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Activated sludge process is one of the most widely used processes for wastewater biological treatment. As the main by-product of activated sludge process，sludge with the high moisture content easily results in various problems such as the difficult transportation，high cost of resource utilization，and low calorific value. Therefore，it is necessary to conduct the sludge dewatering. Flocculation is widely used in sludge dewatering due to its unique advantages including simple operation，fast reaction rate，extensive application scope and excellent dewatering performance. The composition，advantages and disadvantages，and related flocculation mechanisms of various flocculants are introduced in detail. Subsequently，the preparation strategies of various flocculants and their application in sludge dewatering are systematically summarized. Finally，the outlooks of various flocculants on sludge dewatering are presented in order to provide a reference for future research on the application of flocculation technology on sludge dewatering.

Postoperative complications of colorectal cancer (CRC) are the main cause of postoperative death and seriously affect the quality of life and survival time of patients. The application of a clinical prediction model for postoperative complications of CRC can help promptly identify high-risk patients. Accordingly, reasonable intervention measures can be actively taken to reduce the incidence of postoperative complications of CRC. A scientific basis can also be provided to improve the prognosis of patients. In this work, literature on the risk-factor analysis and prediction-model construction of postoperative complications of CRC at home and abroad in recent years was collected and reviewed. The evaluation content and efficiency of the clinical prediction models in postoperative complications of CRC were summarized. Their advantages and disadvantages were also analyzed. The purpose of this study was to provide a reference for the subsequent optimization of such models and the development of a strong, clinically practical, and universal risk-screening tool for postoperative complications of CRC.

Based on the intrinsic geometry, a new type of mechanism with single degree of freedom is designed which can translate along the intersecting line. According to the theory of azimuth feature set, the degree of freedom of the mechanism is calculated and its kinematics are analyzed. A coordinate system is established to solve the forward and inverse position solutions of the mechanism and it is verified. Based on the schematic diagram of the mechanism, a three-dimensional model is established with a specific dimension parameter, and the kinematics simulation is carried out in Recurdyn. The output characteristics of the terminal running trajectory are analyzed, and the feasibility of the mechanism is verified.

Based on the topology design theory and method of parallel mechanism of position and orientation characteristic (POC) theory, a new 3-DOF parallel mechanism with cylindrical translation and one rotation (2T1R) is designed, and the topological characteristics of the mechanism are analyzed, including the calculation of the position and orientation characteristic set, degrees of freedom and coupling degrees; then the kinematics of the mechanism is analyzed, the forward and inverse solutions of the position of the mechanism are solved and verified; finally, based on the forward and inverse position solutions of the mechanism, the work space and rotation capacity of the mechanism are analyzed. The work of this research lays a foundation for the subsequent scale optimization, error analysis and dynamic analysis of the mechanism.

Personality is a stable construction, which is associated with thoughts, emotions and behaviors of human. Any technology involved in understanding, analyzing and predicting human behaviors may benefit from personality recognition. Accurately recognizing the personality will contribute to the research of human-computer interaction, recommendation system, cyberspace security, etc. Social media provides high-quality data for personality recog-nition, while classic personality measurement methods such as self-report scales and projective tests can no longer match the social media data in the age of big data. Moreover, the current mainstream personality recognition methods based on machine learning still have lots of room for performance improvement. Therefore, this paper investigates the literature on personality recognition based on social media data, introduces the background knowledge of personality recognition and summarizes the research status according to the data types of recognition model input used, specifically based on social text data, social image data, social application data and multimodal data. Finally, this paper proposes seven future research directions of personality recognition based on social media data.

This paper briefly reviewed the history of China-made PLC and presented the domestic market situation of PLC, mainstream products of China-made PLC, PLC standards, and relevant national policies. And the existing gaps between foreign and domestic PLC products were analyzed from some aspects including PLC performance indicators, logic programming software, network protocols, product categories, and standardization. Then this paper tried to propose a solution to develop the China-made PLC, combining the characteristics of PLCs of European, American, and Japanese systems. Led by the key projects in the fields of defense industry and critical infrastructure, the China-made PLC will be developed and used based on the domestic independent software and hardware.

Objective: To realize rapid nondestructive testing of rice freshness. Methods: A portable device system based on nano color-sensitive sensors combined with near-infrared spectroscopy was developed. The spectral data of the corresponding color-sensitive sensors were collected for the identification of multi-gradient rice adulteration and the prediction of freshness across batches of rice. Results: Using Si-CARS-PLS to extract spectral characteristic variables, the recognition rate of discriminative model was the highest after modeling by LDA algorithm, and the recognition rate of training set and prediction set were 97.22% and 95.83%, respectively. At the same time, PLSR model predicted that cross-batch data had stronger stability. The correlation coefficients （Rc, Rp） of the training set and the prediction set of different batches of rice sample data were all stable at about 0.95, the root mean square errors （RMSEC, RMSEP） were all lower than 0.2, and the relative analysis errors （RPD） were all greater than 3. Conclusion: The system has the characteristics of high accuracy, convenience and good robustness of prediction model, and has a good application prospect in the field detection of rice freshness.

In this study, we established a method for the determination of metronidazolem, dimetridazole and ronidazole in honey by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The analytes were extracted from samples, purified using a cation exchange cartridge, separated on a C18 (3.0 mm × 100 mm, 1.7 μm) chromatographic column, detected in the multiple reaction monitoring (MRM) mode, and quantitated by an internal standard method. Metronidazolem, dimetridazole and ronidazole were separated well and their calibration curves were linear in the concentration range of 1.0–100.0 ng/mL. The relative standard deviation for replicate determinations was 1.6%–3.7% (n = 6), and the average recoveries for spiked samples ranged from 95.0% to 101.4%. This method has the advantages of low matrix interference, simple operation and high accuracy, an is suitable for the determination of the residues of metronidazolem, dimetridazole and ronidazole in honey.

