Search

The poor mechanical properties of self-healing hydrogels limited their applications in the fields of biomedicine and industry. Here, a series of self-healing polymeric ionic liquid (PIL) hydrogels with high mechanical strength and electrical conductivity were prepared through hydrophobic association. Hydrophilic monomer vinyl ionic liquids (VILs) based on choline and amino acids, acrylamide (AAm) and hydrophobic monomers stearyl methacrylate (C18) were copolymerized in a micellar solution of sodium dodecyl sulfate (SDS); meanwhile, bacterial cellulose was introduced to enhance the mechanical strength of hydrogels. The resultant hydrogels exhibited excellent mechanical strength (5.8 MPa), extensive elongation at break (4250%) and outstanding self-healing efficiency (85%) without any external intervention. Even after healing, the tensile strength of most hydrogels could reach 2.5–3.9 MPa. At the same time, the incorporation of ILs endowed hydrogels with good electrical conductivity (a maximum of 1.258 S/m). These excellent properties predicted the potential application of the obtained PIL hydrogels in the fields of biomedicine and industry.

Accurate and up-to-date built-up area mapping is of great importance to the science community, decision-makers, and society. Therefore, satellite-based, built-up area (BUA) extraction at medium res...

China's environmental problems are not only related to the fundamental interests of the broad masses of the people, but also to China's national security and international image. At present, China's environmental protection work is facing a complex situation. Pollution sources can be divided into natural pollution sources and man-made pollution sources. Natural sources of pollution refer to places where nature releases harmful substances or causes harmful effects to the environment, such as active volcanoes. Man-made pollution source refers to the pollution source formed by human activities, and is also the main object of environmental protection research and control. Among the man-made pollution sources, air pollution sources, water pollution sources and soil pollution sources can be classified according to the main objects of pollution. Among them, air pollution sources and water pollution sources have the greatest impact on human life. Therefore, it has become an important subject worthy of in-depth discussion to take automatic and electronic measures for potential environmental pollution incidents, discover environmental pollution problems in time, reduce the probability of environmental pollution incidents, and even put some major environmental pollution incidents in their infancy. In this paper, deep learning method is used to classify the existing key pollution source video. Water pollution experiments show that the accuracy of video counting reaches 93.1%, which is better than other video processing schemes. The operation time of the system reaches acceptable range, and a solution to meet the real-time requirement is put forward.

The Shuangyu gas reservoir in Southwest China is a fracture ultra deep carbonate gas reservoir. Its reservoir buried depth is more than 7000m, and the reservoir temperature is about 160°C. In the early stage, the vertical well acid fracturing method was used to obtain the natural gas production, but the gas production decreased rapidly after acid fracturing, which is difficult to achieve economic and efficient development. Segmented acid fracturing of horizontal wells is an effective way to greatly increase gas production. However, segmented acid fracturing of horizontal wells in Shuangyu ultra deep carbonate gas reservoir faces the following problems: (1)High reservoir temperature, fast acid rock reaction speed and short length of acid corrosion fracture. (2) The diameter of horizontal well is small (114.3mm), packer and other tools can not be put into the wellbore. In this paper, through laboratory tests, an acid fracturing fluid with temperature resistance of 160 °C and friction resistance of 35% of water is developed. The fracturing fluid has the characteristics of high viscosity and high concentration of hydrogen ions, which can not only fracture the formation, but also carry out effective acid etching on the fracture during the injection process. Through the experiment, the temporary plugging agent which can meet the temperature of 160 °C and the combination of temporary plugging agent can form a sealing layer in the fracture with the width of 4mm, and the maximum sealing pressure can reach 20MPa. The research results have been successfully applied in well y616. After fracturing, the daily output of natural gas in well Y616 is 1 million cubic meters, which is more than three times that of the surrounding vertical wells.

IntroductionPrimary Sjögren’s syndrome (pSS) is a systemic autoimmune disorder that primarily affects the exocrine glands such as the lacrimal and the salivary glands. Dry eye disease (DED) is one of the most prevalent manifestations of pSS and is usually classified into aqueous-deficient dry eye and evaporative dry eye. Sjögren’s syndrome dry eye (SSDE) is generally described as aqueous-deficient dry eye. However, as the leading pathophysiological mechanism of evaporative dry eye, meibomian gland dysfunction (MGD) also has influence on SSDE, which has been shown in recent studies. We speculate that SSDE is more than just an aqueous-deficient dry eye. While no related systematic review and meta-analysis has been published, the present study is designed to derive a better understanding of the association between MGD and SSDE.Methods and analysisThe Preferred Reporting items for Systematic Reviews and Meta-Analysis for Protocols 2015 statement was used to prepare this protocol. PubMed, Embase, Web of Science, Cochrane Database, China National Knowledge Infrastructure and Wan Fang Database will be searched from their inception to 31 October 2021, with restrictions to publications in English or Chinese. Two reviewers will independently carry out data extraction and quality assessment. The diagnosis of pSS will meet the standard diagnostic criteria, such as American College of Rheumatology/European League against Rheumatism Classification Criteria (ACR/EULAR) or American-European Consensus Group Classification criteria (AECG), and the definition of MGD and DED will differ between studies. The quality of included studies will be judged using the Newcastle-Ottawa Quality Scale. We will carry out this meta-analysis using RevMan V.5.4.1. The incidence of MGD in patients with SSDE will be indicated as OR with 95% CI.Ethics and disseminationEthical approval is not required as this meta-analysis is performed based on published studies and does not involve human participants. The results will be published in a peer-reviewed journal.PROSPERO registration numberCRD42021226017.

