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The EHV transformer is one of the core transmission equipment of China’s EHV/UHV transmission system, and its reliability directly affects the operational stability of the power grid. In the long-term operation process, affected by sudden short-circuit, resonance, and aging, it will produce faults such as loose winding, loose iron core, inverted discharge, and shim block shedding. Most of the methods for transformer fault diagnosis need off-line monitoring and cause some damage to transformer equipment. At present, the use of transformer acoustic signal for diagnosis and identification is in the initial stage, and there is still room for improvement in the accuracy of fault diagnosis, and how to classify the unbalanced data samples also deserves attention. Therefore, an EHV transformer fault diagnosis method based on acoustic perception and sparse autoencoder-BNCNN is proposed in this paper. Firstly, the sparse autoencoder is trained to ensure the processing ability of the network on imbalanced data. Then, the high-step CNN network is optimized using the BN layer, the data is fed into the improved CNN network for feature extraction, and the classifier outputs the fault diagnosis results. Finally, the transformer acoustic data are processed, and the dataset is formed to evaluate the performance of the proposed method. The experimental results show that the proposed method not only improves the accuracy substantially compared with other methods but also has the ability to handle imbalanced data.

Aiming for on-line monitoring of typical mechanical faults of transformer, a two-channel MEMS digital silicon wheat voice print detection system is developed, which consists of two channel MEMS digital silicon wheat array, the host of sound acquisition system and the upper computer software with the function of displaying real-time waveform and spectrum analysis. The device can operate reliably in complex environments by adjusting the array spacing, improving the power quality, and adjusting the network protocol. Further, different test environments were built to test the resolution performance of the equipment to the center frequency of the sound source at different distances and angles of the sound source. The results show that the system can realize the resolution of the center frequency of different sound source distance and Angle, and still has a good center frequency resolution performance in the case of environmental noise. At the same time, the system has strong stability, environmental adaptability, easy installation and non-contact detection characteristics, can be used for real-time detection and diagnosis of transformer faults.

Under the normalization of epidemic control in COVID-19, it is essential to realize fast and high-precision face recognition without feeling for epidemic prevention and control. This paper proposes an innovative Laplacian pyramid algorithm for deep 3D face recognition, which can be used in public. Through multi-mode fusion, dense 3D alignment and multi-scale residual fusion are ensured. Firstly, the 2D to 3D structure representation method is used to fully correlate the information of crucial points, and dense alignment modeling is carried out. Then, based on the 3D critical point model, a five-layer Laplacian depth network is constructed. High-precision recognition can be achieved by multi-scale and multi-modal mapping and reconstruction of 3D face depth images. Finally, in the training process, the multi-scale residual weight is embedded into the loss function to improve the network's performance. In addition, to achieve high real-time performance, our network is designed in an end-to-end cascade. While ensuring the accuracy of identification, it guarantees personnel screening under the normalization of epidemic control. This ensures fast and high-precision face recognition and establishes a 3D face database. This method is adaptable and robust in harsh, low light, and noise environments. Moreover, it can complete face reconstruction and recognize various skin colors and postures.

During coronavirus (SARS-CoV2) the number of fraudulent transactions is expanding at a rate of alarming (7352421 online transaction records). Additionally, the Master Card (MC) usage is increasing. To avoid massive losses, companies of finance must constantly improve their management information systems for discovering fraud in MC. In this paper, an approach of advancement management information system for discovering of MC fraud was developed using sequential modeling of data depend on intelligent forecasting methods such as deep Learning and intelligent supervised machine learning (ISML). The Long Short-Term Memory Network (LSTM), Logistic Regression (LR), and Random Forest (RF) were used. The dataset is separated into two parts: the training and testing data, with a ratio of 8:2. Also, the advancement of management information system has been evaluated using 10-fold cross validation depend on recall, f1-score, precision, Mean Absolute Error (MAE), Receiver Operating Curve (ROC), and Root Mean Square Error (RMSE). Finally various techniques of resampling used to forecast if a transaction of MC is genuine/fraudulent. Performance for without re-sampling, with under-sampling, and with over-sampling is measured for each Algorithm. Highest performance of without re-sampling was 0.829 for RF algorithm-F score. While for under-sampling, it was 0.871 for LSTM algorithm-RMSE. Further, for over-sampling, it was 0.921 for both RF algorithm-Precision and LSTM algorithm-F score. The results from running advancement of management information system revealed that using resampling technique with deep learning LSTM generated the best results than intelligent supervised machine learning.

