Age of Loop Information with Flexible Transmission Enabled Communication and Control Co-Design in Industrial IoT

Precise and low-latency information transmission through communication systems is essential in the Industrial Internet of Things(IIoT).However,in an industrial system,there is always a coupling relationship between the control and communication components.To improve the system's overall performance,exploring the co-design of communication and control systems is crucial.In this work,we propose a new metric±Age of Loop Information with Flexible Transmission(AoLI-FT),which dynamically adjusts the maximum number of uplink(UL)and downlink(DL)transmission rounds,thus enhancing reliability while ensuring timeliness.Our goal is to explore the relationship between AoLI-FT,reliability,and control convergence rate,and to design optimal blocklengths for UL and DL that achieve the desired control convergence rate.To address this issue,we first derive a closed-form expression for the upper bound of AoLI-FT.Subsequently,we establish a relationship between communication reliability and control convergence rates using a Lyapunov-like function.Finally,we introduce an iterative alternating algorithm to determine the optimal communication and control parameters.The numerical results demonstrate the significant performance advantages of our proposed communication and control co-design strategy in terms of latency and control cost.

Dynamics of information diffusion and disease transmission in time-varying multiplex networks with asymmetric activity levels

While the interaction between information and disease in static networks has been extensively investigated,many studies have ignored the characteristics of network evolution.In this study,we construct a new two-layer coupling model to explore the interactions between information and disease.The upper layer describes the diffusion of disease-related information,and the lower layer represents the disease transmission.We then use power-law distributions to examine the influence of asymmetric activity levels on dynamic propagation,revealing a mapping relationship characterizing the interconnected propagation of information and diseases among partial nodes within the network.Subsequently,we derive the disease outbreak threshold by using the microscopic Markov-chain approach(MMCA).Finally,we perform extensive Monte Carlo(MC)numerical simulations to verify the accuracy of our theoretical results.Our findings indicate that the activity levels of individuals in the disease transmission layer have a more significant influence on disease transmission compared with the individual activity levels in the information diffusion layer.Moreover,reducing the damping factor can delay disease outbreaks and suppress disease transmission,while improving individual quarantine measures can contribute positively to disease control.This study provides valuable insights into policymakers for developing outbreak prevention and control strategies.

Studying the co-evolution of information diffusion,vaccination behavior and disease transmission in multilayer networks with local and global effects

Today,with the rapid development of the internet,a large amount of information often accompanies the rapid transmission of disease outbreaks,and increasing numbers of scholars are studying the relationship between information and the disease transmission process using complex networks.In fact,the disease transmission process is very complex.Besides this information,there will often be individual behavioral measures and other factors to consider.Most of the previous research has aimed to establish a two-layer network model to consider the impact of information on the transmission process of disease,rarely divided into information and behavior,respectively.To carry out a more in-depth analysis of the disease transmission process and the intrinsic influencing mechanism,this paper divides information and behavior into two layers and proposes the establishment of a complex network to study the dynamic co-evolution of information diffusion,vaccination behavior,and disease transmission.This is achieved by considering four influential relationships between adjacent layers in multilayer networks.In the information layer,the diffusion process of negative information is described,and the feedback effects of local and global vaccination are considered.In the behavioral layer,an individual's vaccination behavior is described,and the probability of an individual receiving a vaccination is influenced by two factors:the influence of negative information,and the influence of local and global disease severity.In the disease layer,individual susceptibility is considered to be influenced by vaccination behavior.The state transition equations are derived using the micro Markov chain approach(MMCA),and disease prevalence thresholds are obtained.It is demonstrated through simulation experiments that the negative information diffusion is less influenced by local vaccination behavior,and is mainly influenced by global vaccination behavior;vaccination behavior is mainly influenced by local disease conditions,and is less influenced by global disease conditions;the disease transmission threshold increases with the increasing vaccination rate;and the scale of disease transmission increases with the increasing negative information diffusion rate and decreases with the increasing vaccination rate.Finally,it is found that when individual vaccination behavior considers both the influence of negative information and disease,it can increase the disease transmission threshold and reduce the scale of disease transmission.Therefore,we should resist the diffusion of negative information,increase vaccination proportions,and take appropriate protective measures in time.

