High Degree Centrality Research Articles

Risk assessment for autonomous navigation system based on knowledge graph

This paper presents a knowledge graph construction framework based on the Adversarial Interpretive Structure Model (AISM), aiming to reveal the failure interdependencies of autonomous navigation assemblies. The autonomous navigation system is divided into 6 systems with 43 failure modes. The hierarchical correlations between multiple systems are quantified by establishing the distance matrix and adjacency matrix, which are defined as triples to serve the entities and attributes for the knowledge graph. The centrality metrics are applied to evaluate the importance of nodes and to assess the stability of the knowledge graph. The coordination relationships between autonomous navigation systems and the impact associations of failure modes are discussed to validate the reasonability of the proposed framework. The results indicate that The NAVTEX operation panel and The installation base of the temperature sensor are the most essential components in the hierarchical results. The positioning system has a high degree centrality and betweenness centrality, while the server system has the highest closeness centrality. The propagation path from Decrease in positioning accuracy to The installation base of the temperature sensor is investigated, providing the methodological and data basis for decision-making.

War, art, and sanctions: social network analysis on the NACP’s databases of sanctioned Russian individuals and art collectors

AbstractUntil March 2024, Ukraine’s National Agency on Corruption Prevention (NACP) was tasked with maintaining databases of individuals sanctioned for their support of Russian military action in Ukraine and monitoring works of art owned by these individuals to prevent their being sold in violation of sanctions. The present study gathered data from the NACP’s War & Sanctions and War & Art databases through a combination of web scraping and API requests. These data were then examined using measures of network analysis (including degree centrality, eigenvector centrality, and clustering coefficient), which allowed the visualisation and statistical analysis of the network of Russian elite. This research is the first that has conducted scientific analysis on these datasets. Investigation determined that the social network of individuals in the upper strata of Russian society is vast and moderately centralised, with over a dozen clusters around influential Russian oligarchs, politicians, and celebrities. It also determined that art collectors have a more central role in this network than non-collectors of art. The network exhibits a clear presence of hubs and nodes with high degree centrality and a high degree-betweenness centrality correlation, indicating that information can flow quickly and efficiently. However, the network’s dependence on a few key individuals could constitute a vulnerability, with the extraction or isolation of these figures potentially disrupting the network’s function and coordination. This research also identified inconsistencies in the imposition of sanctions, creating grey areas that could allow the continued trade of cultural goods, potentially bypassing international restrictions.

Evaluating the Conceptual Development of Healthcare Leadership Literature with a Network Approach

Analysis of co-word networks can be used in discovering themes that reflect both the cognitive structure of the scientific field and the gap in the field, besides following the conceptual change in the scientific field. This study aims to reveal the development of the "healthcare leadership" literature, which started to attract more attention with the COVID-19 Pandemic. The authors searched for all articles written in English between 2020 and 2023 in Web of Science Core Collection and Scopus databases with the key phrases “healthcare leadership”, “healthcare leader”, “health care leadership” or “health care leader” as the query to search the following fields within a record: Title, Abstract, and Author Keywords. 134 articles in the Web of Science database and 585 articles in the Scopus database were reached. Duplicate articles (271) were first eliminated, then articles with one keyword (12) were excluded since one keyword is not appropriate for network analysis. Consequently, 1,360 unique keywords of 2,372 keywords from 436 articles were entered into the analysis scope. A network of whole years was constructed based on word frequency co-occurrence matrices with keywords from all years included, besides a network for each year. Descriptive statistics such as network density, connected components, and total number of ties were calculated for the whole network. Centrality measures were calculated for the network of whole years and each year. Network density and number of the connected components were found as 0.012 and 27 respectively. Leadership, COVID-19/Pandemic, healthcare, healthcare leadership, qualitative research, and nurse were the first six concepts with the highest degree centrality, betweenness centrality, and eigenvector centrality for the network of whole years. Thus, it was possible to see both the general view of the cognitive structure of the healthcare leadership literature and conceptual change in the literature over the years. The findings have the potential to provide important clues for trends and future research directions in this field.

