Multiple Connections Research Articles

Shuddhi and The Nav-Hindu in Goa: Dynamics of Identity Politics

In a much-publicised event in 1928, a section of the Goan Catholics of tribal origin reconverted to Hinduism. In this highly charged political context, the act of shuddhi framed the event as a straightforward homecoming, masking conflicting underlying interests. Analysing the palpable tensions around this event allows better understanding of how shuddhi was made possible and perceived at the local level, appreciating specifically the vulnerability of the Christian Gauda tribal population. Drawing on archival and ethnographic research, this article rethinks shuddhi in Goa through the changing dynamics of politics, in which new definitions of Hindu identity were being worked out by the Hindu elite. Examining the relationships between the Portuguese colonial state, the local Hindu elite, the reconverts and multiple trans-local connections, the article identifies the formation of a new identity of the reconverts within the broader context of caste and community relations.

IL-22 in Atopic Dermatitis.

Atopic dermatitis (AD) is a prevalent and chronic inflammatory skin condition characterized by a multifaceted pathophysiology that gives rise to diverse clinical manifestations. The management of AD remains challenging due to the suboptimal efficacy of existing treatment options. Nonetheless, recent progress in elucidating the underlying mechanisms of the disease has facilitated the identification of new potential therapeutic targets and promising drug candidates. In this review, we summarize the newest data, considering multiple connections between IL-22 and AD. The presence of circulating IL-22 has been found to correlate with the severity of AD and is identified as a critical factor driving the inflammatory response associated with the condition. Elevated levels of IL-22 in patients with AD are correlated with increased proliferation of keratinocytes, alterations in the skin microbiota, and impaired epidermal barrier function. Collectively, these factors contribute to the manifestation of the characteristic symptoms observed in AD.

Cross-talk between oxidative stress and lipid metabolism regulators reveals molecular clusters and immunological characterization in polycystic ovarian syndrome

BackgroundChanges in the oxidative stress and lipid metabolism (OSLM) pathways play important roles in polycystic ovarian syndrome (PCOS) pathogenesis and development. Consequently, a systematic analysis of genes related to OSLM was conducted to identify molecular clusters and explore new biomarkers that are helpful for the diagnostic of PCOS.MethodsGene expression and clinical data from 22 PCOS women and 14 normal women were obtained from the GEO database (GSE34526, GSE95728, and GSE106724). Consensus clustering identified OSLM-related molecular clusters, and WGCNA revealed co-expression patterns. The immune microenvironment was quantitatively assessed utilizing the CIBERSORT algorithm. Multiple machine learning models and connectivity map analyses were subsequently applied to explore potential biomarkers for PCOS, and nomograms were employed to develop a predictive multigene model of PCOS. Finally, the OSLM status of PCOS and the hub genes expression profiles were preliminarily verified using TUNEL, qRT‒PCR, western blot, and IHC assays in a PCOS mouse model.Results19 differential expression genes (DEGs) related to OSLM were identified. Based on 19 DEGs that were strongly influenced by OSLM, PCOS patients were stratified into two distinct clusters, designated Cluster 1 and Cluster 2. Distinct differences in the immune cell proportions existed in normal and two PCOS clusters. The random forest showed the best results, with the least cross-entropy and the utmost AUC (cross-entropy: 0.111 AUC: 0.960). Among the 19 OSLM-related genes, CXCR1, ACP5, CEACAM3, S1PR4, and TCF7 were identified by a Bayesian network and had a good fit with PCOS disease risk by the nomogram (AUC: 0.990 CI: 0.968–1.000). TUNEL assays revealed more severe DNA damage within the ovarian granule cells of PCOS mice than in those of normal mice (P < 0.001). The RNA and protein expression levels of the five hub genes were significantly elevated in PCOS mice, which was consistent with the results of the bioinformatics analyses.ConclusionA novel predictive model was constructed for PCOS patients and five hub genes were identified as potential biomarkers to offer novel insights into clinical diagnostic strategies for PCOS.

Mapping the Implementation Determinants of Second Dose Measles Vaccination in the World Health Organization African Region: A Rapid Review.

