Scalable Network Research Articles

Fault Diagnosis of Distributed Energy Distribution Network Based on PSO-BP

With the increasing scale of distribution network at distribution time, its complexity grows geometrically, and its fault diagnosis becomes more and more difficult. Aiming at the slow convergence and low accuracy of traditional backpropagation neural network in dealing with single-phase ground faults, the study proposes a backpropagation neural network based on improved particle swarm optimization. The model optimizes the weights and acceleration constants of the particle swarm algorithm by introducing dynamic coefficients to enhance its global and local optimization seeking ability. It is also applied in optimizing the parameters of backpropagation neural network and constructing the routing model and ranging model for fault diagnosis about distributed energy distribution network. The simulation results revealed that the maximum absolute error of the improved method is 0.08. While the maximum absolute errors of the traditional backpropagation neural network and the particle swarm optimized backpropagation neural network were 0.65 and 0.10, respectively. The fluctuation of the relative errors of the research method was small under different ranges of measurements. At 8.0 km, the minimum relative error was 0.39% and the maximum relative error was 2.81%. The results show that the improved method proposed in the study significantly improves the accuracy and stability of fault diagnosis and localization in distribution networks and is applicable to complex distribution network environments. The method has high training efficiency and fault detection capability and provides an effective tool for distribution network fault management.

Purcell Enhancement and Spin Spectroscopy of Silicon Vacancy Centers in Silicon Carbide Using an Ultrasmall Mode-Volume Plasmonic Cavity.

Silicon vacancy (VSi) centers in 4H-silicon carbide have emerged as a strong candidate for quantum networking applications due to their robust electronic and optical properties, including a long spin coherence lifetime and bright, stable emission. Here, we report the integration of VSi centers with a plasmonic nanocavity to Purcell enhance the emission, which is critical for scalable quantum networking. Employing a simple fabrication process, we demonstrate plasmonic cavities that support a nanoscale mode volume and exhibit an increase in the spontaneous emission rate with a measured Purcell factor of up to 48. In addition to investigating the optical resonance modes, we demonstrate an improvement in the optical stability of the spin-preserving resonant optical transitions relative to the radiation-limited value. The results highlight the potential of nanophotonic structures for advancing quantum networking technologies and emphasize the importance of optimizing emitter-cavity interactions for efficient quantum photonic applications.

Quality over quantity - rethinking social participation in dementia prevention: results from the AgeWell.de trial.

Social participation as a protective factor against cognitive decline was one of the targets in the AgeWell.de study, a multi-domain interventional trial in a sample of older adults at increased risk for dementia. This study aimed to examine differential effects of the intervention and other influencing factors on social participation throughout the trial. A longitudinal analysis of study data at the primary follow-up after 24 months (n = 819) was conducted. The Lubben Social Network Scale (LSNS-6) was used to assess quantitative aspects of social networks, and self-reported social activities were classified using a three-tiered categorical framework to capture qualitative aspects. A positive effect of the intervention was observed at the qualitative framework level, with an OR of 1.38 [95% CI: 1.05-1.82] for achieving or maintaining higher social participation at follow-up, while no effect could be detected on quantitative social network characteristics. Later phases of the Covid-19 pandemic showed a negative impact on the level of social participation at follow-up with an OR of 0.84 [95% CI: 0.75-0.95]. These findings suggest that by focusing on qualitative aspects of social participation as a component of dementia prevention, future interventions can promote enriched social interactions within established social networks. German Clinical Trials Register (DRKS) ID DRKS00013555.

