Set Of Networks Research Articles

Properties of a Random Bipartite Geometric Associator Graph Inspired by Vehicular Networks.

We consider a point process (PP) generated by superimposing an independent Poisson point process (PPP) on each line of a 2D Poisson line process (PLP). Termed PLP-PPP, this PP is suitable for modeling networks formed on an irregular collection of lines, such as vehicles on a network of roads and sensors deployed along trails in a forest. Inspired by vehicular networks in which vehicles connect with their nearest wireless base stations (BSs), we consider a random bipartite associator graph in which each point of the PLP-PPP is associated with the nearest point of an independent PPP through an edge. This graph is equivalent to the partitioning of PLP-PPP by a Poisson Voronoi tessellation (PVT) formed by an independent PPP. We first characterize the exact distribution of the number of points of PLP-PPP falling inside the ball centered at an arbitrary location in R2 as well as the typical point of PLP-PPP. Using these distributions, we derive cumulative distribution functions (CDFs) and probability density functions (PDFs) of kth contact distance (CD) and the nearest neighbor distance (NND) of PLP-PPP. As intermediate results, we present the empirical distribution of the perimeter and approximate distribution of the length of the typical chord of the zero-cell of this PVT. Using these results, we present two close approximations of the distribution of node degree of the random bipartite associator graph. In a vehicular network setting, this result characterizes the number of vehicles connected to each BS, which models its load. Since each BS has to distribute its limited resources across all the vehicles connected to it, a good statistical understanding of load is important for an efficient system design. Several applications of these new results to different wireless network settings are also discussed.

Beyond individual, population, and community: Considering information, cell number, and energy flux as fundamental dimensions of life across scales

We suggest rethinking ecology as a set of continuous, interconnected dynamics and spatial networks that would represent an alternative framework to the traditional organizational levels—cell, organism, population, community, and ecosystem. We draw on unifying biological theories—information theory, cell theory, and metabolic theories—to propose a continuous space for living systems that avoids epistemological constraints imposed by a priori assumptions of discrete levels of biological organization. The discrete levels of organization that ecologists have traditionally envisioned can be viewed instead as coarse-grained, localized groupings within continuous dimensions of information (I), cell number (C), and energy flux (E). Moving from levels of organization to emergent properties of information, cells, and energy flux allows us to illustrate how diverse ecological and evolutionary phenomena—such as allometric and diversity-related scaling patterns, predator-prey dynamics, evolutionary trajectories, and alternative stable states—can be represented within the same continuum. We suggest that there may be structure within this information-cells-energetic flux (ICE) framework that unifies ecology from the beginning of life to the present and provides a useful lens through which patterns and processes in nature can be viewed.

On Persistence of Some $\mathcal {W}_{I}$-Endotactic Chemical Reaction Networks

Motivated by the concept of the endotactic network, a kind of special geometric structure in chemical reaction networks developed for persistence analysis, we propose a new notion, named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {W}_{I}$</tex-math></inline-formula> -endotactic network. The corresponding network set is a larger class of set than the endotactic network, let alone the weakly reversible network. Based on an energy-like function, we prove that all 1-dimensional mass-action <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {W}_{I}$</tex-math></inline-formula> -endotactic networks are persistent. Further, we prove some higher dimensional network systems also to support persistence if the system is composed of a series of 1-dimensional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {W}_{I}$</tex-math></inline-formula> -endotactic networks. We discuss two cases for the sub-systems with and without intersecting species, and present the corresponding sufficient conditions to capture persistence. Finally, we use some examples including abstract and real biochemical reaction networks to illustrate our results.

Network and Computational Drug Repurposing Analysis for c-Myc Inhibition in Burkitt Lymphoma.

The treatment rate of Burkitt lymphoma (BL) is still low in low-income countries and among elderly patients. The c-Myc dysregulation induced by mutations is one of the characteristics of BL. However, studies on the downstream signaling pathways of c-Myc are still lacking. This study aimed to identify the signaling pathways regulated by c-Myc. Network and gene set analyses using c-Myc inhibition (i.e., c-Myc knock-down and c-Myc inhibitor treatment) transcriptome datasets for BL cell lines were performed to determine the pathways regulated by c-Myc. In addition, computational drug repurposing was used to identify drugs that can regulate c-Myc downstream signaling pathway. Computational drug repurposing revealed that the ERK/MAPK signaling pathway is regulated by c-Myc in BL and that this pathway can be modulated by vorinostat. Furthermore, in the pharmacogenomics database, vorinostat showed a cell viability half-maximal inhibitory concentration of less than 2 μM in the BL cell lines. The downstream signaling pathway regulated by c-Myc and the drug that can modulate this pathway is presented for the first time.

