Scalable Network Research Articles

Disutility associated with social isolation and loneliness in Germany: results of a population survey using the EQ-5D-5L instrument

BackgroundSocial isolation and loneliness are highly prevalent and may have a negative impact on health-related quality of life (HRQL). The EQ-5D-5L is a widely used questionnaire from which an index value for HRQL based on societal preferences (utility) can be derived. The purpose of this study was to estimate the loss in utility (i.e. disutility) associated with loneliness and social isolation in the German adult population.MethodsData came from a quota sample of individuals aged 18–74 years residing in Germany (n = 5,000) and representing the adult population in terms of age groups, gender and geographic locations. Data collection was conducted online in August and September 2023 by a certified market research firm. The EQ-5D-5L index score based on the German value set was used as outcome. Moreover, the established Lubben Social Network Scale was used to measure social isolation. The De Jong Gierveld tool was used to quantify loneliness. Groups affected by social isolation or loneliness were compared to non-affected groups, respectively. Differences in covariates between affected and non-affected groups were balanced using entropy balancing. Linear regressions were conducted afterwards (using the weights derived from the entropy balancing). Subgroup analyses by sex and age groups as well as various robustness checks were conducted.ResultsThe EQ-5D-5L index score was lower among individuals with social isolation compared to individuals without social isolation (β=-0.04, p < 0.001). Moreover, the EQ-5D-5L index score was lower among individuals with loneliness compared to individuals without loneliness (β=-0.07, p < 0.001). Several robustness checks produced similar results. The findings were almost the same for both women and men and varied only slightly between age groups.ConclusionsWe found a statistically significant and relevant disutility associated with social isolation and, even more pronounced, with loneliness. The magnitude of disutilities is similar to those reported for various chronic diseases. Taking into account the high prevalence of social isolation and, in particular, loneliness, the associated burden in terms of quality-adjusted life years (QALY) lost is likely to be tremendous. The results underline the need to take action against the high prevalence of loneliness and social isolation.

DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules

Modern generative models based on deep learning have made it possible to design millions of hypothetical materials. To screen these candidate materials and identify promising new materials, we need fast and accurate models to predict material properties. Graphical neural networks (GNNs) have become a current research focus due to their ability to directly act on the graphical representation of molecules and materials, enabling comprehensive capture of important information and showing excellent performance in predicting material properties. Nevertheless, GNNs still face several key problems in practical applications: First, although existing nested graph network strategies increase critical structural information such as bond angles, they significantly increase the number of trainable parameters in the model, resulting in a increase in training costs; Second, extending GNN models to broader domains such as molecules, crystalline materials, and catalysis, as well as adapting to small data sets, remains a challenge. Finally, the scalability of GNN models is limited by the over-smoothing problem. To address these issues, we propose the DenseGNN model, which combines Dense Connectivity Network (DCN), hierarchical node-edge-graph residual networks (HRN), and Local Structure Order Parameters Embedding (LOPE) strategies to create a universal, scalable, and efficient GNN model. We have achieved state-of-the-art performance (SOAT) on several datasets, including JARVIS-DFT, Materials Project, QM9, Lipop, FreeSolv, ESOL, and OC22, demonstrating the generality and scalability of our approach. By merging DCN and LOPE strategies into GNN models in computing, crystal materials, and molecules, we have improved the performance of models such as GIN, Schnet, and Hamnet on materials datasets such as Matbench. The LOPE strategy optimizes the embedding representation of atoms and allows our model to train efficiently with a minimal level of edge connections. This substantially reduces computational costs and shortens the time required to train large GNNs while maintaining accuracy. Our technique not only supports building deeper GNNs and avoids performance penalties experienced by other models, but is also applicable to a variety of applications that require large deep learning models. Furthermore, our study demonstrates that by using structural embeddings from pre-trained models, our model not only outperforms other GNNs in distinguishing crystal structures but also approaches the standard X-ray diffraction (XRD) method.

Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light

Optical approaches have made great strides towards the goal of high-speed, energy-efficient computing necessary for modern deep learning and AI applications. Read-in and read-out of data, however, limit the overall performance of existing approaches. This study introduces a multilayer optoelectronic computing framework that alternates between optical and optoelectronic layers to implement matrix-vector multiplications and rectified linear functions, respectively. Our framework is designed for real-time, parallelized operations, leveraging 2D arrays of LEDs and photodetectors connected via independent analog electronics. We experimentally demonstrate this approach using a system with a three-layer network with two hidden layers and operate it to recognize images from the MNIST database with a recognition accuracy of 92% and classify classes from a nonlinear spiral data with 86% accuracy. By implementing multiple layers of a deep neural network simultaneously, our approach significantly reduces the number of read-ins and read-outs required and paves the way for scalable optical accelerators requiring ultra low energy.