Fate of chlordecone in soil food webs in a banana agroecosystem in Martinique

Large quantities of chlordecone-based insecticides were produced and used throughout the world. One of its most important uses was to control the damage caused by Cosmopolites sordidus in banana-growing regions. In the islands of Martinique and Guadeloupe, 18,000 ha of farmland are potentially contaminated. Despite the key role played by soil macrofauna in agroecosystems, there are currently no data on their contamination. The aim of this study was to explore the fate of chlordecone (CLD) and its transfer to different organisms of the soil food web.Seven species of invertebrates representing different taxonomic groups and trophic levels of the soil communities of Martinique were targeted and collected in six experimental banana fields, with a level of contamination within a range of values classically observed. Soil samples and macrofauna from the study sites were analysed for CLD and chlordecol (CLDOH) its main transformation product. The contamination of the soil fauna were related to δ15N (trophic level), proportion of soil ingestion (diet) and types of epidermis (mucus or exoskeleton) in order to study the different mechanisms of macrofauna contamination.Presence of CLD and CLDOH could be quantified in all the soil organisms from contaminated fields. Results showed a significant relationship between the CLD contamination of detritivorous and the ash content of their faeces, suggesting that soil ingestion was the main contamination pathway. In contrast, the exoskeleton-bearing diplopod Trigoniulus coralinus and the soft-bodied earthworm Eudrilus eugeniae, both detritivores with a comparable diet, had similar contamination levels, suggesting that the type of tegument has little influence on bioaccumulation. At the scale of the entire trophic network, a significant relationship was uncovered between δ15N values and CLD contamination of the fauna, therefore providing some in situ evidence for a bioamplification process along the soil food chain.

Identification and Evaluation of Vulnerable Links in a Distribution Network with Renewable Energy Source Based on Minimum Discriminant Information

With the increase in the proportion of photovoltaic and wind power access, the scale and form of distribution networks are becoming more and more complex. The traditional single distribution network vulnerability assessment method is difficult to use to identify the vulnerable links in the distribution network. Therefore, this paper proposes a method for identifying and evaluating vulnerable links in distribution networks based on minimum discriminant information. First, considering the influence of distributed grid connection, an improved probabilistic power flow calculation method is proposed, which improves the calculation efficiency and accuracy. Second, considering the correlation degree, transmission capacity, and voltage stability of branches in the distribution network, the identification index of vulnerable lines is defined. Based on power quality and operating state, the identification index of vulnerable nodes in a distribution network is defined. Finally, based on the indicators of vulnerable nodes and vulnerable lines, the vulnerable links in the distribution network are comprehensively evaluated based on the principle of minimum discriminant information, and the vulnerable links of the entire distribution network are evaluated according to different degrees of vulnerability. The rationality and effectiveness of the proposed method are verified via an example analysis of actual power grid data.

DEVELOPMENT OF SCIENTIFIC FOUNDATIONS AND METHOD OF AUTONOMOUS POWER AND GAS SUPPLY OF REMOTE CONSUMERS FROM GAS DISTRIBUTION NETWORKS

The development and implementation of independent power supply systems with a high degree of autonomy is becoming an urgent task for the development of remote and inaccessible regions of the country, both due to the lack of energy capacities and accessible networks in newly developed areas, and the high cost of allocating separate power lines for remote and compact settlements. The increase in the scale of central networks is associated with high capital investments and long project implementation time, which creates inconvenience for consumers and the development of small and medium-sized enterprises. At the same time, unlike power supply, gasification issues, which are actively being resolved in all regions of the country, make it possible to connect new consumers of natural gas several times faster and cheaper, which is due to the high manufacturability and speed of construction of polyethylene gas pipelines of distribution networks, provided that gas pipelines pass in the region. In addition to lower capital and operating costs, the process of transporting energy resources through pipelines in the form of a primary source — gas — is characterized by significantly lower losses compared to water and electricity. At the same time, network gas in combination with trigeneration technologies and cryogenic hydrocarbon storage can be used not only as boiler fuel, but also as motor fuel, a source for generating electricity, heat and even cold with a replenished autonomous reserve. Thus, as an alternative solution to traditional power systems, it is proposed to use hybrid power systems — systems based on trigeneration plants with gas piston installations, turbine and screw machines, as well as cryogenic gas storage facilities that can provide the population with electricity, heat, cold and motor fuel. The article analyzed the state and development of the fuel and energy complex in Russia, conducted a study of the needs for heat, gas and electricity for an average compact settlement with small and medium-sized enterprises. Analysis and selection of equipment parameters for uninterrupted autonomous power supply of the economy was carried out.

