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Abstract Background and Aims In this study, a pediatric hospital was selected as the study setting, to investigate the effects of the environment of a pediatric operating room child‐friendly space on preschool children under anesthesia. Methods Interviews were conducted in pediatric hospitals through anesthesia of preschool children. This study randomizing children between the ages of 1 and 7 years (seven males, five females, mean age 3.8 years, standard deviation 0.7). Twelve participants took part in this interview, which was an in‐depth interview of preschoolers' perceptions of anesthesia using generalized data analysis to assess young children's perceived preferences and sensitivity to the environment. Preschoolers' preferences for feeling under anesthesia are reflected in four main areas. Results (1) Interesting and engaging environment. (2) Humanized design, free of fear and unfamiliarity. (3) Caring, patient, and compassionate care by healthcare professionals. (4) High cooperation of the child and good doctor–patient relationship. Conclusion The key findings of this study may help designers and clinicians to design a toddler‐friendly built environment where pediatric hospital child‐friendly spaces are designed to benefit their health and maximize their cooperation and psychological recovery.

The Hailar River is located in the Inner Mongolia Autonomous Region of Northeast China. It is a connecting hub of the agricultural pastoral transitional zone on the Hulunbuir grassland, with abundant water and biodiversity resources, and important ecological conservation significance. This study takes the Hailar River as the research area to evaluate the impact and main influencing factors of soil C, N, and P ecological measurement from the upstream to downstream concave convex riverbanks of the Hailar River. The research results show that: (1) The average soil particle size shows differences in the upstream and downstream: the average soil particle size in Section 1 is 31.6–192.3 µm, Section 2 is 21–213 µm, Section 3 is 21–288 µm, and Section 4 is 42–206 µm; the pH value in the upstream area is generally low, while the pH value in the downstream area increases. The reason for this is that the convex bank area has sufficient water, which plays a role in inhibiting salt content; (2) the nutrient content in the concave bank is generally higher in the upstream region than in the downstream region, while the difference in nutrient content between the upstream and downstream regions is relatively small in the convex bank; (3) the nutrient content of concave banks is mostly positively correlated with soil moisture content, while convex banks are positively and negatively correlated with soil moisture content and soil particle size. Research has shown that different cross-sections upstream and downstream, as well as uneven riverbanks, significantly affect soil physicochemical properties and soil C, N, and P ecological measurements. Studying the content of soil C, N, and P in different riparian zones under typical cross-sections can provide new ideas for regional ecological protection and even global C, N, and P cycling.

Water quality has become a significant concern in many river basins in China due to both point and non-point source pollution. The SWAT model assessed pollution reduction scenarios and their effects on Donghe River basin water quality in southwest China. The calibrated model evaluated existing point and non-point emissions. Three schemes reduced point sources by 30, 60, and 90% and non-point sources by 25, 50, and 75%, respectively. Simulations analyzed annual and monthly total phosphorus (TP) concentrations under the scenarios. Results showed that the scenarios effectively improved water quality, meeting Class IV TP standards annually. However, TP exceeded standards in dry months (January–April, December) under all scenarios. A certain degree of negative correlation (R = −0.52, P = 0.11) between TP and rainfall suggests rainfall that influences TP. Comprehensive measures are needed to achieve standards year-round. In summary, the study found that reducing emissions improved Donghe water quality overall but more work is required to meet standards during dry periods. Rainfall correlates with and may affect TP. The work emphasizes implementing comprehensive approaches for year-round water quality improvements in the basin. HIGHLIGHTS An appropriate SWAT model was established for the Donghe River Basin.; Non-point source pollution in the Donghe River Basin accounts for more than half of the total phosphorus emissions.; Rainfall is a critically important factor influencing the variation in total phosphorus concentration in the Donghe River Basin.; The months of January to April and December each year constitute the most critical period for total phosphorus pollution in the Donghe River Basin.;

As the construction of ecological civilization has become more and more important in recent years, ecological restoration and its effect assessment have also received increasing attention. Taking the Wangping coal mine in Beijing as an example, based on Landsat TM/OLI series remote sensing data, we chose five metrics, i.e., fraction vegetation coverage, humidity, heat, dryness, and black particulates, to construct the model for the modified remote sensing ecological index (MRSEI). It was also combined with the Hurst index to conduct dynamic monitoring, spatiotemporal analysis, and prediction studies of the ecological environment quality in the study area. The results showed that: (1) Compared with the RSEI, the first principal component of the MRSEI better integrates the information of each component indicator, has a better average correlation with each indicator, and better reflects the habitat condition in the study area. (2) The mean value of the MRSEI in the study area increased from 0.433 in 1990 to 0.722 in 2021, an increase of 40.03%. (3) From 1990 to 2001, the poor and fair MRSEI-grade areas in the study area were concentrated in the northeastern and southwestern parts of the area. After the ecological restoration project was carried out, the environmental quality of the Wangping coal mine improved year by year, and a small number of poor areas were concentrated in the northeastern border area. (4) The MRSEI of the Wangping coal mine predicted that the future ecological environment of the study area would show a general trend of continuous improvement, but a certain percentage of the area in the northeast had a weak antisustainability trend. The results of the study could provide a reference for ecological restoration planning, sustainable development, and the management of mining areas.

