeding probability analysis of HSRs are defined. Then a finite element model (FEM) of an assumed HSR track-bridge system is constructed, which comprises a five-span simply-supported girder bridge supporting a finite length CRTS II ballastless track. Under different seismic excitations, the seismic displacement response of the rail is calculated; the character of the rail displacement is analyzed; and the exceeding probability of the rail vertical displacement exceeding the allowable standard (2mm) is investigated. Findings – The results show that: (1) The bridge-abutment joint position may form a step-like under seismic excitation, threatening the running safety of high-speed trains under seismic excitations, and the rail displacements at mid-span positions are bigger than that at other positions on the bridge. (2) The exceeding probability of rail displacement is up to about 44% when PGA = 0.01g, which is the level-five risk probability and can be described as 'very likely to happen'. (3) The exceeding probability of the rail at the mid-span positions is bigger than that above other positions of the bridge, and the mid-span positions of the track-bridge system above the bridge may be the most hazardous area for the running safety of trains under seismic excitation when high-speed trains run on bridges. Originality/value – The work extends the seismic hazardous analysis of HSRs and would lead to a better understanding of the exceeding probability for the rail of HSRs under seismic excitations and better references for the alert of the HSR operation.

Developing a quantitative hazard assessment model for spontaneous coal gangue combustion is imperative to ensuring the safety of coal mining operations. In this study, eight types of coal gangue from different coalfields were used to determine the controlling factors for spontaneous combustion. A variety of physical and chemical characterization methods were employed to investigate the physical and chemical characteristics of the samples. Based on the experimental results, six key parameters were identified, namely the aliphatic C–H components, pyrite sulfur content, physical oxygen absorption capacity, ash content, ignition point temperature, and activation energy for thermal decomposition. The results show that the spontaneous combustion hazard of coal gangue is positively correlated with the first three of these and is negatively correlated with the rest. Using the entropy-weight method combined with the analytic hierarchy process, appropriate weighting factors for each parameter were obtained and used to establish a quantitative assessment model for the spontaneous combustion hazard of coal gangue. This model is expected to enhance the current industrial best practices for the prevention and control of spontaneous coal gangue combustion.

With the intensification of global climate change, the frequency of wildfires has markedly increased, presenting an urgent challenge in assessing tripping failures for power systems. This paper proposes an innovative method to evaluate the spatial wildfire-induced tripping risk of transmission lines based on a flame combustion model. Firstly, Bayes theory is employed to assess the spatial probability of wildfire occurrence. Subsequently, Wang Zhengfei’s flame combustion model is utilized to estimate the potential flame height of wildfires along the transmission corridor. Thirdly, the insulation breakdown risk of the transmission line is calculated based on the relative height difference between the flame and the transmission line. Finally, the spatial wildfire-induced tripping risk of the transmission line is then determined by combining the wildfire occurrence probability and the insulation breakdown risk. A case study conducted in Guizhou province, China validates the accuracy of the proposed model. Utilizing ArcGIS, the wildfire occurrence probability distribution in Guizhou is visualized to enhance the efficiency of operation and maintenance. The results indicate that over 80% of wildfire incidents occurred in areas with occurrence probabilities exceeding 50%.

The risk assessment of rockburst intensity is significant for tunnel construction safety. First, the depth of the rockburst (X1), the uniaxial compressive strength of the rocks (X2), the brittleness coefficient of the rocks (X3), the stress coefficients of the rocks (X4), and the elastic energy index (X5) are adopted as the evidence body, and their essential certainty and reliability is determined using the entropy-gray correlation theory. Second, the synthetic certainty reliability of other samples is calculated based on the evidence theory. Relatively to the traditional gray extension model, it can improve the predictive accuracy and determine the certainty and reliability of different evidence bodies. The difference of importance between other evidence bodies can be reflected; and an interval scale can be taken into consideration in the evaluation process, so the proposed theory can reasonably predict the grade criterion which is interval form. Conclusion demonstrated that the suggested approach is entirely consistent with the actual investigation. The proposed model not only considers the unreliability or reliability of the problem but also solves some degrees of uncertainty and ambiguity of the datum; it enhances the predictive efficiency and provides a new way and thought for future risk assessment of rockburst intensity.

