Modularity Values Research Articles

Stable construction and analysis of MDD modular networks based on multi-center EEG data

BackgroundThe modular structure can reflect the activity pattern of the brain, and exploring it may help us understand the pathogenesis of major depressive disorder (MDD). However, little is known about how to build a stable modular structure in MDD patients and how modules are separated and integrated. MethodWe used four independent resting state Electroencephalography (EEG) datasets. Different coupling methods, window lengths, and optimized community detection algorithms were used to find a reliable and robust modular structure, and the module differences of MDD were analyzed from the perspectives of global module attributes and local topology in multiple frequency bands. ResultsThe combination of the Phase Lag Index (PLI) and the Louvain algorithm can achieve better results and can achieve stability at smaller window lengths. Compared with Healthy Controls (HC), MDD had higher Modularity (Q) values and the number of modules in low-frequency bands. In addition, MDD showed significant structural changes in the frontal and parietal-occipital lobes, which were confirmed by further correlation analysis. ConclusionOur results provided a reliable validation of the modular structure construction method in MDD patients and contributed strong evidence for the changes in emotional cognition and visual system function in MDD patients from a new perspective. These results would afford valuable insights for further exploration of the pathogenesis of MDD.

Metacommunity structures of dormant and active zooplankton in two distant mediterranean regions

Abstract Temporary ponds are small‐sized limnic systems that dry periodically. The length of the wet phase defines the hydroregime and determines community assembly in temporary ponds. Zooplankton species have adapted to desiccation by adopting some dormant strategies, such as the formation of resting stages. Hatching from resting stages is a source of recolonization after drought events and the composition of hatching taxa is also influenced by hydroregime. We compared metacommunity structure and composition of the dormant sub‐community (resting stages); the pioneer active sub‐community (fauna present immediately after inundation of ponds); and the cumulative active sub‐community (fauna present at the initial, middle and final phases of hydroperiod) in a set of temporary ponds with semipermanent and seasonal hydroregimes in two areas with mediterranean climate: central Chile and eastern Spain. We aimed to determine the influence of hydroregime and identify whether metacommunity patterns are consistent between regions sharing a mediterranean climate. We selected five semipermanent and five seasonal ponds in each region to sample sediments and active zooplankton sub‐communities. By combining hatching experiments from the sediments with direct counts of active zooplankton from the field samples, we gathered data on both dormant and active sub‐communities to build unweighted bipartite networks of species occurrences in each pond. The obtained networks were analysed to determine seasonal trends in structural features of the metacommunities (diversity, nestedness and modularity) and their relation to hydroregime (semipermanent and seasonal) and geographical location (Chile and Spain). We observed consistent differences in community structure when comparing sub‐communities at different successional stages (dormant, pioneer active and cumulative active) and with different hydroregimes (semipermanent vs. seasonal). Both α‐ and γ‐diversity showed a consistent trend, being highest in cumulative active sub‐communities and lowest in dormant ones, and were consistently higher in seasonal ponds, regardless of the region. In addition, β‐diversity was higher in the pioneer active sub‐community in both regions, with no significant differences between hydroregimes. The species composition of cumulative active sub‐communities were less heterogeneous than that of the pioneer and dormant ones. No consistent patterns were found in modularity and nestedness values across sub‐communities and hydroregimes. In agreement with our hypotheses, we observed a gradual accumulation of taxa as well as community homogenization over time in both regions. Contrary to our expectations, hydroregime did not affect these patterns and, surprisingly, shorter hydroperiods exhibited higher regional diversity. Our results suggest that hydroregime as a single variable does not have a strong explanatory power for metacommunity assembly of temporary ponds. Therefore, a more comprehensive theory is needed to anticipate the effects of the upcoming hydroperiod shortening.

