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The rapid development of trade along the Belt and Road Initiative (BRI) directly affect economic growth and influence carbon emission. This study used fifty-two BRI countries out of the sixty-five countries that signed onto the BRI in 2013 for the empirical study. The panel data extracted is from the period 1993 to 2018. The study utilized the Common Correlated Effects Mean Group (CCEMG) and the Augmented Mean Group (AMG) estimation techniques for the empirical analysis. The findings revealed a positive link between imports and carbon emissions. Contrarily, exports revealed an inverse relationship with carbon emission, while foreign direct investment inflow revealed a positive but insignificant relationship with consumption-based carbon emission. Therefore, it is recommended that to control the environment and maintain ecological balance, there is the need to establish environmental protection clauses in many international treaties and domestic legislation while promoting international trade.

Due to the combined pressures of energy shortage and environmental degradation, bio-liquid fuels have been widely studied as a green, environmentally friendly, renewable petroleum alternative. This article summarizes the various technologies of three generations of biomass feedstocks (especially the second-generation, biomass lignin, and the third-generation, algae raw materials) used to convert liquid fuels (bioethanol, biodiesel, and bio-jet fuel) and analyzes their advantages and disadvantages. In addition, this article details the latest research progress in biomass liquid fuel production, summarizes the list of raw materials, products and conversion processes, and provides personal opinions on its future development. The aim is to provide a theoretical basis and reference for the optimization of existing technology and future research and development of biomass liquid fuels.

Seasonal or monthly databases of the diatom populations in specific bodies of water are needed to infer the drowning site of a drowned body. However, existing diatom testing methods are laborious, time-consuming, and costly and usually require specific expertise. In this study, we developed an artificial intelligence (AI)-based system as a substitute for manual morphological examination capable of identifying and classifying diatoms at the species level. Within two days, the system collected information on diatom profiles in the Huangpu and Suzhou Rivers of Shanghai, China. In an animal experiment, the similarities of diatom profiles between lung tissues and water samples were evaluated through a modified Jensen-Shannon (JS) divergence measure for drowning site inference, reaching a prediction accuracy of 92.31%. Considering its high efficiency and simplicity, our proposed method is believed to be more applicable than existing methods for seasonal or monthly water monitoring of diatom populations from sections of interconnected rivers, which would help police narrow the investigation scope to confirm the identity of an immersed body.

Forensic scholars are paying more attention to postmortem computed tomography (PMCT) and PMCT angiography (PMCTA), which are gradually becoming effective and practical methods in forensic practice. However, few studies have focused on the application of PMCTA to cardiac ventricular puncture-especially of the right ventricle. In this article, we introduce a pulmonary PMCTA approach by right ventricle cardiac puncture and its potential value in fatalities from pulmonary thromboembolism (PTE). The procedure was performed on 11 males and 6 females. PMCT was performed first; then a biopsy core needle was used for percutaneous puncture of the right ventricle under CT guidance. About 400 mL of contrast media was injected at a rate of 50 mL/8 s, followed by CT scanning. Visualization of the pulmonary artery contrast filling was complete in 9 cadavers, and the pulmonary arteries showed significant filling defects in 8 subjects. Unlike in clinical practice, the phenomenon of postmortem coagulation sometimes occurs in the vascular lumina after death. Therefore, the results of these 8 cases can only suggest or be highly suspicious of death from PTE. Then autopsy and histopathological examination confirmed that 4 of the above 8 patients were diagnosed with PTE; the remaining 4 had postmortem clot including chicken fat clot in the pulmonary artery. Pulmonary PMCTA approach is a simple, convenient, and effective method for the visualization of the pulmonary artery, which can be used as an effective auxiliary tool to identify PTE in forensic practice. It will also provide technical support to further investigate PTE imaging characteristics.

