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Attention-deficit/hyperactivity disorder (ADHD) commonly precedes the initial onset of mania in youth with familial risk for bipolar disorder (BD). Although ADHD youth with and without BD familial risk exhibit different clinical features, associated neuropathophysiological mechanisms remain poorly understood. This study aimed to identify brain functional network abnormalities associated with ADHD in youth with and without familial risk for BD. Resting-state functional magnetic resonance imaging scans were acquired from 37 ADHD youth with a family history of BD (high-risk), 45 ADHD youth without a family history of BD (low-risk), and 32 healthy controls (HC). Individual whole-brain functional networks were constructed, and graph theory analysis was applied to estimate network topological metrics. Topological metrics, including network efficiency, small-worldness and nodal centrality, were compared across groups, and associations between topological metrics and clinical ratings were evaluated. Compared to HC, low-risk ADHD youth exhibited weaker global integration (i.e., decreased global efficiency and increased characteristic path length), while high-risk ADHD youth showed a disruption of localized network components with decreased frontoparietal and frontolimbic connectivity. Common topological deficits were observed in the medial superior frontal gyrus between low- and high-risk ADHD. Distinct network deficits were found in the inferior parietal lobule and corticostriatal circuitry. Associations between global topological metrics and externalizing symptoms differed significantly between the two ADHD groups. Different patterns of functional network topological abnormalities were found in high- as compared to low-risk ADHD, suggesting that ADHD in youth with BD familial risk may represent a phenotype that is different from ADHD alone. [ABSTRACT FROM AUTHOR]

The scientific and reasonable division of geographical units of cultivated land reserve resource areas is an important foundation for comprehensive land consolidation (CLC). Saline–alkali land is an important cultivated land reserve resource, and is significant for ensuring food security. This paper constructs a two-level land consolidation index system for cultivated land reserve resource areas. The Kruskal graph theory clustering algorithm was used to divide the study area into four types of ecological zones. On the basis of ecological zones, the study area was further divided into three types of consolidation units. Finally, the spatial relationship between ecological zoning and land use types was clarified, and the classification distribution of land consolidation potential was determined through an analysis of the CLC of two-level geographical units. Da'an City, Jilin province, China, was selected as the study area. The results of this study show the following: (1) In Da'an City, the conservation priority areas are concentrated in the north, the development priority areas are concentrated in the central and southern areas, and the comprehensive upgrade areas and adjustment rebuilding areas are in the transition zones. (2) The CLC potential trend is low in the north but high in the south in Da'an City. This paper proposes a framework for a geographical unit delineation method for saline–alkali-enriched cultivated land reserve resource zones, and analyzes the spatial layout of ecological protection demands and land consolidation potential in Da'an City. The results and conclusions of this study will provide a reference for CLC in cultivated land reserve resource areas. [ABSTRACT FROM AUTHOR]

With the rapid development of the global economy and trade, the number of ships serving ports in China is increasing continuously. Port traffic is becoming busier, and ship behavior is more complex and changeable. The analysis of ship behavior patterns in port waters has become an urgent problem to improve the efficiency and safety of port areas. In this paper, through the full integration of ship trajectory and port geographic information, the behavior chain of a single ship across the whole process of entering and exiting the port is identified. The traffic complexities and dynamics can be further analyzed by grouping the movement patterns of large ships. Based on graph theory, the port areas can be described as a transportation network in which functional areas are nodes and fairways between different areas are edges. The traffic can be analyzed through the network structure characteristics, such as node degree, node weight, and edge weight, and by their similarities and differences. This methodology provides a quantitative analysis for exploring the behavior patterns of large ships as well as the various traffic complexities. A case study in Tianjin Port has been conducted to verify the proposed model. The results show that it can accurately analyze a ship behavior's regularity, occasion, and correlation. It provides a theoretical reference for the port to schedule and formulate emergency plans. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to investigate alterations in the topological organization of whole-brain functional networks in patients with chronic low back pain (CLBP) and characterize the relationship of these alterations with pain characteristics. Methods: Thirty-three CLBP patients and 34 matched healthy controls (HCs) underwent fMRI scans. A graph-theoretical approach was applied to identify brain network changes in patients suffering from chronic low back pain given its nonspecific etiology and complexity. Graph theory-based analysis was used to construct functional connectivity matrices and extract the features of small-world networks of the brain in both groups. Then, the whole-brain functional connectivity differences were characterized by network-based statistics (NBS) analysis, and the relationship between the altered brain features and clinical measures was explored. Results: At the global level, patients with CLBP showed significantly decreased gamma, sigma, global efficiency, and local efficiency and increased lambda and shortest path length compared with HCs. At the regional level, there were deficits in nodal efficiency within the default mode network and salience network. NBS analysis demonstrated that decreased functional connectivity was present in the CLBP patients, mainly in the frontolimbic circuit and temporal regions. Furthermore, aspects of topological dysfunctions in CLBP were correlated with pain severity. Conclusion: This study highlighted the aberrant topological organization of functional brain networks in CLBP, which may shed light on the pathophysiology of CLBP and support the development of pain management approaches. [ABSTRACT FROM AUTHOR]

This paper explores the phenomenon of functional upgrading in Global Value Chains (GVCs) by applying empirical techniques of Social Network Analysis and graph theory to data from the TiVA database for the period from 1995 to 2018. Specifically, we focus on two (from less to more complex) stages of GVC participation: the global supply of manufacturing inputs and the global supply of business services to be embodied in other countries' exports. We identify which countries are the leading specialized global providers in each stage, analysing their evolutions over time and the relationship between both progressions. It highlights the trajectory of China that has increased its role as a hub in both stages to reach top positions. Using a dynamic panel data model, we find that the role of a country as a specialized global supplier of manufacturing inputs is associate with becoming a prominent global supplier of embodied business services, suggesting a path to functional upgrading in GVCs. [ABSTRACT FROM AUTHOR]

Although consistent evidence has revealed that cognitive impairment is a common sequela in patients withmild stroke, few studies have focused on it, nor the impact of lesion location on cognitive function. Evidence on the neural mechanisms underlying the effects of mild stroke and lesion location on cognitive function is limited. This prompted us to conduct a comprehensive and quantitative study of functional brain network properties in mild stroke patients with different lesion locations. Specifically, an empirical approach was introduced in the present work to explore the impact of mild stroke-induced cognitive alterations on functional brain network reorganization during cognitive tasks (i.e., visual and auditory oddball). Electroencephalogram functional connectivity was estimated from three groups (i.e., 40 patients with cortical infarctions, 48 patients with subcortical infarctions, and 50 healthy controls). Using graph theoretical analysis, we quantitatively investigated the topological reorganization of functional brain networks at both global and nodal levels. Results showed that both patient groups had significantly worse behavioral performance on both tasks, with significantly longer reaction times and reduced response accuracy. Furthermore, decreased global and local efficiency were found in both patient groups, indicating a mild stroke-related disruption in information processing efficiency that is independent of lesion location. Regarding the nodal level, both divergent and convergent node strength distribution patterns were revealed between both patient groups, implying thatmild stroke with different lesion locations would lead to complex regional alterations during visual and auditory information processing, while certain robust cognitive processes were independent of lesion location. These findings provide some of the first quantitative insights into the complex neural mechanisms of mild stroke-induced cognitive impairment and extend our understanding of underlying alterations in cognition-related brain networks induced by different lesion locations, which may help to promote post-stroke management and rehabilitation. [ABSTRACT FROM AUTHOR]

