Communication Resources Research Articles

Взаимосвязь спорта и государственной политики на примере Азербайджана

The relevance of the chosen topic lies in the growing importance of sports as an instrument of state policy, particularly in the context of Azerbaijan. As such, sports activities influence both the domestic and foreign policies of the country by fulfilling their primary functions of social integration, individual socialization, public health promotion, and social mobilization. The novelty of this publication is rooted in the integrated approach to examining the interrelation between politics and sports as a resource for political communication. The article highlights conceptual frameworks regarding the role of sports in political contexts, providing a brief historical overview and modern analysis of sports as an instrument of state policy within the realm of political communication. Additionally, it describes Azerbaijan’s sports activities terms of achieving the country’s domestic and foreign policy goals.

Shaping Quantum Noise through Cascaded Nonlinear Processes in a Dissipation-Engineered Multimode Cavity

Multimode quantum light is enticing for several applications, spanning imaging, spectroscopy, communication, and more. Parametric nonlinear processes have been vital in realizing squeezed and other quantum states of light. However, most work exploiting these processes has focused on generating multimode squeezed vacua and squeezing in mode superpositions (supermodes). Bright squeezing in multiple discrete frequency modes, if realized, could unlock novel applications in quantum-enhanced spectroscopy and optical quantum computing. Here, we show how dissipation engineering of a multimode nonlinear cavity with cascaded three-wave-mixing processes allows us to shape above-threshold frequency combs that feature strong single-mode output amplitude noise squeezing over 10 dB below the shot-noise limit, tunable across the comb. In addition, we demonstrate squeezing for multiple discrete frequency modes above threshold. This bright squeezing arises from enhancement of the (noiseless) nonlinear rate relative to decay rates in the system due to the cascaded generation of photons in a single idler “bath” mode. A natural consequence of the strong nonlinear coupling in our system is the creation of an effective cavity in the synthetic frequency dimension that sustains Bloch oscillations in the modal energy distribution. Bloch mode engineering could provide an opportunity to better control nonlinear energy flow in the synthetic frequency dimension, with exciting applications in quantum random walks and topological photonics. Lastly, we show evidence of long-range correlations in amplitude noise between discrete frequency modes, enabling long-range entanglement in a synthetic frequency dimension and providing a new resource for quantum communication. Published by the American Physical Society 2024

POLICY IMPLEMENTATION TO ACCELERATE STUNTING REDUCTION: A QUALITATIVE STUDY

Background: The prevalence of stunting in Kepahyang Regency in Bengkulu Province is high (24.9%). Despite the policy of Accelerating Stunting Reduction or Percepatan Penurunan Stunting (PPS) outlined in Presidential Decree Number 72 of 2021, results have been suboptimal. Aims: This study aims to analyze and evaluate the implementation of PPS policies and identify supporting and inhibiting factors. Methods: Research using a qualitative approach, focusing on the evaluation of achievements of 11 specific nutrition intervention indicators with policy implementation analysis using the strengthening of Edward III policy analysis. Data collection used in-depth interview guidelines with six stakeholders, focus group discussions (FGD) in two groups, and document observation. Informants are selected by purposive sampling. Data analysis uses content analysis. Results: The implementation of PPS in Kepahyang Regency faces communication problems, limited resources, complex bureaucratic structures, and inadequate supervision, which shows the need for increased coordination and support. Village government support and good social conditions are supporting factors, while lack of communication between the village government and the community and minimal budget support are the main obstacles. Conclusion: Communication issues, resource limitations, bureaucratic complexity, and inadequate supervision are the inhibiting factors. Strong support from local government and health workers is the supporting factor. Keywords: implementation, policy, specific nutrition interventions, stunting

Dynamic event-triggered attitude synchronization of multi-spacecraft formation via a learning Chebyshev neural network control approach

Purpose This paper aims to investigate the attitude synchronization issue of multi-spacecraft formation flying systems under the limited communication resources. Design/methodology/approach The authors propose a distributed learning Chebyshev neural network controller (LCNNC) combining a dynamic event-triggered (DET) mechanism and a learning CNN model to achieve accurate multi-spacecraft attitude synchronization under communication constraints. Findings The proposed method can significantly reduce the internal communication frequency and improve the attitude synchronization accuracy. Practical implications This method requires the low communication resources, has a high control accuracy and is thus suitable for engineering applications. Originality/value A novel DET mechanism-based LCNNC is proposed to achieve the accurate multi-spacecraft attitude synchronization under communication constraints.

