Node Degree Centrality Research Articles

Characteristic signatures of Northern Hemisphere blocking events in a Lagrangian flow network representation of the atmospheric circulation.

In the past few decades, boreal summers have been characterized by an increasing number of extreme weather events in the Northern Hemisphere extratropics, including persistent heat waves, droughts and heavy rainfall events with significant social, economic, and environmental impacts. Many of these events have been associated with the presence of anomalous large-scale atmospheric circulation patterns, in particular, persistent blocking situations, i.e., nearly stationary spatial patterns of air pressure. To contribute to a better understanding of the emergence and dynamical properties of such situations, we construct complex networks representing the atmospheric circulation based on Lagrangian trajectory data of passive tracers advected within the atmospheric flow. For these Lagrangian flow networks, we study the spatial patterns of selected node properties prior to, during, and after different atmospheric blocking events in Northern Hemisphere summer. We highlight the specific network characteristics associated with the sequence of strong blocking episodes over Europe during summer 2010 as an illustrative example. Our results demonstrate the ability of the node degree, entropy, and harmonic closeness centrality based on outgoing links to trace important spatiotemporal characteristics of atmospheric blocking events. In particular, all three measures capture the effective separation of the stationary pressure cell forming the blocking high from the normal westerly flow and the deviation of the main atmospheric currents around it. Our results suggest the utility of further exploiting the Lagrangian flow network approach to atmospheric circulation in future targeted diagnostic and prognostic studies.

Cognitive impairment and associations with structural brain networks, endocrine status, and risk genotypes in newly orchiectomized testicular cancer patients

A higher incidence of cognitive impairment (CI) has previously been reported among orchiectomized testicular cancer patients (TCPs), but little is known about the underlying pathophysiology. The present study assessed CI in newly orchiectomized TCPs and explored the structural brain networks, endocrine status, and selected genotypes. Forty TCPs and 22 healthy controls (HCs) underwent neuropsychological testing and magnetic resonance imaging, and provided a blood sample. CI was defined as a z-score ≤ -2 on one neuropsychological test or ≤ -1.5 on two neuropsychological tests, and structural brain networks were investigated using graph theory. Associations of cognitive performance with brain networks, endocrine status (including testosterone levels and androgen receptor CAG repeat length), and genotypes (APOE, BDNF, COMT) were explored. Compared with HCs, TCPs performed poorer on 6 out of 15 neuropsychological tests, of which three tests remained statistically significant when adjusted for relevant between-group differences (p< 0.05). TCPs also demonstrated more CI than HCs (65% vs. 36%;p = 0.04). While global brain network analysis revealed no between-group differences, regional analysis indicated differences in node degree and betweenness centrality in several regions (p < 0.05), which was inconsistently associated with cognitive performance. In TCPs, CAG repeat length was positively correlated with delayed memory performance (r = 0.36;p = 0.02). A COMT group × genotype interaction effect was found for overall cognitive performance in TCPs, with risk carriers performing worse (p = 0.01). No effects were found for APOE, BDNF, or testosterone levels. In conclusion, our results support previous findings of a high incidence of CI in newly orchiectomized TCPs and provide novel insights into possible mechanisms.

A Knowledge Graph Approach for the Detection of Digital Human Profiles in Big Data

Digital transformation is a long process that changes the managing human profiles in both offline and online approaches. This generates the amount of huge data stored in both relational databases and many others like social networks or graph databases. To exploit effectively big data, several measures and algorithms in Picture Fuzzy Graph (PFG) are applied to solve many complex problems in the real-world problems. The paper has presented a novel approach using a knowledge graph to find a human profile including the detection of humans in large data. In the proposed model, digital human profiles are collected from conventional databases combination with social networks in real-time, and a knowledge graph is created to represent complex-relational user attributes of human profile in large datasets. PFG is applied to quantify the degree centrality of nodes. Furthermore, techniques and algorithms on the graph are used to classify the nodes. The experiments in the knowledge graph implemented to illustrate the proposed model. The main contribution in this paper is to identify the right persons among complex-relational groups, locations in real-time based on large datasets on the social networks, relational databases and graph databases.

