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In the process of continuous promotion in the green electricity market, various risks have emerged due to the inherent uncertainty and complex trading environment of the market. Evaluating and controlling the risks through a multi-dimensional evaluation is crucial for the smooth and healthy development of the green electricity market. This work proposes a risk assessment method for the green electricity market based on the multi-dimensional cloud model. First, the comprehensive indicator evaluation system is established for both pre-transaction and post-transaction dimensions by analyzing the operating mechanism of the green electricity market. Second, each dimensional cloud is formed by the evaluation clouds of all indicator levels. The comprehensive risk level cloud is generated based on the indicator values and the evaluation clouds of each indicator. Finally, the sources of market risk are identified, and the corresponding risk management methods are proposed on the basis of the risk evaluation level and membership degree of each indicator. The case study results demonstrate that the proposed method can accurately evaluate and reduce the potential risks existing in the green electricity market through the two-stage detection mechanism.

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.

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.

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.

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.

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.

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.

Proper management of vesicoureteral reflux (VUR) has remained one of the most controversial and complex issues in pediatric urology. Selecting management strategies and surgical indications of VUR should be based upon age, gender, grading, etiology, bladder function, heredity, urinary tract infection and renal status. And VUR patients should be stratified into different risk levels. And according to individual differences and risk factors, optimal treatments are devised to effectively improve the patient outcomes. [ABSTRACT FROM AUTHOR]

This research was carried out to determine the concentration of heavy metals in Pacific white leg shrimp cultured in farms of Ganaveh, Deylam, Hillah, Mand, Delvar and Shif and its consumption risk. The shrimp samples were prepared based on the standard methodology. The health risk of its consumption was evaluated according evaluated according to the daily intake (DI) index. Our results showed that the shrimp reared in the Deylam site had the maximum values of toxic metals mercury, nickel, and zinc compared to other sites of Bushehr Province. The concentrations of As, Cd, Cu, Fe, Hg, Ni, Pb, and Zn in shrimp muscle were 0.021, 0.028, 11.725. 42.003, 0.095, 3.278, 0.122 and 6.748 mg/kg dry weight (DW), respectively. Based on the findings, the concentration of Cu and Ni were significantly higher than the permissible maximum recommended amounts for human consumption by the world health organization (WHO), and they were 0.38 and 10 mg/kg DW, respectively. On the other hand, the health risk evaluation of the maximum tolerable daily intake (MTDI) of heavy metals showed that daily and continuous consumption of shrimp is safe for consumers in different age groups (children, adults) and despite the high levels of nickel and copper, there is no serious risk to consumers.

In order to comprehensively and objectively evaluate the risks faced by the underground comprehensive pipe corridor with large investment, wide coverage and long operation cycle, this paper takes a pipe corridor in Beijing as the object, based on the whole life cycle theory, identifies the risk factors in six stages of the whole life cycle of the pipe corridor, and establishes a risk index system consisting of 6 second-level and 43 third-level risk indicators. The comprehensive evaluation model of pipeline corridor risk based on AHP and Fuzzy was established, and the results were verified by weighted distribution method. The research results show that the overall risk of the whole life cycle of the studied pipe gallery belongs to the medium risk, of which the risk of abandonment stage is the highest, followed by construction and completion stage, operation stage, design stage, bidding stage and decision-making stage, and put forward targeted management measures.

In order to mitigate global warming and help the country achieve its carbon peaking and carbon neutrality targets at the earliest possible time, the emission-control companies should accomplish the carbon compliance in accordance with relevant national policies and regulations. However, these companies frequently face the failure risk of carbon compliance subjected to various factors, including the national carbon quota policy, local carbon market situation, the verification of carbon offset projects, as well as the effectiveness of carbon reduction technologies. To help the enterprises avoid the risk of carbon-compliance failure and design rational carbon asset management strategy, in this research, the innovative combination of interpretive structural modeling (ISM), Bayesian network model, risk calculation and sensitivity analysis method was formulated. Firstly, the ISM method was used to establish a hierarchical relationship of risk factors that contribute to the failure of carbon compliance. Secondly, the probability prediction model of carbon-compliance failure risk based on the Bayesian network model was established by aid of the Netica software. Thirdly, the risk value of enterprise's carbon compliance failure was quantitatively calculated based on its production operation and carbon asset management. Finally, the sensitivity analysis method was used to identify critical risk factors and design risk control measures for six well-known domestic enterprises, laying good foundation for improving the success rate of carbon compliance and facilitating low-carbon green transformation. Compared to traditional qualitative risk assessment method, this combined approach is capable of realizing the quantitative evaluation of failure risk based on comprehensive investigation and analysis of the production and operational situation, which provides effective technical support to enhance enterprise's compliance awareness and improve low carbon competitiveness.

The Belt and Road Initiative is a major Initiative proposed by Chinese President Xi Jinping in 2013. Research on the risk prevention and control of China's financial investment in countries along the Belt and Road has become a very hot topic in the world. This research focuses on the risk evaluation methods and prevention and control countermeasures of China's foreign investment under the Belt and Road Initiative. First, based on the analysis of the existing studies on economic investment evaluation, an intuitionistic fuzzy multi-attribute evaluation method based on entropy method and G1 method is proposed. The essence of the proposed method is to combine the intuitionistic fuzzy set theory with subjective and objective evaluation methods, which improves the disadvantage of the original evaluation method taking too much subjective factors into consideration. This study applies the proposed method to the economic risk evaluation of China's outward foreign direct investment (OFDI), constructs a 17-indicator economic risk system, and uses this method to rank the importance of the 17 indicators. The more important contribution is that this paper not only achieves improvements at the theoretical level and innovation at the practical level, but also condenses the research conclusions into three pieces of countermeasures and suggestions on China's investment in countries along the Belt and Road. This research can provide theoretical support for Chinese government to make financial investment decisions in countries along the Belt and Road, and can also help countries along the Belt and Road to actively integrate into the Belt and Road Initiative, and promote the high-quality social and economic development of the countries along the Belt and Road. [ABSTRACT FROM AUTHOR]