The primary objective of this work is to introduce a novel approach that modifies the method for analyzing and assessing the risk of water supply network failure. The approach aligns with recommendations from the World Health Organization and the European Union regarding the reliability and safety of water supply to consumers. The presented method for assessing the risk in the water distribution subsystem was based on the vulnerability identifying method (VIM) and involves the determination of the vulnerability index (VI). The VIM vulnerability factors considered encompass the failure rate, chemical stability of water, and issues related to water corrosion properties in water distribution subsystems. The obtained risk assessment includes parameters such as the probability of hazard occurrence, the consequences of these hazards, and vulnerability to them. This concept was evaluated using real operational data from the water distribution subsystem. The estimated risk level, under the given operating conditions, indicates its acceptability. [ABSTRACT FROM AUTHOR]

In the present study, a Guangzhou urban rainstorm waterlogging model which is developed using the hydrodynamic method, combined with refined precipitation forecast data, is used to simulate the ponding depth and risk level of waterlogging points in Guangzhou. The results demonstrate that the model has a hit rate of 65% for the waterlogging risk level of the waterlogging points in the past two years, while the highest hit rate is 72.8% for the precipitation processes with accumulated precipitation of 50～100 mm. The simulated ponding depth for the precipitation event on May 22 is relatively shallower compared with the real situation, and the error is mainly found in the simulation for the ponding depth within 30 cm, accounting for about 64%. Most of the simulated ponding depth is shallower and these ponding points are mainly located in the north-central part of Guangzhou. The model has good simulation ability for ponding with depth below 1 m, while the simulation ability for deep ponding around 2 m is still limited. Moreover, the waterlogging conditions are different in the central urban area under different return periods. Under the 1-hour rain condition, the ponding depth is generally less than 20 cm, and some range within 20～59 cm; under the 3-hour rain condition, the ponding depth is generally between 20～59 cm, with some ranging between 0～20 cm and 60～119 cm. Overall, the simulation results roughly match the actual waterlogging situation, and the model accuracy can meet general operational needs. [ABSTRACT FROM AUTHOR]

Copyright of Agribusiness Forum / Forum Agribisnis is the property of IPB 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.)

The risk of dammed lake has always been the research hotspot of scholars at home and abroad. A reasonable risk assessment method is conducive to scientific emergency response. In this paper, a rapid quantitative assessment method of dammed lake risk based on Bayesian network is proposed, including dammed lake risk assessment module, outburst flood routing simulation module, and outburst loss assessment module. The model puts forward the risk logic topology according to the causality of the dammed lake outburst disaster chain and quantifies the network nodes of each module through statistical data, empirical model, and logical inference combined with the Monte Carlo method, to obtain the possibility of dammed lake outburst and the life loss and economic loss rate of each affected area, to comprehensively evaluate the dammed lake risk. According to the national regulations on the definition of accidental loss and the probability of the life distribution of the dammed lake, combined with the Bayesian model, the risk definition range of the dammed lake is calculated, and the risk classification table of the dammed lake is proposed. Finally, the risk assessment of Baige dammed lake is carried out. The assessment results show that the comprehensive risk value of Baige dammed lake is 0.733 9, which belongs to the highest risk, and class I red alarm should be carried out. This method can quickly and quantitatively evaluate the overall risk of a sudden dammed lake and provide reference for decision-making of rapid emergency response [ABSTRACT FROM AUTHOR]

Copyright of Industrial Minerals & Processing / Huagong Kuangwu yu Jiagong is the property of Industrial Minerals & Processing 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.)

Copyright of Water Conservancy Science & Techonlogy & Economy is the property of Water Conservancy Science & Technology & Economy 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 paper analyzes the characteristics of influencing factors of urban road traffic safety and designs an evaluation method for urban road traffic safety factors based on visual guidance and hazard assessment. A three-dimensional evaluation system structure was established for drivers, vehicles, and roads based on their psychological and physiological factors, driving behavior factors, climate factors, and road facilities; By analyzing the feature norm set that affects driver decision-making, dynamic evaluation of urban road traffic safety under different risk levels is achieved; Taking into account traffic flow, vehicle speed, and fog frequency, a visual guidance model for driving optical flow in dangerous sections of urban road traffic was constructed based on visual guidance and visual psychological feedback models; Based on the dynamic distribution of the impact of urban road traffic safety, measures such as acceleration and deceleration, parking, and distance maintenance have been formulated. The experimental results indicate that the model can accurately achieve the graded evaluation of urban road traffic safety and effectively reduce the probability of accidents. [ABSTRACT FROM AUTHOR]

