Communication Resources Research Articles

Club convergence in regional labor productivity: how do Australian states and territories compare to the US, UK, and Canadian subnational regions?

AbstractDeveloping strategies to enhance productivity growth requires identifying leading and lagging regions, industries, and growth drivers. However, there are limited cross-country studies using subnational data. Our study goes beyond the traditional country convergence approach and estimates labor productivity convergence using Philips and Sul’s club convergence approach and subnational data from 2004 to 2020. We aim to determine whether labor productivity growth rates in Australian states and territories are equal, converging, or divergent as compared to United States, United Kingdom, and Canadian subnational regions. The results show that five Australian jurisdictions, including Western Australia and New South Wales, are in the high labor productivity group (Club 1), while the remaining three i.e., Australian Capital Territory, Victoria, and Queensland, are in the moderate growth group (Club 2). We also used fixed effects models with least squares dummy variable estimators to identify the club’s characteristics. The results show that regions with a high proportion of employees in information and communications technology, energy, mining, and resources experienced higher labor productivity growth than those with a manufacturing sector. Human capital, innovation, and household disposable income were associated with high labor productivity, whereas high old-age dependency and children-to-women ratios were associated with lower growth. These results are consistent with the macroeconomic phenomenon that economic development leads to inequality and polarization in certain regions of a country. Nonetheless, the findings are useful for decision-makers and researchers to use in benchmarking and improving regional strategies by identifying regional peers and factors that influence convergence or divergence that can be improved. The results provide insightful findings for consideration by policymakers seeking to boost labor productivity or to bridge regional gaps in productivity.

Exploring the evolution and collaboration in two-sided matching: a comprehensive bibliometric and topic modeling analysis

PurposeThis study aims to provide a comprehensive analysis of two-sided matching (TSM) research, an interdisciplinary field that integrates both theoretical and practical perspectives. By examining 756 research articles from the Web of Science database, this paper seeks to identify key trends, collaboration patterns and emerging research topics within the TSM domain.Design/methodology/approachThe research utilizes bibliometric analysis combined with a structural topic model to analyze TSM-related articles published between January 1, 2000, and September 30, 2022. The study identifies leading subfields, journals, countries/regions and institutions based on publication volume, total citations and average citations per article. Interaction and collaboration patterns among these entities are examined through co-occurrence and coupling networks. Additionally, five major research topics are identified and explored using topic modeling and co-word networks. This hybrid knowledge mining approach better reveals the inherent structural changes in topic clusters. Topic distribution and network analysis are beneficial in capturing the attention allocation of different entities to knowledge.FindingsThe analysis reveals five prominent research topics in TSM: communication resource allocation, stable matching research, computing task assignment, TSM decision-making and market matching mechanism design. These topics represent the main directions of TSM research. The study also uncovers a shift in research focus from theoretical aspects to practical applications. Furthermore, the distribution of knowledge and interaction patterns among key entities align with the identified research trends.Originality/valueThis study offers a novel and detailed overview of TSM research highlighting significant trends and collaboration patterns within the field. By integrating bibliometric methods with structural topic modeling the study provides unique insights into the evolution of TSM research making it a valuable resource for both academic and professional communities.

Optimization algorithm of UAV‐assisted federated learning resources based on wireless energy harvesting

AbstractFederated learning (FL) enhances data privacy and security by enabling multiple terminals to collaboratively train a global model without transferring data to a central location. To tackle the challenges of limited terrestrial communication resources and device energy in FL, a UAV‐assisted federated learning and energy collection resource optimization algorithm is proposed. This algorithm harvests the working energy of FL terminals from radio frequency signals transmitted by the UAV via wireless energy collection. Considering energy causality and limited resources, we formulate a problem aimed at minimizing the completion time of FL training tasks. As this problem is NP‐hard, we initially employ a greedy algorithm to optimize the UAV's position, then decompose it into three sub‐problems: computing resources, power control, and bandwidth allocation. We derive and establish the optimization objective function for computing resources as convex, obtaining the expression for optimal computing resources. The Brent algorithm, based on golden section interpolation, iteratively solves the power distribution sub‐problem, while the Lagrange‐quasi‐Newton algorithm optimizes bandwidth allocation for each user. Simulation results demonstrate that the proposed algorithm effectively reduces training completion time while maintaining training quality.