Creating a safe and resilient urban environment is a crucial part of sustainable urban development. Therefore, it is imperative that a city's safety resilience is evaluated from various perspectives. To evaluate and improve the resilience of urban fire safety more scientifically, this study proposes a theoretical framework for evaluating urban safety resilience based on the triangle model and an index system including fire hazard, regional characteristics, and fire resilience is established. The entropy weight method and cloud model are used for quantitative evaluation, and the weights and risk level ratings are analyzed and discussed. The results demonstrate that the method considering urban safety resilience plays a significant role in promoting the development of urban fire safety and can provide a reference for policymakers in improving fire services. [ABSTRACT FROM AUTHOR]

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Tunnel boring machines often encounter clogging during excavation in strata with rich clay content. The clogging can damage the cutterhead and interrupt excavation. Cutting tool clogging, also known as mud cake formation, has a strong effect on excavation work efficiency. While current studies are focusing on the mechanism of clogging, engineering practice still heavily relies on qualitative empirical judgement. To quantitatively assess the risk of mud cake formation, we carefully selected 22 influential parameters to reflect the effect of geological, machinal, and operational risk factors in the tunneling process and established a rubric for risk factor contributions using the analytical hierarchy process (AHP). The results imply the liquidity index, plasticity index, cutterhead torque, and total thrust force contribute to around 45% of the total influence on mud cake formation, while machinal factors are less influential than geological and operational factors. We verified the framework with a tunnel section from the Guangzhou Metro Line 22 that had mud cake formation reported. Rings labelled as high risk for mud cake formation by our framework concurred with those rings with mud cake actually observed. Project log and operational parameter variations were incorporated to explore the connection between mud cake formation and treatment. [ABSTRACT FROM AUTHOR]

Copyright of Fly Ash Comprehensive Utilization is the property of Hebei Fly Ash Comprehensive Utilization Magazine Co., Ltd. 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.)

Urban fire risk assessment plays an important role in reducing fire losses, but establishing a simple and efficient risk assessment model has always been a difficult and hot issue. The fire risk assessment index system was established on the basis of full investigation and analysis of fire hazard, regional characteristics, and disaster prevention and mitigation capacity in Lintong District of Xi’an City. Ten evaluation indicators including fire load density, spatial distribution of major hazard sources, key firefighting unit area, and fire water supply capacity were selected, and the weight of each evaluation indicator was determined by using the analytic network process (ANP) structural operation. The 500 m×500 m fishing net was used as the evaluation unit to construct a fire risk assessment model, and the ArcGIS spatial analysis function was used to perform index quantification, evaluation unit division, and evaluation model operation, and finally the fire risk assessment map of Lintong District was obtained. As the evaluation result show that the overall fire risk in Lintong District is higher in the south and lower in the north, high-risk areas accounted for 1.81%, medium-risk areas accounted for 5.82%, sub-risk areas accounted for 16.85%, and low-risk areas accounted for 75.53%, high fire risk areas are mainly concentrated in the five sub-district offices of Lishan, Xiekou, Daiwang, Xinfeng and Beitian. According to the actual investigation on site, these sub-district offices are densely populated and have many major danger sources, indicating that the established risk assessment model is accurate, reliable, simple and efficient, and can provide a useful reference for fire risk assessment in other cities with similar fire-pregnancy environments. [ABSTRACT FROM AUTHOR]

Strainburst poses significant risks to the safety of workers in deep underground engineering. Assessing strainburst potential plays a crucial role in disaster prevention. Due to the anisotropy and heterogeneity of rock mass, it is essential to quantify the strainburst potential from the perspective of probability. This study aims to propose a methodology that integrates Monte Carlo simulation (MCS) and machine learning (ML) algorithms to evaluate the probability of strainburst potential. First, a numerical model based on damage initiation and spalling limit (DISL) method is established. Then, a novel indicator, absolute local energy release rate (ALERR), is proposed to assess strainburst potential, which describes the absolute value of elastic strain energy released after rock mass failure. The location of the maximum value of ALERR is used to determine the depth of outburst pit, which is adopted to evaluate the strainburst potential level. So, the quantitative relationship between strainburst potential and ALERR can be established. Thereafter, a strainburst dataset including six variables and the depth of outburst pit is obtained based on the MCS method and numerical model. To improve the computational efficiency, seven different ML algorithms are integrated with MCS to calculate the probability of strainburst potential, respectively. Finally, the proposed methodology is used for the strainburst potential evaluation in the 3# headrace tunnel of Jinping II hydropower station. Results show that random forest (RF) and gradient boosting (GB) algorithms have better evaluation performance and can be combined with MCS to calculate the probability of strainburst potential reliably. The proposed methodology can provide valuable guidance for the probability assessment of strainburst potential. Highlights: A new methodology that integrates Monte Carlo simulation (MCS) and machine learning (ML) algorithms is proposed to evaluate the probability of strainburst potential. A novel indicator, absolute local energy release rate (ALERR), is proposed to assess strainburst potential. A numerical model, which combines MCS and damage initiation and spalling limit (DISL) approaches, is established to simultaneously simulate the brittle failure behavior of hard rock and variability of rock mechanics parameters. [ABSTRACT FROM AUTHOR]

