Network Size Research Articles

Evaluation of Voltage Fluctuations Effect on Reconfiguration of Distribution Systems

Distribution system operators seriously restrict bus voltage deviations within a small range from the nominal voltage to ensure security aspects. The distribution network reconfiguration technique is often performed to improve these voltage levels, if not to reduce power losses. However, distribution systems feed various kinds of consumers, and their power demand dependency on bus voltages is different based on their load type. Modifying the network configuration changes the system's nodal voltage levels, and then the load power is also impacted after the reconfiguration. Only a few research works in the field of distribution system reconfiguration have included load power dependency to nodal voltages in their proposed models. However, these studies have not discussed whether adding voltage-depend loads to the reconfiguration problem is beneficial or only increases the computational efforts without substantial gains on the optimal solution. With the intention to fill this gap, this article develops a comprehensive analysis of how the change of load according to the voltage affects reconfiguration solutions. This investigation aims to help distribution system researchers and readers understand this phenomenon and highlight the benefits and drawbacks of taking it into account. Also, a robust reconfiguration model for considering load types and nodal voltage is proposed. The model is validated in different network models and sizes, where the obtained results are compared with other models presented in the literature. The results analysis indicates that voltage fluctuations impact the distribution network configuration through voltage effect on power losses and the system nodal loads. Also, load voltage-dependent models are more flexible than constant demand based-formulations in finding feasible solutions in more restricted voltage tolerances.

Social network characteristics impact the recognition of degraded speech by adult cochlear implant users

Social networks influence the quantity and quality of linguistic input experienced in everyday listening environments. In normal-hearing listeners, the variable linguistic input provided by larger and more diverse social networks has been shown to support speech processing. However, for adult cochlear implant (CI) users, limitations in the perception of linguistic and talker details may limit input variability benefits. The current study examined the effects of social network size and diversity on the recognition of spectrotemporally degraded speech by adult CI users. Twenty-six postlingually deafened adult CI users completed a detailed questionnaire about their regular communication partners. Social network size was calculated as the number of regular communication partners, and social network diversity was calculated as the degree of age, education, and accent heterogeneity among communication partners. Social network metrics were compared to vowel, word, and sentence recognition accuracy scores, also controlling for basic auditory ability. Results showed that social network age diversity was moderately to strongly correlated with word and sentence recognition accuracy. Social network size and other diversity metrics were not related to word or sentence recognition accuracy. These findings suggest that more diverse input from different age groups facilitates spoken word recognition in adult CI users.

Pandemic at the Gates: The Improvement of Social Capital and Collective Efficacy in Neighbourhoods of the Greater Jakarta Metropolitan Area

This study seeks to interpret the presence of social capital and collective efficacy among residents of neighbourhoods in the Greater Metropolitan Jakarta Area (GJMA) due to the COVID-19 pandemic. A quantitative study was undertaken by surveying 922 GJMA residents on the basis of their perception of the improvement of five components of social capital. During this period, neighbourhood residents improved their social capital and practised collective efficacy in several concrete forms to meet their common goals and procure the required goods. Although this study finds the shared values and norms, social network size, mutual aid and trust, civic engagement and participation in activities improved during the pandemic, the level of monthly income and marital and tenurial status implicated the quality of social ties and capital.

A Decomposition Approach to Multiagent Systems With Bernoulli Packet Loss

In this paper, we extend the decomposable systems framework to multi-agent systems with Bernoulli distributed packet loss with uniform probability. The proposed sufficient analysis conditions for mean-square stability and $H_2$-performance -- which are expressed in the form of linear matrix inequalities -- scale linearly with increased network size and thus allow to analyse even very large-scale multi-agent systems. A numerical example demonstrates the potential of the approach by application to a first-order consensus problem.

A Lightweight Remote Sensing Aircraft Object Detection Network Based on Improved YOLOv5n

Due to the issues of remote sensing object detection algorithms based on deep learning, such as a high number of network parameters, large model size, and high computational requirements, it is challenging to deploy them on small mobile devices. This paper proposes an extremely lightweight remote sensing aircraft object detection network based on the improved YOLOv5n. This network combines Shufflenet v2 and YOLOv5n, significantly reducing the network size while ensuring high detection accuracy. It substitutes the original CIoU and convolution with EIoU and deformable convolution, optimizing for the small-scale characteristics of aircraft objects and further accelerating convergence and improving regression accuracy. Additionally, a coordinate attention (CA) mechanism is introduced at the end of the backbone to focus on orientation perception and positional information. We conducted a series of experiments, comparing our method with networks like GhostNet, PP-LCNet, MobileNetV3, and MobileNetV3s, and performed detailed ablation studies. The experimental results on the Mar20 public dataset indicate that, compared to the original YOLOv5n network, our lightweight network has only about one-fifth of its parameter count, with only a slight decrease of 2.7% in mAP@0.5. At the same time, compared with other lightweight networks of the same magnitude, our network achieves an effective balance between detection accuracy and resource consumption such as memory and computing power, providing a novel solution for the implementation and hardware deployment of lightweight remote sensing object detection networks.