Multi-status Bayesian network for analyzing collapse risk of excavation construction

In response to the need for effective collapse risk analysis in engineering projects, a decision support approach is presented. It is rooted in multi-status Bayesian network (MSBN) and fuzzy set theory, encompassing MSBN construction, risk analysis, and management. This research addresses the causative correlation between influential factors and excavation collapse, considering the inherent uncertainty and fuzziness of the project. To enhance the reliability of evaluation results, an expert confidence index, integrating judgment ability, subjective reliability, and risk preference, is introduced. The approach is applied to an excavation case study, demonstrating its viability and capacity. Furthermore, it offers actionable insights for decision-makers to proactively mitigate accident probabilities. Notably, sensitivity analysis identifies critical risk factors. This research contributes to the field of engineering project risk assessment and management. It serves as a foundation for future research and development, guiding the way for improved strategies and decision support systems.

Practical and Malicious Multiparty Private Set Intersection for Small Sets

Private set intersection (PSI) is a pivotal subject in the realm of privacy computation. Numerous research endeavors have concentrated on situations involving vast and imbalanced sets. Nevertheless, there is a scarcity of existing PSI protocols tailored for small sets. Those that exist are either restricted to interactions between two parties or necessitate resource-intensive homomorphic operations. To bring forth practical multiparty private set intersection solutions for small sets, we present two multiparty PSI protocols founded on the principles of Oblivious Key–Value Stores (OKVSs), polynomials, and gabled cuckoo tables. Our security analysis underscores the resilience of these protocols against malicious models and collision attacks. Through experimental evaluations, we establish that, in comparison to related endeavors, our protocols excel in small-set contexts, particularly in low-bandwidth wide area network (WAN) settings.

Single-cell insights into epithelial morphogenesis in the neonatal mouse uterus.

The uterus is vital for successful reproduction in mammals, and two different types of epithelia (luminal and glandular) are essential for embryo implantation and pregnancy establishment. However, the essential cellular and molecular factors and pathways governing postnatal epithelium maturation, determination, and differentiation in developing uterus are yet to be elucidated. Here, the epithelium of the neonatal mouse uterus was isolated and subjected to single-cell transcriptome (scRNA-seq) analysis. Both the undifferentiated epithelium and determined luminal epithelium were heterogeneous and contained several different cell clusters based on single-cell transcription profiles. Substantial gene expression differences were evident as the epithelium matured and differentiated between postnatal days 1 to 15. Two new glandular epithelium-expressed genes (Gas6 and Cited4) were identified and validated by in situ hybridization. Trajectory analyses provided a framework for understanding epithelium maturation, lineage bifurcation, and differentiation. A candidate set of transcription factors and gene regulatory networks were identified that potentially direct epithelium lineage specification and morphogenesis. This atlas provides a foundation important to discover intrinsic cellular and molecular mechanisms directing uterine epithelium morphogenesis during a critical window of postnatal development.

DL[formula omitted]F: A Deep Learning model for the Local Forecasting of renewable sources

This work presents the first version of DL2F, a powerful Deep Learning model specifically designed to forecast four essential weather variables that influence solar power potential: Global Horizontal Irradiance (GHI), temperature, atmospheric pressure, and relative humidity. The model uses a time series for each variable as input and the forecast provided by the well-known Fifth-Generation Mesoscale Model (MM5) system. Then, it generates new predictions for each variable and the subsequent 3 days. The suggested approach improves the MM5 system’s accuracy, effectively mitigating its tendency to misestimate real data in some specific seasons. In essence, DL2F is a regressor able to refine the forecasts generated by the MM5 system and improve its overall precision. The model’s architecture is based on a set of recurrent neural networks of the Gated Recurrent Unit (GRU) type.

Influencer identification of dynamical networks based on an information entropy dimension reduction method

Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control. Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure. However, these algorithms do not consider network state changes. We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity. By using mean field theory and information entropy to calculate node activity, we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance. We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C. elegans neural network. We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.

Structural connectivity of cytoarchitectonically distinct human left temporal pole subregions: a diffusion MRI tractography study.