Objective: We investigated the temporal expression profiles of long noncoding RNA (lncRNA) and mRNA in the peripheral blood of pigs during development and identified the lncRNAs that are related to the blood-based immune system.Methods: Peripheral blood samples were obtained from the pigs at 0, 7, 28, and 180 days and 2 years of age. RNA sequencing was performed to survey the lncRNA and mRNA transcriptomes in the samples. Short time-series expression miner (STEM) was used to show temporal expression patterns in the mRNAs and lncRNAs. Gene ontology and Kyoto encyclopedia of genes and genomes analyses were performed to assess the genes’ biological relevance. To predict the functions of the identified lncRNAs, we extracted mRNAs that were nearby loci and highly correlated with the lncRNAs.Results: In total of 5,946 lncRNA and 12,354 mRNA transcripts were identified among the samples. STEM showed that most lncRNAs and mRNAs had similar temporal expression patterns during development, indicating the expressional correlation and functional relatedness between them. The five stages were divided into two classes: the suckling period and the late developmental stage. Most genes were expressed at low level during the suckling period, but at higher level during the late stages. Expression of several T-cell-related genes increased continuously during the suckling period, indicating that these genes are crucial for establishing the adaptive immune system in piglets at this stage. Notably, lncRNA TCONS-00086451may promote blood-based immune system development by upregulating nuclear factor of activated T-cells cytoplasmic 2 expression.Conclusion: This study provides a catalog of porcine peripheral blood-related lncRNAs and mRNAs and reveals the characteristics and temporal expression profiles of these lncRNAs and mRNAs during peripheral blood development from the newborn to adult stages in pigs.

The design of the transformer can withstand the mechanical shock during transportation and the large electromechanical power generated by short circuit and switching surge current. However, due to these forces, the windings may be slack, misaligned or deformed. The windings are the core component of the transformer, which directly affects the diagnosis of the transformer and even the entire power system. This paper proposes a feature extraction method for transformer winding vibration signals based on improved variational modal decomposition (IVMD). The improved variational modal decomposition is used to decompose the vibration signals under different states of transformer windings into a series of finite bandwidth eigenmode functions (IMF); on the basis of feature extraction, this paper also introduces the correlation vector machine (RVM), through the pattern recognition algorithm to establish the relationship between the weight divergence and the transformer winding operating conditions, and through the harmony search algorithm to optimize the option to the RVM kernel function. The algorithm proposed in this paper can effectively extract the characteristics of the mechanical vibration signal of the transformer winding and improve the classification accuracy of RVM.

Metal nitrides are a new type of electrocatalysts applied in energy storage and conversion systems. In this report, hierarchical cobalt nitride micros balls with heterostructure are prepared through nitridation of Co(OH)(CO3)0.5·0.11H2O counterparts. The microballs are composed of nanowires, which enchase nanoparticles with heterostructure in terms of Co5.47N/Co3N. The catalyst presented here exhibits a high power conversion efficiency of 7.3% for reduction of I3−, comparable to that of 7.6% for platinum-based catalysts. In alkaline electrolyte (1 mol L−1 KOH), only 55 and 290 mV of overpotentials are required to achieve a current density of 10 mA cm−2 for hydrogen evolution and oxygen evolution reactions, respectively. The superior electrochemical performance of the catalyst is attributed to the synergistic effect resulting from Co5.47N/Co3Nheterostructure.

Collecting a vast amount of face data with identity labels to train a convolutional neural network is an effective mean to learn a discriminative feature representation for face recognition. However, the datasets with larger scale often contain more noisy labels, that directly affects the ultimate performance of the learned model. This paper proposes an end-to-end feature learning method with robustness to noisy label. First, a data filtering method is proposed to automatically online filter out the data with false label, by checking the consistency between the annotated label and the results of top-N prediction. Then the loss functions of softmax and center loss are simply revised to only supervise the reserved feature. Finally, we use MS-Celeb-1M dataset, which contains massive noisy labels, to train a 128-D feature representation without any pre-train or data pre-clean. A single learned model gets an accuracy of 99.43% on LFW test set, that is very close to the model trained using the clean data.

