Network Analysis Research Articles

Design of an AI-based egg fertility detection system for incubators

The conventional approach to determine the fertility of chicken eggs, called candling, is subjective, time-consuming, and ineffective and thereby results in low hatching rates and financial losses. As a result, a more reliable, accurate, and sustainable system that uses artificial intelligence and automated systems to improve the process of assessing egg fertility for incubation. The system proposes capturing of images of eggs at the early stages of incubation by a camera and sent to a cloud server, where a Convolutional Neural Network (CNN) analyses and classifies the data to determine the fertility status of the eggs. The system aims to increase the quality and hatching rate of chicken eggs, lower labour expenses and human error, and boost the poultry industry's sustainability and profitability. The framework of the system is based on the current poultry egg fertility assessment methods in Zimbabwe, which include candling, infrared thermography, ultrasound, heart rate detection, oxygen flux detection, visible or near-infrared transmittance spectroscopy, and thermal imaging. The system is expected to perform better than existing systems in terms of accuracy, speed, and cost-effectiveness. The experimental results show that, the method's average identification time is 0.210 seconds, and its identification accuracy is 98.40 percent. These results demonstrate the viability of the suggested AI-based method for automatically determining an egg's fertility without human intervention, greatly increasing hatcheries' and related businesses' productivity.

The impact of social network and resource endowment of smallholders on sustainable apple production

PurposeSmallholders are essential in ensuring food security; however, smallholder-dominated food production often involves high resource-environmental costs. This study analyzed the factors that differentiate horticultural practices, willingness to adopt technology and social networks between optimized practices (OPT) and farmer practices (FP) to provide localized and systematic solutions for the sustainable apple production.Design/methodology/approachTo explore the approach of smallholder-dominated sustainable apple production, 257 apple producers in the Bohai Bay region, a major apple planting area in China, were investigated. Life cycle assessment (LCA), emergy analysis and social network analysis methods were used for evaluation.FindingsThe results showed that the net economic profit and emergy sustainability index (ESI) in OPT was 15.8 × 104 RMB·ha-1 and 1.2, respectively, which were 126.9 and 128.0% higher than FP. In contrast, greenhouse gas (GHG) emissions under OPT was 29.3% lower than those under FP. OPT has a higher percentage of adoption of scientific fertilizer application and water-saving irrigation technologies compared to FP. OPT has strong learning abilities, more social resources (such as technical training and sharing technical experience with others) and connections with stakeholders in the apple supply chain. Optimizing smallholders' social capital, willingness to adopt technology, behavioral willingness and technological awareness can promote sustainable apple production.Originality/valueConsidering the horticultural practices employed by smallholders in conjunction with their social networks, these factors contributed to the transition of smallholder-led apple production toward sustainability. The findings provided viable options and a theoretical basis for smallholder-dominated crop production to move toward sustainability, with significant implications for policymakers.

Identification and validation of diagnostic and prognostic biomarkers in prostate cancer based on WGCNA

BackgroundProstate cancer (PCa) represents a significant health challenge for men, and the advancement of the disease often results in a grave prognosis for patients. Therefore, the identification of biomarkers associated with the diagnosis and prognosis of PCa holds paramount importance in patient health management.MethodsThe datasets pertaining to PCa were retrieved from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted to investigate the modules specifically associated with the diagnosis of PCa. The hub genes were identified using the LASSO regression analysis. The expression levels of these hub genes were further validated by qRT-PCR experiments. Receiver operating characteristic (ROC) curves and nomograms were employed as evaluative measures for assessing the diagnostic value.ResultsThe blue module identified by WGCNA exhibited a strong association with PCa. Six hub genes (SLC14A1, COL4A6, MYOF, FLRT3, KRT15, and LAMB3) were identified by LASSO regression analysis. Further verification confirmed that these six genes were significantly downregulated in tumor tissues and cells. The six hub genes and the nomogram demonstrated substantial diagnostic value, with area under the curve (AUC) values ranging from 0.754 to 0.961. Moreover, patients with low expression levels of these six genes exhibited elevated T/N pathological stage and Gleason score, implying a more advanced disease state. Meanwhile, their progression-free survival (PFS) was observed to be potentially poorer. Finally, a significant association could be observed between the expression of these genes and the dysregulation of immune cells, along with drug sensitivity.ConclusionsIn summary, our study identified six hub genes, namely SLC14A1, COL4A6, MYOF, FLRT3, KRT15, and LAMB3, which can be utilized to establish a diagnostic model for PCa. The discovery may offer potential molecular targets for clinical diagnosis and treatment of PCa.