What is known and objective The frequent tenofovir disoproxil fumarate (TDF)-related adverse drug reactions (ADRs) are nephrotoxicity and bone toxicity; however, tooth-related ADRs of TDF have not been reported. We describe the case of a 41-year-old Han Chinese man with chronic hepatitis B with TDF-associated tooth loss. Case summary He presented with halitosis, gingival swelling and tooth loss after TDF use. After excluding the possibility of other drug-related ADRs, TDF was considered a possible cause and switched with tenofovir alafenamide fumarate (TAF). After 6 months, the oral symptoms disappeared, with no additional tooth loss. What is new and conclusion This is the first report of such ADRs. The ADR score was 7, indicating tooth loss as a potential TDF-related ADR.

The zero-shot classification algorithm has been widely concerned in recent years, in which the labeling of samples of a new category is unnecessary and the cost of annotations can be reduced in applications. This paper presents a zero-shot method for image classification based on word vectors enhancement and distance metric learning. Specifically, the convolutional neural network (CNN) is employed to extract image feature vectors which have the same dimension as semantic feature vectors. Then, an unsupervised learning method is applied on Wikipedia corpus for extracting word vectors and the skip-gram is used to obtain word vectors. The model of analysis dictionary learning is improved by reducing redundant information in word vectors. The obtained sparse vectors are used as semantic features and a distance metric learning method is employed to measure the distance between image features and semantic features. Finally, the classification is implemented by a nearest neighbor based classifier. The effectiveness of the proposed algorithm is validated on the AwA and CUB data sets. Experimental results demonstrate that the proposed method has good performance in terms of both accuracy and robustness.

The entire world is currently facing problems related to higher rates of human trafficking today, and the research shows that the higher percentage of victims are among children. Although countless children are reported missing every year, many of them remain untraced. This paper presents a novel use of deep learning methodology for fighting against human trafficking and identifying the reported missing child from their photos, with the help of face recognition. The first step is to register the children in the common portal to be verified before crossing the border of their country of origin. Secondly, the public can upload photographs of suspected missing children into a common portal with landmarks and remarks. Uploaded photos are automatically compared with the registered photos of the missing children from the repository, and the classification of the input children’s images is performed. The photo with the best match will be selected from the database. To do that, a deep learning model is trained to correctly identify the missing child from the missing children image database provided, using the facial images uploaded by the public. The Convolutional Neural Network (CNN), a highly effective deep learning technique for image-based methodology, is adopted here for face recognition purposes. Face descriptors are extracted at 99.98% from the images using a pre-trained CNN model VGG16-Face deep architecture.

Vehicle counting aims to calculate the number of vehicles in congested traffic scenes. Although object detection and crowd counting have made tremendous progress with the development of deep learning, vehicle counting remains a challenging task, due to scale variations, viewpoint changes, inconsistent location distributions, diverse visual appearances and severe occlusions. In this paper, a well-designed Vehicle Counting Network (VCNet) is novelly proposed to alleviate the problem of scale variation and inconsistent spatial distribution in congested traffic scenes. Specifically, VCNet is composed of two major components: (i) To capture multi-scale vehicles across different types and camera viewpoints, an effective multi-scale density map estimation structure is designed by building an attention-based mask refinement module. The multi-branch structure with hybrid dilated convolution blocks is proposed to assign receptive fields to generate multi-scale density maps. To efficiently aggregate multi-scale density maps, the attention-based mask refinement is well-designed to highlight the vehicle regions, which enables each branch to suppress the scale interference from other branches. (ii) In order to capture the inconsistent spatial distributions, a spatial-awareness block loss (SBL) based on the region-weighted reward strategy is proposed to calculate the loss of different spatial regions including sparse, congested and occluded regions independently by dividing the density map into different regions. Extensive experiments conducted on three benchmark datasets, TRANCOS, VisDrone2019 Vehicle and CVCSet demonstrate that the proposed VCNet outperforms the state-of-the-art approaches in vehicle counting. Moreover, the proposed idea can be applicable for crowd counting, which produces competitive results on ShanghaiTech crowd counting dataset.

