Research Progress of Garden Microclimate in Recent 15 Years in China:Knowledge Graph Analysis Based on Citespace

Taking CNKI database as the data source and measuring visual map analysis tool Citespace as auxiliary tool,domestic 843 articles in recent 15 years are analyzed,and visualization maps such as time line analysis,institutional cooperation network analysis,author collaboration network analysis,keyword co-occurrence,and emergent words analysis are drawn.Combined with the literature content analysis,four hot spots in the research field of landscape architecture microclimate in China are obtained,namely ENVI-MET,comfort,design strategy and urban green space.The research trend is thermal comfort,human body comfort and winter city.The research results can provide reference for the research on domestic garden microclimate.

Improving landscape Characteristics in Port Said’s El Sallam Garden via Observational and Space Syntax Analysis

This paper provides a comprehensive examination of El Sallam Garden in Port Said City,concentrating on its landscape characteristics and potential for design enhancement.This study looks at how space syntax can be used to assess the impact of a tree planting design’s spatial configuration on an urban park’s visual fields.Trees play an important role in determining the spatial characteristics of an outdoor space.According to space syntax theory,an urban area is a collection of connected spaces that can be represented by a matrix of quantitative properties known as syntactic measures.Computer simulations can be used to measure the quantitative properties of these matrices.This study uses space syntax techniques to assess how tree configurations and garden area which can affect the social structures of small-scale gardens in Port Said.It also looks at how these techniques can be used to predict the social structures of four garden zones in El Sallam Garden.The study includes an observational and space syntax study through comparative analysis of four garden zones in El Sallam garden.The results of the study show that the area and planting configurations of the garden had a significant effect on the syntactic social and visual measures of the urban garden.The conclusions and recommendations can be a useful tool for landscape architects,urban planners,and legislators who want to enhance public areas and encourage social interaction in urban settings.

Effect of graph generation on slope stability analysis based on graph theory

Limit equilibrium method （LEM） and strength reduction method （SRM） are the most widely used methods for slope stability analysis. However, it can be noted that they both have some limitations in practical application. In the LEM, the constitutive model cannot be considered and many assumptions are needed between slices of soil/rock. The SRM requires iterative calculations and does not give the slip surface directly. A method for slope stability analysis based on the graph theory is recently developed to directly calculate the minimum safety factor and potential critical slip surface according to the stress results of numerical simulation. The method is based on current stress state and can overcome the disadvantages mentioned above in the two traditional methods. The influences of edge generation and mesh geometry on the position of slip surface and the safety factor of slope are studied, in which a new method for edge generation is proposed, and reasonable mesh size is suggested. The results of benchmark examples and a rock slope show good accuracy and efficiency of the presented method.

Visualization Analysis for Business Performance of Chinese Listed Companies Based on Gephi

When conducting company performance evaluations,the traditional method cannot reflect the distribution characteristics of the company’s operating conditions in the entire securities market.Gephi is an efficient tool for data analysis and visualization in the era of big data.It can convert the evaluation results of all listed companies into nodes and edges,and directly display them in the form of graphs,thus making up for the defects of traditional methods.This paper will take all the listed companies in the Shanghai and Shenzhen Stock Exchange as the analysis object.First uses tushare and web crawlers to collect the financial statement data of these companies.And then,uses the Economic Value Added model to calculate the EVA of each listed company and build graph data.Next,import the graph data into gephi to generate the distribution graph of all listed companies’performance,and summarize the distribution characteristics of business performance.Finally,select a listed company that you want to analyze in detail,using the traditional DuPont analysis method to conduct micro level visualization analysis of the business performance to find the main factors affecting the company’s operating performance.Incorporating gephi into traditional performance analysis methods will make the results of traditional analytical methods more effective and complete.

MBGM: A Graph-Mining Tool Based on MapReduce and BSP

This paper proposes an analytical mining tool for big graph data based on MapReduce and bulk synchronous parallel （BSP） com puting model. The tool is named Mapreduce and BSP based Graphmining tool （MBGM）. The core of this mining system are four sets of parallel graphmining algorithms programmed in the BSP parallel model and one set of data extractiontransformationload ing （ETE） algorithms implemented in MapReduce. To invoke these algorithm sets, we designed a workflow engine which optimized for cloud computing. Finally, a welldesigned data management function enables users to view, delete and input data in the Ha doop distributed file system （HDFS）. Experiments on artificial data show that the components of graphmining algorithm in MBGM are efficient.

Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications

Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.