Research on factors influencing Chinese parents' support for death education: a cross-sectional survey.

This study aims to explore the factors influencing Chinese parents' attitudes toward death education. Given the current lack of such education in China, this research is particularly significant. Death education is vital for shaping the values of young people and alleviating mental health issues, such as depression and suicidal tendencies. By identifying these influencing factors, this study seeks to provide guidance for policymakers and educators in promoting the development and widespread adoption of death education. To do so, a national cross-sectional quota sample of 12,435 Chinese parents was used. Borrowing from social-ecological theory, the researchers carried out multiple stepwise regression analyses to examine the individual, family, and social-level factors that shape the supportive attitudes of Chinese parents toward death education. The findings revealed that at the individual level, parent (β = 0.04, p < 0.001), education level (β = 0.07, p < 0.001), and religious belief (β = -0.02, p < 0.05) were significant predictors of Chinese parents' support for death education. Meanwhile, at the family and social level, average monthly household income (β = 0.07, p < 0.001), family health (β = 0.03, p < 0.05), family communication (β = 0.02, p < 0.05), social support (β = 0.15, p < 0.001), neighborhood relations (β = 0.11, p < 0.001), and social network size (β = 0.05, p < 0.001) were significant predictors of Chinese parents' supportive attitudes toward death education. Based on these findings, it is suggested that the relevant development, planning, publicity, and public welfare groups and government departments should promote death education, provide more social support, and encourage neighborhood harmony. As higher education and average monthly household income were found to significantly impact the support, the government should improve access to higher education and actively work to increase residents' income to facilitate the development of death education.

TAILOR: InTer-feAture distinctIon fiLter fusiOn pRuning

Filter pruning is an effective method for reducing the size of convolutional neural networks without sacrificing performance. Most filter pruning methods prioritize filters with high information content, but fail to consider that filters with low information content might capture essential features. Moreover, we have discovered that the distinctions among feature maps generated by filters can identify crucial features. Based on this insight, we propose a novel pruning method called inTer-feAture distinctIon fiLter fusiOn pRuning (TAILOR), which fuses the feature distinctions between filters. TAILOR randomly selects multiple filter sets within a convolutional layer and calculates the output feature maps of the next convolutional layer of these sets. Subsequently, an intelligent distinction optimization scheme is proposed to obtain the optimal filter set for filter pruning, which supplants the original convolutional layer. Experimental results indicated that the inter-feature distinctions among filters significantly affect filter pruning. TAILOR outperforms state-of-the-art filter-pruning methods in terms of model prediction accuracy, floating-point operations, and parameter scale. For instance, with VGG-16, TAILOR achieves a 73.89% FLOPs reduction by removing 91.85% of the parameters, while improving accuracy by 0.36% on the CIFAR-10.

Triadic signatures of global human mobility networks.

Global refugee and migrant flows form complex networks with serious consequences for both sending and receiving countries as well as those in between. While several basic network properties of these networks have been documented, their finer structural character remains under-studied. One such structure is the triad significance profile (TSP). In this study, the TSPs of global refugee and migrant flow networks are assessed. Results indicate that the migrant flow network's size and TSP remain stable over the years; its TSP shares patterns with social networks such as trade networks. In contrast, the refugee network has been more dynamic and structurally unstable; its TSP shares patterns with networks in the information-processing superfamily, which includes many biological networks. Our findings demonstrate commonality between migrant and social networks as well as between refugee and biological networks, pointing to possible interdisciplinary collaboration-e.g., application of biological network theories to refugee network dynamics-, potentially furthering theoretical development with respect to both network theory and theories on human mobility.

