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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.

Global warming causes a temperature rise and alteration of other meteorological variables that directly or indirectly affect human comfort. The wet bulb globe temperature (WBGT) incorporates the effects of multiple meteorological variables to provide a reliable measure of human thermal stress. Despite the large significance of WBGT on public health, studies related to characterization and trends assessment of WBGT are limited in the tropical humid region like Peninsular Malaysia due to the unavailability of all meteorological variables required for such analysis. This study employed reanalysis meteorological data of ERA5 to assess the characteristics and changes in hourly, daily, monthly, seasonal and annual outdoor WBGT over peninsular Malaysia for the period 1959–2021 using the Liljegren method. The WBGT values were classified into five categories to assess the human thermal stress levels defined by the United States Department of the Army (USDA). The mean daily WBGT in PM varies from 21.5 °C in the central south elevated region to 30.5 °C in the western coastal region. It always reaches a heat-related illness risk level (31.20 °C) in the afternoon during monsoon and extreme stress conditions during inter-monsoonal periods. The trend analysis revealed an increase in WBGT for all the time scales. The higher increase in the mean and maximum WBGT was estimated in the coastal and south regions, nearly by 0.10 to 0.25 °C/decade. The increase in mean nighttime WBGT was 0.24 °C/decade, while in mean daytime WBGT was 0.11 °C/decade. The increase in WBGT caused a gradual expansion of areas experiencing daily WBGT exceeding a high-risk level for 5 h (11 AM to 3 PM). The information and maps generated in this study can be used for mitigation planning of heat-related stress risk in PM, where temperature extremes have grown rapidly in recent 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.

The rockburst hazard induced by the extreme release of the stress concentrated in rock mass in deep underground mines poses a significant threat to the safety and economy of the mining projects. Therefore, properly managing this hazard is critical for ensuring rock engineering projects’ sustainability. This study proposes comprehensible and practical classifiers for rockburst risk level appraisal by hybridizing K -means clustering with gene expression programming, GEP, logistic regression, LR, and classification and regression tree, CART (i.e., K -mean-GEP-LR and K -means-CART classifiers). A database containing 246 rockburst events with four risk levels of none, light, moderate, and severe was compiled from previous practices. Preliminary statistical analyses were conducted to detect the extreme outliers and determine the critical rockburst indicators. The K -means clustering analysis was performed to identify the main clusters within the database and relabel the rockburst events. The GEP algorithm was then utilized to develop binary models for predicting the occurrence of each class. Then, the likelihood of each class occurrence was determined using LR. Furthermore, the K -means clustering was combined with the CART algorithm to provide another visual tree structure model. The classifiers’ performance evaluation showed 96% and 95% accuracy values in the training and testing stages, respectively, for the K -means-GEP-LR model, while the accuracy values of 98.8% and 93.0% were obtained for the foregoing stages for the K -means-CART classifier. The results showed the robustness and high classification capability of both models. MatLab codes were also provided for the K -means-GEP-LR model, which assists other researchers/engineers in implementing the model in practice.

Conventional health risk assessments do not adequately reflect short-term exposure characteristics following chemical accidents. We aimed to evaluate the efficacy of existing assessment methods and propose a more suitable risk assessment approach for short-term exposure to hazardous chemicals. We analyzed foundational studies used to derive reference concentration (RfC), reference dose (RfD), and minimal risk level (MRL) values and applied these health guidance values (HGVs) to a hypothetical chemical accident scenario. An analysis of the studies underlying each HGV revealed that, except for the RfC for formaldehyde and the RfD for toluene, all values were derived under research conditions comparable to their respective exposure durations. Given the differing toxicity mechanisms between acute and chronic exposures, MRLs that were aligned with the corresponding exposure durations supported more appropriate risk management decisions. The health risk assessment results showed that RfC/RfD-based hazard quotients (HQs) were consistently higher than MRL-based HQs across all age groups and both substances, indicating that RfC/RfD values tend to yield more conservative risk estimates. For formaldehyde, the use of RfC instead of MRL resulted in an additional 208 tiles (2.08 km2) being classified as areas of potential concern (HQ > 1) relative to the MRL-based evaluation. These findings highlighted that the selection of HGVs can significantly influence the spatial extent of areas of potential concern, potentially altering health risk determinations for large population groups. This study provides a scientific basis for improving exposure and risk assessment frameworks under short-term exposure conditions. It also serves as valuable foundational data for developing effective and rational risk management strategies during actual chemical accidents. To the best of our knowledge, this is the first study to apply MRLs to a short-term chemical accident scenario and directly compare them with traditional reference values.

The risk management of cascade reservoir systems (CRSs) is a major public challenge, and the establishment of risk criteria is critical to solving this issue. In this paper, an approach is presented to establish risk criteria of for CRSs in China based on statistical data (reservoir dam break accidents; fatalities due to reservoir dam breaks; the construction date of defective reservoirs; and the current situation of CRSs), F–N curves, F–E curves, F–I curves, and the as low as reasonably practicable (ALARP) principle. This paper has some innovations in the following aspects. First, the characteristics of CRSs are revealed for the first time. Second, risk criteria for CRSs are suggested. Third, the engineering safety level, risk exposure level, and risk level of CRSs in China are determined. The research results of this paper provide abundant information for the operation management, maintenance and reinforcement of CRSs in China.

Forest fires in various parts of the world cause irreversible environmental and economic losses due to the increase in global warming. Determining the risk levels of forest fires is very important in terms of minimizing the negative effects of this fire disaster. In this context, the aim of this study is to mathematically model forest fires from January 2020 to January 2021 in Antalya/Türkiye region with fuzzy logic approach. The model has been created with the climatic and topographic characteristics of the region and the fuzzy logic approach. The results obtained from the fuzzy logic approach have been compared with the real data, and it has been shown that the results are 84% compatible in this model. As a result, with the risk assessments to be made thanks to this model, the most effective intervention will be made in a current forest fire. What we mean by the most effective intervention here is to determine which vehicle group will be used according to the degree of risk of the fire. As a result of all these, a serious contribution will be made to overcome the struggle against a current forest fire disaster and to ensure the sustainability of nature.

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.