Lessons from a crisis: occupational stress in healthcare workers in an acute paediatric teaching hospital in Ireland

BackgroundThe COVID-19 pandemic placed increased pressure on service provision and healthcare worker (HCW) wellness. As the crisis of the pandemic receded, paediatric healthcare staff required an appropriate response to facilitate...

Observer‐Based Switching‐Like Adaptive‐Triggered Resilient Coordination Control of Discrete Singular Systems Under DI Attacks with Uncertain Occurrence Probabilities

ABSTRACTIn this paper, we investigate observer‐based switching‐like adaptive‐triggered resilient state feedback control for discrete singular systems under deception‐injection (DI) attacks. Considering state variables are not completely measurable, a delayed state observer is engineered to reconstruct system states and the occurrence of DI attacks is described by Bernoulli random variables. The upper bound method is used to deal with the DI attacks with jumping patterns and imprecise occurrence probabilities. Taking into account the sporadic nature of DI attacks, switching‐like adaptive‐triggered resilient control method is presented, and the collaboratively designed triggered mechanism and resilient control strategy can not only automatically adjust data transmission based on the system states, but also save communication resources and network bandwidth, and enhance the tolerance to hostile attacks so as to improve safety and reliability of networked singular systems. By utilizing singular value decomposition technique, the noncausal behavior of the singular system is avoided and by applying linear matrix inequality (LMI) technology, the desired gains of controller and observer are achieved concurrently, then neoteric conditions about stochastic admissibility of closed‐loop discrete singular systems are provided. Ultimately, the validity of the proposed resilient coordination control approach is verified via a direct current (DC) motor model.

Deep Learning‐Based Community Detection Attack: Arctic Shipping Data as a Case

ABSTRACTThis study explores advanced methods for community discovery attacks in Vehicular Ad hoc Networks (VANETs) using deep learning techniques. It begins with a comprehensive analysis of VANET characteristics, classifications, routing protocols, and topological structures, followed by an exploration of deep learning applications in network community detection attacks, focusing on state‐of‐the‐art algorithms and evaluation metrics. The core contribution is a novel community discovery attack method for VANETs that integrates graph convolutional neural networks with multi‐head and bidirectional attention mechanisms. The proposed approach is rigorously evaluated through extensive experimentation and in‐depth analysis of results. The study concludes with a critical summary of findings and outlines promising future research directions, aiming to provide innovative strategies for enhancing community discovery attacks in vehicular networks. This research has potential implications for improving communication efficiency, resource allocation, and performance in VANET environments.

Facilitators, barriers, and strategies for supporting shared decision-making with people with intellectual disability: A West Australian primary healthcare professional perspective

ABSTRACT Background Shared decision-making between patients and primary healthcare professionals positively impacts health outcomes. However, people with intellectual disability face additional barriers and require supported shared decision-making (SSDM) to participate. Little is known about how healthcare professionals use SSDM with this population. This paper explores the facilitators and barriers experienced, and strategies/resources employed by healthcare professionals working with people with intellectual disability. Method Ten purposively sampled primary healthcare professionals participated in semi-structured interviews. This descriptive qualitative study used content analysis. Findings were compared with a proposed model of factors influencing triadic (the person with intellectual disability, their caregiver and the healthcare professional) SSDM. Results Five factor categories emerged: previous training/experience; engagement and trust; effective collaboration with caregivers; organisational culture and contexts; and familiarity/confidence with communication support strategies and resources. Conclusions Healthcare professionals can leverage pre-existing skills and knowledge, but provision of targeted professional development may reduce anxiety and increase successful SSDM.

