Context Of Networks Research Articles

Prediction of protein-RNA interactions from single-cell transcriptomic data.

Proteins are crucial in regulating every aspect of RNA life, yet understanding their interactions with coding and noncoding RNAs remains limited. Experimental studies are typically restricted to a small number of cell lines and a limited set of RNA-binding proteins (RBPs). Although computational methods based on physico-chemical principles can predict protein-RNA interactions accurately, they often lack the ability to consider cell-type-specific gene expression and the broader context of gene regulatory networks (GRNs). Here, we assess the performance of several GRN inference algorithms in predicting protein-RNA interactions from single-cell transcriptomic data, and propose a pipeline, called scRAPID (single-cell transcriptomic-based RnA Protein Interaction Detection), that integrates these methods with the catRAPID algorithm, which can identify direct physical interactions between RBPs and RNA molecules. Our approach demonstrates that RBP-RNA interactions can be predicted from single-cell transcriptomic data, with performances comparable or superior to those achieved for the well-established task of inferring transcription factor-target interactions. The incorporation of catRAPID significantly enhances the accuracy of identifying interactions, particularly with long noncoding RNAs, and enables the identification of hub RBPs and RNAs. Additionally, we show that interactions between RBPs can be detected based on their inferred RNA targets. The software is freely available at https://github.com/tartaglialabIIT/scRAPID.

The pre-Colombian roads of geoglyphs sites in the state of acre: the tequinho site road complex

This article prioritized studying the roads and paths that are interconnected to geoglyph-type sites in the Western Brazilian Amazon, not only because of their originality, but also to understand some issues that permeate the universe of geoglyphs, such as how was gave the spatial distribution of these roads in the region, what meaning they had within the sociocultural context of that society. For a more detailed analysis, the Tequinho site was used as the main object of the research, its characteristics and location being what most contributed to its choice, given that it is in the center of the region where the geoglyphs occur, and several roads and paths are linked to the site, and are noticeable in the landscape. The Tequinho site is understood as a public center for cultural and religious events, and its roads and paths are seen as central props in ritualistic ceremonies, as well as markers and boundaries in the landscape. This article also addresses the importance of the Iquiri River, which works, was part of the context of a land-fluvial network in the region of geoglyphs, in the east of the State of Acre, Brazil. During this work, ethno-historical sources were used as a theoretical framework, as well as the perspective of landscape archeology.

Real-Time Environmental Monitoring Using Low-Cost Sensors in Smart Cities with IoT

In recent years, people are getting aware of environmental pollution which results in a growing demand for robust systems that is capable of monitoring environmental conditions in real time. An efficient air quality monitoring system is not only essential for public awareness but also has significant industrial applications. For instance, in mining and heavy industries, there is a risk of air pollution from various hazardous gases, and having an effective monitoring system could be life-saving for employees. Within the context of expensive sensor networks, challenges such as data acquisition, management, connectivity, and energy consumption is getting too high. Internet of Things (IoT) technology addresses these challenges. This study introduces an IoT-based framework designed to track environmental conditions with the help of several sensors. This system enables users to measure temperature, humidity, and the presence of dangerous gases in both indoor and outdoor settings. Data collected is stored on a web server, accessible globally via the internet. Moreover, a specially designed web application provides critical information and allows users to receive notifications for significant environmental fluctuations detected by the sensors. Compared to similar systems, our proposed solution stands out due to its affordability, precision, user-friendly interface, cloud-based architecture, and its comprehensive monitoring and data visualization capabilities. The system underwent rigorous evaluation in various scenarios, demonstrating a high level of accuracy and dependability in its performance.