Industrial control system(ICS) is widely used in water treatment, power, oil, transportation, military industry and other industries. The safety and stability are related to the normal operation of key national infrastructures. ICS are facing with serious security threats with the advancement of digital production and intelligent manufacturing. This paper proposes and designs a set of in-depth ICS security protection system by discussing the typical attack methods and the weaknesses of existing protection measures，and elaborates its technical focus from five aspects: physical security, data security, network security, host and application security, and control security. And then a comprehensive security protection platform is built on the basis of a trusted platform, forming a set of adaptive, closed-loop, security models and mechanisms that can self-defense and recover, maintain the safety of the ICS in three dimensions.It is helpful for security and stability operation of ICS.

Support vector machine (SVM) is a supervised machine learning algorithm based on statistical learning theory. Due to low requirements for data and excellent generalization ability in regression and classification modeling, SVM is used widely in data analysis and mining modeling of intelligent transportation system. This paper first introduces the basic principles and open source tools of SVM. Next, this paper summarizes the applications of SVM in regression prediction of passenger flow, traffic congestion, traffic accident and traffic carbon emission. After that, this paper summarizes the applications of SVM in classified prediction of traffic status estimation, traffic sign recognition and traffic incident detection. This paper compares SVM with other widely used algorithms in intelligent transportation system. The research status of the optimization algorithms and derivative algorithms based on SVM are analyzed. Finally, this paper prospects the optimization and application trend of SVM in the future intelligent transportation system.

The multi-platform-borne Synthetic Aperture Radar (SAR) has become one of the most explored research directions in the domain of SAR. This study discusses the imaging algorithms in multi-platform-borne SARs such as airborne SAR, missile-borne SAR, and spaceborne SAR. First, the establishment of the radar echo model is briefly introduced, including two main points: slant range-model and imaging mode. Subsequently, the imaging algorithms of the aforementioned multi-platform-borne SARs developed and used in recent years are summarized. In addition, the inherent characteristics and challenges are described. Finally, the future development trends of the research are discussed.

The pulse generator is the basic equipment for device drivers and control, widely used in many occasions, such as power electronics, motion control, vehicle control, etc. However, the used pulse generator is weak in communication, which cannot meet high-speed control requirements or be used in the intelligent manufacturing application. To solve the above problem, this paper designed a pulse generator with real-time industrial ethernet EtherCAT and Loongson processor LS1C. The designed pulse generators can form the daisy chain and ring topology by EtherCAT. And the pulse generator is very flexible and suited for many different applications with its four operation modes. Finally, experiment results are presented to demonstrate the operation modes and performance.

Step-wise acid washing was applied to eliminate the indigenous minerals of Huadian and Balikun oil shales.The pyrolysis experiments of raw and demineralized oil shales were carried out by using a thermogravimetric analyzer.The effects of demineralization on pyrolysis were investigated by comparing the thermal weight loss characteristics and kinetic parameters of different oil shale samples.Results showed that after removal of carbonate minerals，the onset temperature of weight loss was decreased by 92 ℃ and 40 ℃，respectively，and the maximum weight loss rate and weight loss were reduced.These indicated that carbonate minerals inhibited the release of small organic molecules at low temperatures and promoted the thermal decomposition of kerogen at high temperatures.Further de-silicate of carbonate-free oil shale increased the maximum weight loss rate of Huadian oil shale，which suggested that silicate minerals suppress kerogen pyrolysis at high temperatures.As to the pyrolysis reaction of oil shale at 350 ℃-510 ℃，de-carbonate decreased and de-silicate increased the apparent activation energy value by about 10 kJ/mol and 30 kJ/mol，respectively.This indicated that carbonates increased while silicates decreased the temperature sensitivity of weight loss rate and silicates played a more pronounced effect than carbonates.

There is a significant impact of ablation angle on the anti-ablation performance of C/C composites. A self-designed oxygen-kerosene ablation system was employed to study the ablation behavior and mechanism of C/C composites. Typical ablation angle of the experiment was 90°, 60°, 45° respectively, and particle concentration of the gas-solid two-phase ablation flow was 1.37%. The microstructure of post-test samples was observed with the scanning electron microscope(SEM), and the ablation rate of samples was also calculated. The result shows that when the ablation angle is 90°, 60°, 45°,the corresponding mass ablation rate for ablation experiment is 0.146,0.123,0.100g/s respectively. With the decrease of ablation angle, the mass ablation rate of samples is decreased gradually; while for the samples affected by particle erosion, the corresponding mass ablation rate is 0.452,0.455,0.432g/s and the line ablation rate is 1.863,1.323,0.843mm/s. The mass ablation rate is approximately unchanged and the line ablation rate is decreased. The smaller the ablation angle is, the more serious the thermalchemical ablation of gas, and thus increase the erosion area of axial fiber bundle under ablation condition and ablation gradient of radial fiber under ablation/erosion condition.

A self-designed oxygen-kerosene ablation system was employed to study the ablation mechanism and performance of the carbon-carbon composites. Particle concentrations of the gas-solid two-phase ablation flow were 0, 1.37%, 2.22%, and 2.64% respectively. The microstructure of post-test samples was analysed through the scanning electron microscope (SEM), and the ablation rate was calculated. The influence principle of particle concentrations on the ablation rate of the carbon-carbon composites was studied, and the mechanism of ablation was analysed. Experiment results show that with no particles involved the mass ablation rate is 0.159g/s and the linear ablation rate is 0.175mm/s, while with particles introduced, the minimum mass ablation rate and linear ablation rate are 0.432g/s and 0.843mm/s respectively, with the increase of particle concentrations, the ablation rate becomes accelerated. Particle erosion makes the ablation of sample seriously intensified, and the ablation gradient of radial fiber on erosion surface is increased with the increase of particle concentrations as well.