Material exchange frequently occurs in gullies, and thus the relationship between a gully-network structure and sediment transport potential has attracted considerable interest. However, previous researches ignored the difficulty of material transport from sources to sinks, and did not quantify the connectivity of a network structure. In this study, we used a graph model structure to model gully networks of six typical sample areas in the Loess Plateau of China and quantified gully network connectivity using four indexes: average node strength, accessibility from sources to sinks, potential flow, and network structural connectivity index. Results show that: (1) Reflected by different quantitative indexes, the trends of gully network connectivity in different regions are similar. From north to south, the connectivity of a sample area first increases and then decreases. (2) The more mature gullies have stronger network connectivity. Small resistance is conducive to material transport in the gullies. (3) The node connectivity index of the gully network shows a significant aggregation distribution in space, and node connectivity on the main channel is often stronger than that on the branch trench. These results not only deepen the understanding of the process and mechanism of loess gully geomorphic development and evolution but also provide a reference for geomorphic studies. [ABSTRACT FROM AUTHOR]

Obstetric studies had long revealed that the human female mental state, although subjective, has a nonlinear relation to the gestation, which could eventually leads to eugenics characteristics. Due to the difference of regions, there are differences between the data, people want to analyze the correlation between the survey data of different regions. Traditional obstetric studies explore health information on understanding and predicting this psychological state. However, most traditional research is based on statistical methods that exploring the correlation between numbers. This type of method lacks an understanding of the natural semantics of obstetric data, so it is prone to problems such as deviation or missing data (such as missing geospatial information) in data analysis. To tackle this problem, we study the use of a generic graph representation on gynecology and obstetric surveys with geospatial features, and propose a bipartite approach, or Geo-SPS, to mine the semantic relationship between low quality health information data. Our solution highlights our unconventional adaptation of novel graph theory from computational physics into biomedical ontology, that carefully maps semantic objects into a bipartite graph. With this tool, Geo-SPS provides a unique approach to semantic similarity metrics. The method supports fast and understandable processing of mixed textual data under different geographic spaces in a graph convolutional neural network. We further evaluate and validate the feasibility of Geo-SPS using a case study on obstetric surveys and health information data from over 3000 pregnant women in three different places from China. Results show that Geo-SPS can effectively represent prenatal mental state from this mixed data set, with an accurate classification on birth defects. [ABSTRACT FROM AUTHOR]

Hydrological connectivity affects the material cycling and energy transfer of ecosystems and is an important indicator for assessing the function of aquatic ecosystems. Therefore, clarification of hydrologic connectivity and its optimization methods is essential for basin water resources management and other problems; however, most of the current research is focused on intermittently flooded areas, especially in terms of optimization, and on hydrological regulation within mature water structures, while research on hydrological connectivity in dry, low rainfall plain areas remains scarce. Based on the graph and binary water cycle theories, this study assessed and hierarchically optimized the structural hydrological connectivity of the Dongliao River Basin (DRB), integrating artificial and natural connectivity, and explored the hydrological connectivity optimization method in the arid plain region at the basin scale to increase connectivity pathways. The spatial analysis and evaluation of hydrological connectivity was also carried out based on the results of the hierarchical optimization, and provided three scenarios for the construction of hydrological connectivity projects in the basin. The hierarchical optimization yielded a total of 230 new water connectivity paths, and the overall hydrological connectivity increased from 5.07 to 7.64. Our results suggest a large spatial correlation in hydrological flow obstruction in the DRB. The center of gravity of circulation obstruction shifted to the south after optimization for different levels of connectivity. With the increase in the optimization level of hydrological connectivity, the national Moran index rose and then fell. The magnitude of the increase in hydrological connectivity effects varied at different optimization levels, and there were sudden points' increase points. From an application point of view, Scenario 1 is necessary and the most cost effective is Scenario 2, which provides a scientific basis for guiding the construction of future ecological projects in the DRB. [ABSTRACT FROM AUTHOR]

Chinese Traditional Gardens (CTGs) are an important part of China's cultural inheritance from the past. Today's China has experienced rapid urbanization, raising the need for a new form of contemporary gardens intended to satisfy peoples' need for traditional culture. Garden paths are important in CTGs; they are designed to show visitors changing views with each step, and to lead them to secluded, quiet places via winding paths. This enhances the ornamental interest of the gardens. Based on plane graphics, this study evaluates the characteristics of three types of garden paths in fourteen traditional gardens and a contemporary antique garden, the Daguan Garden in Beijing. The analysis uses correlation and factor analysis to integrate 28 quantitative path indicators into five aspects of average, scale, network, wide, and aggregation. The 28 indicators can be expressed by six simple indicators: average connection length, number of path sections, alpha index, average width, average tortuous angle, and concentration degree. The results show small variations of garden paths between traditional gardens, but a considerable difference between the contemporary garden and traditional gardens. The research proposes a framework for the quantification and comparison of garden path features that can be applied before and after garden path construction, for both ancient and modern garden styles. This framework generates garden path feature values and theoretical values of six indicators, and is not constrained by the garden scale. Therefore, it provides an accurate and efficient design tool for garden designers. [ABSTRACT FROM AUTHOR]

Roadway multi-fatality crashes have always been a vital issue for traffic safety. This study aims to explore the contributory factors and interdependent characteristics of multi-fatality crashes using a novel framework combining association rules mining and rules graph structures. A case study is conducted using data from 1068 severe fatal crashes in China from 2015 to 2020, and 1452 interesting rules are generated using an association rule mining approach. Several modular rules graph structures are constructed based on graph theory to reflect the interactions and patterns between different variables. The results indicate that multi-fatality crashes are highly associated with improper operations, passenger overload, fewer lanes, mountainous terrain, and run-off-the-road crashes, representing the key variables of factors concerning driver, vehicle, road, environment, and accident, respectively. Furthermore, crashes involving different severity levels, road categories, and terrain are verified to possess unique association rules and independent crash patterns. Moreover, the proportion of severe crashes caused by a combination of human-vehicle-road-environment factors (43%) is much higher than that of normal crashes (3%). This study reveals that the hidden associations between various factors contribute to the overrepresentation and severity of multi-fatality crashes. It also demonstrates that the crash mechanisms involving multi-fatality crashes and their interactions are more complex at the system level than those for normal crashes. The proposed framework can effectively map the intrinsic link between multiple crash factors and potential risks, providing transportation agencies with helpful insights for targeted safety measures and preventive strategies. [ABSTRACT FROM AUTHOR]

Objective: This study is designed to investigate the brain function changed regions in elderly patients with knee osteoarthritis (KOA) and to explore the relationship between neuropsychological tests and resting-state functional magnetic resonance imaging (rs-fMRI) network to clarify the possible mechanism underlying cognitive changes in KOA patients. Materials and methods: Fifty-two patients aged ≥ 65 with KOA and twenty-two healthy-matched controls were recruited in this study. All participants were given rs-fMRI check. We used graph theory analysis to characterize functional connectivity (FC) and topological organization of the brain structural network. The relationship between FC values, topological properties, and the neuropsychological test scores was analyzed. Results: Compared with the controls, fourteen edges with lower functional connectivity were noted in the KOA group. Local efficiency and smallworldness of KOA patients decreased compared to the healthy controls. No significant alterations of nodal topological properties were found between the two groups. There was a significant positive correlation between the AVLT-H (L) and the internetwork of default mode network (DMN) (left/right orbitofrontal Superior cortex) and limbic/cortical areas (left/right caudate, right amygdala). AVLT-H(L) was positively correlated with small-worldness and local efficiency. Conclusion: The results indicated that for elderly KOA patients with declined cognition, topological properties, FC between DMN and subcortical limbic network related regions are significantly decreased compared to healthy controls. These alterations demonstrated a significant correlation with the neuropsychological test scores. [ABSTRACT FROM AUTHOR]

