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Through on-site investigation, the number, locations, and causes of death in the Luding earthquake were obtained. Based on an analysis of the causes of death, 44.1% of the deaths were caused by building collapse, and 54.8% were caused by earthquake-induced geological effects. Therefore, it is particularly important to conduct research on targeted methods for assessing casualties for emergency rescue work after earthquakes. First, this article analyzes the number of deaths as a result of different earthquake intensities and building types and, combined with data from 81 on-site survey points (lethality levels and quantity and ratios of different types of buildings), obtains the regional lethality levels for different intensities and administrative units in the study area (intensity VIII and IX areas). Second, based on on-site investigation results of points at which secondary geological effects (landslides and rolling stones) occurred, and by combining parameters, such as the slope, slope direction, slope curvature, and distance from the fault, this study obtains the risk level of earthquake-induced landslides. On this basis, a coupling analysis is conducted on the seismic landslide risk level and lethality level. The results show a positive correlation between the risk level and the lethality level; the higher the risk level, the higher the corresponding lethality level is, but this relationship is not absolute. The Pearson’s correlation coefficient for the fitting results is 0.70828, the coefficient of determination R2 is 0.50102, and the mean squared (MS) is 0.029. The results of the sensitivity analysis of the two levels show values that are above the average level, with the prediction accuracy of the landslide risk level and lethality level being 0.708 and 0.609, respectively. The fitting results show average values at a moderate level; by constructing a fitting relationship between the landslide risk level and lethality level, the relationship can provide ideas and feasibility for constructing a lethality matrix for secondary geological disasters during earthquakes. Furthermore, it is possible to quickly evaluate the number of deaths due to secondary geological effects of the earthquake.

Healthcare workers handling antineoplastic drugs (ADs) in preparation units run the risk of occupational exposure to contaminated surfaces and associated mutagenic, teratogenic, and oncogenic effects of those drugs. To minimise this risk, automated compounding systems, mainly robots, have been replacing manual preparation of intravenous drugs for the last 20 years now, and their number is on the rise. To evaluate contamination risk and the quality of the working environment for healthcare workers preparing ADs, we applied the Failure Mode Effects and Criticality Analysis (FMECA) method to compare the acceptable risk level (ARL), based on the risk priority number (RPN) calculated from five identified failure modes, with the measured risk level (MRL). The model has shown higher risk of exposure with powdered ADs and containers not protected by external plastic shrink film, but we found no clear difference in contamination risk between manual and automated preparation. This approach could be useful to assess and prevent the risk of occupational exposure for healthcare workers coming from residual cytotoxic contamination both for current handling procedures and the newly designed ones. At the same time, contamination monitoring data can be used to keep track of the quality of working conditions by comparing the observed risk profiles with the proposed ARL. Our study has shown that automated preparation may have an upper hand in terms of safety but still leaves room for improvement, at least in our four hospitals.

Background The study aimed to characterize the prevalence of alcohol consumption and further investigate the relationship between alcohol consumption and type 2 diabetes mellitus (T2DM). Methods We studied 39,259 participants aged 18 to 79 years of the Henan Rural Cohort study. The associations between alcohol consumption and T2DM were examined using the logistic regression models and restricted cubic spline. Results For men, alcohol abstinence was associated with an increased risk of T2DM (1.491(1.265, 1.758)), whereas current drinkers were not associated with T2DM (1.03(0.91, 1.15)). Further analysis of alcohol drinkers revealed that only high-risk drinkers of WHO drinking risk levels increased the risk of T2DM (1.289(1.061,1.566)) compared to never drinkers. The risk of T2DM increased as the age of starting to consume alcohol decreased and as the number of years of consuming alcohol and the alcohol intake increased only in men. We further found that the risk of T2DM decreased as the number of years of abstinence increases and no association between alcohol abstinence and T2DM was found after more than 10 years of abstinence among men. Conclusions Our results suggested that reducing the amount of alcohol consumed and adhering to abstinence from alcohol consumption are beneficial in reducing the risk of T2DM. Trial registration The Henan Rural Cohort Study has been registered at Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). Date of registration: 2015-07-06. http://www.chictr.org.cn/showproj.aspx?proj=11375