Abstract Background Despite technological advancement in the field of healthcare, the worldwide burden of illness caused by cardio-vascular diseases (CVDs) is rising, owing mostly to a sharp increase in developing nations that are undergoing fast health transitions. People have been experimenting with techniques to extend their lives since ancient times. Despite this, technology is still a long way from attaining the aim of lowering mortality rates. Methods From methodological perspective, a design Science Research (DSR) approach is adopted in this research. As such, to investigate the current healthcare and interaction systems created for predicting cardiac disease for patients, we first analyzed the body of existing literature. After that, a conceptual framework of the system was designed using the gathered requirements. Based on the conceptual framework, the development of different components of the system was completed. Finally, the evaluation study procedure was developed taking into account the effectiveness, usability and efficiency of the developed system. Results To attain the objectives, we proposed a system consisting of a wearable device and mobile application, which allows the users to know their risk levels of having CVDs in the future. The Internet of Things (IoT) and Machine Learning (ML) techniques were adopted to develop the system that can classify its users into three risk levels (high, moderate and low risk of having CVD) with an F1 score of 80.4% and two risk levels (high and low risk of having CVD) with an F1 score of 91%. The stacking classifier incorporating best-performing ML algorithms was used for predicting the risk levels of the end-users utilizing the UCI Repository dataset. Conclusion The resultant system allows the users to check and monitor their possibility of having CVD in near future using real-time data. Also, the system was evaluated from the Human-Computer Interaction (HCI) point of view. Thus, the created system offers a promising resolution to the current biomedical sector. Trial Registration Not Applicable.

Southeast Tibet is an area with high frequency of debris flow, and also the place G318 pass through. Debris flow poses a serious threat to local social development and major engineering construction. In this paper, according to the conditions of southeast Tibet, 11 disaster-causing factors such as highway distance, watershed distance, fault distance, elevation, slope, rainfall and NDVI etc. are selected to study the occurrence regularity of debris flow in this area. Each factor data of debris flow point is derived and analyzed in ArcGIS, correlation test is carried out and factor sensitivity grade is established. After calculating the weights by AHP, the risk distribution map of debris flow in southeast Tibet in 2015 was made by combining factor sensitive layers to verify the correctness of the model. Different risk distribution maps are made according to different rainfall sensitivities to predict the future debris flow prone areas. When the rainfall sensitivity of the whole region is 3, nearly half of the southeast Tibet is highly prone to debris flow, and all along G318 are in high risk areas. When the rainfall sensitivity is 2, there is no extremely high risk area in southeast Tibet, the risk index of the whole region is greatly reduced, and the high risk part along G318 accounts for 84.56%. The results show that rainfall has a great influence on the risk distribution of debris flow in the study area. The research results can provide theoretical support for the social development and major project construction in southeast Tibet.

The high penetration of renewables has amplified errors in forecasts of wind and solar power generation that are made to support the operation of power systems. To guarantee the security and reliability of the power system, sufficient dispatchable power generation capacity must be reserved. Various studies of the use of risk assessment to improve operational performance have been carried out. However, most methods of so doing are designed to cover a given risk level of uncertainty, which is determined from forecasts rather than actual results. As renewable generation and load capacity increase, defining the risk level without considering unit commitment (UC) may limit scheduling efficiency. In this study, chance-constrained programming is combined with goal programming to optimize risk-based UC. Solar power generation, wind power generation, and demand forecast errors are handled by the optimization of reserve schedules and adjustments of risk by imposing a penalty cost. The proposed risk-based UC model is transformed into a mixed-integer linear programming (MILP) problem using the equivalent deterministic form and piecewise linear functions to find an efficient solution. Case studies of a realistic Taiwan power system in 2025 are considered; these include peak (Summer 2025) and off-peak (Winter 2025) scenarios. Simulation results reveal that the flexible operation of a battery energy storage system (BESS) (upward/downward reserve) reduces risk costs by a significant amount (19.12%∼100%). Sensitivity analysis reveals how the risk preferences of the System Operator (SO) can be represented in terms of predefined risk levels and confidence intervals to capture the uncertainties in load, wind power generation, and solar power generation forecasts. The spinning reserve level is optimized to reduce the risk cost and the addition of BESS in flexible operations further reduces the risk cost. Lastly, pumped storage units as a downward reserve can reduce wind spillage and excess solar power generation costs.