MadQCI: a heterogeneous and scalable SDN-QKD network deployed in production facilities

Current quantum key distribution (QKD) networks focus almost exclusively on transporting secret keys at the highest possible rate. Consequently, they are built as mostly fixed, ad hoc, logically, and physically isolated infrastructures designed to avoid any penalty to the quantum channel. This architecture is neither scalable nor cost-effective and future, real-world deployments will differ considerably. The structure of the MadQCI QKD network presented here is based on disaggregated components and modern paradigms especially designed for flexibility, upgradability, and facilitating the integration of QKD in the security and telecommunications-networks ecosystem. These underlying ideas have been tested by deploying many QKD systems from several manufacturers in a real-world, multi-tenant telecommunications network, installed in production facilities and sharing the infrastructure with commercial traffic. Different technologies have been used in different links to address the variety of situations and needs that arise in real networks, exploring a wide range of possibilities. Finally, a set of realistic use cases has been implemented to demonstrate the validity and performance of the network. The testing took place during a period close to three years, where most of the nodes were continuously active.

The Hierarchical Coevolutionary Units of Ecological Networks.

In ecological networks, cohesive groups of species may shape the evolution of interactions, serving as coevolutionary units. Ranging across network scales, from motifs to isolated components, elucidating which cohesive groups are more determinant for coevolution remains a challenge in ecology. We address this challenge by integrating 376 empirical mutualistic and antagonistic networks and coevolutionary models. We identified cohesive groups at four network scales containing a significant proportion of potential direct coevolutionary effects. Cohesive groups displayed hierarchical organisation, and potential coevolutionary effects overflowing lower-scale groups were contained by higher-scale groups, underscoring the hierarchy's impact. However, indirect coevolutionary effects blurred group boundaries and hierarchy, particularly under strong selection from ecological interactions. Thus, under strong selection, indirect effects render networks themselves, and not cohesive groups, as the likely coevolutionary units of ecological systems. We hypothesise hierarchical cohesive groups to also shape how other forms of direct and indirect effects propagate in ecological systems.

Technological anxiety: Analysis of the impact of industrial intelligence on employment in China

Employment is the greatest livelihood. Whether the impact of industrial robotics technology materialized in machines on employment in the digital age is an “icing on the cake” or “adding fuel to the fire” needs further study. This study aims to analyze the impact of the installation and application of industrial robots on labor demand in the context of the Chinese economy. First, from the theoretical logic and the economic development law, this study gives the prior judgment and research hypothesis that industrial intelligence will increase jobs. Then, based on the panel data of 269 cities in China from 2006 to 2021, we use the two-way fixed effect model, dynamic threshold model, and two-stage intermediary effect model. The objective is to investigate the impact of industrial intelligence on enterprise labor demand and its path mechanism. Results show that the overall effect of industrial intelligence on the labor force with the installation density index of industrial robots as the proxy variable is the “creation effect”. In other words, advanced digital technology has created additional jobs, and the overall supply of employment in the labor market has increased. The conclusion is still valid after the endogeneity identification and robustness test. In addition, the positive effect has a nonlinear effect on the network scale. When the installation density of industrial robots exceeds a particular threshold value, the division of labor continues to deepen under the combined action of the production efficiency and compensation effects, which will cause enterprises to increase labor demand further. Further research showed that industrial intelligence can increase employment by promoting synergistic agglomeration and improving labor price distortions. This study concludes that in the digital China era, the introduction and installation of industrial robots by enterprises can affect the optimal allocation of the labor market. This phenomenon has essential experience and reference significance for guiding industrial digitalization and intelligent transformation and promoting the high-quality development of people’s livelihood.

Bayesian Lookahead Perturbation Policy for Inference of Regulatory Networks.

The complexity, scale, and uncertainty in regulatory networks (e.g., gene regulatory networks and microbial networks) regularly pose a huge uncertainty in their models. These uncertainties often cannot be entirely reduced using limited and costly data acquired from the normal condition of systems. Meanwhile, regulatory networks often suffer from the non-identifiability issue, which refers to scenarios where the true underlying network model cannot be clearly distinguished from other possible models. Perturbation or excitation is a well-known process in systems biology for acquiring targeted data to reveal the complex underlying mechanisms of regulatory networks and overcome the non-identifiability issue. We consider a general class of Boolean network models for capturing the activation and inactivation of components and their complex interactions. Assuming partial available knowledge about the interactions between components of the networks, this paper formulates the inference process through the maximum aposteriori (MAP) criterion. We develop a Bayesian lookahead policy that systematically perturbs regulatory networks to maximize the performance of MAP inference under the perturbed data. This is achieved by optimally formulating the perturbation process in a reinforcement learning context and deriving a scalable deep reinforcement learning perturbation policy to compute near-optimal Bayesian policy. The proposed method learns the perturbation policy through planning without the need for any real data. The high performance of the proposed approach is demonstrated by comprehensive numerical experiments using the well-known mammalian cell cycle and gut microbial community networks.