The supersonic gas-jet target is an important experimental target for laser wakefield acceleration (LWFA), which has great potential for driving novel radiation sources such as betatron radiation and Compton scattering gamma rays. According to different electron acceleration requirements, it is necessary to provide specific supersonic gas jets with different density profiles to generate electron beams with high quality and high repetition rates. In this study, the interference images and density profiles of different gas-jet targets were obtained through a modified Nomarski interference diagnosis system. The relationships between the gas density and back pressure, nozzle structure, and other key parameters were studied. Targets with different characteristics are conducive to meeting the various requirements of LWFA.

Deep learning based on radiological methods has attracted considerable attention in forensic anthropology because of its superior classification capacities over human experts. However, radiological instruments are limited in their nature of high cost and immobility. Here, we integrated a deep learning algorithm and three-dimensional (3D) surface scanning technique into a portable system for pelvic sex estimation. Briefly, the images of the ventral pubis (VP), dorsal pubis (DP), and greater sciatic notch (GSN) were cropped from virtual pelvic samples reconstructed from CT scans of 1000 individuals; 80% of them were used to train and internally evaluate convolutional neural networks (CNNs) that were then evaluated externally with the remaining samples. An additional 105 real pelvises were documented virtually with a handheld 3D surface scanner, and the corresponding snapshots of the VP, DP, and GSN were predicted by the trained CNN models. The CNN models achieved excellent performance in the external testing using CT-based images, with accuracies of 98.0%, 98.5%, and 94.0% for VP, DP, and GSN, respectively. When the CT-based models were applied to 3D scanning images, they obtained satisfactory accuracies above 95% on the VP and DP images compared to the GSN with 73.3%. In a single-blind trial, a multiple design that combined the three CNN models yielded a superior accuracy of 97.1% with 3D surface scanning images over two anthropologists. Our study demonstrates the great potential of deep learning and 3D surface scanning for rapid and accurate sex estimation of skeletal remains.

Although recent object detectors have shown excellent performance for vehicle detection, they are incompetent for scenarios with a relatively large number of vehicles. In this paper, we explore the dense vehicle detection given the number of vehicles. Existing crowd counting methods cannot directly applied for dense vehicle detection due to insufficient description of density map, and the lack of effective constraint for mining the spatial awareness of dense vehicles. Inspired by these observations, a conceptually simple yet efficient framework, called CODAN, is proposed for dense vehicle detection. The proposed approach is composed of three major components: (i) an efficient strategy for generating multi-scale density maps (MDM) is designed to represent the vehicle counting, which can capture the global semantics and spatial information of dense vehicles, (ii) a multi-branch attention module (MAM) is proposed to bridging the gap between object counting and vehicle detection framework, (iii) with the well-designed density maps as explicit supervision, an effective counting-awareness loss (C-Loss) is employed to guide the attention learning by building the pixel-level constrain. Extensive experiments conducted on four benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art methods. The impressive results indicate that vehicle detection and counting can be mutually supportive, which is an important and meaningful finding.

Logistics activities are an important source of energy consumption and environmental issues. Research conclusions and practical experience show that promoting the development of container multimodal transport is an effective way to reduce the level of carbon footprint. The key to influencing the development of container multimodal transport lies in the cooperation of all participants and links (modes of transport, transport businesses). Evaluating the synergy degree is a key step in this development process. This paper takes the whole process of container multimodal transportation as the research perspective, analyzes the operation process, and treats the process as a production system composed of four subsystems: facilities and equipment, organizational management, business operations, and information interactions. Through in-depth interviews and an analysis of the academic literature and policy documents, we establish a synergy degree evaluation index system and measurement model of container multimodal transport based on synergy theory and case studies. The research results are consistent with the actual situation. From 2015 to 2018, the synergy of container multimodal transport system of China&rsquo, s G port developed slowly, but generally moved in a more orderly direction.