Redundancy is an important attribute of a resilient urban drainage system. While there is a lack of knowledge on where to increase redundancy and its contribution to resilience, this study developed a framework for the optimal network structure of urban drainage systems that considers pipeline redundancies. Graph theory and adaptive genetic algorithms were used to obtain the initial layout and design of the urban drainage system. The introduction of additional water paths (in loop)/redundancies is suggested by the results of complex network analysis to increase resilience. The drainage performances of the urban drainage system with pipeline redundancies, and without redundancies, were compared. The proposed method was applied to the study area in Dongying City, Shandong Province, China. The results show that the total overflow volume of the urban drainage system with pipeline redundancies under rainfall exceeding the design standard (5 years) is reduced by 20–30%, which is substantially better than the network without pipeline redundancies. [ABSTRACT FROM AUTHOR]

The Yuan dynasty is the first non-Han unified dynasty in ancient China. It is subtle to tell the similarities and differences between the classic Chinese poems in the Yuan dynasty written by the writers of Han nationality and non-Han minority nationalities. By graph theory and statistics, this article establishes a novel method for quantifying the similarity between the Han's and non-Han's Yuan poetry. First, we formulate the character co-occurrence networks (CCONs) of the Han's and non-Han's Yuan poem databases collected from 'Database of Ancient Chinese Classics'. The resistance distance, a graph theory concept, is introduced to describe the lexical relation between a pair of characters in the CCONs. Then, we propose the similarity index as the coefficient of variation for the ratios of resistance distances in the CCONs of Han's and non-Han's poem databases, which measures the consistency between the usages of characters in the Han's and non-Han's poems. The calculation results indicate that the Han's and non-Han's poems share a high percentage of common high-frequency characters and there do not exist significantly different lexical features in terms of the usage of these high-frequency characters. The obtained results offer new insights into the quantitative features of this long-debated issue. [ABSTRACT FROM AUTHOR]

Objective: Progressive conditions characterized by cognitive decline, including mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are clinical conditions representing a major risk factor to develop dementia, however, the diagnosis of these pre-dementia conditions remains a challenge given the heterogeneity in clinical trajectories. Earlier diagnosis requires data-driven approaches for improved and targeted treatment modalities. Methods: Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 35 patients with SCD, 19 with MCI, and 36 age-matched healthy controls (HC). A recently developed machine learning technique, Hollow Tree Super (HoTS) was utilized to classify subjects into diagnostic categories based on their FC, and derive network and parcel-based FC features contributing to each model. The same approach was used to identify features associated with performance in a range of neuropsychological tests. We concluded our analysis by looking at changes in PageRank centrality (a measure of node hubness) between the diagnostic groups. Results: Subjects were classified into diagnostic categories with a high area under the receiver operating characteristic curve (AUC-ROC), ranging from 0.73 to 0.84. The language networks were most notably associated with classification. Several central networks and sensory brain regions were predictors of poor performance in neuropsychological tests, suggesting maladaptive compensation. PageRank analysis highlighted that basal and limbic deep brain region, along with the frontal operculum demonstrated a reduction in centrality in both SCD and MCI patients compared to controls. Conclusion: Our methods highlight the potential to explore the underlying neural networks contributing to the cognitive changes and neuroplastic responses in prodromal dementia. [ABSTRACT FROM AUTHOR]

Subjective cognitive decline (SCD) is considered the first stage of Alzheimer's disease (AD). Accurate diagnosis and the exploration of the pathological mechanism of SCD are extremely valuable for targeted AD prevention. However, there is little knowledge of the specific altered morphological network patterns in SCD individuals. In this present study, 36 SCD cases and 34 paired-matched normal controls (NCs) were recruited. The Jensen-Shannon distance-based similarity (JSS) method was implemented to construct and derive the attributes of multiple brain connectomes (i.e., morphological brain connections and global and nodal graph metrics) of individual morphological brain networks. A t-test was used to discriminate between the selected nodal graph metrics, while the leave-one-out crossvalidation (LOOCV) was used to obtain consensus connections. Comparisons were performed to explore the altered patterns of connectome features. Further, the multiple kernel support vector machine (MK-SVM) was used for combining brain connectomes and differentiating SCD from NCs. We showed that the consensus connections and nodal graph metrics with the most discriminative ability were mostly found in the frontal, limbic, and parietal lobes, corresponding to the default mode network (DMN) and frontoparietal task control (FTC) network. Altered pattern analysis demonstrated that SCD cases had a tendency for modularity and local efficiency enhancement. Additionally, using the MK-SVM to combine the features of multiple brain connectomes was associated with optimal classification performance [area under the curve (AUC): 0.9510, sensitivity: 97.22%, specificity: 85.29%, and accuracy: 91.43%]. Therefore, our study highlighted the combination of multiple connectome attributes based on morphological brain networks and offered a valuable method for distinguishing SCD individuals from NCs. Moreover, the altered patterns of multidimensional connectome attributes provided a promising insight into the neuroimaging mechanism and early intervention in SCD subjects. [ABSTRACT FROM AUTHOR]

Objective: Ischemic moyamoya (MMD) disease could alter the cerebral structure, but little is known about the topological organization of the structural covariance network (SCN). This study employed structural magnetic resonance imaging and graph theory to evaluate SCN reorganization in ischemic MMD patients. Method: Forty-nine stroke-free ischemic MMD patients and 49 well-matched healthy controls (HCs) were examined by T1-MPRAGE imaging. Structural images were pre-processed using the Computational Anatomy Toolbox 12 (CAT 12) based on the diffeomorphic anatomical registration through exponentiated lie (DARTEL) algorithm and both the global and regional SCN parameters were calculated and compared using the Graph Analysis Toolbox (GAT). Results: Most of the important metrics of global network organization, including characteristic path length (Lp), clustering coefficient (Cp), assortativity, local efficiency, and transitivity, were significantly reduced in MMD patients compared with HCs. In addition, the regional betweenness centrality (BC) values of the bilateral medial orbitofrontal cortices were significantly lower in MMD patients than in HCs after false discovery rate (FDR) correction for multiple comparisons. The BC was also reduced in the left medial superior frontal gyrus and hippocampus, and increased in the bilateral middle cingulate gyri of patients, but these differences were not significant after FDR correlation. No differences in network resilience were detected by targeted attack analysis or random failure analysis. Conclusions: Both global and regional properties of the SCN are altered in MMD, even in the absence of major stroke or hemorrhagic damage. Patients exhibit a less optimal and more randomized SCN than HCs, and the nodal BC of the bilateral medial orbitofrontal cortices is severely reduced. These changes may account for the cognitive impairments in MMD patients. [ABSTRACT FROM AUTHOR]