Social network analysis approach to identify agricultural key communicators

Aim: This paper employed Social Network Analysis approach to visualize and calculate different social network metrics. It identifies key communicators who play pivotal roles in information flow. Methodology: Research was conducted at Jammulapalem and Perali villages of Bapatla district, Andhra Pradesh, India, during 2022-23 using an exploratory research design. A total of 120 farmers Eigen vector were selected using simple random technique, and data was collected using a well-developed interview schedule. Network metrics such as Degree centrality, betweenness centrality, and eigen vector centrality were computed to evaluate the network structure using R software (version 4.3.1). R packages, namely igraph, statnet and network D3, were used for network creation, analysis and visualization to identify the influential nodes. Results: The study revealed a complex web of relationships among various stakeholders within the agricultural network through a network graph, identifying key communicators with the highest Degree centrality. Interpretation: The focal points identified through Social Network Analysis represent a specific demographic and socio-economic group, typically aged between 35 and 55, primarily medium-scale farmers,with landholdings spanning 10 to 25 acres and high annual income, with educational backgrounds ranging from high school to pre-university college, and wield significant influence within their local communities. They require sensitization, training, practical demonstrations, and personalized support to effectively disseminate agricultural information. Key words: Agricultural Information System Network, Centrality measures, Information Sources, Key Communicators, Network visualization, Social Network Analysis

Effects of the control of an invasive tree on the structure of a plant-frugivore network.

Invasive non-native species are one of the main causes of degradation of ecosystems worldwide. The control of invasive species is key to reducing threats to ecosystem viability in the long term. Observations of structural changes in ecological interaction networks following invasive species suppression can be useful to monitor the success of ecological restoration initiatives. We evaluated the structure of plant-bird frugivory interaction networks in a plant community invaded by the guava tree (Psidium guajava L.) by comparing network metrics before and after control actions. Psidium guajava was relevant in all metrics for the unmanaged network in this study, with high degree centrality and high nestedness contribution. Based on the asymmetry of species interactions, we found that birds were highly dependent on the invasive plant before suppression. Once P. guajava trees were eliminated, bird and plant species richness, total number of interactions, and modularity increased, whereas nestedness and interaction strength asymmetry decreased. The diet of the bird community became more diversified once P. guajava was no longer available and relevant species roles in community structure emerged. Our results corroborate the fact that ecological restoration interventions should include the control of non-native plant species that attract frugivorous animals in order to diversify plant-frugivore interactions and thus maintain biodiversity in natural ecosystems.

텍스트 마이닝을 활용한 교직단체 운영 특성 분석

The purpose of this study is to analyze Teachers’ Organizations’ Operational Characteristics by using text mining. To solve this research problem, press releases issued by Korean Federation of Teachers’ Associations(KFTA), Korean Teachers and Education Workers’ Union(KTU), and Korean Federation of Teachers Unions(KFTU) were collected, and the collected data were analyzed for keyword frequency analysis, centrality and network analysis, CONCOR analysis using Textom, UCINET. Through this, the following research results were derived. First, as a result of keyword frequency analysis, ‘teacher’, ‘education’, and ‘school’ were identified as the top keywords. Second, through keyword frequency analysis, it was possible to identify keywords that were ranked relatively higher by group or derived only from specific groups. Third, as a result of centrality analysis, the keyword with the highest degree centrality was ‘teacher’. Fourth, as a result of CONCOR analysis, 8 clusters were formed like ‘Response and support for civil complaints’, ‘Improvement of field-centered educational environment’, ‘Revision of the law for fair life guidance’, ‘Request for legislation through the National Assembly’, ‘The response to child abuse’, ‘Infringement and protection of teachers’ authority’, ‘Improving the school violence system’, ‘reduction of administrative work’. Based on these results, implications for the management of teachers’ organizations were presented.