Objective To evaluate the effectiveness and safety of rhubarb-based Chinese herbal formulae (RCHF), which are widely used to treat hepatic encephalopathy (HE) in China. Methods Nine online databases were searched from inception to November 22, 2016. Eligible studies were randomized controlled trials of Traditional Chinese Medicine (TCM) treatment for adult patients (≥ 18 years old) with HE. Outcomes such as mortality rate, clinical response rate, blood ammonia level, and alanine aminotransferase were evaluated between TCM group and control group. Results Thirty studies involving 2661 HE patients were analyzed. Most studies used RCHF treatment. Compared with conventional treatment as usual, lactulose, and vinegar, RCHF were associated with significant improvement in clinical response rate [risk ratio (RR) = 1.33, 95% confidence interval (CI) = 1.25, 1.43, I2 = 0%; RR = 1.26, 95% CI = 1.14, 1.38, I2 = 22%; and RR = 1.19, 95% CI = 1.06, 1.33, I2 = 0%, respectively] and significant reductions in levels of blood ammonia and alanine aminotransferase. Only minor RCHF-associated adverse events, such as abdominal pain (0.3%), anal tenesmus (0.3%), and diarrhea (2.3%), were reported, and there were no significant differences in these events between the treatment group and the three types of control group. Conclusion The findings suggest that RCHF may be an alternative treatment option for HE patients. More rigorous multicenter studies with larger samples and longer observational periods are needed to confirm these findings.

Objective To compare the causes, clinical symptoms, laboratory test results, and prognosis in patients with acute liver failure (ALF) induced by traditional Chinese medicines (TCM) and by Western Medicines (WM). Methods The medical histories of patients who were diagnosed with drug-induced ALF (DALF) (n = 96) after hospitalization in the 302 Military Hospital between January 2010 and December 2015 were retrospectively examined. Results Fifty-eight of the 96 DALF patients (60.4%) had a hepatocellular pattern of DALF, 16 patients (16.7%) had a cholestatic pattern, and 22 patients (22.9%) had a mixed pattern. DALF resolved in 24 patients (25.0%). Twenty-five patients (26.0%) developed chronic liver injury, 43 patients (44.8%) died, and 4 patients (4.2%) underwent liver transplantation. There were 42 ALF patients (43.8%) who received WM, and 32 ALF patients (33.3%) who received TCM. TCM-induced ALF patients had a higher average age [42.4 ± 18.4) vs (33.5 ± 17.9) years, P = 0.04] and higher creatinine and urine nitrogen levels [(155.2 ± 108.8) vs (97.5 ± 130.4) mmol/L, P = 0.047; (9.1 ± 7.7) vs (4.3 ± 5.0) mmol/L, P = 0.002, respectively]. Patients with TCM-induced ALF exhibited an increased risk of renal injury [odds ratio (OR), 3.75; 95% confidence interval (CI), 1.330-10.577]. The 14 patients with TCM-induced ALF who died exhibited higher creatinine levels than the 18 patients with TCM-induced ALF patients who survived [(218.7 ± 111.6) vs (105.8 ± 78.4) mmol/L, P = 0.002]. They were also more likely to exhibit ascites (85.7% vs 44.4%, P = 0.017) and hepatorenal syndrome (78.6% vs 22.2%, P = 0.002). Conclusion TCM-induced ALF was more likely to be accompanied by renal injury than was WM-induced ALF, especially in TCM-induced ALF patients who died.

As the technological progress of mobile Internet, smartphone based on Android OS accounts for the vast majority of market share. The traditional encryption technology cannot resolve the dilemma in smartphone information leakage, and the Android-based authentication system in view of biometric recognition emerge to offer more reliable information assurance. In this paper, we summarize several biometrics providing their attributes. Furthermore, we also review the algorithmic framework and performance index acting on authentication techniques. Thus, typical identity authentication systems including their experimental results are concluded and analyzed in the survey. The article is written with an intention to provide an in-depth overview of Android-based biometric verification systems to the readers.

It is difficult to classify Hunshandake sandy land due to complex terrain, an improved classification method is proposed in this paper to promote the effect of classification. Firstly, scattering characteristics of ground objects are extracted. Then, the extracted features is combined to Jensen-Bregman logDet(JBLD) for classification. Finally, Hunshandake sandy land obtained by Radarsat-2 satellite is classified to verify the effectiveness of the method.