The second dose of measles-containing vaccines (MCV2) has significant programmatic relevance in the current immunisation landscape because it serves as both an opportunity to reduce measles immunity gaps and strengthen second year of life vaccination platforms. However, MCV2 coverage remains suboptimal across countries in the World Health Organization (WHO) African Region and this puts a significant number of children at risk of morbidity and mortality from measles despite the availability of an effective vaccine. There is an urgent need to strengthen the implementation of MCV2 but this requires a thorough and systematic understanding of contextual factors that influence it. The literature that describes the determinants of implementation of MCV2 in a manner that adequately accounts for the complexity of the implementation context is scarce. Therefore, the purpose of this rapid review was to explore the implementation determinants of MCV2 in the WHO African Region using systems thinking. Literature search in two databases (PubMed and Google Scholar) were conducted. After screening, a total of 17 eligible articles were included in the study. Thematic analysis of extracted data was performed to identify the implementation determinants, after which they were mapped using the Consolidated Framework for Implementation Research (CFIR). A causal loop diagram (CLD) was used to illustrate the linkages between identified determinants. We found 44 implementation determinants across the five CFIR domains, i.e., innovation, outer setting, inner setting, individual, and implementation process. The majority of identified determinants are within the individual domain followed by the inner setting domain. The CLD showed that multiple contingent connections and feedback relationships exist between the identified implementation determinants within and across CFIR domains. The linkages between the implementation determinants revealed three balancing and reinforcing loops each. The findings suggest that implementation determinants of second-dose measles vaccination in the WHO African Region are complex, with multiple interconnections and interdependencies, and this insight should guide subsequent policies. There is an urgent need for further implementation research with embedded CLD in specific settings to inform the design of tailored systemic strategies to improve the implementation effectiveness of MCV2.

Multi-order graph clustering with adaptive node-level weight learning

Current graph clustering methods emphasize individual node and edge connections, while ignoring higher-order organization at the level of motif. Recently, higher-order graph clustering approaches have been designed by motif-based hypergraphs. However, these approaches often suffer from hypergraph fragmentation issue seriously, which degrades the clustering performance greatly. Moreover, real-world graphs usually contain diverse motifs, with nodes participating in multiple motifs. A key challenge is how to achieve precise clustering results by integrating information from multiple motifs at the node level. In this paper, we propose a multi-order graph clustering model (MOGC) to integrate multiple higher-order structures and edge connections at node level. MOGC employs an adaptive weight learning mechanism to automatically adjust the contributions of different motifs for each node. This not only tackles hypergraph fragmentation issue but enhances clustering accuracy. MOGC is efficiently solved by an alternating minimization algorithm. Experiments on seven real-world datasets illustrate the effectiveness of MOGC.

Goal-Oriented Multiple Access Connectivity for Networked Intelligent Systems

Goal-Oriented Multiple Access Connectivity for Networked Intelligent Systems

A hyper-distance-based method for hypernetwork comparison.

Hypernetwork is a useful way to depict multiple connections between nodes, making it an ideal tool for representing complex relationships in network science. In recent years, there has been a marked increase in studies on hypernetworks; however, the comparison of the difference between two hypernetworks has received less attention. This paper proposes a hyper-distance (HD)-based method for comparing hypernetworks. The method is based on higher-order information, i.e, the higher-order distance between nodes and Jensen-Shannon divergence. Experiments carried out on synthetic hypernetworks have shown that HD is capable of distinguishing between hypernetworks generated with different parameters, and it is successful in the classification of hypernetworks. Furthermore, HD outperforms current state-of-the-art baselines to distinguish empirical hypernetworks when hyperedges are randomly perturbed.

Determining key mammalian species and food web robustness across different land cover vegetation using network analysis

Abstract. Risdiyanto I, Santosa Y, Santoso N, Sunkar A. 2024. Determining key mammalian species and food web robustness across different land cover vegetation using network analysis. Biodiversitas 25: 3175-3190. The ecosystem's food web depicts the intricate energy flow and complex interactions among its diverse organisms. Organisms are not confined to single trophic levels or food chains; they establish multiple food connections with other organisms within the ecosystem. This study aims to identify critical mammalian species and assess the robustness of various ecosystem types, considering the diversity of species presence, through food web analysis. Computational graphic-based network analysis is employed to achieve this goal. Ecosystem types, categorized by their land cover, include plantation/agricultural forests, Bushes and shrubss, forests, and mixed landscapes. Network centrality metrics such as degree, closeness, betweenness, and eigenvector centrality are utilized to evaluate the presence of key species and ecosystem robustness. The relative contribution of mammalian species as connectors and regulators of energy flow in the food web ranges from 8-23% of all nodes involved. Key mammalian species are classified into ecosystem stability and sustainability keys. In Bushes and shrubss ecosystems, key species predominantly consist of mammalian predator species that are crucial for maintaining ecosystem stability through population control. Conversely, in other ecosystems, key species are primarily connectors, ensuring the sustained energy flow. The most resilient food webs are observed in Bushes and shrubss and mixed ecosystems due to their higher biomass growth rates and abundant presence of mammalian species. Utilizing food web analysis can significantly contribute to species and ecosystem conservation efforts by offering a comprehensive understanding of interspecies interactions, food web structures, and ecosystem dynamics.

