Network Analysis Methods Research Articles

Network modeling of problematic social media use components in college student social media users

BackgroundWhile the constitutive features of problematic social media use (PSMU) have been formulated, there has been a lack of studies in the field examining the structure of relationships among PSMU components.MethodThis study employed network analytic methods to investigate the connectivity among PSMU components in a large sample of 1,136 college student social media users (Mage = 19.69, SD = 1.60). Components of PSMU were assessed by the Bergen Social Media Addiction Scale (BSMAS) derived from a components model of addiction. We computed two types of network models, Gaussian graphical models (GGMs) to examine network structure and influential nodes and directed acyclic graphs (DAGs) to identify the probabilistic dependencies among components.ResultRelapse component consistently emerged as a central node in the GGMs and as a parent node of other components in the DAGs. Relapse and tolerance components exhibited strong mutual connections and were linked to the most vital edges within the networks. Additionally, conflict and mood modification nodes occupied more central positions within the PSMU network for the low-BSMAS-score subgroup compared with the high-BSMAS-score subgroup.ConclusionOur findings shed new light on the complex architecture of PSMU and its potential implications for tailored interventions to relieve PSMU.

Spatial Correlation Network and Influencing Factors of Carbon Emissions from Inter-provincial Tourism Transportation in China

Clarifying the spatial correlation network structure from tourism transportation carbon emissions and its influencing factors is crucial for China's tourism and transportation industry to coordinate the planning of carbon reduction governance and realize the sustainable development of the tourism transportation industry. Based on inter-provincial panel data from 2001 to 2021, China's carbon emissions from tourism transportation were measured, and the modified spatial gravity model was used to construct characteristics of provincial spatial networks and their influencing factors, which were analyzed using the social network analysis method and the QAP model. The study showed that ① China's total carbon emissions from tourism and transportation have been growing slowly year by year, showing a distribution pattern of "high in the southeast and low in the northwest," with obvious differences between the eastern and western regions. ② China's carbon emissions from tourism and transportation formed a multi-threaded and complex network of "dense in the east and sparse in the west." The "Matthew effect" in the spatial network was obvious, with eastern provinces such as Beijing, Shanghai, and Guangdong dominating the core and the northwestern and northeastern provinces such as Xinjiang, Qinghai, Heilongjiang, and Liaoning on the periphery. ③ China's carbon emissions from the tourism transportation block model had a clear division structure, and each block had a large number of correlations and received a spatial overflow of carbon emissions from other blocks. ④ Transportation energy intensity and transportation structure had a significant positive effect on the spatial correlation network, while spatial geographic distance, residents' consumption level, and tourism economic efficiency had a significant negative effect on the spatial network.

Quality-aware fuzzy min-max neural networks for dynamic brain network analysis and its application to schizophrenia identification

Dynamic functional connections (dFCs) refer to the observed phenomenon that functional connectivity changes over a short time, which has become a pioneering method in the diagnosis of brain diseases. However, current dynamic brain network analysis methods are insufficient to address the fuzzy information of brain networks caused by the quality issues of rs-fMRI data, and the uncertainty arising from inconsistent data quality across different windows due to missing values and noise in individual windows. These results in unreliable integration of multiple windows. To this end, this paper proposes a dynamic brain network analysis method based on quality-aware fuzzy min–max neural networks (QFMMNet). The individual window of dFCs is treated as a view, and we define three convolution filters to extract features from the brain network under the multi-view learning framework, thereby obtaining multi-view evidence for dFCs. We design the multi-view fuzzy min–max neural networks (MVFMM) based on fuzzy sets to deal with the fuzzy information of the brain network, which takes evidence as input patterns to generate hyperboxes and serves as the classification layer of each view. A quality-aware ensemble module is introduced to deal with uncertainty, which employs Dempster-Shafer Theory (D-S theory) to directly model the uncertainty and evaluate the dynamic quality-aware weighting of each view. At the decision level of model, a weighted fusion strategy is employed for multi-view fusion based on the dynamic quality-aware weighting, thereby the quality of each view is fully considered to improve the trustworthiness of the decision. We verified the effectiveness of QFMMNet through comparison with advanced algorithms on three real schizophrenia datasets, and the results show that QFMMNet significantly improved the classification performance of brain disease diagnosis tasks. The accuracy on the Huaxi, COBRE, and Xiangya datasets reached 87.14%, 86.67%, and 85.27%, respectively.

