Analysis Of Network Structure Research Articles

Institutional analysis of water governance in the Colorado River Basin, 1922–2022

The 1922 Colorado River Compact started the long history of water governance in the Colorado River Basin. Over the last century, the institutional structure has shaped water governance in the basin. However, an understanding of the long-term evolution is lacking. This study examines how water management strategies have evolved at the basin scale by incorporating institutional, temporal, and network structure analysis methods to examine long-term changes. Content analysis was employed to systematically investigate encouraged and/or discouraged water management actions at different rule levels. The water governance network was examined at four points in time to map the institutional structure, actors, and governance level at which rules are issued and targeted. Using institutional analysis, we found constitutional, operational, and collective-choice level rules for water supply, storage, movement, and use have been altered via layering of new governance rules without major rule or responsibility alteration. The network analysis results indicate that key decision-making positions have remained and actors who issue and are targeted by the rules lack significant change. We found original positions of power have been maintained, potentially stagnating the space for problem-solving and management strategy renegotiation. Our results indicate that path dependency has shaped water governance and who is able to influence decision-making.

언어네트워크 분석을 통한 예비영유아교사의 수업시연 내용 연구: 멘토링 중심 수업시연대회에 따른 변화

Objectives In this study, we conducted a language network analysis of the content of the changing lesson demonstrations as pre-service early childhood teacher experienced mentoring-centered lesson demonstrations. Methods For this reason, after 264 chapters of the 1st Selection Tournament Warrior Materials and mentoring were conducted for 34 participants in the Class Trial Competition, 362 chapters of the 2nd Final Selection Tournament Warrior materials were analyzed. Data were compared by keyword frequency analysis, network structure analysis, centrality analysis, and CONCOR analysis using KrKwic and UCINET 6 programs. Results First of all, In the first round of competitions, Guys was mentioned most often after We, and in the second round of competitions, Teacher was mentioned after We. Second, the relationships between keywords in the utterances in the second round competition were more concentrated than those in the first round competition. In terms of degree centrality, betweenness centrality, and eigenvector centrality, Guys was ranked in the top 3 in the first selection competition, and Teacher was ranked in the top 2 in the second selection competition. Third, according to the CONCOR analysis, two main clusters appeared in the first round of competitions is teacher-centered interaction and explanation about the content of the activity. Two main clusters appeared in the second round of competitions is child-centered interaction and a method cluster was formed for young children to participate in activities. Conclusions The results of this study suggest that it is necessary to develop a curriculum in collaboration between universities and early childhood educational institution in order to improve the professional abilities of pre-service early childhood teachers.

SURG-19. THE SYNDROME OF THE SUPPLEMENTARY MOTOR AREA: CLINICAL COURSE AND PREDICTORS OF OUTCOME AFTER RESECTION OF PREMOTOR LESIONS

Abstract OBJECTIVE Supplementary motor area (SMA) syndrome frequently occurs after resection of premotor lesions. Interhemispheric connectivity including the contralateral SMA might be crucially involved in its development and recovery. We aimed to identify predictors of outcome by quantifying preoperative structural alterations in the non-affected contralateral hemisphere. METHODS We retrospectively searched our database for patients developing SMA syndrome after resection of premotor lesions between 2013 and 2023. Clinical characteristics and imaging data were collected. Structural network analysis was performed using the previously validated morphometric inverse divergence network. RESULTS 27 patients with a mean age of 39.2 ± 17.7 years developed SMA syndrome, including 13/27 patients (48.1%) with primary brain tumors and 14/27 patients (51.9%) with intractable lesional epilepsy. Postoperative SMA syndrome was commonly characterized by contralateral upper extremity hemiparesis (19/27 patients, 70.4%) and aphasia in case of dominant hemisphere involvement (13/13 patients, 100%). Tumor patients were less likely to fully recover from SMA syndrome after 7 days compared to epilepsy patients (2/13 [15.4%] versus 9/14 [64.3%] patients, p=0.018). Most patients (23/27, 85.2%) experienced full recovery of SMA syndrome after a median follow-up of 5 months (IR 3-16 months), with no difference between patients with tumors or epilepsy (p=0.999). Preoperative contralateral intra-hemispheric connectivity was similar between patients receiving tumor or focal resection (0.183 vs 0.177, p=0.295). However, imaging analyses demonstrated baseline affection of SMA connectivity in patients with brain tumors as characterized by reduced cortical thickness of the contralateral frontal lobe and the cingulate gyrus compared to epilepsy patients. CONCLUSION While full recovery from SMA syndrome is common, SMA syndrome is frequently less severe and more transient in patients with epilepsy. This might be due to affection of the contralateral SMA network in tumor patients due to the infiltrative nature of the disease. Imaging analyses might assist in identifying network affections at baseline.