Barriers to organisational development in higher education lifelong learning initiatives

From an economic human capital perspective, higher education lifelong learning initiatives should include the professional development of individuals as well as organisational development. This indicates the dissolving of boundaries between individual and organisational development and that successful professional development occurs at both individual and aggregated levels. Based on the networked mode, higher education institutions could be closely linked to their surrounding society, embracing a two-way relationship with a hybrid character, including multiple connections with branches and/or organisations. In such initiatives, formal education and informal work-related tasks are blurred and both are emphasised as equally important. In focus are collaborative features and the idea to link theoretical reasoning to authentic organisational problems. To offer higher education lifelong learning for organisational development, Mid Sweden University initiated a project called BUFFL in collaboration with eight organisations belonging to an established network of banks and insurance companies. This paper aims at reporting barriers to organisational development in higher education lifelong learning. It answers the following research question: Which barriers could be identified when implementing a hybrid and networked approach to higher education lifelong learning for organisational development? A case-study-inspired approach was conducted. In total, 328 registrations from the collaborating organisations were documented in the BUFFL courses. In addition to open-ended questions and Likert scale questions in the course evaluations, the methods also included conversations with leading representatives of the collaborating organisations. Although several barriers were highlighted in line with the innovation resistance theory, the main barrier seems to be that no organisation applied lifelong learning as a tool for strategic organisational development. The study suggests that a crucial barrier breaker to reduce or eliminate the main barrier is a course on strategically managed competence development for leading representatives of participating organisations. A combined focus on individuals and organisations could also stimulate leading representatives to take a holistic approach to the organisation’s competence development. This includes increased responsibility to ensure that investments in the competence area are beneficial for the organisation. Bringing company-relevant data as input to the courses could ensure the establishment of strong links between theoretical perspectives and work-related practices.

Solving Mazes: A New Approach Based on Spectral Graph Theory

The use of graph theory for solving labyrinths and mazes is well known, understanding the possible paths as the connections between the nodes that represent the corners or bifurcations. This work presents a new idea: minimizing the length of the optimal path formulated as a topology optimization problem. The maze is mapped with finite elements, and then, through the eigenvalues of the Laplacian matrix of the graph, a constraint is imposed over the connectivity between the input and the output. Several 2D examples are provided to support this approach, allowing for unequivocally finding the shortest path in mazes with multiple connections between entrance and exit, resulting in an nonheuristic algorithm.

Attention mechanism based on deep learning for defect detection of wind turbine blade via multi-scale features

Abstract An enhanced wind turbine blade surface defect detection algorithm, CGIW-YOLOv8, has been introduced to tackle the problems of uneven distribution of defect samples, confusion between defects and background, and variations in target scales that arise during drone maintenance of wind turbine blades. This algorithm is given based on the YOLOv8 model. Initially, a data augmentation method based on geometric changes and Poisson mixing was used to enrich the dataset and address the problem of uneven sample distribution. Subsequently, the incorporation of the Coordinate Attention (CA) mechanism into the Backbone network improved the feature extraction capability in complex backgrounds. In the Neck, the Reparameterized Generalized Feature Pyramid Network (Rep-GFPN) was introduced as a path fusion strategy and multiple cross-scale connections are fused, which effectively enhances the multi-scale expression ability of the network. Finally, the original CIOU loss function was replaced with Inner-WIoU, which was created by applying the Inner-IoU loss function to the Wise-IoU loss function. It improved detection accuracy while simultaneously speeding up the model's rate of convergence. Experimental results show that the mAP of the method for defect detection reaches 92%, which is 5.5% higher than the baseline network. The detection speed is 120.5 FPS, which meets the needs of real-time detection.

Connections within the Multiplicative Field: A Case Study of Adán’s Mathematical Thinking

ABSTRACT Mathematical coherence is a goal within the Common Core State Standards for Mathematics. One aspect of this coherence is how student mathematical thinking is developed across concepts. Unfortunately, mathematics is often taught as isolated ideas across grades. The multiplicative field is an area of study that needs to be examined as a space to develop connections across concepts. This paper uses a case study of mathematical thinking from one 5th-grade student over a school year to demonstrate the connections in his thinking across whole numbers, fractions, and graphing work. Analyzing his solution strategies with three frameworks detailing multiplicative, proportional, and functional thinking, his strategies show a developing understanding of proportional reasoning, and functional thinking as he is building his multiplicative thinking and connections from whole numbers to fraction problem-solving strategies to graphing. Examining this student’s mathematical thinking points to the opportunity for connections across the multiplicative field through teaching mathematics in a manner more responsive to student sense-making.