From local networks to national reform? Programmatic groups in the regionalization of German health policy

AbstractBefore policy reform initiatives reach the agenda of national decision makers, such initiatives often start at a small‐scale, local level. The Programmatic Action Framework (PAF) emphasizes the importance of biographical connections to understand how a group of policy actors forms, and how it develops programs that may lead to decade‐long policy change. This article applies the PAF to study the emergence of a programmatic group behind initiatives on regionalization in German health policy through discourse analysis and expert interviews. The study contributes to understanding social networks behind policy programs and social‐psychological, partly informal drivers of policy change. The results show that many PAF conditions have already been met in the German health policy subsystem. We describe a network of policy actors with local and organizational drivers that has led to nationwide cooperation on regionalization.Related ArticlesCaliskan, Cantay. 2020. “The Influence of Elite Networks on Green Policy Making.” Politics & Policy 48(6): 1104–37. https://doi.org/10.1111/polp.12382.Petridou, Evangelia, Per Becker, and Jörgen Sparf. “Policy Entrepreneurs in Public Administration: A Social Network Analysis.” Politics & Policy 49(2): 414–45. https://doi.org/10.1111/polp.12400.Svallfors, Stefan, Erica Falkenström, Corrie Hammar, and Anna T. Höglund. 2022. “Networked Reports: Commissioning and Production of Expert Reports on Swedish Health Care Governance.” Politics & Policy 50(3): 580–97. https://doi.org/10.1111/polp.12462.

Embeddedness in complex networks: Theoretical central debate, policy implications, and research agenda in ethnic entrepreneurship

Objective: This article explores ethnic entrepreneurship as an emerging organizational phenomenon, aiming to provide a comprehensive overview through a systematic literature review. Method: Triangulation employed bibliometric analysis, network analysis, citation and co-citation analysis, co-citation network, and a meta-analysis of bibliometrics over the period. It also includes content analysis demonstrating study characteristics, key findings, and suggestions for future research. Key Findings: The crucial role of ethnic networks in identity valorization and overcoming obstacles, exerting a moderating influence on entrepreneurial decisions, was identified. Furthermore, a broader theoretical framework for understanding the ethnic entrepreneurship phenomenon is synthesized. Theoretical/Methodological Contributions: A combined method of analyses is employed, offering an approach that can be adopted by similar research. The theoretical framework in which ethnic entrepreneurship studies are anchored is presented, emphasizing social immersion, co-ethnic network building, and Economic Sociology. Relevance/Originality: In a growing global migration movement, individuals have become entrepreneurs in the pursuit of ethnic valorization, equality, and survival. They face various barriers in host countries, influencing the decision to undertake entrepreneurship and their business. This study fills a gap by elucidating how research explains the interplay of these emerging phenomena. Social/Management Contributions: The article identifies future directions for ethnic entrepreneurship research, addressing crucial issues such as gender, network construction, financing, international refugee policies, public policies, ethnicity, ethnic markets, marginalization, and prejudice. Its conclusions offer valuable insights for managers, researchers, and policymakers.

Spectral Bipartition via Gap Cut on DNA Sequences

Deoxyribonucleic Acid (DNA) and graph partitioning are two distinct fields of study which can be linked in the structure of biological networks. Graph partitioning has been extensively studied but not its application in the biological field. This research explored on the application of spectral graph partitioning in DNA splicing, aiming to simulate the cleavage of DNA by performing spectral bipartition on DNA sequences in a DNA splicing system. This research incorporates Fiedler theory and algebraic graph theory, which are commonly utilized in network analysis and the analysis of graph connectivity. Some DNA sequences of even length are selected and expressed in graphical representations. The adjacency matrix, Laplacian matrix, and degree matrix are computed from the graphs, as well as the Fiedler value and Fiedler vector associated with the graphs. Gap cut is used as a method of spectral bipartition which produces two partitions of DNA sequence of unequal lengths. The generalizations of gap cut on DNA sequences of even length are provided as lemmas and theorem.