Effective methods for quantifying complexity based on improved ordinal partition networks: Topological dispersion entropy and weighted topological dispersion entropy

Topological permutation entropy is based on ordinal partition networks (OPNs) to approximate topological entropy of low-dimensional chaotic systems. But the ordinal patterns of OPN ignore the magnitude of the amplitude value. To solve the problem, we propose topological dispersion entropy (TDE) and weighted topological dispersion entropy (WTDE) based on dispersion patterns to characterize the complexity of a system. Furthermore, WTDE strengthens the topological structure analysis of complex networks by weighting the adjacency matrix of the improved ordinal partition networks, thereby more accurately capturing the dynamic evolution of time series. The proposed methods are comprehensively evaluated by numerical experiments. The results show that the performance of both TDE and WTDE is significantly better than TPE. Especially, WTDE has good stability to parameters, data length, and noise. Finally, combining support vector machines and K-Nearest Neighbor, TDE and WTDE are applied to the classification of physiological data and mechanical failure data.

A special contribution from spatial ability to math word problem solving: Evidence from structural equation modelling and network analysis

There is a growing body of research into the factors contributing to math word problem solving. However, these studies usually use limited number of potential predictors (precluding assessing of their contribution in comparison with other factors or “g” general intelligence) and some predictors (such as analogical and hypothetical reasoning) are largely omitted. Thus, the aim of the current study was to explore contributions of different types of reasoning to math word problem solving and whether these contributions have added value compared with each other and general cognitive ability. Chinese schoolchildren in Grades 3 (N = 199; Mage = 102.4 months), 4 (N = 162; Mage = 114.6), 5 (N = 174; Mage = 126.1) and 6 (N = 180; Mage = 138.6) completed 8 tasks tapping into spatial, mechanical, verbal, mathematic, hypothetical and analogical reasoning. Our data showed that when 6 general cognitive factors load onto General cognitive ability factor in a Structural Equation Model (SEM), only spatial visualization has additional contribution to Word problem solving factor. Gaussian Graphical models (GGMs) showed that 2 verbal tasks and spatial visualization showed stable (present in at least 3 out of 4 grades) contributions to both word problem solving tasks. Analogical reasoning showed contribution to process of word problem solving only. To sum up, both SEM and GGMs converged on the importance of spatial ability for math word problems solving. Our results call for verbal and spatial ability to be routinely assessed and targeted by educational interventions within math curriculum.

Navigating the social media landscape: unraveling the intricacies of safety perceptions

The emergence of social media has greatly enriched the channels and sources of information through which individuals interact. However, there remains a lack of empirical research on its actual impact on personal safety. This study employed both Structural Equation Modeling (SEM) and network analysis to explored the mechanisms by which social media engagement affects safety perceptions. Additionally, it examined gender differences from both micro-level perspectives (including general trust and perceived social support) and macro-level viewpoints (including personal belief in a just world). The findings revealed that perceived social support, general trust, and personal belief in a just world played a fully mediating role. A multi-group analysis indicated that males were more susceptibility to the influence of social media engagement, with their general trust exhibiting negligible impact on perceived safety. Consequently, this study argues that attention should be given not only how social media affects personal beliefs and safety perceptions, but also its impact on behavioral outcomes.

