Semiannual oscillation,annual oscillation,quasibiennial oscillation,and solar cycle variation of the OH airglow emission in the mesopause region

The vertically integrated emission rate,centroid altitude,peak emission rate,and peak height of the hydroxyl(OH)airglow were calculated from Thermosphere Ionosphere Mesosphere Energetics and Dynamics(TIMED)/Sounding of the Atmosphere using Broadband Emission Radiometry(SABER)observations to study the seasonal and interannual variations in the intensity and location of the OH emission.The emission rate is inversely proportional to the height of the emission,with the semiannual oscillation dominating at low latitudes and the annual oscillation dominating at higher latitudes.The OH emission is modulated by the quasibiennial oscillation at the equator,and the quasibiennial oscillation signal is weak at other latitudes.We represented the vertical transport of atomic oxygen by using atomic oxygen concentrations obtained from a global atmospheric model,the Specified Dynamics Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension simulations.Compared with the amplitudes of the migrating diurnal tide(DW1)calculated from temperature data observed by TIMED/SABER,we found that both the vertical transport of atomic oxygen and DW1 amplitudes in the equatorial region exhibit semiannual oscillation and quasibiennial oscillation,which have a strong correlation with the variations in the amplitude and phase of semiannual oscillation and quasibiennial oscillation in OH emission.It is likely that the DW1 affects the vertical transport of atomic oxygen that is involved in the reaction to produce O3,thus affecting the OH emission.We analyzed the relationship between OH emission and solar activity by using the solar radio flux at 10.7 cm as a proxy for solar activity.The results showed that the OH emission is well correlated with solar activity,and the modulation of OH emission by solar activity has a significant latitudinal variation.The small correlation between emission height and solar activity indicates that solar activity modulates OH emission mainly through chemical rather than dynamic processes.

Joint Optimization of Energy Consumption and Network Latency in Blockchain-Enabled Fog Computing Networks

Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things(IoT)devices.To ensure the security of private data,in this paper,we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network.A reputation model is proposed to update the credibility of the fog nodes(FN),which is used to select blockchain nodes(BN)from FNs to participate in the consensus process.According to the Rivest-Shamir-Adleman(RSA)encryption algorithm applied to the blockchain system,FNs could verify the identity of the node through its public key to avoid malicious attacks.Additionally,to reduce the computation complexity of the consensus algorithms and the network overhead,we propose a dynamic offloading and resource allocation(DORA)algorithm and a reputation-based democratic byzantine fault tolerant(R-DBFT)algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network security.Simulation results demonstrate that the proposed algorithm could efficiently reduce the network overhead,and obtain a considerable performance improvement compared to the related algorithms in the previous literature.

Combining prognostic value of serum carbohydrate antigen 19-9 and tumor size reduction ratio in pancreatic ductal adenocarcinoma

BACKGROUND Pancreatic ductal adenocarcinoma(PDAC)is a common cancer with increasing morbidity and mortality due to changes of social environment.AIM To evaluate the significance of serum carbohydrate antigen 19-9(CA19-9)and tumor size changes pre-and post-neoadjuvant therapy(NAT).METHODS This retrospective study was conducted at the Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment,Chongqing University Cancer Hospital.This study specifically assessed CA19-9 levels and tumor size before and after NAT.RESULTS A total of 156 patients who completed NAT and subsequently underwent tumor resection were included in this study.The average age was 65.4±10.6 years and 72(46.2%)patients were female.Before survival analysis,we defined the post-NAT serum CA19-9 level/pre-NAT serum CA19-9 level as the CA19-9 ratio(CR).The patients were divided into three groups:CR<0.5,CR>0.5 and<1 and CR>1.With regard to tumor size measured by both computed tomography and magnetic resonance imaging,we defined the post-NAT tumor size/pre-NAT tumor size as the tumor size ratio(TR).The patients were then divided into three groups:TR<0.5,TR>0.5 and<1 and TR>1.Based on these groups divided according to CR and TR,we performed both overall survival(OS)and disease-free survival(DFS)analyses.Log-rank tests showed that both OS and DFS were significantly different among the groups according to CR and TR(P<0.05).CR and TR after NAT were associated with increased odds of achieving a complete or near-complete pathologic response.Moreover,CR(hazard ratio:1.721,95%CI:1.373-3.762;P=0.006),and TR(hazard ratio:1.435,95%CI:1.275-4.363;P=0.014)were identified as independent factors associated with OS.CONCLUSION This study demonstrated that post-NAT serum CA19-9 level/pre-NAT serum CA19-9 level and post-NAT tumor size/pre-NAT tumor size were independent factors associated with OS in patients with PDAC who received NAT and subsequent surgical resection.