Cascade Vertical Federated Learning Towards Straggler Mitigation and Label Privacy Over Distributed Labels

Vertical federated learning (VFL) enables collaborative machine learning on vertically partitioned data with privacy-preservation. Most VFL methods face three daunting challenges in real-world applications. First, most existing VFL methods assume that at least one party holds the complete set of labels of all data samples. However, this assumption often violates the nature of many practical scenarios, where the parties only have partial labels. Second, the heterogeneity and dynamic of computational and communication resources in participated parties may cause the straggler problem and slow down training convergence. Third, the confidential label information could be exposed through malicious parties during VFL. To address these challenges, we propose a novel VFL algorithm named Cascade Vertical Federated Learning (CVFL), in which partitioned labels can be fully utilized to train neural networks with privacy-preservation. To mitigate the straggler problem, we design a novel optimization objective to increase straggler's contribution to the trained models. To mitigate the label privacy risks, we design a novel defense approach to protect the label privacy of CVFL. We conduct comprehensive experiments and the results demonstrate the effectiveness and efficiency of CVFL. Further, the proposed defense approach can achieve a better tradeoff between label privacy and model utility than two widely-used defense approaches.

Efficient Asynchronous Multi-Participant Vertical Federated Learning

Vertical Federated Learning (VFL) is a private-preserving distributed machine learning paradigm that collaboratively trains machine learning models with participants whose local data overlap largely in the sample space, but not so in the feature space. Existing VFL methods are mainly based on synchronous computation and homomorphic encryption (HE). Due to the differences in the communication and computation resources of the participants, straggling participants can cause delays during synchronous VFL model training, resulting in low computational efficiency. In addition, HE incurs high computation and communication costs. Moreover, it is difficult to establish a VFL coordinator (a.k.a. server) that all participants can trust. To address these problems, we propose an efficient Asynchronous Multi-participant Vertical Federated Learning method (AMVFL). AMVFL leverages asynchronous training which reduces waiting time. At the same time, secret sharing is used instead of HE for privacy protection, which further reduces the computational cost. In addition, AMVFL does not require a trusted entity to serve as the VFL coordinator. Experimental results based on real-world and synthetic datasets demonstrate that AMVFL can significantly reduce computational cost and improve the accuracy of the model compared to five state-of-the-art VFL methods.

Fixed/preassigned-time non-chattering synchronization of nonlinear coupled Cohen-Grossberg neural networks via event-triggered control

In this paper, the studies on fixed-time and preassigned-time synchronization for a kind of nonlinear coupled Cohen-Grossberg neural networks (CGNNs) are researched by event-triggered non-chattering control. Firstly, differing from some existed fixed-time synchronization control strategies, a power-law feedback controller without sign function is adopted to achieve non-chattering control effect. To save communication and computational resources, an event-triggered mechanism (ETM) is introduced, which can naturally avoid Zeno behavior. By designing preassigned time parameters as controller parameters, the preassigned-time synchronization of dynamic networks is studied. Then, based on the designed control mechanism, sufficient conditions for fixed-time and preassigned-time synchronization of the nonlinear coupled CGNNs are given. Finally, the validity and generality of the obtained results are verified by numerical simulation.

Blockchain-based lightweight trusted data interaction scheme for cross-domain IIoT

To facilitate cross-domain data interaction in the Industrial Internet of Things (IIoT), establishing trust between multiple administrative domains is essential. Although blockchain technology has been proposed as a solution, current techniques still suffer from issues related to efficiency, security, and privacy. Our research aims to address these challenges by proposing a lightweight, trusted data interaction scheme based on blockchain, which reduces redundant interactions among entities. We enhance the traditional Practical Byzantine Fault Tolerance (PBFT) algorithm to support lightweight distributed consensus in large-scale IIoT scenarios. Introducing a composite digital signature algorithm and incorporating veto power minimizes resource consumption and eliminates ineffective consensus operations. The experimental results show that, compared with PBFT, our scheme reduces latency by 27.2%, thereby improving communication efficiency and resource utilization. Furthermore, we develop a lightweight authentication technique specifically for cross-domain IIoT, leveraging blockchain technology to achieve distributed collaborative authentication. The performance comparisons indicate that our method significantly outperforms traditional schemes, with an average authentication latency of approximately 151 milliseconds. Additionally, we introduce a trusted federated learning (FL) algorithm that ensures comprehensive trust assessments for devices across different domains while protecting data privacy. Extensive simulations and experiments validate the reliability of our approach.

