Influencer identification of dynamical networks based on an information entropy dimension reduction method

Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control.Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure.However, these algorithms do not consider network state changes.We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity.By using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance.We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network.We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.

Research on the Reform of School-Enterprise Cooperation Teaching and Education Mode for Computer Majors

This paper discusses the innovative methods of school-enterprise cooperation education mode in computer applied talent training.An innovative training model based on school-enterprise cooperation is proposed to promote the cultivation of students’practical and innovative skills,so as to better adapt to the needs of society.By analyzing the key links and influencing factors of the training mode,this paper puts forward some concrete suggestions and measures to provide guidelines for universities and enterprises in personnel training.

Unmasking gastric carcinoma: Unveiling diagnostic biomarkers and the role of critical care

We discuss the findings of Wu et al on the utility of neutrophil-to-lymphocyte ratio,platelet-to-lymphocyte ratio,and systemic immune-inflammatory index as diagnostic markers for gastric carcinoma(GC).We commend the study's contributions to the field and suggest a prospective study to validate these markers'sensitivity and specificity for early GC detection.We also propose developing surveillance protocols that incorporate these markers with other diagnostic methods to enhance clinical decision-making.Furthermore,we highlight the need for a more diverse patient cohort to assess the generalizability of these markers across different ethnic groups and demographic factors.Our suggestions aim to refine the application of these markers in clinical practice and to understand their potential in diverse clinical scenarios.

An improved GCN−TCN−AR model for PM_(2.5) predictions in the arid areas of Xinjiang,China

As one of the main characteristics of atmospheric pollutants,PM_(2.5) severely affects human health and has received widespread attention in recent years.How to predict the variations of PM_(2.5) concentrations with high accuracy is an important topic.The PM_(2.5) monitoring stations in Xinjiang Uygur Autonomous Region,China,are unevenly distributed,which makes it challenging to conduct comprehensive analyses and predictions.Therefore,this study primarily addresses the limitations mentioned above and the poor generalization ability of PM_(2.5) concentration prediction models across different monitoring stations.We chose the northern slope of the Tianshan Mountains as the study area and took the January−December in 2019 as the research period.On the basis of data from 21 PM_(2.5) monitoring stations as well as meteorological data(temperature,instantaneous wind speed,and pressure),we developed an improved model,namely GCN−TCN−AR(where GCN is the graph convolution network,TCN is the temporal convolutional network,and AR is the autoregression),for predicting PM_(2.5) concentrations on the northern slope of the Tianshan Mountains.The GCN−TCN−AR model is composed of an improved GCN model,a TCN model,and an AR model.The results revealed that the R2 values predicted by the GCN−TCN−AR model at the four monitoring stations(Urumqi,Wujiaqu,Shihezi,and Changji)were 0.93,0.91,0.93,and 0.92,respectively,and the RMSE(root mean square error)values were 6.85,7.52,7.01,and 7.28μg/m^(3),respectively.The performance of the GCN−TCN−AR model was also compared with the currently neural network models,including the GCN−TCN,GCN,TCN,Support Vector Regression(SVR),and AR.The GCN−TCN−AR outperformed the other current neural network models,with high prediction accuracy and good stability,making it especially suitable for the predictions of PM_(2.5)concentrations.This study revealed the significant spatiotemporal variations of PM_(2.5)concentrations.First,the PM_(2.5) concentrations exhibited clear seasonal fluctuations,with higher levels typically observed in winter and differences presented between months.Second,the spatial distribution analysis revealed that cities such as Urumqi and Wujiaqu have high PM_(2.5) concentrations,with a noticeable geographical clustering of pollutions.Understanding the variations in PM_(2.5) concentrations is highly important for the sustainable development of ecological environment in arid areas.

Phase-matching enhanced quantum phase and amplitude estimation of a two-level system in a squeezed reservoir

Squeezed reservoir engineering is a powerful technique in quantum information that combines the features of squeezing and reservoir engineering to create and stabilize non-classical quantum states. In this paper, we focus on the previously neglected aspect of the impact of the squeezing phase on the precision of quantum phase and amplitude estimation based on a simple model of a two-level system(TLS) interacting with a squeezed reservoir. We derive the optimal squeezed phase-matching conditions for phase φ and amplitude θ parameters, which are crucial for enhancing the precision of quantum parameter estimation. The robustness of the squeezing-enhanced quantum Fisher information against departures from these conditions is examined, demonstrating that minor deviations from phase-matching can still result in remarkable precision of estimation. Additionally, we provide a geometric interpretation of the squeezed phase-matching conditions from the classical motion of a TLS on the Bloch sphere. Our research contributes to a deeper understanding of the operational requirements for employing squeezed reservoir engineering to advance quantum parameter estimation.