Privacy-Preserving and Lightweight Verification of Deep Packet Inspection in Clouds

In the trend of network middleboxes as a service, enterprise customers adopt in-the-cloud deep packet inspection (DPI) services to protect networks. As network misconfigurations and hardware failures notoriously exist, recent efforts envision to ensure the execution integrity of DPI services in untrusted clouds. However, they either require enterprise customers to know proprietary DPI rulesets of cloud providers or introduce forbidden overhead in the network context. In the paper, we propose a privacy-preserving and lightweight verification scheme that efficiently checks whether in-the-cloud DPI services run correctly without leaking private DPI rulesets. Particularly, our design introduces one trusted third party to perform privacy-preserving and trustworthy ruleset evaluation and DPI execution verification. Meanwhile, it devises a novel DPI ruleset authentication method that enables tamper-proof DPI operations and facilitates fast proof generation. The proofs can be verified without requiring the verifier to always maintain all rulesets. To further reduce the verification costs while resisting cloud cheating behaviors like bias treatments of packets, it employs a commitment-based delayed sampling mechanism which requires the DPI services to first demonstrate that all packets have been processed before receiving sampling decisions. Moreover, extensive experiments are conducted based on Click modules. The results show that the proposed scheme is practical and only incurs the real-time overhead of 10-20 microseconds.

Achieving social influence across gender and time: Are dominance and prestige equally viable for men and women?

The dual framework of social rank allocation discusses dominance and prestige as two viable routes to status or social influence. In doing so, this literature has largely neglected findings demonstrating backlash against men and women for behaving in gender-incongruent ways. Likewise, it remains unclear if dominance and prestige continue to be effective means to status over time. This study investigates the viability of dominance or prestige in contributing to an individual's social influence, conditional on their gender and across time. Using a stereotype-neutral context of an online social network, I unobtrusively tracked individuals' changes in social influence among their network members on Twitter. By analyzing almost 230,000 tweets, it was found that men's influence increased with greater dominance, whereas women's decreased. At the same time, women's influence increased with greater prestige, whereas men's decreased. Network centrality (in-degree centrality) explained this differential interaction pattern. Additionally, longitudinal analysis provided a more nuanced understanding. Over time, role incongruence effects dampened, dominance became less effective, even for men, and prestige became viable for both men and women. Thus, by jointly considering the role of gender and time, this research offers key theoretical caveats to the dual rank framework. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

From pixels to riverscapes: How remote sensing and geospatial tools can prioritize riverscape restoration at multiple scales

AbstractPrioritizing restoration opportunities effectively across entire riverscape networks (i.e., riverine landscape including floodplain and stream channel networks) can be difficult when relying on in‐channel, reach‐scale monitoring data, or watershed‐level summaries that fail to capture riverscape heterogeneity and the information necessary to implement restoration actions. Leveraging remote sensing and geospatial tools to develop spatially continuous information across nested hierarchical scales may support increased understanding of local riverscape reaches in their broader network context. Using riparian (vegetation) and geomorphic (elevation) indicators to assess status of riverscape health, along with a measure of restoration capacity (valley bottom area), could be adapted to fit specific management goals related to riverscape restoration. Frameworks using remotely sensed vegetation and elevation data to prioritize restoration continuously across riverscapes at restoration‐relevant, reach‐scales may uphold the ecosystem services provided by riverscapes. By incorporating local knowledge and identifying caveats for using these datasets, continuous inferences can be applied at network scales (watershed to regional extent and reach‐scale resolution) to prioritize restoration over a wide variety of ecoregions.This article is categorized under: Water and Life > Conservation, Management, and Awareness Water and Life > Methods Water and Life > Stresses and Pressures on Ecosystems

Exploring the Components of Digital Identity on Social Networks Sites: Identifier, Post, Profile Photo, and Selfie

The digital age has ushered in a paradigm shift in self-expression and identity construction, with social network sites (SNSs) serving as vibrant canvases for individuals to shape and project their digital personality. This paper focuses on delving into the intricate components of digital identity on SNS, focusing on four key elements: identifiers, posts, profile photos, and selfies. Based on a descriptive and analytical method, this article attempts to draw up a detailed and nuanced analysis of the multiple facets of digital identity, thus providing a solid basis for in-depth understanding of this complex phenomenon in the specific context of social networks which technically regulate the representation of self. The results obtained show that the construction of a digital identity is framed by the architecture of SNSs, which incite users to manifest their selves, thus producing unlimited digital traces that reflect their personality traits. The results also provide an understanding of the structure of SNSs that aim to raise awareness of presenting a positive self-image in the digital world.