Network structure analysis based on embodied energy of the Australian economy

Energy and economic growth are closely linked and have negative impacts on the environment. Both environmental protection and economic development are significant global focal points of concern. Australian economic growth has slowed but energy consumption has increased. The relationship between economic growth and energy consumption has changed significantly. However, existing methods for accounting energy transfers between industries overlook the role of embodied energy, making them insufficient to fully capture the actual energy usage in both domestic and international trade. This study investigates the structural changes in embodied energy in the Australian economy. The paper integrates sectors as the section nodes for a network and constructs a two-layer network model to analyze energy flows both between and within nodes. The results show that the Australian embodied energy network exhibited significant variations in efficiency and interconnectedness over time, with a trend toward shorter, more interconnected paths from 2015 to 2019, enhancing overall network efficiency. The Australian embodied energy network currently faces significant funding constraints. The findings show that the Australian economic sectors have increased energy consumption in production without contributing to economic growth due to the concentration of resources within the network.

Measuring daily tourism mobility spillover at the intra-metropolis level with mobile positioning data

ABSTRACT Although tourism mobility spillover continues to be a key indicator for tourism management, more innovative research must be conducted at the micro level and high sampling frequency. Against the backdrop of an increasing number of global cities, in this paper, we evaluate the daily tourism mobility spillover inside a worldwide city of China: Shanghai. Based on the Granger causal network model and an original mobile positioning dataset, we analyse the causal relationship between local tourism flows and the spillover effects of tourism mobility within Shanghai. By categorising tourists into ‘local tourists from Shanghai’ and ‘tourists from out of Shanghai’, we reveal a significant causal relationship between Shanghai districts and flows generated by ‘tourists from out of Shanghai’. The analysis of the causal network structure also reveals key districts and points of interest that significantly contribute to congestion in tourism mobility and Shanghai's dynamics. This econometric approach offers policymakers a valuable tool to monitor mobility drivers and optimise flows within the city.

Dynamic Interplay of Loop Motions Governs the Molecular Level Regulatory Dynamics in Spleen Tyrosine Kinase: Insights from Molecular Dynamics Simulations.

The spleen tyrosine kinase (Syk) is a key regulator in immune cell signaling and is linked to various mechanisms in cancer and neurodegenerative diseases. Although most computational research on Syk focuses on novel drug design, the molecular-level regulatory dynamics remain unexplored. In this study, we utilized 5 × 1 μs all-atom molecular dynamics simulations of the Syk kinase domain, examining it in combinations of activation segment phosphorylated/unphosphorylated (at Tyr525, Tyr526) and the "DFG"-Asp protonated/deprotonated (at Asp512) states to investigate conformational variations and regulatory dynamics of various loops and motifs within the kinase domain. Our findings revealed that the formation and disruption of several electrostatic interactions among residues within and near the activation segment likely influenced its dynamics. The protein structure network analysis indicated that the N-terminal and C-terminal anchors were stabilized by connections with the nearby stable helical regions. The P-loop showed conformational variation characterized by movements toward and away from the conserved "HRD"-motif. Additionally, there was a significant correlation between the movement of the β3-αC loop and the P-loop, which controls the dimensions of the adenine-binding cavity of the C-spine region. Overall, understanding these significant motions of the Syk kinase domain enhances our knowledge of its functional regulatory mechanism and can guide future research.

Revealing the brain behind travel: an analysis of neuro-tourism research using structural topic models and network analysis

ABSTRACT Neuro-tourism is a growing field that could revolutionize the tourism industry by uncovering the neurological mechanisms behind the tourist experience. However, the underlying mechanisms and interrelationships shaping the evolving landscape of neuro-tourism research have not yet been fully elucidated. This study analyzes research trends in neuro-tourism from 2006 to 2024 and identifies academic collaboration networks. We utilized structural topic modeling and network analysis to uncover the underlying academic topics in neuro-tourism. Our analysis found eight growing and four declining subfields in neuro-tourism. The network analysis showed significant contributions from countries, institutions, and journals. This research adds to existing theory by explaining the mechanisms and connections impacting the evolving neuro-tourism research field. It provides valuable insights for scholars and researchers studying the intersection of neuroscience and tourism. Understanding how research is categorized in databases like the Web of Science can help make strategic publishing decisions, encouraging interdisciplinary collaboration and innovation.

