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ObjectivesTo assess the current status of injury prevention (IP) core competency among medical students majoring in public health in China and to advocate for incorporating IP in the medical curriculum.MethodsThe study used purposive sampling in eight medical universities in China in 2017, including 420 undergraduates and 763 graduates, using self-administered questionnaires based on the core competency instrument for IP with five domains (31 items): A) injury analysis and assessment (8 items), B) IP project planning and implementation (7 items), C) communication (6 items), D) community practice (5 items), and E) leadership and systematic thinking (5 items). The higher score indicated the higher level of proficiency of the ability (scores ranged from 1 to 5). We used linear regression model to test the effect of IP course experience on the core competency mean score after adjusting for potential confounders.ResultsThe total mean score was 2.78 (SD=0.76, median=2.9, range=1–4.55) and 2.68 (SD=0.75, median=2.81, range=1–4.45) for undergraduates and graduates, respectively. There were 60% and 36% of undergraduates and graduates who have ever taken IP course, respectively. IP course class hours were positively associated with core competency level (P<0.05) across five domains (except for domain D) and the total.ConclusionThe core competency level is relatively low among public health students in China. Setting IP courses should be considered as an effective way to improve students’ core competency. It is a step moving towards the IP education promotion, and further boosting the field of public health.

Skyline query computes all the “best” elements which are not dominated by any other elements and thus is very important for decision-making applications. Recently, it is generalized to skyband query and a k-skyband query returns those elements dominated by no more than k, of other elements. To incorporate the skyband operator into the stream engine for monitoring skybands over sliding windows, space usage estimation for skyband operator becomes a critical issue in the query optimizer. In this paper, we firstly introduce the skyband sketch as the cost model. Based on the cost model, we propose an approach for estimating the space usage of skyband operator over sliding windows of data streams under the assumptions of statistical independence across dimensions, no duplicate values over each dimension, and dimension domains totally ordered. Experiments verify that our approaches can estimate the space usage effectively over arbitrarily distributed data. To the best of our knowledge, this is the first work that attempts to address the issue and proposes effective approaches to solve it.

The wrong wiring of the power supply region directly affects the accuracy of the metering data. This paper based on the existing data of the power supply regions, defined four indicators which include minimum negative line loss rate, maximum positive line loss rate, minimum power factor and maximum number of negative power values. Then by using the four indicators before and after the wrong wiring rectification of the historical wrong wiring regions and the decision tree algorithm, the wrong wiring regions identification model was built. The identification model was used to identify the wrong wiring regions in all regions of the power company, and the regions identified as wrong wiring by the model were inspected on-site. The inspection result shows that the precision of the model is satisfactory, which can effectively improve the inspection and rectification efficiency of the wrong wiring regions.

Line loss is an important factor to measure the economy of power system. In order to explore the main factors affecting line loss, the line loss data of 60 stations in a city are screened out by using power information collection system. With the voltage level data as the index, the stations are clustered by the fuzzy clustering algorithm to form effective differential grouping. According to different types of stations, the curve similarity method and regional factor analysis method are used to analyze the weather conditions and the influence degree of the station factors on line loss. The main influencing factors of line loss are obtained. According to the analysis results of each type of stations, the gray correlation analysis algorithm is used to rank the significant factors according to the influence degree. Taking a certain area line loss data as an example, the influencing factors of line loss abnormality are studied. The results show that according to the characteristics of each type of platform area, the model can analyze the factors of each type of platform area and judge the influencing factors at different levels, so as to achieve the effect of targeted and hierarchical governance for different regions.

Skyline query computes all the “best” elements which are not dominated by any other elements and thus is very important for decision-making applications. Recently, it is generalized to skyband query and a k-skyband query returns those elements dominated by no more than k, of other elements. To incorporate the skyband operator into the stream engine for monitoring skybands over sliding windows, space usage estimation for skyband operator becomes a critical issue in the query optimizer. In this paper, we firstly introduce the skyband sketch as the cost model. Based on the cost model, we propose an approach for estimating the space usage of skyband operator over sliding windows of data streams under the assumptions of statistical independence across dimensions, no duplicate values over each dimension, and dimension domains totally ordered. Experiments verify that our approaches can estimate the space usage effectively over arbitrarily distributed data. To the best of our knowledge, this is the first work that attempts to address the issue and proposes effective approaches to solve it.

The IoT is considered as one of the most important supporting technologies of the smart grid and the smart grid is considered as one of the most important application areas of the IoT. However, the electric IoT has vast amount and many kinds of sensors, very high data acquisition frequency, and a highly heterogeneous network, which lead to the challenge that if the raw time-series data gathered by sensors is all transmitted to the sensing data center via network and then compressed and reserved, the bandwidth and the computing resource requirements of the network and the sensing data center, respectively, are unacceptable. In this paper, in order to cope with the challenge, we propose an in-network time-series data compression algorithm, i.e., the DSDT (Distributed Swinging Door Trending) algorithm, for the electric IoT, which utilizes the computing resource of the sensors to compress the raw sensing data, and then transmits the compressed data to sensing data center where the data will be further compressed and reserved. In this way, the bandwidth and the computing resource requirements of the network and the sensing data center, respectively, are significantly reduced. A performance study shows the superiority of the algorithm.