Post-traumatic Reactions and Social–Emotional Competence Among Chinese High School Students Experiencing COVID-19 Lockdown: A Network Analysis

Post-traumatic Reactions and Social–Emotional Competence Among Chinese High School Students Experiencing COVID-19 Lockdown: A Network Analysis

The heterogeneity of insomnia symptoms for emerging workers in the digital economy: Latent profile and network analysis

The heterogeneity of insomnia symptoms for emerging workers in the digital economy: Latent profile and network analysis

Performance analysis of underwater wireless sensor networks using Reinforcement Learning

This article presents a novel routing protocol named DROR, specifically tailored for underwater wireless sensor networks (UWSNs) to tackle the challenge of void regions. DROR integrates Reinforcement Learning (RL) and Opportunistic Routing (OR) in a recipient-oriented approach, considering the energy limitations and the unique underwater setting. It incorporates a mechanism for void rehabilitation, allowing packets to circumvent void nodes and maintain continuous moving for dependable transmission. Furthermore, a dynamic scheduling strategy based on relative Q-values ensures proficient packet forwarding along the most efficient routing path. Simulation outcomes illustrate the efficacy of the suggested protocol concerning delay, PDR, and energy tax in UWSNs with varying Range, Depths, Packet sizes, and moving radius.

Relationship between the rumen microbiome and liver transcriptome in beef cattle divergent for feed efficiency

BackgroundFeed costs account for a high proportion of the variable cost of beef production, ultimately impacting overall profitability. Thus, improving feed efficiency of beef cattle, by way of determining the underlying genomic control and selecting for feed efficient cattle provides a method through which feed input costs may be reduced whilst also contributing to the environmental sustainability of beef production. The rumen microbiome dictates the feed degradation capacity and consequent nutrient supply in ruminants, thus potentially impacted by feed efficiency phenotype. Equally, liver tissue has been shown to be responsive to feed efficiency phenotype as well as dietary intake. However, although both the rumen microbiome and liver transcriptome have been shown to be impacted by host feed efficiency phenotype, knowledge of the interaction between the rumen microbiome and other peripheral tissues within the body, including the liver is lacking. Thus, the objective of this study was to compare two contrasting breed types (Charolais and Holstein-Friesian) divergent for residual feed intake (RFI) over contrasting dietary phases (zero-grazed grass and high-concentrate), based on gene co-expression network analysis of liver transcriptome data and microbe co-abundance network of rumen microbiome data. Traits including RFI, dry matter intake (DMI) and growth rate (ADG), as well as rumen concentrations of volatile fatty acids were also included within the network analysis.ResultsOverall, DMI had the greatest number of connections followed by RFI, with ADG displaying the fewest number of significant connections. Hepatic genes related to lipid metabolism were correlated to both RFI and DMI phenotypes, whilst genes related to immune response were correlated to DMI. Despite the known relationship between RFI and DMI, the same microbes were not directly connected to these phenotypes, the Succiniclasticum genus was however, negatively connected to both RFI and ADG. Additionally, a stepwise regression analysis revealed significant roles for both Succiniclasticum genus and Roseburia.faecis sp. in predicting RFI, DMI and ADG.ConclusionsResults from this study highlight the interactive relationships between rumen microbiome and hepatic transcriptome data of cattle divergent for RFI, whilst also increasing our understanding of the underlying biology of both DMI and ADG in beef cattle.

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data

Brokering school improvement through a school–university partnership: a longitudinal social network analysis of middle leadership development

Brokering school improvement through a school–university partnership: a longitudinal social network analysis of middle leadership development

Aging characterization of dielectric materials using microstrip spiral resonator sensor: application in high frequency range, up to 18 GHz