Energy-Aware Ch Selection And Optimized Routing Algorithm In Wireless Sensor Networks Using Wmba And Qoga

WSN is widely used in different applications, such as smart grid, water municipals, and health care, for monitoring the health condition of a patient, etc. But, the major challenge of WSN is energy efficiency. Energy Efficient-based WSN has always been a research focus to improve its performance. Aiming at optimal energy utilization to improve life expectancy with guaranteed QoS, an optimal CH selection and optimal routing algorithm in WSN is proposed. The work has carried out the selection of the CH based on various factors, such as residual energy of the nodes, proximity, distance to the base station, cost, node degree, and node centrality or coverage, using the proposed WMBA. After that, an optimal path selection using QOGA is done between CH and BS based on various factors, such as distance, residual energy, and node degree, under the MKMA clustering process. The experiment shows that the proposed methodology achieves better life expectancy for the WSN by achieving an energy consumption of 975J for the entire round of 2000 and a PDR of 93.56%, and remains to be energy efficient compared to the existing state-of-art methods.

Different alternations of static and dynamic brain regional topological metrics in schizophrenia and obsessive-compulsive disorder

IntroductionThough schizophrenia (SZ) and obsessive-compulsive disorder (OCD) are conceptualized as distinct clinical entities, they do have notable symptom overlap and a tight association. Graph-theoretical analysis of the brain connectome provides more indicators to describe the functional organization of the brain, which may help us understand the shared and disorder-specific neural basis of the two disorders.ObjectivesTo explore the static and dynamic topological organization of OCD and SZ as well as the relationship between topological metrics and clinical variables.MethodsResting state functional magnetic resonance imaging data of 31 OCD patients, 49 SZ patients, and 45 healthy controls (HC) were involved in this study (Table 1). Using independent component analysis to obtain independent components (ICs) (Figure 1), which were defined as nodes for static and dynamic topological analysis.ResultsStatic analysis showed the global efficiency of SZ was higher than HC. For nodal degree centrality, OCD exhibited decreased degree centrality in IC59 (located in visiual network) (P = 0.03) and increased degree centrality in IC38 (located in salience network) (P = 0.002) compared with HC. Dynamic analysis showed OCD exhibited decreased dynamics of degree centrality in IC38 (P = 0.003) compared with HC, which showed a negative correlation with clinical scores in OCD. While SZ showed decreased dynamics of degree centrality in IC76 (located in sensory motor network) compared with OCD (P=0.009), which showed a positive correlation with clinical scores in SZ (Figure 2).ConclusionsThese changes are suggestive of disorder-specific alternation of static and dynamic brain topological organization in OCD and SZ.

Changes in Degree Centrality of Network Nodes in Different Frequency Bands in Parkinson's Disease With Depression and Without Depression.