Space for Justice in Sustainable Development Goal 7 (SDG7) Discourse: Examining Hashtag Co‐Occurrence Networks and Energy Justice Messaging on Social Media

ABSTRACTDespite extensive research on energy justice, little has been written about whether scholarly discourse on energy justice has translated into public social media discourse. This paper addresses this gap by examining Sustainable Development Goal 7 (SDG7) communications on social media. To date, this study is the first research utilizing social network analysis (SNA) and computational methods to examine social justice messaging related to SDG7 on Twitter (currently known as X) and identify central topics associated with SDG7 communications. The data used include 6059 tweets containing “#sdg7,” resulting in a hashtag co‐occurrence network with 2769 nodes and 22,667 edges. It shows that while related SDG goals, like Goal 3 on good health and well‐being, and themes, such as solar energy and African energy issues, are often discussed alongside SDG7, justice‐related issues like racial justice and important transition perspectives, such as energy justice or just transitions, are less frequently linked to it in the Twitter sphere. The findings suggest a need to better integrate justice topics into SDG7 conversations to shape more inclusive and impactful energy policies.

Deconstruction and analysis of global biodiversity loss transfer network based on the social network analysis method.

Biodiversity is crucial for maintaining ecosystem stability and achieving sustainable development. However, global biodiversity loss is a common challenge faced by most countries. Therefore, based on the data from the International Union for Conservation of Nature (IUCN) Red List of Threatened Species and the Eora database, we used the multi-regional input-output (MRIO) model to calculate biodiversity loss in 188 countries. We constructed a global biodiversity loss transfer network from the binary and weighted perspectives and deconstructed the evolution characteristics and the factors influencing the network from the "relationship" perspective using social network analysis (SNA) and quadratic assignment procedure (QAP) method. The global biodiversity loss transfer network had a typical network structure with dense connections, demonstrating spatial correlation characteristics. The countries with top in- and out-degree centrality rankings were developed and large-scale emerging economies and developing countries in Africa, respectively, implying that the former are responsible for "importing" large amounts of biodiversity and transferring biodiversity loss to the latter. The block model analysis indicated that the transfer network was divided into different functional blocks, with biodiversity spillover effects. The QAP analysis revealed that the differences in geographical adjacency, per capita GDP, urbanization rate, environmental regulation, and agricultural land proportion explained 3.627% of the changes in the global biodiversity loss transfer network. Our results suggested that the relationships of biodiversity loss transfer among countries should be considered by policymakers to address biodiversity challenges. Therefore, governments should recognize the remote responsibility, reduce unsustainable consumption and production, develop sustainable trade, and make trade policies considering the transfer of biodiversity impacts.

Explaining civilian control of militarisation in Indonesia: The case of military law amendment

This study explores the civilian control of militarisation practice in Indonesia, which refers to the discursive politics involving citizenry in the legitimisation mechanism of the use of military force for defence and non-defence objectives. To explore the empirical manifestation of the concept, the study applies the discourse network analysis method, which combines qualitative content analysis and social network analysis to investigate the public debates over the military law amendment. Accordingly, the study collected a dataset of 227 statements from 55 actors in 118 news articles on the military law amendment from May to July 2023. The analysis reveals that, first, the discourse was dominated by actors from non-governmental organisations, think tanks, and academia. Second, the discourse was driven by several main concerns, such as the return of military dual-function doctrine, the current state of military personnel occupying civilian posts, and the urgency of public participation in the deliberation process of the amendment. This article argues that the configuration of the dominant actors and the prominent issues reflect the prevalence of inward-looking defence policy orientation issue in Indonesia.