Predictive current control strategy has captured much concern recently in power converters. As for the multi-pulse flexible-topology thyristor rectifiers (m-PFTTR), the symmetry of the input line-to-line voltages has great influence upon the implementation of the control strategy. Existing literatures have mainly focused on the implementation under ideal condition, i.e., the input line-to-line voltages, being critical to the predictive current controller, were supposed to be symmetrical. In fact, the unbalance of the input line-to-line voltages usually exists in the windings of the zigzag transformer at front end and the A/D converter of the digital signal processor (DSP). This paper presents a new method to dynamically regulate the input line-to-line voltages to ensure the symmetry of each other. It is realized through the instantaneous correction of the bias values in the course of sampling, and the correction is embodied in the modified control algorithm. The paper includes analyses of the m-PFTTR as well as the control algorithm, simulation and experiments. Simulation and experimental results demonstrate the effectiveness of the presented method.

Rolling bearing is one of the most commonly used components in rotating machinery. It's so easy to be damaged that it can cause mechanical fault. Therefore, it is of great significance for its condition monitoring and fault diagnosis. However, the traditional diagnosis methods still suffer from two problems, which are (1) the information density of the monitoring data is low because of huge monitoring data amount, and (2) the requirements of domain expertise and prior knowledge for sensitive feature extraction. Aiming at above problems, a new diagnosis method based on compressed sensing (CS) and convolution neural network (CNN) is proposed in this paper. The method consists of three key steps. First, the original monitoring signals are converted into compressed sensing domain for reducing data amount and improving its information density by using compressed sensing method. Second, the compressed signal is used as the input of the convolution neural network to extract sensitive features adaptively, and to realize the fault intelligence diagnosis. Finally, several groups of experiments are carried out to validate the feasibility of the proposed method in this paper, and the diagnostic accuracy achieves 93.7%, which is far higher than the traditional methods.

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer death globally. More accurate and reliable diagnostic methods/biomarkers are urgently needed. Joint application of metabolomics and transcriptomics technologies possesses the high efficiency of identifying key metabolic pathways and functional genes in lung cancer patients. In this study, we performed an untargeted metabolomics analysis of 142 NSCLC patients and 159 healthy controls; 35 identified metabolites were significantly different between NSCLC patients and healthy controls, of which 6 metabolites (hypoxanthine, inosine, L-tryptophan, indoleacrylic acid, acyl-carnitine C10:1, and lysoPC(18:2)) were chosen as combinational potential biomarkers for NSCLC. The area under the curve (AUC) value, sensitivity (SE), and specificity (SP) of these six biomarkers were 0.99, 0.98, and 0.99, respectively. Potential diagnostic implications of the metabolic characteristics in NSCLC was studied. The metabolomics results were further verified by transcriptomics analysis of 1027 NSCLC patients and 108 adjacent peritumoral tissues from TCGA database. This analysis identified 2202 genes with significantly different expressions in cancer cells compared to normal controls, which in turn defined pathways implicated in the metabolism of the compounds revealed by metabolomics analysis. We built a fully connected network of metabolites and genes, which shows a good correspondence between the transcriptome analysis and the metabolites selected for diagnosis. In conclusion, this work provides evidence that the metabolic biomarkers identified may be used for NSCLC diagnosis and screening. Comprehensive analysis of metabolomics and transcriptomics data offered a validated and comprehensive understanding of metabolism in NSCLC.

In order to conform the development trend of distribution network, the requirement of terminal need to have the ability of Internet of Things (IoT) is becoming more and more prominent. Firstly, the paper expounds the development trend of low-voltage distribution network and emphasizes the technical transformation of the transformer terminal unit. Secondly, it puts forward the software and hardware architecture of the transformer terminal unit which is suitable for the IoT. Then, it introduces the key technologies related to the transformer terminal unit. Finally, it analyzes the next stage of the work plan for the improvement of the function.