Climate change has caused habitat fragmentation and reduced connectivity. The Fen River Basin in Shanxi Province, China is an important habitat for the central population of the brown-eared pheasant (BEP). The effects of climate change need to be considered in the conservation planning of BEP habitats. We used a species dispersion model to determine the BEP core habitat and graph theory to explore the connectivity of the BEP's main habitats. The pinch point areas of BEP dissemination were determined by circuit theory. Least-cost pathways were used to identify the critical corridors for BEP dissemination. A gap analysis was conducted to estimate the efficiency of BEP conservation measures. Under the future climate scenarios, BEP habitats decreased by between 54.69% and 97.63%, and the connectivity of the main habitats was reduced by a similar magnitude. The BEP core habitat shifted to the southwestern region under the influence of climatic conditions. Currently, 90.84% of the species' critical habitat remains unprotected. Due to climate change, the core habitat in the future was projected to differ from the current protected area. Enhancing the protection of the pinch point region may aid in the restoration of habitat connectivity. [ABSTRACT FROM AUTHOR]

Background: Stressful life events (SLEs) are well-established proximal predictors of the onset of depression. However, the fundamental causes of interindividual differences in depression outcomes are poorly understood. This study addressed this depression susceptibility mechanism using a well-powered sample of adults living in China.Methods: Healthy participants with SLEs (n = 185; mean = 47.51 years, 49.73% female), drawn from a longitudinal study on the development of depression, underwent diffusion tensor imaging, interleukin-6 (IL-6) level measurement, and trimonthly standardized clinical and scale evaluations within a two-year period.Results: Receiver operating characteristic analyses indicated that reduced feeder connection and HIP.R nodal efficiency improved the predictive accuracy of post-SLEs depression (ORfeeder = 0.623, AUC = 0.869, P < 0.001; ORHIP = 0.459, AUC = 0.855, P < 0.001). The successfully established path analysis model confirmed the significant partial effect of SLEs-IL-6-white matter (WM) network differences-depression (onset and severity) (x2/8 = 1.453, goodness-of-fit [GFI] = 0.935, standard root-mean-square error of approximation [SRMR] = 0.024). Females, individuals with lower exercise frequency (EF) or annual household income (AHI) were more likely to have higher IL-6 level after SLEs (βint-female⁎SLEs = -0.420, P < 0.001; βint-exercise⁎SLEs = -0.412, P < 0.001; βint-income⁎SLEs = -0.302, P = 0.005).Limitations: The sample size was restricted due to the limited incidence rate and prospective follow-up design.Conclusions: Our results suggested that among healthy adults after SLEs, those who exhibited abnormal IL-6-WM differences were susceptible to developing depression. Females, lower AHI or EF might account for an increased risk of developing these abnormal IL-6-WM differences. [ABSTRACT FROM AUTHOR]

Purpose: Idiopathic rapid eye movement Sleep Behavior Disorder (iRBD) is considered as a prodromal and most valuable warning symptom for Parkinson's disease (PD). Although iRBD and PD without RBD (nRBD-PD) are both α-synucleinopathies, whether they share the same neurodegeneration process is not clear enough. In this study, the pattern and extent of neurodegeneration were investigated and compared between early-stage nRBD-PD and iRBD from the perspective of whole-brain functional network changes. Methods: Twenty-one patients with iRBD, 23 patients with early-stage nRBD-PD, and 22 matched healthy controls (HCs) were enrolled. Functional networks were constructed using resting-state functional MRI (fMRI) data. Network topological properties were analyzed and compared among groups by graph theory approaches. Correlation analyses were performed between network topological properties and cognition in the iRBD and nRBD-PD groups. Results: Both patients with iRBD and patients with early-stage nRBD-PD had attention, executive function, and some memory deficits. On global topological organization, iRBD and nRBD-PD groups still presented small-worldness, but both groups exhibited decreased global/local efficiency and increased characteristic path length. On regional topological organization, compared with HC, nRBD-PD presented decreased nodal efficiency, decreased degree centrality, and increased nodal shortest path length, while iRBD presented decreased nodal efficiency and nodal shortest path. For iRBD, brain regions with decreased nodal efficiency were included in the corresponding regions of nRBD-PD. Nodal shortest path changes were significantly different in terms of brain regions and directions between nRBD-PD and iRBD. Attention deficits were correlated with local topological properties of the occipital lobe in both iRBD and nRBD-PD groups. Conclusion: Both global and local efficiency of functional networks declined in nRBD-PD and iRBD groups. The overlaps and differences in local topological properties between nRBD-PD and iRBD indicate that iRBD not only shares functional changes of PD but also presents distinct features. [ABSTRACT FROM AUTHOR]

One of the main transportation problems of biggest modern cities is the excessively high load on ground transport, which is why the development of subway networks is of particular importance. This article analyzes the development of the spatial structure of subway networks in China. Currently China shows an intensive growth of existing networks and massive openings of new networks, which makes it the most suitable object for studying the evolution of subway networks. The methodology developed by K. Kansky and S. A. Tarkhov was used as the theoretical basis of this study. The study was conducted by analyzing the dynamics of the main quantitative and topomorphological indicators of subway networks during their passage through the stages of spatial evolution. The following indicators were used: the number of subways, the total length of the network, the number of cycles in the network, the number of topological layers and the number of cycles in each of them, the number of branching tiers, the area of topological layers and their share in the cyclic core of the network, the distribution of the length between the elements of the network structure, average cycle size, topological limit, cyclization index and circuity index. We identified the patterns for passing the stages of evolutionary development by the networks of Chinese subways; also, we found common features that define the “Chinese” type of subway, we identified a new subtype of networks. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three quantitative indicators formed the integrated landscape connectivity. • Graph theory connectivity indices have been improved by livestock density and slope. • The integrated landscape connectivity can well quantify DOC variations in farm pond. • The interaction of three indicators explained over 20% of DOC contents. The landscape connectivity in farm pond catchments leads to complex hydrologic and biogeochemical processes, which is important to the carbon cycle of the watershed. However, little attention has been paid to analyzing the influence of landscape connectivity on water dissolved organic carbon (DOC) content in farm pond catchments. In this study, we developed an integrated landscape connectivity framework that encompasses landscape connectivity metrics, graph theory connectivity indices, and spatial distance. And the graph theory connectivity indices were constructed based on the modified resistance surface using a function that integrated landscape development intensity, slope, and livestock density. Using DOC content data from 86 farm ponds monitored between June 2017 and May 2018. The results demonstrated that the average water DOC content in farm ponds was higher during the dry season (5.35 ± 2.03 mg/L) compared to the wet season (4.27 ± 1.40 mg/L). The individual effects of landscape connectivity metrics and graph theory connectivity indices on DOC content were more prominent during the dry season, while the spatial distance had a more significant influence in the wet season. The interactive effects of determinant variables had a more substantial influence on pond water DOC content than their individual effects during both wet and dry seasons. The interactive effect variation ratio (IEVR) of all variables was positive in both seasons (0.143–0.777). Combining landscape connectivity metrics, graph theory connectivity indices, and spatial distance (22.0–24.3%) improved the interpretation of DOC content in comparison to using separate (0.4–12.1%) or paired variables (0.3–19.3%). Our findings suggest that integrated landscape connectivity is necessary to further understand the role of ponds in the organic carbon cycling of surface water. These strategies should not only focus on landscape connectivity metrics, but also consider the influences of graph theory connectivity indices and spatial distance in farm pond catchments. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new method to describe hydrological connectivity in plain river networks. • Graph theory and comprehensive connectivity indices were integrated in this framework. • The hydrological connectivity decreased by 50% over the past century due to hydraulic facilities. • The area of lake wetlands decreased sharply but the importance of the same lake patches increased. • Determine the optimal hydrological connectivity is a challenge. The proliferation of hydraulic facilities worldwide has greatly hindered the lake wetlands ecosystem services through impacting hydrological connectivity. However, developing a general method to evaluate hydrological connectivity in the plain river-lake wetlands networks heavily influenced by hydraulic facilities remains challenging. In this study, we present a novel framework for the hydrological connectivity evaluation of lake wetlands whilst considering different types of hydraulic facilities. By generalising lake wetlands into patches and rivers into edges, a river topological network connecting lake wetlands was constructed based on graph theory, and connectivity indices were calculated through three dimensions: individual patches - inter-patches - and study area, respectively. Using detailed historical topographic maps and high-resolution satellite data, this new framework, combining graph theory and connectivity indices, is applied to a river-lake wetlands district with dense sluices/pumping stations in eastern China. Results show that (1) Although the number of lake wetlands remained relatively stable over the past century, the average area decreased by 18.44%, and the density of hydraulic engineering boomed to 2.2/km2. (2) The increases in hydraulic facilities and the decrease in lake wetlands areas significantly reduced the degree of hydrological connectivity, resulting in the overall connectivity value decreasing from 0.97 in the 1910s to 0.45 in 2019. (3) The importance of the same lake wetlands patch has progressively heightened in the last hundred years. This study provides an effective method for quantifying hydrological connectivity in plain river-lake wetlands with dense hydraulic facilities and could shed light on lake wetlands protection and restoration. [ABSTRACT FROM AUTHOR]

