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.

Analysis and Suggestions on Management Strategies in Eco-industrial Parks in China

In the research, management strategies and problems of eco-industrial parks were mainly analyzed and some suggestions were proposed based on eco-industrial park, circular economy and ecologization of industrial park in China, providing references for ecologicalization policy design of industrial parks in China.

Open multilevel hierarchic intelligent control system for eco-industrial system

In allusion to the characteristics of the open complex giant system, an open multilevel hierarchic intelligent control system is established for the eco-industrial system. With the idea of the open engineering system, using the hall for workshop of metasynthetic engineering （HWME）, intelligent control techniques, the expert system and the design of experiments are integrated within the framework of the nonlinear multiobjective decision support system to develop a robust, top-level design specification so as to make the system have the quality of adaptive control, self-organizing, self-learning and robustness. Finally, an illustrative example is given to clarify the effectiveness of the method.

Calculation of the influence of Green Technology Innovation to Eco-Industrial Management in China

The function of Green Technology Innovation （GTI） in the Eco-Industrial Management （ELM） appears more and more important, but the research on how much this function plays is scarce. The influence of Green Technology Innovation to the Eco-Industrial Management has gradually received the academic and the industrial attention. In this article an empirical research was attempted to inspect whether such influence exists and to what value. The study attempts to empirically explore the influencing degree of GTI on EIM in China based on the double logarithmic regression equation.

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.

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.

Industry-City Integration in Eco-industrial Park of China

Industry-city integration in industrial park has stepped into quick development with the continuous advancement of urbanization in China. Relying on the developed ecological industry chain,sustainable development ability and good ecological service,eco-industrial park is at the forefront of industry-city integration,and accumulates rich experience in practice. The operation mechanism model includes government development,development of large state-owned enterprises,and public-private partnership. The space structures involve the relation between central urban district and new industrial town,overall layout of production,living and ecological space in eco-industrial park. In the aspect of system innovation,park management system,household registration management system,cultural system of community and environmental governance system face the challenges.

Research on the Industrial Symbiosis Supporting System of Eco-industrial Park

The establishment and development of the industrial symbiosis of eco-industrial park are affected by several factors.Based on the formed industrial symbiosis supporting system chart,this article analyzes the microcosmic supporting system and macroscopic supporting system.In the microcosmic supporting system,it elaborates five aspects including key enterprises,service intermediaries in the park,symbiotic enterprises,competitors and the public in detail.Then it describes the macroscopic supporting system from four aspects of governmental participation,technological innovation,educational promotion and cultural support.Finally,combining with the current construction status of the eco-industrial park in China,it proposes the countermeasures and suggestions to build the supporting system for the eco-industrial park and provides theoretical support for the faster and better construction of the eco-industrial park in China.

Research on the Knowledge System Framework of Eco-industrial Engineering

With intensified contradiction between the rapid devel- opment of modem industry and the carrying capacity of natural environment, coordinating the relationship between economic benefits and ecological benefits is a significant issue to be solved for modem industrial engineering. This paper firstly conducts the comparative analysis between traditional and ecological industry as well as traditional and modem industrial engineering, and in- dicates that eco-industrial engineering is an inevitable choice for sustainable development of modem industrial engineering; Then, based on industrial ecology, environmental economics and sustain- able development theory, the connotation and definition of eco- industrial engineering are proposed, and the theoretical and practi- cal development of the concepts of eco-industrial engineering are further investigated. Furthermore, the research subject, research content, academic system architecture and evaluation methods of eco-industrial engineering are discussed. Finally, combining the current scientific and technological development, we put forward the important scientific value and practical significance of con- structing eco-industrial engineering for the utilization of these concepts.

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.

Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow field.In this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored before.In our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.This method significantly reduces the workload of data measure-ment in practical engineering applications.Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects.Furthermore,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field points.Compared with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields.This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios.The code is available at https://github.com/zhangzm0128/MSU.