The risk evaluation of tailing ponds is characterized by randomness, fuzziness and numerous uncertainties in the evaluation process. In order to effectively guarantee the operational safety of tailing ponds, a tailing pond risk evaluation method based on a combined assignment-cloud model is proposed. From a system analysis perspective, this paper applies the physical-substance-human-reason (WSR) system theory to explore the factors affecting the safe operation of tailings ponds from three aspects; subject factors, object factors and organisational forms, and constructs a WSR-based tailings pond risk evaluation index system. Secondly, the improved G1 method and CRITIC method are used to assign subjective and objective weights to the evaluation indicators respectively, and the game theory algorithm calculates the combination coefficients with the best weights to derive the combination weights. Finally, the cloud model theory is combined to calculate the relative affiliation and comprehensive characteristic value of each indicator, and the risk level is classified based on the principle of maximum affiliation, and the visualisation effect is presented in the form of a cloud chart. The evaluation method is validated using a tailings pond in Yunnan Province as an example, and the results are consistent with the actual situation, and compared with unknown measures and other methods. [ABSTRACT FROM AUTHOR]

To study the occurrence and spatial distribution of antibiotics in the Nanming River in Guiyang, China, 27 water samples were collected. Twelve antibiotics were analyzed by solid phase extraction (SPE) combined with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), among which a total of eight antibiotics were detected with a maximum frequency of 91.2%, indicating the prevalence of antibiotics in the study area. The total antibiotic concentrations ranged from 2.3 to 1097.4 ng/L and were dominated by fluoroquinolones according to wide spatial distribution. The least contaminated sites were located in suburbs, while the most severe contamination occurred in sites affected by urban areas. The spatial distribution is vulnerable to human activities such as wastewater treatment plants (WWTPs). The self-organizing map (SOM) atlas showed that antibiotics were mainly divided into three components: sulfonamides (SAs), tetracyclines (TCs), and fluoroquinolones (FQs), and the sampling sites were divided into four categories. Ofloxacin (OFX) was the dominant contributor to ecological risk with the risk quotient (RQ) > 1 at 23 sampling points. The good positive correlation between FQs and Na+/ (Na+ +Ca2+) implied that FQs could be used as a symbol of urban river syndrome. By combining traditional indicators and emerging pollutants (co-tracers), this study provides a more accurate representation of anthropogenic pollutants in urban rivers. [ABSTRACT FROM AUTHOR]

[Display omitted] • A framework is designed to evaluate the privacy risks of personal data practices. • Privacy indicators are defined in terms of personal data based on standards. • A privacy policy analysis model is designed to identify data practices for evaluation. • The privacy indicators of mobile applications are evaluated with real-world datasets. Personal data have become the key to data-driven services and applications whereas privacy requirements are now strongly imposed by regulations. Meanwhile, people find it difficult to understand whether the services and applications handle personal data to comply with their agreements and regulations. Therefore, the need for privacy indicators, which summarize privacy contents as forms of privacy scoring, labels, etc., has increased to empower the users' rights by providing understandable information about privacy. For firm privacy indicators, proper criteria and methods for evaluating the level of privacy risks and compliance are required. Accordingly, this paper proposes a privacy scoring framework for services in the context of handling personal data, inspired by six standardized indicators. This paper introduces detailed information on standardized indicators and proposes privacy indicators to quantify privacy scores. Also, this paper proposes methods for evaluating privacy policy based on a set of machine learning-based hierarchical binary classifiers and processes for quantifying the level of privacy risks and compliance from privacy-related information. Through analyzing privacy policies and data access lists of more than 10,000 mobile applications on Google Play Store and investigating case studies on privacy scoring of some mobile applications, this paper shows the feasibility of the proposed framework. [ABSTRACT FROM AUTHOR]

Elevated road plays a very important role as corridors in urban traffic network, and the occurrence of traffic accidents often causes a great impact. In that sense, we propose a unique and enhanced Autoencoder (AE) to identify elevated road traffic accident (RTA) risk based on traffic anomaly detection in an unsupervised manner. An attention mechanism is introduced to extract the traffic condition features considering traffic spatiotemporal variation characteristics. Additionally, an enhanced loss is also introduced to optimize the ability of unsupervised anomaly detection (UAD) approach to detect anomalous RTA risk and persistent anomalous traffic condition, which can significantly boost the anomaly detection performance using the contaminated traffic condition datasets. To assess the RTA risk, the evaluation mechanism and discriminant threshold are used to quantitatively analyze the detected abnormal traffic condition. Finally, experiments on real traffic datasets demonstrate the effectiveness of the model. [ABSTRACT FROM AUTHOR]

A snapshot screening was carried out in an urban river at the end of a dry period in the water and sediments to assess the presence and environmental risk for the following CECs: paracetamol, ibuprofen, diclofenac, clofibric acid, carbamazepine, ofloxacin, caffeine, tonalide, galaxolide, and bisphenol-A. Concomitantly, the occurrence and environmental risk of sixteen PAHs congeners, six indicator PCBs, and twelve dioxin-like PCBs were evaluated in sediments. The most abundant and ubiquitous CECs were bisphenol A (BPA) and caffeine, and the total contents in the surface water varied between 90.95–212.18 and 3.17–167.38 ng·L−1, respectively. The concentrations found in lixiviates ranged from 134.94–772.85 (BPA) and 14.43–92.60 ng·L−1 (caffeine). Other CECs were detected in lower concentrations, and their presence varied between sampled sites. Values of total PAHs congeners in sediment varied between 10.39–52.91 ng·g−1 dw. The majority of the detected PAHs seem to have a pyrolitic origin with a small petrogenic contribution. Total PCBs' concentrations ranged from 5.06 to 6.13 ng·g−1 dw. Despite the relatively low concentration of most of the detected compounds, the overall environmental risk, considering the screened compounds altogether, cannot be considered negligible. The obtained results are discussed in terms of other data available (though highly dispersed) in the literature. A four-color alert system is included to inform about the level of risk associated with the amount of each CEC, PAH, and PCB. [ABSTRACT FROM AUTHOR]

This paper presents a method for evaluation floor water inrush risk using combined weighting and analytic hierarchy process. The method is validated by a case study at the Pengzhuang coal mine in Shandong Province, China. First, the relevant geological background, hydrogeological data and related data were collected and analyzed, and the risk evaluation index of water inrush from the floor was determined. Then, combined with principal component analysis, entropy weight method and analytic hierarchy process, a comprehensive evaluation model was constructed to determine the comprehensive weights of each surge evaluation index. Finally, based on the GIS geographic information processing system, the optimized comprehensive weighting mathematical model was combined with the vulnerability index method to determine the water inrush risk division. The results show that the improved integrated weighting model is more accurate than the improved comprehensive weighting model. At the same time, the evaluation results obtained by comparing the traditional water inrush coefficient method have verified the reasonableness of the evaluation model. The results can provide a theoretical basis for the safe mining of coal seam above confined groundwater. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University: Engineering Science is the property of Editorial Office of Journal of Zhengzhou University: Engineering Science 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.)