In the modern era of rapid development of information technology and mobile Internet, mobile terminals represented by Android smart phones are applied to various fields of life, and the number of users is far more than that of traditional PC terminals. Mobile financial applications marked by mobile banking and mobile payment have higher access control requirements, while traditional text passwords, dynamic passwords and verification codes have hidden dangers of being stolen and copied. How to apply biometrics to mobile identity authentication with high security requirements has become a hot issue for scholars all over the world. Lip reading and speech biometrics are studied in this paper, and a multi-biometric fusion authentication system based on both on Android smart phone platform is developed.

A growing number of cancer deaths draws attractions in the areas of bioinformatics worldwide. It is worth noting that cancer microarray data is composed of tens of thousands of genes, but there are only a few dozens of genes that are really relative to cancers. Thus, it is particularly import to pick out the relevant genes by means of microarray data analysis. Gene selection and classification techniques capture the essence of cancer mechanism and represent a suite of the refreshing cancer cure. In the paper, we summarize 4 types of cancer microarrays which show up in reference providing their attributes and download websites. Furthermore, we also review most extensively studied gene selection and classification techniques. Meanwhile, comparison and analysis on accuracies of gene selection and classification methods in view of remarkably influential algorithms are outlined with unique and randomized algorithms. Thus, the most correlative cancer genes are selected, Leukemia and Colon cancer as typical representatives are listed in the survey. From the microcosmic perspectives, these emerging techniques are considerably conductive to solve the dimensional curse of cancer microarray data applied in the treatment of cancer.

UAV remote sensing has been widely used in emergency rescue, disaster relief, environmental monitoring, urban planning, and so on. Image recognition and image location in environmental monitoring has become an academic hotspot in the field of computer vision. Convolution neural network model is the most commonly used image processing model. Compared with the traditional artificial neural network model, convolution neural network has more hidden layers. Its unique convolution and pooling operations have higher efficiency in image processing. It has incomparable advantages in image recognition and location and other forms of two-dimensional graphics tasks. As a new deformation of convolution neural network, residual neural network aims to make convolution layer learn a kind of residual instead of a direct learning goal. After analyzing the characteristics of CNN model for image feature representation and residual network, a residual network model is built. The UAV remote sensing system is selected as the platform to acquire image data, and the problem of image recognition based on residual neural network is studied, which is verified by experiment simulation and precision analysis. Finally, the problems and experiences in the process of learning and designing are discussed, and the future improvements in the field of image target location and recognition are prospected.

Several studies have suggested that the thalamic centromedian-parafascicular (CM/PF or the PF in rodents) is implicated in the pathophysiology of Parkinson's disease (PD). However, inconsistent changes in the neuronal firing rate and pattern have been reported in parkinsonian animals. To investigate the impact of a dopaminergic cell lesion on PF extracellular discharge in behaving rats, the PF neural activities in the spike and local field potential (LFP) were recorded in unilaterally 6-hydroxydopamine- (6-OHDA) lesioned and neurologically intact control rats during rest and limb movement. During rest, the two PF neuronal subtypes was less spontaneously active, with no difference in the spike firing rates between the control and lesioned rats; only the lesioned rats reshaped their spike firing pattern. Furthermore, the simultaneously recorded LFP in the lesioned rats exhibited a significant increase in power at 12-35 and 35-70Hz and a decrease in power at 0.7-12Hz. During the execution of a voluntary movement, two subtypes of PF neurons were identified by a rapid increase in the discharge activity in both the control and lesioned rats. However, dopamine lesioning was associated with a decrease in neuronal spiking fire rate and reshaping in the firing pattern in the PF. The simultaneously recorded LFP activity exhibited a significant increase in power at 12-35Hz and a decrease in power at 0.7-12Hz compared with the control rats. These findings indicate that 6-OHDA induces modifications in PF spike and LFP activities in rats during rest and movement and suggest that PF dysfunction may be an important contributor to the pathophysiology of parkinsonian motor impairment.

In recent years, researchers have proven the release of silver ions (Ag(+)) from silver nanoparticles (Ag NPs) significantly affects their toxicity to bacteria and other organisms. Due to the difficulty in maintaining a steady flux of a high concentration of Ag(+), it is still challenging to develop a highly efficient, stable, and biocompatible Ag NP-based antimicrobial material. To circumvent this issue, we developed a new Ag-based bactericide through the fabrication of sunlight-driven and ultrafine silver/silver chloride anchored on reduced graphene oxide (Ag/AgCl/rGO). This stable Ag/AgCl nanophotocatalyst with negligible release of Ag(+) generated a high amount of oxidative radicals, killing the bacteria, thus achieving both high bactericidal efficiency and stability. Moreover, functionalization of the nanomaterial with poly(diallyldimethylammonium chloride) (PDDA) gives it a highly adsorptive capacity, which allows it to capture the bacteria and possibly enhances the bactericidal activity. In vivo histopathological studies showed that the Ag/AgCl/rGO nanomaterial could obviously promote the regeneration of the epidermis, which indicated the good biomedical potential of Ag/AgCl/rGO nanomaterial in burn wound healing.