A typical stereo matching algorithm involves calculating of the matching cost and smoothness constraint. Recently some algorithms show the good performances by learning the matching cost, but the learning of smoothness constraint have been little researched. This paper focuses on both aspects by using convolutional neural networks. The proposed method first learns a Euclidean embedding for each image using a convolutional neural network with a triplet-based loss function, where the matching cost is directly computed by the squared L2 distances between two vectors in the embedding space. Then we use similar convolutional neural networks to learn the smoothness constraint, and a generalized Semiglobal Matching algorithm is proposed to estimate the disparity. The proposed method has a comparable performance with the state-of-the-art algorithms by using smaller and shallower networks, and the experiments show that learning both of matching cost and smoothness constraint has positive effects for stereo matching.

Waste incineration is a process of full combustion reaction between waste and oxygen at high temperature. It is a new problem whether the special natural environmental conditions of Tibet Plateau, such as low air pressure, low oxygen content and low temperature, will affect the waste incineration in the plateau area. In this work, the influence of different parameters on MSW incineration efficiency and flue gas emission were investigated. The results showed that the temperatures exhibited a significant impact on the flue gas emission. Under the lower temperature, CO was determined to be the main pollutant. With the increase of temperature, NOx became the main pollutant. The optimal temperature range of flue gas emission was between 800ºC to 900ºC. A slight negative pressure in incinerator was benefit for incineration system safety and flue gas emissions. The optimal range was -50Pa to 0Pa. Lower oxygen content (3%-6%) in the incinerator affected the incineration efficiency and flue gas emission. Meanwhile, the high oxygen content had no obvious impact on the flue gas emission, but the cost increased and the service life of the incinerator was affected. The optimal oxygen content in the incinerator was controlled at 6%-8%. Furthermore, the air temperatures, pressures and oxygen content in the natural environment had no significant effect on MSW incineration process. Increasing the air volume would bring about the increase of N 2 in the incinerator. This work provides the basic data support for MSW incineration technology in plateau area.

Gene expression profiles of blood can reflect the physiopathologic status of the immune system. The dynamic microRNA (miRNA) expression profiles of peripheral blood from pigs at different developmental stages, and how differential expression of miRNAs might relate to immune system development, are unknown. In this study, peripheral blood samples taken at five developmental stages were used to construct 15 miRNA libraries (three biological replicates/stage): 0 days (newborn), 30 days (weaning), 60 days (weaned), and 180 and 360 days (puberty). We identified 295 known mature miRNAs. Hierarchical clustering of the miRNA expression profile showed significant differences between individuals at the neonatal and postnatal stages. Functional enrichment analysis revealed that miRNAs differentially expressed between pairwise comparisons of the developmental stages were over-represented in immune-related pathways such as toll-like receptor signaling. The time-course of expression of the over-representated miRNAs exhibited a pattern of steady decline over time, for both the complete miRNA compendium and immune-related miRNAs. We identified six marker miRNAs that were highly negatively correlated with chronologic age and enriched for genes involved in immune-related pathways. This study of a peripheral blood miRNA transcriptome offers insight into immune system development in swine and provides a resource for pig genome annotation.

This paper presents an in-depth study on the indirect drying characteristics and interface renewal. By comparing the drying rate curve, it is found that shortening the surface drying stage, which contains a repeated evaporation-diffusion-condensation process of moisture, is the key to improve the total drying efficiency. By stirring the coal and realizing the interface renewal between the bottom layer and the surface layer, the drying efficiency reached seven times than that of static indirect drying. Based on indirect heat transfer with high heat and mass transfer rate, a new type of indirect heat transfer moving bed coal moisture control device is designed. At the same time, Fluent fluid mechanics software is used for mathematical modeling and simulation experiments. It is proved that the designed moving bed coal moisture control device has a good application prospect in coal pre-drying technology.

