Comprehensive integration of single-cell transcriptomic data illuminates the regulatory network architecture of plant cell fate specification

Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors(TFs)in intricate regulatory networks in a cell-type specific manner.Here we introduced a comprehensive single-cell transcriptomic atlas of Arabidopsis seedlings.This atlas is the result of meticulous integration of 63 previously published scRNA-seq datasets,addressing batch effects and conserving biological variance.This integration spans a broad spectrum of tissues,including both below-and above-ground parts.Utilizing a rigorous approach for cell type annotation,we identified 47 distinct cell types or states,largely expanding our current view of plant cell compositions.We systematically constructed cell-type specific gene regulatory networks and uncovered key regulators that act in a coordinated manner to control cell-type specific gene expression.Taken together,our study not only offers extensive plant cell atlas exploration that serves as a valuable resource,but also provides molecular insights into gene-regulatory programs that varies from different cell types.

Single event effects evaluation on convolution neural network in Xilinx 28 nm system on chip

Convolutional neural networks(CNNs) exhibit excellent performance in the areas of image recognition and object detection, which can enhance the intelligence level of spacecraft. However, in aerospace, energetic particles, such as heavy ions, protons, and alpha particles, can induce single event effects(SEEs) that lead CNNs to malfunction and can significantly impact the reliability of a CNN system. In this paper, the MNIST CNN system was constructed based on a 28 nm systemon-chip(SoC), and then an alpha particle irradiation experiment and fault injection were applied to evaluate the SEE of the CNN system. Various types of soft errors in the CNN system have been detected, and the SEE cross sections have been calculated. Furthermore, the mechanisms behind some soft errors have been explained. This research will provide technical support for the design of radiation-resistant artificial intelligence chips.

A Sensor Network Coverage Planning Based on Adjusted Single Candidate Optimizer

Wireless sensor networks(WSNs)are widely used for various practical applications due to their simplicity and versatility.The quality of service in WSNs is greatly influenced by the coverage,which directly affects the monitoring capacity of the target region.However,low WSN coverage and uneven distribution of nodes in random deployments pose significant challenges.This study proposes an optimal node planning strategy for net-work coverage based on an adjusted single candidate optimizer(ASCO)to address these issues.The single candidate optimizer(SCO)is a metaheuristic algorithm with stable implementation procedures.However,it has limitations in avoiding local optimum traps in complex node coverage optimization scenarios.The ASCO overcomes these limitations by incorporating reverse learning and multi-direction strategies,resulting in updated equations.The performance of the ASCO algorithm is compared with other algorithms in the literature for optimal WSN node coverage.The results demonstrate that the ASCO algorithm offers efficient performance,rapid convergence,and expanded coverage capabilities.Notably,the ASCO achieves an archival coverage rate of 88%,while other approaches achieve coverage rates below or equal to 85%under the same conditions.

A CNN-Based Single-Stage Occlusion Real-Time Target Detection Method

Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.

Application of Smith Predictor Based on Single Neural Network in Cold Rolling Shape Control

Flatness is one of the most important criterion factors to evaluate the quality of the steel strip. To improve the strip＇ s flatness quality, the most frequently used methodology is to employ the closed-loop automatic shape control system. However, in the shape control system, the shape-meter is always installed at the down way of the exit of the cold rolling mill and can not sense the changes of the strip flatness in the rolling gap directly. This kind of installation results in the delay of the feedback in the control system. Therefore, the stability and response performance of the system are strongly affected by the delay. At present, there is still no mature way to design controllers for systems with time delay. Although the conventional PID controller used in most practical applications has the capability to compensate the delay, the effect of the compensation is limited, especially for the systems with long time delay. Smith predictor, as a compensator for solving this problem, is now widely used in industry systems. However, the request of highly precise model of the system and the poor adaptive performance to the changes of related parameters limit the application of the Smith predictor in practice. In order to overcome the drawbacks of the Smith predictor, a new Smith predictor based on single neural network PID （SNN-PID） is proposed. Because the single neural network is employed into the Smith predictor to improve the controller＇s self-adaptability, the adaptive capability to the varying parameters of the system is improved. Meanwhile, for the purpose of solving the problems such as time-consuming and complicated calculation of the neural networks in real time, the learning coefficient of neural network is divided into several stages as usually done in expert control system. Therefore, the control system can obtain fast response due to the improved calculation speed of the neural networks. In order to validate the performance of the proposed controller, the experiment is conducted on the shape control system in a 300 mm four-high reversing cold rolling mill. The experimental results show that the SNN-PID with Smith predictor controller can effectively compensate the delay effects and achieve better control performance than the conventional PID controller.

