Network Links Research Articles

A secure and lightweight trust evaluation model for enhancing decision-making in resource-constrained industrial WSNs.

The dependability of nodes in an Industrial Wireless Sensor Network (IWSN) is vital for precise decision-making and the overall functioning of the network. Unreliable nodes have the potential to result in incorrect data, which can undermine the reliability of monitoring and control systems. Inaccurate decision-making in IWSNs can result in operational failures, safety risks, inadequate resource utilization, elevated energy consumption, and susceptibility to security vulnerabilities. Trust models tackle these challenges by recognizing and reducing the impact of unreliable nodes, improving the precision of data, and strengthening the security of the IWSNs. To overcome these challenges, this research work introduces a "Novel approach for Trust Utilization and Reliability Enhancement" (NATURE) in IWSNs. The unique approach employed in proposed NATURE model is distinguished by its multi-level clustered model, which improves attack mitigation, reduces communication overhead, and enhances overall network efficiency. This model is particularly effective in industrial environments where the network structure and the nature of data traffic are highly dynamic. The use of a multifactor trust estimation framework allows NATURE to assess the trustworthiness of SNs based on a comprehensive set of criteria, including behavior patterns, energy consumption, and communication reliability. By operating on multiple levels, NATURE can dynamically adjust trust functions to accurately distinguish between trustworthy and faulty SNs, thereby improving network reliability. Moreover, NATURE employs the temporal decay factor to prioritize recent node behaviors, ensuring that obsolete actions have minimal impact. Additionally, it uses the dynamic adjustment factor to balance the influence of negative interactions, encouraging reliable and responsive trust evaluations. Additionally, NATURE integrates a dynamically adjustable logical time window to enhance monitoring precision and adaptability, outperforming fixed-length windows in anomaly detection. NATURE integrates an Optimal Lead Node Election Algorithm (OLNEA) to improve cluster leader selection process. OLNEA considers network density, link quality, and Lead Node (LN), battery life, ensuring competent data aggregation and load balancing. By periodically selecting robust LNs and seamlessly switching to alternatives, NATURE promotes reliability and mitigates the impact of low LN battery levels. Additionally, NATURE employs a trust-based attacks detection algorithm to fortify IWSN security. This algorithm employs keen methods to verify data integrity, monitor energy levels, and ensure message authenticity, effectively safeguarding the IWSN from malevolent attacks and ensuring the safe transmission of data. Experimental results highlight NATURE's exceptional performance across significant metrics when compared to existing trust schemes. In a WSN of 500 SNs with 30% being malicious, NATURE detects malicious behavior with a 97% accuracy, outperforming other models. Even with 50% malicious SNs, NATURE maintains a high detection accuracy of 91%, again surpassing alternative approaches. Additionally, NATURE significantly reduces energy consumption while achieving efficient throughput rates, underscoring its effectiveness in challenging network conditions.

Research on the Technology and Application of Remote Wireless Transmission of Digital Image Signal

Abstract. With the increasing demand for remote real-time high-quality video transmission, especially in the context of large area applications of unmanned aerial vehicle (UAV) and remote location instant live broadcast, traditional wired transmission methods have been surpassed by digital transmission methods due to their superior performance in image quality, transmission distance and delay. Therefore, this study focuses on the application of digital image wireless transmission in remote scenarios such as UAVs, including key digital transmission technologies such as Orthogonal Frequency Division Multiplexing (OFDM) and Coded Orthogonal Frequency Division Multiplexing (COFDM), as well as public network link communication technologies such as 4G and 5G, by means of a literature study and a case study. It also analyzes the development of WiFi, Lightbridge and OcuSync graphics transmission systems using DJI brand products as case studies. The analysis shows that digital image wireless transmission, especially COFDM and advanced proprietary systems such as DJI's OcuSync, has significant advantages in terms of transmission distance, interference immunity, encryption techniques, and reusability. However, factors such as building obstruction, frequency interference, and weather conditions can affect the transmission quality. To mitigate these challenges, solutions are proposed, such as auxiliary signal enhancement systems, adaptive frequency hopping and software-defined radio technologies.

Score-driven exponential random graphs: A new class of time-varying parameter models for temporal networks.

Motivated by the increasing abundance of data describing real-world networks that exhibit dynamical features, we propose an extension of the exponential random graph models (ERGMs) that accommodates the time variation of its parameters. Inspired by the fast-growing literature on dynamic conditional score models, each parameter evolves according to an updating rule driven by the score of the ERGM distribution. We demonstrate the flexibility of score-driven ERGMs (SD-ERGMs) as data-generating processes and filters and show the advantages of the dynamic version over the static one. We discuss two applications to temporal networks from financial and political systems. First, we consider the prediction of future links in the Italian interbank credit network. Second, we show that the SD-ERGM allows discriminating between static or time-varying parameters when used to model the U.S. Congress co-voting network dynamics.

GB-RVFL: Fusion of randomized neural network and granular ball computing

The random vector functional link (RVFL) network is a prominent classification model with strong generalization ability. However, RVFL treats all samples uniformly, ignoring whether they are pure or noisy, and its scalability is limited due to the need for inverting the entire training matrix. To address these issues, we propose granular ball RVFL (GB-RVFL) model, which uses granular balls (GBs) as inputs instead of training samples. This approach enhances scalability by requiring only the inverse of the GB center matrix and improves robustness against noise and outliers through the coarse granularity of GBs. Furthermore, RVFL overlooks the dataset’s geometric structure. To address this, we propose graph embedding GB-RVFL (GE-GB-RVFL) model, which fuses granular computing and graph embedding (GE) to preserve the topological structure of GBs. The proposed GB-RVFL and GE-GB-RVFL models are evaluated on KEEL, UCI, NDC and biomedical datasets, demonstrating superior performance compared to baseline models.

