Application of virtual reality technology improves the functionality of brain networks in individuals experiencing pain

Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.

Unlocking the future:Mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response

BACKGROUND Mitochondrial genes are involved in tumor metabolism in ovarian cancer(OC)and affect immune cell infiltration and treatment responses.AIM To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks.METHODS Prognosis,immunotherapy efficacy,and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus.Mitochondrial genes were sourced from the MitoCarta3.0 database.The discovery cohort for model construction was created from 70% of the patients,whereas the remaining 30% constituted the validation cohort.Using the expression of mitochondrial genes as the predictor variable and based on neural network algorithm,the overall survival time and immunotherapy efficacy(complete or partial response)of patients were predicted.RESULTS In total,375 patients with OC were included to construct the prognostic model,and 26 patients were included to construct the immune efficacy model.The average area under the receiver operating characteristic curve of the prognostic model was 0.7268[95% confidence interval(CI):0.7258-0.7278]in the discovery cohort and 0.6475(95%CI:0.6466-0.6484)in the validation cohort.The average area under the receiver operating characteristic curve of the immunotherapy efficacy model was 0.9444(95%CI:0.8333-1.0000)in the discovery cohort and 0.9167(95%CI:0.6667-1.0000)in the validation cohort.CONCLUSION The application of mitochondrial genes and neural networks has the potential to predict prognosis and immunotherapy response in patients with OC,providing valuable insights into personalized treatment strategies.

Resting-state brain network remodeling after different nerve reconstruction surgeries:a functional magnetic resonance imaging study in brachial plexus injury rats

Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.

A priority-based dynamic load transfer algorithm for cellular/WLAN integrated networks

For the integration network of a cellular network and a wireless local area network （WLAN）, a priority-based dynamic load transfer （PDLT） algorithm is proposed. The dynamic vertical handoffs by call admission control are jointly determined by the network conditions and the traffic characteristics in combination with the location-condition of mobile terminals. When there is no bandwidth resource available in the cellular network or WLAN, the proposed PDLT algorithm allows an incoming voice call or data call within the overlapping area of the cellular network and the WLAN to be directed to the spare network; meanwhile, by dynamically computing the occupancy of the bandwidth resource, the proposed PDLT algorithm also allows an ongoing voice call or data communication to be transferred to the network with a sufficient bandwidth resource according to the given threshold to balance the number of voice/data calls in the two networks. The analysis results of a two-dimensional Markov model and the simulation results show that the PDLT algorithm can effectively enhance the whole integrated network＇ s traffic, reduce the blocking probability of new calls and increase the data throughput, and thus decrease the response time for various services.

可用于WLAN的双频MIMO天线设计

设计了一款可用于WLAN的双频MIMO天线。天线的正面为L型单极子,背面为金属接地板。通过在接地板上加载耦合枝节使天线具有双频特性。同时,为了天线的小型化,对耦合枝节进行了弯折处理,减小天线尺寸。在两个天线单元之间加载T型寄生枝节和刻蚀矩形缝隙来提高天线的隔离度。使用电磁仿真软件和矢量网络分析仪对天线进行仿真和实测,结果表明:天线的工作频段为2.4~2.55 GHz和5.1~6.3 GHz,覆盖了WLAN的频段,同时在低频段内隔离度高于19 dB,高频段内隔离度高于25 dB。此外,天线的包络相关系数小于0.01且具有全向辐射特性。天线整体性能较好,可以满足WLAN的需求。

一种面向WLAN的分布式分层并行仿真方法

针对采用分布式并行方法仿真WLAN(wireless local area network)场景时存在的随终端节点个数增加而效率降低的问题,提出了一种面向WLAN的分布式分层并行仿真方法。基于WLAN的星状网络拓扑结构,令仿真接入节点的进程为主进程,负责WLAN全网中其他仿真节点的时间同步;将所有仿真终端节点的进程均匀分为若干组,由组长负责该组内进程的同步。在主进程广播仿真开始事件后,组长进程先收集本组组员终端节点推进结束消息,当收齐后再向主进程汇报。形成“主进程-组长进程-组员进程”的3层分层结构。在不同计算负荷下,仿真分析并得到了分层仿真方法的时间增益因子闭合表达式。仿真结果表明,与现有不分层的仿真方法相比,当平均计算负荷为1.2倍单位时长、节点个数为100时,所提分层仿真方法的增益可达50%。

Design of a USIM and ECC based handover authentication scheme for 5G-WLAN heterogeneous networks

A variety of wireless communication technologies have been developed to provide services to a large number of users.The future integrated 5G-WLAN wireless networks will support seamless and secure roaming,and various types of real-time applications and services,which will be the trend of next-generation computing paradigm.In this paper,we discuss the privacy and security problems in 5G-WLAN heterogeneous networks and present a logical 5G-WLAN integrated architecture.We also propose a novel USIM and ECC based design of handover authentication for next-generation 5G-WLAN heterogeneous networks that can provide secure and seamless Internet connectivity.Our scheme has the features of strong security and better performance in terms of computation cost,energy cost,and storage cost as compared with the state-of-the-art schemes.