Estimation of cancer cell migration in biomimetic random/oriented collagen fiber microenvironments

Increasing data indicate that cancer cell migration is regulated by extracellular matrixes and their surrounding biochemical microenvironment,playing a crucial role in pathological processes such as tumor invasion and metastasis.However,conventional two-dimensional cell culture and animal models have limitations in studying the influence of tumor microenvironment on cancer cell migration.Fortunately,the further development of microfluidic technology has provided solutions for the study of such questions.We utilize microfluidic chip to build a random collagen fiber microenvironment(RFM)model and an oriented collagen fiber microenvironment(OFM)model that resemble early stage and late stage breast cancer microenvironments,respectively.By combining cell culture,biochemical concentration gradient construction,and microscopic imaging techniques,we investigate the impact of different collagen fiber biochemical microenvironments on the migration of breast cancer MDA-MB-231-RFP cells.The results show that MDA-MB-231-RFP cells migrate further in the OFM model compared to the RFM model,with significant differences observed.Furthermore,we establish concentration gradients of the anticancer drug paclitaxel in both the RFM and OFM models and find that paclitaxel significantly inhibits the migration of MDA-MB-231-RFP cells in the RFM model,with stronger inhibition on the high concentration side compared to the low concentration side.However,the inhibitory effect of paclitaxel on the migration of MDA-MB-231-RFP cells in the OFM model is weak.These findings suggest that the oriented collagen fiber microenvironment resembling the late-stage tumor microenvironment is more favorable for cancer cell migration and that the effectiveness of anticancer drugs is diminished.The RFM and OFM models constructed in this study not only provide a platform for studying the mechanism of cancer development,but also serve as a tool for the initial measurement of drug screening.

The Effect of Procrastination on Physical Exercise among College Students—The Chain Effect of Exercise Commitment and Action Control

Background:Exercise procrastination is prevalent among college students,causing decline in physical fitness.It is imperative to investigate the mechanism affecting college students’physical activity behaviors.This study was aimed at investigating the effect of procrastination on college students’physical exercise behavior,and the chain mediation effects of exercise commitment and action control(AC),to provide a theoretical basis for interventions targeting physical exercise behavior among college students.Methods:A questionnaire survey was conducted using convenience sampling.The General Procrastination Scale,Exercise Commitment Scale,Action Control Scale,and Physical Activity Rating Scale-3 questionnaires were used.Participants were 581 college students(age 19.27±0.94 years;243 males and 338 females).Statistical methods of regression analysis and structural equation modeling(SEM)were applied.Results:Procrastination,exercise commitment,and action control were found to be significant predictors of physical exercise behavior.Among these predictors,exercise commitment and action control showed full mediation effects in the relationship between procrastination and physical exercise behavior,and explained 25.48%and 30.77%of the total variance,respectively.The chain mediation effect of exercise commitment-action control was significant,accounting for 22.60%of the total variance,and the total indirect effect was 79.33%.Conclusion:Therefore,higher procrastination was associated with less participation in physical exercise behavior among college students.Improvements in exercise commitment and volitional decision-making ability for physical exercise behavior promoted physical exercise behavior,and increased exercise commitment promoted volitional decision-making ability among the students.The chain reaction effect of exercise commitment and action control also buffered the negative effects of procrastination on physical exercise behavior,thereby increasing physical exercise behavior among college students.

Bioinspired Passive Tactile Sensors Enabled by Reversible Polarization of Conjugated Polymers

Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors consume less power,but lack the capability to resolve static stimuli.Here,we address this issue by utilizing the unique polarization chemistry of conjugated polymers for the first time and propose a new type of bioinspired,passive,and bio-friendly tactile sensors for resolving both static and dynamic stimuli.Specifically,to emulate the polarization process of natural sensory cells,conjugated polymers(including poly(3,4-ethylenedioxythiophen e):poly(styrenesulfonate),polyaniline,or polypyrrole)are controllably polarized into two opposite states to create artificial potential differences.The controllable and reversible polarization process of the conjugated polymers is fully in situ characterized.Then,a micro-structured ionic electrolyte is employed to imitate the natural ion channels and to encode external touch stimulations into the variation in potential difference outputs.Compared with the currently existing tactile sensing devices,the developed tactile sensors feature distinct characteristics including fully organic composition,high sensitivity(up to 773 mV N^(−1)),ultralow power consumption(nW),as well as superior bio-friendliness.As demonstrations,both single point tactile perception(surface texture perception and material property perception)and two-dimensional tactile recognitions(shape or profile perception)with high accuracy are successfully realized using self-defined machine learning algorithms.This tactile sensing concept innovation based on the polarization chemistry of conjugated polymers opens up a new path to create robotic tactile sensors and prosthetic electronic skins.

Effect of annealing treatment on the structural, optical, and electrical properties of Al-doped ZnO thin films

Highly conductive and transparent Al-doped ZnO （AZO） thin films were prepared from a zinc target containing Al （1.5 wt.%） by direct current （DC） and radio frequency （RF） reactive magnetron sputtering. The structural, optical, and electrical properties of AZO films as-deposited and submitted to annealing treatment （at 300 and 400℃, respectively） were characterized using various techniques. The experimental results show that the properties of AZO thin films can be further improved by annealing treatment. The crystallinity of ZnO films improves after annealing treatment. The transmittances of the AZO thin films prepared by DC and RF reactive magnetron sputtering are up to 80% and 85% in the visible region, respectively. The electrical resistivity of AZO thin films prepared by DC reactive magnetron sputtering can be as low as 8.06 x 10-4 Ωcm after annealing treatment. It was also found that AZO thin films prepared by RF reactive magnetron sputtering have better structural and optical properties than that prepared by DC reactive magnetron sputtering.

V2X Offloading and Resource Allocation in SDN-Assisted MEC-Based Vehicular Networks

As an important application scenario of 5G, the vehicular network has a huge amount of computing data, which brings challenges to the scarce network resources. Mobile edge computing(MEC) sinks cloud services to the edge of network, which reduces the delay jitter caused by remote cloud computing. Software-defined networking(SDN) is an emerging network paradigm with the features of logic centralized control and programmability. In this paper, we construct an SDN-assisted MEC network architecture for the vehicular network. By introducing SDN controller, the efficiency and flexibility of vehicular network are improved, and the network state can be perceived from the global perspective. To further reduce the system overhead, the problem of vehicle to everything(V2X) offloading and resource allocation is proposed, where the optimal offloading decision, transmission power control, subchannels assignment, and computing resource allocation scheme are given. The optimization problem is transformed into three stages because of the heterogeneity of the offloaded tasks and the NP-hard property of the problem. Firstly, the analytic hierarchy process is used to select initial offloading node, then stateless Q-learning is adopted to allocate transmission power, subchannels and computing resources. In addition, the offloading decision is modeled as a potential game, and the Nash equilibrium is proved by the potential function construction. Finally, the numerical results show that the proposed mechanism can effectively reduce the system overhead and achieve better results compared with others’ algorithms.

Design and Implementation of Initial Cell Search in 5G NR Systems

The initial cell search plays an important role during the process of downlink synchronization establishment between the User Equipment(UE) and the base station. In particular, the uncertainty of the synchronization signals on the frequency domain and the flexibility of frame structure configuration have brought great challenges to the initial cell search for the fifth-generation(5G) new radio(NR). To solve this problem, firstly, we analyze the physical layer frame structure of 5G NR systems. Then, by focusing on the knowledge of synchronization signals, the 5G NR cell search process is designed, and the primary synchronization signal(PSS) timing synchronization algorithm is proposed, including a 5G-based coarse synchronization algorithm and conjugate symmetry-based fine synchronization algorithm. Finally, the performance of the proposed cell search algorithm in 5G NR systems is verified through the combination of Digital Signal Processing(DSP) and personal computer(PC). And the MATLAB simulation proves that the proposed algorithm has better performance than the conventional cross-correlation algorithm when a certain frequency offset exists.

