Network Links Research Articles

Research on position tracking control of electro-hydraulic actuator system combining feedforward inverse model controller and exponential functional link network adaptive controller

The electro-hydraulic actuator system plays a crucial role in indoor high-frequency simulation experiments. Many components of the system exhibit unavoidable nonlinear characteristics, which reduce its dynamic position response capability. Additionally, most algorithms are unsuitable for practical high-frequency conditions due to either local instability or high computational complexity. To improve position tracking accuracy under high-frequency control, a novel combined control strategy is proposed, integrating an appropriate feedforward inverse model controller (FFIMC) with an exponential functional link network adaptive controller (EFLNAC). Initially, the system has reached a stable state under the action of the PID controller. Subsequently, the FFIMC estimates both the model and the inverse model of the PID system by applying the AIC technique developed through the DCT-VSSNLMS adaptive algorithm, which extends the system’s bandwidth. Following this, the EFLNAC, as the outermost loop, acquires the inverse model of the FFIMC system and updates internal parameters in real-time, thereby enhancing the system’s resistance to nonlinear disturbances. Finally, the combined controller is validated through simulations and experiments under various working conditions. The results show a significant increase in the PID system’s bandwidth and demonstrate that the tracking error of the signal is maintained within 0.079 mm. Therefore, the combined controller proves to be an effective solution for high-precision, high-frequency displacement tracking, offering the advantages of low computational complexity, high accuracy, and strong effectiveness.

Species Diversity and Network Diversity in the Human Lung Cancer Tissue Microbiomes.

This study explores the relationship between microbial diversity and disease status in human lung cancer tissue microbiomes, using a sample size of 1,212. Analysis divided the data into primary tumor (PT) and normal tissue (NT) categories. Differences in microbial diversity between PT and NT were significant in 57% of comparisons, although dataset dependence was a factor in the diversity levels. Shared species analysis (SSA) indicated no significant differences between PT and NT in over 90% of comparisons. Network diversity assessments revealed significant differences between NT and PT regarding species relative abundances and network link abundances for q=0-3. Additionally, at q=0, significant variations were found between NT and LUSC in network link probabilities, illustrating the diversity in species interactions. Our findings suggest a stable overall microbiome diversity and composition in lung cancer patients' lung tissues despite patients with diagnosed lung tumors, indicating modified microbial interactions within the tumor. These results highlight an association between altered microbiome interaction patterns and lung tumors, offering new insights into the ecological dynamics of lung cancer microbiomes.

Adversarial nonnegative matrix factorization for temporal link prediction

Temporal link prediction has been extensively studied and widely applied in various applications, aiming to predict future network links based on the historical networks. However, most existing methods ignore the behavior of previous network updating information in temporal networks. To address these issues, we propose a novel link prediction model based on adversarial nonnegative matrix factorization, which fuses graph representation and adversarial learning to perform temporal link prediction. Specifically, we add a bounded adversary matrix to the input matrix to provide the robustness against real perturbations. Then, our model fully exploits the impact of snapshots by using communicability. Simultaneously, we utilize the cosine similarity to extract the node similarity and map it to low-dimensional latent representation to preserve the local structure. Additionally, we provide effective updating rules to learn the parameters of this model. Extensive experiments results on six real-world networks demonstrate that the proposed method outperforms several classical and the state-of-art matrix-based methods.

Zero-cost upgrade to a multi-fiber network with partial lane-change capabilities

Growing capacity requirements are leading to the deployment of multiple fibers in each optical network link. Even though deploying state-of-the-art multi-fiber network architectures with stacked and independent fiber layers simplifies network design and control, spectrum can be used more efficiently if the optical-network nodes allow fiber layers to be interconnected, i.e., if the so-called lane change is enabled. Unfortunately, lane change in high-degree optical nodes requires wavelength selective switches (WSSs) with a high number of ports, which is prohibitively costly or even unfeasible with current WSS technology. Instead, lane change in low-degree optical nodes can be enabled at no extra cost, using WSS ports that are otherwise left empty. In this study, we describe our proposal for a multi-fiber network with partial lane-change capabilities and perform a simulative study to identify the advantages of this architecture, as well as discuss the emerging resource allocation challenges associated with it. We demonstrate that, by enabling lane change in degree-2 nodes, we can increase network throughput by 3% and restore 5%–8% more traffic in the case of single- and double-link failures at no additional equipment cost.