In view of feature that temperature of motor could increase in abnormal condition, the paper proposed a design scheme of temperature monitoring system of motor based on 1-wire bus. Taking motor of fuming furnace of some company as an example, it introduced hardware structure and design of upper computer software of the system in details and gave problems needed pay attention to in process of installation and debugging of the system. The actual application showed that the system has high measuring precision and meets engineering requirements.

Synthetic minority oversampling technique (SMOTE) has become one of the mainstream methods for dealing with unbalanced data due to its ability to effectively deal with minority samples, and many SMOTE improvement algorithms have been proposed, but very little research existing considers popular algorithmic-level improvement methods. Therefore a more comprehensive analysis of existing SMOTE class algorithms is provided. Firstly, the basic principles of the SMOTE method are elaborated in detail, and then the SMOTE class algorithms are systematically analyzed mainly from the two levels of data level and algorithmic level, and the new ideas of the hybrid improvement of data level and algorithmic level are introduced. Data-level improvement is to balance the data distribution by deleting or adding data through different operations during preprocessing; algorithmic-level improvement will not change the data distribution, and mainly strengthens the focus on minority samples by modifying or creating algorithms. Comparison between these two kinds of methods shows that, data- level methods are less restricted in their application, and algorithmic-level improvements generally have higher algorithmic robustness. In order to provide more comprehensive basic research material on SMOTE class algorithms, this paper finally lists the commonly used datasets, evaluation metrics, and gives ideas of research in the future to better cope with unbalanced data problem. [ABSTRACT FROM AUTHOR]

As an important branch of intelligent transportation system, road traffic flow prediction plays an important role in congestion prediction, path planning. The spatiotemporal polymorphism and complex correlation of road traffic flow data force the transformation and upgrading of road traffic flow prediction methods in the era of big data. In order to mine the time-space characteristics of traffic flow, scholars have proposed various methods, including model fusion, model algorithm improvement, data definition conversion, etc, in order to improve the prediction accuracy of the model. In order to reasonably summarize all kinds of traffic flow prediction methods, they are divided into three categories according to the types of methods used: statistics based methods, machine learning based methods, and depth learning based methods. This paper summarizes and analyzes the new models and algorithms in recent years by summarizing various traffic flow prediction methods, aiming to provide research ideas for relevant researchers. Finally, the methods of traffic flow prediction are summarized and prospected, and the exploration direction of the future traffic flow prediction field is given. [ABSTRACT FROM AUTHOR]

To Address the issue of increased core size and weight in low-frequency transformers compared to conventional power-frequency transformers， a lightweight design method for a 35 kV low-frequency transformer is proposed. This method involves multi-objective optimization of transformer losses， weight， and dimensions. Firstly， the initial structure of the low-frequency transformer is determined based on national standards and design manuals. Subsequently， transformer size， weight， and losses are selected as optimization objectives. Following this， specific optimization variables are chosen based on the characteristics of low-frequency transformers， and corresponding constraints are defined. Finally， a genetic algorithm is utilized to optimize the low-frequency transformer. A comparative analysis reveals a reduction of 11.04% in total losses， along with a decrease of 4.36% and 6.71% in core and coil weights， respectively， and a 12.85% reduction in core center distance.

[Objectives] SSR (Simple Sequence Repeat) markers are a very useful method for species identification, molecular linkage mapping, and genetic diversity studies. In order to develop molecular markers for Anoplophora chinensis, SSR markers were identified based on transcriptome sequence. This study provides a basis for studies of genetic diversity and the integrated control of A. chinensis. [Methods] The software MISA was used to explore all microsatellite loci in the transcriptome database of A. chinensis. Primers were designed using the software Primer Premier 3.0, and screened using PCR amplification and electrophoresis. [Results] A total of 2 360 SSRs were identified through software analysis; a frequency of 25.31%. These had 1758 sequences, accounting for 18.85% of the total number of sequences. Most repeat types were mononucleotide motifs (79.03%) followed by trinucleotide motifs (12.54%). The dominant repeat type was A/T (99.30%). The proportion of SSR length to 10-11 bp was highest (56.10%), the maximum number of repeats was 10 and the number of SSR loci was 1188 (50.34%). These results indicate the feasibility of using polymorphism analysis to develop SSR markers for A. chinensis.A total of 60 primer pairs were randomly selected and verified by PCR amplification, 53 of which were able to amplify the expected products; a primer availability rate as high as 88% which could be used in future studies. [Conclusion] The information analysis of SSR loci and the design and validation of primers will be helpful for gene mining, and for research on population genetic structure, genetic diversity, evolutionary relationships and the integrated control of A. chinensis. [ABSTRACT FROM AUTHOR]