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 protocol.Due to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate users.Moreover,the security design of the wireless structure is vulnerable to versatile attacks.For example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false messages.Althoughmany security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world applications.This paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks effectively.The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack.The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in theWLAN.The proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related work.The experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

Resource Allocation for Integrated Voice and Data Services in Cellular/WLAN Networks

Wireless Local Area Network(WLAN) with high data bit rates can be used with cellular network to achieve higher level of Quality of Service(QoS) by sharing their total resources efficiently.The integration between cellular and WLAN networks should be ensured considering different channel-allocation strategies of both networks and efficient resource management techniques should be developed.In this paper,we propose a new call admission scheme to use the coupled resource effectively.The proposed scheme,by taking the different resource sharing strategies for two access networks,limits the new,horizontal and vertical handoff voice and data call arrivals with respect to their call-level QoS requirements.Numerical results show that the proposed integrated cellular/WLAN network model uses the resources more effectively and achieves all upper bound QoS requirements for voice and data users as compared with the non-integrated network model.

A dynamic programming algorithm for network selection in 3G/WLAN

An essential characteristic of the 4th Generation(4G) wireless networks is integrating various heterogeneous wireless access networks.This paper considers the network selection for both admission and handoff strategy problems in heterogeneous network of 3G/WLAN.A novel dynamic programming algorithm is proposed by taking heterogeneous network characteristics,user mobility and different service types into account.The specificity of our approach is that it puts the situations in a new model and makes decisions in stages of different states.Simulation results validate that the proposed scheme can obtain better new call blocking and handoff dropping probability performance than traditional schemes while ensuring quality-of-services(QoS) for both real-time and data connections.

基于IRF-PF算法的WLAN室内定位方法

针对室内复杂环境下无线局域网接收信号强度的时变波动性,提出一种基于改进随机森林算法并融合粒子滤波的室内定位方法。首先,通过自适应滑动窗口选取接收信号强度较为平滑的信号接入点作为定位指纹特征建立决策树,从而减少特征空间的噪声;然后,利用bagging抽样后的袋外数据赋予决策树不同的投票权重等级,增大决策树类别间判别差异性,优化随机森林算法在目标位置与接收信号强度间弱映射关联性场景下定位精度低的问题,依据改进的随机森林算法建立位置坐标与接收信号强度间的映射模型,计算定位点的位置;最后,为了提高定位准确性与稳定性,将依据改进随机森林算法计算出的位置作为粒子滤波的观测量,利用粒子滤波对目标位置进行估计和预测。在Zenodo数据集上的实验结果表明:提出的算法在定位误差2 m范围内精度达到75.8%,在4 m范围内精度已经高达94.8%;相比于传统随机森林算法,能够将2 m内的定位精度提高11.5%;与K近邻、岭回归和贝叶斯等室内定位算法相比,定位精度和算法实时性也得到明显提升。

Radio Access Selection in Integrated UMTS/WLAN Networks

Heterogeneous networks combine different access technologies. An important problem in such networks is the selection of the most suitable radio access network. To perform this task efficiently, a lot of information is required, such as signal strength, QoS, monetary cost, battery consumption, and user preferences. These are well known issues and a considerable effort has been made to tackle them using a number of solutions. These efforts improve the performance of vertical handover but also add considerable complexity. In this paper, we introduce an enhanced algorithm for radio access network selection, which is simple, flexible and applicable to future mobile systems. Its main characteristics are the distribution of the radio access selection process among the mobile terminal and the core network, the evaluation of mobile terminal connections separately and the primary role of user preferences in the final decision. The performance of the algorithm is evaluated through simulation results, which show that the algorithm provides a high rate of user satisfaction. It decreases the messages required for the vertical handovers in the whole network and it alleviates the core network from the processing of unnecessary requests.

