Magnetic Nonreciprocity in a Hybrid Device of Asymmetric Artificial Spin-Ice-Superconductors

Controlling the size and distribution of potential barriers within a medium of interacting particles can unveil unique collective behaviors and innovative functionalities.We introduce a unique superconducting hybrid device using a novel artificial spin ice structure composed of asymmetric nanomagnets.This structure forms a distinctive superconducting pinning potential that steers unconventional motion of superconducting vortices,thereby inducing a magnetic nonreciprocal effect,in contrast to the electric nonreciprocal effect commonly observed in superconducting diodes.Furthermore,the polarity of the magnetic nonreciprocity is in situ reversible through the tunable magnetic patterns of artificial spin ice.Our findings demonstrate that artificial spin ice not only precisely modulates superconducting characteristics but also opens the door to novel functionalities,offering a groundbreaking paradigm for superconducting electronics.

Opinion consensus incorporating higher-order interactions in individual-collective networks

In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this paper, a two-layer network consisting of an individual-opinion layer and a collective-opinion layer is constructed, and a dissemination model of opinions incorporating higher-order interactions(i.e. OIHOI dissemination model) is proposed. Furthermore, the dynamic equations of opinion dissemination for both individuals and groups are presented. Using Lyapunov's first method,two equilibrium points, including the negative consensus point and positive consensus point, and the dynamic equations obtained for opinion dissemination, are analyzed theoretically. In addition, for individual opinions and collective opinions,some conditions for reaching negative consensus and positive consensus as well as the theoretical expression for the dissemination threshold are put forward. Numerical simulations are carried to verify the feasibility and effectiveness of the proposed theoretical results, as well as the influence of the intra-structure, inter-connections, and higher-order interactions on the dissemination and evolution of individual opinions. The main results are as follows.(i) When the intra-structure of the collective-opinion layer meets certain characteristics, then a negative or positive consensus is easier to reach for individuals.(ii) Both negative consensus and positive consensus perform best in mixed type of inter-connections in the two-layer network.(iii) Higher-order interactions can quickly eliminate differences in individual opinions, thereby enabling individuals to reach consensus faster.

Actor-Critic-Based UAV-Assisted Data Collection in the Wireless Sensor Network

Wireless Sensor Network(WSN)is widely utilized in large-scale distributed unmanned detection scenarios due to its low cost and flexible installation.However,WSN data collection encounters challenges in scenarios lacking communication infrastructure.Unmanned aerial vehicle(UAV)offers a novel solution for WSN data collection,leveraging their high mobility.In this paper,we present an efficient UAV-assisted data collection algorithm aimed at minimizing the overall power consumption of the WSN.Firstly,a two-layer UAV-assisted data collection model is introduced,including the ground and aerial layers.The ground layer senses the environmental data by the cluster members(CMs),and the CMs transmit the data to the cluster heads(CHs),which forward the collected data to the UAVs.The aerial network layer consists of multiple UAVs that collect,store,and forward data from the CHs to the data center for analysis.Secondly,an improved clustering algorithm based on K-Means++is proposed to optimize the number and locations of CHs.Moreover,an Actor-Critic based algorithm is introduced to optimize the UAV deployment and the association with CHs.Finally,simulation results verify the effectiveness of the proposed algorithms.

Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks

Large-scale wireless sensor networks(WSNs)play a critical role in monitoring dangerous scenarios and responding to medical emergencies.However,the inherent instability and error-prone nature of wireless links present significant challenges,necessitating efficient data collection and reliable transmission services.This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs.The primary goal is to enhance the reliability of data collection and transmission services,ensuring a comprehensive and practical approach.Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability.Additionally,it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops.These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios.Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes.The evaluation spans varying sensor densities,wireless channel conditions,and packet transmission rates,showcasing the protocol’s superiority in ensuring reliable and efficient data transfer.Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs.By prioritizing fairness,reliability,and efficiency,the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services,thereby offering a valuable contribution to the field of medical event-drivenWSNs.

Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection,which is then used as input to the CNN.The customized Convolutional Neural Network method is the date augmented-based CNN model to generate‘fake data’or‘fake images’.This study was carried out using Python and its libraries.We used 242 films from the dataset gathered by the Deep Fake Detection Challenge,of which 199 were made up and the remaining 53 were real.Ten seconds were allotted for each video.There were 318 videos used in all,199 of which were fake and 119 of which were real.Our proposedmethod achieved a testing accuracy of 91.47%,loss of 0.342,and AUC score of 0.92,outperforming two alternative approaches,CNN and MLP-CNN.Furthermore,our method succeeded in greater accuracy than contemporary models such as XceptionNet,Meso-4,EfficientNet-BO,MesoInception-4,VGG-16,and DST-Net.The novelty of this investigation is the development of a new Convolutional Neural Network(CNN)learning model that can accurately detect deep fake face photos.

A dialogue between gut microbiota and network pharmacology

The gastrointestinal system of the human body is home for a large number and rich variety of microbes,including bacteria,viruses,fungi and protozoa,which are collectively known as the gut microbiota(the largest and most intricate“organ”within the body).Till now,the groundbreaking research has emerged elucidating the pivotal role of the gut microbiota in human health and disease.The normal gut microbiota,after long evolution with the host,has formed an interdependent and mutually limited homeostasis system,which can produce beneficial metabolites for preserving intestinal integrity and preventing the onset of leaky gut syndrome along with subsequent immune activation and inflammation.Although the number of gut microbial metabolites is not fully understood,several metabolites(e.g.,short-chain fatty acids and branched-chain amino acids)have been tested to determine positive or negative effects on the development of specific diseases,which may be a new direction for the prevention and treatment of diseases.

Collecting aerosol in airflow with a magnetically stabilized fluidized bed

A magnetically stabilized fluidized bed (MSB) is a highly efficient filter that takes the advantage of both fluidized beds and fixed beds. This paper presents the research to collect aerosol in airflow with a MSB. The filtering model of MSB is established with its parameters including magnetic Geld intensity, gas superficial velocity, average grain-size, and bed height on the collection efficiency of MSB. The model is verified by experiments.

Traffic Labeller： Collecting Internet Traffic Samples with Accurate Application Information

Traffic classification research has been suffering from a trouble of collecting accurate samples with ground truth.A model named Traffic Labeller(TL) is proposed to solve this problem.TL system captures all user socket calls and their corresponding application process information in the user mode on a Windows host.Once a sending data call has been captured,its 5-tuple {source IP,destination IP,source port,destination port and transport layer protocol},associated with its application information,is sent to an intermediate NDIS driver in the kernel mode.Then the intermediate driver writes application type information on TOS field of the IP packets which match the 5-tuple.In this way,each IP packet sent from the Windows host carries their application information.Therefore,traffic samples collected on the network have been labelled with the accurate application information and can be used for training effective traffic classification models.

Core collection construction of tea plant germplasm in Anhui Province based on genetic diversity analysis using simple sequence repeat markers

The tea plant[Camellia sinensis(L.)O.Kuntze]is an industrial crop in China.The Anhui Province has a long history of tea cultivation and has a large resource of tea germplasm with abundant genetic diversity.To reduce the cost of conservation and utilization of germplasm resources,a core collection needs to be constructed.To this end,573 representative tea accessions were collected from six major tea-producing areas in Anhui Province.Based on 60 pairs of simple sequence repeat(SSR)markers,phylogenetic relationships,population structure and principal coordinate analysis(PCoA)were conducted.Phylogenetic analysis indicated that the 573 tea individuals clustered into five groups were related to geographical location and were consistent with the results of the PCoA.Finally,we constructed a core collection consisting of 115 tea individuals,accounting for 20%of the whole collection.The 115 core collections were considered to have a 90.9%retention rate for the observed number of alleles(Na),and Shannon’s information index(I)of the core and whole collections were highly consistent.Of these,39 individuals were preserved in the Huangshan area,accounting for 33.9%of the core collection,while only 10 individuals were reserved in the Jinzhai County,accounting for 8.9%of the core set.PCoA of the accessions in the tea plant core collection exhibited a pattern nearly identical to that of the accessions in the entire collection,further supporting the broad representation of the core germplasm in Anhui Province.The results demonstrated that the core collection could represent the genetic diversity of the original collection.Our present work is valuable for the high-efficiency conservation and utilization of tea plant germplasms in Anhui Province.