The semi-arid region of northern China is highly sensitive to environmental changes, especially the Hulunbuir Grassland, which has an essential ecological status and a fragile environment. This study focused on the NDVI characteristics of three different ecosystems and their dominant influencing factors. It proposed a method to show the immediate effects of factors influencing NDVI on a statistical level. The results showed that: (1) NDVI of floodplain wetland > NDVI of meadow > NDVI of sand ribbon. There were obvious differences among the three ecosystems, and the spatial distribution of NDVI was consistent with altitude. (2) The main explanatory factors were the phenological period, humidity, temperature, accumulated precipitation, runoff, and evaporation, which accounted for 68.8% of the total explanation. (3) Phenological period, humidity, and precipitation were positively correlated with NDVI. Temperature and evaporation had a positive effect on NDVI within a certain range. This study revealed the differences in environmental factors in different ecosystems, enriched the theory of NDVI influencing factors, and provided a scientific basis for future NDVI research and regional ecological conservation.

User interests modeling has been exploited as a critical component to improve the predictive performance of recommender systems. However, with the absence of explicit information to model user interests, most approaches to recommender systems exploit users activities (user generated contents or user ratings) to inference the interest of users. In reality, the relationship among users also serves as a rich source of information of shared interest. To this end, we propose a framework which avoids the sole dependence of user activities to infer user interests and allows the exploitation of the direct relationship between users to propagate user interests to improve system's performance. In this paper, we advocate a novel modeling framework. We construct a probabilistic user interests model and propose a user interests propagation algorithm (UIP), which applies a factor graph based approach to estimate the distribution of the interests of users. Moreover, we incorporate our UIP algorithm with conventional matrix factorization (MF) for recommender systems. Experimental results demonstrate that our proposed approach outperforms previous methods used for recommender systems.

Continued construction of reservoirs around the world promotes socio-economic development and severely affects the ecological and hydrological processes of rivers and floodplain wetlands. In this study, the Yimin River in Inner Mongolia, China, was taken as the research area. The water inundation guarantee rate (WIGR) was used as the model basis to characterize the inundation probability of the floodplain area. The comparative study of the remote sensing data of the 38 periods before the construction of the reservoir, and the 14 periods after the construction of the reservoir, shows that: due to the impact of the reservoir construction, the submerged area of the water body (WIGR greater than zero) decreased from 49.03 km2 to 39.32 km2, a total reduction of 9.71 km2; the very low water inundation guarantee rate area (WIGR value of 0–20%) was the most affected, with a decrease of 12.14 km2, while the area of other areas with a WIGR value greater than 20% increased by 3.43 km2. In addition, the affected floodplain wetlands have significant spatial distribution characteristics: first, they are mainly distributed in the high-curvature river reach areas, and in this study 77.96% of the three high-curvature river reach areas accounted for 26.04% of the area, with area loss in very low WIGR areas; the second is that it is distributed far from the outside of the river channel, while the WIGR value in some areas near the river channel is increased. This study provides a technical reference for downstream wetland protection based on the WIGR model.

Constantly increasing vegetation changes pose serious challenges to the sustainable use of global ecosystems. Thus, facing the increasingly serious climate and ecological environment problems and improving vegetation coverage is crucial to the sustainable development of the region. Along these lines, in this work, a monitoring model of vegetation cover change was proposed and developed by using Landsat TM (1989, 1999, and 2011) and Landsat OLI-TIRS (2021) data. More specifically, it was used to assess vegetation change. Based on this model, the vegetation change in the core area of Hulun Buir Grassland was systematically analyzed., From the acquired results, the existence of spatial differences in the vegetation coverage changes in the study area were demonstrated. The total area of vegetation coverage changes was 758.95 km2, and the area from low vegetation coverage to high vegetation coverage was 456.41 km2, accounting for 60.14% of the total change area. The area from high vegetation coverage to low vegetation coverage was 302.57 km2, accounting for 39.86% of the total change area, whereas the area of the area without vegetation coverage was 1963.92 km2, accounting for 72.13% of the study area, and the overall vegetation coverage is improving. Vegetation cover change monitoring models can also be used to reveal and describe large-scale vegetation landscape changes and obtain clear vegetation change results through easy-to-obtain data; our work suggests that in the process of pursuing regional economic development and accelerating urbanization, industrialization, and agricultural modernization, human beings should assume more responsibilities and pursue the sustainable development of the natural environment. The results of this work are of great importance to further study the potential driving mechanism of the vegetation coverage changes and provide theoretical guidance for relevant managers to formulate vegetation restoration measures.