The rapid development of global transportation network and information communication technology has an important impact on regional economy and social spatial structure, and promotes the transformation of regional research paradigm. The coordinated development of urban agglomeration and regional functional linkage from the perspective of flow space has become an important issue in urban regional research. Different from the traditional central place theory that emphasizes the differences in scale and level of cities within the region, the flow space perspective helps to understand how the regional factor flow organizes cities of all scales and levels in the form of network with the background of space-time compression, and promotes the functional linkage and coordinated development within the region on the basis of reshaping the urban spatial relationship. Based on the literature review of relevant research at home and abroad, this paper constructs an analytical framework for the relationship between regional spatial factor flow and function collaboration, and discusses the regional function collaborative mechanism of urban agglomeration from three aspects: "how to reshape the regional spatial network by factor flow", "how to restructure the urban hierarchy by infrastructure connectivity" and "how to strengthen the regional functional linkage by functional complementarity". On this basis, taking the urban agglomeration of San Francisco Bay Area as an example, this paper discusses the evolution progress of San Francisco Bay Area from "single center agglomeration development" to "double-core symbiotic evolution", "polycentric development" and "generic network functional linkage development" from three aspects of liquidity, connectivity and complementarity. The study found that: 1) In the first stage (before the 1930s), fact flow strengthened the connection between San Francisco and Oakland, connectivity improved the status of Oakland, and functional complementarity promoted the symbiosis of the two cities. 2) In the second stage (1940s-1980s), the flow and converge of factors towards the south bay promoted the tripartite growth of San Francisco, Oakland and San Jose, transport connectivity strengthened the core-peripheral connection, and factor convection and functional reciprocity promoted the multi-center linkage development. 3) In the third stage (since 1980s), factor distribution and transportation network connectivity contributed to the generic network spatial structure, dislocation and complementary development strengthened the regional circulation and realized the functional linkage of the Bay Area. Based on the perspective of flow space, this paper reviews the basic situation of the development of world-class bay area urban agglomerations and excavates the internal mechanism of regional functional coordinated development driven by factor flow, which has reference significance for guiding China's cities from competition to competition and cooperation and establishing a new mechanism of regional coordinated development. The research shows that to promote the development of China's bay area in the future, we should: 1) Comply with the law of factor flow, innovate the mechanism and remove barriers to factor mobility, and promote the free flow of factors inside and outside the region; 2) Give play to the guiding role of the government and non-governmental organizations to improve the connectivity of software and hardware infrastructure within the region; 3) Explore the regional consultation mechanism, break through the regional administrative barriers, and formulate the regional development strategy of functional complementation and linkage with the help of multiple subject forces.

On the basis of fully understanding the scientific connotation of coordinated development of urban agglomeration in the Guangdong-Hong Kong-Macao Greater Bay Area, the evaluation index system of coordinated development is constructed from four dimensions: industrial coordinative innovation, environmental coordinative governance, service coordinative sharing and institutional coordinative arrangement. The theory and method of space of flows, big data, network analysis and interlocking network model are comprehensively used to quantitatively analyze the interurban relationship, liquidity, connectivity and boundary effect. The research shows that: 1) Interurban capital flow is concentrated between Guangzhou and Shenzhen, and interurban knowledge flow is concentrated among Hong Kong, Guangzhou and Shenzhen. Macao, Zhuhai, Zhongshan and Jiangmen have higher level of collaborative environmental governance, while Shenzhen-Dongguan-Huizhou and Guangzhou-Foshan have lower level of collaborative environmental governance. The interurban service coordination sharing level of infrastructure such as transportation, communications, logistics and banking is higher among cities of Guangzhou-Shenzhen, Guangzhou-Foshan and Shenzhen-Dongguan. And Hong Kong and Macao have institutional advantages in cross-border regional coordination. 2) Guangzhou and Shenzhen, Guangzhou and Foshan, Shenzhen and Dongguan, and Hong Kong and Shenzhen have the best level of coordinated development in the Guangdong-Hong Kong-Macao Greater Bay Area. At the same time, Guangzhou and Dongguan, Shenzhen and Foshan show a linkage trend. Although the de-boundary trend is obvious and the interurban liquidity is enhanced, the coordinated development level of the Guangdong-Hong Kong-Macao Greater Bay Area is generally not high. 3) The collaborative capacity of Guangzhou and Shenzhen is the strongest, and there is still much room for improvement in the collaborative capacity of the two international cities, Hong Kong and Macao. It is of great practical value to deeply understand the typical characteristics and potential trends of the coordinated development of urban agglomeration in the Guangdong-Hong Kong-Macao Greater Bay Area for the construction of international first-class bay area and world-class urban agglomeration.

Thermophilic can keep stability and activity at high temperature, which are ideal materials to study stability of proteins. Developing a valuable method to identify thermostability of protein would be helpful for protein engineering. In the present study, amino acid composition and physicochemical properties of protein have been though of being related to the thermostability of protein. A reliable benchmark dataset including 915 thermophilic proteins and 793 non-thermophilic proteins is constructed for training and testing the proposed model in this article. We define proteins samples using physicochemical properties and component of amino acid, so we design a descriptor which will combine dipeptide composition with nine physiochemical properties of amino acids. The results by support vector machine (SVM) with 5-fold cross-validation show that the best accuracy is 92.74% by using 290 features when the parameter gap is 0, indicating that our model holds very high potential to become a useful tool for the research on protein thermostability. [ABSTRACT FROM AUTHOR]

The light interference methane detector has many advantages, such as high stability, wide measurement range, etc. However, the artificial reading and low level of automation make it no communication ability with monitoring system. To solve this problem,the authors made an improvement based on the measurement of interferogram intensity. The optical configuration was modified to guarantee the one-to-one relationship between the intensity of interferogram and the methane volume fraction. The authors used photoelectric elements to capture the interferogram and calculated the methane volume fraction with the variation of interferogram intensity. Given that the zero-drift error was affected by environmental factors, the authors modeled the relationship between pressure and measurement error by theoretical analysis, which was used to compensate the measurement error. Then the relationship between temperature and measurement error was experimentally determined. The authors thus obtained the temperature compensation model by linear interpolation. The effectiveness of compensation models and the stability of designed system were verified by accuracy test, pneumatic test, temperature test and long-term stability test. [ABSTRACT FROM AUTHOR]