Passive Integrated Sensing and Communication Scheme Based on RF Fingerprint Information Extraction for Cell-Free RAN

This paper investigates how to achieve integrated sensing and communication(ISAC)based on a cell-free radio access network(CF-RAN)architecture with a minimum footprint of communication resources.We propose a new passive sensing scheme.The scheme is based on the radio frequency(RF)fingerprint learning of the RF radio unit(RRU)to build an RF fingerprint library of RRUs.The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side.The receiver extracts the channel parameters from the signal and estimates the channel environment,thus locating the reflectors in the environment.The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture.Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.

Electronic band structures of topological kagome materials

The kagome lattice has garnered significant attention due to its ability to host quantum spin Fermi liquid states.Recently,the combination of unique lattice geometry,electron–electron correlations,and adjustable magnetism in solid kagome materials has led to the discovery of numerous fascinating quantum properties.These include unconventional superconductivity,charge and spin density waves(CDW/SDW),pair density waves(PDW),and Chern insulator phases.These emergent states are closely associated with the distinctive characteristics of the kagome lattice's electronic structure,such as van Hove singularities,Dirac fermions,and flat bands,which can exhibit exotic quasi-particle excitations under different symmetries and magnetic conditions.Recently,various quantum kagome materials have been developed,typically consisting of kagome layers stacked along the z-axis with atoms either filling the geometric centers of the kagome lattice or embedded between the layers.In this topical review,we begin by introducing the fundamental properties of several kagome materials.To gain an in-depth understanding of the relationship between topology and correlation,we then discuss the complex phenomena observed in these systems.These include the simplest kagome metal T_(3)X,kagome intercalation metal T X,and the ternary compounds AT_(6)X_(6)and RT_(3)X_(5)(A=Li,Mg,Ca,or rare earth;T=V,Cr,Mn,Fe,Co,Ni;X=Sn,Ge;R=K,Rb,Cs).Finally,we provide a perspective on future experimental work in this field.

Test Stability of Uster Advanced Fiber Information System(AFIS)

To study test stability of Advanced Fiber Information System(AFIS),card sliver produced in two experiments(12 plans in each experiment)were tested by AFIS.By a statistic analysis of the test results,the number of test times that can get a reliable test reliability(hereinafter this number of test times is referred to as Reliable Test Times,RTT)of test parameters and the coefficient of variation(CV%)values of 30 test results of each experiment plan were obtained.It's concluded that some parameters,such as length,seed coat nep(SCN)size,nep size and immature fiber content(IFC),etc.are very reliable by a test of ten or more times,but other parameters,such as SCN content,trash content,and visible foreign matter(VFM)content,etc.are not reliable until they are tested over 100 times.

A Planar 4-Bit Reconfigurable Antenna Array Based on the Design Philosophy of Information Metasurfaces

Inspired by the design philosophy of information metasurfaces based on the digital coding concept,a planar 4-bit reconfigurable antenna array with low profile of 0.15λ0(whereλ0is the free-space wavelength)is presented.The array is based on a digital coding radiation element consisting of a 1-bit magnetoelectric(ME)dipole and a miniaturized reflection-type phase shifter(RTPS).The proposed 1-bit ME dipole can provide two digital states of"0"and"1"(with 0°and 180°phase responses)over a wide frequency band by individually exciting its two symmetrical feeding ports.The designed RTPS is able to realize a relative phase shift of 173°.By digitally quantizing its phase in the range of 157.5°,additional eight digital states at intervals of 22.5°are obtained.To achieve low sidelobe levels,a 1:16 power divider based on the Taylor line source method is employed to feed the array,A prototype of the proposed 4-bit antenna array has been fabricated and tested,and the experimental results are in good agreement with the simulations.Scanning beams within a±45°range were measured with a maximum realized gain of 13.4 dBi at12 GHz.The sidelobe and cross-polarization levels are below-14.3 and-23.0 dB,respectively.Furthermore,the beam pointing error is within 0.8°,and the 3 dB gain bandwidth of the broadside beam is 25%.Due to its outstanding performance,the array holds potential for significant applications in radar and wireless communication systems.