Identifying the systemic importance and systemic vulnerability of financial institutions based on portfolio similarity correlation network

The indirect correlation among financial institutions, stemming from similarities in their portfolios, is a primary driver of systemic risk. However, most existing research overlooks the influence of portfolio similarity among various types of financial institutions on this risk. Therefore, we construct the network of portfolio similarity correlations among different types of financial institutions, based on measurements of portfolio similarity. Utilizing the expanded fire sale contagion model, we offer a comprehensive assessment of systemic risk for Chinese financial institutions. Initially, we introduce indicators for systemic risk, systemic importance, and systemic vulnerability. Subsequently, we examine the cross-sectional and time-series characteristics of these institutions’ systemic importance and vulnerability within the context of the portfolio similarity correlation network. Our empirical findings reveal a high degree of portfolio similarity between banks and insurance companies, contrasted with lower similarity between banks and securities firms. Moreover, when considering the portfolio similarity correlation network, both the systemic importance and vulnerability of Chinese banks and insurance companies surpass those of securities firms in both cross-sectional and temporal dimensions. Notably, our analysis further illustrates that a financial institution’s systemic importance and vulnerability are strongly and positively associated with the magnitude of portfolio similarity between that institution and others.

Assessing Betweenness Centrality for Multimodal Transport Network – A Case Study of Delhi Metropolitan Area

This study introduces an approach for analysis of network centrality, with a focus on betweenness centrality in complex urban transport systems. Central to this research is the development of a Path Evaluation Function (PEF), a tool that enriches traditional centrality assessments for transport networks by factoring in critical network attributes like link capacity and travel time. This approach is particularly adept at unraveling the intricacies of betweenness centrality in multimodal transportation networks. Using this approach, the study delves into the dynamics of betweenness centrality within the Delhi Metropolitan transport network, an evolving urban landscape characterized by diverse transit modes. The analysis reveals a marked concentration of high betweenness centrality along key transport corridors, highlighting their role in facilitating urban mobility. This refined understanding of betweenness centrality, obtained through the PEF, offers deeper insights into how crucial nodes and links function within the network, thereby enabling more informed decision-making in urban planning and policy. Furthermore, the versatility of the PEF extends beyond betweenness centrality, showing promise as a tool for evaluating other centrality measures within varied network contexts.

Distributed Data-Driven Learning-Based Optimal Dynamic Resource Allocation for Multi-RIS-Assisted Multi-User Ad-Hoc Network

This study investigates the problem of decentralized dynamic resource allocation optimization for ad-hoc network communication with the support of reconfigurable intelligent surfaces (RIS), leveraging a reinforcement learning framework. In the present context of cellular networks, device-to-device (D2D) communication stands out as a promising technique to enhance the spectrum efficiency. Simultaneously, RIS have gained considerable attention due to their ability to enhance the quality of dynamic wireless networks by maximizing the spectrum efficiency without increasing the power consumption. However, prevalent centralized D2D transmission schemes require global information, leading to a significant signaling overhead. Conversely, existing distributed schemes, while avoiding the need for global information, often demand frequent information exchange among D2D users, falling short of achieving global optimization. This paper introduces a framework comprising an outer loop and inner loop. In the outer loop, decentralized dynamic resource allocation optimization has been developed for self-organizing network communication aided by RIS. This is accomplished through the application of a multi-player multi-armed bandit approach, completing strategies for RIS and resource block selection. Notably, these strategies operate without requiring signal interaction during execution. Meanwhile, in the inner loop, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm has been adopted for cooperative learning with neural networks (NNs) to obtain optimal transmit power control and RIS phase shift control for multiple users, with a specified RIS and resource block selection policy from the outer loop. Through the utilization of optimization theory, distributed optimal resource allocation can be attained as the outer and inner reinforcement learning algorithms converge over time. Finally, a series of numerical simulations are presented to validate and illustrate the effectiveness of the proposed scheme.