Along with the increase of car ownership, the efficient and intelligent vehicle monitoring system has become urgent needs of many communities and companies. Vehicle access monitoring system is an important part to achieve vehicle management. This system uses QT/E ( QtEmbedded $ programming language, based on Arm processor and RFID) technology. In the vehicle entrance fitted with this system, legitimate vehicles can be identified, and be allowed through without stopping. Sqlite database is used to process large amounts of data. Portable design based on Arm processor has more advantages in cost and instaHation. The interface using QT/E and touch screen provide graphical and humanized interface, which is beautiful and easy to operate. This system meets the needs of automated management of vehicle access. [ABSTRACT FROM AUTHOR]

This paper mainly studies the coil modulus of weft plain and 1 x 1 rib knitted fabric knitted by 75% acrylic and 25% wool blended yarn. To knit according to the knitting process of weft plain stitch and 1 x 1 rib stitch, then unravel the fabric to measure the total length of yarn that the coil number is know. Finally, calculate the coil modulus of two different stitches according to the formula. The experimental results show that the coil modulus of weft plain stitch may be initially marked as 19. 5 and that of 1 x 1 rib stitch may be initially marked as 20. 5. The coil modulus not only involves to the fabric appearance quality, moreover regarding the control yarn and the gray cloth tensity fluctuation, enhances the winding length uniformity, the improvement fabric property has the reference value. [ABSTRACT FROM AUTHOR]

The natural dye of Asiatic wormwood is extracted, and the optimum extracting method is selected. The dyeing properties are tested, including colorant dissolvability, spectral distribution,thermo-stability, anti-ultraviolet light, resistance of metal ion, resistance to oxidation and reduction. then by using orthogonal test,the effects of dyeing temperature,the amount of mordant, pH, time on K/S,DE and Integ value in the process of bottom chrome method for wool fabrics are researched. Finally, the optimum dyeing process of bottom chrome method is obtained: when the amount of mordant is 1.4%(o.w.f), pH is 5,temperature is 75°C,dyeing time is 30min. Meanwhile, the soaping fastness and rubbing fastness of wool fabric are tested. [ABSTRACT FROM AUTHOR]

[Purpose/significance] Open access (OA) has been developing for more than a decade, and the corresponding resources are growing and richer. It is useful to review the literature of OA resource impact evaluation in time. It will help to clarify the research context, improve academic achievements and OA resources influence. [Method/process] From the perspective of literature analysis and citation analysis, SCI, HISTCITE and CNKI tools were used to collect literatures, citations, highly-cited papers, research institutions, core authors and source publications of OA resource influence evaluation, so as to comb domestic and foreign research profiles and reveal important research force and literature. [Result/conclusion] It is found that the influence of OA resources continue to attract the attention of the researchers. The United States and Britain are the first study echelons, followed by India, Canada, Germany and China. The important foreign literature researches OA influence from the OA paper citation, considering that the golden OA is now the popular publishing mode, and the domestic important literature mainly researches the impact of OA journals with citation, network links, impact factor, and the characteristics of factors. OA journals can be developed as a focus to enhance the impact of OA.

In recent years, convolutional neural networks (CNN) have gained significant attention in the research of electrocardiogram (ECG) signal diagnosis due to their excellent detection and recognition capabilities. However, the large size of parameters and computational requirements of CNN models make it challenging to deploy them on resource-constrained wearable embedded devices. Pruning techniques can be used to compress the network size, but the irregular distribution of weights after sparsity pruning still brings problems in efficiently processing non-zero data in the pruned network model. To address this issue, an efficient sparse convolutional cardiac arrhythmia classification accelerator is designed. Firstly, a sparse compressed dataflow approach using multi-batch partitioning is proposed to improve computational efficiency. Secondly, a sparse-aware processing element (PE) array is designed to perform convolution operations, addressing the issue of load imbalance caused by pruning. FPGA simulation results demonstrate that the designed sparse convolution accelerator achieves a throughput of 53.84 billion operations per second (GOP·s-1) at the clock frequency of 200 MHz, with power consumption of 0.263 W and energy efficiency of 204.72 GOP·W-1. It achieves a 5-class classification accuracy of 98%. Compared to previous sparse accelerator designs, it can achieve a maximum acceleration of 2.0-2.4 times and an energy efficiency improvement of 4.5-6.1 times, making it suitable for wearable ECG classification devices.(近年来，卷积神经网络（convolutional neural networks，CNN）因其优异的检测与识别能力，在心电图（electrocardiogram，ECG）信号的诊断研究中备受瞩目。然而，CNN模型因参数规模和算力需求较大，难以用于资源受限的可穿戴嵌入式设备。虽然使用剪枝技术可压缩网络规模，但稀疏化剪枝后权重的不规则分布使修剪后的网络模型依然存在无法高效处理非零数据的问题。为此，设计了一种高效的稀疏化卷积心律失常分类加速器。首先，采用多批次划分的稀疏压缩数据流方法提高计算效率；其次，设计了稀疏感知的计算阵列（processing element，PE）完成卷积运算，解决剪枝带来的负载不平衡问题。在现场可编程门阵列（field programmable gate array，FPGA）平台上的仿真结果显示，所设计的稀疏化卷积加速器在200 MHz时钟频率下的吞吐率为53.84 GOP·s-1、平均功耗为0.263 W、能效比为204.72 GOP·W-1、五分类准确率达98%，相较于以往的稀疏化加速器，最高可以实现2.0~2.4倍的加速、4.5~6.1倍的能效比提升，适用于可穿戴的ECG分类设备。)