Identifying pollinator‐friendly sites within urban green spaces for sustainable urban agriculture

AbstractIntroductionThe relentless urban expansion has led to the depletion of forests and agricultural lands. Yet, pinpointing viable zones for urban agriculture, particularly concerning pollinator presence, remains a formidable task for city planners.Materials and MethodsOur study introduces a novel methodology to identify optimal locations for cultivating pollinator‐dependent crops. Leveraging species distribution models (SDMs), we generated habitat suitability maps for 243 pollinating insects, including bees (41 species), butterflies (140 species), and hoverflies (62 species) across South Korea. Subsequently, employing Graphab 2.8 software, we categorised 10 major cities in South Korea based on the connectivity of green space patches. Clusters exhibiting greater green space coverage, size, and connectivity displayed higher modularity values. Utilising ArcGIS software's zonal function, we computed key statistical parameters—minimum, maximum, mean, and sum values—of the pollinator taxa maps within each cluster.ResultsOur analysis unveiled a robust positive correlation between cluster modularity and the cumulative distribution of pollinators. Regions with heightened modularity exhibited richer pollinator populations, suggesting their suitability for urban agriculture for pollinator‐dependent crops.ConclusionThis study proposes that employing graph theory‐based clustering of urban green spaces can effectively delineate areas conducive to urban agriculture. This strategic identification of green space clusters with optimal pollinator abundance holds promise for urban agricultural planning and sustainability initiatives.

How retrieval processes change with age: Exploring age differences in semantic network and retrieval dynamics.

This study investigated the impact of age on semantic memory networks and retrieval dynamics using a single-list free recall paradigm, involving 318 participants. The younger group, with 175 participants aged 25-55 years (M = 46.68 years; SD = 10.69), and the older group, consisting of 143 participants aged 61-88 years (M = 68.71 years; SD = 6.09), completed a word recall test to assess delayed recall performance. Semantic memory networks were constructed from recall data by analyzing the co-occurrence and sequence of recalled words. We observed significant differences in network structure, where the older group displayed higher average shortest path length and modularity values, indicative of less integrated networks, while the younger group exhibited a higher clustering coefficient, suggesting a more interconnected network. In terms of retrieval dynamics, both groups showed a temporal contiguity effect with forward asymmetry. However, this effect was less pronounced in older adults. The study also identified participants that diverted from the average dynamic curves: one subgroup relied on nontemporal mechanisms, and the other employed a backward direction in memory search. Participants utilizing forward temporal associations demonstrated the highest recall performance. Overall, our findings suggest that lower free recall performance in older adults may be related to a diminished capacity to reinstate temporal context for retrieval and distinct differences in their semantic memory network structure. Specifically, older adults appear to exhibit networks with a less flexible, small-world-like structure. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Performance Evaluation of Python Libraries for Community Detection on Large Social Network Graphs

The development of social networking platforms has transformed the way people interact. The increasing number of social media users generates vast amounts of data and creates communities among users that are interesting to analyze. Community detection holds significant benefits in understanding network structures and providing insights into these communities. Community detection can be performed using various libraries and algorithms. However, the abundance of options can be challenging in selecting the appropriate library and algorithm. Therefore, this research evaluates the performance of community detection algorithms on social network graph datasets using several Python libraries such as NetworkX, igraph, Scikit-Network, and CDlib, as well as Louvain and Label Propagation algorithms. The findings of this study reveal that igraph is an ideal library, with fast execution time, efficient memory usage, and user-friendliness. Additionally, the Louvain algorithm is ideal for community detection and exhibits high modularity values.

Signs of criticality in social explosions

The success of an on-line movement could be defined in terms of the shift to large-scale and the later off-line massive street actions of protests. The role of social media in this process is to facilitate the transformation from small or local feelings of disagreement into large-scale social actions. The way how social media achieves that effect is by growing clusters of people and groups with similar effervescent feelings, which otherwise would not be in touch with each other. It is natural to think that these kinds of macro social actions, as a consequence of the spontaneous and massive interactions, will attain the growth and divergence of those clusters, like the correlation length of statistical physics, giving rise to important simplifications on several statistics. In this work, we report the presence of signs of criticality in social demonstrations. Namely, similar power-law exponents are found whenever the distributions are calculated either considering time windows of the same length or with the same number of hashtag usages. In particular, the exponents for the distributions during the event were found to be smaller than before the event, and this is also observed either if we count the hashtags only once per user or if all their usages are considered. By means of network representations, we show that the systems present two kinds of high connectedness, characterised by either high or low values of modularity. The importance of analysing systems near a critical point is that any small disturbance can escalate and induce large-scale—nationwide—chain reactions.