Breaking Spatial Constraints: A Dimensional Perspective-Based Analysis of the Eco-Efficiency of Cultivated Land Use and Its Spatial Association Network

Global urbanization has caused enormous challenges that seriously threaten ecological security and the food system. Thus, there is a need for finding an optimal solution for the eco-efficiency of cultivated land use (ECLU) that can promote the development of new-type urbanization, while ensuring the sustainable utilization of limited cultivated-land resources. The quantitative system of multi-scale ECLU used in existing studies is inadequate; it is necessary to establish a measurement system from the perspective of geographical spatial relationship that uses evaluation as a key basis for management. In this study, we considered the Changchun Metropolitan Area and a representative urban–rural transition area as the target regions and customized new ECLU evaluation systems for different scales. The super slack-based measure and gravity and social network analyses methods were applied to evaluate the ECLU and explore the structural characteristics of its spatial association network. The average ECLU value for the Changchun Metropolitan Area was 0.974; the results indicated that most of the study area was eco-efficient. The value of ECLU for the urban–rural transition area varied from 0.022 to 1.323; thus, the highly efficient cultivated land was mainly distributed around the urban built-up area. The spatial association network of ECLU revealed that the overall spatial correlations were relatively weak, with a significant “bipolar” division of ECLU; furthermore, the network hierarchy and stability needed improvement. Moreover, we noted distant attraction capacity and siphoning effects outside regional boundaries. In the Changchun Metropolitan Area, it manifested as a monocentric radiation, with Changchun City as the center. In the urban–rural transition area, the cultivated land in proximity to the newly built urban area was more likely to experience spatial spillover. These findings have important implications for strengthening land-use management and advancing sustainable agricultural development for new-type urbanization. Our study can be used by policymakers and stakeholders to design sustainable urban cities, while improving land-use management and optimizing resource use.

A New Approach for Predicting Strength Based on Temperature-Time History Using Two-Parameter Maturity ANN Models.

One widely used method to predict concrete strength development based on temperature variations during curing is the equivalent maturity time (te) method. This method uses the activation energy (Ea) as its key parameter, which reflects the cement's sensitivity to temperature. However, research shows that the Ea value varies depending on factors such as cement type, water/cement ratio, temperature, and additives. The permanent subject of discussion is the question of what value of the Ea parameter should be assumed. In this paper, a new approach is proposed by using a neural network analysis method to develop a strength-temperature history model. It was assumed that the ANN-fc% = f(Q, E, T, t) model would have 4 inputs: hydration heat (Q), activation energy (Ea), temperature (T), and time (t). The research was conducted on mortars using 6 cements, at curing temperatures ranging from 5 to 35 °C, assessing strength over a 90 day period. The results showed that the ANN analysis method allows for estimating the relative compressive strength with sufficient accuracy. Analysis of the input nodes indicated that Q influences early strength gain, while Ea affects later strength development. The application of the ANN model for calculating strength based on temperature changes during maturation was illustrated.

The role of stakeholders in the development of ChAdOx1-S COVID-19 vaccine

COVID-19, an infectious disease caused by the virus SARS-CoV-2, has become a global pandemic and has been classified as a Public Health Emergency of International Concern. Considering vaccines as an effective action to shorten viral transmission and to reduce mortality, stakeholders have worked together to develop vaccines, which played a vital role in containing this pandemic worldwide. In the development of the adenovirus-based ChAdOx1-S vaccine, it is interesting to note the speed at which it was created, with safety, breaking paradigms, relying on connections and influences at various levels, showing that a vaccine can be produced in a very short period of time if specific stakeholder dynamics is engaged. Herein, we combined the concepts and methods of Stakeholder Theory and Social Network Analysis within this complex scenario, indeed, the positive results delivered to society reinforce the principles of the Stakeholder Theory. Moreover, Social Network Analysis revealed that the ChAdOx1-S vaccine ecosystem had 30 nodes of interconnection. Overall, the data summarized presently discussed indicate the need for preparedness within the vaccine development ecosystem, important for future research into the management and development process of vaccines designed to cope with future global or regional health emergencies. Key points related to the complexity of a health emergency, considering its risks to public health and society. A coordinated, interdisciplinary, and systemic response between the different actors is emphasized. This study focuses on analyzing the actors involved in the activities related to the development of the ChAdOx1-S vaccine against COVID-19, taking into account the paradigms of time and specificity of the pandemic emergency. Considering the network analysis, for this vaccine, new institutional arrangements have emerged, significantly expanding the limits of knowledge, since the initial research was based on more than a decade of knowledge production.