ОТРИЦАТЕЛЬНЫЕ КОНСТРУКЦИИ В СВЯЗНОМ ТЕКСТЕ

Negative constructions in a connected text Summary The article examines the use of aspectual forms in negative constructions as a means of organizing a coherent text. In the description, we used the distinction between left- and right-sided multiple connections carried out by various elements of the structure. The state of anticipation is the main component of the described situation. Modal shades of the possibility of action are perceived as an additional semantic component in a minimal context, logically determined by the moment of expectation. We distinguish two types of expected situations: 1) the description of the situation ends with the occurrence (realization of the expected action); 2) the implementation of the expected action does not occur contrary to the objective prerequisites for its implementation. The context conveys the completion of the situation, indicating the fact of the occurrence of the action. The state of prolonged absence of the expected action is conveyed implicitly. The linguistic expression of the described denotative situation may include negative constructions such as не + imperfective aspect. The fact of the occurrence of an event is accompanied by a continuation of the previous state, but when negated, the perfective aspect is used. The actualization of what is behind the negation in the passage − the carrier subtext, the implicit confirmation of the positive content is perceived at the level of the semantic structure of the text as a whole. Key words: text, semantic connection, negation, types of verb, expected action, actualization, context, situation

BPNN‐based flow classification and admission control for software defined IIoT

AbstractFlow admission control (FAC) aims to efficiently manage the service requests while maximizing the network utilization. With multiple connection requests, access delay or even service interruption may occur. This paper proposes a novel FAC approach to reduce the contention between the end nodes and ensure high utilization of the networking resources for software defined IIoT. First, incoming flows are classified into different priorities using back propagation neural network based on selected features representing the current network status. Second, with the designed flow admission policies, bandwidth and buffer size are estimated with stochastic network calculus model. Finally, the thresholds of the proposed FAC scheme are dynamically decided based on the above two parameters. Various flows are admitted or rejected via the proposed FAC to maintain real time processing. Unlike traditional FAC schemes rely on static priority systems, the proposed scheme leverages machine learning technique for dynamic flow prioritization and the stochastic network calculus model for precise estimation. Computer simulation reveals that the proposed scheme accurately classifies the flows, and substantially decreases the transmission delay and improves the network utilization compared to the existing FAC schemes. This highlights the superiority of the proposed scheme meeting the demands of software defined IIoT.

Broadening the scope: Multiple functional connectivity networks underlying threat conditioning and extinction

Abstract Threat learning processes are thought to be foundational to anxiety and fear-related disorders. However, the study of these processes in the human brain has largely focused on specific brain regions, owing partly to the ease of translating between these regions in human and nonhuman animals. Moving beyond analyzing focal regions of interest to whole-brain dynamics and connectivity during threat learning is essential for understanding the neuropathology of fear-related disorders in humans. In this study, 223 participants completed a 2-day Pavlovian threat conditioning paradigm while undergoing fMRI. Participants completed threat acquisition and extinction. Extinction recall was assessed 48 hours later. Using a data-driven group independent component analysis (ICA), we examined large-scale functional connectivity networks during each phase of threat learning. Connectivity networks were tested to see how they responded to conditioned stimuli during early and late phases of threat acquisition and extinction as well as during early trials of extinction recall. A network overlapping with the default mode network involving hippocampus, ventromedial prefrontal cortex (vmPFC), and posterior cingulate was implicated in threat acquisition and extinction. Another network overlapping with the salience network involving dorsal anterior cingulate cortex (dACC), mPFC, and inferior frontal gyrus was implicated both in threat acquisition and in extinction recall. Other networks overlapping with parts of the salience, somatomotor, visual, and frontoparietal networks were involved in the acquisition or in the extinction of learned threat responses. These findings help support the functional cooperation of specific brain regions during threat learning in a model-free fashion while introducing new findings of spatially independent functional connectivity networks during threat and safety learning. Rather than being a single process in a core network of regions, threat learning involves multiple brain networks operating in parallel performing different functions at different timescales. Understanding the nature and interplay of these dynamics will be critical for comprehensive understanding of the multiple processes that may be at play in the neuropathology of anxiety and fear-related disorders.