Cloud Network Collaborative SFC Deployment Under Heterogeneous Resource Of Power White Box Network

The construction of new electric power systems has led to a multiplication in the number of access nodes for electric power IoT information communication, accompanied by a deepening trend in business cloudification. However, due to the insufficient resource linkage and regulation capabilities of the electric power communication network on the cloud side, network side, and edge side, coupled with the lack of a convenient and unified cloud-network resource convergence and control mechanism, the existing electric power communication architecture is gradually finding it difficult to fully meet the demand for deterministic multi-service bearer. Therefore, it is necessary to evolve the original network equipment within the network toward a white box model, opening up interfaces for fine-grained sensing and full-area control, in order to provide more accurate and controllable network transmission services. In this paper, we first propose a cloud-network cooperative resource scheduling architecture for the electric power white box network. This architecture establishes a bearer network to achieve high consistency and cooperativeness of cloud-side end computing power through white box switches, uniformly schedules the global resources of the cloud-network convergence network, and improves the network service quality between cloud-side and side-side. Furthermore, we design a multi-dimensional re-source scheduling method for cloud-network synergy in the electric power white box network. This involves constructing a white box network virtualized heterogeneous resource (VHR) model and a heterogeneous resource control flow (HRCF) model. We transform the problem of merged deployment of service function chains (SFCs) into an SFC merger deployment problem by jointly controlling heterogeneous information, communication, storage, and arithmetic resources, along with the white box switch-specific pipelining resources in the white box network. We then carry out merger optimization of the same kind of function chains. Additionally, we propose a G-GS algorithm based on heuristic methods. Simulation results demonstrate that the method presented in this paper can significantly reduce the cost consumption of white box pipeline resources in the electric power white box network, thereby reducing the processing delay of SFC and improving the quality of electric power service bearing.

The Impact of 5G and Data Science on Digital Marketing: An Analytical Review and Research Agenda

Objective: This study aims to evaluate the impact of 5G technology across various sectors, focusing on its applications in smart cities, digital marketing, and industrial use cases, with the goal of understanding how 5G enhances connectivity, efficiency, and user experience. Theoretical Framework: The research is based on theories of digital transformation and emerging technologies, highlighting the role of artificial intelligence and advanced connectivity in the evolution of urban, commercial, and industrial systems. Method: A systematic literature review (SLR) was conducted, analyzing studies published from 1996 to 2023. The research utilized social network analysis, content analysis, and business case analysis to gain a comprehensive understanding of the effects of 5G. Results and Discussion: The findings reveal that 5G technology significantly improves connectivity, operational efficiency, and user experience, facilitating real-time communication and advanced resource management. The discussion contextualizes these findings within the theoretical framework, highlighting the benefits and challenges of 5G. Implications of the Research: The study emphasizes implications for digital infrastructure and services, suggesting the need for strategic investments and policies to fully leverage the potential of 5G. Originality/Value: This study provides an integrated and novel view of 5G, distinguishing itself from previous research by offering a holistic perspective on advanced applications.

H∞$$ {\boldsymbol{H}}_{\mathbf{\infty}} $$ Control With Event‐Triggered Mechanism for T‐S Fuzzy System Under Multiple Cyber‐Attacks

ABSTRACTThe problem of event‐triggered control for networked Takagi–Sugeno (T‐S) fuzzy system under aperiodic DoS attacks and deception attacks is investigated. First, an event generator is introduced in the Sensor‐to‐Controller channel to determine the transmission of data. At the same time, the Sensor‐to‐Controller channel is assumed to be subjected to deception attacks that are randomly distributed but not Bernoulli distributed. Next, the impact of aperiodic DoS attacks on the Controller‐to‐Actuator channel is further considered, and the DoS attack behavior is described in terms of attack period and frequency. The article designs an adaptive resilience event‐triggered mechanism (ARETM), which is aimed at circumventing the ineffective data updating in the “active” phase of the DoS attacks, thereby realizing the effective saving of communication resources and mitigating the adverse effects of DoS attacks. Then, the switched fuzzy system is established to cope with the different states of the DoS attackers. Using the piecewise Lyapunov function, a design method for the controller gains and the ARETM matrix is obtained, which allows the system to be stabilizable and obtain performance under the control action. Finally, the effectiveness of the ARETM‐based control strategy is confirmed by simulation experiment.