Reconstruction of High-Resolution Solar Spectral Irradiance Based on Residual Channel Attention Networks

The accurate measurement of high-resolution solar spectral irradiance (SSI) and its variations at the top of the atmosphere is crucial for solar physics, the Earth’s climate, and the in-orbit calibration of optical satellites. However, existing space-based solar spectral irradiance instruments achieve high-precision SSI measurements at the cost of spectral resolution, which falls short of meeting the requirements for identifying fine solar spectral features. Therefore, this paper proposes a new method for reconstructing high-resolution solar spectral irradiance based on a residual channel attention network. This method considers the stability of SSI spectral features and employs residual channel attention blocks to enhance the expression ability of key features, achieving the high-accuracy reconstruction of spectral features. Additionally, to address the issue of excessively large output features from the residual channel attention blocks, a scaling coefficient adjustment network block is introduced to achieve the high-accuracy reconstruction of spectral absolute values. Finally, the proposed method is validated using the measured SSI dataset from SCIAMACHY on Envisat-1 and the simulated dataset from TSIS-1 SIM. The validation results show that, compared to existing scaling coefficient adjustment algorithms, the proposed method achieves single-spectrum super-resolution reconstruction without relying on external data, with a Mean Absolute Percentage Error (MAPE) of 0.0302% for the reconstructed spectra based on the dataset. The proposed method achieves higher-resolution reconstruction results while ensuring the accuracy of SSI.

Reconfigurable entanglement distribution network based on pump management of a spontaneous four-wave mixing source.

Leveraging the unique properties of quantum entanglement, quantum entanglement distribution networks support multiple quantum information applications and are essential to the development of quantum networks. However, practical implementation poses fundamental challenges to network scalability and flexibility. Here, we propose a reconfigurable entanglement distribution network scheme based on tunable multipump excitation of a spontaneous four-wave mixing (SFWM) source and a time-sharing method. We characterize the two-photon correlation under different pump conditions to demonstrate the effect of pump degenerate and pump nondegenerate SFWM processes on the two-photon correlation and its tunability. Then, as a benchmark application, a 10-user fully connected quantum key distribution network is established in a time-sharing way with triple pump lights. Our results provide a promising networking scheme for large-scale entanglement distribution networks owing to its scalability, functionality, and reconfigurability.

Future-Proof Networks: NFV’S Role In 5g and Beyond

Riding the wave of the multifaceted rapid change in communication technologies and universal deployment of 5G, Network Functions Virtualization, or NFV, has emerged as a disruptive framework for adaptable, scalable, and cost-efficient networks. This can be because NFV decouples network functions from specialized hardware, affording the flexibility that addresses growing demands in ultra-reliable low-latency communications, enhanced mobile broadband, and massive machine-type communications. These capabilities are essential for meeting 5G’s requirements and preparing for the more complex challenges anticipated with 6G. As 6G transition begins, the role of NFV has to be reassessed in order to meet the increased challenges for ultra-low latency, massive connectivity, and unprecedented network reliability. This paper considers the ability of NFV to evolve with new networking standards by unpacking its potentials, limitations, and opportunities for improvement. This research determines factors critical to the scalability, flexibility, and efficiency of NFV in various network scenarios through simulations, case studies, and performance metric analyses. In doing this, it raises the need for advancing NFV architecture to stay relevant. Rather than only looking at current applications, this work addresses new future needs and maybe even answers issues for future stringent network standards that do not compromise on profitability. Such insights will be useful for developing future-proof infrastructures, positioning NFV as a cornerstone in nextgeneration networks such as 6G and beyond.

A few-shot oil spill segmentation network guided by multi-scale feature similarity modeling

Segmentation of oil spills with few-shot samples using UAV optical and SAR images is crucial for enhancing the efficiency of oil spill monitoring. Current oil spill semantic segmentation predominantly relies on SAR images, rendering it relatively data-dependent. We propose a flexible and scalable few-shot oil spill segmentation network that transitions from UAV optical images to SAR images based on the image similarity of oil spill regions in both types of images. Specifically, we introduce an Adaptive Feature Enhancement Module (AFEM) between the support set branch and the query set branch. This module leverages the precise oil spill information from the UAV optical image support set to derive initial oil spill templates and subsequently refines and updates the query oil spill templates through training to guide the segmentation of SAR oil spills with limited samples. Additionally, to fully exploit information from both low and high-level features, we design a Feature Fusion Module (FFM) to merge these features. Finally, the experimental results demonstrate the effectiveness of our network in enhancing the performance of UAV optical-to-SAR oil spill segmentation with few samples. Notably, the SAR oil spill detection accuracy reaches 75.88% in 5-shot experiments, representing an average improvement of 5.3% over the optimal baseline model accuracy.