A Study on Internet News for Patient Safety Campaigns: Focusing on Text Network Analysis and Topic Modeling.

Background/Objectives: This study aimed to identify the main issues related to public patient safety campaigns reflected in Korean online news. This study utilized a text-mining method to identify keywords and topics related to patient safety campaigns. Methods: The data collection period was from 1 January 2022 to 31 December 2023, and 4110 news articles were extracted. Through data preprocessing, 2661 duplicated news and 1213 unrelated news were removed, and 236 news were selected. Using the NetMiner program, keyword co-occurrence frequency calculation, keyword centrality analysis, and topic modeling analysis were performed. Results: The results showed that the most frequently mentioned keywords with high degree centrality, betweenness centrality, and closeness centrality in online news were "hospital", "medical", "medicine", "project", and "treatment". The topics of online news related to the patient safety campaign were "patient-centered care for medical safety", "health promotion projects at a regional institution", "hand hygiene education to prevent infection", "healthcare quality improvement through the Mint Festival", and "safe use of medicines". Conclusions: This study analyzed patient safety campaign news topics using text network analysis and topic modeling. It was confirmed that patient safety campaigns are essential for fostering a patient safety culture, improving medical quality, and encouraging patient participation in hospitals. Therefore, to build a safe medical environment, it is necessary to establish an effective patient safety campaign for not only medical staff providing medical care, but also patients and their caregivers, and for this, cooperation and participation from various professional occupations are necessary.

Network theory and migration: Avoiding misapplications and misinterpretations

Network analysis is becoming a popular tool for the study of animal movement. Proliferation of software enables researchers to use network measures without reflecting on the underlying mathematics and biology. One common use characterizing movement networks, such as migration, with centrality measures. Predominantly developed to evaluate social systems, these measures now appear in applications across disciplines that vary greatly in system properties. Each network measure has a very specific mathematical definition, with implicit assumptions about system properties to which they are applied that often make them inappropriate for migration. We report mismatches between mathematical assumptions and applications of network measures to migration, primarily with bird examples. For example, of 11 trajectory-transmission network-flow processes observed in the real world, only two are applicable to bird migration, neither of which allows valid application of classical centrality measures. We also identify misinterpretations of network measures, such as nodes with the highest degree centrality as locations where individuals intermingle. Other misinterpretations include that a high betweenness score translates to a node that is frequently used or is an important site along a main migratory route. Finally, networks are used in different ways, ranging from descriptive visualization to quantitative prediction and we identify common cases, such as migratory connectivity, where this leads to miscommunication. Network-theoretic approaches to answer ecological research and conservation questions associated with migration are numerous, but new measures need to be developed for animal migration. We provide a guide for researchers of animal movement for measure selection, development, and documentation.

Resting-State Network Analysis Reveals Altered Functional Brain Connectivity in Essential Tremor.

Introduction: Essential tremor (ET) comprises motor and non-motor-related features, whereas the current neuro-pathogenetic basis is still insufficient to explain the etiologies of ET. Although cerebellum-associated circuits have been discovered, the large-scale cerebral network connectivity in ET remains unclear. This study aimed to characterize the ET in terms of functional connectivity as well as network. We hypothesized that the resting-state network (RSN) within cerebrum could be altered in patients with ET. Methods: Resting-state functional magnetic resonance imaging (fMRI) was used to evaluate the inter- and intra-network connectivity as well as the functional activity in ET and normal control. Correlation analysis was performed to explore the relationship between RSN metrics and tremor features. Results: Comparison of inter-network connectivity indicated a decreased connectivity between default mode network and ventral attention network in the ET group (p < 0.05). Differences in functional activity (assessed by amplitude of low-frequency fluctuation, ALFF) were found in several brain regions participating in various RSNs (p < 0.05). The ET group generally has higher degree centrality over normal control. Correlation analysis has revealed that tremor features are associated with inter-network connectivity (|r| = 0.135-0.506), ALFF (|r| = 0.313-0.766), and degree centrality (|r| = 0.523-0.710). Conclusion: Alterations in the cerebral network of ET were detected by using resting-state fMRI, demonstrating a potentially useful approach to explore the cerebral alterations in ET.