An efficient Protocol for Enhanced Flynet Communication in Presence of Partitioning

This paper presents an adaptive fault-tolerant resource allocation protocol tailored for FlyNet, a dynamic aerial network characterized by its mobility and three-dimensional operational space. Addressing the challenges of network partitioning and resource allocation in aerial networks, the proposed algorithm dynamically adjusts to the network's changing topology, ensuring consistent and efficient resource management. Through robust fault tolerance mechanisms, the protocol enhances FlyNet's reliability, maintaining seamless communication and optimal resource utilization even amid node mobility and disruptions. Simulation results demonstrate the algorithm's effectiveness in adapting to dynamic environments, maximizing resource utilization, and minimizing communication delays.

Self-triggered consensus resilient control for multi-agent systems against sensor deception attacks based on a single parameter learning method

This paper presents a self-triggered consensus resilient control method for nonlinear multi-agent systems (MASs) under sensor deception attacks. A single parameter learning method is integrated into backstepping technique to simplify design procedure. The neural networks are utilized to compensate for unknown dynamics of the MASs. Moreover, a self-triggered mechanism is presented for MASs to refrain from continuously monitoring triggering conditions and conserve communication resources. The designed controller can resist sensor deception attacks and guarantee that all signals of the MASs are uniformly bounded. An expository simulation example reveals the virtue of the presented method.

Dynamic Self‐Triggered Parallel Control of Guidance Intercept Systems With Constrained Inputs via Adaptive Dynamic Programming

ABSTRACTThis paper introduces an anti‐saturation optimization algorithm based on dynamic self‐triggered adaptive dynamic programming (ADP) for bounded acceleration guidance interception. By establishing a nonlinear input‐constrained guidance intercept and control system, the smooth bounded function is used to constrain the system input to ensure that the system operates in a controllable range. Subsequently, the appropriate performance function that can accurately reflect the system is designed. In alignment with the advantages of parallel control and ADP, a group of parallel systems are constructed by modeling the derivation of control input. A self‐learning control framework is explored, facilitating virtual‐actual interaction and mutual reinforcement between multiple controllers, optimizing the management of the interception system. Furthermore, event‐triggered control (ETC) policies are devised, which can be updated only when needed by setting trigger conditions, thus saving data resources. The stability proof of the closed‐loop system is given to ensure that the system can keep stable operation when the trigger condition is satisfied. On this basis, a dynamic self‐triggered control (DSTC) with soft computing is put forward, enabling trigger instant calculation without real‐time system monitoring and further extending the trigger interval. Simulation results evidence that the devised guidance scheme can intercept the maneuvering target at a reduced expenditure of communication resources.

Проблемы социализации и инкультурации в современных условиях

Russian society today is influenced by the process of globalization, which is manifested in objective phenomena such as technical and technological development, as well as subjective manifestations-changes in the value-normative system. The consequence of these processes is an anthropological crisis, the replacement of traditional agents of socialization with new ones – information and telecommunication resources, the Internet. The young generation of Russians, who most actively use new technologies in their everyday and professional lives, is at risk. Using methods of document analysis, synthesis, and analogy, the author identifies permanent features of the processes of socialization and enculturation such as the interplay of conscious and unconscious mechanisms and the law of imitation/copying of the most significant models and values. The article points out that in the modern conditions of the information society, such a sought-after goal of socialization and enculturation as the formation of an integral personality capable of reproducing and transmitting basic values and norms to subsequent generations turns out to be difficult to achieve. The fact of modernity is also becoming the allocation of the Internet as an influential agent of socialization. Employing descriptive, comparative methods, and formal legal source analysis, the author concludes that, alongside the formal-legal influence of the state on the functioning of information and communication resources, there is a necessity for systemic influence from civil society institutions and the education sector as a whole, which can establish appropriate axiological foundations in society and the state.