A rapid method was developed for the simultaneous determination of flupyradifurone and its metabolites 6-chloronicotinic acid and difluoroacetic acid in milk using ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). A simple and efficient dispersive solid phase extraction method was used to purify milk samples, and the purification strategy was evaluated. After improved quick, easy, cheap, effective, rugged and safe (QuEChERS) treatment, 0.1% formic acid in acetonitrile was used as the mobile phase for chromatographic separation on a Shim-pack-GIST C18 column, and the detection was performed in the multiple reaction monitoring (MRM) mode. The external standard method with matrix standard curves was used for quantification. The results showed that under the optimal conditions, good linearity (determination coefficient greater than 0.996) was observed for flurofuranone, 6-chloronicotinic acid and difluoroacetic acid in the concentration ranges of 0.005–0.5, 0.005–0.5 and 0.025–2.5 mg/L, respectively. The average recoveries of flupyradifurone and its metabolites at three spiked concentrations varied from 70.2% to 97.6%, with relative standard deviations (RSDs) between 3.6% and 5.3%, and the limits of detection (LODs) ranged from 0.01 to 0.05 mg/kg, much lower than the national maximum residue limits. This method was characterized by high accuracy, high sensitivity, and good reproducibility. Therefore, it is suitable for large batches of milk samples, and can provide reliable technical support for the management of the pesticide flupyradifurone.

As people’s consumption levels and health awareness have risen, the consumer demand for functional foods is steadily increasing. Medium-chain fatty acids (MCFAs), an important class of dietary fatty acids, have been regarded as high-value functional foods due to their unique transportation system and the fact that they can be metabolized rapidly in the body while producing high energy. Beginning with an overview of the types and structures of MCFAs, this paper goes on to elaborate the metabolic pathway of MCFAs in the human body and then summarize recent research on MCFAs in the adjuvant treatment of different diseases. This article focuses on the positive role of MCFAs in the adjuvant treatment of Alzheimer’s disease, epilepsy, cancer, diabetes, obesity, and other diseases, and expounds their action mechanism. Finally, an outlook on the future development of MCFAs is given. Overall, this review provides a theoretical basis for promoting the application of MCFAs in the fields of food and medicine.

Fatigue driving is one of the main factors that threaten the safety of drivers and traffic. Efficient and accurate fatigue driving detection method can effectively ensure the safety of drivers and their surrounding traffic, maintain traffic order, and reduce property losses and casualties. The fatigue driving detection method based on the driver’s physiological characteristics and vehicle driving information has many limitations, such as being unfriendly to the driver and having numerous influencing factors. Therefore, the fatigue driving detection method based on the driver’s facial features has become a research hotspot. Firstly, the facial feature performance of fatigue driving is described, and advantages, disadvantages and application scenarios of common public datasets in the field of fatigue driving are summarized. Secondly, the advantages and disadvantages of common face detection algorithms in the field of fatigue driving detection are analyzed and studied by using open datasets and comparative experiments. Then, this paper generalizes the process of detection methods based on driver facial features, whose methods and technologies used in the key steps are reviewed. Furthermore, fatigue discriminant parameters and methods of fatigue driving results prediction are summarized. Finally, this paper ends with a discussion of current challenges of fatigue driving detection methods based on driver facial features and looks forward to the future research.

研究芝麻中香兰素的含量水平以及香兰素在芝麻油加工过程的转移情况，以便为芝麻油中含有内源性香兰素提供佐证。使用高分辨质谱仪和液相色谱-串联质谱仪对芝麻中的香兰素进行定性和定量分析，对39种不同品种、来源的芝麻中的香兰素含量进行了测定、分析，并对芝麻油加工过程中香兰素的含量变化进行了研究。结果表明，不同地域的芝麻中香兰素的含量水平不同，且在加工过程中香兰素能够转移至芝麻油中，可能导致芝麻油中内源性的香兰素含量范围较大。因此，不能将芝麻油中检出香兰素作为“假香油”的判定标准，建议持续研究芝麻及芝麻油中内源性香兰素的含量水平并进行科学分析。 In order to provide evidence for the endogenous vanillin in sesame oil, the content level of vanillin in sesame and the transfer of vanillin in sesame oil processing were studied. The vanillin in sesame was qualitatively and quantitatively analyzed by high-resolution mass spectrometry and liquid chromatography-tandem mass spectrometry. The vanillin content in 39 kinds of sesame from different varieties and sources were determined and analyzed, and the change of vanillin content during sesame oil processing was studied. The results showed that the content level of vanillin in sesame planted in different regions was various. Moreover, vanillin could be transferred to sesame oil during the processing of sesame, which might lead to a large range of endogenous vanillin content in sesame oil. In conclusion, the detection of vanillin in sesame oil can not be used as the judgement standard of fake sesame oil. It is suggested that the content level of endogenous vanillin in sesame and sesame oil should be continuously studied and scientifically analyzed.

With the development of deep learning, object detection technology has gradually changed from traditional manual detection methods to deep neural network detection methods. Among many object detection algorithms based on deep learning, the one-stage object detection method based on deep learning is widely used because of its simple network structure, fast running speed and higher detection efficiency. However, the existing one-stage object detection methods based on deep learning do not have ideal detection results for small target objects in the detection process due to the lack of feature information, low resolution, complicated background information, unobvious details and higher positioning accuracy, which reduces the detection accuracy of the model. Aiming at the existing problems of one-stage object detection method based on deep learning, a large amount of one-stage small object detection technologies based on deep learning are studied. Firstly, the optimization methods for small object detection are systematically summarized from the aspects of Anchor Box, network structure, IoU (intersection over union) and loss function in the one-stage object detection methods. Secondly, the commonly used small object detection datasets and their application fields are listed, and the detection graphs on each small object detection dataset are given. Finally, the future research direction of one-stage small object detection methods based on deep learning is investigated.

