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The purpose of this study was to investigate the mediating role of teachers' intrinsic and extrinsic motivation in the relationship between principals' perceived distributed leadership and organizational learning capability in schools.

The study employs a quantitative research design and a survey methodology. Data were collected from 400 teachers in Bahrain.

The results reveal that teachers' intrinsic and extrinsic motivation fully mediates the relationship between principals' perceived distributed leadership and organizational learning capability in schools.

The study contributes to the literature on distributed leadership, organizational learning and motivation by highlighting the important mediating role of teachers' intrinsic and extrinsic motivation in the relationship between principals' perceived distributed leadership and organizational learning capability. The study also has practical implications for school administrators by suggesting that distributed leadership practices can be an effective strategy for promoting organizational learning capability in schools.

The purpose of this study is to examine the effect of distributed leadership on organizational commitment and the role of trust and open group climate as moderator variables in this relationship.

To test the conceptual model and research hypotheses empirically, the authors collected data based on an investigation over a sample of 318 engineers in the Information Technology telecommunication sector in Tunisia. The results were analyzed using factor analysis and structural equation modeling.

The results show a significant and positive impact of the support function, of the participation in decision-making and cooperation on organizational commitment. The authors find evidence for the existence of a positive moderating effect of trust and affective climate at the level of the causal link between distributed leadership and organizational commitment dimensions.

These results provide useful indications for managers within the framework of leadership style that is more appropriate to the group’s proper functioning. Throughout this work, managers will know that distributed leadership is adapted to create a social climate based on dialogue and trust, an essential element of distributed leadership. Supervision and authority should give up a coercive vision in a more cooperative and constructive approach. Coordination should be founded on a horizontal and transversal vision of the organization.

Distributed leadership is increasingly seen as a key vehicle for firms’ improvement and renewal. However, research on this concept was largely conducted in the field of education and health. Studies dealing with small and medium-sized companies are rather scarce. There are not, to the best of the authors’ knowledge, any works in the Tunisian context because the majority of the reference studies are Anglo-Saxon. The originality and value of this research lies in its anchoring in the context. Moreover, this study provides empirical evidence of the importance of the role of the affective climate on organizational commitment. Indeed, engagement is a behavioral and attitudinal indicator of organizational climate. This paper is intended to provide a stimulus for exploring the distributed leadership area in terms of shaping thinking and designs for organizational change to enhance organizational commitment in a highly digital world.

In this paper, two omni‐directional mobile vehicles are designed and controlled implementing distributed mechatronics controllers. Omni‐directionality is the ability of mobile vehicle to move instantaneously in any direction. It is achieved by implementing Mecanum wheels in one vehicle and conventional wheels in another vehicle. The control requirements for omni‐directionality using the two above‐mentioned methods are that each wheel must be independently driven, and that all the four wheels must be synchronized in order to achieve the desired motion of each vehicle.

Distributed mechatronics controllers implementing Controller Area Network (CAN) modules are used to satisfy the control requirements of the vehicles. In distributed control architectures, failures in other parts of the control system can be compensated by other parts of the system. Three‐layered control architecture is implemented for; time‐critical tasks, event‐based tasks, and task planning. Global variables and broadcast communication is used on CAN bus. Messages are accepted in individual distributed controller modules by subscription.

Increase in the number of distributed modules increases the number of CAN bus messages required to achieve smooth working of the vehicles. This requires development of higher layer to manage the messages on the CAN bus.

The limitation of the research is that analysis of the distributed controllers that were developed is complex, and that there are no universally accepted tool for conducting the analysis. The other limitation is that teh mathematical models of the mobile robot that have been developed need to be verified.

In the design of omni‐directional vehicles, reliability of the vehicle can be improved by modular design of mechanical system and electronic system of the wheel modules and the sensor modules.

The paper tries to show the advantages of distributed controller for omni‐directional vehicles. To the author's knowledge, that is a new concept.

The purpose of this paper is to propose distributed learning-based three different metaheuristic algorithms for the identification of nonlinear systems. The proposed algorithms are experimented in this study to address problems for which input data are available at different geographic locations. In addition, the models are tested for nonlinear systems with different noise conditions. In a nutshell, the suggested model aims to handle voluminous data with low communication overhead compared to traditional centralized processing methodologies.

Population-based evolutionary algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and cat swarm optimization (CSO) are implemented in a distributed form to address the system identification problem having distributed input data. Out of different distributed approaches mentioned in the literature, the study has considered incremental and diffusion strategies.

Performances of the proposed distributed learning-based algorithms are compared for different noise conditions. The experimental results indicate that CSO performs better compared to GA and PSO at all noise strengths with respect to accuracy and error convergence rate, but incremental CSO is slightly superior to diffusion CSO.

This paper employs evolutionary algorithms using distributed learning strategies and applies these algorithms for the identification of unknown systems. Very few existing studies have been reported in which these distributed learning strategies are experimented for the parameter estimation task.

This monograph defines distributed intelligence and discusses the relationship of distributed intelligence to data base, justifications for using the technique, and the approach to successful implementation of the technique. The approach is then illustrated by reference to a case study of experience in Birds Eye Foods. The planning process by which computing strategy for the company was decided is described, and the planning conclusions reached to date are given. The current state of development in the company is outlined and the very real savings so far achieved are specified. Finally, the main conclusions of the monograph are brought together. In essence these conclusions are that major savings are achievable using distributed intelligence, and that the implementation of a company data processing plan can be made quicker and simpler by its use. However, careful central control must be maintained so as to avoid fragmentation of machine, language skills, and application taking place.