Analyzing the general method of establishing a support vector machine evaluation model, this paper discusses the application of this model in the risk assessment of overseas mining investment. Based on the analysis of the risk assessment index system of overseas mining investment, the related parameters of the optimal model were ascertained by training the sample data of 20 countries collected in 2015 and 2016, and the investment risk of 8 test samples was evaluated. All 8 samples were correctly identified, with an error rate of 0. South Africa's mining investment risk in 2016 was assessed using the risk evaluation model for overseas mining investment based on a support vector machine, and it was rated as grade IV (general investment risk). The results show that the model can provide a new solution for the judgment and deconstruction of the risk of overseas mining investment. [ABSTRACT FROM AUTHOR]

Bank erosion is a key process of channel evolution in alluvial rivers, which can cause severe damage to riparian levees and influence the local river regime. Since the operation of the Three Gorges Project (TGP), the downstream reach has suffered continuous channel degradation and obvious bank erosion processes, putting great pressure on flood control management. A hybrid fuzzy procedure was thus proposed to evaluate bank erosion risk by combining the analytic hierarchy process and the entropy weight method, and this procedure was applied to evaluate the bank erosion risk in the Jingjiang Reach (JR) of the Middle Yangtze River. The Lower Jingjiang Reach (LJR) is a typical meandering channel, and the bank erosion is particularly serious. The weights of different influencing factors were firstly calculated by the proposed procedure, and the results showed that the dominant factor in the category of hydraulic factors was the curvature of the main flow (with a weight of 0.269), whereas the dominant factor in the category of boundary conditions was bank protection with the maximum weight of 0.378. Secondly, the occurrence possibility of bank erosion was presented using a linear function based on the calculated weights of different influencing factors. It was found that the river bank would be at a high risk as the bank erosion possibility was greater than 0.5; otherwise, the river bank would be at a low risk. The performance of the proposed procedure was evaluated by the measured bank erosion processes at two typical sections in the JR from 2003 to 2020, and satisfying results were obtained with an accuracy of 92%. [ABSTRACT FROM AUTHOR]

Metal accumulation in the marine environment can increase the metal content of fish living in it. For this reason, it is essential to evaluate the nutrition from fish consumption and assess metal toxicity risk. Ten species of demersal fish, which are part of the daily diet of coastal residents, were sampled using trammel net in the East China Sea. Levels of the microelements, the constant elements, and the toxic elements of muscle tissue were analyzed. Spiny red gurnard had the highest concentrations of calcium, copper, and iron, whereas pufferfish and threadfin porgy were good sources of zinc and magnesium. The levels of lead, cadmium, and inorganic arsenic in all samples were below the limit values according to Chinese national standards. Although some concerns were raised about the concentration of mercury (Hg) in threadfin porgy, silver croaker, and fivespot flounder, molar ratios (selenium, Se:Hg) and the Se health benefit values (HBVSe) indicated that they were safe for human consumption. Additionally, the estimated weekly intake and % weekly contribution of 10 fish species with different elements were provided, giving a reference for the people's healthy consumption. [ABSTRACT FROM AUTHOR]

As a new construction mode, the prefabricated building is the main direction of the transformation and upgrading process of the Chinese construction industry. In an earthquake area, prefabricated building damage is often reported; therefore, it is important to evaluate the failure risk of its structure under seismic effect. In this paper, combined with the vulnerability and building depreciation theory of prefabricated buildings, as well as a seismic hazard analysis of engineering sites, we propose a quantitative method for evaluating the structural failure risk of prefabricated buildings under seismic effect during their service life. In order to illustrate the proposed method, a residential prefabricated building as described in the previous study is used as a case study. The structural failure risk value and overall risk level of the case under seismic effect are calculated to verify the feasibility and effectiveness of the seismic risk evaluation method. The results demonstrate that the proposed method benefits and assists risk management in the decision making and disaster prevention and mitigation regarding prefabricated buildings. [ABSTRACT FROM AUTHOR]

(1) Background: Inventory management is a key point in the achievement of the virtuous cycle and sustainable development of the real estate industry. In response to the practical needs of city-based policies, this paper constructs a new research approach of "evolution dynamics—risk analysis—performance evaluation—policy design" of real estate inventory, and conducts a case study on the Yangtze River Delta. (2) Methods: This paper studies the change characteristics, trends and spatial patterns of real estate inventory changes in the Yangtze River Delta based on Geographic Information System software, and quantitatively evaluates the risk level and management performance of real estate inventory by introducing the Boston Consulting Group Matrix for corporate management and the Super- Data Envelopment Analysis Model for operations research, providing a basis for policy design. (3) Results: First, the "destocking" policy has gained results to some extent and diversified the inventory evolution, thus alleviating or curbing the negative trend in most cities. Second, the real estate inventory in the Yangtze River Delta is divided into high, low, potential and zero pressure zones by risk levels, and the proportion of cities with increased, decreased and unchanged risk levels is essentially the same. Third, the average real estate inventory management performance index has been steadily improving, but overall, it is still unsatisfactory, with cities in an effective state accounting for 40% and below for a long time. Fourth, real estate inventory and its management performance both show significant spatial effects, with cold and hot spot cities characterized by a "center-periphery" spatial pattern in geographical distribution, and the cities in the study area are classified into four types: super-efficiency, efficiency, inefficiency, and super-inefficiency. Fifth, the real estate inventory in most cities is continuing to grow positively, and a small number of cities have been in the high-risk zone for a long time or become new members of the high-risk zone, making the government and enterprises still faced up with great pressure and challenges in inventory management with the risk level further increased but management performance growing slowly. (4) Conclusions: The study area is divided into four types of policy areas, that is, red key area, yellow important area, green auxiliary area, and path-dependent area, and suggestions for optimization are made from the perspectives of risk control, performance improvement, benchmarking recommendation, and redundancy governance, providing a basis for the government's real estate inventory management policy design and the enterprise's high-quality development decision. [ABSTRACT FROM AUTHOR]