Cache-MCDM: A hybrid caching scheme in Mobile Named Data Networks based on multi-criteria decision making

Robust and reliable content retrieval in a Mobile Ad Hoc Network (MANET) is challenged by its intermittent connectivity and mobility characteristics. In-network caching and name-based content retrieval attributes of Named Data Networks (NDN) can counterattack the stated challenges of MANETs. The distance- or hop-based caching schemes, proposed mainly for infrastructure NDN, admit contents either close to the content source, client, or midway, which are trivial to realize since content mainly travels from the network core (content sources) to the edge (clients). In NDN-based MANETs, the mobility attribute causes source and consumer nodes to change their locations frequently. Therefore, the content can travel from the network core to the edge and vice-versa. Under such circumstances, the traditional hop-based schemes that cache at particular points, for instance, either near the source, client, or halfway, may vandalize the efficient content retrieval in MANETs. Thus, proposing a new strategy capable of caching at any of the stated caching points considering varying network contexts, for instance, location change of source and consumer, is inevitable. Such a strategy can scale well in terms of network resource usage and significantly improve PDR and content retrieval latency performances in NDN-based MANET. In this direction, we propose an AHP-based hybrid caching scheme, namely Cache-MCDM, that equips each router with a set of different hop-based caching options and employs AHP-TOPSIS method to pick the most suitable caching option. The proposed method models the suitable option selection as a Multi-Criteria Decision Making (MCDM) problem. Simulation reveals that Cache-MCDM is more effective and efficient than hop-based probabilistic caching (HPC), opportunistic caching (OppProb), probabilistic in-network caching (ProbCache), and SourceCacheProb (SCP), in terms of (i) PDR by approximately 3%, 15%, 10%, and 11%, respectively, (ii) latency by approximately 25%, 145%, 75%, and 77%, respectively, (iii) retransmission ratio by approximately 7%, 30%, 25%, and 25%, respectively, and (iv) cache replacement rate by approximately 8%, 100%, 80%, and 25%, respectively.

A formal security analysis of the fast authentication procedure based on the security context in 5G networks

A formal security analysis of the fast authentication procedure based on the security context in 5G networks

Sparse deep belief network coupled with extended local fuzzy active contour model-based liver cancer segmentation from abdomen CT images.

Liver cancer from abdominal CT images must be accurately segmented for the purpose of diagnosis with treatment planning. But, the similarity in gray values between the liver and the surrounding tissues poses a challenge. To address this, a novel sparse deep belief network coupled with extended local fuzzy active contour model-based liver cancer segmentation from abdomen CT images (SDBN-ELFAC-LCS-CT) is proposed. This method incorporates dynamic adaptive pooling and residual modules in SDBN to improve the feature selection and generalization ability. Additionally, the 3D reconstruction is performed to refine segmentation results. The proposed SDBN-ELFAC-LCS-CT approach is implemented in MATLAB. The performance of the proposed SDBN-ELFAC-LCS-CT achieves dice coefficients that were up to 96.16% higher and 75.88%, 88.75%, and 71.16% lower. Volumetric overlap error compared with existing models, like basic ensembles of vanilla-style deep learning modes, increases liver segmentation from CT imageries (BEVS-LCS-CT), an incorporated 3 dimensional sparse deep belief network along enriched seagull optimization approach for liver segmentation (3DBN-ESOA-LCS-CT) and iterative convolutional encoder-decoder network and multiple scale context learning for segmenting liver (ICEDN-LCS-CT), respectively.

Stable state estimation in a network context

Power grid, communications, computer and product reticulation networks are frequently layered or subdivided by design. The OSI seven-layer computer network model and the electrical grid division into generation, transmission, distribution and associated markets are cases in point. The layering divides responsibilities and can be driven by operational, commercial, regulatory and privacy concerns. From a control context, a layer, or part of a layer, in a network isolates the authority to manage, i.e. control, a dynamic system with connections into unknown parts of the network. The topology of these connections is fully prescribed but the interconnecting signals are largely unavailable, through lack of sensing and even prohibition. Accordingly, one is driven to simultaneous input and state estimation methods. This is the province of this paper, guided by the structure of these network problems. We study a class of algorithms for this joint task, which if the system has transmission zeros outside the unit circle leads to an unstable and unimplementable estimator. Two modifications to the algorithm to ameliorate this problem were recently proposed involving (a) replacing the troublesome subsystem with its outer factor from its inner-outer factorization or (b) using the Kalman filter for a high-variance white signal model for the unknown inputs. The outer factor has only stable transmission zeros and so is stably invertible. The Kalman filter is stable by design. Here, we establish the connections between the original estimation problem for state and input signal and the outputs/estimates from the algorithm applied solely to the outer factor. We further show that both fixes coincide.