Dynamic Evolution of the Power Structure and Contracting State Status in the Regional Trade Agreement Network and Its Trade Feedback Effects

ABSTRACT Against the backdrop of the serious setback in WTO multilateral negotiations, countries around the world have signed a large number of regional trade agreements (RTAs) to further reduce trade barriers and promote trade liberalization. These RTAs have formed a complex network of crisscrossing and mutually reinforcing dependencies that reshape the trade effects of individual RTAs. This paper uses the complex network analysis method to discuss the construction and development issues of RTAs among countries around the world and reveals the historical changes in the power structure of the economic and trade rules network and the dynamic evolution of the network status of core countries in international trade since 1995. Based on this, the feedback mechanism of the overall structure of the network and the node country status on the trade effect of RTAs is explored and tested. The analysis results show the following: (1) The global RTA network follows a power law distribution, the unbalanced characteristics of bipolarization in international economic and trade rules are very significant, and the gap in national network status is enormous. European countries are almost always in the center of the network, and China’s network status has not improved significantly. (2) The network shows obvious competitive and balancing relationships, and the pattern of international economic and trade rules has a trend toward short-term multi-polarization. (3) The test results of the trade effect feedback mechanism of the network show that the redundancy relationship of the overall network and the concentration of power structure have a negative influence on the trade effect of RTAs, while the bridging cluster function and transitivity of the overall network have a positive feedback effect on the trade effect of RTAs. The core position and control of a single country in the network form a positive feedback and high return on the trade effect of RTAs.

Exploring the Relationship Between Visual Perception of the Urban Riverfront Core Landscape Area and the Vitality of Riverfront Road: A Case Study of Guangzhou

The vitality of riverfront districts, as a crucial component of urban livability, is profoundly influenced by human visual perception of the surrounding environment. This study takes the Pearl River in Guangzhou as an example and explores the relationship between the visual perception of the urban riverfront core landscape area and the vitality of Riverfront Road. Employing subjective environment perception prediction methods and analyzing the riverfront landscape pictures captured by the research team, we quantified six essential perceptual dimensions. Furthermore, we evaluated the vitality of Riverfront Road through a four-step process: 1. measuring key visual indices of Riverfront Road, including the green view index (GVI), water view index (WVI), sky view index (SVI), and building view index (BVI); 2. evaluating the proximity of cultural landmarks to Riverfront Road; 3. calculating the convenience of driving, buses, and subways for Riverfront Road with the network analysis method; 4. deriving the vitality value of Riverfront Road through the combination of hotspot data from Baidu. With the application of random forest and result comparisons, we obtained a comprehensive analysis of the correlation between visual perception of the urban riverfront core landscape area and the vitality of Riverfront Road. The results reveal the significant correlation between these two factors and highlight that visual perception of the old city landscape area is superior to that of the new city, although the cultural landmarks and transportation convenience play essential roles in the improvement of vitality in Riverfront Road. It is evident that relying solely on visual design may fail to prominently boost vitality. Overall, spatial design should adopt a multidimensional approach, integrating various factors such as transportation convenience, social interaction venues, cultural activities, etc., to create a cohesive vitality network.

Spatial Correlation Network Characteristics and Influencing Factors of Industrial Carbon Emission Efficiency of Cities in Pearl River Basin

China has experienced rapid development of urbanization and industrialization, and urban industrial carbon reduction has become the key to green and sustainable development. Exploring urban industrial carbon emissions and its spatial correlation characteristics is of great importance for promoting collaborative cooperation between cities and achieving the "dual carbon" goal. Considering 49 cities in the Pearl River Basin as research objects, the super slacks-based measure model was used to measure the urban industrial carbon emission efficiency from 2010 to 2020, and exploratory spatiotemporal data analysis was used to explore its spatial pattern. On this basis, we used social network analysis and quadratic assignment procedure （QAP） methods to explore the spatial correlation network characteristics and influencing factors of urban industrial carbon emission efficiency. The results indicated that the industrial carbon emission efficiency of the Pearl River Basin showed a downward trend. Additionally, a strong spatial imbalance and heterogeneity was observed. The Pearl River Delta Region always dominated the spatial correlation network of carbon emission efficiency, forming a multi-level composite network system with the core network as the primary component and the conventional network as the auxiliary component. Economically developed cities, such as Guangzhou, Shenzhen, and Dongguan have long been in the center of the network and have extensive connections with other cities, while playing an intermediary role. The level of urbanization, science and technology, and industrialization have an increasingly significant positive impact on the strength of the connection between urban industrial carbon emission networks. The Pearl River Basin industrial carbon emission efficiency network has significant individual differences and hierarchical characteristics. According to the individual centrality, network role, and clustering characteristics, combined with the corresponding influencing factors, the Pearl River Basin urban collaborative carbon emission reduction strategy was formulated. This study can provide reference for the formulation and implementation of regional low-carbon collaborative development strategies.