대학 비교과 교육과정 질 관리를 위한 환류 핵심이슈 도출

Objectives The purpose of this study is to derive key issues related to feedback in the extracurricular curriculum at the university level and suggest directions for system improvements. Methods Therefore, the literature review for this study encompassed 117 academic journal articles published in South Korea during the last decade (2014-2023). Details of each Introduction and Conclusion were extracted, keywords were refined, and the main keywords were identified through TF or TF-IDF. Additionally, a semantic network analysis was conducted to determine connections between keywords. A CONCOR analysis was performed to clarify associations between cooccurring keywords. The results were also visualized. Results First, the top 10 most frequent feedback-related core keywords in non-credit extracurricular programs follow: writing, learning community, core competencies, academic achievement,tutoring, university life, online, self-directed learning, thinking skills, and collaboration. Second, the top five keywords with high degree centrality in the semantic network analysis were learning community-academic achievement-university life-tutoring-collaboration. The top five keywords with closeness centrality were collaboration-creativity-problem solving-career-learning community. The top five keywords with eigenvector centrality were learning community-academic achievement-collaboration-self-directed learning-tutoring. The top five keywords with betweenness centrality were collaboration-creativity-problem-solving-career-learning community. Third, as a result of the CONCOR analysis of the main keywords, the following four clusters were found: demand and performance management, system improvement, content and method improvement, and response to environmental change. Conclusions Based on these findings, the following measures are emphasized: (1) the higher effectiveness of IR related to non-credit extracurricular programs, (2) the preparation of strategies to identify and support prospective transfer students, (3) the examination of management systems to improve the operation of non-credit extracurricular programs, and (4) efforts to ensure the continuity and consistency of policies.

An exploration of the regulatory regime of the Indian nuclear industry

India has always attached great importance to the development of its nuclear industry. India's nuclear industry started early with the establishment of the Atomic Energy Commission (AEC) in 1948 and the Department of Atomic Energy (DAE) in 1954. The current management system in India is that the Prime Minister exercises the highest decision-making power, the Atomic Energy Commission assists the Prime Minister in decision-making; the Department of Atomic Energy is in charge of the nuclear industry; research and development institutions and state-owned enterprises under the Department of Atomic Energy implement specific decisions. The characteristics of India's nuclear industry system are: direct supervision by the Prime Minister and full safeguards; high degree of centralisation and unified management; integration of the military and civilian sectors and the use of the civilian sector as a cover for the military; and adherence to the “"three-step” nuclear energy development route. India's remarkable achievements in the face of a prolonged international embargo are attributable to the nuclear industry management system that has been in place in India for more than 60 years, which is worthy of our reference.

빅데이터를 활용한 유보통합에 대한 사회적 인식 분석

The purpose of this study is to find out the social discourse of ECEC integration using big data. Big data related to ECEC integration from 2013 to 2023 were collected using Textorm. The collected raw data were refined, and among them 50 keywords were selected as a result of frequency analysis. The results of the study are as follows. First, the social discourse of ECEC integration showed high frequency in the order of ‘teacher’, ‘child care center’, ’kindergarten’, ‘child care’, and ‘qualification’. As a result of cluster analysis, social discourse was formed centering on three core discourses such as teacher qualification acquisition, policy direction, and consultation subject. Second, major keywords such as ‘child care center’, and ‘kindergarten’ have a higher degree of centrality, while word such as ‘policy’, and ‘Korea National Open University’ have a higher closeness centrality. Third, the ego network analysis was conducted on the main keywords with higher centrality and frequency, such as ‘teacher’ and ‘qualification’. In addition, the ego network analysis was performed and visualized the main keywords and visualized. Through this study, the social discourse was investigated and implications for approach of ECEC integration were discussed.