The Impact of Brexit on the British Constitutional Law

Brexit as a hybrid phenomenon, that is, both legal and political, is the next stage in the debate on the place and role of the European Union (EU). The analysis of the impact of EU legislation on the shape of British constitutional law requires establishing the admissibility of reversing existing constitutional modifications. The author will argue that the age of globalisation and establishing legal network links, the participation of states in supernational organisations can result in irreversible modifications of the scope and meaning of constitutional law. In a simplified way, individual legal systems in a legal pluralism are “open”. The changes taking place are irreversible because they concern the foundation of competences as well as the system position of individual national authorities.

Research on Heterogeneous Information Network Link Prediction Based on Representation Learning

A heterogeneous information network, which is composed of various types of nodes and edges, has a complex structure and rich information content, and is widely used in social networks, academic networks, e-commerce, and other fields. Link prediction, as a key task to reveal the unobserved relationships in the network, is of great significance in heterogeneous information networks. This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks. This paper introduces the basic concepts of heterogeneous information networks, and the theoretical basis of representation learning, and discusses the specific application of the deep learning model in node embedding learning and link prediction in detail. The effectiveness and superiority of these methods on multiple real data sets are demonstrated by experimental verification.

The disruption index is biased by citation inflation

Abstract A recent analysis of scientific publication and patent citation networks by Park et al. (Nature, 2023) suggests that publications and patents are becoming less disruptive over time. Here we show that the reported decrease in disruptiveness is an artifact of systematic shifts in the structure of citation networks unrelated to innovation system capacity. Instead, the decline is attributable to ‘citation inflation’, an unavoidable characteristic of real citation networks that manifests as a systematic time-dependent bias and renders cross-temporal analysis challenging. One driver of citation inflation is the ever-increasing lengths of reference lists over time, which in turn increases the density of links in citation networks, and causes the disruption index to converge to 0. The impact of this systematic bias further stymies efforts to correlate disruption to other measures that are also time-dependent, such as team size and citation counts. In order to demonstrate this fundamental measurement problem, we present three complementary lines of critique (deductive, empirical and computational modeling), and also make available an ensemble of synthetic citation networks that can be used to test alternative citation-based indices for systematic bias. Peer Review https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00333

Estimation of vessel link-level travel time distribution: A directed network-driven approach

Accurate vessel travel time estimation is essential for operational efficiency and route optimization. Despite the prevalent use of the automatic identification system (AIS) in garnering multifaceted real-time data, ambiguities and inaccuracies in travel time predictions persist. It leads to planning uncertainties, inefficient resource allocations, and heightened operational costs in maritime logistics. To addresses these issues, this paper proposed a nuanced method to enhance the precision and reliability of vessel travel time estimations. Firstly, a directed maritime network is constructed by extracting essential information from AIS-based historical vessel trajectories. This lays the foundation for the subsequent analytical processes. Secondly, the non-parametric kernel density estimation (KDE) is applied to this constructed network, enabling the estimation of vessel travel time distributions across various network links. The non-parametric KDE is used in combination with AIS data, which improves the specificity and accuracy of the travel time estimations at the link level. Finally, this paper employs cellular automata (CA) simulations to validate the accuracy of the KDE-based estimations. Comparison between the simulation results and real-world data reveals a high degree of accuracy in the proposed method, confirming its applicability and effectiveness in estimating vessel travel times.

Soil carbon pools and microbial network stability depletion associated with wetland conversion into aquaculture ponds in Southeast China