Impact of non-diffusive interactions on Turing instability

Observational studies of the chemical reactions have shown that the uniform state, driven by the diffusion differences of activator and inhibitor species, can seed the spontaneous emergence of spatially organized patterns—also known as Turing instability. The presence of diffusive interaction between the species in discrete media can naturally form the structure of the network Laplacian, thereby resulting in that the Turing instability can be better understood via the eigenvectors and eigenvalues of the graph Laplacian. However, it is still unclear whether and under which conditions the Turing instability can be induced in the absence of diffusive transport. In this work, to overcome the limitation of network Laplacian matrix that only has non-positive eigenvalues, we investigate the effects of non-diffusive (or no-local) coupling between the species on the Turing instability for the directed and undirected networks, respectively, where the interaction matrix may produce the positive and negative eigenvalues. In particular, we first derive the critical conditions that the Turing instability occurs by employing the linear stability analysis. Then we apply them to the networked systems governed by the FitzHugh–Nagumo dynamics and obtain the critical range of eigenvalues of the interaction matrix. Finally, we use the Girko’s circular law to predict the eigenvalues’ distribution, and show, via extensive numerical simulations, the transition process of the Turing instability depending on network size, network connectivity and connection weights (intraspecific interaction strength). Our results reveal that in the absence of diffusive transport, the Turing instability still can arise, offering a new perspective on the generation mechanism of the Turing instability.

The local topology of dynamical network models for biology

Abstract The search for motifs—recurrent patterns in network topology—has led to the identification of universal classes of complex systems across diverse fields and has served as a quantitative tool to reveal common properties in both evolved and designed networks. In this study, we investigate the presence and significance of network superfamilies—previously identified through the census of triadic motifs—in the largest data set of dynamic, biological network models. We present triad significance profiles of 71 existing biological network models, all experimentally inspired. The generated data are treated in an unbiased manner and consistently clustered into two classes using several unsupervised techniques. The more prevalent class does exhibit a strong correlation with the superfamily of sensory transmission networks, which are characterized by the feedforward loop motif commonly found in signal-processing systems. Surprisingly, the remaining class shows a better correlation with the superfamily of word-adjacency networks. To better understand this, the results are analysed for varying network size thresholds, and their connection to the effect of model building activity is examined. It is highlighted that the more the model focuses on smaller portions of the regulatory network, as a result of the coarse-graining of the boundary dynamics and the peripheral regions of the network, the more its topology starts resembling that of ‘sentences’ of word-adjacency networks.

Social Network of the Executive Managers and the Performance of Iranian Start-Up Businesses; Evidence based on the Findings of the Global Entrepreneurship Monitor (GEM)

Social Network of the Executive Managers and the Performance of Iranian Start-Up Businesses; Evidence based on the Findings of the Global Entrepreneurship Monitor (GEM)

FV-EffResNet: an efficient lightweight convolutional neural network for finger vein recognition.

Several deep neural networks have been introduced for finger vein recognition over time, and these networks have demonstrated high levels of performance. However, most current state-of-the-art deep learning systems use networks with increasing layers and parameters, resulting in greater computational costs and complexity. This can make them impractical for real-time implementation, particularly on embedded hardware. To address these challenges, this article concentrates on developing a lightweight convolutional neural network (CNN) named FV-EffResNet for finger vein recognition, aiming to find a balance between network size, speed, and accuracy. The key improvement lies in the utilization of the proposed novel convolution block named the Efficient Residual (EffRes) block, crafted to facilitate efficient feature extraction while minimizing the parameter count. The block decomposes the convolution process, employing pointwise and depthwise convolutions with a specific rectangular dimension realized in two layers (n × 1) and (1 × m) for enhanced handling of finger vein data. The approach achieves computational efficiency through a combination of squeeze units, depthwise convolution, and a pooling strategy. The hidden layers of the network use the Swish activation function, which has been shown to enhance performance compared to conventional functions like ReLU or Leaky ReLU. Furthermore, the article adopts cyclical learning rate techniques to expedite the training process of the proposed network. The effectiveness of the proposed pipeline is demonstrated through comprehensive experiments conducted on four benchmark databases, namely FV-USM, SDUMLA, MMCBNU_600, and NUPT-FV. The experimental results reveal that the EffRes block has a remarkable impact on finger vein recognition. The proposed FV-EffResNet achieves state-of-the-art performance in both identification and verification settings, leveraging the benefits of being lightweight and incurring low computational costs.