Metropolitan-scale heralded entanglement of solid-state qubits.

A key challenge toward future quantum internet technology is connecting quantum processors at metropolitan scale. Here, we report on heralded entanglement between two independently operated quantum network nodes separated by 10 kilometers. The two nodes hosting diamond spin qubits are linked with a midpoint station via 25 kilometers of deployed optical fiber. We minimize the effects of fiber photon loss by quantum frequency conversion of the qubit-native photons to the telecom L-band and by embedding the link in an extensible phase-stabilized architecture enabling the use of the loss-resilient single-click entangling protocol. By capitalizing on the full heralding capabilities of the network link in combination with real-time feedback logic on the long-lived qubits, we demonstrate the delivery of a predefined entangled state on the nodes irrespective of the heralding detection pattern. Addressing key scaling challenges and being compatible with different qubit systems, our architecture establishes a generic platform for exploring metropolitan-scale quantum networks.

Extracting semantic link network of words from text for semantics-based applications

Extracting semantic link network of words from text for semantics-based applications

Translation, cross-cultural adaptation and scale validation to assess professionalism among medical students

Introduction: the professionalism of the physician should be present not only in the technical attributes, such as decision making and clinical reasoning, but also personal and interpersonal attributes, such as empathy, ethics, commitment, sense of social responsibility, altruism, teamwork and confidentiality. However, these attributes should be worked already during the medical course and includes the need to be assessed. Objective: To translate, cross-culturally adapt and validate a scale to assess professionalism in medical students. Method: We conducted a methodological validation study of the Professionalism Assessment Scale for Medical Students developed in Slovenia and authorized by the main author. The study was developed in four phases: translation and retranslation phase; cross-cultural adaptation phase; test and retest phase; and final application phase of the scale. The study population in the final phase consisted of a medical student and the sample was of convenience. The questionnaire was developed through the open LimeSurvey software and available via link in social networks. Data analysis was performed in Epi Info 7.2.4.0 and study approved by the institution’s Research Ethics Committee. Results: 82 students participated, with a mean age of 21 years (SD = 1.8) and predominance of males. Before answering the Professional Assessment Scale (PAS), 73.2% said they knew how to define what professionalism was and 47.3% said they had a medical relative. Participants achieved very high PAS scores, with only 4 participants (4.88%) scoring less than 100. The mean score was 107 on a scale of no more than 110 points. The PAS presented a Cronbach alpha coefficient of 0.7. Conclusion: The study revealed that the students of the educational institution have a high degree of professionalism and the validation of the scale in its final stage reached an acceptable level of reliability and can be used in other studies.

Academic collaboration recommendation based on graph neural network and multi-attribute embedding

Academic collaboration recommendation (ACR) can help researchers find potential partners for research and thus promote academic innovation. Recent works mostly use graph learning-based methods to explore various ways of combining node information with topology, which consists of multiple steps, including network construction, node feature extraction, network representation learning and link prediction. One limitation is that they only conduct research on co-authorship networks and ignore citation relationship between publications. Besides, they tend to use attributes from a single dimension of researchers and do not take attributes of researchers from multiple dimensions into consideration at the same time. To address the above issues, we present the multi-dimensional attributes enhanced heterogeneous (MAH) network representation learning method, which constructs heterogeneous networks with both co-authorship and citation information and makes use of multi-dimensional attributes. Three research questions are addressed in this work: (RQ1) Is our proposed method effective on academic collaboration recommendation compared with existing state-of-the-art methods? (RQ2) Does incorporating citation information into co-authorship network help improve the performance of academic collaboration recommendation? (RQ3) How does fusing multi-dimensional attributes affect the performance of academic collaboration recommendation? A publicly available real-world data set is used in our experiments. The superior performance of MAH compared with baseline methods demonstrates that the proposed multi-dimensional feature-based researcher profile can enrich node information in academic network and effective researcher representations can be learned by applying graph representation learning methods on the network.

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.

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.

A EVALUASI KINERJA CWDM 8 CHANNEL DENGAN VARIASI PANJANG GELOMBANG: FOKUS POWER LINK BUDGET, SNR DAN BER

The need for internet connection continues to grow and is the main priority of the Indonesian population. The use of fiber optic as a transmission system aims to increase data transfer speed and is resistant to interference. The use of CWDM technology can overcome high traffic volumes by dividing the wavelength. PT. Surya Citra Media has a network that connects the SCTV tower office with the IVM office in Daan Mogot. This study examines the performance of the CWDM fiber optic network link SCTV Tower - IVM Daan Mogot. The CWDM used has a capacity of 8 channels with a wavelength of 1310nm to 1450nm with a channel spacing of 20nm. This study was conducted to evaluate the 8-channel CWDM fiber optic network through performance measurements. The performance of the fiber optic network is focused on the parameters of the power link budget, SNR and BER. The results showed a power link budget of -9.407 dB and an SNR value with the smallest value of 37.0864 and the largest of 37.6150 dB. While the BER value ranges from 10-10 to 10-11. So that the network can be declared feasible based on the evaluation results.