The pedunculopontine nucleus (PPN) is a new deep brain stimulation target for treating Parkinson's disease (PD). But the alterations of the PPN electrophysiological activities in PD are still debated. To investigate these potential alterations, extracellular single unit and local field potential (LFP) activities in the PPN were recorded in unilateral hemispheric 6-hydroxydopamine (6-OHDA) lesioned rats and in control rats, respectively. The spike activity results revealed two types of neurons (Type I and Type II) with distinct electrophysiological characteristics in the PPN. Both types of neurons had increased firing rate and changed firing pattern in lesioned rats when compared to control rats. Specifically, Type II neurons showed an increased firing rate when the rat state was switched from rest to locomotion. The LFP results demonstrated that lesioned rats had lower LFP power at 0.7-12Hz and higher power at 12-30Hz than did control animals in either resting or locomotor state. These findings provide a better understanding of the effects of 6-OHDA lesion on neuronal activities in the PPN and also provide a proof of the link between this structure and locomotion, which contributes to better understanding the mechanisms of the PPN functioning in the pathophysiology of PD.

With the wide use of lithium battery, its online monitoring and residual capacity prediction have been paid much attention. There are two methods for predicting the residual capacity of lithium batteries, namely model method and data-driven method. The traditional model method requires in-depth understanding of the material characteristics and aging mechanism of the battery. However, it is difficult to establish an accurate model due to the complex electrochemical reactions in the battery and the vulnerability to external factors. The data-driven law has been applied more widely because of its good applicability and flexibility. This paper presents a method of battery capacity prediction based on fuzzy logic and neural networks. The lithium battery data published by PCoE are selected for the test, and the results show that the prediction error of the method for the residual capacity of single battery is less than 2%, which indicates that the method has a good applicability for the complex nonlinear system of lithium battery pack, and can obtain accurate battery capacity prediction value, and it has a good application prospect.

Bacterial cellulose(BC) is a kind of extracellular polymer synthesized by bacteria and it has very wide applications in many fields. However, the application of BC in a large commercial scale can still not be fulfilled due to the low yield and demanding for BC membranes with very different properties. To this end, a new BC-producer Komagataeibacter rhaeticus TJPU03 was isolated from rotten orange peel, which produced 8.28 ± 0.27 g/L(dry weight) in standard HS medium at the 10th day. The membrane is easier to be purified by one-step alkaline treatment and the produced BC(K-BC) membranes possess homogeneous, looser and more porous three-dimensional network composed by thinner cellulose fibrils. However, the wet K-BC possesses stronger mechanical properties and exhibits lower toxicity and higher cytocompatibility to mammalian cell. Owing to the more porous and homogeneous network, K-BC possesses high loading capacity of cell and protein drugs. Also, it exhibits sustained-controlled release ability for proteinaceous drug. The high yield of this strain and the special characteristics of K-BC predict this strain to be a very promising BC-producer and broad applications of K-BC in the fields of wound healing, scaffolds of tissue engineering, tissue repair and regeneration.

