Frequency Of Information Exchange Research Articles

The Effect of Cyberbullying on Stress and Productivity of Remote Employees During COVID-19

ABSTRACT The purpose of this study was to enhance our understanding of cyberbullying from a conservation of resources perspective with the goal of advancing theory and providing practitioners with information aimed at well-being interventions. We examined perceived stress as an explanatory mechanism of the relationship between cyberbullying and work productivity in remote workers. Information exchange frequency and quality were considered as potential resources that were hypothesized to mitigate this indirect effect. A sample of 269 adults in the United States who were working remotely as a result of COVID-19 were recruited through Amazon’s Mechanical Turk. The results of this time-lagged study indicated that cyberbullying did indeed exhibit an indirect effect on productivity via stress. However, contrary to expectations, this indirect effect was stronger for those with higher information exchange quality rather than lower information exchange quality. The indirect effect was unaffected by information exchange frequency. We encourage future scholars to dig deeper into when resources may be detrimental to employee well-being. We also encourage practitioners to consider ways to reduce both cyberbullying and worker stress with interventions such as technology etiquette training and careful planning of information exchange.

Safety communication in construction projects in Vietnam from contractors’ perspective

Little is known about how specific participants send and receive safety information and training whilst safety is always an essential factor contributing to the success of a project. Research examining the patterns of safety communication, especially in emerging markets like Vietnam, is therefore necessary. Data was collected through case studies and interviews with six contractor’s managers in different projects. This paper presents a social network analysis approach that has been used a wide range of applications and disciplines to analyse and measure how safety communication occurs between project participants. The results show that there is a clustering in the way the organization communicates about occupational safety. At the same time, there is a relationship between the frequency of exchanging information on occupational safety and the satisfaction with the work safety in the project of the contractor's managers. Initially, it can be surmised that the frequency of information exchange on occupational safety in Vietnam is similar to that in many other countries. In addition, the projects have a variety of occupational safety communication channels. In terms of applicability, metrics like ones presented in this paper can perfectly serve as a leading set of safety performance indicators, which can be rapidly measured and modeled at the outset of a project and throughout project implementation.

An Event-Triggered Approach for Gradient Tracking in Consensus-Based Distributed Optimization

This paper is concerned with a communication-efficient algorithm update scheme for solving distributed convex optimization problems by introducing a distributed event-triggered approach. Compared with real-time consensus-based distributed optimization algorithms in the literature, this paper focuses on extending the real-time gradient tracking scheme and proposes a novel distributed event-triggering condition to reduce the frequency of information exchange between agents in a network. The proposed event-triggered approach for consensus-based distributed optimization algorithms not only avoids the real-time consecutive communication and the coordinated computation between agents but reduces the computation load of algorithm execution. Furthermore, the proposed event-triggering condition depends on only local states from neighbors at only their event times and does not require a global and homogeneous sampling period. In addition, this paper analytically shows that the proposed consensus-based distributed optimization algorithm based on an event-triggered approach can also converge to the exact optimal solution with a linear convergence rate even if the real-time consecutive communication between agents is replaced with sporadic communication.

Video Content Placement At the Network Edge: Centralized and Distributed Algorithms

In the traditional video streaming service provisioning paradigm, viewers typically request video content through a central Content Delivery Network (CDN) server. However, because of the uncertain wide area network delays, the (remote) viewers usually suffer from long video streaming delay, which affects the quality of experience. Multi-Access Edge Computing (MEC) offers a way to shorten the video streaming delay by building small-scale cloud infrastructures at the network edge, which are in close proximity to the viewers. In this paper, we present novel centralized and distributed algorithms for the video content placement problem in MEC. In the proposed centralized video content placement algorithm, we leverage the Lyapunov optimization technique to formulate the video content placement problem as a series of one-time-slot optimization problems and apply an Alternating Direction Method of Multipliers (ADMM)-based method to solve each of them. We further devise a distributed Multi-Agent Reinforcement Learning (MARL)-based method with value decomposition mechanism and parallelization policy update method to solve the video content placement problem. The value Decomposition mechanism deals with the credit assignment among multiple agents, which promotes the cooperative optimization of the global target and reduces the frequency of information exchange. The parallelization of policy network can speed up the convergence process. Simulation results verify the effectiveness and superiority of our proposed centralized and distributed algorithms in terms of performance.