The purpose of this paper is to explore the relationship between perceived distributed leadership and job satisfaction among nurses. Leadership is central to improving quality care. Reports following investigations of poor care standards, identified inadequate leadership as a contributory factor and called for a new kind of leadership. One alternative is distributed leadership. Evidence suggests associations between leadership and job satisfaction but, there is a paucity of research examining associations between distributed leadership and job satisfaction: the purpose of this study was to address this gap.

A cross-sectional survey design was used and data collected via questionnaires. Using census sampling, 350 nurses in a hospital in Malta were selected. A response rate of 50% (n = 176) was achieved. Data were analysed using Spearman’s correlation coefficient and multiple regression. Ethical approval was obtained from relevant committees/individuals.

Results indicated a moderate application of perceived distributed leadership and application of all components of distributed leadership could be improved. Nurses were neither satisfied nor dissatisfied with their jobs. Correlation analysis showed a positive relationship between distributed leadership and job satisfaction. Multiple regression showed that commitment and participative decision-making were major predictors of job satisfaction while supervision by managers had a negative effect.

Improving distributed leadership is a priority in the nursing profession.

To the best of the authors’ knowledge, this study is the first to show that distributed leadership has a positive effect on job satisfaction among nurses. Supervision, a constituent of distributed leadership, was associated with reduced job satisfaction, therefore reducing this is paramount.

Knowledge work (KW) is a well‐researched topic. However, KW is difficult to measure and little consensus has been reached on elements that affect knowledge work productivity on a team level. The current theories neglect teams working in distributed geographical areas. The purpose of the paper is to integrate recent literature on knowledge work productivity (KWP) in distributed teams and give an overview of the elements affecting it.

The paper presents an overview of research performed in the field of knowledge work productivity. The authors integrate theories of different fields of management theory (knowledge management, intellectual capital and learning), and work and organizational psychology. This paper answers three questions: What is knowledge work? What is knowledge work productivity? Which elements hinder or enable knowledge work productivity in distributed teams of global technology companies?

The authors define the crucial elements that either hinder or enable KWP: team tasks, team structure and processes, the physical, virtual and social workspaces as well as organizational context. The paper presents an integrative model of KWP in distributed teams of global technology companies.

Distributed teams are common in global companies. By understanding the elements that affect KWP, companies can stimulate or decrease specific elements in order to improve productivity of their distributed knowledge workers.

This paper integrates theories from different disciplines in order to create an understanding about knowledge work and its productivity for further research.

The purpose of this paper is to focus on distributed leadership in schools and explore the implications arising from this model of leadership for those in formal leadership positions. It considers how the role of the principal, in particular, is affected and changed as leadership is more widely shared within the organization.

The paper draws upon a wide range of research literature to explore the available empirical evidence about distributed leadership and organizational outcomes. The analysis focuses particularly on the evidence base concerning distributed leadership and student learning outcomes.

This analysis of the available evidence highlights the potential for distributed leadership to make a difference to organizational change and improvement. It suggests that principals need to relinquish power and authority; that there is an inevitable shift away from leadership as position to leadership as interaction and that principals will need to build a high degree of reciprocal trust to negotiate successfully the fault lines of formal and informal leadership practice.

The paper contributes a contemporary overview of literature about the impact of distributed leadership and analyses the implications for the role of the school principal.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.

Localization is a fundamental problem in wireless sensor networks. In many applications, sensor location information is critical for data processing and meaning. While the global positioning system (GPS) can be used to determine mote locations with meter precision, the high hardware cost and energy requirements of GPS receivers often prohibit the ubiquitous use of GPS for location estimates. This high cost (in terms of hardware price and energy consumption) of GPS has motivated researchers to develop localization protocols that determine mote locations based on cheap hardware and localization algorithms. The purpose of this paper is to present a comprehensive review of wireless sensor network localization techniques, and provide a detailed overview for several distance‐based localization algorithms.

To provide a detailed summary of wireless sensor network localization algorithms, the authors outline a tiered classification system in which they first classify algorithms as distributed, distributed‐centralized, or centralized. From this broad classification, the paper then further categorizes localization algorithms using their protocol techniques. By utilizing this classification system, the authors are able to provide a survey of several wireless sensor network localization algorithms and summarize relative algorithm performance based on the algorithms' classification.

There are numerous localization algorithms available and the performance of these algorithms is dependent on network configuration, environmental variables, and the ranging method implemented. When selecting a localization algorithm, it is important to understand basic algorithm operation and expected performance. This tier‐based algorithm classification system can be used to gain a high‐level understanding of algorithm performance and energy consumption based on known algorithm characteristics.

Localization is a widely researched field and given the quantity of localization algorithms that currently exist, it is impossible to present a complete review of every published algorithm. Instead, the paper presents a holistic view of the current state of localization research and a detailed review of ten representative distance‐based algorithms that have diverse characteristics and methods. This review presents a new classification structure that may help researchers understand, at a high‐level, the expected performance and energy consumption of algorithms not explicitly addressed by our work.