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There have been many recent fears of severe house-price decreases in some provinces in China causing a nationwide collapse of the housing market. Therefore, this paper aims to clarify the linkage structure of China's housing market and its risk contagion routes. Given monthly data of provincial housing and stock-market capital returns from 2001M01 to 2019M12, on the basis of graph theory, this paper first explores the linkage structure of provincial housing markets. Relying on the linkage structure, this paper then simulates the effect of unexpected negative shocks from the stock market on the probabilities of a housing-market collapse based on the epidemic model. The results show that (i) consistently with practical evidence, the probability of housing-market collapse is relatively high in the southwest of China and (ii) reducing housing-market linkage, such as through a blocking mechanism, to prevent collapse is helpful. [ABSTRACT FROM AUTHOR]

The irregular forbidden zone (FZ) is a common phenomenon with giant hydropower plants developed in the past two decades in China. Irregular shapes of FZs significantly challenge hydro unit commitment (HUC). This paper proposes a novel MILP based framework for HUC considering irregular FZ related constraints including the FZ constraint, effects of linearization errors in both the net head and the output, and the FZ crossing constraint. In the framework, the FZ constraint is handled by the optimal convex partitioning algorithm and the common structure-based formulation method. Inspired by the planar translating robot placement problem, linearization errors are considered by the Minkowski sum method. To handle the FZ crossing constraint, we then propose a graph theory-based approximate formulation method. The framework is integrated into a HUC model with an objective of peak shaving. The model is then tested with a batch of real-world instances of a cascade hydropower system formed by ten giant units with highly irregular FZs. The results show our framework can effectively consider the irregular FZ related constraints. The major advantage of our framework is its ability to handle the irregular shapes of FZs without any tedious and error-prone manual processing. [ABSTRACT FROM AUTHOR]

Anthropogenic activities alter the underlying surface conditions and arrangements of landscape features in a drainage basin, interfering with the pollutant (e.g., dissolved nitrogen, phosphorus) transport network configuration and altering the hydrological response. Assessing the impact of anthropogenic activities on hydrological connectivity for natural-artificial catchment is critical to understand the hydrological-driven ecosystem processes, services and biodiversity. However, quantifying this impact at catchment scale remains challenging. In this study, a new framework was proposed to quantify the impact of anthropogenic activities on hydrological connectivity combined with graph theory and network analysis. This framework was exemplified in a natural-artificial catchment of the Yangtze River basin of China. Based on remote sensing and field-investigated data, three transport networks were constructed, including natural transport network (N1), ditch–road transport network (N2), and terrace–dominated transport network (N3), which reflected the different human intervention. The results showed that human intervention improved the connectivity of the nodes and enhanced the complexity of the catchment transport network structure. Anthropogenic activities significantly decreased the hydrological structural connectivity of the catchment. In particular, compared with the N1 network, the critical nodes for hydrological connectivity which were judged by connectivity indexes were reduced by 92.94% and 95.29% in the N2 and N3 network, respectively. Furthermore, the ditch–road construction had a greater impact than terraces in decreasing hydrological structural connectivity at catchment scale. This framework has proven effective in quantifying the hydrological connectivity analysis under different human intervention at the catchment scale and facilitates the improvement of catchment management strategies. • A new framework was proposed to identify catchment hydrological connectivity. • Graph theory combined with network analysis were integrated into the framework. • Three transport networks were constructed to exemplify the framework. • Ditches, roads, and terraces significantly decreased hydrological connectivity. [ABSTRACT FROM AUTHOR]

[Display omitted] • A network modeling framework suitable for various types of heterogeneous risk factors in urban floods has been proposed. • Simulation of the cascading effects of heterogeneous hazard-bearing bodies in the city from a functional availability perspective under continuous flood attack. • The heterogeneous hazard-bearing bodies are interdependent and networked, which causes flood damage to spread more quickly and widely. • The greater degree and more propagating hazard-bearing bodies are key factors influencing the spread of flood risk and the cascading failure of functionalities in urban areas. Increasing urban flooding posed serious threat to urban safety and sustainable development. The heterogeneity of hazard-bearing bodies is one of the important reasons for the variability of cascading failure scenarios in floods. Most previous studies have focused on failure propagation risks among specific types of hazard-bearing bodies, making it impossible to conduct a systematic analysis of the damage caused by persistent flooding in cities. In contrast to conventional graph representations, the heterogeneous hazard-bearing bodies network (HHBBN) based on heterogeneous functional graph theory was constructed in this paper and used to capture the relevant associations of urban resources in a flood. The HHBBN takes into account heterogeneity such as sensitivity and service diffusivity, and functionalities related to hazard-bearing bodies are represented as nodes, with service relationships and geographical associations depicted as edges. To further study the performance change of HHBBN in urban areas during rainstorm flooding, the cascading failure of HHBBN under continuous disturbance was also simulated. The proposed network modeling and cascading failure simulation methods were tested in Futian District of Shenzhen City, China. The case results demonstrate that the heterogeneous hazard-bearing bodies are interdependent and networked, which causes flood damage to spread more quickly and widely. Furthermore, the larger degree and more propagating hazard-bearing bodies are the key factors affecting the cascading failure of heterogeneous hazard-bearing bodies. [ABSTRACT FROM AUTHOR]

Summary: With the rapid development of microgrids, dynamic regional multi‐microgrid networks are emerging as an efficient and flexible solution in smart distribution networks. To optimize the dynamic networking of multi‐microgrid for better economics and reliability, this article proposes an evolutionary optimization method based on graph theory. This article first reviews the need for dynamic networking in multi‐microgrids. Then a dynamic networking model is proposed based on graph theory and solved by the nondominated sorting genetic algorithm II, which can provide a set of Pareto‐optimal solutions efficiently. The proposed method is tested by a regional multi‐microgrid network in an industrial park in Northern China. The results showed that the daily operating costs of the regional multi‐microgrid can be reduced by 17.4% with the proposed dynamic networking method. When faults are considered, the daily operation costs can be reduced by 11.0%, and the system average interruption frequency index value is reduced from 0.27 to 0.19. The results also demonstrated the efficiency of the proposed algorithm over other evolutionary computing methods in terms of both runtime and convergence with promising applications in real world scenarios. Through simulations in the IEEE 33‐node system, different operation programs are provided according to the preferences of operators under normal conditions. The operating cost and active power loss are reduced under failure conditions, which shows the effectiveness of the proposed method. The work in this article is expected to provide some reference for the application of regional multi‐microgrid. [ABSTRACT FROM AUTHOR]