Research on Social Network Data Analysis and Criminal Network Identification in Criminal Law

Abstract The public security system is increasingly utilizing technology and big data for criminal network identification, making the identification of criminal suspects through social data a crucial aspect of current criminal network identification. Based on our research into the verbal features of networks used to portray criminal suspects, we constructed a criminal network identification system using social networks and complex network theory. We then designed the overall framework and main functional modules. We established the case data warehouse of public security agencies and built the social relationship network of criminal suspects. This paper compares the effectiveness of the complex network (CN) algorithm with other community delineation algorithms and utilizes the criminal network identification system it constructs for case data mining and analysis. The extended modularity values of the CN algorithm proposed in this paper on four real datasets are the largest among all algorithms, and the community delineation effect is optimal. In the mining of criminal gangs, a1, a3, and a15 represent the heads of the gangs, while a56 shares similar duty content with a1, a3, and a15, albeit at a lower rank. A65 is in charge of connecting and communicating with gang members.

Multiple sclerosis is associated with differences in semantic memory structure.

Although language is often considered to be largely intact in multiple sclerosis (MS), word-finding difficulties are a common complaint. Recent work suggests that declines in language are not solely the result of motoric and cognitive slowing that is most strongly associated with MS. Network science approaches have been effectively used to examine network structure as it relates to clinical conditions, aging, and language. The present study utilizes a network science approach to investigate whether individuals with MS exhibit less interconnected and resilient semantic networks compared to age-matched neurotypical peers. We used semantic fluency data from 89 participants with MS and 88 neurotypical participants to estimate and analyze the semantic network structure for each participant group. Additionally, we conducted a percolation analysis to examine the resilience of each network. Network measures showed that individuals with MS had lower local and global clustering coefficients, longer average shortest path lengths, and higher modularity values compared to neurotypical peers. Small-worldness, network portrait divergence measures, and community detection analyses were consistent with these results and indicated that macroscopic properties of the two networks differed and that the semantic network for individuals with MS was more fractured than the neurotypical peer network. Moreover, a spreading activation simulation and percolation analysis suggested that the semantic networks of individuals with MS are less flexible and activation degrades faster than those of age-matched neurotypical participants. These differing semantic network structures suggest that language retrieval difficulties in MS partially result from decline in language-specific factors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Extraction of brain function pattern with visual-capture-task fMRI using dynamic time-window method in ADHD children

Attention deficit/Hyperactivity disorder (ADHD) has a great impact on children's development. This paper uses a novel adaptive brain state extraction algorithm to construct a dynamic time-window brain network, which captures the brain function pattern characteristics of ADHD children with higher temporal resolution. The test data were acquired by functional magnetic resonance imaging (fMRI) obtained from 23 children with ADHD during the visual-capture-task [age: (8.27 ± 2.77)]. A spatial standard deviation method is used after the initial data processing, to extract the brain activity pattern state; An improved clustering algorithm is constructed to verify the changes made to the dynamic time-window brain network model. There can be seen clear differences between each state within 0.05 s after the test. The results show that our improved new framework can effectively obtain the characteristics of dynamic brain functional connection strength changes during the task. In addition, the new algorithm is able to capture the dynamic changes of the brain network, with an 80 % improvement compared to traditional methods for the average modularity value Q. This work demonstrates a novel approach to find out the pattern changes between dynamic brain function connections, which can be of great significance for the adjuvant treatment of children with ADHD.