Unsupervised Meteorological Downscaling Based on Dual Learning and Subgrid-scale Auxiliary Information

Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable energy.Although deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale uncertainty.This article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate downscaling.Such a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the upscaling.This dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping process.Experimental findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and quantitatively.Specifically,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation coefficient.In summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.

Digital Economy and Corporate Innovation-Washing--An Empirical Study on How Information Mitigates Policy Distortion

As stated in the Report to the 20th National Congress of the Communist Party of China(CPC),innovation remains at the heart of China’s modernization drive,and it is vital to optimize the allocation of innovation resources,deepen structural scientific and technological reforms,and enhance the overall performance of China’s innovation system.Government incentives have boosted firm R&D and innovation efforts;however,they have also triggered an innovation dilemma where enterprises,capitalizing on their informational advantages,resort to innovation-washing behaviors that undermine the intended purpose of the policies.Based on the information asymmetry theory,this paper conducts an empirical study on how the digital economy affects firms’innovation-washing behavior.The development of the regional digital economy could suppress firm innovation-washing behavior in the region,and such a mitigation effect is primarily caused by an increase in the number of digital industry professionals.According to our heterogeneity analysis,the digital economy has a greater impact on firm innovation-washing behavior for certain types of enterprises,including non-state-owned enterprises(non-SOEs),small and medium-sized enterprises(SMEs),enterprises in less competitive industries,and enterprises in unfavorable business environments.Our mechanism analysis revealed that the digital economy may restrain innovation-washing behavior by reducing information asymmetry between enterprises and external stakeholders.In terms of economic outcomes,the digital economy has the potential to directly influence firm innovation output while also indirectly mitigating the subsequent decline in innovation output by discouraging innovation-washing.This paper enriches the research findings on how the digital economy breaks down“information silos”and offers a potential solution to the“emphasis on input and quantity over quality and efficiency”phenomenon in science and technology innovation practices.

Combining machine learning algorithms with traditional methods for resolving the atomic-scale dynamic structure of monolayer MoS_(2) in high-resolution transmission electron microscopy

High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-corrected transmission electron microscopy constrain resolution.A machine learning algorithm is developed to determine the aberration parameters with higher precision from small,lattice-periodic crystal images.The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide(MoS_(2))monolayer with 25 variables(14 aberrations)resolved in wide ranges.Using these measured parameters,the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS_(2)monolayers.The images were acquired due to the unexpected movement of the specimen holder.Four-dimensional data extraction reveals time-varying atomic structures and ripple.In particular,the atomic evolution of the sulfur-vacancy point and line defects,as well as the edge structure near the amorphous,is visualized as the resolution has been improved from about 1.75?to 0.9 A.This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.

Design and Implementation of VPDN-based 3G Wireless Meteorological Information Transmission System

[Objective] This study was to construct VPDN-based 3G wireless meteorological information transmission system, in order to achieve the backup transmission of real-time meteorological services in all levels of meteorological stations. [Method] The current situations and business requirements of municipal meteorological networks are analyzed, putting forward the idea of backup routes that using VPDN-based 3G wireless meteorological information transmission system as the ground transmission line. And the network structure, operation mode and the system implementation are described in detail. [Result] VPDN-based 3G wireless meteorological information transmission system realizes the backup of ground line. Compared with the Modem dial-up, it is more stable and with faster transferring speed. With the rapid development of communication technology and computer technology, the information transmission system will be more efficient and stable, and will play a greater role in meteorological information transmission. [Conclusion] With the continuous development of modern meteorology, various types of meteorological data increase, presenting higher demand for the meteorological information transmission system. The established VPND-based 3G wireless meteorological information transmission system of this study provides a solution for backup transmission of real-time meteorological services.