Economical quantum secure direct communication network with single photons

In this paper a scheme for quantum secure direct communication （QSDC） network is proposed with a sequence of polarized single photons. The single photons are prepared originally in the same state （0） by the servers on the network, which will reduce the difficulty for the legitimate users to check eavesdropping largely. The users code the information on the single photons with two unitary operations which do not change their measuring bases. Some decoy photons, which are produced by operating the sample photons with a Hadamard, are used for preventing a potentially dishonest server from eavesdropping the quantum lines freely. This scheme is an economical one as it is the easiest way for QSDC network communication securely.

A production prediction method of single well in water flooding oilfield based on integrated temporal convolutional network model

Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed and verified. This method is started from data processing, the correspondence between water injectors and oil producers is determined according to the influence radius of the water injectors, the influence degree of a water injector on an oil producer in the month concerned is added as a model feature, and a Random Forest(RF) model is built to fill the dynamic data of water flooding. The single well history is divided into 4 stages according to its water cut, that is, low water cut, middle water cut, high water cut and extra-high water cut stages. In each stage, a TCN based prediction model is established, hyperparameters of the model are optimized by the Sparrow Search Algorithm(SSA). Finally, the models of the 4 stages are integrated into one whole-life model of the well for production prediction. The application of this method in Daqing Oilfield, NE China shows that:(1) Compared with conventional data processing methods, the data obtained by this processing method are more close to the actual production, and the data set obtained is more authentic and complete.(2) The TCN model has higher prediction accuracy than other 11 models such as Long Short Term Memory(LSTM).(3) Compared with the conventional full-life-cycle models, the model of integrated stages can significantly reduce the error of production prediction.

Single Mobile Sink Based Energy Efficiency and Fast Data Gathering Protocol for Wireless Sensor Networks

Recently, the exponential rise in communication system demands has motivated global academia-industry to develop efficient communication technologies to fulfill energy efficiency and Quality of Service (QoS) demands. Wireless Sensor Network (WSN) being one of the most efficient technologies possesses immense potential to serve major communication purposes including civil, defense and industrial purposes etc. The inclusion of sensor-mobility with WSN has broadened application horizon. The effectiveness of WSNs can be characterized by its ability to perform efficient data gathering and transmission to the base station for decision process. Clustering based routing scheme has been one of the dominating techniques for WSN systems;however key issues like, cluster formation, selection of the number of clusters and cluster heads, and data transmission decision from sensors to the mobile sink have always been an open research area. In this paper, a robust and energy efficient single mobile sink based WSN data gathering protocol is proposed. Unlike existing approaches, an enhanced centralized clustering model is developed on the basis of expectation-maximization (EEM) concept. Further, it is strengthened by using an optimal cluster count estimation technique that ensures that the number of clusters in the network region doesn’t introduce unwanted energy exhaustion. Meanwhile, the relative distance between sensor node and cluster head as well as mobile sink is used to make transmission (path) decision. Results exhibit that the proposed EEM based clustering with optimal cluster selection and optimal dynamic transmission decision enables higher throughput, fast data gathering, minima delay and energy consumption, and higher

PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for Image Super-Resolution

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)research.However,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction performance.These methods commonly use standard convolutions,which increase the convolutional operation cost of the model.In this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this problem.Specifically,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image reconstruction.Additionally,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature representations.Inspired by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an image.MFFB can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature information.As the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

New Precoded Spatial-Multiplexing for an Erasure Event in Single Frequency Networks

In this paper, we propose a new spatial-multiplexing(SM) scheme employing an orthogonal precoder over Rayleigh-fading channels for an erasure event in single frequency networks(SFN). To optimize the precoder, the average bit error rate(BER) is evaluated and minimized through a mathematical analysis. Compared to an ordinary SM, the proposed scheme guarantees identical BER performance under non-erasure fading channels and achieves a greatly improved performance under erasure fading channels, especially for a higher erasure-ratio and SNR values. This improvement is mainly due to the increase in the diversity gain incurred by the optimized precoder over the erasure event. We also compare the performance of the proposed SM to that of the conventional constellation-rotation(CR) scheme applied to the single antenna SFN systems. The results of a computer simulation show that the performance of the new scheme is more effective than that of a conventional CR across all simulation cases.