The temporal pole (TP) is considered one of the major paralimbic cortical regions, and is involved in a variety of functions such as sensory perception, emotion, semantic processing, and social cognition. Based on differences in cytoarchitecture, the TP can be further subdivided into smaller regions (dorsal, ventrolateral and ventromedial), each forming key nodes of distinct functional networks. However, the brain structural connectivity profile of TP subregions is not fully clarified. Using diffusion MRI data in a set of 31 healthy subjects, we aimed to elucidate the comprehensive structural connectivity of three cytoarchitectonically distinct TP subregions. Diffusion tensor imaging (DTI) analysis suggested that major association fiber pathways such as the inferior longitudinal, middle longitudinal, arcuate, and uncinate fasciculi provide structural connectivity to the TP. Further analysis suggested partially overlapping yet still distinct structural connectivity patterns across the TP subregions. Specifically, the dorsal subregion is strongly connected with wide areas in the parietal lobe, the ventrolateral subregion with areas including constituents of the default-semantic network, and the ventromedial subregion with limbic and paralimbic areas. Our results suggest the involvement of the TP in a set of extensive but distinct networks of cortical regions, consistent with its functional roles.

Designing electricity distribution networks: The impact of demand coincidence

With the global effort to reduce carbon emissions, clean technologies such as electric vehicles and heat pumps are increasingly introduced into electricity distribution networks. These technologies considerably increase electricity flows and can lead to more coincident electricity demand. In this paper, we analyze how such increases in demand coincidence impact future distribution network investments. For this purpose, we develop a novel model for designing electricity distribution networks, called the distribution network reconfiguration problem with line-specific demand coincidence (DNRP-LSDC). Our analysis is two-fold: (1) We apply our model to a large sample of real-world networks from a Swiss distribution network operator. We find that a high demand coincidence due to, for example, a large-scale uptake of electric vehicles, requires a substantial amount of new network line construction and increases average network cost by 84% in comparison to the status quo. (2) We use a set of synthetic networks to isolate the effect of specific network characteristics. Here, we show that high coincidence has a more detrimental effect on large networks and on networks with low geographic consumer densities, as present in, e.g., rural areas. We also show that expansion measures are robust to variations in the cost parameters. Our results demonstrate the necessity of designing policies and operational protocols that reduce demand coincidence. Moreover, our findings show that operators of distribution networks must consider the demand coincidence of new electricity uses and adapt investment budgets accordingly. Here, our solution algorithms for the DNRP-LSDC problem can support operators of distribution networks in strategic and operational network design tasks.

Collaborative deep semi-supervised learning with knowledge distillation for surface defect classification

Defect inspection plays a vital role in ensuring high-quality production in industrial automation. While supervised approaches have been successful, they rely on costly labeled data. To address this limitation, semi-supervised methods have gained popularity, utilizing both labeled and unlabeled data for training. This research addresses the challenge of noisy semi-supervised training caused by incorrect pseudo-labels in Convolutional Neural Network based models. To enhance the accuracy and reliability of pseudo-label selection, a novel collaborative learning strategy with knowledge distillation for defect classification is proposed. The proposed approach involves training a set of teacher networks collaboratively, allowing them to collectively determine the pseudo-labels for each unlabeled image and improving the quality of pseudo-labeling. Subsequently, each teacher network is trained using these pseudo-labeled data. Finally, the acquired collaborative knowledge is transferred to a single student network, reducing model complexity, memory requirements, and enabling faster inference during deployment. The proposed approach demonstrates competitive performance on three publicly available defect classification datasets: NEU steel surfaces, SLS laser powder beds, and Surface Textures, achieving results comparable to the state-of-the-art. Notably, remarkable accuracy is achieved even with limited labeled data during training. For instance, on the SLS dataset, the proposed approach achieves 97% accuracy, which is comparable to the state-of-the-art’s 98% accuracy when using 100% of labeled data. Remarkably, the proposed approach accomplishes this level of accuracy using only 3% of the labeled training data, showcasing its efficiency and effectiveness in leveraging limited labeled data to achieve impressive results. Source code is available at https://github.com/.11Source code will be made available after the acceptance of this manuscript.