Introduction A low-cost chemical sensor for ammonia (NH3) detection is highly demanded nowadays for several important applications. Two-dimensional (2D) nanomaterials such as graphene/reduced graphene oxide (rGO), transitional metal dicharcogenides, black phosphor and transition metal carbonitride (MXene) have found a promising application in the fields of gas sensing electronics at room temperature due to their unique microstructure, electrical properties and excellent gas adsorptions ability. Decoration of the secondary phase such as noble metals and metal oxide nanocrystals to form heterojunctions with two-dimensional materials can effectively improve the inadequacy of two-dimensional materials based gas sensors. It takes advantages of the difference in the Fermi level of each phase to make the electrons transfer from one material to the other and thus enriching the electron concentration on the surface of one material that is dominant in the electrical transport. In this way, more ionized oxygen is chemisorbed, leading to the improvement of the gas sensing properties [1]. In this work, 2D MXene heterojunctions incorporated with SnO2 nanoparticles have been synthesized by using hydrothermal method. The as-fabricated MXene/SnO2 heterojunction based chemiresistive-type sensor showed excellent sensitivity to different concentrations of ammonia from 0.5-100 ppm at room temperature. The response has about 20 times higher than those reported in literature to 100 ppm NH3 and an excellent selectivity. Preparation and characterizations The Ti3AlC2 was purchased directly from Beijing Forsman Corp. The concentrated hydrochloric acid (HCl) was diluted to 9M. 2g of lithium fluoride (LiF) was added into 20ml diluted hydrochloric acid and stirred with magnetic mixer. 2g Ti3AlC2 was slowly and evenly dispersed in the solution over a period of several minutes and subsequently stirred at 35℃ for 48h to obtain the MXene. 70 mg of MXene powder was dispersed in 30ml of deionized water after grinding and sonicated for 30min. 73.5mg of stannic chloride pentahydrate (SnCl4·5H2O) was added into the suspension and stirred with magnetic stirrer at room temperature. The suspension was then loaded in a 50ml Teflon-lined autoclave. The autoclave was sealed and heated in an KSL-1200X hydrothermal system at 180℃ for 12h to obtain the MXene-SnO2 hybirds. The samples were then characterized by XRD, SEM, XPS, Raman and TEM-ED. The MXene/SnO2 based chemirestive-type sensor was measured by a static volumetric method. The resistance of the sensor is directly measured by a digital multimeter (Agilent 34410A). The responses of the sensors were defined as the relative change in the resistances of the sensors in the air and those in the tested gas. Results and Conclusions Fig.1a shows the XRD of the prepared samples. Appearance and down-shift of the peak at ~10 oC indicates the successful achievements of MXene and thus the MXene-SnO2 composite heterojunctions [2-3].Fig.1 (b-c) show the micrographs of the surface morphology of the MXene and heterojunctions. The etched MXene material has obvious layered structure with sizes ranging from 2 μm to 4 μm. The thickness of MXene is about 75 nm indicating the sample contains about 100 single layers of MXene. The granular SnO2 nanoparticles are intimately attached to the surface of MXene. Fig.2 (b) shows the Raman spectra of MXene and Mxene/SnO2 heterojunction. The appearance of both D-band and G-band in the MXene/SnO2 heterojunction sample indicated that MXene remained well during the hydrothermal reaction. Fig.2(c) shows the Ti core level (2p) spectra of the MXene and MXene/SnO2. Ti - X corresponds to titanium carbide or titanium oxynitride. Fig.2d indicates that the peaks at 530.1 and 532 eV correspond to oxygen vacancies and O=C-OH, respectively, suggesting that these and surface-adsorbed oxygen tend to form more active sites, thereby enhancing sensing capability. The sensors using MXene/SnO2 heterojuction shows excellent response and selectivity to low-level NH3 from 0.5-9 ppm at RT (Fig.3). Since the work function of MXene (4.465 eV) was slightly lower than that of SnO2 (4.7 eV), Schottky-type junctions were formed across MXene/SnO2 interfaces (Fig.4). Electrons would transfer from SnO2 to MXene, and the depletion layer was deepened on the surface of MXene. MXene could then chemically adsorb more oxygen, and the response of the MXene/SnO2 heterojunction was thus significantly enhanced. References [1] J. M.Walker, S. A.Akbar, P. A.Morris, Synergistic Effects in Gas Sensing Semiconducting Oxide Nano-Hetero- structures: A review, Sens. Actuators B 2019, 286, 624-640. doi: 10.1016/j.snb.2019.01.049 [2] S.J.Kim,H-J.Koh, C. E. Ren, O.Kwon, K.Maleski,S-Y.Cho, B.Anasori, Ch-K.Kim, Y-K.Choi, J. Kim, Y.Gogotsi, H-T.Jung, Metallic Ti3C2TX MXene Gas Sensors with Ultrahigh Signal-to-Noise Ratio, ACS Nano 2018, 12, 986−993.doi: 10.1021/acsnano.7b07460. [3] E. Lee, A.Vahidi, B. C. Prorok,Y. S. Yoon, M.Beidaghi, D-J.Kim, Room Temperature Gas Sensing of Two-Dimensional Titanium Carbide, ACS Appl. Mater. Interf. 2017, 9, 37184-37190.doi: 10.1021/acsami. 7b11055. Figure 1

China is one of the countries with the most natural disasters in the world. Natural disasters seriously endanger people’s safety and economic situations. Therefore, it is very necessary to strengthen the prediction and research on the characteristics and the causes of various natural disasters. Based on the Internet distributed and incremental crawling technology, this system crawls the knowledge literatures, disaster news and professional disaster data from domestic and overseas on natural disasters, as well as drawing on the advanced knowledge management concept, the captured data will be displayed in the form of knowledge tree. Integrating the authoritative global typhoon live maps and orthophotos which acquired by UAV to better monitor and predict typhoons; This system determined that the SSM framework is used as the system framework, combined MySQL and Redis database to store data stably and efficiently. In addition, the Echarts component can be used to analyze and visualize the crawled data. Big data analysis techniques such as knowledge graphs and related algorithms for literature analysis, data cleaning to better data store and display. In the end, it will enable the researchers to more easily and efficiently carry out the knowledge sharing, collaborative cooperation and interactive communication, which has the certain research significance and use value.

