Analysis on the application of FMEA in 'instrument and equipment surface cleaning and disinfection' in hospitals based on standardization and cleaning and disinfection information system management.

Purpose: To analyze the application of 'instrument and equipment surface cleaning and disinfection' in hospitals based on standardization and the management of cleaning and disinfection information systems. Methods: Employees and all cleaning and disinfected instruments and equipment from 56 inpatient departments in our hospital were selected as the subjects of observation. The period before the intervention (January 2023) was designated as the control group, while the period after the intervention (July 2023) was designated as the study group. In the control group, the instruments and equipment under routine management were disinfected. The research team applied the Failure Mode and Effects Analysis (FMEA) method to clean and disinfect the surfaces of instruments and equipment on the basis of standardization and cleaning and disinfection information system management. Employees' theoretical knowledge points and operational skill scores before and after the intervention were compared and evaluated. The changes in the risk priority coefficient (RPN) values of high-risk factors were analyzed. Fifty-six clinical medical staff from 56 inpatient departments in the hospital were selected to evaluate the clinical satisfaction of the cleaning and disinfection management of instruments and equipment before and after the intervention, and the clinical satisfaction of the two groups was compared. Results: The scores of theoretical knowledge and operational skills of the staff in the research group were significantly higher than those in the control group. The passing rates of theoretical knowledge and operational skills in the control group and the research group were 44.64 and 94.64% respectively, and 55.36 and 96.43%, respectively. The qualified rate of theoretical knowledge and operational skills of staff in the study group was significantly higher than that in the control group (p < 0.05). The RPN scores of medical personnel, environment, system and system guarantee factors in the control group were 80, 80, 80, and 100, respectively. The RPN scores of medical personnel factors, environmental factors, system factors and system guarantee factors in the research group were 6, 24, 24, and 36, respectively. Conclusion: Through standardization and cleaning and disinfection information system management, the theoretical knowledge and technical operation capabilities of cleaning can be effectively improved.

Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification.

BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) analysis is crucial in diagnosing cardiovascular diseases (CVDs). It is important to consider both temporal and spatial features in ECG analysis to improve automated CVDs diagnosis. Significant progress has been made in automated CVDs diagnosis based on ECG with the continuous development of deep learning. Current most researches often treat 12-lead ECG signals as synchronous sequences in Euclidean space, focusing primarily on extracting temporal features while overlooking the spatial relationships among the 12-lead. However, the spatial distribution of 12-lead ECG electrodes can be more naturally represented using non-Euclidean data structures, which makes the relationships among leads more consistent with their intrinsic characteristics. METHODS: This study proposes an innovative method, Convolutional Residual Graph Neural Network (Conv-RGNN), for ECG classification. The first step is to segment the 12-lead ECG into twelve single-lead ECG, which are then mapped to nodes in a graph that captures the relationships between the different leads through spatial connections, resulting in the 12-lead ECG graph. The graph is then used as input for Conv-RGNN. A convolutional neural network with a position attention mechanism is used to extract temporal sequence information and selectively integrate contextual information to enhance semantic features at different positions. The spatial features of the 12-lead ECG graph are extracted using the residual graph neural network. RESULTS: The experimental results indicate that Conv-RGNN is highly competitive in two multi-label datasets and one single-label dataset, demonstrating exceptional parameter efficiency, inference speed, model performance, and robustness. CONCLUSION: The Conv-RGNN proposed in this paper offer a promising and feasible approach for intelligent diagnosis in resource-constrained environments.

Wastewater treatment process enhancement based on multi-objective optimization and interpretable machine learning.

Optimization and control of wastewater treatment process (WTP) can contribute to cost reduction and efficiency. A wastewater treatment process multi-objective optimization (WTPMO) framework is proposed in this paper to provide suggestions for decision-making in setting parameters of WTP. Firstly, the prediction models based on Extreme Gradient Boosting (XGB) with Bayesian optimization (BO) are developed for predicting effluent water quality (EQ) and energy consumption (EC) for different influent quality and process parameter settings. Then, the SHapley Additive exPlanations (SHAP) algorithm is used to complement the interpretability of machine learning to quantitatively evaluate the impact of different features on the predicted targets. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the Technique for Ordering Preferences on Similarity of Ideal Solutions (TOPSIS) is introduced to solve and make decisions on the multi-objective optimization problem. The WTPMO applicability is validated on Benchmark Simulation Model 1 (BSM1). The results show that BOXGB achieves accurate prediction for EQ and EC with R2 values of 0.923 and 0.965, respectively, indicating that BO can effectively select the model hyperparameters in XGB. Based on SHAP supplemented the interpretability of the model to fully explain how the influent water quality and decision variables affect the EQ and EC of the WTP. In addition, the optimized process parameters are determined based on NSGA-II and TOPSIS, and the EC optimization rate is 1.552% while guaranteeing water quality compliance. Overall, this research can effectively achieve the optimization of WTP, ensure that the effluent water quality meets the standards while reducing energy consumption, assist Wastewater treatment plants (WWTPs) to achieve more intelligent and efficient operation and maintenance management, and provide strong support for environmental protection and sustainable development goals.

Impact of physical and mental fatigue on construction workers' unsafe behavior based on physiological measurement.

INTRODUCTION: Construction worker fatigue is an important factor leading to unsafe behavior, a major cause of construction accidents. Uncovering the impact mechanism of fatigue on workers' unsafe behavior can prevent construction accidents. However, it is difficult to effectively measure workers' fatigue onsite and analyze the impact of worker fatigue on their unsafe behavior. METHOD: This research analyzes the relationship between the physical and mental fatigue of construction workers and their unsafe behavior via physiological measurement based on a simulated experiment on handling tasks. RESULTS: It is found that: (a) both physical fatigue and mental fatigue have negative effects on workers' cognitive ability and motion ability, and the negative effects are more serious under the combination of the two types of fatigue; (b) mental fatigue can easily change workers' risk propensity, making them more willing to face risks, and in a state of the two types of fatigue, they are more likely to make choices with less pay and higher risk; (c) the number of signal identification errors is positively correlated with LF (low frequency)/HF (high frequency), and negatively correlated with the standard deviation of normal-to-normal intervals (SDNN), while the number of footstep control errors is negatively correlated with the time elapsed between two successive R waves (RR interval) and skin temperature (SKT). PRACTICAL APPLICATIONS: These findings can enrich construction safety management theory from a perspective of quantified fatigue and facilitate safety management practices on construction sites, thus contributing to the body of knowledge and practices of construction safety management.