At present, APT attack detection has become the focus of the network security protection field. APT attacks are one of the most difficult attacks in cyber attacks. The complexity and variability of APT attack behavior greatly increases the difficulty of attack detection. In order to cope with APT attack, some well-known network security companies at home and abroad have developed a commercial APT intrusion detection system. This highly targeted attack can not be identified by the traditional intrusion detection system. Therefore, in order to deal with this new type of cyber attack. The paper proposes a new method to detect APT attack from different organizations. Data mining algorithm is used to analyze every organization’s APT network attack behavior and obtain association rules, so as to customize the design of the Snort rules and apply them to intrusion detection system. Experiments have shown that the evaluation index of the intrusion detection system using the extended Snort rule is significantly better than the traditional Snort intrusion detection system when detecting the same test data. The precision of the extended Snort intrusion detection system is as high as 98.3%, and the false alarm rate is almost 0, which ultimately achieves the purpose of APT detection.

介绍了 Ф 420 mm熔石英高次非球面透镜的加工与检测方法。对现有数控加工工艺进行了优化，通过分工序加工方式，依次采用机器人研磨、抛光和离子束修形技术完成了透镜的加工。进行非球面透镜检测时，考虑透镜的凹面为球面，利用球面波干涉仪对其面形进行了直接检测，剔除干涉仪标准镜镜头参考面误差后，透镜凹面的精度达到0.011λ-RMS；针对透镜的凸面为高次非球面，采用基于背后反射自准法的零位补偿技术对其进行面形检测，其精度达到0.013λ-RMS。最后，采用一块高精度标准球面镜对加工后透镜的透射波前进行了自消球差检测，得到其波前误差为0.013λ-RMS。试验结果表明，非球面透镜各项技术指标均满足设计要求。所述工艺方法亦适用于更大口径的非球面透镜及其他类型非球面光学元件的高精度加工.

In recent years, human activity recognition (HAR) has attracted a lot of attention due to its wide application, such as indoor positioning and navigation. This paper proposes a MconvLSTM to construct a multi-unit deep network structure,which can effectively improve the accuracy of HAR. Firstly, the input data is dimensionally expanded. Secondly, multiple convLSTM module are used to input data from different sensors to achieve partial weight sharing. Multiple outputs are merged finally. The experimental results show that the partial weight sharing mechanism and dimension expansion effectively improve the extraction of single sensor features, aiming to improve the activity recognition rate. Using public UCI datasets for testing, the accuracy is significantly improved compared to traditional convLSTM network results.

The vertical external cavity surface emitting laser (VECSEL) is one of the hottest research fields of semiconductor lasers, due to its high power and good beam quality. However, there are few reports about how to systematically design the active region of VECSEL. In this paper, the gain design of quantum wells, which are the most important region within the VECSEL, is carried out. To achieve low power consumption under high temperature condition, epitaxial structure of the VECSEL is optimized by using the commercial software PICS3D. Firstly, the relationship between the structure of quantum well and the gain is simulated by the k·p method. Then, the gain spectra of quantum wells at different carrier densities and temperatures are compared with each other, and the optimal composition and thickness of quantum well are thus determined. The temperature drift coefficient is 0.36 nm/K, obtained by simulating the drift of the gain peak wavelength at the working temperature. Finally, the gain spectra of quantum wells with five different barriers are compared with each other. The slight blue shift of the gain peak in the quantum well with five different barriers accommodates the different emission thermal drifts of the quantum well at high temperature operation. With the GaAsP barriers on both sides of quantum well the gain characteristics of quantum wells can be improved efficiently. The designed structure is deposited by the MOCVD system. According to the reflection spectrum of the gain chip, measured by ellipsometer, the stop-band over 100 nm is centered at the about 970 nm wavelength, confirming accurate growth of the VECSEL. The 808 nm pump laser is focused on the surface of VECSEL chip at an incident angle from 30° to 50°. The VECSEL light-light characteristics are tested under the output coupling mirror with different reflectivity. The output power of VECSEL with a 97.7% reflectance output coupling mirror reaches 9.82 W at the pumping power of 35 W, without saturating the power curve. By using the external mirrors with different reflectivity, there appears the wavelength shift with the pumping power changing from 0.216 nm/W to 0.16 nm/W. Thus, the internal heating effects are different for VECSEL with different mirrors. The divergence angles at two orthogonal directions are 9.2° and 9.0°, respectively. And the circle profile of optical field shows good symmetry.