Preparation and performance study of waterborne epoxy resin/non-covalent modified graphene oxide hydrogen barrier coatings

Hydrogen embrittlement in hydrogen pipelines can cause severe economic losses and safety hazards, thus research on efficient hydrogen barrier coatings is of great importance. In this study, a more efficient and easily operable non-covalent modification method was employed to modify carboxymethyl chitosan (CMCS) onto graphene oxide (GO), resulting in a significant improvement in the dispersibility and uniform distribution of GO in waterborne epoxy resin. Subsequently, a hydrogen barrier coating was prepared by incorporating the modified GO into waterborne epoxy resin. The influence of different doping concentrations on the coating performance was investigated. The basic properties of the coatings were characterized using macroscopic analysis, SEM, contact angle measurements, adhesion tests and electrochemical techniques. The corrosion resistance and hydrogen barrier effect was evaluated through electrochemical hydrogen permeation experiments. Results showed that The modified graphene oxide has good compatibility with epoxy resin. When the content of CGO was 0.25 %, the dispersion was best. the CGO coatings has some corrosion resistance with approximately 10 MPa adhesive force and 105 Modular value at the beginning of immersion. The CGO coatings significantly improved the hydrogen resistance of ×80 steel. The lowest hydrogen permeation current density was observed at 0.25 wt% CGO concentration, which indicated optimal hydrogen permeation inhibition capability.

A study of innovations in legal governance with respect to the safety of artificial intelligence

Abstract This paper aims to promote the safe development of artificial intelligence and improve legal policies. Combined with the cluster analysis algorithm, it analyzes the safety risks as well as legal defects of artificial intelligence. The Laplace matrix is derived using the similarity matrix, and the feature vector space is constructed by analyzing the associated features of artificial intelligence safety. Combining the spectral clustering algorithm, legal assessment indexes for artificial intelligence safety were constructed. The modular metric value method is utilized to assess the clustering effect of laws on the safety of artificial intelligence. Analyzing the security risks of artificial intelligence, improved legal policies are proposed from the perspective of technology and privacy. The results show that the effect of improving privacy protection policy on privacy protection is 0.85, and the effect of clarifying subject rights is 0.9. The introduction of laws should consider social ethics, and the effect degree of ethical principles is 0.75. Clarifying subject rights can help avoid technological risks to a certain extent, and improving privacy protection policies can help protect users’ privacy.

Direct measurement of nonlocal quantum states without approximation

Abstract Efficient acquiring information from a quantum state is important for research in fundamental quantum physics and quantum information applications. Instead of using standard quantum state tomography method with reconstruction algorithm, weak value was proposed to directly measure density matrix elements of quantum state. Recently, similar to the concept of weak value, modular values was introduced to extend the direct measurement scheme to nonlocal quantum wave function. However, this method still involves approximation, which leads to inherent low-precision. Here, we propose a new scheme which enables direct measurement for ideal value of the nonlocal density matrix element without taking approximations. Our scheme allows more accurate characterization of nonlocal quantum states, and therefore has greater advantages in practical measurement scenarios.

Algebraic de Rham theory for the relative completion of 𝑆𝐿₂(ℤ)

We develop an explicit algebraic de Rham theory for the relative completion of S L 2 ( Z ) {\mathrm {SL}}_2(\mathbb {Z}) . This allows the construction of iterated integrals involving modular forms of the second kind, generalizing iterated integrals of holomorphic modular forms that were previously studied by Brown and Manin. These newly constructed iterated integrals provide all ‘multiple modular values’ defined by Brown.

The role of the thalamus in modular functional networks in temporal lobe epilepsy with cognitive impairment.