BackgroundDepression induces an early onset of Parkinson’s disease (PD), aggravates dyskinesia and cognitive impairment, and accelerates disease progression. However, it is very difficult to identify and diagnose PD with depression (PDD) in the early clinical stage. Few studies have suggested that the changes in neural networks are associated with PDD, while degree centrality (DC) has been documented to be effective in detecting brain network changes.ObjectivesThe objectives of this study are to explore DC changes between patients with PDD and without depression (PDND) and to find the key brain hubs involved with depression in PD patients.MethodsOne hundred and four PD patients and 54 healthy controls (HCs) underwent brain resting-state functional magnetic resonance imaging. The Data Processing and Analysis of Brain Imaging and Resting-State Functional Magnetic Resonance Data Analysis Toolkit were used for processing and statistical analysis. The DC value of each frequency band was calculated. One-way analysis of variance and a two-sample t-test for post hoc comparison were used to compare the differences of the DC values in different frequency bands among PDD, PDND, and healthy control group. Gaussian random field was used for multiple comparison correction. Pearson correlation analysis was performed between each individual’s DC map and clinical indicators.ResultsThe DC value of different brain regions changed in PDD and PDND in different frequency bands. The prefrontal lobe, limbic system, and basal ganglia were the main brain regions involved. PDD patients showed a wider range and more abnormal brain areas in the slow-4 frequency band (0.027–0.073 Hz) compared to the HCs. PDD showed a decreased DC value in the medial frontal gyrus, bilateral cuneus gyrus, right lingual gyrus, bilateral supplementary motor area (SMA), bilateral superior frontal gyrus, and left paracentral lobule, but an increased DC value in the bilateral brainstem, midbrain, bilateral parahippocampal gyrus, cerebellum, left superior temporal gyrus, bilateral insula, left fusiform gyrus, and left caudate nucleus in the traditional frequency band (0.01–0.08 Hz) compared to PDND patients. PDND patients displayed more abnormal functions in the basal ganglia in the slow-4 frequency band.ConclusionThe DC changes in PDD and PDND are frequency dependent and frequency specific. The medial frontal gyrus, SMA, and limbic system may be the key hubs for depression in PD.

Quantification of Cognitive Function in Alzheimer's Disease Based on Deep Learning.

Alzheimer disease (AD) is mainly manifested as insidious onset, chronic progressive cognitive decline and non-cognitive neuropsychiatric symptoms, which seriously affects the quality of life of the elderly and causes a very large burden on society and families. This paper uses graph theory to analyze the constructed brain network, and extracts the node degree, node efficiency, and node betweenness centrality parameters of the two modal brain networks. The T test method is used to analyze the difference of graph theory parameters between normal people and AD patients, and brain regions with significant differences in graph theory parameters are selected as brain network features. By analyzing the calculation principles of the conventional convolutional layer and the depth separable convolution unit, the computational complexity of them is compared. The depth separable convolution unit decomposes the traditional convolution process into spatial convolution for feature extraction and point convolution for feature combination, which greatly reduces the number of multiplication and addition operations in the convolution process, while still being able to obtain comparisons. Aiming at the special convolution structure of the depth separable convolution unit, this paper proposes a channel pruning method based on the convolution structure and explains its pruning process. Multimodal neuroimaging can provide complete information for the quantification of Alzheimer’s disease. This paper proposes a cascaded three-dimensional neural network framework based on single-modal and multi-modal images, using MRI and PET images to distinguish AD and MCI from normal samples. Multiple three-dimensional CNN networks are used to extract recognizable information in local image blocks. The high-level two-dimensional CNN network fuses multi-modal features and selects the features of discriminative regions to perform quantitative predictions on samples. The algorithm proposed in this paper can automatically extract and fuse the features of multi-modality and multi-regions layer by layer, and the visual analysis results show that the abnormally changed regions affected by Alzheimer’s disease provide important information for clinical quantification.

Bypass Schemes of Urban Transit Station Based on Complex Network Theory

To study detour scheme after failure of urban transit station, urban transit network model is constructed by considering the reciprocal of urban transit line departure time interval as the weight. In accordance with the characteristics of the traffic flow, the network is studied under different time intervals, namely, morning, evening, and flat rush hours. First, an urban transit network topological characteristic index analysis model was constructed by combining ABC management method to identify the key elements of each indicator. Second, on the basis of the research on topological characteristic indicators, average path length and global efficiency of the network were used to measure the urban transit network connectivity, and an urban transit network station failure optimization scheme was designed. The optimization effect was judged by the changes in the two index values before and after the bus stop detour in accordance with the number of stations in the bypass area, namely, two stations, one station, and zero stations. Finally, the reliability of the model is studied, considering the urban transit network in Huangdao District of Qingdao. Through the analysis of node degree, second-order node degree, centrality, and other indicators, the urban transit network in Huangdao District of Qingdao was found to have evident small-world characteristics, and the top ranking stations of node degree value and centrality index are mainly distributed in the new urban edge planning area and the urban center area. In addition, for stations with the same rank, the node centrality value at each time interval has the following order: morning rush hours> evening rush hours> flat rush hours. The top 10 urban transit stations with node degree value are selected as the failure stations for optimization analysis. Results show that the average shortest path length of the optimized network decreases, the global efficiency of the network increases, and the optimized scheme is reliable.

