Service Quality Evaluation Model of Public Living Facilities in a Community

Accurate evaluating the service quality of public living facilities in a community by quantitative method is significant to urban planning. However, the performances of existing methods are usually limited for service quality evaluation due to single data source or single index. To solve the above problems, we propose a service quality evaluation model of public living facilities in a community. Firstly, POI data and subjective residents\u27 satisfaction evaluation data was pre-processed for data preparation. Then, the four evaluation indicators included in the model were established, namely, accessibility, diversity, selectivity, and satisfaction. Finally, after the completion of the calculation of the four indexes, standardized processing of the calculation results was performed, and the entropy method was used to assign different weights to the indexes, thereby achieving the quantitative evaluation of the service quality of community public living facilities. We chose the central urban area of Chengdu, China, as a case study for modeling analysis, and the case study successfully estimated the service quality and spatial difference of community living facilities. The results of this model can provide a reliable basis for future urban planning and the location of commercial facilities

Postpartum practices of puerperal women and their influencing factors in three regions of Hubei, China

BACKGROUND: 'Sitting month' is a Chinese tradition for women's postpartum custom. The present study aims to explore the postpartum dietary and health practices of puerperal women and identify their influential factors in three selected regions of Hubei, China. METHODS: A cross-sectional retrospective study was conducted in the selected urban, suburban and rural areas in the province of Hubei from 1 March to 30 May 2003. A total of 2100 women who had given birth to full-term singleton infants in the past two years were selected as the participants. Data regarding postpartum practices and potentially related factors were collected through questionnaire by trained investigators. RESULTS: During the puerperium, 18% of the participants never ate vegetables, 78.8% never ate fruit and 75.7% never drank milk. Behaviour taboos such as no bathing, no hair washing or teeth brushing were still popular among the participants. About half of the women didn't get out of the bed two days after giving birth. The average time they stayed in bed during this period was 18.0 h. One third of them didn't have any outdoor activities in that time periods. The educational background of both women and their spouses, location of their residence, family income, postnatal visit, nutrition and health care educational courses were found to be the influencing factors of women's postpartum practices. CONCLUSION: Traditional postpartum dietary and health behaviours were still popular among women in Hubei. Identifying the factors associated with traditional postpartum practices is critical to develop better targeting health education programs. Updated Information regarding postpartum dietary and health practices should be disseminated to women

Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration

Safe Reinforcement Learning (RL) aims to find a policy that achieves high rewards while satisfying cost constraints. When learning from scratch, safe RL agents tend to be overly conservative, which impedes exploration and restrains the overall performance. In many realistic tasks, e.g. autonomous driving, large-scale expert demonstration data are available. We argue that extracting expert policy from offline data to guide online exploration is a promising solution to mitigate the conserveness issue. Large-capacity models, e.g. decision transformers (DT), have been proven to be competent in offline policy learning. However, data collected in real-world scenarios rarely contain dangerous cases (e.g., collisions), which makes it prohibitive for the policies to learn safety concepts. Besides, these bulk policy networks cannot meet the computation speed requirements at inference time on real-world tasks such as autonomous driving. To this end, we propose Guided Online Distillation (GOLD), an offline-to-online safe RL framework. GOLD distills an offline DT policy into a lightweight policy network through guided online safe RL training, which outperforms both the offline DT policy and online safe RL algorithms. Experiments in both benchmark safe RL tasks and real-world driving tasks based on the Waymo Open Motion Dataset (WOMD) demonstrate that GOLD can successfully distill lightweight policies and solve decision-making problems in challenging safety-critical scenarios

Mining significant local spatial association rules for multi-category point data

Spatial association rule mining can reveal the inherent laws of spatial object interdependence and is an important part of spatial data mining. Most of the existing algorithms for mining local spatial association rules are oriented towards the spatial association between two categories of points and cannot fully reflect the spatial heterogeneity of complex spatial relations among multiple categories of points. In addition, the interactions between points in different categories are often asymmetrical. However, the existing algorithms ignore this asymmetry. To address the above problems, an algorithm for mining local spatial association rules for point data of multiple categories based on position quotients is proposed. First, the proximity relationship between points is determined by an adaptive filter, and the spatial weight value is given according to Gaussian kernel function. Then, the multivariate local colocation quotient of each point is calculated to measure the strength of the local regional spatial association rule. Finally, the Monte Carlo simulation function is used to generate a random sample distribution to test the significance of the results. The algorithm is verified on artificial simulation data and real Point of Interest (POI) data. The experimental results show that the algorithm can identify significant association regions of different spatial association rules for point sets