This current work aims to propose a complete microwave procedure for dielectric materials aging characterization. To achieve this aim, a microstrip spiral resonator sensor, based on printed circuit board (PCB) technology, is designed to attend polyvinyl chloride (PVC) aging behavior through microwave measurements. The resonator is composed of a spiral microstrip, excited by coupling 50-Ohm transmission line on the top layer of the substrate; meanwhile a rectangular slot is etched in the bottom layer, behind the spiral shape to achieve high inductive effect. Structure optimization was conducted using high-frequency structure simulator (HFSS). The sensor is then used as a holder of the UV-aged PVC material while reflection (S11) and transmission (S21) coefficients are measured by vector network analyzer (VNA) in high frequency range, up to 18 GHz. The aging effect is finally perceived by observing the frequency shift of the resonance frequency between the empty and the aged-PVC loaded sensor, together with a variation in the S21 parameter amplitude. It is then found that for 864 h of UV-exposure is provided more than 0.45% frequency shift and about 4.67% decrease in electromagnetic energy transmission. Moreover, comparison between S11 and S21 results reveals that using S11 parameter is more practical to investigate material aging as it allows for simple visual checking of the frequency shift.

Industry 5.0's pillars and Lean Six Sigma: mapping the current interrelationship and future research directions

PurposeIn the evolving landscape of Industry 5.0 (I5.0), which emphasises sustainability, human-centricity and resilience, mapping the current interrelationship and future research directions, the role of Lean Six Sigma (LSS) methodology remains underexplored. Our study addresses this gap by examining the potential of LSS to support I5.0 while identifying areas for further investigation.Design/methodology/approachThis study's multifaceted approach, which includes systematic literature review (SLR), bibliographic network analysis (BNA) and expert validation (EV), provides a holistic exploration of the interaction between LSS and I5.0 as the basis for well-founded conclusions.FindingsThe analysis yields several valuable insights. Firstly, it demonstrates the absence of a direct link between LSS and I5.0. Secondly, the substantial body of literature analysed establishes connections between LSS and its pillars. Thirdly, the analysis identifies points of intersection, difference and similarity between LSS and I5.0, highlighting the potential of LSS to facilitate implementation of I5.0 through its proven methodologies, continuous improvement culture, risk management, error learning, human–machine collaboration and training and skill development.Originality/valueThis study pioneers the effort to realise the latent potential of LSS in the context of I5.0. Its systematic identification of the synergies between these paradigms fills a critical gap in the literature and gives policymakers, managers and researchers a guide for informed decision-making to maximise the benefits of I5.0 for individuals, companies, society and the planet.

Exploring the Genetic and Molecular Connection between Autism and Huntington's Disease Via Transcriptomics and Biological Interaction Networks Analysis

Exploring the Genetic and Molecular Connection between Autism and Huntington's Disease Via Transcriptomics and Biological Interaction Networks Analysis

Coparenting Patterns of Parents of an Infant With Complex Congenital Heart Disease: Qualitative Identification and Network Analysis of Effects of Infant Illness Severity and Guided Participation Intervention

Coparenting Patterns of Parents of an Infant With Complex Congenital Heart Disease: Qualitative Identification and Network Analysis of Effects of Infant Illness Severity and Guided Participation Intervention

Network Issue Agenda Setting on Facebook: Exploring the Interplay Between Polarized Campaigns and Party Supporters

This study undertook an analysis of network agenda setting during the 2020 U.S. Presidential campaign, focusing on the interactions between the campaigns and their respective supporters within the context of a polarized social media environment. By employing social network analysis techniques to examine issue agendas, the study revealed a relatively weak correlation between the agendas of the campaigns and their affiliated supporters on Facebook. Conversely, it found a notable association between entities sharing the same ideological orientation—party supporters displayed a higher degree of engagement with their own party’s campaign, and vice versa. The implications of these findings, from a theoretical, methodological, and practical standpoint, have been thoroughly discussed.

Ensemble for evaluating diagnostic efficacy of non-invasive indices in predicting liver fibrosis in untreated hepatitis C virus population