SHINE: A Scalable Heterogeneous Inductive Graph Neural Network for Large Imbalanced Datasets

SHINE: A Scalable Heterogeneous Inductive Graph Neural Network for Large Imbalanced Datasets

Gate-based quantum neurons in hybrid neural networks

Abstract Quantum computing is conceived as a promising and powerful next-generation platform for information processing and it has been shown that it could bring significant accelerations to certain tasks, compared to its classical counterparts. With recent advances in noisy intermediate-scale quantum (NISQ) devices, we can process classical data from real-world problems using hybrid quantum systems. In this work, we investigate the critical problem of designing a gate-based hybrid quantum neuron under NISQ constraints to enable the construction of scalable hybrid quantum deep neural networks (HQDNNs). We explore and characterize diverse quantum circuits for hybrid quantum neurons and discuss related critical components of HQDNNs. We also utilize a new schema to infer multiple predictions from a single hybrid neuron. We further compose a highly customizable platform for simulating HQDNNs via Qiskit and test them on diverse classification problems including the iris and the wheat seed datasets. The results show that even HQDNNs with the simplest neurons could lead to superior performance on these tasks. Finally, we show that the HQDNNs are robust to certain levels of noise, making them preferred on NISQ devices. Our work provides a comprehensive investigation of building scalable near-term gate-based HQDNNs and paves the way for future studies of quantum deep learning via both simulations on classical computers and experiments on accessible NISQ devices.

Scalable multiple network inference with the joint graphical horseshoe

Scalable multiple network inference with the joint graphical horseshoe

A Survey of Resource Allocation in V2V Communications

In this paper, a comprehensive analysis of resource allocation in vehicle-to-vehicle (V2V) communication systems is presented, which is crucial for enhancing traffic control and improving road safety and driving enjoyment. A variety of resource allocation strategies are classified and examined in this paper, including game theory-based approaches, spectrum allocation, energy allocation, location-based allocation, and machine learning-based procedures. Open research questions and areas that need further study in the field of V2V communication are highlighted for future benefit. This survey helps readers make informed decisions about system design, implementation, and improvement by providing a comprehensive overview of the latest developments in V2V resource allocation. It also indicates areas that need further research and possible directions to enhance the security, flexibility, and scalability of V2V communication networks.

FaultSeg3D plus: A comprehensive study on evaluating and improving CNN-based seismic fault segmentation

Convolutional neural networks (CNNs) have been widely used for seismic fault segmentation and show more powerful performance than conventional attribute-based methods to obtain a fault map with noise-free and continuously trackable fault features. However, CNN-based methods face the potential problem of poor generalization in field seismic images, and factors affecting fault segmentation remain incompletely studied or unexplored. Moreover, the existing pixel-wise metrics, borrowed from the natural image segmentation tasks, cannot fairly or reasonably evaluate the fault segmentation results. We first develop a distance-based metric to provide a geologically more reasonable evaluation on fault interpretation. We then use the most commonly used U-net architecture as an example to study how the CNN-based fault segmentation is affected by some significant factors such as training data, many kinds of network hyperparameters, and scaling and rotation in the inference step. Experimental results show that a training data set with more realistic reflection features and multiple sampling rates can enrich the data set variations in the structure and waveform signatures, thus significantly enhancing the fault segmentation. Moreover, a novel loss function we developed outperforms others with notable margins. Last, but not least, it is necessary to apply a test-time augmentation strategy by merging predictions with multiple scales and rotations in the reference step because the CNN does not preserve transformation invariance. Based on the studies, we optimally train a properly designed CNN and apply it to multiple field examples, where we obtain accurate, clean, continuous fault detections, and quantitatively evaluate them with manual interpretations.