Altered brain functional network dynamics in obsessive-compulsive disorder.

Obsessive–compulsive disorder (OCD) is a debilitating and disabling neuropsychiatric disorder, whose neurobiological basis remains unclear. Although traditional static resting‐state magnetic resonance imaging (rfMRI) studies have found aberrant functional connectivity (FC) in OCD, alterations in whole‐brain FC and topological properties in the context of brain dynamics remain relatively unexplored. The rfMRI data of 29 patients with OCD and 40 healthy controls were analyzed using group independent component analysis to obtain independent components (ICs) and a sliding‐window approach to generate dynamic functional connectivity (dFC) matrices. dFC patterns were clustered into three reoccurring states, and state transition metrics were obtained. Then, graph‐theory methods were applied to dFC matrices to calculate the variability of network topological organization. The occurrence of a state (State 1) with the highest modularity index and lowest mean FC between networks was increased significantly in OCD, and the fractional time in brain State 1 was positively correlated with anxiety level in patients. State 1 was characterized by having positive connections within default mode (DMN) and salience networks (SAN), and negative coupling between the two networks. Additionally, ICs belonging to DMN and SAN showed lower temporal variability of nodal degree centrality and efficiency in patients, which was related to longer illness duration and higher current obsession ratings. Our results provide evidence of clinically relevant aberrant dynamic brain activity in OCD. Increased functional segregation among networks and impaired functional flexibility in connections among brain regions in DMN and SAN may play important roles in the neuropathology of OCD.

THE RESILIENCE OF COMPLEX NETWORK: AN APPROACH FOR RELEVANT NODES EXTRACTION

In this paper, a new algorithm to select the relevant nodes — those that maintain the cohesion of the network — of the complex network is presented. The experiments on most of the real complex networks show that the proposed approach outperforms centrality measures as node degree, PageRank algorithm and betweenness centrality. The rationale of the algorithm for extracting relevant nodes is to discover the self-similarity of the network. As seen in the algorithm, throughout the extraction sequence of relevant nodes, differences are advised with node degree, PageRank algorithm and betweenness centrality. Finally, empirical evidence is considered to show that complex network robustness is a nonlinear function of the small-worldness measure.

Neuroanatomical Alterations in Patients With Tinnitus Before and After Sound Therapy: A Combined VBM and SCN Study.

Many neuroanatomical alterations have been detected in patients with tinnitus in previous studies. However, little is known about the morphological and structural covariance network (SCN) changes before and after long-term sound therapy. This study aimed to explore alterations in brain anatomical and SCN changes in patients with idiopathic tinnitus using voxel-based morphometry (VBM) analysis 24 weeks before and after sound therapy. Thirty-three tinnitus patients underwent magnetic resonance imaging scans at baseline and after 24 weeks of sound therapy. Twenty-six age- and sex-matched healthy control (HC) individuals also underwent two scans over a 24-week interval; 3.0T MRI and high-resolution 3D structural images were acquired with a 3D-BRAVO pulse sequence. Structural image data preprocessing was performed using the VBM8 toolbox. The Tinnitus Handicap Inventory (THI) score was assessed for the severity of tinnitus before and after treatment. Two-way mixed model analysis of variance (ANOVA) and post hoc analyses were performed to determine differences between the two groups (patients and HCs) and between the two scans (at baseline and on the 24th week). Student-Newman-Keuls (SNK) tests were used in the post hoc analysis. Interaction effects between the two groups and the two scans demonstrated significantly different gray matter (GM) volume in the right parahippocampus gyrus, right caudate, left superior temporal gyrus, left cuneus gyrus, and right calcarine gyrus; we found significantly decreased GM volume in the above five brain regions among the tinnitus patients before sound therapy (baseline) compared to that in the HC group. The 24-week sound therapy group demonstrated significantly greater brain volume compared with the baseline group among these brain regions. We did not find significant differences in brain regions between the 24-week sound therapy and HC groups. The SCN results showed that the left superior temporal gyrus and left rolandic operculum were significantly different in nodal efficiency, nodal degree centrality, and nodal betweenness centrality after FDR correction. This study characterized the effect of sound therapy on brain GM volume, especially in the left superior temporal lobe. Notably, sound therapy had a normalizing effect on tinnitus patients.