Модернизация резервуарного парка региональной дирекции материально-технического снабжения железнодорожного транспорта

Objective: current system of organizing material and technical supply (MTS) in the Russian Railways Holding evaluation, determining the place and role of the regional supply department (RSD) in it, and identifying the most significant aspects of its activities. Methods: methods of comparative analysis of industry methodological documents and regulations for the functioning of both the entire MTS system and its individual departments, methods for calculating coefficients assessing the condition and use of the RSD fuel tank farm, statistical analysis and data processing, were used to achieve the goal of the study. Materials from scientific works on the chosen topic and periodicals were used to carry out the research. Results: highlighted the distinctive features of the MTS system in railway transport, that are expressed in a management and financing system high degree of centralization, in a number of financial and organizational restrictions of the regional material and technical supply department activities, reducing its functions in the procurement and delivery processes to operational control. The state and efficiency of using the fuel resource storage system in the department are evaluated, some proposals are made for its modernization. Practical significance: based on the calculations and comparative analysis of the results obtained, the advantages of modernizing the existing storage system for fuel and energy resources using modular storage technologies in directions with insignificant and changing cargo flows are substantiated. Those proposals can be used to improve the efficiency of the system for providing fuel resources to railway transport organizations.

Topological Organization of the Brain Network in Patients with Primary Angle-closure Glaucoma Through Graph Theory Analysis.

Primary angle-closure glaucoma (PACG) is a sight-threatening eye condition that leads to irreversible blindness. While past neuroimaging research has identified abnormal brain function in PACG patients, the relationship between PACG and alterations in brain functional networks has yet to be explored. This study seeks to examine the influence of PACG on brain networks, aiming to advance knowledge of its neurobiological processes for better diagnostic and therapeutic approaches utilizing graph theory analysis. A cohort of 44 primary angle-closure glaucoma (PACG) patients and 44 healthy controls participated in this study. Functional brain networks were constructed using fMRI data and the Automated Anatomical Labeling 90 template. Subsequently, graph theory analysis was employed to evaluate global metrics, nodal metrics, modular organization, and network-based statistics (NBS), enabling a comparative analysis between PACG patients and the control group. The analysis of global metrics, including small-worldness and network efficiency, did not exhibit significant differences between the two groups. However, PACG patients displayed elevated nodal metrics, such as centrality and efficiency, in the left frontal superior medial, right frontal superior medial, and right posterior central brain regions, along with reduced values in the right temporal superior gyrus region compared to healthy controls. Furthermore, Module 5 showed notable disparities in intra-module connectivity, while Module 1 demonstrated substantial differences in inter-module connectivity with both Module 7 and Module 8. Noteworthy, the NBS analysis unveiled a significantly altered network when comparing the PACG and healthy control groups. The study proposes that PACG patients demonstrate variations in nodal metrics and modularity within functional brain networks, particularly affecting the prefrontal, occipital, and temporal lobes, along with cerebellar regions. However, an analysis of global metrics suggests that the overall connectivity patterns of the entire brain network remain unaltered in PACG patients. These results have the potential to serve as early diagnostic and differential markers for PACG, and interventions focusing on brain regions with high degree centrality and nodal efficiency could aid in optimizing therapeutic approaches.

Integrative analysis of Iso-Seq and RNA-seq data reveals transcriptome complexity and differential isoform in skin tissues of different hair length Yak