BACKGROUND Hepatitis C virus (HCV) infection progresses through various phases, starting with inflammation and ending with hepatocellular carcinoma. There are several invasive and non-invasive methods to diagnose chronic HCV infection. The invasive methods have their benefits but are linked to morbidity and complications. Thus, it is important to analyze the potential of non-invasive methods as an alternative. Shear wave elastography (SWE) is a non-invasive imaging tool widely validated in clinical and research studies as a surrogate marker of liver fibrosis. Liver fibrosis determination by invasive liver biopsy and non-invasive SWE agree closely in clinical studies and therefore both are gold standards. AIM To analyzed the diagnostic efficacy of non-invasive indices [serum fibronectin, aspartate aminotransferase to platelet ratio index (APRI), alanine aminotransferase ratio (AAR), and fibrosis-4 (FIB-4)] in relation to SWE. We have used an Artificial Intelligence method to predict the severity of liver fibrosis and uncover the complex relationship between non-invasive indices and fibrosis severity. METHODS We have conducted a hospital-based study considering 100 untreated patients detected as HCV positive using a quantitative Real-Time Polymerase Chain Reaction assay. We performed statistical and probabilistic analyses to determine the relationship between non-invasive indices and the severity of fibrosis. We also used standard diagnostic methods to measure the diagnostic accuracy for all the subjects. RESULTS The results of our study showed that fibronectin is a highly accurate diagnostic tool for predicting fibrosis stages (mild, moderate, and severe). This was based on its sensitivity (100%, 92.2%, 96.2%), specificity (96%, 100%, 98.6%), Youden’s index (0.960, 0.922, 0.948), area under receiver operating characteristic curve (0.999, 0.993, 0.922), and Likelihood test (LR+ > 10 and LR- < 0.1). Additionally, our Bayesian Network analysis revealed that fibronectin (> 200), AAR (> 1), APRI (> 3), and FIB-4 (> 4) were all strongly associated with patients who had severe fibrosis, with a 100% probability. CONCLUSION We have found a strong correlation between fibronectin and liver fibrosis progression in HCV patients. Additionally, we observed that the severity of liver fibrosis increases with an increase in the non-invasive indices that we investigated.

Multistakeholder Initiatives and Sustainability? A Governance Analysis using the German Initiative on Sustainable Cocoa (GISCO) as a Case Study

AbstractThis paper examines the Multistakeholder initiative (MSI) “German Initiative on Sustainable Cocoa (GISCO)”. MSIs represent arenas in which heterogeneous actors from governments, businesses and civil society come together to achieve sustainability goals that they cannot achieve otherwise. The self-defined goals of GISCO are first, to improve the living conditions of cocoa farmers and their families; second, to conserve and protect natural resources and biodiversity; and third, to increase the share of sustainably produced cocoa. Although all stakeholder groups share these goals, they have different agendas and conflicting interests. Despite numerous case studies, no theoretical basis has been established on the functioning and success of negotiations in MSIs. Therefore, the question arises as to how the governance of an MSI can be captured empirically to explain (un)achieved outcomes of the collaboration. The contribution of this paper is the development of a theoretical framework and its application to the case study. Minutes of 84 meetings and 18 qualitative expert interviews were analyzed by social network analysis and qualitative content analysis using MaxQDA to identify (a) influential actors, (b) collaboration structures and (c) processes as well as (d) topics discussed to explain (not) achieved outcomes regarding the self-defined goals. The results provide detailed insight into the governance of an MSI. The MSI helps members to extend their individual networks and to learn from each other, but quickly reaches its limits in achieving the self-imposed common goals. One reason for this is the lack of representation of actors from the Global South, despite addressing environmental and living conditions there in two out of the three GISCO goals. Furthermore, it is shown that the structures and processes of decision-making within the MSI are designed in such a way that a lack of hierarchical directives, sanctions and other decision-making mechanisms makes negotiation-based compromises difficult. Consequently, the power of each stakeholder group to use their veto right to delay or prevent the changes required to achieve common goals cannot be overcome.

Brain network changes after the first seizure: an insight into medication response?

Abstract After a first epileptic seizure, anti-seizure medications (ASM) can change the likelihood of having a further event. This prospective study aimed to quantify brain network changes associated with taking ASM monotherapy. We applied graph theoretical network analysis to longitudinal resting-state functional MRI (fMRI) data from 28 participants who had recently experienced their first seizure. Participants were imaged before and during long-term ASM therapy, with a mean inter-scan interval of 6.9 months. After commencing ASM, we observed an increase in the clustering coefficient and a decrease in network path length. Brain changes after ASM treatment were most prominent in the superior frontoparietal and inferior fronto-temporal regions. Participants with recurrent seizures display the most pronounced network changes after ASM treatment. This study shows changes in brain network function after ASM administration, particularly in participants with recurrent seizures. Larger studies that ideally include control cohorts are required to understand further the connection between ASM-related brain network changes and longer-term seizure status.