Thermoswitchable catalysis to inhibit and promote plastic flow in vitrimers.

Acid-base catalysis is a common strategy to induce covalent bond exchanges in dynamic polymer networks. Strong acids or strong bases can promote rapid network rearrangements, and are simultaneously preferred catalysts for chemical reactions where maximum efficiency at the lowest possible temperature is aimed for. However, within the context of dynamic polymer networks, the incorporation of highly active catalysts can negatively affect the longer term application potential. Network dynamicity can diminish through catalyst ageing or quenching and highly active catalysts may prematurely activate bond exchanges, leading to dimensional instability and thus low creep resistance of the polymer networks. Herein, we present several examples where we explicitly explored weak acids (carboxylic acids) as catalysts for dynamic bond exchanges, using vinylogous urethanes (VU) as a well-understood protic acid catalysed vitrimer chemistry. Surprisingly, we have found that the sought-after long-term stability offered by a weak acid does not necessarily bring lower activity at high temperature. In fact, the weak acids show a remarkable thermoswitchable catalytic behaviour, going from an inactive hydrogen bonded state to an active state where the polymer matrix is protonated, with a profound impact on the network reactivity and rheology. Carboxylic acids with different electronic or steric environments show clear reactivity trends and their fine-tuning resulted in the most thermally responsive VU vitrimers studied to date. Our findings point out that catalyst choice and design for vitrimers is only poorly informed by catalyst performance in more traditional chemical reactions (in solvent), and that a more tailored catalyst design holds great promise for the field of vitrimers.

An improved design of circularly polarized microstrip antenna for 5G communication

This research paper explores the performance of four circularly polarized microstrip antenna designs tailored for 5G applications. As 5G technology continues to advance, antenna design is pivotal in realizing its potential. The designs, labeled Design 1 through 4, undergo rigorous analysis, encompassing axial ratio, gain, and impedance bandwidth. Notably, Designs 2 and 3, featuring strategic slot cuts, exhibit strong axial ratios suitable for circular polarization within the 5G frequency range. Design 4 introduces an L-shaped feeding line, showcasing unique characteristics albeit with trade-offs. Conversely, Design 1, without slot cuts, demonstrates suboptimal circular polarization performance. This study discusses the significance of these findings within the context of 5G networks, emphasizing the relevance of antenna design choices. As 5G technology evolves, this research offers valuable insights for optimizing antennas in the quest for efficient 5G communication systems.

AutoTarget: Disease-Associated druggable target identification via node representation learning in PPI networks

Drug target discovery, a pivotal early stage in drug development, is resource-intensive and crucial for ensuring drug efficacy. This study presents AutoTarget, a novel computational pipeline designed to identify disease-associated druggable targets by applying node representation learning to protein–protein interaction (PPI) networks. AutoTarget uses node2vec + for node classification, incorporating neighborhood context and structural equivalence in PPI networks derived from the STRING database. Data from the Therapeutic Target Database (TTD) and DisGeNET were integrated to identify known drug targets and gene-disease associations, respectively. Each protein is embedded into a 128-dimensional vector space, capturing local network structures and enabling the identification of structurally equivalent proteins. A Naïve Bayes classifier, trained on these embeddings, achieved a recall of 0.90 and an F1 score of 0.79 in predicting potential drug targets. AutoTarget identified 3,979 novel potential druggable target proteins out of 19,333 proteins in the PPI network, which were mapped to 23,363 diseases using DisGeNET. This creates a comprehensive resource for disease-specific drug target exploration. Case studies on triple-negative breast cancer and obesity demonstrated AutoTarget’s capability to identify both established and emerging targets, such as CD44, MAPK3, and GIP. Visualization of embedding vectors using t-SNE revealed clear separations between functional protein families, including nuclear proteins, growth factor receptors, and the G proteins within the kinase proteins. This supports the method’s ability to capture biologically relevant information. However, limitations were noted, including the inability to distinguish between different types of disease-associated proteins based solely on network features. Overall, this study advances the application of machine learning and network theory for identifying druggable targets across a wide range of diseases. AutoTarget provides researchers with a valuable tool for expediting the discovery of novel druggable targets, potentially streamlining the drug discovery process. The AutoTarget code and database are publicly available to facilitate further research.