Background With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and intervention program, which screens stroke and its risk factors and conducts high-risk population interventions for people aged above 40 years old all over China. In this program, stroke risk factors include hypertension, diabetes, dyslipidemia, smoking, lack of exercise, apparently overweight and family history of stroke. People with more than two risk factors or history of stroke or transient ischemic attack (TIA) are considered as high-risk. However, it is impossible for this criterion to classify stroke risk levels for people with unknown values in fields of risk factors. The missing of stroke risk levels results in reduced efficiency of stroke interventions and inaccuracies in statistical results at the national level. In this paper, we use 2017 national stroke screening data to develop stroke risk classification models based on machine learning algorithms to improve the classification efficiency. Method Firstly, we construct training set and test sets and process the imbalance training set based on oversampling and undersampling method. Then, we develop logistic regression model, Naïve Bayesian model, Bayesian network model, decision tree model, neural network model, random forest model, bagged decision tree model, voting model and boosting model with decision trees to classify stroke risk levels. Result The recall of the boosting model with decision trees is the highest (99.94%), and the precision of the model based on the random forest is highest (97.33%). Using the random forest model (recall: 98.44%), the recall will be increased by about 2.8% compared with the method currently used, and several thousands more people with high risk of stroke can be identified each year. Conclusion Models developed in this paper can improve the current screening method in the way that it can avoid the impact of unknown values, and avoid unnecessary rescreening and intervention expenditures. The national stroke screening program can choose classification models according to the practice need.

Extreme weather events pose significant risks to power systems, necessitating effective risk forewarning and management strategies. A few existing researches have concerned the correlation of the extreme weather in different regions of power system, and traditional operation risk assessment methods gradually cannot satisfy real-time requirements. This motivates us to present an intelligent risk forewarning method for the operation of power systems considering multi-region extreme weather correlation. Firstly, a novel multi-region extreme weather correlation model based on vine copula is developed. Then, a risk level classification method for power system operations is introduced. Further, an intelligent risk forewarning model for power system operations is proposed. This model effectively integrates the multi-region extreme weather correlation and the risk level classification of the system. By employing the summation wavelet extreme learning machine, real-time monitoring and risk forewarning of the system’s operational status are achieved. Simulation results show that the proposed method can rapidly identify potential risks and provides timely risk forewarning information, helping enhance the resilience of power system operations.

This study investigated the effect of moderate risk level (8 µg/kg) AFB1 in diet supplemented with or without adsorbents on lactation performance, serum parameters, milk AFM1 content of healthy lactating cows and the AFM1 residue exposure risk in different human age groups. Forty late healthy lactating Holstein cows (270 ± 22 d in milk; daily milk yield 21 ± 3.1 kg/d) were randomly assigned to four treatments: control diet without AFB1 and adsorbents (CON), CON with 8 μg/kg AFB1 (dry matter basis, AF), AF + 15 g/d adsorbent 1 (AD1), AF + 15 g/d adsorbent 2 (AD2). The experiment lasted for 19 days, including an AFB1-challenge phase (day 1 to 14) and an AFB1-withdraw phase (day 15 to 19). Results showed that both AFB1 and adsorbents treatments had no significant effects on the DMI, milk yield, 3.5% FCM yield, milk components and serum parameters. Compared with the AF, AD1 and AD2 had significantly lower milk AFM1 concentrations (93 ng/L vs. 46 ng/L vs. 51 ng/L) and transfer rates of dietary AFB1 into milk AFM1 (1.16% vs. 0.57% vs. 0.63%) (p < 0.05). Children aged 2–4 years old had the highest exposure risk to AFM1 in milk in AF, with an EDI of 1.02 ng/kg bw/day and a HI of 5.11 (HI > 1 indicates a potential risk for liver cancer). Both AD1 and AD2 had obviously reductions in EDI and HI for all population groups, whereas, the EDI (≥0.25 ng/kg bw/day) and HI (≥1.23) of children aged 2–11 years old were still higher than the suggested tolerable daily intake (TDI) of 0.20 ng/kg bw/day and 1.00 (HI). In conclusion, moderate risk level AFB1 in the diet of healthy lactating cows could cause a public health hazard and adding adsorbents in the dairy diet is an effective measure to remit AFM1 residue in milk and its exposure risk for humans.