Digital pioneers: Mormon mommy bloggers and building the “Bloggernacle”

This article examines the influence of Mormon mommy bloggers (MMBs), as key web architects and content creators starting in the early 2010s. MMBs, referring here to Mormon content creators whose blogs focused on topics such as childrearing, domesticity, and lifestyle themes, were significant players during Web 2.0 through their usage of the longform blog. MMBs transformed the invisibilized domestic labor of mothering and housekeeping into monetizable content within the Mormon blogosphere or “Bloggernacle.” The aspirational monetization and professionalization of the blog offered a tangible occupation for Mormon stay-at-home mothers in a religious culture where working outside the home was discouraged. MMBs, through blogging, attempted to situate themselves not simply as caretakers but enterprising, digital cultural creators aligning themselves with a (neo)liberal feminist ethos of entrepreneurialism and individualistic influencing. Using a corpus of web archives from Brigham Young University’s digital collections, this article enlists the Archives Research Compute Hub (ARCH) to process archival data into derivatives to illuminate this underexplored period of web history, employing the methods of feminist thematic and social network analysis. By combining cultural and quantitative analysis of MMBs, this article highlights how MMBs were crucial creators who paved the way for contemporary trends of feminized influencing and the uneasy blending of feminist and commercial content which has increasingly defined contemporary mother-influencers.

Development of Digital Literacy Evaluation Model for Tourism Management of Undergraduate Students

Background and Aims: The digital economy, digital transformation of tourism enterprises, and new forms of employment have created new demands for the study and future career development of tourism management among undergraduate students. The digital literacy level of these students directly impacts their future employment opportunities, skill development, and even the pace of digital development in tourism professional education. The objectives of this research are 1) to extract the components of digital literacy in the tourism management of undergraduate students and 2) to develop a digital literacy evaluation model for the tourism management of undergraduate students. Methodology: The research reviews relevant theoretical concepts, including digital literacy and information literacy. The processes in this research are as follows: 1) reviewing the theoretical basis of the study 2) defining related concepts such as digital literacy, information literacy, etc. Reviewed research on the digital literacy for tourism management of undergraduate students, providing a theoretical and practical foundation for this paper's research. 2) To take 1,308 tourism management undergraduate students in China as the survey subjects, the current status of their digital literacy was investigated. To analyze the survey results and extract the components of digital literacy among tourism management undergraduate students. 3) We selected 21 experts, used the Delphi method to determine the components of the digital literacy evaluation model, and then used the network analysis method to determine the weight of each component. Finally, we designed a digital literacy evaluation model that is suitable for undergraduate students studying tourism management. Results: The study's results indicate that the model clearly demonstrates the relationships and relative importance of the components of tourism management for undergraduate students. Teachers can effectively use this model to assess students' digital literacy levels and provide them with personalized support and guidance. Students can use this model to improve their digital literacy levels and adapt to the digital age's development sequentially and purposefully. Conclusion: The model clearly shows how tourist management instructional features are weighted. Teachers can measure students' digital literacy with this strategy. Students' digital tourist management skills are improving as teachers customize aid and advise. This method could streamline and personalize education to better prepare students for tourism.

Comorbidity of problematic substance use and other addictive behaviors and anxiety, depression, and post-traumatic stress disorder: a network analysis.