Copyright of Industrial Minerals & Processing / Huagong Kuangwu yu Jiagong is the property of Industrial Minerals & Processing 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.)

Construction activities involve a high level of risk in many ways. The most risky aspect is Occupational Health and Safety (K3). The Surakarta Furniture IKM Sentra Building Project is a 4-storey steel structure, requiring a lot of workers and heavy equipment, so there is a potential risk of industrial accidents. Among other aspects of the implementation of occupational safety and health that must be considered, is an analysis of the level of occupational health and safety risks in such a way as to enable employers to manage or deal with existing risks in such a way. That occupational safety and health risks associated with work are minimized. This paper aims to identify and analyze the level of occupational safety and health (OSH) risks in the Surakarta Furniture IKM Center project. The research method was carried out by interviewing and distributing questionnaires to respondents, namely staff, supervisors, owners, and foremen with reference to PUPR Ministerial Regulation NO 10 of 2021. The identification results obtained 35 OHS risk variables in the Surakarta IKM Furniture Center Project. The results of the analysis of the level of occupational safety and health (OSH) risk in the Surakarta Furniture IKM Center development project obtained work with a high level of risk, namely steel work, therefore the contractor must pay attention to the Occupational Safety and Health (OSH) of the work. Starting from using PPE, reminding workers of the dangers in the event of an accident, using safety belts, installing safety lines, installing nets, and outreach about the dangers of steel work. [ABSTRACT FROM AUTHOR]

Risk management is one of the important aspects in business management, especially in the hospitality industry. Risk can come from a variety of sources, one of which is the behavior of perpetrators who do not assess the danger. This case study will analyze the risk management carried out by Hotel Atria Serpong related to perpetrators who do not assess danger. In this case study, Hotel Atria Serpong has clear policies and procedures related to risk management. However, when dealing with perpetrators who do not assess harm, these policies and procedures appear to be less effective. Offenders who do not assess the danger may pose a safety risk to guests and hotel employees. To solve this problem, Hotel Atria Serpong can take several actions. First, increase employee training and awareness related to identifying and handling perpetrator behavior that does not assess harm. Second, strengthen existing policies and procedures, adding specific steps to be taken when confronting perpetrators who do not assess harm. Third, improve the physical security of the hotel, for example by installing CCTV in areas that are prone to unsafe actions. In this case, Hotel Atria Serpong needs to pay attention to factors that can affect the effectiveness of the actions taken. For example, employee training and awareness should be tailored to the characteristics of hotel guests and visitors. Policies and procedures must also be adjusted to the applicable laws and regulations in Indonesia. In conclusion, effective risk management must be able to identify and deal with risks from a variety of sources, including the behavior of actors who do not assess harm. Hotel Atria Serpong can improve risk management by taking appropriate actions and paying attention to factors that can affect its effectiveness. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering 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.)

Covid-19 impacted several sectors such as economic, political, social, sports and art activities etc. On the other hand, it influenced handwashing times, awareness, and risk levels for the hospital staff in Sulaimaniyah City. In this study, the effects of Covid-19 on awareness, risk level, and hand cleaning have been focused on by hospital staff in Sulaimaniyah City hospitals. A qualitative method using an electronic questionnaire was applied for data collection. The total number of participants was 404 hospital staff. The percentage ratio of female employees is higher than males in the health sector in the Sulaimaniyah Hospitals. Awareness by the hospital staff has increased during the Covid-19 pandemic. Participants aged more than 55 years were more at risk because of their age, and females more than 55 years had the most threat. Covid-19 impacted the increasing rate of washing hands by 38% and using materials for cleaning hands by 46% for the hospital staff in Sulaimaniyah City. The average grand total change percentage was 41%. Water consumption increased by 135% in the Sulaimaniyah Hospitals throughout the Covid-19 pandemic. On the other hand, fewer working hours in the hospitals led to a high-water consumption ratio among the employees in the hospitals.