Image registration based on matrix perturbation analysis using spectral graph

We present a novel perspective on characterizing the spectral correspondence between nodes of the weighted graph with application to image registration. It is based on matrix perturbation analysis on the spectral graph. The contribution may be divided into three parts. Firstly, the perturbation matrix is obtained by perturbing the matrix of graph model. Secondly, an orthogonal matrix is obtained based on an optimal parameter, which can better capture correspondence features. Thirdly, the optimal matching matrix is proposed by adjusting signs of orthogonal matrix for image registration. Experiments on both synthetic images and real-world images demonstrate the effectiveness and accuracy of the proposed method.

Nonequilibrium free energy and information flow of a double quantum-dot system with Coulomb coupling

We build a double quantum-dot system with Coulomb coupling and aim at studying connections among the entropy production,free energy,and information flow.By utilizing concepts in stochastic thermodynamics and graph theory analysis,Clausius and nonequilibrium free energy inequalities are built to interpret local second law of thermodynamics for subsystems.A fundamental set of cycle fluxes and affinities is identified to decompose two inequalities by using Schnakenberg's network theory.Results show that the thermodynamic irreversibility has energy-related and information-related contributions.A global cycle associated with the feedback-induced information flow would pump electrons against the bias voltage,which implements a Maxwell demon.

Integrated causal inference modeling uncovers novel causal factors and potential therapeutic targets of Qingjin Yiqi granules for chronic fatigue syndrome

Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a clinically approved formula for treating post-COVID-19;however,its potential as a drug target for treating CFS remains largely unknown.This study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating CFS.Methods:This prospective cohort analysis included 4,212 adults aged≥65 years who were followed up for 7 years with 435 incident CFS cases.Causal modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS.A proteome-wide,two-sample Mendelian randomization(MR)analysis was employed to explore the proteins associated with the identified causal factors of CFS,which may serve as potential drug targets.Furthermore,we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated proteins.Results:Among 4,212 participants(47.5%men)with a median age of 69 years(interquartile range:69–70 years)enrolled in 2004,435 developed CFS by 2011.Causal graph analysis with multivariate logistic regression identified frequent cough(odds ratio:1.74,95%confidence interval[CI]:1.15–2.63)and insomnia(odds ratio:2.59,95%CI:1.77–3.79)as novel causal factors of CFS.Proteome-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule(ESAM)was causally linked to both chronic cough(odds ratio:1.019,95%CI:1.012–1.026,P=2.75 e^(−05))and insomnia(odds ratio:1.015,95%CI:1.008–1.022,P=4.40 e^(−08))in CFS.The major bioactive compounds of QJYQ,ginsenoside Rb2(docking score:−6.03)and RG4(docking score:−6.15),bound to ESAM with high affinity based on virtual screening.Conclusions:Our integrated analytical framework combining epidemiological,genetic,and in silico data provides a novel strategy for elucidating complex disease mechanisms,such as CFS,and informing models of action of traditional Chinese medicines,such as QJYQ.Further validation in animal models is warranted to confirm the potential pharmacological effects of QJYQ on ESAM and as a treatment for CFS.

BPGM: A Big Graph Mining Tool

The design and implementation of a scalable parallel mining system target for big graph analysis has proven to be challenging. In this study, we propose a parallel data mining system for analyzing big graph data generated on a Bulk Synchronous Parallel （BSP） computing model named BSP-based Parallel Graph Mining （BPGM）. This system has four sets of parallel graph mining algorithms programmed in the BSP parallel model and a well-designed workflow engine optimized for cloud computing to invoke these algorithms. Experimental results show that the graph mining algorithm components in BPGM are efficient and have better performance than big cloud-based parallel data miner and BC-BSP.

Identifying Composite Crosscutting Concerns with Scatter-Based Graph Clustering

Identifying composite crosscutting concerns（CCs） is a research task and challenge of aspect mining.In this paper,we propose a scatter-based graph clustering approach to identify composite CCs.Inspired by the state-of-the-art link analysis tech-niques,we propose a two-state model to approximate how CCs tangle with core modules.According to this model,we obtain scatter and centralization scores for each program element.Espe-cially,the scatter scores are adopted to select CC seeds.Further-more,to identify composite CCs,we adopt a novel similarity measurement and develop an undirected graph clustering to group these seeds.Finally,we compare it with the previous work and illustrate its effectiveness in identifying composite CCs.