Adaptive Predefined‐Time Fuzzy Control for Nonlinear Systems Subject to Unknown Control Direction and Mismatched Disturbances Based on Command Filter Technique

ABSTRACTThis article proposes an adaptive predefined‐time event triggered fuzzy tracking control strategy for strict‐feedback nonlinear systems with unknown control direction and mismatched disturbances by applying command filters. The Nussbaum‐type function is employed to tackle the unknown control direction problem, and the fuzzy logic systems (FLSs) are applied to model the nonlinear functions. And then, command filters are adopted to deal with the problems of complexity explosion, and the event‐triggered technique is exploited to conserve communication resources. With the help of backstepping mechanism and predefined‐time stability theory, the constructed adaptive control scheme is proposed to ensure all signals in the closed‐loop system are bounded and that the tracking error converges to a residual set. Finally, a simulation example proves the validity of presented control mechanism.

Holistic Patient Care: A Narrative Review on the Synergy of Internal Medicine, Anesthesiology, and Surgical Practices

Introduction: holistic patient care, which integrates the efforts of Internal Medicine, Anesthesiology, and Surgical Practices, has been shown to improve patient outcomes across the continuum of care. This review aimed to explore the synergy between these specialties and highlight how their collaboration contributes to comprehensive, patient-centered care.Method: a narrative review was conducted using peer-reviewed literature from databases such as PubMed, Scopus, and Google Scholar. The search focused on articles published in the last 15 years, using terms related to interdisciplinary collaboration in healthcare. Data from relevant studies were synthesized and grouped into preoperative, intraoperative, and postoperative phases, with emphasis on how each specialty contributes to holistic care.Results: the review demonstrated that collaboration between these specialties is essential for optimizing preoperative risk management, ensuring intraoperative stability, and enhancing postoperative recovery. Internal Medicine played a key role in managing chronic conditions preoperatively, while Anesthesiology tailored anesthesia plans and managed perioperative pain. Surgical Practices, guided by insights from the other specialties, executed patient-centered surgeries. Despite the benefits, challenges such as communication barriers and resource limitations were identified.Conclusion: this review highlighted the critical role of interdisciplinary collaboration in holistic patient care. While the synergy between Internal Medicine, Anesthesiology, and Surgical Practices improved patient outcomes, addressing communication challenges and resource constraints is essential for further enhancing care quality.

Event-Triggered Fuzzy Adaptive Predefined-Time Control for Fractional-Order Nonlinear Systems with Time-Varying Deferred Constraints and Its Application

This paper focuses on the fuzzy adaptive predefined-time control for fractional-order nonlinear systems with time-varying deferred constraints. First, a modified dynamic surface control technique is introduced to address the problem of computational complexity exposed in the backstepping framework, and the interval type-2 fuzzy logic systems are applied to model the unknown nonlinearities of the systems. Next, a shifting function and the barrier Lyapunov function with variational barrier bounds are formulated to deal with the constraints issue. Particularly, the constraint conditions can be satisfied within a predetermined time, even if they are transgressed initially. Furthermore, a switching threshold event-triggered controller is devised to balance the control energy and communication resources. With the help of the predefined-time stability criterion, it is proven that the presented predefined-time event-triggered controller can ensure that all the signals involved in the closed-loop system are bounded and the tracking error fluctuates to a small neighborhood of the origin in a predefined-time interval. Finally, two simulation examples are provided to confirm the effectiveness of the put-forward control algorithm.