Based on the cooperation patent data between 31 provinces in China from 1985 to 2017, this paper portrays the topological structure, spatial pattern and proximity mechanism of China's inter-regional innovation cooperation network by the graph theory, spatial statistical methods and the quadratic assignment procedure model. The conclusions are as follows: (1) the topological structure of China's inter-regional innovation cooperation forms a hierarchical network with Beijing as the core and it exhibits a typical small-world property and 'core–periphery' structure. (2) In terms of spatial distribution characteristics, the 'quadruple' with the Beijing–Tianjin region, Yangtze River Delta, Pearl River Delta and Sichuan as the peaks has become the backbone of the inter-regional innovation cooperation network in China. At the same time, the spatial differentiation of the three indicators of degree centrality, median centrality and intensity centrality is obvious. (3) In terms of proximity mechanism, geographical, institutional and cognitive proximity exert a significant effect on China's inter-regional innovation cooperation network linkage. However, there are significant differences in the effects of various proximity elements on China's regional innovation network. [ABSTRACT FROM AUTHOR]

This paper explores the phenomenon of international servicification of manufacturing from the period 1995 to 2011. By applying empirical techniques of Social Network Analysis and graph theory, we find that the network of flows of intermediate services embodied in manufacturing exports is still slightly dense and would not correspond to a traditional centre-periphery structure. The mapping shows a numerous, highly cohesive group of countries, with China, the USA and Germany as central economies and an increasing leading role of Asian economies, which would indicate their commitment to upgrading within global value chains. We go a step further by empirically analysing the impact of the countries' centrality in the global network of intermediate services on manufacturing competitiveness. Our findings reveal that, together with the level of embodiment of intermediate services into manufacturing exports, who the providers of those services inputs are is a key determining factor for manufacturing competitiveness. [ABSTRACT FROM AUTHOR]

When designing the underground logistics system, it is necessary to consider the uncertainty of logistics nodes, high cost, and high risk. This paper employed the theories of uncertain graph and dynamic programming to solve the network planning problem of underground logistics system. Firstly, we proposed the concepts of uncertainty measure matrix and vertices structure uncertainty graph by using uncertainty measure and uncertainty graph. Secondly, vertices structure uncertainty graph of the underground logistics system was constructed based on our proposed vertices structure uncertainty graph and the uncertainty of logistics nodes. Thirdly, the dynamic programming model of the underground logistics system was established, and its solution algorithm was also designed by improving simulated annealing. Finally, the correctness and feasibility of the method was validated by using a numerical example of the underground logistics system in Xianlin district, Nanjing City in China. [ABSTRACT FROM AUTHOR]

Severe PM 2.5 (particulate matter with aerodynamic diameter smaller than 2.5 μm) pollution in China, with adverse impact on human health and global climate change, has raised great concern in recent years. The volatile organic compound (VOC) is suggested to be a significant precursor of PM 2.5 , but which VOC structure contributes the most to the aerosol particulate formation is still an open question. With large space occupation and complex structures (cyclic, linear and branched), VOCs together with inorganics may play a critical role in atmospheric new particle formation (NPF). To explore the effect of molecular structure on nucleation in NPF, a multi-component (H2SO4-HNO3-NH3-VOC) kinetic model is established based on the gas kinetic theory and graph theory. As the most common VOCs in the atmosphere, alkanes with 2–15 carbons are selected for model analysis. The results show that alkanes especially those with a cyclic structure or carbon number ≥4 results in the highest rate of NPF within the species considered in this study. The dominant size of nuclei, as well as the concentration of particles (larger than 1 nm), is enhanced by the increasing carbon number in alkanes. Taking the alkanes with same carbon number for comparison, the contribution to the nucleation by molecular structure is found to follow the order cyclic>linear>branched, and inversely proportion to branching number. Image 1 • A new insight of the effect of VOCs on aerosol particulates is presented. • Graph theory is applied to describe nucleation for the first time. • VOC can enhance atmospheric nucleation in air pollution. • Cyclic VOCs contribute most to the nucleation, followed by linear and branched. [ABSTRACT FROM AUTHOR]

An in-depth analysis of the urban road network structure plays an essential role in understanding the distribution of urban functional area. To concentrate topologically densely connected road segments, communities of urban roads provide a new perspective to study the structure of the network. In this study, based on OpenStreetMap (OSM) roads and points-of-interest (POI) data, we employ the Infomap community detection algorithm to identify the hierarchical community in city roads and explore the shaping role roads play in urban space and their relation with the distribution of urban functional areas. The results demonstrate that the distribution of communities at different levels in Guangzhou, China reflects the urban spatial relation between the suburbs and urban centers and within urban centers. Moreover, the study explored the functional area characteristics at the community scale and identified the distribution of various functional areas. Owing to the structure information contained in the identification process, the detected community can be used as a basic unit in other urban studies. In general, with the community-based network, this study proposes a novel method of combining city roads with urban space and functional zones, providing necessary data support and academic guidance for government and urban planners. [ABSTRACT FROM AUTHOR]

• Build a multi-objective method for improving LEN functional and structural connectivity. • Identify key locations for improving structural connectivity based on graph theory. • Introduce "integral index of probable connection" into network link additional technique. • Search corridor paths with maximum functional connectivity using greedy algorithm. • Use hierarchical rules with different priorities to set the greedy algorithm. By building connectivity between ecological spaces, the landscape ecology network (LEN) can effectively prevent further habitat fragmentation and loss in urbanizing areas. The existing LEN construction method fails to consider the multiple optimization goals of improving network structural and functional connectivity. This paper aimed to provide a novel method to improve connectivity for existing LENs in urbanizing areas. We took an urbanizing area in China as an example and extract existing LEN based on ecological priorities. On this basis, from the dimension of structural connectivity optimization, we used the network link additional technique based on "integral index of probable connection" to identify 50 optimal locations of additional corridors that could significantly improve the network structural connectivity. From the dimension of functional connectivity enhancement, we applied the greedy algorithm based on hierarchical rules with different priorities to search for corridor paths with maximum ecological function and low ecological and construction resistance. The optimized LEN contained 43 ecological sources and 74 ecological corridors. The dual dimension method proposed in this paper can improve the functional and structural connectivity of existing LEN, and greatly save the optimization time and efficiency of the network, to provide targeted ecological space protection schemes for urbanizing areas. [ABSTRACT FROM AUTHOR]

The synchronization of power generators is an important condition for the proper functioning of a power system, in which the fluctuations in frequency and the phase angle differences between the generators are sufficiently small when subjected to stochastic disturbances. Serious fluctuations can prompt desynchronization, which may lead to widespread power outages. Here, we model the stochastic disturbance by a Brownian motion process in the linearized system of the non-linear power systems and characterize the fluctuations by the variances of the frequency and the phase angle differences in the invariant probability distribution. We propose a method to calculate the variances of the frequency and the phase angle differences. For the system with uniform disturbance-damping ratio, we derive explicit formulas for the variance matrices of the frequency and the phase angle differences. It is shown that the fluctuation of the frequency at a node depends on the disturbance-damping ratio and the inertia at this node only, and the fluctuations of the phase angle differences in the lines are independent of the inertia. In particular, the synchronization stability is related to the cycle space of the network. We reveal the influences of constructing new lines and increasing capacities of lines on the fluctuations in the phase angle differences in the existing lines. The results are illustrated for the transmission system of Shandong Province of China. For the system with non-uniform disturbance-damping ratio, we further obtain bounds of the variance matrices. [ABSTRACT FROM AUTHOR]