Noise-improved information transmission in a nonlinear threshold array for Gaussian mixture noise

To discuss further the dependence of stochastic resonance on signals, nonlinear systems and noise, especially on noise, the binary input signal buried in Gaussian mixture noise through a nonlinear threshold array is studied based on mutual information. It is obtained that Gaussian mixture noise can improve the information transmission through the array. Both stochastic resonance （SR） and suprathreshold stochastic resonance （SSR） can be observed in the single threshold system and in the threshold array. The parameters in noise distribution affect the occurrence of SR and SSR. The efficacy of information transmission can be significantly enhanced as the number of threshold devices in the array increases. These results show further the dependence of SR and SSR on the noise distribution, and also extend the applicability of SR and SSR in information transmission.

Improved Ant Colony-Genetic Algorithm for Information Transmission Path Optimization in Remanufacturing Service System

The information transmission path optimization(ITPO) can often a ect the e ciency and accuracy of remanufactur?ing service. However, there is a greater degree of uncertainty and complexity in information transmission of remanu?facturing service system, which leads to a critical need for designing planning models to deal with this added uncer?tainty and complexity. In this paper, a three?dimensional(3D) model of remanufacturing service information network for information transmission is developed, which combines the physic coordinate and the transmitted properties of all the devices in the remanufacturing service system. In order to solve the basic ITPO in the 3D model, an improved 3D ant colony algorithm(Improved AC) was put forward. Moreover, to further improve the operation e ciency of the algorithm, an improved ant colony?genetic algorithm(AC?GA) that combines the improved AC and genetic algorithm was developed. In addition, by taking the transmission of remanufacturing service demand information of certain roller as example, the e ectiveness of AC?GA algorithm was analyzed and compared with that of improved AC, and the results demonstrated that AC?GA algorithm was superior to AC algorithm in aspects of information transmission delay, information transmission cost, and rate of information loss.

SSD Real-Time Illegal Parking Detection Based on Contextual Information Transmission

With the improvement of the national economic level,the number of vehicles is still increasing year by year.According to the statistics of National Bureau of Statics,the number is approximately up to 327 million in China by the end of 2018,which makes urban traffic pressure continues to rise so that the negative impact of urban traffic order is growing.Illegal parking-the common problem in the field of transportation security is urgent to be solved and traditional methods to address it are mainly based on ground loop and manual supervision,which may miss detection and cost much manpower.Due to the rapidly developing deep learning sweeping the world in recent years,object detection methods relying on background segmentation cannot meet the requirements of complex and various scenes on speed and precision.Thus,an improved Single Shot MultiBox Detector(SSD)based on deep learning is proposed in our study,we introduce attention mechanism by spatial transformer module which gives neural networks the ability to actively spatially transform feature maps and add contextual information transmission in specified layer.Finally,we found out the best connection layer in the detection model by repeated experiments especially for small objects and increased the precision by 1.5%than the baseline SSD without extra training cost.Meanwhile,we designed an illegal parking vehicle detection method by the improved SSD,reaching a high precision up to 97.3%and achieving a speed of 40FPS,superior to most of vehicle detection methods,will make contributions to relieving the negative impact of illegal parking.

Effects of evacuation assistant's leading behavior on the evacuation efficiency: Information transmission approach

Evacuation assistants are expected to spread the escape route information and lead evacuees toward the exit as quickly as possible. Their leading behavior influences the evacuees＇ movement directly, which is confirmed to be a decisive factor of the evacuation efficiency. The transmission process of escape information and its function on the evacuees＇ movement are accurately presented by the proposed extended dynamic communication field model. For evacuation assistants and eevacuees, their sensitivity parameter of static floor field（SFF）, kL S, and kS, are fully discussed. The simulation results e indicate that the appropriate kL Sis associated with the maximum kSof evacuees. The optimal combinations of kL Sand e kSwere found to reach the highest evacuation efficiency. There also exists an optimal value for evacuation assistants＇ information transmission radius.