A Study on the Mechanisms Influencing Youth Intermittent Disengagement Behavior on Short Video PlatformsBased on the Moderating Role of Self-Control Ability

As a product of the technological revolution in information media, short video platforms feature an increasingly complex scale of information, functional settings, and social networks. These complexities pose risks of perceived overload on the user level, leading to negative usage behaviors such as usage fatigue and intermittent disengagement. Based on the Stimulus-Organism-Response (S-O-R) theoretical framework, this study constructs a model of the mechanisms influencing intermittent disengagement behaviors among young users on short video platforms. Innovatively, the study introduces the moderating role of self-control theory. Data were collected through an online questionnaire, yielding 423 valid samples, and analyzed using structural equation modeling, mediation effect modeling, and moderation effect modeling. Results indicate that perceived information overload has a significant positive impact on the occurrence of intermittent disengagement among youth; usage fatigue partially mediates the pathway from perceived information overload to intermittent disengagement and fully mediates the pathway from perceived functional overload to intermittent disengagement; self-control ability positively moderates the relationship between usage fatigue and intermittent disengagement.

Cavity-mediated iSWAP oscillations between distant spins

Direct interactions between quantum particles naturally fall off with distance. However, future quantum computing architectures are likely to require interaction mechanisms between qubits across a range of length scales. In this work, we demonstrate a coherent interaction between two semiconductor spin qubits 250 μm apart using a superconducting resonator. This separation is several orders of magnitude larger than for the commonly used direct interaction mechanisms in this platform. We operate the system in a regime in which the resonator mediates a spin–spin coupling through virtual photons. We report the anti-phase oscillations of the populations of the two spins with controllable frequency. The observations are consistent with iSWAP oscillations of the spin qubits, and suggest that entangling operations are possible in 10 ns. These results hold promise for scalable networks of spin qubit modules on a chip.

Scalability and Security in Blockchain Networks: Evaluation of Sharding Algorithms and Prospects for Decentralized Data Storage

This article addresses the issues of scalability and security in blockchain networks, with a focus on sharding algorithms and decentralized data storage. Key challenges include the low throughput and high transaction latency in public networks such as Bitcoin and Ethereum. Sharding is examined as a method to enhance performance through data distribution, but it raises concerns regarding node management and reliability. Sharding schemes, such as Elastico, OmniLedger, Pyramid, RepChain, and SSchain, are analyzed, each presenting its own advantages and drawbacks. Alternative architectures like Directed Acyclic Graphs (DAGs) demonstrate potential for improved scalability but require further refinement to ensure decentralization and security. Protocols such as Brokerchain, Meepo, AHL, Benzene, and CycLedger offer unique approaches to addressing performance and transaction consistency issues. This article emphasizes the need for a comprehensive approach, including dynamic sharding, multi-level consensus, and inter-shard coordination. Additionally, a conceptual model is proposed that incorporates the sharding of transactions, states, and networks, which enables greater scalability and efficiency.

TD-GCN: A novel fusion method for network topological and dynamical features

The topological and dynamical features of complex networks hold abundant information. How to fully utilize this information for more accurate network structure mining is a significant issue. In this paper, we propose a novel method that simultaneously takes into account both network topological and dynamical features via graph convolutional networks (TD-GCN). Specifically, we obtain the topological features of the network by using the second-order adjacency matrix of the complex network, which captures indirect connections between nodes, for a more detailed representation of network structure, and use the SIS model to generate node state data in the complex network as the dynamical features of the network. The network topological and dynamical features are fused through the graph convolutional neural network. To verify the effectiveness and applicability of our method, we conduct extensive experiments on both simulated networks and real-world networks with various network scales. We comprehensively compare the proposed method with other existing methods in the domains of network link prediction and network node ranking learning. The experimental results show that our method can better capture the characteristic information in complex networks and has better performance compared with other methods.