Abnormal intra-network architecture in extra-striate cortices in amblyopia: a resting state fMRI study

Background:Amblyopia(lazy eye)is one of the most common causes of monocular visual impairment.Intensive investigation has shown that amblyopes suffer from a range of deficits not only in the primary visual cortex but also the extra-striate visual cortex.However,amblyopic brain processing deficits in large-scale information networks especially in the visual network remain unclear.Methods:Through resting state functional magnetic resonance imaging(rs-fMRI),we studied the functional connectivity and efficiency of the brain visual processing networks in 18 anisometropic amblyopic patients and 18 healthy controls(HCs).Results:We found a loss of functional correlation within the higher visual network(HVN)and the visuospatial network(VSN)in amblyopes.Additionally,compared with HCs,amblyopic patients exhibited disruptions in local efficiency in the V3v(third visual cortex,ventral part)and V4(fourth visual cortex)of the HVN,as well as in the PFt,hIP3(human intraparietal area 3),and BA7p(Brodmann area 7 posterior)of the VSN.No significant alterations were found in the primary visual network(PVN).Conclusion:Our results indicate that amblyopia results in an intrinsic decrease of both network functional correlations and local efficiencies in the extra-striate visual networks.

Aberrant functional connectivity network in subjective memory complaint individuals relates to pathological biomarkers

Background:Individuals with subjective memory complaints(SMC)feature a higher risk of cognitive decline and clinical progression of Alzheimer’s disease(AD).However,the pathological mechanism underlying SMC remains unclear.We aimed to assess the intrinsic connectivity network and its relationship with AD-related pathologies in SMC individuals.Methods:We included 44 SMC individuals and 40 normal controls who underwent both resting-state functional MRI and positron emission tomography(PET).Based on graph theory approaches,we detected local and global functional connectivity across the whole brain by using degree centrality(DC)and eigenvector centrality(EC)respectively.Additionally,we analyzed amyloid deposition and tauopathy via florbetapir-PET imaging and cerebrospinal fluid(CSF)data.The voxel-wise two-sample T-test analysis was used to examine between-group differences in the intrinsic functional network and cerebral amyloid deposition.Then,we correlated these network metrics with pathological results.Results:The SMC individuals showed higher DC in the bilateral hippocampus(HP)and left fusiform gyrus and lower DC in the inferior parietal region than controls.Across all subjects,the DC of the bilateral HP and left fusiform gyrus was positively associated with total tau and phosphorylated tau181.However,no significant between-group difference existed in EC and cerebral amyloid deposition.Conclusion:We found impaired local,but not global,intrinsic connectivity networks in SMC individuals.Given the relationships between DC value and tau level,we hypothesized that functional changes in SMC individuals might relate to pathological biomarkers.

Aberrant Global and Regional Topological Organization of the Fractional Anisotropy-weighted Brain Structural Networks in Major Depressive Disorder

Background： Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder （MDD）. Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods： The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results： Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions： All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network.

A novel testability model for health management of heading attitude system

Prognostics and health management （PHM） is very important to guarantee the reliability and safety of aerospace systems, and sensing and test are the precondition of PHM. Integrating design for testability into early design stage of system early design stage is deemed as a fundamental way to improve PHM performance, and testability model is the base of testability analysis and design. This paper discusses a hierarchical model-based approach to testability modeling and analysis for heading attitude system health management. Quantified directed graph, of which the nodes represent components and tests and the directed edges represent fault propagation paths, is used to describe fault-test dependency, and quantitative testability information is assigned to nodes and directed edges. The fault dependencies between nodes can be obtained by functional fault analysis methodology that captures the physical architecture and material flows such as energy, heat, data, and so on. By incorporating physics of failure models into component, the dynamic process of a failing or degrading component can be projected onto system behavior, i.e., system symptoms. Then, the analysis of extended failure modes, mechanisms and effects is utilized to construct fault evolution-test dependency. Using this integrated model, the designers and system analysts can assess the test suite＇s fault detectability, fault isolability and fault predictability. And heading attitude system application results show that the proposed model can support testability analysis and design for PHM very well.

Oscillation-specific nodal alterations in early to middle stages Parkinson’s disease

Background:Different oscillations of brain networks could carry different dimensions of brain integration.We aimed to investigate oscillation-specific nodal alterations in patients with Parkinson’s disease(PD)across early stage to middle stage by using graph theory-based analysis.Methods:Eighty-eight PD patients including 39 PD patients in the early stage(EPD)and 49 patients in the middle stage(MPD)and 36 controls were recruited in the present study.Graph theory-based network analyses from three oscillation frequencies(slow-5:0.01–0.027 Hz;slow-4:0.027–0.073 Hz;slow-3:0.073–0.198 Hz)were analyzed.Nodal metrics(e.g.nodal degree centrality,betweenness centrality and nodal efficiency)were calculated.Results:Our results showed that(1)a divergent effect of oscillation frequencies on nodal metrics,especially on nodal degree centrality and nodal efficiency,that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed,which visually showed that network was perturbed in PD;(2)PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities,which was consistently detected within all three oscillation frequencies;(3)the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients;(4)logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level;(5)occipital disruption within high frequency(slow-3)made a significant influence on motor impairment which was dominated by akinesia and rigidity.Conclusions:Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages.