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In order to strengthen the safety management of coal slurry preparation systems, a dynamic risk assessment method was established by using the bow-tie (BT) model and the Structure-variable Dynamic Bayesian Network (SVDBN). First, the BT model was transformed into a static Bayesian network (BN) model of the failure of a coal slurry preparation system by using the bow-tie model and the structural similarity of the Bayesian cognitive science, based on the SVDBN recursive reasoning algorithm. The risk factors of the coal slurry preparation system were deduced using the Python language in two ways, and at the same time, preventive measures were put forward according to the weak links. In order to verify the accuracy and feasibility of this method, the simulation results were compared with those obtained using GeNIe software. The reasoning results of the two methods were very similar. Without considering maintenance factors, the failure rate of the coal slurry preparation system gradually increases with increasing time. When considering maintenance factors, the reliability of the coal slurry preparation system will gradually be maintained at a certain threshold, and the maintenance factors will increase the reliability of the system. The proposed method can provide a theoretical basis for the risk assessment and safety management of coal slurry preparation systems. [ABSTRACT FROM AUTHOR]

Objective: We conducted a systematic evaluation of the therapeutic efficacy and complications of tolterodine and α-adrenergic receptor blockers in alleviating ureteral stent-related symptoms. Methods: Until August 2023, we conducted a comprehensive literature search on PubMed, Embase, Web of Science, and Cochrane Library to identify randomized controlled trials evaluating the efficacy and complications of tolterodine and α-adrenergic receptor blockers in treating ureteral stent-related symptoms. Two reviewers independently screened studies and extracted data. The scores from various domains of the Ureteral Stent Symptom Questionnaire (USSQ) were summarized and compared, and statistical analysis was performed using RevMan 5.4.0 software. Results: A total of 8 studies met the inclusion criteria for our analysis. These studies were conducted at different centers. All studies were randomized controlled trials, involving a total of 487 patients, with 244 patients receiving α-adrenergic receptor blockers and 243 patients receiving tolterodine. The results showed that tolterodine demonstrated significantly better improvement in body pain (MD, 1.56; 95% CI [0.46, 2.66]; p = 0.005) (MD, 0.46; 95% CI [0.12, 0.80]; p = 0.008) (MD, 3.21; 95% CI [1.89, 4.52]; p = 0.00001) among patients after ureteral stent placement compared to α-adrenergic receptor blockers at different time points. Additionally, at 4 weeks, tolterodine showed superior improvement in general health (MD, 0.15; 95% CI [0.03, 0.27]; p = 0.01) and urinary symptoms (MD, 1.62; 95% CI [0.59, 2.66]; p = 0.002) compared to α-adrenergic receptor blockers, while at 6 weeks, tolterodine showed better improvement in work performance (MD, -1.60; 95% CI [-2.73, -0.48]; p = 0.005) compared to α-adrenergic receptor blockers. Additionally, the incidence of dry mouth (RR, 4.21; 95% CI [1.38, 12.87]; p = 0.01) is higher with the use of tolterodine compared to α-adrenergic receptor blockers. However, there were no significant statistical differences between the two drugs in other outcomes. Conclusion: This meta-analysis suggests that tolterodine is superior to α-adrenergic receptor blockers in improving physical pain symptoms after ureteral stent placement, while α-adrenergic receptor blockers are more effective than tolterodine in enhancing work performance. Additionally, the incidence of dry mouth is higher with the use of tolterodine compared to α-adrenergic receptor blockers. However, higher-quality randomized controlled trials are needed to further investigate this issue. [ABSTRACT FROM AUTHOR]

The short-term prediction of single well production can provide direct data support for timely guiding the optimization and adjustment of oil well production parameters and studying and judging oil well production conditions. In view of the coupling effect of complex factors on the daily output of a single well, a short-term prediction method based on a multi-agent hybrid model is proposed, and a short-term prediction process of single well output is constructed. First, CEEMDAN method is used to decompose and reconstruct the original data set, and the sliding window method is used to compose the data set with the obtained components. Features of components by decomposition are described as feature vectors based on values of fuzzy entropy and autocorrelation coefficient, through which those components are divided into two groups using cluster algorithm for prediction with two sub models. Optimized online sequential extreme learning machine and the deep learning model based on encoder-decoder structure using self-attention are developed as sub models to predict the grouped data, and the final predicted production comes from the sum of prediction values by sub models. The validity of this method for short-term production prediction of single well daily oil production is verified. The statistical value of data deviation and statistical test methods are introduced as the basis for comparative evaluation, and comparative models are used as the reference model to evaluate the prediction effect of the above multi-agent hybrid model. Results indicated that the proposed hybrid model has performed better with MAE value of 0.0935, 0.0694 and 0.0593 in three cases, respectively. By comparison, the short-term prediction method of single well production based on multi-agent hybrid model has considerably improved the statistical value of prediction deviation of selected oil well data in different periods. Through statistical test, the multi-agent hybrid model is superior to the comparative models. Therefore, the short-term prediction method of single well production based on a multi-agent hybrid model can effectively optimize oilfield production parameters and study and judge oil well production conditions. [ABSTRACT FROM AUTHOR]