The supervision of security risk level of carbofuran pesticide residues can guarantee the food quality and security of residents effectively. In order to predict the potential key risk vegetables and regions, this paper constructs a security risk assessment model, combined with the k-means++ algorithm, to establish the risk security level. Then the evaluation index value of the security risk model is predicted to determine the security risk level based on the deep learning model. The model consists of a convolutional neural network (CNN) and a long short-term memory network (LSTM) optimized by an arithmetic optimization algorithm (AOA), namely, CNN-AOA-LSTM. In this paper, a comparative experiment is conducted on a small sample data set of independently constructed security risk assessment indicators. Experimental results show that the accuracy of the CNN-AOA-LSTM prediction model based on attention mechanism is 6.12% to 18.99% higher than several commonly used deep neural network models (gated recurrent unit, LSTM, and recurrent neural networks). The prediction model proposed in this paper provides scientific reference to establish the priority order of supervision, and provides forward-looking supervision for the government.

Grain processing products constitute an essential component of the human diet and are among the main sources of heavy metal intake. Therefore, a systematic assessment of risk factors and early-warning systems are vital to control heavy metal hazards in grain processing products. In this study, we established a risk assessment model to systematically analyze heavy metal hazards and combined the model with the K-means++ algorithm to perform risk level classification. We then employed deep learning models to conduct a multi-step prediction of risk levels, providing an early warning of food safety risks. By introducing a voting-ensemble technique, the accuracy of the prediction model was improved. The results indicated that the proposed model was superior to other models, exhibiting the overall accuracy of 90.47% in the 7-day prediction and thus satisfying the basic requirement of the food supervision department. This study provides a novel early-warning model for the systematic assessment of the risk level and further allows the development of targeted regulatory strategies to improve supervision efficiency.

The coast of Ghana faces a number of challenges, including coastal erosion and flooding. These problems degrade the coast, jeopardize ecosystems, threaten human lives and well-being, limit economic possibilities, and make people more predisposed to natural disasters (World Bank, 2020 ). The development of a multi-sector investment action plan is a prerequisite for sustainably developing and strengthening the resilience of the Ghanaian coastal zone. This plan seeks to determine an integrated and sustainable system for coastal governance, and to simulate solutions that enhance environmental, and socio-economic benefits. The plan will lead to benefits such as preservation of biodiversity and ecosystems, increased economic opportunity and productivity, and improved and protected livelihoods. Accounting for the vulnerability of the Ghanaian coast to coastal erosion and flooding, a risk level assessment is carried out for the identification and prioritization of areas at significant risk of erosion and flooding where management strategies should be implemented. The assessment consists of the examination of the Ghanaian coastline and the calculation of a Coastal Index from hazard intensities and exposure of the population, land use, economic activities, ecosystems, transport networks, utilities, and services. Disaster risk management and adaptation measures are identified to address management aims for the various coastal archetypes that comprise Ghana’s coastline. The management aims consist of maintaining freshwater sources, ensuring good agricultural and aquaculture practices, promoting local tourism, maintaining biodiversity, and protecting the population, infrastructure, and key assets. This paper summarizes the benefits at the macro-scale of investing in coastal risk reduction measures and presents the co-benefits associated with prioritizing the implementation of ecosystem-based management solutions, such as ecosystem conservation or restoration, dune, and beach management. An action plan is then developed for the implementation of steps and long-term options for a set of preferred pathways. The action plan consists of priority investment measures defined at the conceptual level, with associated costs and timeframes over 10 years.

Scientific risk assessment of dammed lakes is vitally important for emergency response planning. In this study, based on the evolution process of the disaster chain, the logic topology structure of dammed lake risk was developed. Then, a quantitative risk assessment model of dammed lake using Bayesian network is developed, which includes three modules of dammed lake hazard evaluation, outburst flood routing simulation, and loss assessment. In the model, the network nodes of each module were quantified using statistical data, empirical model, logical inference, and Monte Carlo method. The failure probability of a dammed lake, and the losses of life and property were calculated. This can be multiplied to assess the risk a dammed lake imposes after the uniformization of each loss type. Based on the socio-economic development and longevity statistics of dammed lakes, a risk-level classification method for dammed lakes is proposed. The Baige dammed lake, which emerged in China in 2018, was chosen as a case study and a risk assessment was conducted. The obtained results showed that the comprehensive risk index of Baige dammed lake is 0.7339 under the condition without manual intervention, identifying it as the extra-high level according to the classification. These results are in accordance with the actual condition, which corroborates the reasonability of the proposed model. The model can quickly and quantitatively evaluate the overall risk of a dammed lake and provide a reference for decision-making in a rapid emergency response scenario.