The inundation state is one of the main factors affecting the development of river floodplain wetlands, and the water inundate frequency (WIF) is a commonly used characterisation tool. This study combines remote sensing data with ground hydrological observation data to propose the water inundate guarantee rate (WIGR) as a new tool to characterise the inundation probability in river floodplain areas. It further conducts a comparative study between WIGR and WIF in the Yimin River, located in China's Inner Mongolia Autonomous Region. The results show that the inundation state maps of the study area are generally consistent using WIF and WIGR. However, there are observed differences in specific regions. In regions with very low assurance rates, it covers a larger area of 2.90 km2 using WIGR relative to WIF. Meanwhile, The area of other low, medium, high and very high areas is relatively lower. The reason for the difference is that the selected 52 phase remote sensing data corresponds to a low flow guarantee rate, which is generally medium and high. Further, the flow guarantee rate introduced in the WIGR analysis process plays a corrective role in the final analysis results. Compared with WIF, the WIGR algorithm is systematic, continuous, and has certain statistical significance, which has higher scientific credibility and accuracy. Specifically, the simple operation of the WIGR algorithm shows great potential to be applied to other related studies. Nevertheless, further improvement of the WIGR algorithm should focus on the influence of the atmospheric environment on remote sensing data and the acquisition of hydrological data. The WIGR proposed in this study can provide technical support for the conservation of river floodplain wetlands and open new ideas for studying river floodplain wetland ecosystems.

With the development of smart phones and online to offline, spatial platforms, such as TaskRabbit, are getting famous and popular. Tasks on these platforms have three main characters: they are in real-time dynamic scenario, they are on the road networks, and some of them have multiple skills. However, existing studies do not take into account all these three things simultaneously. Therefore, an important issue of spatial crowdsourcing platforms is to assign workers to tasks according to their skills on road networks in a real-time scenario. In this paper, we first propose a practical problem, called online multi-skilled task assignment on road networks (OMTARN) problem, and prove that the OMTARN problem is NP-Hard and no online algorithms can achieve a constant competitive ratio on this problem. Then, we design a framework using batch-based algorithms, including fixed and dynamic batch-based algorithm, and we show that how the algorithms update the batch. After that, we use the hierarchically separated tree structure to accelerate our algorithms. Finally, we implement all the algorithms of the OMTARN problem and clarify their strengths and weaknesses by testing them on both synthetic and real datasets.

Tremendous efforts have been invested in the unsupervised outlier detection research, which is conducted on unlabeled data set with abnormality assumptions. With abundant related labeled data available as auxiliary information, we consider transferring the knowledge from the labeled source data to facilitate the unsupervised outlier detection on target data set. To fully make use of the source knowledge, the source data and target data are put together for joint clustering and outlier detection using the source data cluster structure as a constraint. To achieve this, the categorical utility function is employed to regularize the partitions of target data to be consistent with source data labels. With an augmented matrix, the problem is completely solved by a K-means - a based method with the rigid mathematical formulation and theoretical convergence guarantee. We have used four real-world data sets and eight outlier detection methods of different kinds for extensive experiments and comparison. The results demonstrate the effectiveness and significant improvements of the proposed methods in terms of outlier detection and cluster validity metrics. Moreover, the parameter analysis is provided as a practical guide, and noisy source label analysis proves that the proposed method can handle real applications where source labels can be noisy.

A muon facility—EMuS (Experimental Muon Source)—at China Spallation Neutron Source (CSNS) has been studied since 2007. CSNS, which is designed to deliver a proton beam power of 100 kW at Phase-I, and will serve multidisciplinary research based on neutron scattering techniques, has just completed construction, and is ready to open to general users from September 2018. As an additional platform to CSNS, EMuS aims to provide different muon beams for multiple applications, among which, magnetism study by μSR techniques is a core part. By using innovative designs, such as a long target in conical shape situating in superconducting capture solenoids and forward collection method, EMuS can provide very intense muon beams with a proton beam of 5 kW and 1.6 GeV, from surface muons, decay muons, and high momentum muons to slow muons. In this article, the design aspects of EMuS, including general design, target station, muon beamlines, and μSR spectrometer, as well as prospects for applications on magnetism studies, will be reviewed.