Dialogue intent detection and semantic slot filling are two critical tasks in nature language understanding (NLU) for task-oriented dialog systems. In this paper, we present an attention-based encoder-decoder neural network model for joint intent detection and slot filling, which encodes sentence representation with a hybrid Convolutional Neural Networks and Bidirectional Long Short-Term Memory Networks (CNN-BLSTM), and decodes it with an attention-based recurrent neural network with aligned inputs. In the encoding process, our model firstly extracts higher-level phrase representations and local features from each utterance using convolutional neural network, and then propagates historical contextual semantic information with a bidirectional long short-term memory network layer architecture. Accordingly, we could obtain sentence representation by merging the two architectures mentioned above. In the decoding process, we introduce attention mechanism in long short-term memory networks that can provide additional sematic information. We conduct experiment on dialogue intent detection and slot filling tasks with standard data set Airline Travel Information System (ATIS). Experimental results manifest that our proposed model can achieve better overall performance.

Poster Febrer

Many studies have confirmed that the role of online learning collaboration group is very important. For large-scale online learning, how to effectively find the learning collaboration group is a difficult problem. The online learning forum is the main place for learners to learn and communicate, so it is the main venue for learning collaboration groups to implement collaborative learning. In the implementation process of learning collaboration, there are two characteristics between learning team members. First, there is interaction between them. Second, the contents of their discussion have high relevance. In this paper, the study uses these two important characteristics and carries out two-stage clustering algorithm based on the interaction structure and interactive contents of learners to find the potential learning collaboration groups in the large-scale online learning forum. The experimental results show that the method proposed in this paper is effective. This has practical significance for large-scale online learning support services.

The involvement of dopamine (DA) neuron loss in the etiology of Parkinson's disease has been well documented. The neural mechanisms underlying the effects of DA loss and the resultant motor dysfunction remain unknown. To gain insights into how loss of DA disrupts the electrical processes in the cortico-subcortical network, the present study explores the effects of DA neuron depletion on electrical activity in the primary motor cortex (M1), on the external and the internal segment of the globus pallidus (GPe and GPi respectively), and on their temporal relationships. Comparison of local field potentials (LFPs) in these brain regions from unilateral hemispheric DA neuron depleted rats and neurologically intact rats revealed that the spectrum power of LFPs in 12–70 Hz (for M1, and GPe) and in 25–40 Hz (for GPi) was significantly greater in the DA depleted rats than that in the control group. These changes were associated with a shortening of latency in LFP activities between M1 and GPe, from several hundred milliseconds in the intact animals to close to zero in the DA depleted animals. LFP oscillations in M1 were significantly more synchronized with those in GPe in the DA depleted rats compared with those in the control rats. By contrast, the synchronization of oscillation in LFP activities between M1 and GPi did not differ between the DA depleted and intact rats. Not surprisingly, rats that had DA neuron depletion spent more time along the ladder compared with the control rats. These data suggest that enhanced oscillatory activity and increased synchronization of LFPs may contribute to movement impairment in the rat model of Parkinson's disease.

Recently, increasing attentions have been concentrated upon the judgement of video-based traffic accidents, which serves as a crucial technology of intelligent transportation system (ITS). This paper introduces a novel vehicle tracking method based on projection extreme learning machine (PELM) by taking consideration of poor performance in the dealing with high-dimensional and low-samples. Firstly, samples near the target in the first frame of a video is employed as training samples to the PELM classifier. The algorithm uses projection vectors instead of random assignment as the weights of the input layer. Then the PELM classifier is trained with the samples near the target in the following frames to update parameters and obtain the target location with the maximum response. Finally, the fitting error and maximum-range trajectory are adopted to judge whether the vehicle is collided or not. Simulation experiments have shown the proposed method can achieve quite satisfying results.

Motivated by the security problems caused by the shortage of reinforcement quantity and the sparse distribution of steel bars, this paper proposes a novel steel bars counting method based on machine vision which focus on processing videos and images to obtain the quantity of steel bars. Firstly, we use image capture technology to convert the video into an image. Then preprocessing and edge detection of the above images are used to set up the region of interest through template matching. Finally, we use the Hough transform line detection technique to extract the lines and get the number of bars in the calibration area or in the entire video. Experiments show that the accuracy ratio of steel bars counting is up to 100%, and at the same time this method can detect a many steel bars in the video rapidly, reducing the workload of people.

The research on classification method of malicious code is helpful for researchers to understand attack characteristics quickly, and help to reduce the loss of users and even the states. Currently, most of the malware classification methods are based on supervised learning algorithms, but it is powerless for the small number of labeled samples. Therefore, in this paper, we propose a new malware classification method, which is based on semi-supervised learning algorithm. First, we extract the impactful static features and dynamic features to serialize and obtain features of high dimension. Then, we select them with Ensemble Feature Grader consistent with Information Gain, Random Forest and Logistic Regression with \(L_1\) and \(L_2\), and reduce dimension again with PCA. Finally, we use Learning with local and global consistency algorithm with K-means to classify malwares. The experimental results of comparison among SVM, LLGC and K-means + LLGC show that using of the feature extraction, feature reduction and classification method, K-means + LLGC algorithm is superior to LLGC in both classification accuracy and efficiency, the accuracy is increased by 2% to 3%, and the accuracy is more than SVM when the number of labeled samples is small.