Obtaining genetic variation information from indica rice hybrid parents and identification of loci associated with heterosis are important for hybrid rice breeding. Here, we resequence 1,143 indica accessions mostly selected from the parents of superior hybrid rice cultivars of China, identify genetic variations, and perform kinship analysis. We find different hybrid rice crossing patterns between 3- and 2-line superior hybrid lines. By calculating frequencies of parental variation differences (FPVDs), a more direct approach for studying rice heterosis, we identify loci that are linked to heterosis, which include 98 in superior 3-line hybrids and 36 in superior 2-line hybrids. As a proof of concept, we find two accessions harboring a deletion in OsNramp5 , a previously reported gene functioning in cadmium absorption, which can be used to mitigate rice grain cadmium levels through hybrid breeding. Resource of indica rice genetic variation reported in this study will be valuable to geneticists and breeders. Hybrid rice cultivars are widely planted around the world. Here, the authors resequence 1,143 indica accessions, focusing on the parents of superior hybrid rice lines in China, and reveal genetic loci that are associated with heterosis via measuring frequency of parental variation difference (FPVD).

As an environmental parameter, the chlorophyll-a concentration (Chl-a) is essential for monitoring water quality and managing the marine ecosystem. However, current mainstream Chl-a inversion algorithms have limited accuracy and poor spatial and temporal generalization in Case II waters. In this study, we constructed a quantitative model for retrieving the spatial and temporal distribution of Chl-a in the Bohai–Yellow Sea area using Geostationary Ocean Color Imager (GOCI) spectral remote sensing reflectance (Rrsλ) products. Firstly, the GOCI Rrsλ correction model based on measured spectral data was proposed and evaluated. Then, the feature variables of the band combinations with the highest correlation with Chl-a were selected. Subsequently, Chl-a inversion models were developed using three empirical ocean color algorithms (OC4, OC5, and YOC) and four machine learning methods: BP neural network (BPNN), random forest (RF), AdaBoost, and support vector regression (SVR). The retrieval results showed that the machine learning methods were much more accurate than the empirical algorithms and that the RF model retrieved Chl-a with the best performance and the highest prediction accuracy, with a determination coefficient R2 of 0.916, a root mean square error (RMSE) of 0.212 mg·m−3, and a mean absolute percentage error (MAPE) of 14.27%. Finally, the Chl-a distribution in the Bohai–Yellow Sea using the selected RF model was derived and analyzed. Spatially, Chl-a was high in the Bohai Sea, including in Laizhou Bay, Bohai Bay, and Liaodong Bay, with a value higher than 4 mg·m−3. Chl-a in the Bohai Strait and northern Yellow Sea was relatively low, with a value of less than 3 mg·m−3. Temporally, the inversion results showed that Chl-a was considerably higher in winter and spring compared to autumn and summer. Diurnal variation retrieval effectively demonstrated GOCI’s potential as a capable tool for monitoring intraday changes in chlorophyll-a concentrations.

Osteosarcoma is a highly malignant tumor with unsatisfactory therapeutic outcomes achieved by chemotherapy, radiotherapy, and surgery. As an emerging oncological treatment, immunotherapy has shown potential in the clinical management of many tumors but has a poor response rate in osteosarcoma. The immunosuppressive microenvironment in osteosarcoma is the main reason for the ineffectiveness of immunotherapy, in which the low immune response rate of immune effector cells and the high activation of immunosuppressive cells contribute to this outcome. Therefore, modulating the function of the immune microenvironment in osteosarcoma is expected to remodel the immunosuppressive microenvironment of osteosarcoma and enhance the efficacy of immunotherapy. This article reviews the role of immune cells in the progression of osteosarcoma, describes the corresponding regulatory tools for the characteristics of different cells to enhance the efficacy of osteosarcoma immunotherapy, and concludes the prospects and future challenges of osteosarcoma immunotherapy.

Current road extraction models from remote sensing images based on deep learning are computationally demanding and memory-intensive because of their high model complexity, making them impractical for mobile devices. This study aimed to develop a lightweight and accurate road extraction model, called Road-MobileSeg, to address the problem of automatically extracting roads from remote sensing images on mobile devices. The Road-MobileFormer was designed as the backbone structure of Road-MobileSeg. In the Road-MobileFormer, the Coordinate Attention Module was incorporated to encode both channel relationships and long-range dependencies with precise position information for the purpose of enhancing the accuracy of road extraction. Additionally, the Micro Token Pyramid Module was introduced to decrease the number of parameters and computations required by the model, rendering it more lightweight. Moreover, three model structures, namely Road-MobileSeg-Tiny, Road-MobileSeg-Small, and Road-MobileSeg-Base, which share a common foundational structure but differ in the quantity of parameters and computations, were developed. These models varied in complexity and were available for use on mobile devices with different memory capacities and computing power. The experimental results demonstrate that the proposed models outperform the compared typical models in terms of accuracy, lightweight structure, and latency and achieve high accuracy and low latency on mobile devices. This indicates that the models that integrate with the Coordinate Attention Module and the Micro Token Pyramid Module surpass the limitations of current research and are suitable for road extraction from remote sensing images on mobile devices.