The Impact of Deployment of National Police Reservists on the Overall Security Incidents in Elgeyo Marakwet County, Kenya

The use of police reservists has been recognized as a key strategy in enhancing security, especially in conflict-prone regions like Elgeyo Marakwet County, Kenya. Their unique advantage lies in their proximity to the local community, quick response capabilities, and familiarity with both the terrain and the people they serve. These traits, combined with their role in community policing, have proven vital in addressing resource-based conflicts and cattle rustling. Despite challenges such as limited personnel and the presence of firearms, reservists have significantly contributed to reducing violence and fostering better relations between law enforcement and local communities. The data used in this paper was part of a study that examined the relationship between the deployment of National Police Reservists and security incidents in Elgeyo Marakwet County, Kenya. Through a cross-sectional survey, data was collected from community members through a questionnaire (n = 362), interviews (the County Commander, Subcounty Commanders, Officer Commanding Police Stations and administrative Chiefs; n = 7) and FGDs (n = 7 police reservists). The Friedman Test was conducted to evaluate whether the deployment of NPRs led to reduction in the frequency of security incidents. Qualitative data, was analysed thematically. The study established that the deployment of National Police Reservists (NPRs) had a significant impact on crime reduction (χ²(4) = 11.800, p = .019). The community considered the police reservists as effective in maintaining peace and security, with residents reporting increased safety and expressing strong trust in their abilities. There was a high level of trust in the reservists' ability to protect the community. The potential challenges in the deployment of National Police Reservists include logistical constraints, inadequate communication infrastructure, limited training and resource shortages. The study recommends the continued deployment of NPRs while monitoring and evaluating their impact regularly. In addition, there is need to build on the strong community trust in NPRs by enhancing community engagement and communication. There is need to invest to increase resource allocation to the NPRs as well as address the logistical constraints faced by reservists.

Distributed leader-following bipartite consensus for one-sided Lipschitz multi-agent systems via dual-terminal event-triggered mechanism

This article analyses leader-following bipartite consensus for one-sided Lipschitz multi-agent systems by dual-terminal event-triggered output feedback control approach. A distributed observer is designed to estimate unknown system states by employing relative output information at triggering time instants, and then an event-triggered output feedback controller is proposed. Dual-terminal dynamic event-triggered mechanisms are proposed in sensor–observer channel and controller–actuator channel, which can save communication resources to a great extent, and the Zeno behavior is ruled out. A new generalized one-sided Lipschitz condition is proposed to handle the nonlinear term and achieve bipartite consensus. Some stability conditions are presented to guarantee leader-following bipartite consensus. Finally, one-link robot manipulator systems are introduced to demonstrate the availability of the designed scheme. The results demonstrate that the agents of the robot manipulators can track the reference trajectories bi-directionally, and effectively reduce communication resources by 61.22% and 68.04% at the sensor–observer and controller–actuator channels, respectively.

Non-Periodic Quantized Model Predictive Control Method for Underwater Dynamic Docking

This study proposed an event-triggered quantized model predictive control (ETQMPC) method for the dynamic docking of unmanned underwater vehicles (UUVs) and human-occupied vehicles (HOVs). The proposed strategy employed a non-periodic control approach that initiated the non-linear model predictive control (NMPC) optimization and state sampling based on tracking errors and deviations from the predicted optimal state, thereby enhancing computing performance and system efficiency without compromising the control quality. To further conserve communication resources and improve information transfer efficiency, a quantitative feedback mechanism was employed for sampling and state quantification. The simulation experiments were performed to verify the effectiveness of the method, demonstrating excellent docking trajectory tracking performance, robustness against bounded current interference, and significant reductions in computational and communication burdens. The experimental results demonstrated that the method outperformed in the docking trajectory tracking control performance significantly improved the computational and communication performance, and comprehensively improved the system efficiency.

Communication, Socialization, and Literacy for Adults With Developmental Disabilities: An Interview Study of Parents' Perspectives and Experiences.

The study highlights the significance of continuous support and services in communication, socialization, and literacy skills for adults with developmental disabilities (DDs). The aim of the study was to gather in-depth information on the individualized needs, lived experiences, and expectations of adults with DD by interviewing their parents. A total of 14 parents of adults with DD participated in one-on-one semistructured interviews. During the interviews, parents were asked to describe (a) their children's current communication, socialization, reading and writing skills; (b) the types of communication, socialization, and literacy support their children were receiving; and (c) the types of support that their children needed. Interviews were video- and audio-recorded, transcribed, and analyzed qualitatively using thematic analysis. Thematic analysis revealed three main themes and eight subthemes: (1) insufficient support and services for adults with DD (i.e., lack of competent professionals and lack of literacy support), (2) approaches that the parents attempted for their children with DD (i.e., parents' responsibilities, technologies, and pseudoscience), and (3) recommendations for ongoing support and services (i.e., novel technologies, social media, and motivation). The study highlights crucial gaps between the participating parents' desire for individualized, consistent, and innovative support and services for their adult children with DD and scarcity of resources in communication, socialization, and literacy. The dearth of evidence-based support and resources leads to easy access to pseudoscience. Future research is warranted to develop innovative, customized, and highly motivating programs in communication, socialization, and literacy for adults with DD. https://doi.org/10.23641/asha.27214407.