Research shows that autoimmune encephalitis is associated with sleep disorders. Paraneoplastic neurological syndrome (PNS) with Ma2 antibodies can cause sleep disorders, particularly narcolepsy and rapid eye movement sleep behavior disorder (RBD). Limbic encephalitis (LE) and Morvan syndrome, associated with voltage - gated potassium channel (VGKC)-complex antibodies, which include leucine-rich glioma-inactivated 1 (LGI1) antibody and contactin-associated protein 2 (Caspr2), can result in profound insomnia and other sleep disorders. Central neurogenic hypoventilation are found in patients with anti-N-methyl-D-aspartate (NMDA) receptor encephalitis, whereas obstructive sleep apnea (OSA), stridor and parasomnia are prominent features of encephalopathy associated with IgLON5 antibodies. Sleep disorders are cardinal manifestations in patients with autoimmune encephalitis. Immunotherapy possiblely can improve clinical symptoms and prognosis in a positive way. DOI: 10.3969/j.issn.1672-6731.2017.10.004

Healthy biological community composition and diversity are the basis for soil ecosystems to provide complete ecological functions and services. The current technical bottleneck in soil pollution control is the lack of systematic and comprehensive biodiversity indicators. In this study, the compound-polluted soil in an industrial park was taken as the object, and environmental DNA (eDNA)technology was used to monitor and analyze the composition of the biological community in the polluted soil. The environmental DNA-species sensitivity distribution (eDNA-SSD) method was constructed to diagnose the key stress factors and their ecological threshold. The results showed that:① compound pollution did not have a significant impact on the α -diversity of soil organisms in the park but led to changes in the composition of biological communities and dominant species with high relative abundance. The dominant groups in the polluted soil were Proteobacteria (25.54%), Ascomycota (15.65%), Actinobacteria (8.75%), and Chloroflexi (7.64%). ② Environmental factors accounted for 51.27% of the differences in soil biodistribution, and aniline, sulfate, petroleum hydrocarbon, and lead were the key stress factors for soil biomes. Among them, aniline had the most significant effect on the community distribution of bacteria ( P =0.030), sulfate on fungi ( P =0.025), and metazoans ( P =0.032). ③ The ecological risk threshold (hazardous concentration for 5% of species, HC 5 ) of pollutants with 95% species as the protection target showed a gradient decreasing trend of sulfate (470.00 mg·kg -1 ), petroleum hydrocarbon (17.00 mg·kg -1 ), lead (13.00 mg·kg -1 ), arsenic (6.20 mg·kg -1 ), 4-chloroaniline (0.16 mg·kg -1 ), and aniline (0.11 mg·kg -1 ). Nevertheless, the existing soil risk control value was significantly higher than the ecological risk value, indicating that the existing control standards targeting human health were not suitable for protecting soil health. The comprehensive pollution index showed that the soil pollution in the park was mostly mild pollution accompanied by heavy pollution at individual points. The biological evaluation method based on eDNA-SSD could achieve the comprehensive evaluation of composite pollution in the absence of the benchmark value of pollutants and at the same time better distinguish the differences in soil biological community structure and composition under different pollution levels ( P =0.053). In conclusion, the environmental DNA soil biomonitoring and assessment method developed in this study has guiding significance and application value for soil ecosystem restoration assessment.

With the rise of individualized medicine, precision medicine and translational medicine in the big data era, the application of Internet big data in the medical field is becoming an irresistible trend. Confronted with this new trend, it is urgent to improve the abilities of young uro-andrological surgeons in clinical practice and research exploration with the help of big data. This paper explored the methods of cultivating the abilities of young uro-andrological surgeons in clinical practice and research using big data bases.

Drug combination is a common clinical phenomenon. However, the scientific implementation of drug combination is li-mited by the weak rational evaluation that reflects its clinical characteristics. In order to break through the limitations of existing evaluation tools, examining drug-to-drug and drug-to-target action characteristics is proposed from the physical, chemical and biological perspectives, combining clinical multicenter case resources, domestic and international drug interaction public facilities with the aim of discovering the common rules of drug combination. Machine learning technology is employed to build a system for evaluating and predicting the rationality of clinical drug combinations based on "drug characteristics-repository information-artificial intelligence" strategy, which will be debugged and validated in multi-center clinical practice, with a view to providing new ideas and technical references for the safety and efficacy of clinical drug use.

Objective: To evaluate changes of postoperative thyroid-stimulating hormone（TSH） and total cholesterol（TC） levels in serum, and to analyze the risk of hypercholesterolemia in female patients with differentiated thyroid cancer（DTC） receiving levothyroxine after total thyroidectomy. Method: Female patients with DTC in the first affiliated hospital of hebei north university, who underwent total thyroidectomy, were analyzed retrospectively. All patients were divided into four groups according to follow-up TSH levels:<0.03 mIU/L, 0.03-0.29 mIU/L, 0.30-4.20 mIU/L and>4.20 mIU/L. Changes in TC levels before and after surgery were compared and the occurrence of hypercholesterolemia was analyzed. Result: The lower postoperative TSH level groups had higher preoperative TC levels than the higher TSH level groups. Compared with preoperative level, the TC levels remained unchanged in 0.03-0.29 mIU/L group, but decreased in <0.03 mIU/L group, and markedly increased in greater than 0.3 mIU/Lgroups with TSH levels after surgery. The preoperative-to-follow-up change in TC levels had positive correlation with follow-up TSH level. Compared with those with TSH levels of 0.03-0.29 mIU/L, the risk of TC marginal elevation and hypercholesterolemia in the patients with TSH of 0.30-4.20 mIU/L was found to be 1.16 times and 1.91 times higher, respectively. For patients with TSH levels of >4.2 mIU/L, the risk of TC marginal elevation and hypercholesterolemia was 1.29 times and 3 times, respectively. The increase in TSH was consistent with the increase in the incidence of hypercholesterolemia （ P for trend=0.003）. Conclusion: Women with DTC who are using levothyroxine to maintain TSH after surgery should be aware of the risk of hypercholesterolemia due to inadequate replacement., Competing Interests: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose., (Copyright© by the Editorial Department of Journal of Clinical Otorhinolaryngology Head and Neck Surgery.)