In order to develop a secure and competent technique to express the human immune gene for fighting infections, we cloned and expressed the BD2/3 using VR1020 (a eukaryotic expression plasmid). BD2/3 contains human β-defensin 2 (BD2) and human BD3. To explore safe and effective DNA delivery molecules in vitro and in vivo, the fusion genes of BD2/3 were used as an immune-labelled gene to verify transfection effectivness of modified chitosan (CS). Plasmid of VR1020-BD2/3 was packed with biomaterials: CS, average molecular weight: 25000D; polyethylene glycol-O-chitosan-polyethylenimine (PEG-O-CS-PEI); liposomes (LP); polyamine cationic liposomes (PCL); polyamine cationic liposomes of protamine (PCL-protamine) by ionotropic gelation. We observed that BD2/3 fusion gene showed high bioactivity in vitro and in vivo. The BD2/3 fusion protein inhibited the proliferation of bacteria (S. aureus, S. pneumoniae, P. aeruginosa and E. coli). The Kunming mice were immune to these nanoparticles and we analyzed their delivery efficiency and gene expression effect. BD2/3 results in multiple changes of innate and required immune system of mice. BD2/3 increases expression of IgG, IgG1, IgG2a, IL-2, IL-6, IFN-γ, as well as of lymphocytes and monocytes. Following challenge with virulent E. coli, CD4+ and CD8+ positive T-cell counts were highly elevated in the BD2/3 immunized mice, resulting in higher survival rates of mice. These results indicate that nanoparticles containing modified CS and BD2/3 are potentially safe and effective drugs in vivo to improve the immunity against bacterial infection and enhance innate immunity and adaptive immunity against infectious diseases.

Highlights • Knockdown of TGF-β1 gene expression by shRNA inhibited PRRSV replication in pig immune cells and increased the viability of infected cells. • Silencing TGF-β1 gene expression by shRNA enhanced the antiviral immunity and reduced PRRSV yield in the infected cells. • Silence of TGF-β1 gene expression by shRNA can facilitate the control of PRRSV infection in pigs., Transforming growth factor beta 1 (TGF-β1) was of importance in the pathogenesis of porcine reproductive and respiratory syndrome virus (PRRSV). To determine whether knockdown of TGF-β1 gene expression could facilitate the control of PRRSV infection, specific sequences for expressing shRNA targeted to porcine TGF-β1 gene were synthesized and cloned into pSilencer 3.1-H1 neovector. Then they were used to transfect peripheral blood mononuclear cells of Tibetan pig (Tp-PBMCs) followed by PRRSV inoculation. The positive recombinant plasmids were screened for inhibition of TGF-β1 gene expression by real-time quantitative RT-PCR. Conversely, the mRNA level of PRRSV in shRNA treated Tp-PBMCs dramatically decreased, and there were significant increases of the transcription of immune genes, such as interleukin-2 (IL-2), interleukin-4 (IL-4), interferon-alpha (IFN-α), interferon-gamma (IFN-γ), tumor necrosis factor-alpha (TNF-α), toll-like receptor 3 (TLR3), toll-like receptor 7 (TLR7), Myeloid differentiation primary response gene (88) (MyD88), and interleukin-27p28 (IL-27p28). However, the expressions of IL-8 and IL-10 genes significantly reduced in comparison to the control infected cells. In addition, transfection with the shRNA plasmids significantly elevated the viability of immune cells. Therefore the knockdown of TGF-β1 gene expression by shRNA not only inhibits the replication of PRRSV but also improves immune responsiveness following viral infection, suggesting a novel way to facilitate the control of PRRSV infection in pigs.

Background Species living at high altitude are subject to strong selective pressures due to inhospitable environments (e.g., hypoxia, low temperature, high solar radiation, and lack of biological production), making these species valuable models for comparative analyses of local adaptation. Studies that have examined high-altitude adaptation have identified a vast array of rapidly evolving genes that characterize the dramatic phenotypic changes in high-altitude animals. However, how high-altitude environment shapes gene expression programs remains largely unknown. Findings We generated a total of 910 Gb of high-quality RNA-seq data for 180 samples derived from 6 tissues of 5 agriculturally important high-altitude vertebrates (Tibetan chicken, Tibetan pig, Tibetan sheep, Tibetan goat, and yak) and their cross-fertile relatives living in geographically neighboring low-altitude regions. Of these, ∼75% reads could be aligned to their respective reference genomes, and on average ∼60% of annotated protein coding genes in each organism showed FPKM expression values greater than 0.5. We observed a general concordance in topological relationships between the nucleotide alignments and gene expression–based trees. Tissue and species accounted for markedly more variance than altitude based on either the expression or the alternative splicing patterns. Cross-species clustering analyses showed a tissue-dominated pattern of gene expression and a species-dominated pattern for alternative splicing. We also identified numerous differentially expressed genes that could potentially be involved in phenotypic divergence shaped by high-altitude adaptation. Conclusions These data serve as a valuable resource for examining the convergence and divergence of gene expression changes between species as they adapt or acclimatize to high-altitude environments.