New Technique to develop Intelligent High reliable &amp; Efficient Unmanned Power Generation Modular

Primary aim of this research project is to develop Modular Generator Total Power Output: 1 MW or greater, integrated with High density diesel (NATO F76) generator. This modular comprises of Generators, Fuel Cells (MTBF), Gas Turbine Generators. Note that novelty will be sought on how to configure installation of multiple power units to allow for easy access for maintenance, quick removal and replacement as well as optimize operating life via controls. This proposed power modular will Operate in a marine environment condition such as salt air ingestion, ships motion in high sea states, shock, vibration. Note that novelty will be pursued on how to best optimize design to combine multiple power unit connections to a single ship interface.[1] Key words. Novelty, Corrective Maintenance and Preventive maintenance and Intelligent module.

Network Slicing in 6G: A Strategic Framework for IoT in Smart Cities.

The emergence of 6G communication technologies brings both opportunities and challenges for the Internet of Things (IoT) in smart cities. In this paper, we introduce an advanced network slicing framework designed to meet the complex demands of 6G smart cities' IoT deployments. The framework development follows a detailed methodology that encompasses requirement analysis, metric formulation, constraint specification, objective setting, mathematical modeling, configuration optimization, performance evaluation, parameter tuning, and validation of the final design. Our evaluations demonstrate the framework's high efficiency, evidenced by low round-trip time (RTT), minimal packet loss, increased availability, and enhanced throughput. Notably, the framework scales effectively, managing multiple connections simultaneously without compromising resource efficiency. Enhanced security is achieved through robust features such as 256-bit encryption and a high rate of authentication success. The discussion elaborates on these findings, underscoring the framework's impressive performance, scalability, and security capabilities.

Conceptual framework for determining the treewidth of distribution grids

While distribution grids are often operated radially, they are typically designed to be more redundant, so that each load has multiple connections to the main grid. For complex networks like these, the notion of treewidth can be used to quantify their complexity. In this paper, we propose a new conceptual framework and derive an exact formula for computing treewidth with the help of our constructs. We argue that our framework effectively captures complexities in the structure of distribution grids and has a potential to simplify the calculation of treewidth. After analysing our findings, we hypothesise that the treewidth of distribution grids will typically be low implying that some difficult power system problems can be solved on them in parameterised polynomial time with dynamic programming. We demonstrate this with an example problem of dividing a distribution grid into tree-like operational subgraphs around the primary substations so that no voltage violations occur.

Antibiotics combination exposure increases the resistance to Aeromonas hydrophila infection by improving the microbial network of zebrafish

Normal intestinal microbiota are critical for host health, whereas the use of antibiotics will alter the composition of intestinal microbiota. Understanding the influence of antibiotics on intestinal microbiota of host will further advance our knowledge of host-microbe-antibiotic interactions. Here zebrafish was used as a model to investigate the effect of antibiotics on host susceptibility to pathogen infection. Metronidazole treatment (Group J) and antibiotics combination treatment (metronidazole, ceftazidime, and levofloxacin (Group L)) significantly altered the microbiota composition at 0 and 5 days post-exposure (dpe). At 15 dpe, the intestinal microbial composition of each group tended to be consistent. Meanwhile, the survival rate of group L after Aeromonas hydrophila challenge was significantly higher than that of the control group at 15 dpe. The network analysis indicated antibiotics exposure increased the nodes, edges, and proportion of positive correlations in the network. What's more, after antibiotics combination exposure, the number of nodes with multiple connections and the average degree of network also increased. These findings indicated that antibiotics could increase the resistance to pathogen infection by improving the microbial network, which provides a new perspective on the prevention and treatment of fish diseases.

Coloniality, Race, and Europeanness: Britain’s Borders after Brexit

Abstract The scholarship on the politics of immigration often frames governments’ responses to far-right mobilization as a return to border closures and a rowing back on neoliberalism. In this article, I draw on and expand the scholarship on coloniality to address the limitations of this diagnosis. Specifically, I explore the role of political mobilization in the making of the post-Brexit border regime. My research draws on the analysis of legal and policy initiatives between 2020 and 2023 and twenty-three research interviews with individuals who express their opposition to immigration via engagement in think tanks, grassroots organizations and vigilante groups. The interview data indicates multiple connections between these milieus and shows that each engages in action repertoires beyond the nation-state. And while this prompts border closures, the post-Brexit border regime also encodes openings and loopholes for the circulation of financial elites and precariously employed workers. Thus, I argue that state and non-state actors co-produce a neoliberal border regime of stratified rights, partial inclusions, and gradual exclusions. These variegated entitlements draw on and reinvigorate the racial order of coloniality. The post-Brexit immigration regime enables the free mobility of those racialized as “West European,” facilitates disposable labor mobility of those racialized as “Eastern European,” and restricts the movement of those racialized as “non-European.” This racial imaginary does not only operate via binary distinctions of (non)-Britishness but puts people in complex hierarchical relations to “Europeanness.”