On hierarchical clustering-based approach for RDDBS design

Distributed database system (DDBS) design is still an open challenge even after decades of research, especially in a dynamic network setting. Hence, to meet the demands of high-speed data gathering and for the management and preservation of huge systems, it is important to construct a distributed database for real-time data storage. Incidentally, some fragmentation schemes, such as horizontal, vertical, and hybrid, are widely used for DDBS design. At the same time, data allocation could not be done without first physically fragmenting the data because the fragmentation process is the foundation of the DDBS design. Extensive research have been conducted to develop effective solutions for DDBS design problems. But the great majority of them barely consider the RDDBS's initial design. Therefore, this work aims at proposing a clustering-based horizontal fragmentation and allocation technique to handle both the early and late stages of the DDBS design. To ensure that each operation flows into the next without any increase in complexity, fragmentation and allocation are done simultaneously. With this approach, the main goals are to minimize communication expenses, response time, and irrelevant data access. Most importantly, it has been observed that the proposed approach may effectively expand RDDBS performance by simultaneously fragmenting and assigning various relations. Through simulations and experiments on synthetic and real databases, we demonstrate the viability of our strategy and how it considerably lowers communication costs for typical access patterns at both the early and late stages of design.

A Narrow-Down Approach Based on Machine Learning for Indoor Localization

Over the past decade, the demand and research for indoor localization have burgeoned and Wi-Fi fingerprinting approach has been widely considered because it is cheap and accessible. However, most existing methods lack in terms of positioning accuracy and high computational complexity. To cope with these issues, we formulate a two-stage, coarse and accurate positioning narrow-down approach (NDA). Furthermore, a three-step source domain refinement (SDR) scheme that involves outlier removal, stable AP’s weight enhancement, and a data averaging technique by applying the K-means clustering algorithm is also proposed. The collaboration of SDR scheme with the training data selection, area division, and overlapping schemes reduces the computational complexity and improves coarse positioning accuracy. The effect of the proposed SDR scheme on the performance of the support vector machine (SVM) and random forest algorithms is also presented. In the final/accurate positioning phase, a set of lightweight neural networks (DNNs), trained on different sub-areas, predict the user’s location. This approach significantly increases positioning accuracy while reducing the online computational complexity at the same time. The experimental results show that the proposed approach outperforms the best solutions presented in the literature.

Rotational Transitions in the N2 + Ion Induced by Collisions with Helium Atoms in Cold Helium Plasmas. A Quasiclassical Trajectory Study.

Cross-sections of state-to-state rotational transitions in electronically ground-state 14 (X2 ) ions induced by collisions with 4 He atoms have been calculated using a quasiclassical trajectory method and a set of artificial neural networks representing the /He potential energy surface. The training points for the neural networks have been calculated at a MCSCF (multi-configuration self-consistent field)/aug-cc-pVQZ level. A broad range of the /He collision energy has been considered ( eV) and the efficiency of vibrational transitions in the ion has also been analyzed. It has been found that vibrational transitions are negligible with respect to rotational transitions up to eV and that above this energy, both rotational and vibrational transitions in are marginal in the /He collisions.

Scroll Waves and Filaments in Excitable Media of Higher Spatial Dimension.

Excitable media are ubiquitous in nature, and in such systems the local excitation tends to self-organize in traveling waves, or in rotating spiral-shaped patterns in two or three spatial dimensions. Examples include waves during a pandemic or electrical scroll waves in the heart. Here we show that such phenomena can be extended to a space of four or more dimensions and propose that connections of excitable elements in a network setting can be regarded as additional spatial dimensions. Numerical simulations are performed in four dimensions using the FitzHugh-Nagumo model, showing that the vortices rotate around a two-dimensional surface which we define as the superfilament. Evolution equations are derived for general superfilaments of codimension two in an N-dimensional space, and their equilibrium configurations are proven to be minimal surfaces. We suggest that biological excitable systems, such as the heart or brain which have nonlocal connections can be regarded, at least partially, as multidimensional excitable media and discuss further possible studies in this direction.

Quasi-invariant and attracting sets of competitive neural networks with time-varying and infinite distributed delays

This paper focuses on the quasi-invariant set (QIS), global attracting set (GAS) and global exponential attracting set (GEAS) of competitive neural networks (CNNs) with time-varying and infinite distributed delays. For these purposes, based on the characteristics of nonnegative matrix and M-matrix, a new bidirectional delay integral inequality and a novel integro-differential inequality are first established. From the founded integral inequality, the existence conditions of the QIS and the GAS of the discussed system are obtained. Besides, the existence conditions of the GEAS are also given by the proposed integro-differential inequality, which gets rid of the construction of complex Lyapunov functions and functionals. The frameworks of the QIS, GAS and GEAS are also given. A numerical example is analyzed to confirm the validity of the obtained results in the end.