White matter alterations associated with chronic cannabis use disorder: a structural network and fixel-based analysis

Cannabis use disorder (CUD) is associated with adverse mental health effects, as well as social and cognitive impairment. Given prevalence rates of CUD are increasing, there is considerable efforts, and need, to identify prognostic markers which may aid in minimising any harm associated with this condition. Previous neuroimaging studies have revealed changes in white matter (WM) organization in people with CUD, though, the findings are mixed. In this study, we applied MRI-based analysis techniques that offer complimentary mechanistic insights, i.e., a connectome approach and fixel-based analysis (FBA) to investigate properties of individual WM fibre populations and their microstructure across the entire brain, providing a highly sensitive approach to detect subtle changes and overcome limitations of previous diffusion models. We compared 56 individuals with CUD (median age 25 years) to a sample of 38 healthy individuals (median age 31.5 years). Compared to controls, those with CUD had significantly increased structural connectivity strength (FDR corrected) across 9 edges between the right parietal cortex and several cortical and subcortical regions, including left orbitofrontal, left temporal pole, and left hippocampus and putamen. Utilizing FBA, WM density was significantly higher in those with CUD (FWE-corrected) across the splenium of the corpus callosum, and lower in the bilateral cingulum and right cerebellum. We observed significant correlation between cannabis use over the past month and connectivity strength of the frontoparietal edge, and between age of regular use and WM density of the bilateral cingulum and right cerebellum. Our findings enhance the understanding of WM architecture alterations associated with CUD.

Rheological analysis of network structure of raw natural rubber prepared by different processing techniques

The performance of raw natural rubber (NR) is dominated by its network structure, particular the levels of long chain branching (LCB) and entanglement. Here, three type of raw rubbers were prepared using different processing methods for commercial grade NRs. Linear and nonlinear viscoelastic behaviors, as well as stress relaxation, were analyzed to access their network structures. The third relative harmonic, phase angle, and first to second quarter-period integral ratio of stress curve were utilized as indicators for the nonlinearity of samples. By combining these values with flow activation energy and characteristic times, it can be confirmed that naturally coagulated NR showed higher elasticity than acid-coagulated NR due to high levels of LCB and entanglement, and hot air drying could lead to chain degradation. These findings correlated with molecular weight parameters, gel content and Mooney viscosity results. This research establishes a method for monitoring raw NR quality and predicting its properties.

Structure matters: Assessing the statistical significance of network topologies.

Network analysis has found widespread utility in many research areas. However, assessing the statistical significance of observed relationships within networks remains a complex challenge. Traditional node permutation tests are often insufficient in capturing the effect of changing network topology by creating reliable null distributions. We propose two randomization alternatives to address this gap: random rewiring and controlled rewiring. These methods incorporate changes in the network topology through edge swaps. However, controlled rewiring allows for more nuanced alterations of the original network than random rewiring. In this sense, this paper introduces a novel evaluation tool, the Expanded Quadratic Assignment Procedure (EQAP), designed to calculate a specific p-value and interpret statistical tests with enhanced precision. The combination of EQAP and controlled rewiring provides a robust network comparison and statistical analysis framework. The methodology is exemplified through two real-world examples: the analysis of an organizational network structure, illustrated by the Enron-Email dataset, and a social network case, represented by the UK Faculty friendship network. The utility of these statistical tests is underscored by their capacity to safeguard researchers against Type I errors when exploring network metrics dependent on intricate topologies.