Hybrid BWO-IACO Algorithm for Cluster Based Routing in Wireless Sensor Networks

Wireless Sensor Network (WSN) comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region. As the nodes in WSN operate on inbuilt batteries, the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime. To enhance energy efficiency and network longevity, clustering and routing techniques are commonly employed in WSN. This paper presents a novel black widow optimization (BWO) with improved ant colony optimization (IACO) algorithm (BWO-IACO) for cluster based routing in WSN. The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads (CHs). The BWO algorithm derives a fitness function (FF) using five input parameters like residual energy (RE), inter-cluster distance, intra-cluster distance, node degree (ND), and node centrality. In addition, IACO based routing process is involved for route selection in inter-cluster communication. The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm (KHA). The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network. The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime. The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects. A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.

Alterations of Brain Networks in Alzheimer’s Disease and Mild Cognitive Impairment: A Resting State fMRI Study Based on a Population-specific Brain Template

This study aimed to investigate the alterations in brain networks in patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) based on a population-specific brain template. Previous studies on AD brain networks using graph theory rarely adopted brain templates specific for certain ethnicities. In this study, patients were divided into 3 groups: AD (n = 24), MCI (n = 27), and healthy controls (HCs, n = 33), and all of the subjects are Chinese. Functional brain networks were constructed for each group based on a Chinese brain template using resting-state functional magnetic resonance imaging (rs-fMRI) data; several graph metrics were calculated. Graph metrics with significant differences after false discovery rate (FDR) correction were analyzed with respect to correlations with four neuropsychological test scores: Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Activities of Daily Living (ADL), and Clinical Dementia Rating (CDR), which assessed the subjects’ cognitive functions and ability to engage in ADL. Graph metrics including assortativity coefficient, nodal degree centrality, nodal clustering coefficient, nodal efficiency, and nodal local efficiency of the frontal gyrus and cerebellum were significantly altered in AD and MCI compared with HC. Several graph metrics were significantly correlated with cognitive function and the ability to engage in daily activities. The findings suggest that altered graph metrics in the frontal gyrus may reflect brain plasticity, and that patients with MCI may have unique graph metric alterations in the cerebellum. Future graph analysis studies on functional brain networks in AD and MCI based on population-specific brain atlases for particular ethnicities may prove valuable.

Multiplex Centrality Measurements Applied to Islanded Microgrids

This paper presents four centrality measurements applied to an alternating current (AC) microgrid (MG) modeled as a multiplex network. The MG secondary control is separated into a frequency and a power-sharing layers, each one with a different adjacency matrix. A physical layer is also considered with an admittance matrix representing the impedances among the inverters. Centrality measures are used to determine the importance of nodes in separate layers, thereafter adjacency and Laplacian matrices are redefined to calculate the role of nodes in the multiplex system. First, a global adjacency matrix is calculated by the matrix sum of each adjacency matrix. Second, the adjacency matrix is calculated by a supra-Laplacian matrix. The first eigenvalue of the perturbed matrix is used to determine the diffusivity in the network using as leaders the sets obtained by the centrality measures. The role of the nodes in the system is verified in a simulated MG model of 37 nodes. Degree centrality and energy Laplacian measures present similar sets of nodes; however, the fastest set of nodes is found using the Eigenvector measurements for uniform and supra Laplacian approach.

Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization

Wireless Sensor Networks (WSNs) consist of a large number of spatially distributed sensor nodes connected through the wireless medium to monitor and record the physical information from the environment. The nodes of WSN are battery powered, so after a certain period it loose entire energy. This energy constraint affects the lifetime of the network. The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. At present, clustering and routing algorithms are widely used in WSNs to enhance the network lifetime. In this study, the Butterfly Optimization Algorithm (BOA) is employed to choose an optimal cluster head from a group of nodes. The cluster head selection is optimized by the residual energy of the nodes, distance to the neighbors, distance to the base station, node degree and node centrality. The route between the cluster head and the base station is identified by using Ant Colony Optimization (ACO), it selects the optimal route based on the distance, residual energy and node degree. The performance measures of this proposed methodology are analyzed in terms of alive nodes, dead nodes, energy consumption and data packets received by the BS. The outputs of the proposed methodology are compared with traditional approaches LEACH, DEEC and compared with some existing methods FUCHAR, CRHS, BERA, CPSO, ALOC and FLION. For example, the alive nodes of the proposed methodology are 200 at 1500 iterations which is higher compared to the CRHS and BERA methods.

An Early Assessment of Relationship between Spatial Diffusion of Covid-19 and Evolving Worldwide Commercial Aviation Network: Exploring Global and Local Changes in Aviation Networks Using Node Degree Centrality

An Early Assessment of Relationship between Spatial Diffusion of Covid-19 and Evolving Worldwide Commercial Aviation Network: Exploring Global and Local Changes in Aviation Networks Using Node Degree Centrality

An efficient content placement scheme based on normalized node degree in content centric networking

Content-centric networking (CCN) becomes an imminent Internet architecture that emphasizes the data-centric approach for information retrieval instead of searching for the hosts in the network. CCN offers data (content) caching and its distribution capabilities to reduce average latency, the load of data servers, and improve network bandwidth utilization during content delivery. In this direction, the performance of node degree centrality and distance-based autonomous caching strategies have been investigated. Then, a novel content caching strategy called content placement based on normalized node degree and distance (CPNDD) is proposed. The CPNDD scheme considers content provider distance and node degree centrality parameters together to select the optimal cache locations for comprehensive utilization of the available caching capacities and a further reduction in average latency during content retrieval. The weights for these caching parameters have been determined via widespread simulations on the Abilene networks. The proposed caching scheme is implemented and tested in the simulated environment of ndnSIM and compared with peer competing schemes in CCN. The execution outcomes are examined for different caching capacities (50 and 100), Zipf popularity skewness factors (0.7 and 1.0) and request rates (50/s and 100/s). The simulation executions illustrate that CPNDD caching strategy escalates the cache hit probability and hop-reduction ratio up-to $$8\%$$ and $$9\%$$ as compared to existing schemes. Hence, the proposed content placement scheme improves the performance of CCN networks considerably and makes it suitable for the applications of Industry 4.0.

Nutrient utilization efficiency, ruminal fermentation and microbial community in Holstein bulls fed concentrate-based diets with different forage source