To optimize the economics of electro-dewatering system in a new multi-layer vertical electric field for activated sludge biosolids, different-sized experiments were carried out in this study, including bench-scale, pilot-scale and industrial-scale applications. A bench-scale system was established to determine the optimal parameters of the new structure, including the mechanical pressure level, dewatering extent, processing time and sludge cake thickness. Then, a pilot-scale automated electro-dewatering system was developed to investigate the cathode fouling problems during continuous operations. A complete industrial-scale electro-dewatering system was established with 80 t/d of sludge dewatering capacity based on the above work. The operational stability, the service life of the core parts and the economics were evaluated during a long time operating. Under various optimized parameters and control modes, 69% of the moisture in mechanically dewatered sludge could be reduced within 7–8 min. The average energy consumption was 0.123 kWh/kg dewater at 41 kg dewater /(m 2 h) of anode processing capacity and the anode side temperature was lower than 65 °C, when the anode utilization efficiency was 88.2%. The industrial-scale tests with optimized parameters demonstrated considerable economic benefits.

The state evaluation of power equipment is an important basis for maintenance scheduling. This paper starts from the performance state data of power transformer and aims to study the matrix model and mathematical analysis method that is applicable to the state evaluation parameter system of power equipment in 220 kV substations. Different from the traditional threshold deduction method, this paper is more inclined to propose a closed loop model by finding the correlation among the internal key parameters of the system by reducing the dimension of the state matrix and increasing the dimension of iterations. This paper presents the basic state evaluation parameter system from various aspects which are based on the multi-source information of 220 kV substations. The dimensionality reduction of the matrix is carried out for the criterion matrix of the nature.

The transmission line is realized by boosting the electric energy from the generator with a transformer and then connecting it to the transmission line through the circuit breaker and other control equipment. Structure, transmission lines are divided into overhead transmission lines and cable lines. At present, the real-time state acquisition of overhead transmission line mainly depends on the online monitoring device installed by itself. After the equipment obtains the monitoring information, it will send the relevant information to the remote monitoring system through the network formed by the network. However, the length of the working time of the online monitoring device is limited by the battery, so improving the power utilization rate of the transmission line monitoring device can significantly extend the working time of the device. This paper proposes adirectional transmission data technology which can solve the problems of high energy consumption and poor signal transmission quality of monitoring devices, and effectively extend the network lifetime.

In order to alleviate the pressure of line operation and maintenance, it is necessary to use advanced image recognition methods to identify hidden line hazards. In this paper, we extract features from hidden targets and perform target recognition using deep convolutional neural network. Firstly, we collect a large number of photos with hidden dangers for transmission line. Secondly, we classify those samples according to the detection targets. Thirdly, the deep convolutional neural network model is trained and tuned by using the sample images of various detection objects. When deployed on the server, identification of hidden dangers such as cranes, tower cranes, excavators, and mountain fires reaches acceptable accuracy.

Since the emergence of NADC30-like porcine reproductive and respiratory syndrome virus (PRRSV) in China in 2013, PRRSVs have undergone rapid evolution. In this study, a novel variant of PRRSV strain (designated SCcd17) was successfully isolated from piglets with clinical signs in Sichuan Province in China in 2017, and the complete genomic sequence was determined. The genome of this new isolate was 15,015 nucleotides (nt) long, and comparative analysis revealed that SCcd17 exhibited 90.2%, 85.2%, 84.9%, and 84.0% nucleotide similarity to PRRSVs NADC30, JXA1, CH-1a, and VR-2332, respectively. Phylogenetic analysis indicated that the SCcd17 strain was classified into the NADC30-like sub-genotype, in which all the strains contained the unique discontinuous 131-amino acid deletion in nonstructural protein 2 (nsp2) when compared to VR-2332-like viruses. Notably, extensive amino acid substitutions were observed in nsp2 and a unique single amino acid deletion at position 33 of the GP5 is being described for the first time. Strikingly, recombination analysis revealed that SCcd17 was the result of recombination between the NADC30-like, JXA1-like, and VR-2332-like strains at five recombination breakpoints: nsp1α (nt 641), nsp3 (nt 5141), nsp10 (nt 9521), open reading frame 3 (ORF3) (nt 12,581), and ORF4 (nt 13,021). The genomic data of SCcd17 will be helpful for understanding the role of genomic recombination in the evolution of PRRSV.