As the complexity of banking technology systems increases, the prevention of technological risk becomes an endless battle. Currently, most banks rely on the experience and subjective judgement of experts and employees to allocate resources for technological risk management, which does not effectively reduce the frequency of technology-related incidents. Through an analysis of mainstream risk management models, this study proposes a technology-based risk assessment system based on machine learning. It first identifies risk factors in bank IT, preprocesses the sample data, and uses different regression prediction models to train the processed data to build an intelligent assessment model. The experimental results indicated that the Genetic Algorithm–Backpropagation Neural Network model achieved the best performance. Based on assessment indicators, indicator weight values, and risk levels, commercial banks can develop targeted prevention and control measures by applying limited resources to the most critical corrective actions, thereby effectively reducing the frequency of technology-related incidents.

Every construction project, whether high-tech or simple, certainly has risks in its implementation process, therefore it is necessary to establish an Occupational Safety and Health Management System to minimize Occupational Safety and Health (K3) risks, but there are still many construction companies that are lacking in identifying K3 risks. and implementing an Occupational Health and Safety Management System (SMK3) in the process of implementing a construction project. The purpose of this study was to determine the level of OSH risk based on work items, as well as to measure the level of implementation of SMK3 in a construction project. In this research, the object of the case study is the construction project of the Administration Office Building at I Gusti Ngurah Rai Airport, Bali, during the lower structure work. After carrying out risk identification, 35 risk questions and 5 jobs are obtained, namely Borepile, Pilecap, Pendestal Column, Sloff, Roof Frame, then the results of the identification are processed and the risk level is obtained from the implementation of the Administration Office Building Construction Project at I Gusti Ngurah Rai Airport, Bali. Enter the Medium Category with a risk value of 8.69, while for the implementation of the Occupational Safety and Health Management System (SMK) the Administration Office Building Construction Project at I Gusti Ngurah Rai Airport, Bali is included in the good category with a total application value of 83%. Hopefully this research can become a reference in identifying K3 risks and a reference in implementing the Occupational Health and Safety Management System (SMK).

Copyright of Corrosion & Protection in Petrochemical Industry is the property of Corrosion & Protection in Petrochemical Industry 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.)

Background: Despite technological advancement in the field of healthcare, the worldwide burden of illness caused by cardio-vascular diseases (CVDs) is rising, owing mostly to a sharp increase in developing nations that are undergoing fast health transitions. People have been experimenting with techniques to extend their lives since ancient times. Despite this, technology is still a long way from attaining the aim of lowering mortality rates. Methods: From methodological perspective, a design Science Research (DSR) approach is adopted in this research. As such, to investigate the current healthcare and interaction systems created for predicting cardiac disease for patients, we first analyzed the body of existing literature. After that, a conceptual framework of the system was designed using the gathered requirements. Based on the conceptual framework, the development of different components of the system was completed. Finally, the evaluation study procedure was developed taking into account the effectiveness, usability and efficiency of the developed system. Results: To attain the objectives, we proposed a system consisting of a wearable device and mobile application, which allows the users to know their risk levels of having CVDs in the future. The Internet of Things (IoT) and Machine Learning (ML) techniques were adopted to develop the system that can classify its users into three risk levels (high, moderate and low risk of having CVD) with an F1 score of 80.4% and two risk levels (high and low risk of having CVD) with an F1 score of 91%. The stacking classifier incorporating best-performing ML algorithms was used for predicting the risk levels of the end-users utilizing the UCI Repository dataset. Conclusion: The resultant system allows the users to check and monitor their possibility of having CVD in near future using real-time data. Also, the system was evaluated from the Human-Computer Interaction (HCI) point of view. Thus, the created system offers a promising resolution to the current biomedical sector. Trial Registration: Not Applicable. [ABSTRACT FROM AUTHOR]

Compared with other industries such as manufacturing, the construction industry has a higher danger of fatalities. In Korea, the risk level in the construction industry is managed using the fatality rate per 10,000 construction workers. However, this statistic is lacking in determining the exact risk level because it does not consider the exact number of workers and fails to reflect the specific characteristics of the construction industry. In this study, the fatality rate is deduced by considering the facility type and the project size based on total cost. From the results obtained, considering the facility type, "Assembly" is seen to be the most dangerous facility type. Considering the project size based on total cost, "Less than 0.008 billion dollars" is the most dangerous construction scale. Considering both the facility type and the project size based on total cost, it was confirmed that the overall fatality rate could exceed the fatality rate respective to each facility type and project size. Using the proposed method, it is possible to determine the quantitative risk level considering specific characteristics of the construction industry. [ABSTRACT FROM AUTHOR]