Online travel has developed dramatically during the past three years in China. This results in a large amount of unstructured data like tourism reviews from which it is hard to extract useful knowledge. In this paper, a DWWP system consisting of domain-specific new words detection ( DW ) and word propagation ( WP ) is presented. DW deals with the negligence of user-invented new words and converted sentiment words by means of AMI (Assembled Mutual Information). Inspired by social networks, the new method WP incorporates manually calibrated sentiment scores, semantic and statistical similarity information, which improves the quality of sentiment lexicon in comparison with existing data-driven methods. Experimental results show that DWWP improves seventeen percentage points compared with graph propagation and four percentage points compared with label propagation in terms of accuracy on Dataset I and Dataset II, respectively. [ABSTRACT FROM AUTHOR]

Airport hubs’ indirect connectivity is important for any aviation network. Indirect connectivity describes the capacity of airport hubs to provide indirect connections over the airline network. As the Chinese aviation industry has experienced development, this paper offers a comparative analysis of indirect connectivity for Chinese airport hubs between 2010 and 2015. We investigate wave-system structures, weighted indirect connectivity (WIC) and indirect connections of the top ten airport hubs in China. In the spatial analysis, this paper surveys the spatial patterns of indirect connections at four important airport hubs. Beijing-Capital airport has strong indirect connectivity worldwide. Pudong and Hongqiao airports worked together to maximize the spatial coverage of Shanghai. Guangzhou-Baiyun airport has sufficient indirect connections across southern China, and it intends to expand its spatial influence into northern cities. [ABSTRACT FROM AUTHOR]

The effectiveness of rapid rail transit system is analyzed using tools of complex network for the first time. We evaluated the effectiveness of the system in Beijing quantitatively from different perspectives, including descriptive statistics analysis, bridging property, centrality property, ability of connecting different part of the system and ability of disease spreading. The results showed that the public transport of Beijing does benefit from the rapid rail transit lines, and the benefit of different regions from RRTS is gradually decreased from the north to the south. The paper concluded with some policy suggestions regarding how to promote the system. This study offered significant insight that can help understand the public transportation better. The methodology can be easily applied to analyze other urban public systems, such as electricity grid, water system, to develop more livable cities. [ABSTRACT FROM AUTHOR]

As a sustainable transportation mode, high-speed railway (HSR) has become an efficient way to meet the huge travel demand. However, due to the high acquisition and maintenance cost, it is impossible to build enough infrastructure and purchase enough train-sets. Great efforts are required to improve the transport capability of HSR. The utilization efficiency of train-sets (carrying tools of HSR) is one of the most important factors of the transport capacity of HSR. In order to enhance the utilization efficiency of the train-sets, this paper proposed a train-set circulation optimization model to minimize the total connection time. An innovative two-stage approach which contains segments generation and segments combination was designed to solve this model. In order to verify the feasibility of the proposed approach, an experiment was carried out in the Beijing-Tianjin passenger dedicated line, to fulfill a 174 trips train diagram. The model results showed that compared with the traditional Ant Colony Algorithm (ACA), the utilization efficiency of train-sets can be increased from 43.4% (ACA) to 46.9% (Two-Stage), and 1 train-set can be saved up to fulfill the same transportation tasks. The approach proposed in the study is faster and more stable than the traditional ones, by using which, the HSR staff can draw up the train-sets circulation plan more quickly and the utilization efficiency of the HSR system is also improved. [ABSTRACT FROM AUTHOR]

Plastic pollution control, involving the whole life cycle management of plastic production, consumption, sorting, recycling, and disposal, has become necessary for global sustainable development. Research on public attitudes is vital to understanding whether plastic pollution control policies are being successfully implemented and the degree to which the public is involved. However, few studies have assessed public attitudes toward plastic pollution control from the whole life cycle perspective, especially using big data. Based on China's whole life cycle management policy of plastics, this study collected more than 200,000 relevant comments and user information from Sina Weibo to analyze and evaluate public attitudes and opinions toward plastic pollution control. Spatial-temporal analysis was conducted to discover the regional and temporal differences in public attention. Using a sentiment classification method based on semantic analysis, the emotional tendencies of the public attitudes toward ten subdivided plastic pollution control links were studied. It was found that more people held a positive attitude and paid more attention to reusing and sorting links, while the negative emotions were concentrated on the collection and sorting links. Using a topic modeling method, the negative opinions in various links were revealed, such as lack of supervision and industry standards; over packaging or insufficient packaging; food safety problems caused by the reuse; high costs, poor use and possibly greater waste of substitutes; unclear sorting rules and insufficient supporting measures. Graph theory was applied to display these opinions. Finally, some policy implications derived from the discussions are given., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

In the high-speed urbanization process of China, the urban population has been increasing significantly, leading to a high-density aggregation of population. However, the sharp increase in population density has not produced commensurate improvements in the road networks. On the contrary, the population increase induced a serious evacuation vulnerability, which cities experience during various hazards and catastrophic events. Therefore, research on evacuation vulnerability is important to urban planning. To assess the evacuation vulnerability, the optimal and worst scenarios should be considered because all possible evacuation plans occur between these extremes. However, most previous evacuation vulnerability studies are based on the worst-case scenario, only providing an upper bound of a potential evacuation assessment. To provide a more comprehensive theoretical basis for decision-makers to understand the consequences caused by all possible evacuations, this paper proposes an optimal evacuation vulnerability assessment model that provides the lower bound on potential evacuation difficulties. The model is solved by a stepwise spreading algorithm based on Graph Theory. Subsequently, to evaluate the effectiveness of the model, the study adopts the model to assess the evacuation capability of different road network topologies. A comparison with previous research was performed. The model was demonstrated in an application to the South Luogu Alley of Beijing, China. The significance of this paper is that the combination of our model with previous research may provide a more complete theoretical basis for an evacuation vulnerability assessment. [ABSTRACT FROM AUTHOR]

Type 2 diabetes mellitus is accompanied by cognitive impairment and is associated with an increased risk of dementia. Damage to brain structures such as white matter network disruption may underlie this cognitive disturbance. In the present study, 886 non-diabetic and 163 type 2 diabetic participants completed a battery of neuropsychological tests. Among them, 38 diabetic patients and 34 non-diabetic participants that matched the patients for age/sex/education received a magnetic resonance imaging-based diffusion tensor imaging. Then we calculated the topological properties of the white matter network using a graph theoretical method to investigate network efficiency differences between groups. We found that type 2 diabetic patients had inferior performances compared to the non-diabetic controls, in several cognitive domains involving executive function, spatial processing, memory, and attention. We also found that diabetic patients exhibited a disrupted topological organization of the white matter network (including the global network properties, i.e., network strength, global efficiency, local efficiency and shortest path length, and the nodal efficiency of the right rolandic operculum) in the brain. Moreover, those global network properties and the nodal efficiency of the right rolandic operculum both had positive correlations with executive function in the patient group. The results suggest that type 2 diabetes mellitus leads to an alteration in the topological organization of the cortical white matter network and this alteration may account for the observed cognitive decline. [ABSTRACT FROM AUTHOR]