Curvature-enhanced graph convolutional network for biomolecular interaction prediction

Geometric deep learning has demonstrated a great potential in non-Euclidean data analysis. The incorporation of geometric insights into learning architecture is vital to its success. Here we propose a curvature-enhanced graph convolutional network (CGCN) for biomolecular interaction prediction. Our CGCN employs Ollivier-Ricci curvature (ORC) to characterize network local geometric properties and enhance the learning capability of GCNs. More specifically, ORCs are evaluated based on the local topology from node neighborhoods, and further incorporated into the weight function for the feature aggregation in message-passing procedure. Our CGCN model is extensively validated on fourteen real-world bimolecular interaction networks and analyzed in details using a series of well-designed simulated data. It has been found that our CGCN can achieve the state-of-the-art results. It outperforms all existing models, as far as we know, in thirteen out of the fourteen real-world datasets and ranks as the second in the rest one. The results from the simulated data show that our CGCN model is superior to the traditional GCN models regardless of the positive-to-negative-curvature ratios, network densities, and network sizes (when larger than 500).

Associations between Sleep Quality and Serum Levels of Neurofilament Light in Individuals with Premanifest Huntington Disease

Abstract Objectives To evaluate the associations between sleep quality and serum levels of neurofilament light (NfL) protein in individuals with premanifest Huntington disease (HD). Materials and Methods We recruited 28 individuals with premanifest HD from a pre-existing database (of the Huntington's Environmental Research Optimisation Scheme, HEROs). The participants filled out the Pittsburgh Sleep Quality Index (PSQI), a subjective measure of sleep quality, and blood was collected via routine venepuncture to measure peripheral NfL levels. Results The PSQI scores (median: 5.0; interquartile range: 4.0–7.5) indicated poor sleep quality. General linear modelling revealed no significant (p = 0.242) association between PSQI scores and NfL levels. No significant differences were found between individuals with good and poor sleep quality for any demographic variable collected. Discussion Contrary to studies on other neurological conditions, there was no association between sleep quality and NfL levels in individuals with premanifest HD. This was unexpected, given the influence of environmental factors (such as social network size) on neurodegeneration in individuals with premanifest HD.

Attractor neural networks with double well synapses.

It is widely believed that memory storage depends on activity-dependent synaptic modifications. Classical studies of learning and memory in neural networks describe synaptic efficacy either as continuous or discrete. However, recent results suggest an intermediate scenario in which synaptic efficacy can be described by a continuous variable, but whose distribution is peaked around a small set of discrete values. Motivated by these results, we explored a model in which each synapse is described by a continuous variable that evolves in a potential with multiple minima. External inputs to the network can switch synapses from one potential well to another. Our analytical and numerical results show that this model can interpolate between models with discrete synapses which correspond to the deep potential limit, and models in which synapses evolve in a single quadratic potential. We find that the storage capacity of the network with double well synapses exhibits a power law dependence on the network size, rather than the logarithmic dependence observed in models with single well synapses. In addition, synapses with deeper potential wells lead to more robust information storage in the presence of noise. When memories are sparsely encoded, the scaling of the capacity with network size is similar to previously studied network models in the sparse coding limit.

Microbial community and network responses across strong environmental gradients: How do they compare with macroorganisms?

The way strong environmental gradients shape multispecific assemblages has allowed us to examine a suite of ecological and evolutionary hypotheses about structure, regulation and community responses to fluctuating environments. But whether the highly diverse co-occurring microorganisms are shaped in similar ways as macroscopic organisms across the same gradients has yet to be addressed in most ecosystems. Here, we characterize intertidal biofilm bacteria communities, comparing zonation at both the "species" and community levels, as well as network attributes, with co-occurring macroalgae and invertebrates in the same rocky shore system. The results revealed that the desiccation gradient has a more significant impact on smaller communities, while both desiccation and submersion gradients (surge) affect the larger, macroscopic communities. At the community level, we also confirmed the existence of distinct communities within each intertidal zone for microorganisms, similar to what has been previously described for macroorganisms. But our results indicated that dominant microbial organisms along the same environmental gradient exhibited less differentiation across tidal levels than their macroscopic counterparts. However, despite the substantial differences in richness, size and attributes of co-occurrence networks, both macro- and micro-communities respond to stress gradients, leading to the formation of similar zonation patterns in the intertidal rocky shore.

On size-independent sample complexity of ReLU networks

We study the sample complexity of learning ReLU neural networks from the point of view of generalization. Given norm constraints on the weight matrices, a common approach is to estimate the Rademacher complexity of the associated function class. Previously [9] obtained a bound independent of the network size (scaling with a product of Frobenius norms) except for a factor of the square-root depth. We give a refinement which often has no explicit depth-dependence at all.

