This paper aims to systematically analyze the current state of plastic waste legal supervision in China and to propose a vision for future governance frameworks. In recent years, along with the vigorous rise of emerging industries such as the express delivery industry and takeaway services, the consumption of plastic products has increased sharply. This trend has triggered profound reflection and high vigilance on the issue of plastic waste supervision. This trend has triggered profound reflection and acute vigilance regarding the regulation of plastic waste. Although the Chinese government has initiated multiple regulatory measures and achieved certain outcomes, from a macroscopic perspective, the issue of plastic waste pollution remains grave, and the relevant legal and regulatory system presents a complex situation with limited enforcement efficacy. Hence, it is exceptionally urgent and significant to deeply explore and formulate legislative strategies aimed at alleviating and regulating plastic waste pollution. This paper is dedicated to systematically analyzing the current state of plastic waste legal supervision from both international and domestic dimensions, and meticulously outlining the regulatory framework for plastic waste governance in China. Through the application of legal norm research methods, this paper dissects the flaws and challenges existing in the current governance mechanisms and further conducts a comparative study of the successful practices in this field in developed countries like the United States, with the intention of drawing valuable experiences. On this basis, this paper not only offers a forward-looking outlook on China’s future legislative tendencies in plastic waste pollution but also innovatively proposes a series of new insights and recommendations. These explorations aim to provide a more solid theoretical foundation and practical guidance for the governance approach to plastic waste pollution in China, promote the improvement and enhancement of the enforcement effectiveness of environmental regulations, and thereby effectively confront the global challenge of plastic pollution.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

Underground station passenger flow is large, the number of parcels carried by passengers is large and varied, and the parcels carried have an impact on the fire hazard and evacuation of the station. In order to determine the weights of the passenger luggage risk and environmental factor index system in the fire risk evaluation of underground stations in a more realistic way, an optimized and improved hierarchical analysis method for determining the judgement matrix is proposed, which improves the traditional nine-scaled method and adopts the three-scaled method for the four major categories of luggage, namely, handbags, rucksacks, portable power tools and trolley cases. The advantage of this method is that there is no need for consistency judgement in determining packages with a wide range of types and uncertain contents, thus simplifying the calculation. Meanwhile, the reasonableness and reliability of the method is verified by combining it with an actual metro station fire risk assessment system.

The healthcare sector is progressively modest and patients expect higher service quality; therefore, healthcare practitioners’ and academic researchers’ attention upsurges in exploring service quality, intensifying satisfaction and generating behavioral intention. Despite the significance of the healthcare sector and the importance of quality-related matters, there is a paucity of research and publications dealing with healthcare service quality. This conceptual review evaluates the service quality in Pakistani healthcare sector rendering patients’ perspective. The proposed model emphasizes patients’ switching intention caused by poor or inadequate service quality through intervening constructs of satisfaction and alternative attractiveness. Additionally, current review explored the alternative attractiveness as mediator which was neglected in healthcare context. The model also attempts to propose the association between alternative attractiveness and outcome variable by switching costs regarding patients’ perspectives. The conceptual framework enables hospital managers to comprehend how patients assess healthcare quality provided in the presence of alternatives. The perception of patients would assist them in allocating healthcare resources and hospital management attain performance feedback through service quality parameters. Present review developed an inclusive framework as a novel injector in healthcare sector for patients’ perceived service quality.