Performance analysis and comparison of PoW,PoS and DAG based blockchains

In the blockchain,the consensus mechanism plays a key role in maintaining the security and legitimation of contents recorded in the blocks.Various blockchain consensus mechanisms have been proposed.However,there is no technical analysis and comparison as a guideline to determine which type of consensus mechanism should be adopted in a specific scenario/application.To this end,this work investigates three mainstream consensus mechanisms in the blockchain,namely,Proof of Work(PoW),Proof of Stake(PoS),and Direct Acyclic Graph(DAG),and identifies their performances in terms of the average time to generate a new block,the confirmation delay,the Transaction Per Second(TPS)and the confirmation failure probability.The results show that the consensus process is affected by both network resource(computation power/coin age,buffer size)and network load conditions.In addition,it shows that PoW and PoS are more sensitive to the change of network resource while DAG is more sensitive to network load conditions.

Stochastic resonance of coupled time-delayed system with fluctuation of mass and frequency and its application in bearing fault diagnosis

The stochastic resonance behavior of coupled stochastic resonance(SR)system with time-delay under mass and frequency fluctuations was studied.Firstly,the approximate system model of the time-delay system was obtained by the theory of small time-delay approximation.Then,the random average method and Shapiro-Loginov algorithm were used to calculate the output amplitude ratio of the two subsystems.The simulation analysis shows that increasing the time-delay and the input signal amplitude appropriately can improve the output response of the system.Finally,the system is applied to bearing fault diagnosis and compared with the stochastic resonance system with random mass and random frequency.The experimental results show that the coupled SR system taking into account the actual effect of time-delay and couple can more effectively extract the frequency of the fault signal,and thus realizing the diagnosis of the fault signal,which has important engineering application value.

Roles of ABA Signal Transduction during Higher Plant Seed Development and Germination

ABA is one of the 5 phytohormones in higher plants, which is also the most important hormone that regulates higher plants in response to environmental stress, by ABA signal transduction. Understanding ABA signal transduction at the molecular level is crucial to biology and ecology, and rational breeding complied with corresponding eco-environmental changes. Great advancements have taken place over the past 10 years by application of the Arabidopsis experimental system. Many components involved in ABA signal transduction have been isolated and identified and a clear overall picture of gene expression and control for this transduction has become an accepted fact. On the basis of the work in our laboratory, in conjunction with the data available at the moment, the authors have attempted to integrate ABA signal transduction pathways into a common one and give some insights into the relationship between ABA signal transduction and other hormone signal transduction pathways, with an emphasis upon the ABA signal transduction during higher plant seed development. A future challenge in this field is that different experimental systems are applied and various receptors and genes need to be characterized through the utilization of microarray chips.

A User Participation Behavior Prediction Model of Social Hotspots Based on Influence and Markov Random Field

Hotspot topic trends can be captured by analyzing user attributes and historical behavior in social network. In this paper, we propose a user participation behavior prediction model for social hotspots, based on user behavior and relationship data, to predict user participation behavior and topic development trends. Firstly, for the complex factors of user behavior, three dynamic influence factor functions are defined, including individual, peer and community influence. These functions take timeliness into account using a time discretization method. Secondly, to determine laws of individual behavior and group behavior within a social topic, a hotspot user participation behavior prediction model is proposed and associated with the basic concepts of randora field and Markov property in information diffusion. The experimental results show that the model can not only dynamically predict the individual behavior, but also grasp the development trends of topics.

Simulation modelling of potato virus Y spread in relation to initial inoculum and vector activity

Potato virus Y(PVY)is a non-persistent virus that is transmitted by many aphid species and causes significant damage to potato production.We constructed a spatially-explicit model simulating PVY spread in a potato field and used it to investigate possible effects of transmission efficiency,initial inoculum levels,vector behavior,vector abundance,and timing of peak vector activity on PVY incidence at the end of a simulated growing season.Lower PVY incidence in planted seed resulted in lower virus infection at the end of the season.However,when populations of efficient PVY vectors were high,significant PVY spread occurred even when initial virus inoculum was low.Non-colonizing aphids were more important for PVY spread compared to colonizing aphids,particularly at high densities.An early-season peak in the numbers of noncolonizing aphids resulted in the highest number of infected plants in the end of the season,while mid-and late-season peaks caused relatively little virus spread.Our results highlight the importance of integrating different techniques to prevent the number of PVY-infected plants from exceeding economically acceptable levels instead of trying to control aphids within potato fields.Such management plans should be implemented very early in a growing season.

New flavone and isoflavone glycoside from Belamcanda chinensis

The new flavone, 5,4′-dihydroxy-6,7-methylenedioxy-3′-methoxyflavone, and one new isoflavone glycoside, 3′,5′-dimethoxy irisolone-4′-O-β-d-glucoside were isolated from the rhizomes of Belamcanda chinensis. Their structures were established based on the spectroscopic methods.