Design and Analysis of Traffic Information Transmission System Based on Visible Light Communication

Visible Light Communication( VLC) based on LED is a new wireless communication technology with high response rate and good modulation characteristics in the wavelengths of 380- 780 nm. Compared with conventional methods,the waveband of VLC is harmless to human and safe to communication because of no magnetism radiation. An audio information transmission system using LED traffic lights is presented based on VLC technology. The system is consisted of transmitting terminal,receiving terminal and communication channel. Some experiments were made under real communication environment. The experimental results showed that the traffic information transmission system works steadily with good communication quality and achieves the purpose of transmitting audio information through LED traffic lights,with a data transfer rate up to 250 kbps over a distance of 5 meters.

Transmission of Meteorological Information to a Cockpit and Application of ADS-B

Unpredictable meteorological information, especially that endangers flight safety, should be transmitted to a cockpit to make a flight crew obtain meteorological early warning that affects flight safety as soon as possible and adopt countermeasures, thereby avoiding the occurrence of flight safety accidents. At present, there are limited ways of transmitting meteorological information to a cockpit, and the application of Automatic Dependent Surveillance-Broadcast （ADS-B） is a new way of transmitting meteorological information to a cockpit.

Modeling and Statistical Properties Research on Online Real-Time Information Transmission Network

In this paper, the model of the online real-time information transmission network, such as wechat, micro-blog, and QQ network, is proposed and built, based on the connection properties between users of the online real-time information transmission network, and combined with the local world evolving characteristics in complex network, then the statistical topological properties of the network is obtained by numerical simulation. Furthermore, we simulated the process of information transmission on the network, according to the actual characteristics of the online real-time information transmission. Statistics show that the degree distribution presents the characteristics of scale free network, presenting power law distribution, while the average path length, the average clustering coefficient and the average size of the network also has a power-law relationship, moreover, the model parameters has no effect on power-law exponent. The spread of information on the network represents obvious fluctuation scaling, reflecting the characteristics that information transmission fluctuates over time.

Optimal transmission strategy for spatially correlated MIMO systems with channel statistical information

In real multiple-input multiple-output (MIMO) systems, the perfect channel state information (CSI) may be costly or impossible to acquire. But the channel statistical information can be considered relatively stationary during long-term transmission. The statistical information can be obtained at the receiver and fed back to the transmitter and do not require frequent update. By exploiting channel mean and covariance information at the transmitter simultaneously, this paper investigates the optimal trans- mission strategy for spatially correlated MIMO channels. An upper bound of ergodic capacity is derived and taken as the per- formance criterion. Simulation results are also given to show the performance improvement of the optimal transmission strategy.

Secure Transmission Scheme Based on Dual-Thresholds for Simultaneous Wireless Information and Power Transfer

This paper studies a simultaneous wireless information and power transfer system with multiple external eavesdroppers and internal curious users.We model the random network by Poisson cluster process in consideration of the case where eavesdroppers hide around certain targets.Focusing on the users that work in harvesting-transmitting mode with time switching receivers,we establish communication model via time division multiple access.On this basis,we propose a lightweight secure transmission scheme based on dual-thresholds for physical-layer security enhancement,which consists of two protocols applied to the downlink(DL) and uplink(UL) transmission respectively.In the DL,we design a dynamic information-power switching transmission protocol based on signal-to-noise ratio threshold,which provides an opportunistic approach to reform the fixed period allocation of information and power transfer;in the UL,an energy threshold is proposed for users to control the transmission,which is called a user-led on-off transmission protocol.Furthermore,we give a comprehensive performance analysis for the proposed scheme in terms of delay,reliability,security and secrecy throughput.Based on the analysis results,we optimize the two thresholds and the DL-UL allocationcoefficient to maximize the secrecy throughput.Simulation results show the proposed scheme can bring about a substantial secrecy gain.