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Fluke Networks发布基于WLAN的无线威胁自动更新解决方案,有效抵御无线安全威胁和攻击

AirMagnet无线安全研究小组监测最新出现的基于WLAN的安全威胁,创建新的安全策略,并自动地推送更新到用户驻地WLAN安全监控中心,确保AirMagnet Enterprise 9.0的客户WLAN网络安全固若金汤。

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

Social-ecological perspective on the suicidal behaviour factors of early adolescents in China:a network analysis

Background In early adolescence,youth are highly prone to suicidal behaviours.Identifying modifiable risk factors during this critical phase is a priority to inform effective suicide prevention strategies.Aims To explore the risk and protective factors of suicidal behaviours(ie,suicidal ideation,plans and attempts)in early adolescence in China using a social-ecological perspective.Methods Using data from the cross-sectional project‘Healthy and Risky Behaviours Among Middle School Students in Anhui Province,China',stratified random cluster sampling was used to select 5724 middle school students who had completed self-report questionnaires in November 2020.Network analysis was employed to examine the correlates of suicidal ideation,plans and attempts at four levels,namely individual(sex,academic performance,serious physical llness/disability,history of self-harm,depression,impulsivity,sleep problems,resilience),family(family economic status,relationship with mother,relationship with father,family violence,childhood abuse,parental mental illness),school(relationship with teachers,relationship with classmates,school-bullying victimisation and perpetration)and social(social support,satisfaction with society).Results In total,37.9%,19.0%and 5.5%of the students reported suicidal ideation,plans and attempts in the past 6 months,respectively.The estimated network revealed that suicidal ideation,plans and attempts were collectively associated with a history of self-harm,sleep problems,childhood abuse,school bullying and victimisation.Centrality analysis indicated that the most influential nodes in the network were history of self-harm and childhood abuse.Notably,the network also showed unique correlates of suicidal ideation(sex,weight=0.60;impulsivity,weight=0.24;family violence,weight=0.17;relationship with teachers,weight=-0.03;school-bullying perpetration,weight=0.22),suicidal plans(social support,weight=-0.15)and suicidal attempts(relationship with mother,weight=-0.10;parental mental llness,weight=0.61).Conclusions This study identified the correlates of suicidal ideation,plans and attempts,and provided practical implications for suicide prevention for young adolescents in China.Firstly,this study highlighted the importance of joint interventions across multiple departments.Secondly,the common risk factors of suicidal ideation,plans and attempts were elucidated.Thirdly,this study proposed target interventions to address the unique influencing factors of suicidal ideation,plans and attempts.

The Convergence of WLAN and CDMA 2000 1x Networks

The technical solutions to the convergence of WLAN and CDMA 2000 1x are presented and compared.Problems brought on by network convergence are analyzed,and solutions to problems such as the coexistence of packet data service nodes(PDSNs),user i- dentification,roaming,and handoff are discussed in detail.

WLAN indoor location method based on artificial neural networkt

WLAN mdoor location method based on artificial neural network （ANN） is analyzed. A three layer feed-forward ANN model offers the benefits of reducing time cost of the layout of an indoor location system, saving storage cost of the radio map establishment and enhancing real-time capacity in the on-line phase. According to the analysis of SNR distributions of recorded beacon signal samples and discussion about the multi-mode phenomenon, the one map method is proposed for the purpose of simplifying ANN input values and increasing location performances. Based on the simulations and comparison analysis with other two typical indoor location methods, K-nearest neighbor （KNN） and probability, the feasibility and effectiveness of ANN-based indoor location method are verified with average location error of 2.37m and location accuracy of 78.6% in 3m.

Fast Handover Pre-Authentication Protocol in 3GPP-WLAN Heterogeneous Mobile Networks

The demand of ubiquitous communications drives the development of advanced mobile technologies. Meanwhile, recent increases in mobile data usage and the emergence of new IP service applications constitute the motivation to integrate 3GPP cellular mobile systems with broadband WLANs. Since 3GPP and WLAN systems complement each other in terms of infrastructure and network coverage and bandwidth, 3GPP-WLAN Heterogeneous Mobile Networks based on the 3GPP-based Home Network (3GHN) are proposed for meeting the growing demands in high-speed data access on any mobile devices. However, heterogeneous radio access technologies and architectures lead to many interworking issues, such as network transparency, security mechanism, seamless handover, and quality of service. Among of them, security and handover are the major motives to ensure the confidentiality, reliability and continuity of services in 3GPP-WLAN Heterogeneous Mobile Networks. This paper proposes fast handover pre-authentication protocol to reduce the handover authentication latency and authentication signaling overhead during the whole handover session. The proposed protocol supports Intra-Domain Handover Pre-Authentication (Intra-HO Pre-Auth) and Inter-Domain Handover Pre-Authentication (Inter-HO Pre-Auth) for preauthenticating the Mobile User (MS) prior to performing an Inter-domain Handover (Inter-domain HO)/Intra-domain Handover (Intra-domain HO) process. Meanwhile, the reduction in retrieving new Authentication Vector sand key sets from the Home Location Register/Home Subscriber Service/Authentication Center in 3GHN achieves for minimized redundant authentication signaling transactions between 3GPP domains and WLAN domains. In addition, this paper provides simulation results which show that the proposed Intra-HO Pre-Auth achieves 49% handover authentication performance improvement compared to EAP-AKA, and the proposed Inter-HO Pre-Auth achieves 26% handover authentication performance improvement as well.

Image super‐resolution via dynamic network

Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.