Among those with common mental health disorders (e.g. mood, anxiety, and stress disorders), comorbidity of substance and other addictive disorders is prevalent. To simplify the seemingly complex relationships underlying such comorbidity, methods that include multiple measures to distill which specific addictions are uniquely associated with specific mental health disorders rather than due to the co-occurrence of other related addictions or mental health disorders can be used. In a general population sample of Jewish adults in Israel (N = 4002), network analysis methods were used to create partial correlation networks of continuous measures of problematic substance (non-medical use of alcohol, tobacco, cannabis, and prescription sedatives, stimulants, and opioid painkillers) and behavioral (gambling, electronic gaming, sexual behavior, pornography, internet, social media, and smartphone) addictions and common mental health problems (depression, anxiety, and post-traumatic stress disorder [PTSD]), adjusted for all variables in the model. Strongest associations were observed within these clusters: (1) PTSD, anxiety, and depression; (2) problematic substance use and gambling; (3) technology-based addictive behaviors; and (4) problematic sexual behavior and pornography. In terms of comorbidity, the strongest unique associations were observed for PTSD and problematic technology-based behaviors (social media, smartphone), and sedatives and stimulants use; depression and problematic technology-based behaviors (gaming, internet) and sedatives and cannabis use; and anxiety and problematic smartphone use. Network analysis isolated unique relationships underlying the observed comorbidity between common mental health problems and addictions, such as associations between mental health problems and technology-based behaviors, which is informative for more focused interventions.

Comparing NIRA and Traditional Network Approaches: AStudy Case With Antisocial Personality Disorder Traits.

This study explores the NodeIdentifyR algorithm (NIRA) as a novel network analysis method for examining Antisocial Personality Disorder (ASPD) traits. Using a sample of 2230 Brazilian adults (aged 18-73 years) who responded to ASPD-related factors of the Personality Inventory for DSM-5 (PID-5), we applied NIRA to an ASPD network and compared its results with traditional network analysis methods. Our findings revealed that deceitfulness emerged as the most central trait across both methodologies. NIRA provided additional insights, indicating that simulated decreases in the likelihood of irresponsibility reduced the presence of other traits, while a simulated increase in deceitfulness amplified the likelihood of other ASPD pathological traits. Our results suggest that traditional network centrality measures converge with NIRA's simulated increase results, but NIRA's simulated decrease provides additional information not captured by traditional centrality estimates. We recommend further research to validate these findings across different psychopathologies and refine NIRA use in clinical settings. The insights from this study could serve as a foundation for developing targeted interventions and enhancing our understanding of ASPD trait dynamics.

Compartmentality, commonist impulses, and the path to pluriversal transformation: an Australian perspective

ABSTRACT This article combines theoretical exploration with empirical analysis to critically engage with the pluriversal theory-discourse. The first part introduces the concept of ‘compartmentality,’ suggesting that internalized ontologies of modernism, colonialism, and capitalism likely disrupt societal interconnectedness within the ‘actually existing pluriverse,’ weakening its transformative capacity. By constructing and operationalizing the notion of ‘commonist tendency’ as a counteracting impulse, it sets the conceptual stage for an empirical examination of potential pathologies and their theoretical implications. A novel methodological framework is outlined to explore the Australian pluriverse as a case study. The second part applies quantitative analysis to secondary data from a nationwide meta-mapping survey to evaluate the influence of compartmentality among the participant initiatives. The study employs social network analysis and multivariate methods to uncover the social-organizational determinants of the commonist tendency. It also highlights the existing capacities for overcoming divisions while exposing the subtle yet pervasive forces that hinder the pluriverse from realizing its envisioned potential.

Species specificity and specificity diversity (SSD) framework: a novel method for detecting the unique and enriched species associated with disease by leveraging the microbiome heterogeneity

