This paper presents the method of 3D human body data acquisition based on 3D scanning and the non-contact fit evaluation of clothing based on the distribution features of residual space between clothing and human body. A Kinect camera is employed to collect point cloud data. The points cloud is treated by Geomagic for noise reduction and surface reconstruction. By analyzing the residual space between clothing and human body, the fit distribution of scanned clothing can be achieved. The fit evaluation will make contributions to clothing design, online sales of clothing and virtual try-on.

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.

This paper introduces the "second Five-Year" period,Comprehensive management of rural pollution in Dalian,Ecological protection and construction of major initiatives,Establish and improve the rural garden, village and township enterprises in villages and the development of circular economy model,Actively promote the sustainable development of circular economy in rural areas,Care at home and abroad for economic development of rural circulation and reference reference peers.

Child influenza is an acute infectious disease that places substantial burden on children and their families. Real-time accurate prediction of child influenza epidemics can aid scientific and timely decision-making that may reduce the harm done to children infected with influenza. Several models have been proposed to predict influenza epidemics. However, most existing studies focus on adult influenza prediction. This study demonstrates the feasibility of using the LASSO (least absolute shrinkage and selection operator) model to predict influenza-like illness (ILI) levels in children between 2017 and 2020 in Shanghai, China. The performance of the LASSO model was compared with that of other statistical influenza-prediction techniques, including autoregressive integrated moving average (ARIMA), random forest (RF), ordinary least squares (OLS), and long short-term memory (LSTM). The LASSO model was observed to exhibit superior performance compared to the other candidate models. Owing to the variable shrinkage and low-variance properties of LASSO, it eliminated unimportant features and avoided overfitting. The experimental results suggest that the LASSO model can provide useful guidance for short-term child influenza prevention and control for schools, hospitals, and governments.