Exclusion or Subsidization? A Competitive Analysis of Quality Regulation Strategy for Two-Sided Platforms

With two-sided platforms becoming an increasingly ubiquitous business model, quality is a vital factor for the success of high-technology platforms that face fierce competition. To maintain competency, high-technology platforms commonly use two quality regulation strategies: the exclusion strategy (E strategy), in which the platform denies access to low-quality complementors, and the subsidization strategy (S strategy), in which the platform provides a fixed subsidy to high-quality complementors. This paper investigates the optimal quality regulation strategy for platforms in a duopoly setting. We examine and compare three scenarios: (i) both platforms adopt the exclusion strategy, i.e. mode EE, (ii) both platforms adopt the subsidization strategy, i.e. mode SS, and (ii) one platform adopts the subsidization strategy while the other adopts the exclusion strategy, i.e. mode SE. First, we find that although the developer network size is larger and the platforms charge developers higher access fees under SS, the average quality and the consumer access fees are lower under SS than under EE, leading to lower profits for platforms. Second, under SE, in comparison with the platform that adopts the exclusion strategy, the platform that uses the subsidization strategy achieves lower average quality and larger network sizes on both sides but may set higher or lower access fees on both sides. Moreover, the platform under the subsidization strategy profits more (less) when the operation cost on the developer side is high (low). Third, asymmetric mode SE does not necessarily induce moderate outcomes for market participants compared to modes EE and SS. We also examine the equilibrium mode by considering platforms’ optimal strategies for quality regulation. Our analyses reveal that as the operation cost on the developer side increases, the equilibrium mode evolves from EE to SE/ES and then to SS. These results and insights are robust to several alternative assumptions.

Environmental performance factors: insights from CSR-linked compensation, committees, disclosure, targets, and board composition

ABSTRACT This research investigates the factors influencing Corporate Environmental Performance (CEP) using S&P 500 firm-level data spanning 2001-2022. Drawing on existing literature, the study argues that corporate environmental performance (CEP) is positively affected by certain corporate characteristics. Specifically, the study reveals that CSR-linked compensation, the presence of a CSR committee, disclosure policies, and environmental targets are positively associated with CEP. Furthermore, the study shows that the inclusion of independent directors and female board members can also lead to higher CEP scores. Additionally, the findings indicate that analyst coverage and network size are positively linked to CEP. Robustness tests, including 2SLS, subsample analysis, and alternative measures, provide further support for the primary conclusions. The study's results contribute to the growing body of research on the importance of corporate characteristics in promoting sustainable business practices.

Design and Analysis of a Bandwidth Aware Adaptive Multipath N-Channel Routing Protocol for 5G Internet of Things (IoT)

Large numbers of mobile wireless nodes that can move randomly and join or leave the network at any moment make up mobile ad-hoc networks. A significant number of messages are delivered during information exchange in populated regions because of the Internet of Things' (IoT) exponential increase in connected devices. Congestion can increase transmission latency and packet loss by causing congestion. More network size, increased network traffic, and high mobility that necessitate dynamic topology make this problem worse. An adaptive Multipath Multichannel Energy Efficient (AMMEE) routing strategy is proposed in this study, in which route selection strategies depend on forecasted energy consumption per packet, available bandwidth, queue length, and channel utilization. While multichannel uses a channel-ideal assignment process to lessen network collisions, multipath offers various paths and balances network strain. The link bandwidth is split up into a few sub-channels in the multichannel mechanism. To reduce network collisions, several source nodes simultaneously access the channel bandwidth. The cooperative multipath multichannel technique offers several paths from a single source or from several sources to the destination without colliding or becoming congested. The AMMEE routing approach is the basis for path selection. A load- and bandwidth-aware routing mechanism in the proposed AMMEE chooses the path based on node energy and forecasts their lifetime, which improves network dependability. The outcome demonstrates a comparative analysis of various multichannel medium access control (MMAC) techniques, including Parallel Rendezvous Multi Channel Medium Access Protocol (PRMMAC), Quality of Service Ad hoc On Demand Multipath Distance Vector (QoS-AOMDV), Q-learning-based Multipath Routing (QMR), and Topological Change Adaptive Ad hoc On-demand Multipath Distance Vector (TA-AOMDV) and the proposed AMMEE method. The results show that the AMMEE approach outperforms alternative systems. Doi: 10.28991/ESJ-2024-08-01-018 Full Text: PDF