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The selection of evaluation units in geological hazard evaluation systems is crucial for the evaluation results. In an evaluation system, relevant geological evaluation factors are selected and the study area is divided into multiple regular or irregular independent units, such as grids, slopes, and basins. Each evaluation unit, which includes evaluation factor attributes and hazard point distribution data, is placed as an independent individual in a corresponding evaluation model for use in a calculation, and finally a risk index for the entire study area is obtained. In order to compare the influence of the selection of grid units or slope units—two units frequently used in geological hazard evaluation studies—on the accuracy of evaluation results, this paper takes Yuanyang County, Yunnan Province, China, as a case study area. The area was divided into 7851 slope units by the catchment basin method and 12,985,257 grid units by means of an optimal grid unit algorithm. Nine evaluation factors for geological hazards were selected, including elevation, slope, aspect, curvature, land-use type, distance from a fault, distance from a river, engineering geological rock group, and landform type. In order to ensure the objective comparison of evaluation results for geological hazard susceptibility with respect to grid units and slope units, the weighted information model combining the subjective weighting AHP (analytic hierarchy process) and the objective statistical ICM (information content model) were used to evaluate susceptibility with both units. Geological risk evaluation results for collapses and landslides under heavy rain (25–50 mm), rainstorm (50–100 mm), heavy rainstorm (150–250 mm), and extraordinary rainstorm (>250 mm) conditions were obtained. The results showed that the zoning results produced under the slope unit system were better than those produced under the grid unit system in terms of the distribution relationship between hazard points and hazard levels. In addition, ROC (receiver operating characteristic) curves were used to test the results of susceptibility and risk assessments. The AUC (area under the curve) values of the slope unit system were higher than those of the grid unit system. Finally, the evaluation results obtained with slope units were more reasonable and accurate. Compared with the results from an actual geological hazard susceptibility and risk survey, the evaluation results for collapse and landslide geological hazards under the slope unit system were highly consistent with the actual survey results. [ABSTRACT FROM AUTHOR]

Coal still occupies a key position in China's energy consumption structure, and ensuring safe production in coal mines is a key focus for ensuring energy security. Spontaneous combustion fires in coal mines are a serious threat to the sustainability of safe production in coal mines. In order to prevent coal mine fire risk scientifically and effectively and to assess the level of disaster risk effectively and rationally, a study was conducted on the risk of spontaneous combustion fires in underground coal mines. An evaluation cloud model of spontaneous combustion fire risk in coal mines integrating the interval gray number with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) was established. Seventeen representative risk evaluation indicators were selected, and a coal mine spontaneous combustion fire risk evaluation index system was constructed based on four aspects: personnel, machinery, environment, and management. The interval gray number theory was introduced to improve the classical DEMATEL analysis method, which fully expresses the expert empirical knowledge and solves the problem of ambiguity and randomness in the semantic expression of expert evaluation. The relative importance of each indicator was determined by analyzing the influence relationships between risk evaluation indicators through the improved DEMATEL. A cloud model capable of transforming quantitative descriptions and qualitative concepts was used for comprehensive evaluation of risk, and based on the results of DEMATEL analysis, a comprehensive evaluation cloud model of coal mine spontaneous combustion fire risk was formed. Finally, the validity and practicality of the model were verified by using a mine in Shenmu City, Shaanxi Province, China as an example. This study provides a powerful tool to prevent spontaneous combustion fires in coal mines and makes a positive contribution to the sustainable development of coal mine safety management. [ABSTRACT FROM AUTHOR]

In the aftermath of power disturbance in power grid with high-penetration renewable integration， the frequency response trajectory is more likely to present large frequency deviation and high rate of change， which leads to a significant effect on frequency protection action. In order to accurately predict the frequency response characteristics of the system under the strong random operation mode， this paper uses the fast kernel learning algorithm based on the pseudo-inverse of the kernel matrix and integrates the key feature quantities obtained from the common mode frequency analysis to propose a frequency risk assessment method for power grid with high-penetration renewable integration. This method uses a set of mutually independent sample data to directly construct the regular term function through the pseudo-inverse operation of the kernel matrix， avoiding the convergence problems caused by the iterative solution of general machine learning algorithms without reducing the generalization ability of the learning results. The analysis of an example in the IEEE 39-node system verifies the effectiveness of the proposed method.