Cognitive deficit is common in patients with temporal lobe epilepsy (TLE). Here, we aimed to investigate the modular architecture of functional networks associated with distinct cognitive states in TLE patients together with the role of the thalamus in modular networks. Resting-state functional magnetic resonance imaging scans were acquired from 53 TLE patients and 37 matched healthy controls. All patients received the Montreal Cognitive Assessment test and accordingly were divided into TLE patients with normal cognition (TLE-CN, n = 35) and TLE patients with cognitive impairment (TLE-CI, n = 18) groups. The modular properties of functional networks were calculated and compared including global modularity Q, modular segregation index, intramodular connections, and intermodular connections. Thalamic subdivisions corresponding to the modular networks were generated by applying a 'winner-take-all' strategy before analyzing the modular properties (participation coefficient and within-module degree z-score) of each thalamic subdivision to assess the contribution of the thalamus to modular functional networks. Relationships between network properties and cognitive performance were then further explored. Both TLE-CN and TLE-CI patients showed lower global modularity, as well as lower modular segregation index values for the ventral attention network and the default mode network. However, different patterns of intramodular and intermodular connections existed for different cognitive states. In addition, both TLE-CN and TLE-CI patients exhibited anomalous modular properties of functional thalamic subdivisions, with TLE-CI patients presenting a broader range of abnormalities. Cognitive performance in TLE-CI patients was not related to the modular properties of functional network but rather to the modular properties of functional thalamic subdivisions. The thalamus plays a prominent role in modular networks and potentially represents a key neural mechanism underlying cognitive impairment in TLE.

Studi Deskriptif Kesiapan Kerja, Kompetensi Kejuruan, dan Efikasi Diri Siswa SMK Jurusan AKL Pasca Pandemi Covid-19

Following the Covid-19 pandemic, the competition in Indonesia has become more stringent. The workforce continues to grow, but the number of existing jobs is not yet able to accommodate it. So strategies are needed to face job competition, and in support of job preparedness competence and self-efficiency is required. The purpose of this research is to provide an overview of the conditions of preparedness for work, professional competence and self-efficiency of AKL students that vary in the period after the pandemic in one of the SMK as the research material for achieving the objectives of the establishment of SMK in general and the purpose of the educational unit in particular. This research method uses descriptive quantitative methods with research subjects of students of class XII SMKN Mojoagung major Accounting and Finance Institute of the academic year 2022/2023. The sample size is obtained from the Slovin formula of 105 students.with the technique of sampling using purposive sampler. Data is obtained from a likert scale questionnaire that is scattered and analyzed descriptively for its modular values and then classified according to the categories of each variable. The results of the research obtained that student work readiness in the ready category, professional competence in the good category, and self-efficiency in the high category. Further research is expected to explain the influence or causality relationship of the variables of work readiness, professional competence, and self-efficiency. For AKL SMKN Mojoagung, the results of this research can be used as a reference and evaluation material in order to maintain and enhance the potential of its students.

Complementary Assets, Organizational Modularization, and Platform Enterprise Value Innovation

In the platform economy, platform enterprises connect producers and customers, matching diverse supply with diverse demand to achieve profit goals. The big data assets and marketing relationships owned by platform enterprises are complementary assets, and their modular organizational structure is crucial for enhancing enterprise value innovation. The existing literature lacks in-depth exploration of the impact of complementary assets on platform enterprises. Therefore, the research purpose of this article is to investigate the impact of complementary assets and organizational modularity on platform enterprise value innovation. Taking Meituan as the research object, this paper adopts the single case-study method of grounded theory coding to explore the relationship between complementary assets, organizational modularity, and platform enterprise value innovation. Research has shown that organizational modularity includes two dimensions, dependency and structural arrangement, while value innovation includes three dimensions, customer value innovation, product value, and innovation ability. Among them, complementary assets and organizational modularity both have a positive impact on platform enterprise value innovation, and organizational modularity plays a moderating role in the impact of complementary assets on platform enterprise value innovation. The research results have enriched the research on complementary assets, organizational modularity, and value innovation theory, providing a theoretical basis and inspiration for enterprise organizational design and better utilization of complementary assets.

Thermal compensation design of achromatic andapochromatic optical systems using a 3D glass chart.

It is important to determine the ideal combination of housing materials, groups of refractive materials, and their optical powers for athermalizing achromatic and apochromatic optical systems. This study proposes a combined design approach that utilizes three or more glass types to resolve thermal aberrations and defocus achromatic and apochromatic optical systems. It selects a suitable glass type using a 3D glass chart and calculates the optical power analytically. Furthermore, a temperature-insensitive optical system with a 450-750nm band based on refractive materials (CDGM Glass Co., Ltd.) is designed, with the modular transfer function value of the center field of view decreasing by less than 0.024 in the temperature range of -40∘ C to +80∘ C and the secondary spectrum aberration decreasing by over three times and being maintained within 0.08mm.