BackgroundDifferentiating the microbiome changes associated with diseases is challenging but critically important. Majority of existing efforts have been focused on a community level, but the discerning power of community or holistic metrics such as diversity analysis seems limited. This prompts many researchers to believe that the promise should be downward to species or even strain level—effectively and efficiently identifying unique or enriched species in diseased microbiomes with statistical rigor. Nevertheless, virtually, all species-level approaches such as differential abundance and differential network analysis methods exclusively rely on species abundances without considering species distribution information, while it can be said that distribution is equally, if not more, important than abundance in shaping the spatiotemporal heterogeneity of community compositions.ResultsHere, we fill the gap by developing a novel framework—species specificity and specificity diversity (SSD)—that synthesizes both abundance and distribution information to differentiate microbiomes, at both species and community scales, under different environmental gradients such as the healthy and diseased treatments. The proposed SSD framework consists of three essential elements. The first is species specificity (SS), a concept that reincarnates the traditional specialist-generalist continuum and is defined by Mariadassou et al. (Ecol Lett 18:974-82, 2015). The SS synthesizes a species’ local prevalence (distribution) and global abundance information and attaches specificity measure to each species in a specific habitat (e.g., healthy or diseased treatment). The second element is a new concept to introduce here, the (species) specificity diversity (SD), which is inspired by traditional species (abundance) diversity in community ecology and measures the diversity of specificity (a proxy for metacommunity heterogeneity, essentially) with Renyi’s entropy. The third element is a pair of statistical tests based on the principle of permutation tests.ConclusionsThe SSD framework can (i) identify and catalogue lists of unique species (US), significantly enriched species (ES) in each treatment based on SS and specificity permutation (SP) test and (ii) measure the holistic differences between assemblages (or treatments) based on SD and specificity diversity permutation (SDP) test. Both capacities can be enabling technologies for general comparative microbiome research including risk assessment, diagnosis, and treatment of microbiome-associated diseases.

Jagoan : Transportation Digitalization Innovation Through Application-Based Geotrail Development to Creat a Tourism Center of Excellent in the Geopark of Pasuruan Distric

Abstract Bromo Tengger Semeru Geopark is one of the well-known tourist destinations included in the National Tourism Strategic Area in Pasuruan Regency. As for the Regional Medium-term Development Plan of East Java Province 2019-2024, the Bromo Tengger Semeru Geopark is included in the priority program for developing the National Tourism Strategic Area through increasing geotrail site connectivity and developing a tourism center of excellence. However, the implementation of this program has not been well integrated due to limited access to information and transportation regarding tourist variations gate, and alternative transportation routes. This makes tourists only focus on Mount Bromo caldera tourism, resulting in fewer visitation to geo-site tourist destinations, biodiversity tourism, and cultural tourism around aspiring national geopark. For this reason, it would be beneficial if tourists could access and obtain information regarding all variations of geopark tourism destination in one mobile application called JAGOAN. This application provides a tour package selection feature along with travel schedules, and alternative transportation routes (geotrail) which are integrated with google map directions, booking and reservations, geopark events, weather conditions, and other important information in real-time. The scenarios for tourist visits in the National Tourism Strategic Area and geo-trails were analyzed using scoring and network analysis methods. Meanwhile, making an application prototype involves the process of feature planning, creating user flow, sketching lofi, creating digital designs, and prototyping. It is hoped that the results of the analysis of determining geotrail routes and developing application-based tourism will make it easier for tourists to obtain information so that they can increase tourist visits to support the realization of a tourism center of excellence in the Bromo Tengger Semeru Aspiring Geopark, Pasuruan Regency.

Combined Physiological and Transcriptomic Analysis Reveals Key Regulatory Networks and Potential Hub Genes Controlling Chilling Tolerance During Soybean Germination.

Chilling is an important limiting factor for seed germination of soybean (Glycine max [L.] Merr.). To reveal the regulatory mechanism of chilling tolerance during the soybean germination stage, based on previous studies, the chilling tolerance line R48 and chilling sensitive line R89 in chromosome segment substitution lines were selected for physiological index determination and transcriptome sequencing. It was found that reactive oxygen species (ROS) scavenging system related enzymes, ROS, and osmotic regulators were significantly different between the two lines. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes enrichment were performed on the differentially expressed genes obtained by transcriptome sequencing. It was found that terms or pathways related to flavonoids, unsaturated fatty acids, and abscisic acid were highly enriched. In addition, weighted gene coexpression network analysis (WGCNA) method was used to analyze the physiological index data and transcriptome sequencing data. Four main coexpression modules significantly related to physiological indicators were obtained, and the hub genes in each module were screened according to eigengene-based connectivity value. Haplotype analysis of important candidate genes using soybean germplasm resources showed that there were significant differences in germination indexes between different major haplotypes of Glyma.17G163200. Based on the results of enrichment analysis and WGCNA, the regulation model of low temperature tolerance during soybean germination was preliminarily drawn. This study will provide theoretical guidance for analyzing the molecular regulation mechanism of cold tolerance in soybean germination stage.