Dynamic self-triggered protocol for Takagi-Sugeno fuzzy positive Markov switching systems

This study analyzes a novel dynamic self-triggered protocol for positive nonlinear stochastic switching systems, which integrates diverse key elements encompassing Markov switching parameters, Takagi-Sugeno (T-S) fuzzy, and positivity. The performance evaluation of T-S fuzzy positive Markov switching systems is conducted via a self-triggered mechanism relying solely on historical data for determining subsequent executions, thereby eliminating the requirement of continuous monitoring. A dynamic self-triggered mechanism is introduced with adjustable thresholds to further reduce the conservation of communication resources. By utilizing matrix decomposition technique and implementing an innovative optimization approach distinct from traditional linear matrix inequality method, the positivity and stochastic stability of the system are attained. The achieved results are validated through the Lotka-Volterra model.

Formation tracking control for networked heterogeneous MSV systems with actuator faults: A distributed optimal event-triggered fixed-time sliding mode fault-tolerant control approach

This paper studies the formation tracking problem in networked heterogeneous marine surface vessel systems (NHMSVs) subject to model perturbations, marine environmental disturbances, and actuator faults. A distributed optimal event-triggered fixed-time sliding mode fault-tolerant control (EFSMFC) method is proposed, which can achieve formation tracking within a fixed time that is only related to the control parameters. At the same time, a distance-based Floyd distributed optimization method is used to reduce the high communication cost caused by the redundancy of the network communication topology. In addition, an event-triggered strategy is integrated into the design of the controller to reduce the update frequency of control signals and reduce waste of communication resources. Finally, using Lyapunov stability theory, the global fixed-time convergence of system errors is proved. The simulation results demonstrate the feasibility of the proposed method, exhibiting superior robustness and speed compared to existing control methods.

Event-triggered [formula omitted] control for partial unknown Markov jump discrete-time complex networks with distributed delay

Periodic event triggering, an important control mechanism, effectively saves communication resources and energy, making it highly valuable in networked control systems. This study focuses on the comprehensive utilization of periodic sampling pattern and event triggering mechanism to achieve synchronization control of a class of discrete-time Markov jump complex dynamic networks (CDNs) with H∞ performance index. Firstly, a comprehensive CDNs model is established incorporating Markovian jump parameters with incomplete transition probabilities, time-varying delays, and external random disturbances, making it more readily implementable in practical engineering scenarios. Secondly, a novel periodic event-triggered control strategy is proposed. In this strategy, the threshold parameters and weighting matrix in the event-triggering condition are mode-dependent, which can achieve better resource efficiency and effectively avoid the Zeno phenomenon. Thirdly, a discontinuous Lyapunov–Krasovskii functional is designed to capture information about discrete periodic sampling patterns. By applying free-weighting matrix, Jensen’s inequality, and Wirtinger’s inequality, sufficient conditions and controller for CDNs synchronization are derived with lower conservativeness. Finally, a simulation example is provided to demonstrate the feasibility and effectiveness of the theoretical results obtained.

A dynamic event-triggered approach for observer-based formation control of multi-agent systems with designable inter-event time

This paper addresses the leader-following formation control problems for generic linear multi-agent systems under directed topology with designable inter-event time. A synthesis approach combining controller and observer design is developed under a dynamic event-triggered communication and control scheme. The proposed feedback control, state estimation, and event-triggered rules are distributed, and only local information is required for each agent to implement these algorithms. The proposed dynamic event-triggered protocol incorporates model-based estimation and clock-like auxiliary dynamic variables to prolong the inter-event time and economize the network resources. Furthermore, the inter-event time is designable, which allows more flexible tuning of communication frequency with only minor performance degradation. Sufficient conditions for formation control are established by linear matrix inequalities. The proposed method exhibits significant improvement over the dynamic event-triggered control methods described in the existing literature. Compared to the existing static event-triggered strategy, the proposed approach significantly reduces the utilization of communication resources while preserving asymptotic convergence to the desired formation. Comparative simulations demonstrate the validity and effectiveness of the proposed theoretical results.