EBPT-CRA: A clustering and routing algorithm based on energy-balanced path tree for wireless sensor networks

With the rapid development of internet of things (IoT), the scale of wireless sensor networks (WSNs) is gradually expanding, and it is necessary to study the energy saving and balancing algorithm for WSNs in multi-sensor nodes and large deployment range scenarios. A clustering and routing algorithm based on an energy-balanced path tree (EBPT-CRA) is proposed for large-scale WSNs. First, the energy-balanced path tree (EBPT) is constructed, and then the aggregation betweenness of the nodes are calculated to obtain the cluster head competition coefficients of the nodes. Second, an iterative process is executed to select the cluster heads and build the cluster trees to complete the clustering and intra-cluster routing. Finally, the inter-cluster routing is determined based on the node residual energy and the communication energy. The simulation results demonstrate that the EBPT-CRA can better save and balance the node energy, while improve the network lifetime, throughput and service capability in large-scale WSNs.

Deep reinforcement learning assisted memetic scheduling of drones for railway catenary deicing

Icy rainfall and snowfall in 2024 Spring Festival struck the high-speed railway catenary systems and caused serious traffic disruptions in central and eastern China. Deicing drones are an effective method in response to these freezing events due to their fast speed and high environmental tolerance. However, the large disaster-affected area and the large scale and complexity of catenary networks make deicing drone scheduling a very difficult problem. In this paper, we formulate two versions of deicing drone scheduling problem, one for single drone scheduling and the other for multiple drone scheduling. Unlike most existing vehicle/drone routing problems, our problem aims to minimize the total negative effect caused by the freezing events on train operations, which reflects the prime concern of the decision-maker and is highly complex. To efficiently solve the problem, we propose a reinforcement learning assisted memetic optimization algorithm, which integrates global mutation and a set of neighborhood search operators adaptively selected by deep reinforcement learning. Computational results on real-world problem instances demonstrate its significant performance advantages over selected popular optimization algorithms in the literature.

Fault diagnosis method of rolling bearing based on noise reduction enhanced multi-frequency scale network

Fault diagnosis method of rolling bearing based on noise reduction enhanced multi-frequency scale network

Profiles of Met and Unmet Care Needs in the Oldest Old Primary Care Patients with Cognitive Disorders and Dementia: Results of the AgeCoDe and AgeQualiDe Study.

The prevalence of mild cognitive impairment (MCI) and dementia is increasing as the oldest old population grows, requiring a nuanced understanding of their care needs. Few studies have examined need profiles of oldest old patients with MCI or dementia. Therefore, this study aimed to identify patients' need profiles. The data analysis included cross-sectional baseline data from N = 716 primary care patients without cognitive impairment (n = 575), with MCI (n = 97), and with dementia (n = 44) aged 85+ years from the multicenter cohort AgeQualiDe study "needs, health service use, costs and health-related quality of life in a large sample of oldest old primary care patients [85+]". Patients' needs were assessed using the Camberwell Assessment of Needs for the Elderly (CANE), and latent class analysis identified need profiles. Multinomial logistic regression analyzed the association of MCI and dementia with need profiles, adjusting for sociodemographic factors, social network (Lubben Social Network Scale [LSNS-6]), and frailty (Canadian Study of Health and Aging-Clinical Frailty Scale [CSHA-CFS]). Results indicated three profiles: "no needs," "met physical and environmental needs," and "unmet physical and environmental needs." MCI was associated with the met and unmet physical and environmental needs profiles; dementia was associated with the unmet physical and environmental needs profile. Patients without MCI or dementia had larger social networks (LSNS-6). Frailty was associated with dementia. Integrated care should address the needs of the oldest old and support social networks for people with MCI or dementia. Assessing frailty can help clinicians to identify the most vulnerable patients and develop beneficial interventions for cognitive disorders.

Amygdalar involvement in respiratory dysfunction.

The brainstem has long been recognized as the major respiratory control center, but it has become increasingly appreciated that areas upstream of the brainstem modulate respiration and airway defensive behaviors. This review aims to define the role of the amygdala, a key temporal brain region essential for limbic function, in respiration and airway defenses. We summarize literature describing roles for the amygdala in control of respiration, swallow, cough, airway smooth muscle contraction, and mucus secretion. We emphasize the need to understand how the amygdala regulates these functions both at a local scale and network scale and identify knowledge gaps for current and future investigations. Lastly, we highlight literature suggesting that amygdala dysfunction may contribute to respiratory dysfunction.