A QoS-aware vertical handoff algorithm based on predictive network information

In converged heterogeneous wireless networks, vertical handoff is an important issue in radio resource management and occurs when an end user switches from one network to another （e.g., from wireless local area network to wideband code division multiple access）. Efficient vertical handoff should allocate network resources efficiently and maintain good quality of service （QoS） for the end users. The objective of this work is to determine conditions under which vertical handoff can be performed. The channel usage situation of each access network is formulated as a birth-death process with the objective of predicting the avaliable bandwidth and the blocking probability. A reward function is used to capture the network bandwidth and the blocking probability is expressed as a cost function. An end user will access the certain network which maximizes the total function defined as the combination of the reward fimction and the cost function. Simulation results show that the proposed algorithm can significantly improve the network performance, including higher bandwidth for end users and lower new call blocking and handoff call blocking probability for networks.

Research on Architecture of Logistics Public Information Platform

On the basis of the discussion on main functions of logistics public information platform, and the summary of general functions of some typical cases at home and abroad, such as China electronic port and TradeLink, the logistics public information platform architecture is introduced from a technical point of view. It contains multiple levels, such as cloud computing platform and data storage layer, and discusses some new technologies available. Finally, the important trend of the logistics public information platform development is discussed.

Hybrid Chinese Information Retrieval Model Based on the Combination of Keyword and Concept

A hybrid model that is based on the Combination of keywords and concept was put forward. The hybrid model is built on vector space model and probabilistic reasoning network. It not only can exert the advantages of keywords retrieval and concept retrieval but also can compensate for their shortcomings. Their parameters can be adjusted according to different usage in order to accept the best information retrieval result, and it has been proved by our experiments.

Neural Network Based Algorithm and Simulation of Information Fusion in the Coal Mine

The concepts of information fusion and the basic principles of neural networks are introduced. Neural net-works were introduced as a way of building an information fusion model in a coal mine monitoring system. This assures the accurate transmission of the multi-sensor information that comes from the coal mine monitoring systems. The in-formation fusion mode was analyzed. An algorithm was designed based on this analysis and some simulation results were given. Finally,conclusions that could provide auxiliary decision making information to the coal mine dispatching officers were presented.

Neurodynamics analysis of brain information transmission

This paper proposes a model of neural networks consisting of populations of perceptive neurons, inter-neurons, and motor neurons according to the theory of stochastic phase resetting dynamics. According to this model, the dynamical characteristics of neural networks are studied in three coupling cases, namely, series and parallel coupling, series coupling, and unilateral coupling. The results show that the indentified structure of neural networks enables the basic characteristics of neural information processing to be described in terms of the actions of both the optional motor and the reflected motor. The excitation of local neural networks is caused by the action of the optional motor. In particular, the excitation of the neural population caused by the action of the optional motor in the motor cortex is larger than that caused by the action of the reflected motor. This phenomenon indicates that there are more neurons participating in the neural information processing and the excited synchronization motion under the action of the optional motor.

Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study

Objective: Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography(CEM) images.Methods: In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system(MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion(AFF)algorithm that could intelligently incorporates multiple types of information from CEM images. The average freeresponse receiver operating characteristic score(AFROC-Score) was presented to validate system’s detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve(AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases,comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists’ performance.Results: On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909[95% confidence interval(95% CI): 0.822-0.996] and 0.912(95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists’ average reading time of 3.2 min. The average performance of all radiologists was also improved to varying degrees with MDCS assistance.Conclusions: MDCS demonstrated excellent performance in the detection and classification of breast lesions,and greatly enhanced the overall performance of radiologists.

A strip thickness prediction method of hot rolling based on D_S information reconstruction

To improve prediction accuracy of strip thickness in hot rolling, a kind of Dempster/Shafer(D_S) information reconstitution prediction method(DSIRPM) was presented. DSIRPM basically consisted of three steps to implement the prediction of strip thickness. Firstly, iba Analyzer was employed to analyze the periodicity of hot rolling and find three sensitive parameters to strip thickness, which were used to undertake polynomial curve fitting prediction based on least square respectively, and preliminary prediction results were obtained. Then, D_S evidence theory was used to reconstruct the prediction results under different parameters, in which basic probability assignment(BPA) was the key and the proposed contribution rate calculated using grey relational degree was regarded as BPA, which realizes BPA selection objectively. Finally, from this distribution, future strip thickness trend was inferred. Experimental results clearly show the improved prediction accuracy and stability compared with other prediction models, such as GM(1,1) and the weighted average prediction model.