Classification of Crack Coordinates through Wavelet - Analysis and CNN: Analysis of Data Preparation and Network Structure

For the two-dimensional localization of acoustic emission events (AE), artificial intelligence (AI) was applied. AE events were generated by breaking pencil leads on a granite plate measuring with dimensions of 50 cm × 50 cm × 6 cm. A total of 128 measurements were carried out at each of the 81 positions on the top surface of the granite plate. The recorded signals with 16 channels were processed using continuous wavelet transformation (CWT). The resulting RGB images served as input data for a convolutional neural network (CNN). The transformed images of the measured AE signals were converted into a four-dimensional input by considering them as deeper input, allowing for improved data capture. The result was analysed using a binary classifier according to the one-versus-all method. However, due to limited computational capacity, processing the four-dimensional data posed a challenge, resulting in significant programming effort. The results of this investigation provide insights into the potential of using AI for the localization of AE events.

PubMed captures more fine-grained bibliographic data on scientific commentary than Web of Science: a comparative analysis

BackgroundResearch commentaries have the potential for evidence appraisal in emphasising, correcting, shaping and disseminating scientific knowledge.ObjectivesTo identify the appropriate bibliographic source for capturing commentary information, this study compares comment data...

Identification of novel candidate predisposing genes in familial nonmedullary thyroid carcinoma implicating DNA damage repair pathways.

The genetic basis of nonsyndromic familial nonmedullary thyroid carcinoma (FNMTC) is still poorly understood, as the susceptibility genes identified so far only account for a small percentage of the genetic burden. Recently, germline mutations in DNA repair-related genes have been reported in cases with thyroid cancer. In order to clarify the genetic basis of FNMTC, 94 genes involved in hereditary cancer predisposition, including DNA repair genes, were analyzed in 48 probands from FNMTC families, through targeted next-generation sequencing (NGS). Genetic variants were selected upon bioinformatics analysis and in silico studies. Structural modeling and network analysis were also performed. In silico results of NGS data unveiled likely pathogenic germline variants in 15 families with FNMTC, in genes encoding proteins involved in DNA repair (ATM, CHEK2, ERCC2, BRCA2, ERCC4, FANCA, FANCD2, FANCF, and PALB2) and in the DICER1, FLCN, PTCH1, BUB1B, and RHBDF2 genes. Structural modeling predicted that most missense variants resulted in the disruption of networks of interactions between residues, with implications for local secondary and tertiary structure elements. Functional annotation and network analyses showed that the involved DNA repair proteins functionally interact with each other, within the same DNA repair pathway and across different pathways. MAPK activation was a common event in tumor progression. This study supports that rare germline variants in DNA repair genes may be accountable for FNMTC susceptibility, with potential future utility in patients' clinical management, and reinforces the relevance of DICER1 in disease etiology.

Decarbonization, hegemonic projects, and the green growth policy-planning network: the case of Québec

ABSTRACT Since 2010, grassroots-led socio-ecological movements in Québec, Canada played a key role in overturning carbon extractivist proposals. Building on their successes, these groups now aim to move energy transition debates toward a broader conception of transition that includes radical social justice and post-capitalist alternatives. Meanwhile, corporate actors and the state enlisted major environmental NGOs and union federations into various technocentric ‘green growth’ projects. These hegemonic and counter-hegemonic struggles define how transition unfolds in the province, yet few have studied how actual social actors organize to carry out these divergent responses to the climate crisis. We develop a structural analysis of the green growth policy-planning network in Québec. Starting from five organizations at the core of transition debates, we analyze the network of board interlocks they are embedded in. We describe the overall structure of the network and its main corporate, civil society, and individual actors. Analysis outlines the possibility of a new hegemonic bloc forming, positioned around the green growth project and the cleantech sector, close to achieving dominance in Quebec, that would threaten deeper decarbonization efforts. Thus, despite the recent ban on petroleum extraction, like elsewhere, energy transition in Quebec still faces deep social and ecological contradictions.