BackgroundThe hair follicle development process is regulated by sophisticated genes and signaling networks, and the hair grows from the hair follicle. The Tianzhu white yak population exhibits differences in hair length, especially on the forehead and shoulder region. However, the genetic mechanism is still unclear. Isoform sequencing (Iso-seq) technology with advantages in long reads sequencing. Hence, we combined the Iso-seq and RNA-seq methods to investigate the transcript complexity and difference between long-haired yak (LHY) and normal-haired yak (NHY).ResultsThe hair length measurement result showed a significant difference between LHY and NHY on the forehead and the shoulder (P-value < 0.001). The skin samples from the forehead and the shoulder of LHY and NHY were pooled for isoform sequencing (Iso-seq). We obtained numerous long transcripts, including novel isoforms, long non-coding RNA, alternative splicing events, and alternative polyadenylation events. Combined with RNA-seq data, we performed differential isoforms (DEIs) analysis between LHY and NHY. We found that some hair follicle and skin development-related DEIs, like BMP4, KRT2, IGF2R, and COL1A2 in the forehead skin; BMP1, KRT1, FGF5, COL2A1, and IGFBP5 in the shoulder skin. Enrichment analysis revealed that DEIs in both two comparable groups significantly participated in skin and hair follicle development-related pathways, like ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathways. The results indicated that the hair follicle development of Tianzhu white yak may influence the hair length difference. Besides, the protein-protein interaction (PPI) network of DEIs showed COL2A1 and COL3A1 exhibited a high degree of centrality, and these two genes were suggested as potential candidates for the hair length growth of Tianzhu white yak.ConclusionsThe results provided a comprehensive analysis of the transcriptome complexity and identified differential transcripts that enhance our understanding of the molecular mechanisms underlying the variation in hair length growth in Tianzhu white yak.

Social network analysis of a decade-long collaborative innovation network between hospitals and the biomedical industry in China.

Collaborative innovation between hospitals and biomedical enterprises is crucial for ensuring breakthroughs in their development. This study explores the structural characteristics and examines the main roles of associated key actors of collaborative innovation between hospitals and biomedical enterprises in China. Using the jointly owned patent data within the country's healthcare industry, a decade-long collaborative innovation network between hospitals and biomedical enterprises in China was established and analyzed through social network analysis. The results revealed that the overall levels of collaborative innovation network density, collaborative frequency, and network connectivity were significantly low, especially in less-developed regions. In terms of actors with higher degree centrality, hospitals accounted for the majority, whereas a biomedical enterprise in Shenzhen had the highest degree centrality. Organizations in underdeveloped and northwest regions and small players were more likely to implement collaborative innovation. In conclusion, a collaborative innovation network between hospitals and biomedical enterprises in China demonstrated high dispersion and poor development levels. Stimulating organizations' initiatives for collaborative innovation may enhance quality and quantity of such innovation. Policy support and economic investments, strategic collaborative help, and resource and partnership optimization, especially for small players and in less-developed and northwest regions, should be encouraged to enhance collaborative innovation between hospitals and the biomedical industry in China and other similar countries or regions.

Analysis of the Driving Mechanism of Urban Carbon Emission Correlation Network in Shandong Province Based on TERGM

Analyzing the driving factors and mechanisms of urban carbon emission correlation networks can provide effective carbon reduction decision-making support for Shandong Province and other regions with similar industrial characteristics. Based on industrial carbon emission data from various cities in Shandong Province from 2013 to 2021, the spatial correlation network of carbon emission was established by using a modified gravity model. The characteristics of the network were explored by using the Social Network Analysis (SNA) method, and significant factors affecting the network were identified through Quadratic Assignment Procedure (QAP) correlation analysis and motif analysis. The driving mechanism of the carbon emission correlation network was analyzed by using Temporal Exponential Random Graph Models (TERGMs). The results show that: (1) The spatial correlation network of urban carbon emission in Shandong Province exhibits multi-threaded complex network correlations with a relatively stable structure, overcoming geographical distance limitations. (2) Qingdao, Jinan, and Rizhao have high degree centrality, betweenness centrality, and closeness centrality in the network, with Qingdao and Jinan being relatively central. (3) Shandong Province can be spatially clustered into four regions, each with distinct roles, displaying a certain “neighboring clustering” phenomenon. (4) Endogenous network structures such as Mutual, Ctriple, and Gwesp significantly impact the formation and evolution of the network, while Twopath does not show the expected impact; FDI can promote the generation of carbon emission reception relationships in the spatial correlation network; IR can promote the generation of carbon emission spillover relationships in the spatial correlation network; GS, differences in GDP, differences in EI, and similarities of IR can promote the generation of organic correlations within the network; on the temporal level, the spatial correlation network of urban carbon emission in Shandong Province has shown significant stability during the study period.