Optimization of D2D emergency communication resources for UAV relay based on DA-STD3

The allocation of communication resources is extremely important for the device-to-device (D2D) communication network with unmanned aerial vehicle (UAV) relays, but this field currently mainly focuses on the communication between UAVs and ground devices. While neglecting the comprehensive optimization of air-to-ground communication and internal ground communication. This paper studies the optimization problem of communication for ground D2D devices based on UAV relays in urban environment. We study the comprehensive optimization considering the air-to-ground communication and the mobile ground communication devices. We construct the D2D communication neighbor table, and the terrestrial D2D devices will sort and select the best D2D link for pairing based on the Euclidean distance and gain set around them. We construct a communication model for this scenario and propose an improved Dual-Actor Network Softmax Twin Delayed Deep Deterministic Policy Gradient algorithm based on deep reinforcement learning. We perform transmit power optimization to address the interference caused by D2D communication using frequency division multiplexing technology. By designing the reward function and training mechanism, we can achieve good power control and reduce communication interference. The results of the simulation analysis performed in this scenario show an effective reduction in communication latency and an increase in D2I (device-to-infrastructure) channel capacity, D2D connectivity success, and energy efficiency. The feasibility and effectiveness of the algorithm is verified by comparing the baseline algorithm.

Integral sliding-mode based interval type-2 fuzzy control for efficient networked nonlinear active suspension systems with input uncertainty

An event-triggered integral sliding mode control (ETISMC) approach for the networked nonlinear active suspension systems (ASSs) via interval type-2 (IT-2) T-S fuzzy logic is investigated in this paper. Firstly, in order to deal with the uncertainty in system masses, the nonlinearity of the suspension spring stiffness and damping coefficient, and the unknown actuator nonlinearity simultaneously, an ASSs model based on the IT-2 T-S fuzzy method is constructed. Secondly, a discrete event-triggered method is designed to process the sampled data and reduce the usage of vehicle CAN network communication resources. Thirdly, the H infinity robust method is used to handle the road disturbance and the sliding mode control (SMC) is adopted to deal with the actuator input uncertainty. To better integrate the SMC and event-triggered control (ETC), an ETISMC approach is explored for the ASSs. Finally, the numerical simulations and experiments are conducted to validate the effectiveness of the proposed strategy in this work.

Event-triggered consensus adaptive filters for target localization

Distributed filters show strong robustness by using certain resources of communications and calculations to collaboratively estimate or track an unknown dynamic process of interest over a sensor network. In this paper, an event-triggered mechanism (ETM) is introduced for least mean square (LMS)-based consensus adaptive filters to deal with applications with communication resource constraints. An upper bound of the estimation errors for the proposed event-triggered consensus adaptive filters is established under a cooperative information condition without independent or stationary signal assumptions. To verify the effectiveness and resource saving properties of the proposed event-triggered consensus LMS-based filters, numerical simulations for target localization using bearing-only measurements of multiple unmanned aerial vehicles are provided. It is proved that the ETM provides settable thresholds to artificially adjust the proportions between the estimation accuracy and the resource consumption. Finally, experimental results are given to further show the performance and applicability of the proposed algorithm in practical engineering.

Event‐Triggered Robust Control of Robot Manipulators

ABSTRACTThis paper proposes a new event‐triggered robust control system for robot manipulators, based on two event‐triggered mechanisms (ETMs), which lead to intermittent communication not only for the sensor‐to‐controller channel but also for the controller‐to‐actuator channel. The proposed control system enjoys the advantage of requiring fewer resources of data communication and less signal updating of the control actuators. The control performance of the proposed control system is analyzed based on the Lyapunov approach. Furthermore, the absence of the Zeno behavior in the triggering sequence is proved rigorously. Finally, experiments are carried out on a two‐link robot manipulator system to support the theoretical results.