Teachers' professional development is a hot topic in the process of education reform and development.In the context of urban and rural integration,teachers' professional development in primary and middle schools has got more and more widespread concern.This research,through questionnaire and interview,investigates the teachers' professional situation of the primary and middle schools in F area and Y area in Chongqing;the research contains teachers' professional knowledge,professional ability,professional emotion,professional ethics and training.Results reveal that,under the background of urban and rural integration,teachers' professional development is satisfied,but not all the same.So,it is necessary to carry out the concept of urban and rural integration,promote the interaction between urban and rural teachers and improve the teachers' professional development.We need to enhance teachers occupational happiness and sense of honor and to implement diversified training to promote the effective development of primary and middle school teachers.
From Wei to Tang dynasty,Chinese landscape painting were mainly viridescence landscapes.And in Yuan dynasty,quantities of intellectuals unable to achieve his ambition had to live in forests and indulged in painting and calligraphy.The rise and mature of literati paintings completely replaced the viridescence landscape painting.Viridescence landscape continued to decline.Instead ink landscape dominated nearly a thousand years.This change enriched people's spiritual life,which is an inexorable law governing art development,and promoted the nonstop development of Chinese landscape painting.
Object detection is a crucial task in autonomous driving. Currently, object-detection methods for autonomous driving systems are primarily based on information from cameras and light detection and ranging (LiDAR), which may experience interference from complex lighting or poor weather. At present, the 4-D ( ${x}$ , ${y}$ , ${z}$ , ${v}$ millimeter-wave radar can provide a denser point cloud to achieve 3-D object-detection tasks that are difficult to complete with traditional millimeter-wave radar. Existing 3-D object point-cloud-detection algorithms are mostly based on 3-D LiDAR; these methods are not necessarily applicable to millimeter-wave radars, which have sparser data and more noise and include velocity information. This study proposes a 3-D object-detection framework based on a multiframe 4-D millimeter-wave radar point cloud. First, the ego vehicle velocity information is estimated by the millimeter-wave radar, and the relative velocity information of the millimeter-wave radar point cloud is compensated for the absolute velocity. Second, by matching between millimeter-wave radar frames, the multiframe millimeter-wave radar point cloud is matched to the last frame. Finally, the object is detected by the proposed multiframe millimeter-wave radar point-cloud-detection network. Experiments are performed using our newly recorded TJ4DRadSet dataset in a complex traffic environment. The results showed that the proposed object-detection framework outperformed the comparison methods based on the 3-D mean average precision. The experimental results and methods can be used as the baseline for other multiframe 4-D millimeter-wave radar-detection algorithms.
MongoDB is a NoSQL database which stores the data in form of key-value pairs. It is an open-source, document database which is being used in many data-center applications (e.g. Google, Facebook, etc.) because of its high performance, high availability and automatic scaling. For this kind of data intensive applications, low latency and high throughput are extremely important. However, the existing MongoDB is built upon Boost. Asio, which is a cross-platform C++ library for network and low-level I/O. It can provide a great degree of portability, but at the price of performance due to the limitation of Ethernet network and TCP/IP protocol. This makes MongoDB hard to meet the performance requirements of data intensive applications. The High Performance Computing(HPC) domain has developed high performance networks such as InfiniBand for many years, which provides higher bandwidth and lower latency than Ethernet. These kind of networks also provide advanced features, such as Remote Direct Memory Access(RDMA), to achieve better performance. In this paper, we present a modern RDMA capable design of MongoDB. The performance evaluation on QDR (32 Gbps) shows that our RDMA design achieves 2.84X and 1.93X peak speedup over 1 Gigabit Ethernet (1 GigE) and IP-over-InfiniBand (IPoIB) in all experiments. To the best of our knowledge, this is first MongoDB design utilizing high performance RDMA capable interconnects.
Federated learning (FL) attempts to train a global model by aggregating local models from distributed devices under the coordination of a central server. However, the existence of a large number of heterogeneous devices makes FL vulnerable to various attacks, especially the stealthy backdoor attack. Backdoor attack aims to trick a neural network to misclassify data to a target label by injecting specific triggers while keeping correct predictions on original training data. Existing works focus on client-side attacks which try to poison the global model by modifying the local datasets. In this work, we propose a new attack model for FL, namely Data-Agnostic Backdoor attack at the Server (DABS), where the server directly modifies the global model to backdoor an FL system. Extensive simulation results show that this attack scheme achieves a higher attack success rate compared with baseline methods while maintaining normal accuracy on the clean data.