You change the way you talk: Examining the network, toxicity and discourse of cross-platform users on Twitter and Parler during the 2020 US Presidential Election

This study examines code-switching behaviours of cross-platform social media users specifically between Twitter and Parler during the 2020 US Presidential Election. Utilising social identity theory as a framework, we examine messages related to voter fraud by users who migrated from Twitter to Parler following Twitter bans. Our analysis covers 38,798 accounts active on both platforms, analysing 1.5 million tweets and more than 100,000 parleys. The key findings of the study are as follows: First, we discovered differing levels of network homophily between high degree centrality and low-degree centrality cross-platform users, illustrating how individuals with varying degrees of influence engage differently across platforms. Second, we observed higher toxicity levels in heterogeneous networks, which include both in-group and out-group members, compared with homogeneous networks that are primarily composed of in-group members. This suggests the level of toxicity in online spaces correlates with the level of group diversity. Third, we found that cross-platform users created distinctive discourse community with in-group and out-group members, indicating that content and discussions within these networks are influenced by the social identity dynamics of the users. Our study contributes to the current research in political communication and information science by proposing comparative user analyses across multiple social media platforms. Focusing on a critical period of platform transition during a contentious political event, our study offers insights into the dynamics of online communities and the shifting nature of political language used by social media users.

Формирование федеративных отношений в Российской Федерации: опыт, проблемы, перспективы развития

The article analyzes the stages of improving federal relations in the Russian Federation. The author states that the improvement of federal relations contributes to the solution of many issues concerning Russian society. It is noted that the federal government and the authorities of the subjects came to a compromise on the issue of interest to them, which was reflected in the formation of the Federation Council — the upper house of the country's parliament, its reforms were analyzed. It is proposed to transfer to the Federation Council the issue of submitting candidates to the regional parliament for the post of head of the region, since in the Russian Federation, characterized by a high degree of centralization of the public administration system and a significant amount of powers belonging to the federal center in relations with the regions, it is the solution of this issue that would be most relevant, as a body of people's representation in the highest echelon of power. It is concluded that the creation of federal districts and the appointment of presidential plenipotentiaries strengthened the vertical of power. The author notes that the most important direction of the federal reform is the restoration of the internal state sovereignty of Russia and the supremacy of federal power.

Aberrant functional connectivity associated with drug response in patients with newly diagnosed epilepsy.

To analyze the local functional activity and connectivity features of the brain associated with drug response inpatients newly diagnosed with epilepsy (NDE) who are naïve to anti-seizure medication (ASM). Recruited patients, underwent functional magnetic resonance imaging at baseline, and were assigned to the well-controlled (WC, n = 28) or uncontrolled (UC, n = 11) groups based on their response to ASM. Healthy participants were included in the control group (HC, n = 29). The amplitudes of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) were used to measure local functional activity, and voxel-wise degree centrality (DC) and seed-based functional connectivity (FC) were used to evaluate the connecting intensity of the brain areas. Compared to the HC and WC groups, the UC group had higher ALFF values in the left posterior central gyrus (PoCG.L) and left inferior temporal gyrus (ITG.L) and higher DC in the bilateral PoCG (Gaussian random field correction, voxel-level P < 0.001, and cluster-level P < 0.05). Both PoCG and ITG.L in the UC group showed stronger FC with multiple brain regions, mainly located in the occipital and temporal lobes, compared to the HC or WC group, while the WC group showed decreased or similar FC compared to the HC group. Excessive enhancement of brain functional activity or connecting intensity in ASM-naïve patients with NDE may be associated with a higher risk of poor drug response.