Wetlands, which are ecosystems with the highest soil surface carbon density, have been severely degraded and replaced by artificial reclamation for fish and shrimp ponds in recent years. This transformation is causing intricate shifts in soil carbon pools and microbial stability. In this study, we examined natural wetlands and reclaimed aquaculture ponds in Southeast China to analyze the structure and network stability of soil microbial communities following the reclamation of estuarine wetlands and to elucidate the microbial-mediated mechanisms for regulating soil organic carbon (SOC). The aquaculture ponds presented significantly less average SOC content than the natural wetlands (p < 0.05). ACE, Chao1, and Shannon's indices of bacteria and fungi were decreased in aquaculture ponds. Less numbers of nodes and edge links in the co-occurrence network of soil fungi and bacteria in aquaculture ponds. This suggests reduced correlation and stability within the microbial network of aquaculture ponds. Decomposers in soil fungi (e.g. Dung Saprotroph) reduced. Reduced proportions of key phyla Ascomycota, Basidiomycota and Rozellomycota in the soil fungal network. Reduced proportions of key phyla Proteobacteria, Chloroflexi and Desulfobacterota in the soil bacterial network. In conclusion, our results suggest that converting wetland paddocks to intensive aquaculture ponds results in carbon pool loss and reduces soil microbial network stability. The results highlight the importance of protecting or moderately restoring mangrove wetlands along the coast of southeastern China. It is also predicted that such measures may enhance the storage capacity of soil carbon pools and improve the stability of carbon sequestration by soil microorganisms, thus offering a potential solution for mitigating global climate change.

RIS-Embedded UAVs Communications for Multi-Hop Fully-FSO Backhaul Links in 6G Networks

RIS-Embedded UAVs Communications for Multi-Hop Fully-FSO Backhaul Links in 6G Networks

Urban eco-efficiency of China: Spatial evolution, network characteristics, and influencing factors

Urban eco-efficiency (EE) refers to the ability of a city to balance economic growth with environmental protection through the optimal use of resources and environmental management, reflecting the city’s performance in sustainable development. Quantitative EE analysis is a crucial evaluation criterion for measuring the process of sustainable development (SD). To assess the current state of sustainable development in China, this study employs the slacks-based super efficiency model (Super-SBM) to measure the the urban eco-efficiency in China (CUEE) of 284 cities. The study utilizes Kernel Density Estimation (KDE) and Spatial Autocorrelation Analysis (SAA) to investigate the fundamental patterns of CUEE in terms of inter-city differences and relationships. Complex Network Analysis (CNA) and Link Prediction methods are applied to examine the characteristics and future trends of the CUEE network. Additionally, Geodetector and Geographically Weighted Regression (GWR) are used to analyze the influencing factors of CUEE. The findings are as follows: Overall CUEE is relatively low but shows a steady increase. The spatial association network of CUEE is becoming more complex, with increasing association strengths. The Chengdu-Chongqing Economic Circle (CCEC) is expected to further enhance its spatial association strength and may emerge as a new core area. The effectiveness of the interactions among selected factors is greater than that of individual factors. Degree centrality (X1) is identified as a core explanatory factor influencing CUEE, showing a positive correlation with CUEE. Scientific expenditure (X5) is also positively correlated with CUEE. Local fiscal budget expenditure (X3), industrial output value of foreign-invested enterprises (X7), and total cargo transportation volume (X9) are negatively correlated with CUEE. The analytical framework and research conclusions of this study offer valuable insights into the development of urban ecological efficiency in China and provide constructive recommendations for promoting sustainable urban development.

Care models for coexisting serious mental health and alcohol/drug conditions: the RECO realist evidence synthesis and case study evaluation.