Mapping of functional areas in Spain based on mobile phone data during different phases of the COVID-19 pandemic

ABSTRACT Mobility functional areas are tools based on human mobility that can be useful for spatial and transport planning in delicate situations such as the COVID-19 pandemic. In this work, we aim to map functional areas in Spain from four days corresponding to different phases of the disease. For that goal, mobile phone data provided by Spanish Statistical National Institute (INE) has been used due to its value and potential to provide constantly updated information of mobility at almost-real time. The methodology consists of a network analysis over an origin-destination matrix to obtain modularity values for 3214 population cells provided by the INE. These values were then used to cluster the cells into functional areas. The results show how different confinement and mobility restriction policies influence the amount, size and shape of the functional areas, and therefore, they affect access to services or jobs.

Responses of Fungal Assembly and Co-Occurrence Network of Rhizosphere Soil to Amaranthus palmeri Invasion in Northern China.

The interaction between invasive plants and soil microbial communities is critical for plant establishment. However, little is known about the assembly and co-occurrence patterns of fungal communities in the rhizosphere soil of Amaranthus palmeri. The soil fungal communities and co-occurrence networks were investigated in 22 invaded patches and 22 native patches using high-throughput Illumina sequencing. Despite having little effect on alpha diversity, plant invasion significantly altered the composition of the soil fungal community (ANOSIM, p < 0.05). Fungal taxa associated with plant invasion were identified using linear discriminant analysis effect size (LEfSe). In the rhizosphere soil of A. palmeri, Basidiomycota was significantly enriched, while Ascomycota and Glomeromycota were significantly reduced when compared to native plants. At the genus level, the invasion of A. palmeri dramatically increased the abundance of beneficial fungi and potential antagonists such as Dioszegia, Tilletiopsis, Colacogloea, and Chaetomium, while it significantly decreased the abundance of pathogenic fungi such as Alternaria and Phaeosphaeria. Plant invasion reduced the average degree and average path length, and increased the modularity value, resulting in a less complex but more effective and stable network. Our findings improved the knowledge of the soil fungal communities, network co-occurrence patterns, and keystone taxa in A. palmeri-invaded ecosystems.

Comparing the Effectiveness of Hashtags in Digital Social Movements: A Case Study of #PercumaLaporPolisi and #PolriSesuaiProsedur in Indonesia

Hashtags are used in digital social movements to mobilize netizens and create social change. This study aims to fill in gaps from previous studies by comparing the hashtags #PercumaLaporPolisi and #PolriSesuaiProsedur, which emerged on Twitter as social movements in response to the rape of three children under ten in East Luwu, South Sulawesi. The study incorporates framing analysis and focuses on the frame alignment process to see the efforts of the hashtags #PercumaLaporPolisi and #PolriSesuaiProsedur on mobilizing social media users. This study uses Social Network Analysis (SNA) to analyze the network structure formed from #PercumaLaporPolisi vs. #PolriSesuaiProsedur hashtags and determine the values of reciprocity, centralization, diameter, density, and modularity. The researcher also uses framing alignment analysis to analyze the tweets collected using Python from October 6-17, 2021, and Netlytic software as a tool. This text compares the effectiveness of two Twitter hashtags, #PercumaLaporPolisi, and #PolriSesuaiProsedur, in mobilizing supporters during a campaign about police policies in Indonesia. Network analysis found that #PolriSesuaiProsedur had a more extensive network, higher density, and more robust framing than #PercumaLaporPolisi, which resulted in a more effective mobilization of supporters. The text emphasizes the importance of precise messaging and framing in hashtag activism. This study compared the success of two hashtags, #PercumaLaporPolisi, and #PolriSesuaiProsedur, and found that #PolriSesuaiProsedur was more successful due to its clear framing. This research highlights the significance of precise framing in hashtags for mobilizing supporters in digital social movements. Future studies could investigate the impact of various hashtags on mobilization.