Evaluation Model for Capability of Enterprise Agent Coalition Based on Information Fusion and Attribute Reduction

For the issue of evaluation of capability of enterprise agent coalition,an evaluation model based on information fusion and entropy weighting method is presented. The attribute reduction method is utilized to reduce indicators of the capability according to the theory of rough set. The new indicator system can be determined. Attribute reduction can also reduce the workload and remove the redundant information,when there are too many indicators or the indicators have strong correlation. The research complexity can be reduced and the efficiency can be improved. Entropy weighting method is used to determine the weights of the remaining indicators,and the importance of indicators is analyzed. The information fusion model based on nearest neighbor method is developed and utilized to evaluate the capability of multiple agent coalitions,compared to cloud evaluation model and D-S evidence method. Simulation results are reasonable and with obvious distinction. Thus they verify the effectiveness and feasibility of the model. The information fusion model can provide more scientific,rational decision support for choosing the best agent coalition,and provide innovative steps for the evaluation process of capability of agent coalitions.

Seeking Health Information Online:The Moderating Effects of Problematic Situations on User Intention

Purpose： This study investigates how online user intention in searching health information is affected by problematic situations.Design/methodology/approach： Based on the Theory of Reasoned Action, the Technology Acceptance Model, and Sense-making theory, we propose two dimensions of problematic situations： urgency and severity of health issues being searched online. Data were collected through a questionnaire survey among 214 Wuhan University students and analyzed using hierarchical regression analysis.Findings： Perceived usefulness, perceived ease of use, and subjective norm can influence user intention to seek health information online. The urgency of problematic situations has a negative moderating effect on the relationship between perceived ease of use and user intention and the relationship between subjective norm and user intention. The severity of problematic situations has a negative moderating effect on the relationship between subjective norm and user intention.Research limitations： The respondents of the survey are limited to students in one Chinese university, so whether this study＇s results can be applied to another population or not remains to be verified. In addition, only two dimensions of problematic situations are considered in this study. Practical implications： The paper puts forward the moderating effect of problematic situations and verifies it, which is the compensation for online health information-seeking behavior research. Besides, our analyses have implications for professional design of health care systems and related consumer information searches, and improve their performance. Originality/value： Previous work has reported the effects of problematic situation on user intention to seek health information online, ignoring its influence on other factors. This empirical study extends that work to identify the influence of problematic situation when seeking intention-behavior data in two dimensions, urgency and severity.

Information Collection System of Crop Growth Environment Based on the Internet of Things

Based on the technology of Internet of things, for the issues of large amount data acquisition and difficult real time transport in the data acquisition of crop growth environment, this paper designs one information collection system for crop growth environment. Utilizing the range free location mechanism which defines the node position and GEAR routing mechanism give solutions to the problems of node location, routing protocol applications and so on. This system can realize accurate and automatic real time collection, aggregation and transmission of crop growth environment information, and can achieve the automation of agricultural production, to the maximum extent.

Controlled Quantum Network Coding Without Loss of Information

Quantum network coding is used to solve the congestion problem in quantum communication,which will promote the transmission efficiency of quantum information and the total throughput of quantum network.We propose a novel controlled quantum network coding without information loss.The effective transmission of quantum states on the butterfly network requires the consent form a third-party controller Charlie.Firstly,two pairs of threeparticle non-maximum entangled states are pre-shared between senders and controller.By adding auxiliary particles and local operations,the senders can predict whether a certain quantum state can be successfully transmitted within the butterfly network based on the Z-{10>,|1>}basis.Secondly,when trans-mission fails upon prediction,the quantum state will not be lost,and it will sill be held by the sender.Subsequently,the controller Charlie re-prepares another three-particle non-maximum entangled state to start a new round.When the predicted transmission is successful,the quantum state can be transmitted successfully within the butterfly network.If the receiver wants to receive the effective quantum state,the quantum measurements from Charlie are needed.Thirdly,when the transmission fails,Charlie does not need to integrate the X-{1+>,1->}basis to measure its own particles,by which quantum resources are saved.Charlie not only controls the effective transmission of quantum states,but also the usage of classical and quantum channels.Finally,the implementation of the quantum circuits,as well as a flow chart and safety analysis of our scheme,is proposed.