The feasibility and superiority of the remote fault diagnosis system based on B/S structure is analyzed in this paper. The B/S structure is introduced and compared with C/S structure briefly. The paper summarize frame and main function module of the remote fault diagnosis system and introduce its key technology, such as data acquisition technology, data transmission technology between server and client, intelligent diagnosis technology, database technology etc. The hybrid model of support vector machine (SVM) and hidden markov models(HMM) is used as a intelligent diagnosis method of the system, and a new design which could improve the integrity and privacy of the system database data is applied. According to the diagnostic results to all kinds of simulated faults in the Bently vibration test bed, it shows the system is not only stable, reliable and high accuracy, but also has a certain application value to engineering.
The Approximate Bayesian Computation (ABC) approach has been used to infer demographic parameters for numerous species, including humans. However, most applications of ABC still use limited amounts of data, from a small number of loci, compared to the large amount of genome-wide population-genetic data which have become available in the last few years. We evaluated the performance of the ABC approach for three 'population divergence' models - similar to the 'isolation with migration' model - when the data consists of several hundred thousand SNPs typed for multiple individuals by simulating data from known demographic models. The ABC approach was used to infer demographic parameters of interest and we compared the inferred values to the true parameter values that was used to generate hypothetical "observed" data. For all three case models, the ABC approach inferred most demographic parameters quite well with narrow credible intervals, for example, population divergence times and past population sizes, but some parameters were more difficult to infer, such as population sizes at present and migration rates. We compared the ability of different summary statistics to infer demographic parameters, including haplotype and LD based statistics, and found that the accuracy of the parameter estimates can be improved by combining summary statistics that capture different parts of information in the data. Furthermore, our results suggest that poor choices of prior distributions can in some circumstances be detected using ABC. Finally, increasing the amount of data beyond some hundred loci will substantially improve the accuracy of many parameter estimates using ABC. We conclude that the ABC approach can accommodate realistic genome-wide population genetic data, which may be difficult to analyze with full likelihood approaches, and that the ABC can provide accurate and precise inference of demographic parameters from these data, suggesting that the ABC approach will be a useful tool for analyzing large genome-wide datasets.
Abstract Background Exposure to airborne fine particulate matter (PM 2.5 ) has been reported to be harmful to the human kidney. However, whether the activation of oxidative stress and cell apoptosis plays key roles in the nephrotoxicity caused by PM 2.5 exposure is still poorly understood. The aim of this study was to explore the mechanism of cytotoxicity after PM 2.5 exposure in human proximal tubule epithelial cells (HK-2 cells). Results PM 2.5 exposure resulted in a significant decrease in cell viability, with an increase in LDH release and the early kidney damage marker kidney injury molecule-1 (KIM-1) expression in a dose-dependent manner and time-dependent manner. PM 2.5 exposure induced reactive oxygen species (ROS) generation and markedly elevated apoptosis in HK-2 cells. In addition, PM 2.5 exposure resulted in the activation of antioxidant pathway, as evidenced by the increased expressions of Nrf2, HO-1 and NQO1 and decreased expression of Keap1. Moreover, PM 2.5 exposure also induced the activation of apoptotic pathway, as evidenced by the increased expressions of pro-apoptotic proteins Bax, caspase-3 and caspase-8 and decreased expression of antiapoptotic protein Bcl-2. Conclusions Our results demonstrated that both antioxidant pathway and apoptotic pathway played critical roles in the damage mediated by PM 2.5 in HK-2 cells. This study would give us a strategy to prevent the impairment of renal function by PM 2.5 induced through repression of oxidative stress and apoptosis.
The Cyclone Global Navigation Satellite System (CYGNSS), a publicly accessible spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data, provides a new alternative opportunity for large-scale soil moisture (SM) retrieval, but with interference from complex environmental conditions (i.e., vegetation cover and ground roughness). This study aims to develop a high-accuracy model for CYGNSS SM retrieval. The normalized surface reflectivity calculated by CYGNSS is fused with variables that are highly related to the SM obtained from optical/microwave remote sensing to solve the problem of the influence of complicated environmental conditions. The Gradient Boost Regression Tree (GBRT) model aided by land-type data is then used to construct a multi-variables SM retrieval model with six different land types of multiple models. The methodology is tested in southeastern China, and the results correlate very well with the existing satellite remote sensing products and in situ SM data (R = 0.765, ubRMSE = 0.054 m3m−3 vs. SMAP; R = 0.653, ubRMSE = 0.057 m3 m−3 vs. ERA5 SM; R = 0.691, ubRMSE = 0.057 m3m−3 vs. in situ SM). This study makes contributions from two aspects: (1) improves the accuracy of the CYGNSS retrieval of SM based on fusion with other auxiliary data; (2) constructs the SM retrieval model with multi-layer multiple models, which is suitable for different land properties.
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