Fault Tolerance Management Implementation from Medium-to-Large-Scale Networks

As network infrastructures grow in complexity, ensuring high availability and resilience becomes critical, especially for medium-to-large scale networks. This study focuses on the development and implementation of fault tolerance management within Software-defined networking (SDN) environments, aimed at minimizing downtime and enhancing network reliability. SDN’s centralized control and dynamic programmability provide an ideal framework for implementing efficient fault detection and recovery mechanisms. The proposed model leverages real-time monitoring, redundancy protocols, and adaptive rerouting strategies to mitigate the impact of node or link failures. Key components of the model include failover mechanisms, load balancing, and traffic rerouting algorithms, designed to maintain seamless network operations during failures. Through simulation and testing, the model demonstrates significant improvements in network resilience, reducing recovery time and ensuring uninterrupted service delivery. This research provides a comprehensive guide to implementing fault-tolerant networks using SDN, offering scalable solutions that can be adapted to various network sizes and configurations. The findings emphasize the potential of SDN to revolutionize fault management in modern network infrastructures, making it a crucial consideration for future network design and operations.

The impact of knowledge attributes and life cycle on the evolution of emerging technology innovation network: a TERGM analysis

PurposeThis study aims to explore the factors influencing the evolution of emerging technology innovation network (ETIN) in combination with the key attributes and life cycle of emerging technologies, particularly the impact of multiple knowledge attributes and technology life cycle on the ETIN evolution.Design/methodology/approachThis study collects 5G patent data and their citation information from the Derwent Innovations Index to construct a 5G technology innovation network (5GIN) as a sample network and conducts an empirical analysis of the 5GIN using the temporal exponential random graph model (TERGM).FindingsThe results indicate that during the 5GIN evolution, the network scale continues to expand and exhibits increasingly significant core-periphery structure, scale-free characteristic, small-world characteristic and community structure. Furthermore, the findings suggest that the multiple knowledge attributes based on the key attributes of emerging technologies, including knowledge novelty, coherence, growth and impact, have a significant positive influence on the ETIN evolution. Meanwhile, the temporal evolution of ETIN is also found to be correlated with the life cycle of emerging technologies.Originality/valueThis study extends the exploration of emerging technology research from a complex network perspective, providing a more realistic explanatory framework for the factors influencing ETIN evolution. It further highlights the important role that multiple knowledge attributes and the technology life cycle play within this framework.

Explaining how a psychosocial intervention (PROACTIVE) based on behavioural activation improved outcomes of depression in older adults living in deprived regions of Brazil: The mediating roles of reduced loneliness and stepped care

BackgroundThe PROACTIVE trial was a task-shared, stepped and collaborative care, psychosocial intervention based on psychoeducation and behavioural activation in 715 older adults (60–94 years; mean (SD) 68·6 (6.9) years; 74·1 % female), that was highly effective at improving recovery from depression among older adults in Brazil. Here we investigate mediators of the intervention's effectiveness. MethodsCausal mediation analysis using interventional indirect effects, simultaneously decomposed the total effect of PROACTIVE on recovery from depression (PHQ-9 < 10) into multiple indirect effects including: dose of intervention (numbers of sessions and activities completed); social support (Luben Social Network Scale); perceived loneliness (UCLA questionnaire); and additional sessions offered to participants who did not respond during the initial phase of the stepped care intervention. ResultsOf the intervention's total effect (difference in probability of recovery from depression between the intervention and control arms 0·216 [bias-corrected 95 % CI: 0·149, 0·291]): 13 % was mediated through reduced loneliness (0·028 [0·013, 0·046]); and 25 % through attending additional sessions for participants who did not initially respond to the intervention (0·055 [0·007, 0·102]). LimitationsDue to limitations in our sample size our study may lack power to detect some nuances such as interactions between different mediators. ConclusionsOur findings emphasise the importance of a home-based intervention to improve depression outcomes where participants are encouraged to self-select activities to mitigate against loneliness. Importantly, our findings suggest that the intervention's stepped-care component offering additional sessions to participants who did not experience an early response shows promise in ensuring a sustained recovery from depression.

Software-Based Security Framework for Edge and Mobile IoT

With the proliferation of Internet of Things (IoT) devices, ensuring secure communications has become imperative. Due to their low cost and embedded nature, many of these devices operate with computational and energy constraints, neglecting the potential security vulnerabilities that they may bring. This work-in-progress is focused on designing secure communication among remote servers and embedded IoT devices to balance security robustness and energy efficiency. The proposed approach uses lightweight cryptography, optimizing device performance and security without overburdening their limited resources. Our architecture stands out for integrating Edge servers and a central Name Server, allowing secure and decentralized authentication and efficient connection transitions between different Edge servers. This architecture enhances the scalability of the IoT network and reduces the load on each server, distributing the responsibility for authentication and key management.