• A framework combining knowledge graph and DFMEA is presented to identify to-be-evaluated objectives. • An approach of improved DS evidence fusion with comprehensive weights is proposed to fuse multiple evaluations. • We propose a risk classification approach based on risk trade-off VIKOR to determine component risk levels. • The proposed method is validated on the tunnel boring machine, and comparison and sensitivity analyses are performed. With the development of information technology in manufacturing, product lifecycle iteration is accelerating. Efficiently analyzing and identifying component risks is important to ensure the reliability of products. This paper proposes a new method that combines improved design failure mode and effect analysis (DFMEA) with the knowledge graph for component risk analysis of complex products. A risk evaluation knowledge graph is built for the risk identification and knowledge provision in the process of DFMEA. Graph analysis is used to identify to-be-evaluated objectives based on historical maintenance data, which helps designers avoid the heavy task of identification. The graph matrix of the historical evaluations is used to calculate the comprehensive weights. A Dempster-Shafer (DS) evidence fusion method based on comprehensive weight evaluation information is proposed for the effective fusion of evaluation evidence. As an alternative to the exact risk ranking, we offer a risk trade-off VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method for components classification that classifies components into different risk classes for different risk treatment measures. Finally, the proposed method is validated using a tunnel boring machine as an example, and comparison and sensitivity analyses are performed to demonstrate that the risk value shows a consistent and stable change with expert evaluation. [ABSTRACT FROM AUTHOR]

Microplastics (MPs) have attracted increasing attention worldwide owing to their widespread presence and potential risks to terrestrial and marine ecosystems. Estimating the pollution status and risk levels of MPs in coastal ecosystems is necessary; however, these are poorly understood in coastal megacities. Here, the abundance and characteristics of MPs in seawater, marine sediment, marine organisms, and beaches in the Shenzhen coastal ecosystems and land sources (river and sewage outfall) were simultaneously investigated, and the annual MPs load of rivers and MP-induced ecological risks were evaluated. The results showed that MPs pollution was prevalent in Shenzhen coastal ecosystems, with the average abundances of 2.40 ± 2.48 items/m3, 404.21 ± 431.48 items/kg, 1.66 ± 1.96 items/individual, and 1648.99 ± 1908.19 items/kg in seawater, marine sediment, marine organisms, and beach sands, respectively. The detected MPs were predominantly fibrous/granular, transparent/white, < 1 mm in size, and polyethylene terephthalate/polyethylene/polystyrene. The spatial distribution patterns of marine MPs are influenced mainly by anthropogenic activities and freshwater inflows (rivers and sewage outfalls). Pollution hotspots of MPs were identified in the Pearl River Estuary, which has a high population, gross domestic product, and river and wastewater discharge. Furthermore, the negative correlation between the abundance of MPs in seawater and salinity indicates that freshwater inflow carrying MPs to the sea is an important source of marine MPs pollution. It has been estimated that approximately 8320 billion MPs particles, weighing 274.55 tons, flow into the Shenzhen coast annually through river input. Based on the MPs polymer types and quantities, the ecological risk of MPs pollution in the Shenzhen coastal ecosystem is moderate and deserves further attention. These findings deepen the understanding of MPs pollution, sources, and ecological risks in the southern coastal region of China, and are helpful for employing effective management strategies to control marine MPs pollution. [Display omitted] • Extensive but medium-low levels of MPs contamination along the Shenzhen coast. • PET, PE, and PS were the main polymers on the Shenzhen coast. • Freshwater inflows carrying MPs contributed distribution patterns of marine MPs. • The ecological risk of MPs in Shenzhen coastal ecosystem is moderate. • 8.32 trillion MPs from the five rivers in Shenzhen discharged into the sea per year. [ABSTRACT FROM AUTHOR]

Given the significant threat posed by natural disasters like earthquakes to communities, it is important to assess the associated risks beforehand in order to minimize human and financial losses. This study focuses on identifying existing risks caused by an earthquake crisis by designing a network that evaluates its impact on urban infrastructure. The resulting system dynamics simulation model was used to analyze the frequency of different hazards and estimate the amount of relief goods needed in eight earthquake scenarios. The Fuzzy Cognitive Mapping (FCM) technique was also employed to determine the weight, impact, and effectiveness of each hazard, thus allowing for a ranking of the earthquake crisis scenarios based on their severity. FCM evaluation results showed that the most important and influential risks in an earthquake crisis were fire (with a weight value of 0.124), damage to first-class buildings (0.099), lack of hospital capacity (0.096), and damage to the power grid (0.095). Furthermore, it was observed that mitigating or eliminating the most significant risks would lead to a significant decrease in the demand for relief goods in each scenario. In fact, by addressing the top three earthquake risks, the demand for medicine reduced by 147%. Finally, the ranked results of the earthquake scenarios indicate that fire (with an impact of 12%), damage to the water network, and lack of capacity in relief centers (both with an impact of 6%) are the most and least effective risks, respectively. • Data-driven evaluation for impact of risks on urban infrastructure. • Synergy by integrating the quantitative/semi-quantitative method of FCM with the quantitative method of system dynamics. • Defining scenarios for investigating the effects of high-importance risks. • Considering risks in prioritizing more perilous scenarios. • Evaluating the effects of risks on the required relief goods. [ABSTRACT FROM AUTHOR]

Strengthening the construction of underground gas storage (UGS) is significant for securing national energy and achieving carbon neutrality. However, salt deposition occurs in the wellbore affecting the safety of UGS seriously. How to predict the salt-deposition risk in UGS wells accurately and quickly is still challenging. This work aims at establishing a fast and convenient framework of wellbore salt-deposition risk prediction in UGS wells. Firstly, the influence of the production parameters on wellbore salt-deposition is analyzed by an in-house simulator. Further, an intelligent diagnostic model of salt-deposition risk is proposed based on the machine learning algorithm. The effect of salt deposition on reservoir is analyzed by reservoir salt-deposition simulation model. Finally, downhole video survey is used to validate the modeling accuracy of diagnostic model. Results indicate that the contribution ratio of daily gas production and water-gas-ratio to wellbore salt deposition is 3:2. After the salt deposition, the reduction in water saturation and the permeability of near wellbore areas are 77.14 % and 98.91 %. The modeling accuracy of diagnostic model is 97 %, which is applied in the Y UGS successfully. The proposed framework can predict the salt-deposition risk of UGS wells in real time to help engineers control salt clogging issues. • A novel framework of salt-deposition risk in UGS wells is proposed. • The influence of the dominated factors on wellbore salt-deposition is analyzed quantitatively. • An intelligent diagnostic model is proposed based on machine learning algorithm. • The effect of salt deposition on reservoir is determined. • More aggressive treatment measures of salt deposition are established. [ABSTRACT FROM AUTHOR]