МЕТОД ОЦЕНКИ КООРДИНАТ БЛА ПО ИЗМЕРЕННЫМ ЛОКАЛЬНЫМ РАССТОЯНИЯМ МЕЖДУ ЭЛЕМЕНТАМИ ГРУППЫ

Important problems in the development of mobile robotics are the task of autonomous navigation,automatic movement control and providing a reliable communication channel. For navigation,an unmanned aerial vehicle can use its own inertial navigation system and a satellite navigationsystem. The purpose of this article is to develop a method for reducing errors in the operationof the inertial navigation system of UAVs caused by the presence of random and systematic errors.In this case, we consider the situation of a monotonous increase in the systematic error over time.Usually, navigation data obtained from the satellite does not contain a significant systematic errorin determining the coordinates. However, the satellite signal may be lost for a time significantlylonger than the period of transmission of navigation data from the satellite in normal mode.As a result, there is a problem of increasing the accuracy of the data received from the inertialnavigation system. This problem is particularly relevant for group application of UAVs. Whensolving group control tasks, it becomes necessary to prevent vehicle collisions and possible collisionsalready at the stage of traffic planning. In addition, to solve a number of group tasks, such asmonitoring the terrain, conducting rescue operations, searching for objects in a given area, andjoint cargo transportation, individual objects of the group must move smoothly in space with greataccuracy. This imposes more stringent restrictions on the accuracy of the inertial navigation systemsprocessing and the frequency of information exchange. In this paper, we propose a methodthat allows, based on data obtained from local systems that measure the mutual distances betweenobjects in a group. This information allows correct the estimates of their own coordinates in sucha way as to reduce the standard deviation of the corrected set of points from the true positions ofobjects at a given time. The method also reduces the maximum value of the corresponding deviationin comparison with the original set of estimates obtained from the navigation data of the INS.The method is demonstrated by the example of increasing the accuracy of determining global coordinatesin a group of UAVs.

Absorptive Capacity and Innovation Outcomes in Teams: The Role of CEO Servant Leadership and TMT Information Exchange Frequency on Team Social Integration, Task Conflict and Knowledge Breadth

Absorptive Capacity and Innovation Outcomes in Teams: The Role of CEO Servant Leadership and TMT Information Exchange Frequency on Team Social Integration, Task Conflict and Knowledge Breadth

Consensus-based economic dispatch algorithm in a microgrid via distributed event-triggered control

In this paper, the consensus-based approach for the economic dispatch problem in microgrid via distributed event-triggered control is considered. Its basic principle is to utilise the consensus of the multi-agent systems consisting of generators and loads, which will work cooperatively such that the optimal incremental cost and optimal power output could be obtained to solve the economic dispatch problem. The consensus analysis of the proposed systems focused on the following two physical scenarios: (i) containing the power balance constraint but without generation limit; (ii) the power balance constraint and with generation limit. The corresponding effective distributed event-triggered controller is designed respectively to avoid the continuous communication between the agents, the state updating of each agent depending on its own state and its neighbouring agents at their last triggering time. Compared with the centralised event-triggered mechanism, the proposed distributed scheme could reduce the frequency of information exchange between the agent and its neighbours and save the bandwidth and energy. Simulation indicates the effectiveness of the theoretical results.

Event-Triggered Exponential Synchronization for Complex-Valued Memristive Neural Networks With Time-Varying Delays.

This article solves the event-triggered exponential synchronization problem for a class of complex-valued memristive neural networks with time-varying delays. The drive-response complex-valued memristive neural networks are translated into two real-valued memristive neural networks through the method of separating the complex-valued memristive neural networks into real and imaginary parts. In order to reduce the information exchange frequency between the sensor and the controller, a novel event-triggered mechanism with the event-triggering functions is introduced in wireless communication networks. Some sufficient conditions are established to achieve the event-triggered exponential synchronization for drive-response complex-valued memristive neural networks with time-varying delays. In addition, to guarantee that the Zeno behavior cannot occur, a positive lower bound for the interevent times is explicitly derived. Finally, numerical simulations are provided to illustrate the effectiveness and superiority of the obtained theoretical results.