Deep Reinforcement Learning and Enhanced Optimization for Real-Time Energy Management in Wireless Sensor Networks

Constraints are a major issue in radio-based communication in Wireless Sensor Networks, where each sensor node has a limited amount of power. Conventional clustering and optimization methods have been inappropriate for dynamic conditions which lead to timely energy drainage and reduce the network lifetime. In this research, the novel Deep Reinforcement Learning-Enhanced Hybrid African Vulture and Aquila Optimizer has been proposed that optimizes the dynamic clustering and energy-based parameters in real time. The proposed model is designed for optimizing the Wireless Sensor Networks, by including Deep Reinforcement Learning to adjust the dynamic formation of the base of the cluster on real-time data which leads to efficient energy utilization among all the sensor nodes. It combines the best properties of the Aquila and African Vulture Optimizer to optimize the network lifetime and energy consumption. The network lifetime, which is one of the most crucial characteristics, is optimized by using the global search algorithm of African Vulture Optimiser. In contrast, it is optimized by the localized search of Aquila optimizer to reduce energy consumption. The presented novel African Vulture and Aquila model outperforms the existing methods used convention-based optimization methods. It shows a 20% improvement in energy efficiency and faster convergence with better robustness while keeping the network scalability. The proposed approach is perfectly suited for the scalable WSNs which are mainly used in the environment such as smart cities and IoT systems where a timely adaptation process is inevitable.

МОДЕЛЮВАННЯ ПРОЦЕСУ ФОРМУВАННЯ БЛОКІВ У БЛОКЧЕЙНІ ТА ЙОГО ВПЛИВ НА МАСШТАБОВАНІСТЬ

The article investigates the process of block formation in blockchain networks and the impact of node network architecture and consensus algorithms on their scalability and performance. Analysis of blockchain system scalability is important due to problems that arise when network load increases, particularly the increase in the number of block forks and transaction confirmation times. The research focuses on studying the impact of network delays and the choice of consensus algorithm on the performance and scalability of blockchain networks. The main attention is devoted to mathematical models that describe block formation, as well as the analysis of factors affecting transaction processing speed and throughput. The primary consensus algorithms, such as Proof of Work (PoW) and Proof of Stake (PoS), are considered, and their impact on scalability in implementations based on the Ethereum Virtual Machine (EVM) and Bitcoin is compared. Experimental studies using Geth and Amazon cloud services revealed that the application of the Proof of Stake (PoS) consensus algorithm increases network performance by reducing the complexity of the block formation process in blockchain networks by 99% and accelerates consensus achievement by 70% compared to Proof of Work (PoW). It was also established that increasing the number of nodes from 5 to 50 reduces the network's throughput by almost 10%, and the average confirmation time doubles. The obtained results are aimed at solving the scalability issue by reducing transaction confirmation times for the implementation of decentralized technologies in the Internet of Things (IoT) sphere, where processing speed and storage of large volumes of data are critically important. Keywords: blockchain, block formation, consensus algorithms, decentralized technologies, Ethereum Virtual Machine (EVM), Internet of Things (IoT), mathematical modeling, network delays, scalability.

Accurate and Scalable Graph Convolutional Networks for Recommendation Based on Subgraph Propagation

Accurate and Scalable Graph Convolutional Networks for Recommendation Based on Subgraph Propagation

A Unified Federated Deep Q Learning Caching Scheme for Scalable Collaborative Edge Networks

A Unified Federated Deep Q Learning Caching Scheme for Scalable Collaborative Edge Networks

An Extensive Analysis of Current Software Defined Network Optimization Techniques

In the modern, technologically advanced era, the use of devices connected to the Internet has increased exponentially. This has led to a significant increase in Internet activity. Network scalability, low levels of service, and the need to manage a large number of clients on a single server (which can lead to denial of service (DOS) attacks) are just a few of the consequences of increased traffic if you are a load balancer, . A method proposed in the literature. Although load balancers are popular, studies have shown that they have a few drawbacks, such as not being programmable and exclusive to a single vendor to overcome these issues and the subsequent increase in Internet traffic saw the emergence of Software Defined Networking (SDN) as a paradigm shift. Software defined networking (SDN) enables programmable load balancers and gives customers the freedom to develop and implement their own load-balancing algorithms this exam covers the origins of software-defined networking (SDN) and Open Flow, with impact on weight and density so included. In this article, we will go through various SDN load balancing techniques. This review builds on existing research challenges, their answers, and possible future research directions. Load balancing in intelligent software-defined (SDN) algorithms is typically performed using mathematical tools. Finally, we detail the metrics used to evaluate the performance of the algorithms.