A Review of Telemetry Data Transmission in Unconventional Petroleum Environments Focused on Information Density and Reliability

The paper addresses a literature review of the technologies used in the transmission of measuring and logging data during well drilling. It presents a discussion about efficiency in data density transmission and reliability, especially when it comes to software and automated tools. Initially, this paper analyzes the principle of the telemetry systems, considering the mud pulse telemetry, acoustic telemetry, electromagnetic telemetry and wired drill pipe telemetry. They were detailed highlighting information about functionality, data transmission and its linkage to supporting software. Focus is also given to details of the main advantages and disadvantages of each technology considering the influences of lithology, drilling fluid and formation fluids in the reliability and capacity of data transmission.

Effects of information transmission delay and channel blocking on synchronization in scale-free Hodgkin-Huxley neuronal networks

In this paper,we investigate the evolution of spatiotemporal patterns and synchronization transitions in dependence on the information transmission delay and ion channel blocking in scale-free neuronal networks.As the underlying model of neuronal dynamics,we use the Hodgkin-Huxley equations incorporating channel blocking and intrinsic noise.It is shown that delays play a significant yet subtle role in shaping the dynamics of neuronal networks.In particular,regions of irregular and regular propagating excitatory fronts related to the synchronization transitions appear intermittently as the delay increases.Moreover,the fraction of working sodium and potassium ion channels can also have a significant impact on the spatiotemporal dynamics of neuronal networks.As the fraction of blocked sodium channels increases,the frequency of excitatory events decreases,which in turn manifests as an increase in the neuronal synchrony that,however,is dysfunctional due to the virtual absence of large-amplitude excitations.Expectedly,we also show that larger coupling strengths improve synchronization irrespective of the information transmission delay and channel blocking.The presented results are also robust against the variation of the network size,thus providing insights that could facilitate understanding of the joint impact of ion channel blocking and information transmission delay on the spatiotemporal dynamics of neuronal networks.

Proposal of ZigBee Systems for the Provision of Location Information and Transmission of Sensor Data in Medical Welfare

This paper proposes a scheme to obtain location and vital health information using ZigBee system. ZigBee systems are wireless communication systems defined by IEEE 802.154. In the proposed scheme, location information is obtained using the Link Quality Indication (LQI) function of a ZigBee system, which represents the received signal strength. And, the vital health information are collected from the electrocardiogram monitor, the pulse and blood pressure device, attached to the patient’s body. This information is then transmitted to an outside network by ZigBee systems. In this way, vital health information can be transmitted as ZigBee sensor data while patients with the ZigBee terminal are moving. In the experiments using actual ZigBee devices, the proposed scheme could obtain accurate location and vital health information from the sensor data. Moreover, to achieve high reliability in the actual service, the collected amount of sensor data was confirmed by the theoretic calculation, when a ZigBee terminal passed through ZigBee routers. These results indicate that the proposed scheme can be used to detect the accurate location of the ZigBee terminal. And over 99% of the sensor data on vital health information was obtained when the ZigBee terminal passed through approximately four ZigBee routers.

Scheme for realizing quantum computation and quantum information transfer with superconducting qubits coupling to a 1D transmission line resonator

This paper proposes a simple scheme for realizing one-qubit and two-qubit quantum gates as well as multiqubit entanglement based on de-SQUID charge qubits through the control of their coupling to a 1D transmission line resonator （TLR）. The TLR behaves effectively as a quantum data-bus mode of a harmonic oscillator, which has several practical advantages including strong coupling strength, reproducibility, immunity to 1/f noise, and suppressed spontaneous emission. In this protocol, the data-bus does not need to stay adiabatically in its ground state, which results in not only fast quantum operation, hut also high-fidelity quantum information processing. Also, it elaborates the transfer process with the 1D transmission line.