Revisiting Scheduling in Heterogeneous Networks When the Backhaul Is Limited

We study the impact of the limited capacity of backhaul links on downlink user scheduling in a heterogeneous network comprising macro base stations and small cells. Assuming a tree topology of the backhaul network, we formulate a backhaul-aware global $\alpha$ -fair time-domain user scheduling problem and study it under three different scenarios of backhaul limitations. For the scenario where the backhaul links are not the bottleneck, we derive closed-form scheduling solutions to the scheduling problem under certain assumptions. For the scenario where the backhaul links between the macro base station and the small cells are the bottleneck, we show that the global $\alpha$ -fair user scheduling problem can be decomposed into a set of independent local $\alpha$ -fair user scheduling problems. However, unlike the previous case, a local scheduler in this case is not of a unique type but can be of one of three types, depending on the available backhaul capacity. We completely characterize these three types and also propose a simple heuristic for optimal $\alpha$ -fair scheduling. When the link between the macro base station and the core network is a potential bottleneck, we show how each base station can still perform a local scheduling as in the previous case as long as there is a master problem that allocates feasible virtual backhaul capacities to each BS. However, computing the optimal virtual capacities is complex and expensive in terms of the amount and frequency of information exchanges. For this scenario, we propose realization-agnostic heuristic schemes that are simple to implement and perform quite well.

Spatial Computers for Emergency Support

We present two spatially distributed computing systems that operate in a building and provide intelligent navigation services to people for evacuation purposes. These systems adapt to changing conditions by monitoring the building and using local communication and computation for determining the best evacuation paths. The first system, called distributed evacuation system (DES), comprises a network of decision nodes (DNs) positioned at specific locations inside the building. DNs provide people with directions regarding the best available exit. The second system, called opportunistic emergency support system (OESS), consists of mobile communication nodes (CNs) carried by people. CNs form an opportunistic network in order to exchange information regarding the hazard and to direct the evacuees towards the safest exit. BothDESandOESSemploy sensor nodes deployed at fixed locations for monitoring the hazard.We evaluate the spatial systems using simulation experiments with a purpose-built emergency simulator called DBES.We show how parameters such as the frequency of information exchange and communication range affect the system performance and evacuation outcome.

The Effects of Family Firm Specific Sources of TMT Diversity: The Moderating Role of Information Exchange Frequency

abstractWe examine the relationships among three family firm specific sources of top management team (TMT) diversity (the generation in charge of the family firm, the number of family employees, and the number of employed generations) and family firm performance. By integrating upper‐echelons and team process research, we hypothesize that these TMT diversity sources interact with information exchange frequency among TMT members to affect family firm performance. Multisource survey data with lagged performance measurement, including CEOs and TMT members from 86 family firms, support our hypotheses. We discuss the implications of our findings and develop avenues for future research.

SOBIE:A Novel Super-node P2P Overlay Based on Information Exchange

In order to guarantee both the efficiency and robustness in the Peer-to-Peer (P2P) network, the paper designs a novel Super-node Overlay Based on Information Exchange called SOBIE. Differing from current structured and unstructured, or meshed and tree-like P2P overlay, the SOBIE is a whole new structure to improve the efficiency of searching in the P2P network. The main contributions are 1) to select the super-nodes by considering the aggregation of not only the delay, distance, but also the information exchange frequency, exchange time and query similarity especially; 2) to set a score mechanism to identify and prevent the free-riders. Meanwhile, the SOBIE also guarantees the matching between the physical network and logical network and has small-world characteristic to improve the efficiency. Large number of experiment results show the advantages of the SOBIE including high efficiency and robustness by such different factors as the query success rate, the average query hops, the total number of query messages, the coverage rate and system connectivity.

Exploring the accuracy of SME managers' network perceptions

This paper studies the accuracy of small‐ to medium‐sized (SME) managers' perceptions of their information exchanges with important market actors such as customers, competitors and suppliers. In this way, examines an important dimension of managers' network perceptions, which are assumed to be necessary for optimal utilisation of their networks. By comparing managers' perceived frequency of information exchanges with external actors with an “objective” tracking of their actual behaviour, reveals substantial perceptual errors. Findings are discussed and implications highlighted.