Signature-based portfolio allocation: a network approach

Portfolio allocation represents a significant challenge within financial markets, traditionally relying on correlation or covariance matrices to delineate relationships among stocks. However, these methodologies assume time stationarity and only capture linear relationships among stocks. In this study, we propose to substitute the conventional Pearson’s correlation or covariance matrix in portfolio optimization with a similarity matrix derived from the signature. The signature, a concept from path theory, provides a unique representation of time series data, encoding their geometric patterns and inherent properties. Furthermore, we undertake a comparative analysis of network structures derived from the correlation matrix versus those obtained from the signature-based similarity matrix. Through numerical evaluation on the Standard & Poor’s 500, we assess that portfolio allocation utilizing the signature-based similarity matrix yielded superior results in terms of cumulative log-returns and Sharpe ratio compared to the baseline network approach based on Pearson’s correlation. This assessment was conducted across various portfolio optimization strategies. This research contributes to portfolio allocation and financial network representation by proposing the use of signature-based similarity matrices over traditional correlation or covariance matrices.

The association between child maltreatment, cognitive reappraisal, negative coping styles, and non-suicidal self-injury in adolescents with major depressive disorder

BackgroundNon-suicidal self-injury (NSSI) is a significant public health concern among adolescents with major depressive disorders (MDD). Although previous research has linked child maltreatment (CM) to NSSI, the precise mechanisms remain unclear. This study aims to investigate the association between CM, cognitive reappraisal (CR), negative coping styles (NC) and NSSI in adolescents with MDD, from the perspectives of both Latent Variable Theory and the Network Theory of Mental Disorder.MethodsA sample of 651 adolescents with MDD was recruited from January to December 2023. Data on CM, CR, NC, and NSSI were collected through paper-based self-reported questionnaires. Data analysis primarily involved structural equation modeling and network analysis.ResultsThe reporting rate of NSSI among adolescents with MDD was 48.2%. CM showed a significant positive correlation with NSSI. NSSI was affected by CM through three paths: the mediating role of CR, the mediating role of NC, and the chain mediating role of both CR and NC. Emotional abuse (EA) was the central node, while NSSI, EA, and “The urge to cry quietly when faced with troubles“(NC10) were the key bridge nodes.ConclusionsThis study is the first to use both structural equation modeling and network analysis to explore the explore the relationship between CM, CR, NC, and NSSI in adolescents with MDD, providing a theoretical basis for future early prevention and targeted interventions for adolescents with MDD.

Rational Engineering of a β-Glucosidase (H0HC94) from Glycosyl Hydrolase Family I (GH1) to Improve Catalytic Performance on Cellobiose.

The conversion of lignocellulosic feedstocks by cellulases to glucose is a critical step in biofuel production. β-Glucosidases catalyze the final step in cellulose breakdown, producing glucose, and are often the rate-limiting step in biomass hydrolysis. The specific activity of most natural and engineered β-glucosidase is higher on the artificial substrate p-nitrophenyl β-d-glucopyranoside (pNPGlc) than on the natural substrate, cellobiose. We report an engineered β-glucosidase (Q319A H0HC94) with a 1.8-fold higher specific activity (366.3 ± 36 μmol/min/mg), a 1.5-fold increase in kcat (340.8 ± 27 s-1), and a 3-fold increase in catalytic efficiency (236.65 mM-1 s-1) over H0HC94 (WT) on cellobiose. Molecular dynamic simulations and protein structure network analysis indicate that the Q319A H0HC94 active site pocket is significantly remodeled compared to the WT, leading to changes in enzyme conformation, better accessibility of cellobiose inside the active site pocket, and higher enzymatic activity. This study shows the impact of rational engineering of a nonconserved residue to increase β-glucosidase substrate accessibility and catalytic efficiency by reducing crowding interaction between cellobiose and active site pocket residues near the gatekeeper region and increasing pocket volume and surface area. Thus, rational engineering of previously characterized enzymes could be an excellent strategy to improve cellulose hydrolysis.