Accurate medium and long-term load forecasting plays an important role in the planning, construction and operation of power system. In this paper, a medium and long term electricity demand forecasting method of different industries based on deep belief network is put forward. In order to cope with many complicated factors in medium and long term electricity demand forecasting, the proposed method takes advantage of powerful function modeling ability of deep belief network and selects a number of quantifiable main influencing factors as input variables, and establishes a medium and long term electricity demand forecasting model based on deep belief network for each load types classified of different industries. Simulation example shows that the proposed method has higher prediction accuracy than the traditional neural network algorithm.

With the rapid development of Internet cloud computing technology, services such as cloud storage and cloud disk become a user data storage and backup tool. But at the same time, security of user data has also been great threatened. In this condition, a security enhanced cloud storage access control system based on attributes has been designed and implemented. The system generates the access policy on the client side, implements the access control through the XACML framework, stores user data by using symmetric encryption mechanism at first. The attribute-based encryption mechanism is used to protect the small-scale data–symmetric encryption Key, the data stored in the cloud, provides cloud storage users a safe, flexible, fine-grained and scalable access control function. According to the experimental results, the system can meet the functional requirements, provide a secure access control function. It’s in line with the design expectations at security and performance.

Deteriorated stencil is the main cause of product quality failure for solder paste printing. Frequent stencil cleaning helps to reduce the quality loss, but usually results in an increased cost of downtime. In this paper, an approach for controlling the stencil cleaning is proposed and the optimal decision which balances the quality losses and the downtime losses is obtained based on renewal reward theorem. The degradation of the stencil printing capability is modelled by a Markov chain, and the product quality loss is estimated.

This paper introduces the DP-QPSK modulation coherent reception technology,the overall design and other key is sues for 100 Gbit/s DP-QPSK modulation. Using the technologies based on DSP, it realizes the long-range transmissions of 100 Gbit/s optical systems to achieve optical signal dispersion compensation, polarized solution reuse and phase estimation. The effect of this scheme is verified with OptiSystem and the simulation results indicate that this with the help of DSP module for processing of the received signal, the last constellation is ideal and scheme is implemented simply and has high reliability, and it also has reference significance for the optimization of coherent optical detection. The results show that the technical plan is easy and reliable, which has reference significance on optimization of coherent optical detecting hardware.

In order to consider the uncertainty of DG and loads, the interval number is used to describe the uncertainty of the wind speed and loads. The optimal selection and configuration of distributed generators model is established for distribution systems. The largest investment income for independent power producer of DG is taken as the objective function, and interval power flow is introduced to determine whether the planning scheme is a violation of nodal voltage constraints or power transmission constraints. The model was resolved by the proposed harmony search algorithm. Compared with the traditional probabilistic optimization model, the proposed model is introduced to cope with the uncertainties in optimal selection and configuration of DG and avoid the need for any probability information or membership functions of renewable energy sources and loads.

Cloud Storage, which provides cost-efficient and scalable storage services, has emerged as a hot paradigm today. However, in the resource outsourcing environment such as cloud storage, the resource owner is separated from the resource superintendent, and the authorization decision is made by the untrusted outsourcing server, as a result of which the correct enforcement of the access control policies cannot be ensured. To keep the data confidential against unauthorized parties, cryptographic access control must be applied. In this paper, we present a new cryptographic approach of implementing Core Role based Access Control Model named ABE-RBAC. We use Attribute Based Encryption (ABE) to manage users, roles, permissions, as well as user role assignments (URA) and role permission assignments (RPA), which enables the resource owner to fully control the authorization management, and ensures the proper enforcement of access control polices. This is the first cryptographic core RBAC enforcement that completely conform to the standard GB/T 25062-2010.

The Internet has become an indispensable part of people’s work and life. It provides favorable communication conditions for malwares. Therefore, malwares are endless and spread faster and become one of the main threats of current network security. Based on the malware analysis process, from the original feature extraction and feature selection to malware detection, this paper introduces the machine learning algorithm such as clustering, classification and association analysis, and how to use the machine learning algorithm to malware and its variants for effective analysis.