Experimental Study on the Resistance and Splash Performances of Water Collecting Devices for Mechanical Draft Cooling Towers

In recent years,water collecting systems,with the associated advantages of energy saving and noise reduction,have become the foundation for the development of a scheme to optimize the structure of cooling towers.To explore the feasibility of this approach for mechanical draft cooling towers,a small-scale experimental device has been built to study the resistance and splash performances of three U-type water collecting devices(WCDs)for different water flow rates and wind speeds.The experimental results show that within the considered ranges of wind speed and water flow rate,the pressure drop of the different WCDs can vary significantly.The resistance and local splash performances can also be remarkably different.Some recommendations about the most suitable system are provided.Moreover,a regression analysis of the experimental data is conducted,and the resulting fitting formulas for resistance and splash performance of WCD are reported.

Spatial distribution order parameter prediction of collective system using graph network

In the past few decades, the study of collective motion phase transition process has made great progress. It is also important for the description of the spatial distribution of particles. In this work, we propose a new order parameter φ to quantify the degree of order in the spatial distribution of particles. The results show that the spatial distribution order parameter can effectively describe the transition from a disorderly moving phase to a phase with a coherent motion of the particle distribution and the same conclusion could be obtained for systems with different sizes. Furthermore, we develop a powerful molecular dynamic graph network(MDGNet) model to realize the long-term prediction of the self-propelled collective system solely from the initial particle positions and movement angles. Employing this model, we successfully predict the order parameters of the specified time step. And the model can also be applied to analyze other types of complex systems with local interactions.

Sparse Seismic Data Reconstruction Based on a Convolutional Neural Network Algorithm

At present,the acquisition of seismic data is developing toward high-precision and high-density methods.However,complex natural environments and cultural factors in many exploration areas cause difficulties in achieving uniform and intensive acquisition,which makes complete seismic data collection impossible.Therefore,data reconstruction is required in the processing link to ensure imaging accuracy.Deep learning,as a new field in rapid development,presents clear advantages in feature extraction and modeling.In this study,the convolutional neural network deep learning algorithm is applied to seismic data reconstruction.Based on the convolutional neural network algorithm and combined with the characteristics of seismic data acquisition,two training strategies of supervised and unsupervised learning are designed to reconstruct sparse acquisition seismic records.First,a supervised learning strategy is proposed for labeled data,wherein the complete seismic data are segmented as the input of the training set and are randomly sampled before each training,thereby increasing the number of samples and the richness of features.Second,an unsupervised learning strategy based on large samples is proposed for unlabeled data,and the rolling segmentation method is used to update(pseudo)labels and training parameters in the training process.Through the reconstruction test of simulated and actual data,the deep learning algorithm based on a convolutional neural network shows better reconstruction quality and higher accuracy than compressed sensing based on Curvelet transform.

NOMA Empowered Energy Efficient Data Collection and Wireless Power Transfer in Space-Air-Ground Integrated Networks

As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.

AUV-Aided Data Collection Considering Adaptive Ocean Currents for Underwater Wireless Sensor Networks

Autonomous underwater vehicle(AUV)-assisted data collection is an efficient approach to implementing smart ocean.However,the data collection in time-varying ocean currents is plagued by two critical issues:AUV yaw and sensor node movement.We propose an adaptive AUV-assisted data collection strategy for ocean currents to address these issues.First,we consider the energy consumption of an AUV in conjunction with the value of information(VoI)over the sensor nodes and formulate an optimization problem to maximize the VoI-energy ratio.The AUV yaw problem is then solved by deriving the AUV's reachable region in different ocean current environments and the optimal cruising direction to the target nodes.Finally,using the predicted VoI-energy ratio,we sequentially design a distributed path planning algorithm to select the next target node for AUV.The simulation results indicate that the proposed strategy can utilize ocean currents to aid AUV navigation,thereby reducing the AUV's energy consumption and ensuring timely data collection.

Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.

2023年发表当年被引次数5及其以上的全球棉业相关论文

2024年1月4日,在Web of Science网站以“cotton”或“Gossypium”为“Title”(文题)检索词查询“Web of Science Core Collection”和“Chinese Science Citation DatabaseSM”数据库中2023年发表文章,选择被引次数5及其以上文章68篇。

Endoscopic intramural cystogastrostomy for treatment of peripancreatic fluid collection: A viewpoint from a surgeon

Percutaneous or endoscopic drainage is the initial choice for the treatment of peripancreatic fluid collection in symptomatic patients.Endoscopic transgastric fenestration(ETGF)was first reported for the management of pancreatic pseu-docysts of 20 patients in 2008.From a surgeon’s viewpoint,ETGF is a similar procedure to cystogastrostomy in that they both produce a wide outlet orifice for the drainage of fluid and necrotic debris.ETGF can be performed at least 4 wk after the initial onset of acute pancreatitis and it has a high priority over the surgical approach.However,the surgical approach usually has a better success rate because surgical cystogastrostomy has a wider outlet(>6 cm vs 2 cm)than ETGF.However,percutaneous or endoscopic drainage,ETGF,and surgical approach offer various treatment options for peripancreatic fluid collection patients based on their conditions.

On the Innovation,Design,Construction,and Experiments of OMEGA-Based SSPS Prototype:The Sun-Chasing Project

This study systematically introduces the development of the world’s first full-link and full-system ground demonstration and verification system for the OMEGA space solar power satellite(SSPS).First,the OMEGA 2.0 innovation design was proposed.Second,field-coupling theoretical models of sunlight concentration,photoelectric conversion,and transmitting antennas were established,and a systematic optimization design method was proposed.Third,a beam waveform optimization methodology considering both a high beam collection efficiency and a circular stepped beam shape was proposed.Fourth,a control strategy was developed to control the condenser pointing toward the sun while maintaining the transmitting antenna toward the rectenna.Fifth,a high-efficiency heat radiator design method based on bionics and topology optimization was proposed.Sixth,a method for improving the rectenna array’s reception,rectification,and direct current(DC)power synthesis efficiencies is presented.Seventh,high-precision measurement technology for high-accuracy beam-pointing control was developed.Eighth,a smart mechanical structure was designed and developed.Finally,the developed SSPS ground demonstration and verification system has the capacity for sun tracking,a high concentration ratio,photoelectric conversion,microwave conversion and emission,microwave reception,and rectification,and thus satisfactory results were obtained.

Genetic Diversity Analysis and Constructing Core Collection Based on Phenotypes in Cotton

In that DNA diversity detected nowadays could not mean phenotypic diversity,it is the precondition of breeding project and basic research of crop improvement that genetic diversity analysis and sampling of core collection by phenotypes.Phenotyping and statistic analysis on 9 traits of 92 accessions of cotton germplasm resource from three species（Gossypium hirsutum L.,Gossypium barbadence L.and Gossypium arboreum L.）were conducted.And the statistics（variation coefficient,proportion of special accessions and Shannon-Weaver information index）indicated that initial collection had abundant phenotypic diversity;software NTSYS-pc and the unweighted pair group method of arithmetic（UPGMA）were used for the cluster analysis on genetic similarity coefficient and genetic distance matrix,and the result showed that the genetic relationship among accessions was highly consistent with the pedigree;22 accessions of core collection were selected by software QGAStation,four statistics,such as variance difference percentage（VD%）,mean difference percentage（MD%）,coincidence rate（CR%）and variable rate（VR%）,showed that the genetic diversity of core collection was approximately equal to the initial collection.The results of genetic diversity analysis based on phenotypic data and sampling of core collection would provide reference for breeding projects and basic research.