Graphene-based silver nanoparticles (Ag NPs-GE) material has been developed and demonstrated antibacterial effect against Escherichia coli and Pseudomonas aeruginosa. In this study, the antibacterial activity and mechanism on P. aeruginosa were investigated. The experiments results showed the minimum bactericidal concentration of Ag NPs-GE to P. aeruginosa is 20 μg/ml. When P. aeruginosa were exposed to 20 μg/ml Ag NPs-GE for 1 h, the cell wall was breakdown. In order to study the mechanism of antibacterial effect of Ag NPs-GE, two-dimensional electrophoresis was carried out to compare the protein expressional profiles of P. aeruginosa exposed to 5 μg/ml Ag NPs-GE or 5 μg/ml AgNO3 with the untreated bacteria. Identification of differentially expressed protein was performed by MALDI-TOF/TOF MS. The change of proteomic profile induced by Ag NPs-GE was distinct from that induced by AgNO3. Seven identified proteins were found induced and nine proteins were suppressed by Ag NPs-GE. Five identified proteins were found induced and twenty proteins were suppressed by AgNO3. In addition, either Ag NPs-GE or AgNO3 suppressed the expression of eight proteins, amidotransferase, 30S ribosomal protein S6, bifunctional proline dehydrogenase/pyrroline-5-carboxylate dehydrogenase, arginyl-tRNA synthetase, nitroreductase, acetolactate synthase 3, methionyl-tRNA synthetase and periplasmic tail-specific protease. Furthermore, gene ontology analysis and KEGG pathway analysis were used to characterize the functions of those proteins.

With the steady growth of the economy and the rapid development of modern industrial technology, the problem of environmental pollution has increased. To continue to develop, it is necessary to thoroughly implement the sustainable development strategy, and we must pay more attention to environmental issues. One of the important management tools implemented in China for environmental management is environmental quality monitoring and evaluation. Environmental quality monitoring can scientifically evaluate the environmental quality of a region, scientifically evaluate and forecast the environmental management and environmental engineering, and provide scientific basis for environmental management, environmental engineering, formulation of environmental standards, environmental planning, comprehensive prevention and control of environmental pollution, and ecological environment construction. This paper will discuss the basic principles of neural network and the implementation process of MATLAB and in the MATLAB software implementation and display process. At the same time, the results of different parameters are analyzed through experiments, and the network parameters are constantly adjusted to improve the accuracy of the evaluation results. Taking the regional environment as an example, two monitoring methods are proposed, and a variety of neural network models are used to analyze each prediction method. Case study results show that the latter method has a better prediction effect.

In this paper, a phased array beamforming method based on the rotational platform is proposed. According to this method, the height of a platform can be measured accurately by adjusting the azimuth and elevation angles to keep the beam always point to the nadir point. Thus, the phased array altimeter needs to compensate the intersection change of the beam-point and the nadir point as a consequence of the platform translation and rotation. This paper deduces functions of angle error and phase compensation to calibrate the beam-point by establishing motion model of the platform, provides the method of weight calculating, and analyzes the update rate of weight. The curves illustrating the changes in directions of beam point as azimuth and elevation change respectively are provided by simulating. Finally, by means of simulation, making comparison between compensated and non-compensated antenna beam direction, we find the non-compensated beam is off nadir point with the changing motion of platform, while after compensating, the beam can be calibrated and pointed to the nadir again.

The braided river delta develops in Triassic low oil formation in the block 9 of Tahe oilfield, but its sedimentation evolution process is unclear. By using sedimentation simulation technology, sedimentation process and distribution of braided river delta are studied based on the geological parameters including sequence stratigraphic division, initial sedimentation environment, relative lake level change and accommodation change, source supply and sedimentary transport pattern. The simulation result shows that the error rate between strata thickness of simulation and actual strata thickness is small, and the single well analysis result of simulation is highly consistent with the actual analysis, which can prove that the model is reliable. The study area belongs to braided river delta retrogradation evolution process, which provides favorable basis for fine reservoir description and prediction.

Radio Frequency Identification (RFID) is a technology to exchange messages by radio frequency (RF) between Reader and Tag. As a RFID test system, stability, consistency and adaptation are very important. At a certain budget, the above properties of the test system will be limited. In order to guarantee the performance of the system and get test results as accurate as possible, a lot of solutions should be applied to the whole system. Calibration is a very important part in every system. By proper calibration method, we can get a system with high precision, good stability and consistency. Furthermore, with calibration by hardware of high precision, all the test uncertainties will be able to trace back to that system. Certainly, the system used for calibration will be calibrated by another better system. In such way, we can cost down the test system and at the same time make sure the correctness of the test results. There are two ways of calibration, manually and automatically. Manual calibration requires mass labor which will absolutely raise the cost of total system. If the calibration could be done the same time the RFID system is testing, this will be the fast and most accurate way. In this paper, how to guarantee the accuracy of RFID system will be discussed, especially calibration.