People with severe mental illness who experience co-occurring substance use experience poor outcome including suicide, violence, relapses and use of crisis services. They struggle to access care and treatment due to a lack of an integrated and co-ordinated approach which means that some people can fall between services. Despite these concerns, there is limited evidence as to what works for this population. To undertake a realist evaluation of service models in order to identify and refine programme theories of what works under what contexts for this population. Realist synthesis and evaluation using published literature and case study data. Mental health, substance use and related services that had some form of service provision in six locations in the United Kingdom (five in England and one in Northern Ireland). People with lived experience of severe mental illness and co-occurring substance use, carers and staff who work in the specialist roles as well as staff in mental health and substance use services. Eleven initial programme theories were generated by the evidence synthesis and in conjunction with stakeholders. These theories were refined through focus groups and interviews with 58 staff, 25 service users and 12 carers across the 6 case study areas. We identified three forms of service provision (network, consultancy and lead and link worker); however, all offered broadly similar interventions. Evidence was identified to support most of the 11 programme theories. Theories clustered around effective leadership, workforce development and collaborative integrated care pathways. Outcomes that are meaningful for service users and staff were identified, including the importance of engagement. The requirement for online data collection (due to the COVID-19 pandemic) worked well for staff data but worked less well for service users and carers. Consequently, this may have reduced the involvement of those without access to information technology equipment. The realist evaluation co-occurring study provides details on how and in what circumstances integrated care can work better for people with co-occurring severe mental health and alcohol/drug conditions. This requires joined-up policy at government level and local integration of services. We have also identified the value of expert clinicians who can support the workforce in sustaining this programme of work. People with co-occurring severe mental health and alcohol/drug conditions have complex and multifaceted needs which require a comprehensive and long-term integrated approach. The shift to integrated health and social care is promising but will require local support (local expert leaders, network opportunities and clarity of roles). Further work should evaluate the effectiveness and cost-effectiveness of service models for this group. This study is registered as PROSPERO CRD42020168667. This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR128128) and is published in full in Health Technology Assessment; Vol. 28, No. 67. See the NIHR Funding and Awards website for further award information.

Bilateral fusion low‐light image enhancement with implicit information constraints

AbstractResearch on low‐light image enhancement focuses on improving image quality in dim conditions. Recently, deep learning has driven significant advancements, with many studies using neural networks to enhance low‐light images. However, most focus on complex network designs to increase nonlinearity, often neglecting the implicit information from local image transformations. This paper introduces an improved U‐net‐based method for low‐light enhancement, retaining the original encoding network and adding branch links in the decoding network. The method uses attention feature fusion to handle image noise and gradients separately, adjusting brightness through a gradient‐adaptive transform. This approach optimizes performance using loss functions like peak signal‐to‐noise ratio and colour consistency. Unlike previous methods, the approach emphasizes extracting implicit information from image gradients, achieving enhancement that aligns with the original brightness distribution. The result is enhanced images with high detail similarity to the original, achieved through end‐to‐end inference in experiments.

Study on the planning and scheduling strategies of the hydrogen supply network for road transportation in deep decarbonization scenarios

As the energy decarbonization continues to advance, hydrogen is gaining momentum. With the increasing demand for hydrogen and the lack of hydrogen infrastructure, it is urgent to solve the problem of hydrogen supply network layout planning. In order to reduce redundant construction of hydrogen supply network infrastructure and increase the rationality of its layout, this paper conducts research on the optimal layout planning of hydrogen supply network infrastructure in the transportation sector. By constructing a two-stage real-time scheduling model of hydrogen supply network, the unit cost composition of hydrogen supply network is more complete and accurate, and the location, technology type and quantity of each link of hydrogen supply network are obtained. The results show that the unit cost of China's hydrogen supply network is 27.8 CNY/kg in 2035 and 19.04 CNY/kg in 2050 under the baseline scenario. Compared with the results of existing literature, the two-stage real-time scheduling model can reduce by 7 % of the hydrogen energy supply network unit cost by decreasing the transportation vehicles and the investment capacity of fixed hydrogen storage equipment. The proposed method of the hydrogen supply network planning can provide policy-makers with the selection of sustainability pathways for dynamic hydrogen infrastructure development planning.

Classifying fabric wrinkle via regularized random vector function link optimized by African vultures optimization algorithm

The wrinkling of fabrics not only affects their aesthetic appeal and comfort but also leads to increased wear and a reduced lifespan. Consequently, wrinkle resistance has become a critical metric in evaluating fabric performance. Traditional wrinkle grading methods rely heavily on expert assessments, which are susceptible to visual fatigue, resulting in subjective and inconsistent classification outcomes. To address this issue, this article proposes an automatic wrinkle grading model based on an improved African Vultures Optimization (AVO) algorithm, which optimizes a regularized random vector functional link (RRVFL) network. The model enhances convergence speed and classification accuracy by incorporating the Harris Hawks Optimization (HHO) algorithm to provide a superior initial population for the AVO algorithm. Additionally, the AVO algorithm’s local search capability is strengthened during the optimization process, improving the model’s stability and resistance to overfitting. Experimental results demonstrate that the proposed HHO-AVO-RRVFL model achieves an average classification accuracy of 97.86% across multiple datasets, significantly outperforming other comparative algorithms while also excelling in classification stability and convergence speed.

Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion

Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion

SARS-CoV-2 envelope protein alters calcium signaling via SERCA interactions

The clinical management of severe COVID-19 cases is not yet well resolved. Therefore, it is important to identify and characterize cell signaling pathways involved in virus pathogenesis that can be targeted therapeutically. Envelope (E) protein is a structural protein of the virus, which is known to be highly expressed in the infected host cell and is a key virulence factor; however, its role is poorly characterized. The E protein is a single-pass transmembrane protein that can assemble into a pentamer forming a viroporin, perturbing Ca2+ homeostasis. Because it is structurally similar to regulins such as, for example, phospholamban, that regulate the sarco/endoplasmic reticulum calcium ATPases (SERCA), we investigated whether the SARS-CoV-2 E protein affects the SERCA system as an exoregulin. Using FRET experiments we demonstrate that E protein can form oligomers with regulins, and thus can alter the monomer/multimer regulin ratio and consequently influence their interactions with SERCAs. We also confirm that a direct interaction between E protein and SERCA2b results in a decrease in SERCA-mediated ER Ca2+ reload. Structural modeling of the complexes indicates an overlapping interaction site for E protein and endogenous regulins. Our results reveal novel links in the host-virus interaction network that play an important role in viral pathogenesis and may provide a new therapeutic target for managing severe inflammatory responses induced by SARS-CoV-2.

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.

A new representation learning based maximum power operation towards improved energy management integration with DG controllers for photovoltaic generators using online deep exponentially expanded RVFLN algorithm

To incorporate Local Energy Management System (LEMS) based Tertiary Controller (TC) operation with multiple Photovoltaic based Distributed Generations (PV-DGs) level Primary Controller/ PC: Independent DG Controllers (IDGC) more adequately, a new representation learning (Symmetrical Input-Output weight Encoded Data Compression/ SIODC, with Exponential Expansion based Random Vector Functional Link Network towards Softmax layer’s Control Reference Estimation/ ExRVFLN-SoCRE) based MPPT controller is proposed in this paper in terms of Secondary Controllers (SCs). A new Hybrid LEMS (HyLEMS) is presented towards the proposed Deep Neural Network based SCs (i.e. DNN-SCs) in a distributed (SCdist), as well as centralized (SCcent) manner, to ease the computational, and communication requirements. To investigate that multiple PV-DGs based duty cycle (kth instant Control References/CRs estimation) controllers are considered here with auxiliary Battery Energy Storage Systems (BESS), and AC utility, integrated to Common DC feeder in terms of DC-DC converter dynamics. To avoid Control Reference Estimation Error (CREE) due to initial randomization, optimization subroutines are incorporated for SIODC by generalized semi-supervised learning, and for ExRVFLN-SoCRE by Lagrange multiplier weighted, rms based cost function. The proposed control performance is verified in MATLAB Simulink® based average modeling, and validated through dSPACE DS1104 based RTI with multi-PV (emulators) test-bench as well.

Student-to-Student Connections in Postdigital Networked Learning

AbstractAnalysing learning situations as entanglements of humans and things in networked learning links to contemporary topics that are at the forefront of the postdigital challenge. The literature broadly conceptualises researching networked learning through four different understandings of networks, as a network of people, as situations or contexts, as infrastructure, and as actant. In this study, we explored the fourth way of understanding networked learning by examining how students described their connections with other students while learning online. Using mediated discourse analysis, we analysed 12 student focus groups to gain insights into what supported their connections with other students, paying particular attention to the nature of the networks they engaged in. This led to the identification of six enablers of student-to-student connections: attention, access, collaboration, proximity, timely interaction, and purpose. We demonstrate how the networks described by students when providing accounts of their connections with others can be best described as networks of human and non-human actants. While current research acknowledges that learning is a result of the socio-material entanglements of physical, virtual, and human actants, it is limited in demonstrating how and why such networks are supportive of learning. Our study contributes by showing how the identified enablers and their interrelationships influenced the extent to which networks were found to be supportive of learning. As such, it also supports a more tangible way of articulating the relationship between networked learning and postdigital education.