A novel intrusion detection model for the CAN bus packet of in-vehicle network based on attention mechanism and autoencoder

The attacks on in-vehicle Controller Area Network(CAN)bus messages severely disrupt normal communication between vehicles.Therefore,researches on intrusion detection models for CAN have positive business value for vehicle security,and the intrusion detection technology for CAN bus messages can effectively protect the invehicle network from unlawful attacks.Previous machine learning-based models are unable to effectively identify intrusive abnormal messages due to their inherent shortcomings.Hence,to address the shortcomings of the previous machine learning-based intrusion detection technique,we propose a novel method using Attention Mechanism and AutoEncoder for Intrusion Detection(AMAEID).The AMAEID model first converts the raw hexadecimal message data into binary format to obtain better input.Then the AMAEID model encodes and decodes the binary message data using a multi-layer denoising autoencoder model to obtain a hidden feature representation that can represent the potential features behind the message data at a deeper level.Finally,the AMAEID model uses the attention mechanism and the fully connected layer network to infer whether the message is an abnormal message or not.The experimental results with three evaluation metrics on a real in-vehicle CAN bus message dataset outperform some traditional machine learning algorithms,demonstrating the effectiveness of the AMAEID model.

Design and Implementation of Secure Nodes in the Based-Internet-of-Things Intelligent Household

As one of the most important uses of the Internet of things (IOT), the intelligent household is becoming more and more popular. There are many fragile nodes in the intelligent household and they are bound to encounter some potential risks of hostile attacks, such as eavesdropping, denial of service, error instructs, non-authorized access or fabrication and others. This paper presents a method of design and implement of secure nodes for the intelligent household based on the IOT technology, besides giving the hardware model of nodes, the management of key, the access authentication of network, the transmission of encrypted data, and the alarm based on intrusion detection and other security mechanisms. That is, to improve the security of the based-IOT intelligent household from the viewpoint of nodes security. A test platform is built and the results of simulation prove that the proposed method can effectively improve the security of the intelligent household from access safety and transmission security.

Review of thermal transport and electronic properties of borophene

In recent years, two-dimensional boron sheets （borophene） have been experimentally synthesized and theoretically proposed as a promising conductor or transistor with novel thermal and electronic properties. We first give a general survey of some notable electronic properties of borophene, including the superconductivity and topological characters. We then mainly review the basic approaches, thermal transport, as well as the mechanical properties of borophene with different configurations. This review gives a general understanding of some of the crucial thermal transport and electronic properties of borophene, and also calls for further experimental investigations and applications on certain scientific community.

UPPER SEMI-CONTINUITY OF RANDOM ATTRACTORS FOR A NON- AUTONOMOUS DYNAMICAL SYSTEM WITH A WEAK CONVERGENCE CONDITION

In this paper,we develop the criterion on the upper semi-continuity of random attractors by a weak-to-weak limit replacing the usual norm-to-norm limit.As an application,we obtain the convergence of random attractors for non-autonomous stochastic reactiondiffusion equations on unbounded domains,when the density of stochastic noises approaches zero.The weak convergence of solutions is proved by means of Alaoglu weak compactness theorem.A differentiability condition on nonlinearity is omitted,which implies that the existence conditions for random attractors are sufficient to ensure their upper semi-continuity.These results greatly strengthen the upper semi-continuity notion that has been developed in the literature.

Classification of Domestic Refuse in Medical Institutions Based on Transfer Learning and Convolutional Neural Network

The problem of domestic refuse is becoming more and more serious with the use of all kinds of equipment in medical institutions.This matter arouses people’s attention.Traditional artificial waste classification is subjective and cannot be put accurately;moreover,the working environment of sorting is poor and the efficiency is low.Therefore,automated and effective sorting is needed.In view of the current development of deep learning,it can provide a good auxiliary role for classification and realize automatic classification.In this paper,the ResNet-50 convolutional neural network based on the transfer learning method is applied to design the image classifier to obtain the domestic refuse classification with high accuracy.By comparing the method designed in this paper with back propagation neural network and convolutional neural network,it is concluded that the CNN based on transfer learning method applied in this paper with higher accuracy rate and lower false detection rate.Further,under the shortage situation of data samples,the method with transfer learning and ResNet-50 training model is effective to improve the accuracy of image classification.