Ruminal acidosis can penalize cattle performance and modify ruminal microbiota composition; in that sense, providing quality forage may be a useful tool to cope with such dysfunction. This assay aimed to control animals’ performance and assess ruminal microbiota shifts and interactions when fattening Holstein bulls are fed corn-based concentrate and different quality forages. Thirty animals (from 119 to 332 d of age and from 164 to 511 kg body weight [BW]) were fed corn-based concentrate and were allotted to three experimental treatments or forage sources: barley straw, considered as control (Hordeum vulgare, CTR), oats haylage (Avena sativa, OATS) or vetch haylage (Vicia sativa, VETCH). Both concentrate and forage were supplied ad libitum in a free choice system and animals had free access to drinking water. Bulls’ BW and concentrate intake were automatically recorded on a daily basis. Feces and ruminal fluid were sampled twice, during the growing period (158 d of age and 220 kg BW) and during the finishing period (280 d of age and 434 kg BW), for digestibility, ruminal fermentation and microbial population characterization. No differences in concentrate intake or BW could be detected between CTR and OATS-fed animals; however, VETCH-fed bulls had lower concentrate intake (P < 0.047) and slaughter BW (P = 0.034) than CTR. The use of oats haylage as forage source did not affect nutrient apparent digestibility rates but vetch haylage did penalize dry matter (P = 0.048) and crude protein (P < 0.001) digestibility in finishing animals. Differences in neither ruminal volatile fatty acids concentration nor pH were detected, but acetate-to-propionate ratio increased with the incorporation of vetch haylage in diet (P < 0.041). Ammonia-nitrogen concentration in ruminal fluid was low (20.63 ± 2.55 mg/L) but it significantly improved when oats (P = 0.001, only in finishing) and vetch (P = 0.001) haylage were provided. Core microbial community gathered 75 % of analyzed sequences; however, ruminal microbial community was different between CTR and OATS (P < 0.001) or VETCH (P < 0.001) bulls. Shannon and Simpson diversity indexes were improved by quality forage feeding, mainly during the growing period (P < 0.081 in OATS and P < 0.004 in VETCH). Microbial network analysis revealed that the use of oats or vetch haylage promoted an increase in the overall network complexity, basically in terms of node degree and betweenness centrality. Some lactate-degrader genera were found to be central nodes in the microbial network when quality forage was provided, highlighting their probable implication in ruminal acidosis prevention.

A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study

The Traditional Chinese Medicine (TCM) formula is the main treatment method of TCM. A formula often contains multiple herbs where core herbs play a critical therapeutic effect for treating diseases. It is of great significance to find out the core herbs in formulae for providing evidences and references for the clinical application of Chinese herbs and formulae. In this paper, we propose a core herb discovery model CHDSC based on semantic analysis and community detection to discover the core herbs for treating a certain disease from large-scale literature, which includes three stages: corpus construction, herb network establishment, and core herb discovery. In CHDSC, two artificial intelligence modules are used, where the Chinese word embedding algorithm ESSP2VEC is designed to analyse the semantics of herbs in Chinese literature based on the stroke, structure, and pinyin features of Chinese characters, and the label propagation-based algorithm LILPA is adopted to detect herb communities and core herbs in the herbal semantic network constructed from large-scale literature. To validate the proposed model, we choose chronic glomerulonephritis (CGN) as an example, search 1126 articles about how to treat CGN in TCM from the China National Knowledge Infrastructure (CNKI), and apply CHDSC to analyse the collected literature. Experimental results reveal that CHDSC discovers three major herb communities and eighteen core herbs for treating different CGN syndromes with high accuracy. The community size, degree, and closeness centrality distributions of the herb network are analysed to mine the laws of core herbs. As a result, we can observe that core herbs mainly exist in the communities with more than 25 herbs. The degree and closeness centrality of core herb nodes concentrate on the range of [15, 40] and [0.25, 0.45], respectively. Thus, semantic analysis and community detection are helpful for mining effective core herbs for treating a certain disease from large-scale literature.

A framework for anomaly detection and classification in Multiple IoT scenarios

The investigation of anomalies is an important element in many scientific research fields. In recent years, this activity has been also extended to social networking and social internetworking, where different networks interact with each other. In these research fields, we have recently witnessed an important evolution because, beside networks of people, networks of things are becoming increasingly common. IoT and Multiple IoT scenarios are thus more and more studied. This paper represents a first attempt to investigate anomalies in a Multiple IoT scenario (MIoT). First, we propose a new methodological framework that can make future investigations in this research field easier, coherent, and uniform. Then, in the context of anomaly detection in an MIoT, we define the so-called “forward problem” and “inverse problem”. The definition of these problems allows the investigation of how anomalies depend on inter-node distances, the size of IoT networks, and the degree centrality and closeness centrality of anomalous nodes. The approach proposed herein is applied to a smart city scenario, which is a typical MIoT. Here, data coming from sensors and social networks can boost smart lighting in order to provide citizens with a smart and safe environment.