Cerebral palsy registers around the world: A survey.

To provide a description of cerebral palsy (CP) registers globally, identify which aim to report on CP epidemiology, and report similarities and differences across topics of importance for the sustainability and collaboration between registers. Representatives of all known CP registers globally (n = 57) were invited to participate. The online survey included 68 questions across aims, methodologies, output/impact, and stakeholder involvement. Responses were analysed using descriptive statistics. Forty-five registers participated, including three register networks. Twenty were newly established or under development, including 12 in low- and middle-income countries (LMICs). An epidemiological aim was reported by 91% of registers. Funding is received by 85% of registers, most often from not-for-profit organizations. CP definitions are comparable across registers. While the minimum data set of a register network is used by most registers, only 25% of identified items are collected by all three register networks. Ninety per cent of registers measure research activities/output, and 64% measure research impact. People with lived experience are involved in 62% of registers. There has been a recent surge in CP registers globally, particularly in LMICs, which will improve understanding of CP epidemiology. Ongoing efforts to address identified methodological differences are essential to validate comparison of results and support register collaboration. Cerebral palsy (CP) registers represent an increasing number of regions, including low- and middle-income, worldwide. Most registers collect the minimum data set of a CP register network. Research activities/output and impact are measured by most registers. The majority of registers involve people with lived experience in operation or research.

Relations between scaling exponents in unimodular random graphs

We investigate the validity of the “Einstein relations” in the general setting of unimodular random networks. These are equalities relating scaling exponents: \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} $$\\begin{aligned} d_{w} &= d_{f} + \ ilde{\\zeta }, \\\\ d_{s} &= 2 d_{f}/d_{w}, \\end{aligned}$$ \\end{document} where dw is the walk dimension, df is the fractal dimension, ds is the spectral dimension, and tilde{zeta } is the resistance exponent. Roughly speaking, this relates the mean displacement and return probability of a random walker to the density and conductivity of the underlying medium. We show that if df and tilde{zeta } geqslant 0 exist, then dw and ds exist, and the aforementioned equalities hold. Moreover, our primary new estimate d_{w} geqslant d_{f} + tilde{zeta } is established for all tilde{zeta } in mathbb{R}.For the uniform infinite planar triangulation (UIPT), this yields the consequence dw=4 using df=4 (Angel in Geom. Funct. Anal. 13(5):935–974, 2003) and tilde{zeta }=0 (established here as a consequence of the Liouville Quantum Gravity theory, following Gwynne-Miller 2020 and (Ding and Gwynne in Commun. Math. Phys. 374(3):1877–1934, 2020)). The conclusion dw=4 had been previously established by Gwynne and Hutchcroft (2018) using more elaborate methods. A new consequence is that dw=df for the uniform infinite Schnyder-wood decorated triangulation, implying that the simple random walk is subdiffusive, since df>2.

Sink Node Placement and Partial Connectivity in Wireless Sensor Networks.

This research delves into the aspects of communication and connectivity problems within random Wireless Sensor Networks (WSNs). It takes into account the distinctive role of the sink node, its placement, and application-specific requirements for effective communication while conserving valuable network resources. Through mathematical modeling, theoretical analysis, and simulation evaluations, we derive, compare, and contrast the probabilities of partial and full connectivity within a random WSN, factoring in network parameters and the maximum allowable hop distance/count hmax. hmax captures the diverse range of delay-sensitive requirements encountered in practical scenarios. Our research underscores the significant impact of the sink node and its placement on network connectivity and the sensor connection rate. The results exemplify a noteworthy decline in the sensor connection rate, dropping from 98.8% to 72.5%, upon relocating the sink node from the network center to the periphery. Moreover, as compared with full connectivity, partial connectivity and the sensor connection rate are more suitable metrics for assessing the communication capability of random WSNs. The results illustrate that 1.367 times more energy is required to connect less than 4% of the remote sensors, based on the examined network settings. Additionally, to increase the sensor connection rate slightly from 96% to 100%, an additional 538% more energy is required in multipath fading based on the widely adopted energy consumption model. This research and its outcomes contribute to establishing appropriate performance metrics and determining critical network parameters for the practical design and implementation of real-world wireless sensor networks.