Myeloblastosis (MYB) transcription factors are one of the largest families of transcription factors in higher plants. They play an important role in plant development, defense response processes, and non-biological stresses, i.e., drought stress. Foxtail millet (Setaria italica L.), originated in China, is resistant to drought and low nutrition stresses and has been regarded as an ideal material for studying abiotic stress resistance in monocotyledon. In this study, we ran a transcription profile analysis of zheng 204 under low-nitrogen conditions and identified a MYB-like transcription factor SiMYB42, which was up-regulated under low-nitrogen stress. Phylogenetic tree analysis showed that SiMYB42 belongs to R2R3-MYB subfamily and has two MYB conserved domains. Expression pattern analysis showed that SiMYB42 was significantly up-regulated under various stress conditions, including low-nitrogen stress, high salt, drought and ABA conditions. The results of subcellular localization, quantitative real-time PCR and transcriptional activation analysis indicated that SiMYB42 protein localizes to the nucleus and cell membrane of plant cells, mainly expressed in the leaf or root of foxtail millet, and has transcription activation activity. Functional analysis showed that there was no significant difference between transgenic SiMYB42 Arabidopsis and wild-type (WT) Arabidopsis under normal conditions; however, under low-nitrogen condition, the root length, surface area and seedling fresh weight in transgenic SiMYB42 Arabidopsis, were significantly higher than their counterparts in WT. These results suggest that SiMYB42 transgenic plants exhibit higher tolerance to low-nitrogen stress. Expression levels of nitrate transporters genes NRT2.1, NRT2.4 and NRT2.5, which are the transcriptional targets of SiMYB42, were higher in transgenic SiMYB42 Arabidopsis plants than those in WT; the promoter regions of NRT2.1, NRT2.4 and NRT2.5 all have MYB binding sites. These results indicate that SiMYB42 might enhance foxtail millet tolerance to low-nitrogen condition through regulating the expression of nitrate transporter genes. This study reveals the possible functions of SiMYB42 in a low-nitrogen stress response pathway, and provides a foundation for further understanding the entire regulation network of foxtail millet in response to low-nitrogen stress.

Objective: To assess the value of preoperative serum albumin level in predicting the survival of patients with non-muscle-invasive bladder cancer (NMIBC) undergoing transurethral resection of bladder tumor (TURBT)., Methods: Two hundred and sixteen newly diagnosed patients with NMIBC who underwent TURBT between January, 2007 and April, 2012 were retrospectively analyzed. The patients were categorized into low albumin (<40 g/L) and normal albumin (≥40 g/L) groups. The patient survival was estimated using the Kaplan-Meier method, and univariate and multivariate Cox proportional analyses were used to determine the hazard ratios (HRs) for the overall survival (OS)., Results: Of the patients with available data, 82 (39%) and 127 (61%) patients were classified into low albumin (<40 g/L) and normal albumin (≥40 g/L) groups, respectively. Kaplan-Meier analysis showed a significantly worse 5-year OS in low albumin group than in normal albumin group (P=0.017). In the multivariate Cox regression analysis, after adjusting for confounding variables, the preoperative albumin level remained as an independent predictor for 5-year OS (HR: 3.102, 95%CI: 1.200-8.020, P=0.020)., Conclusion: A low preoperative albumin level predicts a poor 5-year OS in patients with NMIBC who underwent TURBT. Preoperative serum albumin can be a good prognostic factor for predicting survival of the patients with NMIBC treated with TURBT.

Objective: To investigate the clinical application and outcomes of microdissection testicular sperm extraction (micro-TESE) in patients with nonmosaic Klinefelter syndrome (KS)., Methods: A total of 143 nonmosaic KS patients underwent micro-TESE in the Center of Reproductive Medicine of Peking University Third Hospital between July 2012 and August 2016. We analyzed their clinical and follow-up data and evaluated the outcomes., Results: Spermatozoa were successfully retrieved from the testicular tissue in 44.76% (64/143) of the patients, 84.4% (54/64) by unilateral and 15.6% (10/64) by bilateral micro-TESE. Seventy-five of the KS patients were followed up in the years of 2014 and 2015. Of the 34 patients with successful sperm retrieval, 73.52% (25/34) achieved clinical pregnancy and 8 boys and 8 girls were already born in 14 of the 25 cases., Conclusions: The micro-TESE is a useful method for sperm retrieval in nonmosaic KS patients, with high rates of sperm retrieval, clinical pregnancy, and birth of biological offspring.

Genome editing is a novel targeted genome modification biotechnology, which could successfully mutate specific loci as well as generate gene replacement and insertion in various organisms. So far, genome editing technology has been widely applied in investigating gene function and developing valuable traits in both model plants and major crops. In this review, we briefly survey the historical development of genome editing technology, summarize recent progress using the CRISPR/Cas9 system for plant genome editing and explore the potential of the CRISPR/Cas technology in improving forage legumes.

The treatment of Peyronie's disease has been a challenge to urologists, as most of the current oral medications are considered hardly valid and the rest of the therapies require strict control of indications, some of which remain controversial, while the final surgical option may be associated with complications such as penile shortening or erectile dysfunction. Collagenase clostridium histolyticum has been proved to be effective in dissolving the penile cavernosal plaque and approved by the Food and Drug Administration of the USA as the first drug to be used for the treatment of Peyronie's disease. This article presents an introduction to the characteristics, safety, efficacy, and procedures of this new treatment, which may benefit urologists and the patients with Peyronie's disease.