• Degraded or destroyed habitat patches of Rhinopithecus roxellana are located and mapped. • Change detection and MaxEnt are used to determine where to use ecological restoration. • Determine the priority of candidate patches recovery with graph theory. • Provides a quantitative assessment for habitat restoration. • A practical suggestion and replicable framework are provided. Global habitat fragmentation is a major threat to terrestrial wildlife biodiversity, and habitat restoration is an effective method to protect biodiversity. The Rhinopithecus roxellana (R. roxellana) in Hubei Province, is a first-class key protected wild animal in China, and now the fragmentation of its habitat has seriously affected species expansion and genetic diversity. To identify the causes of habitat degradation and the location of recoverable habitat patches, improve habitat connectivity, better respond to species expansion and protect biodiversity effectively. Using two level sets of environmental variables to represent the past and current habitat conditions of R. roxellana in the Shennongjia region. MaxEnt model was firstly used to quantify the suitable habitat value to two levels, and then change detection method determined the habitat degradation areas between the two levels. Finally, the important values of recoverable areas were quantified and ranked with graph theory to clarify the priority of restored patches. The patches affected by degraded or damaged vegetation were found, then the suitable restoration patches were mapped. At last 5 patches as the priority areas of habitat restoration were conserved. Based on MaxEnt, change detection and connectivity analysis, targeted intervention is carried out on the habitat environment of R. roxellana in Shennongjia. As an effective conservation way, restoration of degraded or damaged habitats provides a mechanism to improve habitat connectivity. Here we show a practical method for the increasingly fragmented landscape restoration. [ABSTRACT FROM AUTHOR]

Multimodal visualization of network data is a method considering various types of nodes and visualizing them based on their types, or modes. Compared to traditional network visualization of nodes of the same mode, the new method treats different modes of entities in corresponding ways and presents the relations between them more clearly. In this paper, we apply the new method to visualize movie network data, a typical multimodal graph data that contains nodes of different types and connections between them. We use an improved force-directed layout algorithm to present the movie persons as the foreground and a density map to present films as the background. By combining the foreground and background, the movie network data are presented in one picture properly. User interactions are provided including detailed pie charts visible/invisible, zooming, and panning. We apply our visualization method to the Chinese movie data from Douban website. In order to testify the effectiveness of our method, we design and perform the user study of which the statistics are analyzed. [ABSTRACT FROM AUTHOR]

With the development of the public transportation, bus network becomes complicated and hard to evaluate. Transfer time is a vital indicator to evaluate bus network. This paper proposed a method to calculate transfer times using Space P. Four bus networks in China have been studied in this paper. Some static properties based on graph theory and complex theory are used to evaluate bus topological structure. Moreover, a bus network evolution model to reduce transfer time is proposed by adding lines. The adding method includes four types among nodes with random choice, large transfer time, degree, and small degree. The results show that adding lines with nodes of small degree is most effective comparing with the other three types. [ABSTRACT FROM AUTHOR]

The current study sought to examine the underlying brain changes in individuals with high schizotypy by integrating networks derived from brain structural and functional imaging. Individuals with high schizotypy (n = 35) and low schizotypy (n = 34) controls were screened using the Schizotypal Personality Questionnaire and underwent brain structural and resting-state functional magnetic resonance imaging on a 3T scanner. Voxel-based morphometric analysis and graph theory-based functional network analysis were conducted. Individuals with high schizotypy showed reduced gray matter (GM) density in the insula and the dorsolateral prefrontal gyrus. The graph theoretical analysis showed that individuals with high schizotypy showed similar global properties in their functional networks as low schizotypy individuals. Several hubs of the functional network were identified in both groups, including the insula, the lingual gyrus, the postcentral gyrus, and the rolandic operculum. More hubs in the frontal lobe and fewer hubs in the occipital lobe were identified in individuals with high schizotypy. By comparing the functional connectivity between clusters with abnormal GM density and the whole brain, individuals with high schizotypy showed weaker functional connectivity between the left insula and the putamen, but stronger connectivity between the cerebellum and the medial frontal gyrus. Taken together, our findings suggest that individuals with high schizotypy present changes in terms of GM and resting-state functional connectivity, especially in the frontal lobe. [ABSTRACT FROM AUTHOR]

The article discusses a study that used functional magnetic resonance imaging and graph theory to obtain a view of whole-brain functional connectivity of patients with somatization disorder. The study investigated the changes of voxel-wise functional networks in patients with SD. It reveals the correlation between the z-values of the cluster in the right inferior temporal gyrus and Hamilton Anxiety Scale scores.

[Display omitted] • We evaluated the impacts of different road grades on connectivity at multiple levels with different dispersal distances. • We integrated the analysis framework based on graph theory and circuit theory and proposed a new indicator to identify key patches. • Changes in connectivity are more regular at the landscape and corridor levels influenced by different grades of roads, while more complex at the patch level. • Medium-grade roads have a similar degree of negative impact on patch-level connectivity as high-grade roads. • Species with different dispersal abilities differ in their sensitivity to road impacts at different levels. The development of road networks over the years has caused serious damage to biodiversity. However, few studies have explored the impact of different road grades on ecological network connectivity, especially at multiple levels and at different dispersal distances. Here, we propose an analytical framework based on the integrated graph theory and the circuit theory method, in order to model the ecological network of virtual species, to evaluate connectivity at the landscape, patch, and corridor levels, and to identify the key patches and key corridors that contribute the most to the maintenance of connectivity. The empirical analysis in this study was performed on six scenarios, which were designed by successively integrating different road grades into the landscape. On this basis, the impact of different road grades on the connectivity, key patches, and key corridors in Wuhan, China, were explored. The results showed that: (1) High-grade roads have a significant impact on landscape-patch-corridor connectivity, while medium-grade roads have a similar degree of impact on patch-level connectivity as high-grade roads do. (2) Species with long dispersal ability (25 km) are susceptible to roads at the landscape and corridor levels; species with low and medium dispersal abilities (10, 15 and 20 km) are vulnerable to roads at the patch levels. (3) The importance of key patches and the resistance of key corridors are significantly increased by the influence of roads, while their spatial distribution changes slightly. This integrated framework contributes to an evaluation of the impacts of different grades road on ecological processes, so as to better provide targeted suggestions for biodiversity conservation and transportation planning. [ABSTRACT FROM AUTHOR]

This paper proposes an ontology learning method which is used to generate a graphical ontology structure called ontology graph. The ontology graph defines the ontology and knowledge conceptualization model, and the ontology learning process defines the method of semiautomatic learning and generates ontology graphs from Chinese texts of different domains, the so-called domain ontology graph (DOG). Meanwhile, we also define two other ontological operations-document ontology graph generation and ontology graph-based text classification, which can be carried out with the generated DOG. This research focuses on Chinese text data, and furthermore, we conduct two experiments: the DOG generation and ontology graph-based text classification, with Chinese texts as the experimental data. The first experiment generates ten DOGs as the ontology graph instances to represent ten different domains of knowledge. The generated DOGs are then further used for the second experiment to provide performance evaluation. The ontology graph-based approach is able to achieve high text classification accuracy (with 92.3 % in f-measure) over other text classification approaches (such as 86.8 % in f-measure for tf-idf approach). The better performance in the comparative experiments reveals that the proposed ontology graph knowledge model, the ontology learning and generation process, and the ontological operations are feasible and effective. [ABSTRACT FROM AUTHOR]