Research on single image super-resolution based on very deep super-resolution convolutional neural network

Single image super-resolution(SISR)is a fundamentally challenging problem because a low-resolution(LR)image can correspond to a set of high-resolution(HR)images,while most are not expected.Recently,SISR can be achieved by a deep learning-based method.By constructing a very deep super-resolution convolutional neural network(VDSRCNN),the LR images can be improved to HR images.This study mainly achieves two objectives:image super-resolution(ISR)and deblurring the image from VDSRCNN.Firstly,by analyzing ISR,we modify different training parameters to test the performance of VDSRCNN.Secondly,we add the motion blurred images to the training set to optimize the performance of VDSRCNN.Finally,we use image quality indexes to evaluate the difference between the images from classical methods and VDSRCNN.The results indicate that the VDSRCNN performs better in generating HR images from LR images using the optimized VDSRCNN in a proper method.

Single Phase Induction Motor Drive with Restrained Speed and Torque Ripples Using Neural Network Predictive Controller

In industrial drives, electric motors are extensively utilized to impart motion control and induction motors are the most familiar drive at present due to its extensive performance characteristic similar with that of DC drives. Precise control of drives is the main attribute in industries to optimize the performance and to increase its production rate. In motion control, the major considerations are the torque and speed ripples. Design of controllers has become increasingly complex to such systems for better management of energy and raw materials to attain optimal performance. Meager parameter appraisal results are unsuitable, leading to unstable operation. The rapid intensification of digital computer revolutionizes to practice precise control and allows implementation of advanced control strategy to extremely multifaceted systems. To solve complex control problems, model predictive control is an authoritative scheme, which exploits an explicit model of the process to be controlled. This paper presents a predictive control strategy by a neural network predictive controller based single phase induction motor drive to minimize the speed and torque ripples. The proposed method exhibits better performance than the conventional controller and validity of the proposed method is verified by the simulation results using MATLAB software.

Simulation and heat exchanger network designs for a novel single-column cryogenic air separation process

To realize the industrialization of the novel single-column air separation process proposed in previous work,steady-state simulation for four different configurations of the single-column process with ternary(nitrogen,oxygen and argon)is developed.Then,exergy analysis of the single-column processes is also carried out and compared with the conventional double-column air separation process at the same capacity.Furthermore,based on the steady-state simulation of single-column processes,the different heat exchanger networks(HENs)for the main heat exchanger and subcooler in each process are designed.To obtain better performance for this novel process,optimization of process configuration and operation is investigated.The optimal condition and configuration for this process is consisted as:feedstock is divided into two streams and the reflux nitrogen is compressed at the approximate temperature of 301 K.In addition,HEN is optimized to minimize the utilities.HENs without utilities are obtained for the four different configurations of single-column process.Furthermore,capital costs of the HEN for different cases are estimated and compared.

基于改进的single-pass网络舆情话题发现研究

舆情监控系统解决的关键问题是如何有效且精确地对文本进行聚类,以便从大量Web网页中发现网络舆情热点话题。single-pass算法是话题发现中最常用的文本聚类算法,但其在文本聚类的精度和时效方面存在不足,因而论文在对大量新闻报道语料进行深入分析的基础上,从三个方面对single-pass进行了改进。通过实验求证,发现改进后的single-pass算法在漏检率、误检率和耗费函数等方面有了明显改善。

基于Single-Pass的部队医院网络舆情监控系统设计

针对涉医网络舆情危机的不断出现,提出基于Single-Pass的部队医院网络舆情监控系统设计方法,实现舆情监控系统。根据部队医院的特点,本文对比常见的文本聚类算法,并对Single-Pass算法进行改进,设计适合部队医院的网络舆情监控系统,从而使网络舆论成为监督、改进医院管理和提高医疗质量的推动力。

基于自编码神经网络的Single-Pass聚类事件识别算法

针对传统Single-Pass聚类算法存在的缺陷,提出了一种基于自编码神经网络的Single-Pass聚类算法。通过多个深层的隐藏层对原始数据进行降维,以更好地提取出原始数据的特征信息;并通过对边缘文本重计算来降低误检率,提高聚类精度。实验结果表明,该算法相比传统Single-Pass算法具有更高的聚类准确度,解决了聚类结果受数据顺序影响的问题。

基于Single-Pass的军事网络舆情监控系统设计

军事网络舆论导向是人们获取军事信息的主要来源,这些舆论信息将直接影响人们的判断,甚至危害社会安全。本文基于军事主题特点,对比常见的文本聚类算法,改进传统的Single-Pass算法,设计适合军事主题的网络舆情监控系统,准确率和召回率都高于Single-Pass算法5个百分点以上,达到较好的效果,从而为相关部门制定决策提供可靠依据。

R2N: A Novel Deep Learning Architecture for Rain Removal from Single Image

Visual degradation of captured images caused by rainy streaks under rainy weather can adversely affect the performance of many open-air vision systems.Hence,it is necessary to address the problem of eliminating rain streaks from the individual rainy image.In this work,a deep convolution neural network(CNN)based method is introduced,called Rain-Removal Net(R2N),to solve the single image de-raining issue.Firstly,we decomposed the rainy image into its high-frequency detail layer and lowfrequency base layer.Then,we used the high-frequency detail layer to input the carefully designed CNN architecture to learn the mapping between it and its corresponding derained high-frequency detail layer.The CNN architecture consists of four convolution layers and four deconvolution layers,as well as three skip connections.The experiments on synthetic and real-world rainy images show that the performance of our architecture outperforms the compared state-of-the-art de-raining models with respects to the quality of de-rained images and computing efficiency.

CT reconstruction from a single X-ray image for a particular patient via progressive learning

Computed tomography(CT)has enjoyed widespread applications,especially in the assistance of clinical diagnosis and treatment.However,fast CT imaging is not available for guiding adaptive precise radiotherapy in the current radiation treatment process because the conventional CT reconstruction requires numerous projections and rich computing resources.This paper mainly studies the challenging task of 3 D CT reconstruction from a single 2 D X-ray image of a particular patient,which enables fast CT imaging during radiotherapy.It is widely known that the transformation from a 2 D projection to a 3 D volumetric CT image is a highly nonlinear mapping problem.In this paper,we propose a progressive learning framework to facilitate 2 D-to-3 D mapping.The proposed network starts training from low resolution and then adds new layers to learn increasing high-resolution details as the training progresses.In addition,by bridging the distribution gap between an X-ray image and a CT image with a novel attention-based 2 D-to-3 D feature transform module and an adaptive instance normalization layer,our network obtains enhanced performance in recovering a 3 D CT volume from a single X-ray image.We demonstrate the effectiveness of our approach on a ten-phase 4 D CT dataset including 20 different patients created from a public medical database and show its outperformance over some baseline methods in image quality and structure preservation,achieving a PSNR value of 22.76±0.708 dB and FSIM value of 0.871±0.012 with the ground truth as a reference.This method may promote the application of CT imaging in adaptive radiotherapy and provide image guidance for interventional surgery.

Real-Time Method for Detecting Harmonic and Reactive Currents of Single-Phase Circuits

According to the characteristics of single-phase circuits and demand of using active filter for real-time detecting harmonic and reactive currents, a detecting method based on Fryze＇s power definition is proposed. The results of theoretical analysis and simula- tion show that the proposed method is effective in realtime detecting of instantaneous harmonic and reactive currents in single-phase circuits. When only detecting the total reactive currents, this method does not need a phase-locked loop circuit, and it also can be used in some special applications to provide different compensations on the ground of different requirements of electric network. Compared with the other methods based on the theory of instantaneous reactive power, this method is simple and easy to realize.