According to a certain type of flying in the no imitation gull wing design straight wing with man. Then the bionic wing and conventional airfoil are simulated, and compared the two kinds of wing aerodynamic parameters. The study found, hale no flow man-machine bionic wing design can effectively improve the wing, reduce the upper wing surface flow separation, reduce the lift loss, reduce the pressure drag, improve the lift drag ratio of the wings. In this paper, the simulation results for the design of the UAV wing provides certain reference.

With reference to a certain type of flying drones with imitation airfoil design a seagull flat wings and on the basis of its wing tip winglet in this paper. Through to the numerical simulation of two wings, it is concluded that the bionic wing aerodynamic performance is superior to the conventional airfoil wing, after adding wing tip winglet bionic wings effectively reduced the downwash velocity, reduce the induced drag, makes the wing aerodynamic performance is improved. Provide theoretical reference for the design of the uav wing

First of all, a new multi-axis wrist force sensor with double bending beams was designed to meet the requirements of the intelligent manipulator. The new structure was easy to process and install. The impact of deformation, error and asymmetry caused by machining and aging treatment was avoided, and the performance of sensor was improved. Secondly, mechanical model of the sensor was established, and the force analyses from different directions were made. Thirdly, after strain gauges were pasted and the bridge was designed, loading tests to single beam and whole sensor were carried out, theoretical calculation and finite element analysis were completed, and the relative error was analyzed. Finally, the causes of relative error in different loading test were studied, and a solution of sensor calibration was put forward.

Previous studies reported thatNaja naja atravenom (NNAV) inhibited inflammation and adjuvant arthritis. Here we investigated the role of NNAV in regulation of immune responses in mice. Oral administration of NNAV to normal mice showed significant increase in natural killer cell activity, B lymphocyte proliferation stimulated by lipopolysaccharides, and antibody production in response to sheep red blood cells. Meanwhile, NNAV markedly decreased T lymphocyte proliferation stimulated by concanavalin A, arrested the cell cycle at G0/G1phase, and suppressed CD4 and CD8 T cell divisions. Furthermore, NNAV inhibited the dinitrofluorobenzene-induced delayed-type hypersensitivity reaction. This modulation of immune responses may be partly attributed to the selective increase in Th1 and Th2 cytokines (IFN-γ, IL-4) secretion and inhibition of Th17 cytokine (IL-17) production. In dexamethasone-induced immunosuppressed mice, NNAV restored the concentration of serum IgG and IgM, while decreasing the percentage of CD4 and CD8 T-cell subsets. These results indicate that NNAV enhances the innate and humoral immune responses while inhibiting CD4 Th17 and CD8 T cell actions, suggesting that NNAV could be a potential therapeutic agent for autoimmune diseases.

The analysis on fault characteristic of Distribution Electronic Power Transformer (D-EPT) can help design its operation and protection scheme, hence improve the reliability of the D-EPT as well as the distribution power grid. The DC pole-to-pole short-circuit fault at DC-Link is analyzed with R-L-C resonant mode and free-wheeling mode, and the theoretical analysis on the short-circuit voltage and current is based on several influence factors and relevant system parameters, which indicates that the fault characteristics are related to the configuration, operation and system parameters of D-EPT.

ARGO-YBJ is a full coverage layer of Resistive Plate Counters (RPCs) covering an area of about 5.800 m 2 to be installed at the YangBaJing Laboratory (Tibet, China) at 4.300 m a.s.l . The DAQ system is based on a Central Station which receives the detector information from distributed readout electronics. Data from each RPC are acquired from a Receiver Card which reads out and digitizes the space and time information from 10 pick-up pads and gives out the pad multiplicity for trigger purposes. The Receiver Cards are hosted in Local Stations which Group 12 RPCs. On trigger occurrence, each Local Station sends the collected data to the Central Station. Both the detector and the electronics allow 1 ns accuracy in time measurements. In this work a detailed description of the Local Station and the Trigger System are presented.