Topological Modeling Research on the Functional Vulnerability of Power Grid under Extreme Weather

The large-scale interconnection of the power grid has brought great benefits to social development, but simultaneously, the frequency of large-scale fault accidents caused by extreme weather is also rocketing. The power grid is regarded as a representative complex network in this paper to analyze its functional vulnerability. First, the actual power grid topology is modeled on the basis of the complex network theory, which is transformed into a directed-weighted topology model after introducing the node voltage together with line reactance. Then, the algorithm of weighted reactance betweenness is proposed by analyzing the characteristic parameters of the power grid topology model. The product of unit reliability and topology model’s characteristic parameters under extreme weather is used as the index to measure the functional vulnerability of the power grid, which considers the extreme weather of freezing and gale and quantifies the functional vulnerability of lines under wind load, ice load, and their synergistic effects. Finally, a simulation using the IEEE-30 node system is implemented. The result shows that the proposed method can effectively measure the short-term vulnerability of power grid units under extreme weather. Meanwhile, the example analysis verifies the different effects of normal and extreme weather on the power grid and identifies the nodes and lines with high vulnerability under extreme weather, which provides theoretical support for preventing and reducing the impact of extreme weather on the power grid

Predicting erosion-induced water inrush of karst collapse pillars using inverse velocity theory

Although the impact of Karst Collapse Pillars (KCPs) on water inrush has been widely recognized and studied, few have investigated the fluid-solid interaction, the particles migration inside KCPs, and the evolution feature of water inrush channels. Moreover, an effective approach to reliably predict the water inrush time has yet to be developed. In this work, a suite of fully coupled governing equations considering the processes of water flow, fracture erosion, and the change of rock permeability due to erosion were presented. The inverse velocity theory was then introduced to predict the water inrush time under different geological and flow conditions. The impact of four different controlling factors on the fracture geometry change, water flow, and inrush time was discussed in detail. The results showed that the inverse velocity theory was capable of predicting the occurrences of water inrush under different conditions, and the time of water inrush had a power relationship with the rock heterogeneity, water pressure, and initial particle concentration and an exponential relationship with the initial fracture apertures. The general approach developed in this work can be extended to other engineering applications such as the tunneling and tailing dam erosion

Experimental study on radon exhalation characteristics of coal samples under varying gas pressures

Coal and gas outburst during underground mining operations negatively affects the mining safety and decrease the mining productivity. Current outburst prediction has been largely relying on seismic monitoring which is passive and extremely difficult to quantify. As coal produces radon in the damage process, this unique feature potentially offer a new way to reliably predict the outburst incidents. Therefore, in this study, a series of experiments were carried out under different gas pressures adopting the new coal-rock triaxial-loading radon exhalation test system, which aim to investigate the effect of gas depletion pressures on the characteristics of radon exhalation. The data of stress-strain, radon concentration, and acoustic emission were monitored simultaneously in the dynamic loading processes, and were compared and discussed in detail. Experimental results show that in the loading process, radon concentration shows clear step changes and has good consistency with the results of the coal deformation stages according to the acoustic emission count, verifying the feasibility of coal deformation and fracturing prediction based on radon concentration. Meanwhile, at the early stage of loading, the peak value of radon concentration reduces gradually with the increase in gas pressure. Furthermore, relationship between the maximum value of radon concentration and gas pressure is obtained. This work presents a new method to link gas pressure, coal deformation and fracture prediction together based on radon concentration, which could provide significant guidance for the accurate prediction of coal and rock dynamic disasters. Keywords: Radon exhalation, Coal damage, Acoustic emission counts, Radon concentration, Gas pressur