Potentially toxic element (PTE) pollution from mining activities has raised global concerns because of its significant toxicity, bioaccumulation, and persistence. Therefore, the aim of the study was to establish a solid scientific foundation for precise pollution control, by accurately quantifying PTE pollution sources entering farmlands and assessing their potential ecological risk (PER). Typical PTEs (Cd, Ni, Pb, Cu, Zn, Cr, As, and Hg) were detected in farmlands surrounding a large-scale Pb-Zn mining area. Among the topsoil samples, 89.09 % (Cd), 62.72 % (Pb), 49.09 % (Zn), 45.45 % (Hg), 24.55 % (As), and 0.91 % (Cu) of the samples exceeded their corresponding risk screening values. Source apportioning integrated three approaches: multivariate statistical analysis (MSA), PTE ratio correlation analysis, and positive matrix factorization (PMF). The results showed that 76.7 % of Ni, 76.5 % of Cr, and 54.6 % of Cu were derived from natural sources; 87.6 % of Hg was derived from industrial activities; 89.2 % of Cd, 82 % of Zn, and 77.1 % of Pb originated from solid wastes; and agricultural activities accounted for 75.1 % of As and 23 % of Cu. For further assessment, the PER model was used to quantify the risks of different sources, ranking them from highest to lowest as follows: solid wastes > industrial activities > natural sources > agricultural activities. Therefore, more measures must be taken to control solid waste pollution. Our findings can contribute to pollution source control, targeted soil remediation, and the establishment of effective environmental protection policies. These integrated methods can guide the source apportionment and risk assessment of PTEs. • MSA, PTE ratio correlation, PMF, and PER were integrated for precise source apportionment and risk assessment. • PTE ratio correlation can be used for rapid source identification. • Solid waste is the greatest pollution source and poses the highest PER in Pb-Zn mining areas. [ABSTRACT FROM AUTHOR]

• The risk of pedestrian-vehicle conflicts in the channelized right-turn lane (CRTL) is studied. • A multi-faceted compendium of pedestrian-vehicle conflict risk factors at CRTLs. • The study makes an innovative combination of multiple indicator weight calculation methods. • A evaluation method based on hierarchical-gray entropy-cloud model is proposed. • A visual demonstration of the level of pedestrian-vehicle conflict risk at CRTLs. Channelized right-turn lanes (CRTLs) in urban areas have been effective in improving the efficiency of right-turning vehicles but have also presented negative impacts on pedestrian movement. Pedestrians experience confusion regarding the allocation of road space when crossing crosswalks within these areas, leading to frequent conflicts between pedestrians and motor vehicles. In this paper, considering the characteristics of pedestrian-vehicle conflicts at channelized right-turn lanes as well as the ambiguity and uncertainty of the causes, a comprehensive assignment combined with a cloud model is proposed as a risk evaluation model for pedestrian-vehicle conflicts. The study established a risk indicator system based on three aspects of the transportation system: pedestrians, motor vehicles, and the road environment. Combining the analytic hierarchy process (AHP), grey relational analysis (GRA), and entropy weighting method (EWM) to get the weights of indicator combinations, and then using the cloud model to realize quantitative and qualitative language transformation to complete the risk evaluation. This study employs specific road segments in Qingdao as a validation case for model analysis. The results indicate that the model's evaluation outcomes exhibited a significant level of agreement with the findings from field investigations during both peak and off-peak periods. It is demonstrated that the model has good performance for the safety assessment of pedestrian-vehicle conflicts at CRTL, and it also reflects the ability of the model to assess fuzzy randomness problems. It provides participation value for urban pedestrian-vehicle safety problems as well as applications in other fields. [ABSTRACT FROM AUTHOR]

To explore the characteristics and evaluate the risk of heavy metals in groundwater at a typical smelter-contaminated site, this study focuses on a representative a historical arsenic smelting plant in Southwest China, where the primary historical products were metallic arsenic (∼1000 tons/year) and arsenic trioxide (∼2000 ton/year). The results demonstrated As and Pb as the main pollutants in soil, and As and Cd as main pollutants in groundwater through soil profiling and quarterly groundwater analysis. The maximum As and Pb in the surface soil were 76800 and 2290 mg/kg, respectively, with As vertically infiltrating the deep gravel–sand layer (18–20 m). The groundwater pollution distribution progressively increased along flow direction, influenced by seasonal surface runoff and infiltration fluctuations. The groundwater pollutant concentrations during the dry season notably surpassed those during the wet season, with maximum As and Cd concentrations of 111.64 mg/L and 19.85 μg/L during the dry season, respectively. Furthermore, the analytic hierarchy process (AHP) was applied to evaluate the comprehensive risk of contaminated-site across pollution source load, regional groundwater intrinsic vulnerability, and evaluation of nearby sensitive receptors. The results revealed that the carcinogenic risk of lead in surface soil was moderate to high, while arsenic posed a high carcinogenic risk, contributing to an overall carcinogenic risk proportion of 89.6% in surface soil. Exposure through groundwater intake was identified as the primary pathway, with carcinogenic and noncarcinogenic risks exceeding those through skin contact. The final weights result demonstrated that the principal risk factors are the intrinsic arsenic load and protective target characteristics of regional groundwater at this site. This study provides a reference for comprehensive assessments of similarly contaminated industrial and smelting sites. [Display omitted] • The main soil–groundwater pollutants at the smelting site are As, Pb, and Cd. • The vertical distribution of As and Pb in soil shows surface aggregation. • The flow field and seasonal rainfall affected As distribution in groundwater. • As Loads and protection goals determined the comprehensive risk of groundwater. [ABSTRACT FROM AUTHOR]