As the number of vehicles increases significantly, traffic congestion has become a major social problem in recent years. Such a situation can be alleviated effectively with the emerging of vehicular ad hoc network (VANET)-based real-time path planning systems. However, existing systems face the challenges of poor anti-congestion capability and high complexity. To address the related issues, an road side unit (RSU)-based architecture is proposed in this paper, in which a city is divided into different areas, each with an RSU. Based on the architecture, a decentralized and hierarchical real-time path planning algorithm is proposed. The path planning problem is formulated from two layers, i.e., area path selection in upper layer and intra-area routing in bottom layer, both of which target to minimize the average travel time. Numerical results show that, our proposed algorithm inherits the anti-congestion capability and owns the advantage of low complexity, as compared with the shortest path algorithm and centralized algorithm.

In the early stage of oilfield development, there are only a few wells and well spacing can reach several kilometers. for the alluvial fans and other heterogeneous reservoirs, information from wells alone is not sufficient to derive detailed reservoir information. In this paper, the method of calculating sand thickness through geostatistics inversion is studied, and quantitative relationships between each sedimentary micro-facies are analyzed by combining with single well sedimentary facies. Further, the sedimentary facies plane distribution based on seismic inversion is obtained by combining with sedimentary model, providing the geological basis for the next exploration and deployment.

This paper presents a high-responsivity and high-speed InGaAs/InP PIN photodetector integrated onto the silicon waveguide substrate utilizing the divinyltetramethyldisiloxane-benzocyclobutene (DVS-BCB) adhesive bonding method. A grating coupler is adopted to couple light from the fiber to the silicon waveguide. Light in the silicon photonic waveguide is evanescently coupled into the photodetector. The integrated photodetector structure is first simulated using the FDTD (finite difference time domain) solutions software and the simulation results show a detection efficiency of 95%. According to the simulation result, the integrated photodetector is fabricated. The measured responsivity of the fabricated integrated photodetector with a detection length of 30 μm is 0.89 A/W excluding the coupling loss between the fiber and the grating coupler and the silicon propagation loss at the wavelength of 1550 nm with a reverse bias voltage of 3 V. Measured 3-dB bandwidth is 27 GHz using the Lightwave Component Analyzer (LCA). The eye diagram signal test results indicate that the photodetector can operate at a high speed of 40 Gbit/s. The integrated photodetector is of great significance in the silicon-based optoelectronic integrated chip which can be applied to the optical communication and the super node data transmission chip of the high-performance computer.

A simple and low-energy-consuming approach to synthesize highly stable and dispersive silver nanoparticle-graphene (AgNP-GE) nanocomposites has been developed, in which the stability and dispersivity of the composites are varied greatly with the pH value and temperature of the reaction. The results demonstrate that the optimal reaction conditions are pH 11 at room temperature for 70 min. As-synthesized composites display excellent antimicrobial activity, and can completely inhibit the growth of Escherichia coli cells at a concentration of 20 mg L(-1) (20 ppm). After treatment with 10 ppm AgNP-GE composites, the cells are killed completely within 3 h. The unique structure imparts such good antimicrobial properties to the composites. Firstly, the sheetlike AgNP-GE tends to be adsorbed and accumulated onto the surface of cells, which can change the permeability and enhance the antimicrobial activity. Secondly, Ag(+) released from AgNPs can act on the cells effectively and fully, thereby resulting in cell death.

In LLL images, there is a lot of random flicker noise. When denoising in LLL image, the traditional time-domain recursive noise reduction will bring smear in moving images. An improved algorithm is needed. To improve the time-domain recursive filter, the block that contain moving target in images should not participate the recursive algorithm. Time-spatial recursive denoising for LLL images based on motion detection is proposed to improve the time-domain recursive noise reduction. The blocks that contain moving object and background are processed respectively. Finally, simulation results show the effectiveness of the proposed method.

In this paper, we present a football event detection method by using multiple feature extraction and fusion. Instead of using low-level features, the proposed method is built upon visual, auditory features, text and audio keywords. Promising event detection results have been achieved. By using the proposed method, we have been able to detect the football events accurately. Experimental results have shown our proposed method is effective for football event detection.

This paper proposes a predictive controller based on adaptive resource management techniques for multi-service wireless cellular networks to keep the handoff failure rate (HFR) below a target value regardless of the traffic dynamics and bandwidth requirements of the service types. In this paper, we propose a neural nod feedback algorithm to control the reserved resource. Simulations results indicate that this algorithm can maintain target HFR for any service type even when the traffic load rapidly fluctuates and system parameters vary over a wide range. This algorithm has superior performance in terms of total carried traffic.

Semantic sports video analysis has attracted more and more attention recently. In this paper, we present a football event detection method by using multiple feature extraction and fusion. Instead of using low-level features, the proposed method is built upon visual, auditory features, text and audio keywords. Promising event detection results have been achieved. By using the proposed method, we have been able to detect the football events accurately. Experimental results have shown our proposed method is effective for football event detection.