‘I don’t want to play with you anymore’: dynamic partner judgements in moody reinforcement learners playing the prisoner’s dilemma

Abstract Emerging reinforcement learning algorithms that utilize human traits as part of their conceptual architecture have been demonstrated to encourage cooperation in social dilemmas when compared to their unaltered origins. In particular, the addition of a mood mechanism facilitates more cooperative behaviour in multi-agent iterated prisoner dilemma (IPD) games, for both static and dynamic network contexts. Mood-altered agents also exhibit humanlike behavioural trends when environmental aspects of the dilemma are altered, such as the structure of the payoff matrix used. It is possible that other environmental effects from both human and agent-based research will interact with moody structures in previously unstudied ways. As the literature on these interactions is currently small, we seek to expand on previous research by introducing two more environmental dimensions; voluntary interaction in dynamic networks, and stability of interaction through varied network restructuring. From an initial Erdos–Renyi random network, we manipulate the structure of a network IPD according to existing methodology in human-based research, to investigate possible replication of their findings. We also facilitated strategic selection of opponents through the introduction of two partner evaluation mechanisms and tested two selection thresholds for each. We found that even minimally strategic play termination in dynamic networks is enough to enhance cooperation above a static level, though the thresholds for these strategic decisions are critical to desired outcomes. More forgiving thresholds lead to better maintenance of cooperation between kinder strategies than stricter ones, despite overall cooperation levels being relatively low. Additionally, moody reinforcement learning combined with certain play termination decision strategies can mimic trends in human cooperation affected by structural changes to the IPD played on dynamic networks—as can kind and simplistic strategies such as Tit-For-Tat. Implications of this in comparison with human data is discussed, and suggestions for diversification of further testing are made.

Forking From the Future: How an Interorganizational Network Learned Its Way to New Software Business

Technological disruptions call for new capabilities beyond the reach of a single firm. How does a large interorganizational network learn its way to new Cloud software business while leveraging forking—that is, copying and branching of code common to software development? Such forking promotes commitment and innovation but may compromise joint learning in the network required for developing new capabilities under technological disruption. Elaborating a seminal learning theory in an interorganizational network context, we demonstrate how forking was about to sacrifice long-term aspirations to short-term interests early on. We contribute a new type of forking—forking from the future—and describe how this type of forking harnesses future aspirations and builds collaborative capabilities in the network while reducing tendencies for shortsightedness. Forking from the future consists of the introduction of external perspectives, the implementation of new rules, the use of integrative tools, and generally accelerating collaboration among the network participants from the future back (not from the present forward). As software becomes increasingly important across industries, forking from the future becomes foundational for future competitiveness.

Hate Speech Recognition: The Role of Empathy and Awareness of Social Media Influence

RICONOSCIMENTO DELL’HATE SPEECH: IL RUOLO DELL’EMPATIA E DELLA CONSAPEVOLEZZA DELL’INFLUENZA DEI SOCIAL MEDIA Abstract Hate speech occurs within democratic societies that embrace freedom of expression and is made tangible in the social network context. It is characterized by a specific form of discrimination based on the use of verbal expressions or other media content and, usually, directed at minority groups. Although there is a lack of consensus about a unique and shared definition of hate speech, its social and personal consequences are particularly relevant for the whole society. For these reasons, it seems of crucial importance to identify hate speech recognition antecedents. The present study aimed at analyzing the relationship between hate speech recognition and specific psychological constructs, namely, empathy and awareness of social media influence. More in details, we hypothesized the association between empathy and hate speech recognition was mediated by awareness of social media influence. Data obtained from 146 participants revealed that empathy positively predicted hate speech recognition, and such relationship was mediated by awareness. Implication of such findings are discussed.