Green Design and Planning Resolutions for an Eco-Industrial Town: A Case Study of Polluted Industrial Estate in Rayong Province, Thailand

Map Ta Phut was designated for development in national policy as an Eastern Seaboard Industrial Port. It is located in Rayong Province with growing demand from Eastern Seaboard industries and is heavily affected by serious environmental problems as a'pollution control zone'. While the Joint Standing Committee on Commerce, Industry and Banking, representing the national business sector, claimed that the area generated total revenues of 1.1 trillion baht per year, or 11% of Thailand’s gross domestic product, and employed more than 100,000 workers, the declaration of the area as a pollution control zone severely limited investment and business operations. Thus, controversies arose among the investment business sectors, the residential sector (residents have been affected by a decrease in their quality of life and health) and environmental concern sectors. This paper aims to find an efficient and practical mitigation practice to balance the purposes of the industrial port with protection of surrounding communities and natural resources. The author will apply physical design and planning such as the application of “buffer zones”, “greenbelts”, “set-back”, “green corridor”, “green wall” and “protection strips” along with environmental measurements such as the Air Pollution Tolerance Index (APTI), which can be adapted for pollution protection as best practices of landscape architecture.

Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification

Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.

Computing Power Network:A Survey

With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect.To overcome these problems and improve network efficiency,a new network computing paradigm is proposed,i.e.,Computing Power Network(CPN).Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly.In this survey,we make an exhaustive review on the state-of-the-art research efforts on computing power network.We first give an overview of computing power network,including definition,architecture,and advantages.Next,a comprehensive elaboration of issues on computing power modeling,information awareness and announcement,resource allocation,network forwarding,computing power transaction platform and resource orchestration platform is presented.The computing power network testbed is built and evaluated.The applications and use cases in computing power network are discussed.Then,the key enabling technologies for computing power network are introduced.Finally,open challenges and future research directions are presented as well.

A model of techno-economic evaluation for eco-industrial park project

An eco-industrial park or estate is a community of manufactaring and service businesses located together on a common property. The goat of ElP is to create a win-win harmonious development aspect of ecooomic development and environmental protection. This paper emphasizes that the external .effect of an EIP is its main characteristic of technoeconomic evaluation for eco-industrial park project. From the view of the property, rights, the EIP＇s product is typicalty public-private. The government should take some inca.rares for the quantitative analysis on ecological positive externalities of the enterprises in EIP, and also should adopt Coase＇s Theorem, which supports that the market transaction is the best way to deal with positive externalities （external economics or diseconoraics）, or Pigou＇s Theorem, which holds that the government anti-positive externalities programs are the best way to cope with positive externalities, to internalize the EIP＇s external effects, which is also a fundamental tool to encourage investors to actively invest in EIP projects, Furthermore. this paper thinks that the EIP ＇s income should be equal to the income of staple products of the private property, and that of its by-products of the public property. According to this principle, this paper has put forward three major indicators, net present value （NPV）, internal rate of renan （IRR）, and investment repayment period （IRP）, which are also extensively used indicators in ardinary project techno-economic evaluation model to evaluate EIP technoeconomic effects. Theoretically, the indicatory not only can be used in EIP project evaluation, but also can provide a quantitative measure toot for the government to support EIP＇s construction to the maximum. In the end. a case is analyzed.

Focus on the key problems and dilemmas that arise in developing eco-industrial parks in China

Despite the widespread incorporation of sustainable development into policy discourses, actually achieving the win-win-win scenario of economic, environmental and social development continues to be problematic. Advocates of industrial ecology suggest that shifting the basis of industrial production from a linear to a closed loop system, these gains can be achieved. In recent years, concepts drawn from industrial ecology have been used to plan and develop eeo-industrial parks （EIPs） that seek to increase business competitiveness, reduce waste and pollution, create jobs and improve working conditions. Despite a growing interest in EIPs, there have been few empirically informed studies that seek to explore the potential contribution such EIPs may make to sustainable development. This paper contributes to a developing sympathetic critique of industrial ecology by focusing on the key problems and dilemmas that arise in the course of developing eco-industrial parks, drawing upon empirical work conducted in China. The paper draws upon both an extensive survey of EIPs and in-depth interviews conducted with a range of stakeholders at some sites in China. As the paper reveals, EIPs in China are in their early stages and likewise their contribution to economic development and environmental policy, let alone social policies, is complicated and inchoate. The empirical material reveals that key features of industrial ecology such as inter-firm networking and collaboration in the form of materials interchange and energycascading are either absent or in the early planning stages. In each of the cases studied what is emerging is a form of EIP partly determined by the geographic setting and broader economic realities of the locality. While collaborative behavior between firms is central to EIP development if the potential benefits of industrial ecology are to be realized, it is important to realize that such behavior is difficult to develop from scratch through policy intervention. In conclusion, the paper suggests that expectations must be realistic for the community and location in question. As part of that realism, EIP projects must be designed to allow for a gradual approach, and each phase needs to be financially viable.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.