Amidst the global antimicrobial resistance (AMR) crisis, antibiotic resistance has permeated even the most remote environments. To understand the dissemination and evolution of AMR in minimally impacted ecosystems, the resistome and mobilome of wetlands across the Qinghai-Tibetan Plateau and its marginal regions were scrutinized using metagenomic sequencing techniques. The composition of wetland microbiomes exhibits significant variability, with dominant phyla including Proteobacteria, Actinobacteria, Bacteroidetes, and Verrucomicrobia. Notably, a substantial abundance of Antibiotic Resistance Genes (ARGs) and Mobile Genetic Elements (MGEs) was detected, encompassing 17 ARG types, 132 ARG subtypes, and 5 types of MGEs (Insertion Sequences, Insertions Sequences, Genomic Islands, Transposons, and Integrative Conjugative Elements). No significant variance was observed in the prevalence of resistome and mobilome across different wetland types (i.e., the Yellow River, other rivers, lakes, and marshes) (R =-0.5882, P =0.607). The co-occurrence of 74 ARG subtypes and 22 MGEs was identified, underscoring the pivotal role of MGEs in shaping ARG pools within the Qinghai-Tibetan Plateau wetlands. Metagenomic binning and analysis of assembled genomes (MAGs) revealed that 93 out of 206 MAGs harbored ARGs (45.15 %). Predominantly, Burkholderiales, Pseudomonadales, and Enterobacterales were identified as the primary hosts of these ARGs, many of which represent novel species. Notably, a substantial proportion of ARG-carrying MAGs also contained MGEs, reaffirming the significance of MGEs in AMR dissemination. Furthermore, utilizing the arg_ranker framework for risk assessment unveiled severe contamination of high-risk ARGs across most plateau wetlands. Moreover, some prevalent human pathogens were identified as potential hosts for these high-risk ARGs, posing substantial transmission risks. This study aims to investigate the prevalence of resistome and mobilome in wetlands, along with evaluating the risk posed by high-risk ARGs. Such insights are crucial for informing environmental protection strategies and facilitating the management of water resources on the Qinghai-Tibetan Plateau. [Display omitted] • Consistent resistome and mobilome prevalence observed in various wetlands types. • Human activities may drive pathogens emergence and ARGs distribution shifts. • High-risk ARGs pollution predominated in most Qinghai-Tibetan Plateau wetlands. [ABSTRACT FROM AUTHOR]

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.

The research on fault diagnosis methods based on generative adversarial networks has achieved fruitful results, but most of the research objects are rolling bearings or gears, and the model test data are almost all derived from laboratory bench test data. In the industrial Internet environment, equipment-fault diagnosis is faced with the characteristics of large amounts of data, unbalanced data samples, and inconsistent data file lengths. Moreover, there are few research results on the fault diagnosis of rotor systems composed of shafts, impellers or blades, couplings, and tilting pad bearings. There are still shortcomings in the operational risk evaluation of rotor systems. In order to ensure the reliability and safety of rotor systems, an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty (IACWGAN-GP) model is constructed, a fault diagnosis method based on IACWGAN-GP for tilting pad bearings is proposed, and an intelligent fault diagnosis system platform for equipment in an industrial Internet environment is built. The verification results of engineering case data show that the fault diagnosis model based on IACWGAN-GP can adapt to any length of sequential data files, and the automatic identification accuracy of early faults in tilting pad bearings reaches 98.7%.

The high reliability demand of the machining center emphasizes the accuracy of the fault risk evaluation. In the traditional fault risk evaluation research of the machining center, the influence of fault mode is mostly based on subjective recommendation or does not consider the propagation and diffusion of fault, which makes the risk evaluation results different from the real situation. Therefore, this paper presents a framework to evaluate the fault risk for machining center components. A certain type of machining center is considered as a case study. The fault mode frequency ratio of components is calculated by fault mode analysis. The fault rate calculation is conducted based on the Johnson method. Considering that different fault modes have different influences on fault propagation breadth and depth, the hypergraph theory is used to build a hypernetwork model. The propagation and diffusion influence degree are defined to describe the propagation and diffusion process of faults. Then, the comprehensive influence degree of fault mode is calculated. The risk evaluation is realized by considering the component fault rate, fault mode frequency ratio, and the comprehensive influence degree of fault mode. The method proposed in this paper can provide a reference for the formulation of risk strategies for the machining center.

We investigated the occurrence of organochlorine pollutants (OCs) in the muscle of brown trout and evaluated their potential modulation of parasite infection. The toxicological risk for consumer health was assessed, too. Trout were collected from the Sila National Park (Calabria region, South of Italy). The highest concentrations emerged for the sum of the 6 non-dioxin-like (ndl) indicator polychlorinated biphenyls (Σ6ndl-PCBs), followed by the 1,1,1-trichloro-2,2-di(4-chlorophenyl)-ethane (DDT), dioxin-like PCBs, hexachlorobenzene (HCB), and dieldrin. Measured on lipid weight (LW), the mean value of Σ6ndl-PCBs amounted to 201.9 ng g−1, that of ΣDDTs (the sum of DDT-related compounds) to 100.2 ng g−1, with the major contribution of the DDT-metabolite p,p’-DDE which was detected in all sample units (97.6 ng g−1 on average). Among dioxin-like congeners, PCB 118 showed the highest mean concentration (21.96 ng g−1 LW) and was detected in all sample units. Regression analysis of intestinal parasites on OC concentration was performed, controlling for two potential confounding factors, namely sex and sexual stage. The results evidenced the existence of interactions between the dual stressors in the host-parasite system in the wild. A negative and statistically significant correlation was estimated, suggesting that OCs may decrease parasite infection degree. Regarding the toxicological risk evaluation, OC concentrations were consistently below the current European Maximum Residue Limits.