Stubborn State and Disturbance Observer Co‐Design for Nonlinear Descriptor Systems With δQC$$ \delta \mathrm{QC} $$ via a Dynamic Event‐Triggered Mechanism

ABSTRACTThis article studies the state and disturbance simultaneous estimation problem for a class of nonlinear descriptor systems with using a dynamic event‐triggered mechanism. refers to incremental quadratic constraints, which can provide a unified description of many types of common nonlinear functions. To reduce the negative impact of measurement outliers on the identification estimation, a stubborn state and disturbance observer co‐design scheme is proposed for the first time by embedding dynamic saturation output estimation errors. Meanwhile, a dynamic event‐triggered mechanism is introduced to avoid the need for continuously available output information, which can reduce the pressure on communication resources. By constructing a Lyapunov function, existence conditions of the stubborn state and disturbance observer are obtained in the form of a convex optimization problem so that the error estimation maintains an acceptable estimation performance. Finally, simulation examples illustrate the universality and stubbornness of the proposed observer.

Event‐Triggered Secure Control of Positive Networked Control Systems Under Multi‐Channels Attacks

ABSTRACTThis paper focuses on the secure control of positive networked control systems under multi‐channel attacks characterized by a Bernoulli distribution. Specifically, the sensor‐to‐controller channel suffers from false data injection (FDI) attacks, whereas the controller‐to‐actuator channel suffers from denial of service (DoS) attacks. The objective is to design a static output feedback controller that guarantees the positivity and stochastic stability of the closed‐loop system in the presence of DoS and FDI attacks. To conserve communication resources, we employ an event‐triggered scheme to reduce the frequency of information transmission. Due to the positivity of the system, the proposed event‐triggering mechanism employs the 1‐norm form instead of the 2‐norm form, which allows that the controller parameter is determined via linear programming rather than LMI technique. Finally, two simulation examples are provided to verify the effectiveness of our methods.

Optimizing point-of-sale services in MEC enabled near field wireless communications using multi-agent reinforcement learning

In the next-generation communication system, near-field communication (NFC) is a key enabler of contactless transactions, including mobile payments, ticketing, and access control. With the growing demand for contactless solutions, NFC technology will play a pivotal role in enabling secure and convenient payment experiences across various sectors. In contrast, Internet of Things (IoT) devices such as phones’ Point of Sale (PoS) constitute limited battery life and finite computational resources that act as a bottleneck to doing the authentication in a minimal amount of time. Because of this, it garnered considerable attention in both academic and industrial realms. To overcome this, in this work we consider the Multiple Mobile Edge Computing (MEC) as an effective solution that provides extensive computation to PoS connected to it. To address the above, this work considers the PoS-enabled multi-MEC network to guarantee NFC communication reliably and effectively. For this, we formulate the joint optimization problem to maximize the probability of successful authentication while minimizing the queueing delay by jointly optimizing the computation and communication resources by utilizing a multi-agent reinforcement learning optimization approach. Through extensive simulations based on real-world scenarios, the effectiveness of the proposed approach was demonstrated. The results demonstrate that adjusting the complexity and learning rates of the model, coupled with strategic allocation of edge resources, significantly increased authentication success rates. Furthermore, the optimal allocation strategy was found to be crucial in reducing latency and improving authentication success by approximately 9.75%, surpassing other approaches. This study highlights the importance of resource management in optimizing MEC systems, paving the way for advancements in establishing secure, efficient, and dependable systems within the Internet of Things framework.