Search results

This paper introduces a conceptual lens for leading social change in slums and informal settlements. In line with this aim, the purpose of this case study is to describe the public problem-solving approach of a social change organization situated in an informal settlement through the lens of adaptive leadership, complexity theory and social change leadership (SCL).

This paper follows an engaged reflection tradition. First, the author-practitioners describe an informal settlement case hereafter called ISC in southeast Iran where many people have historically remained undocumented and uneducated. Using complex adaptive systems theory, adaptive leadership and SCL as the conceptual lens, the paper analyzes ISC as a complex adaptive context in which the community and the government are in tension in solving problems, particularly illiteracy. The instrumental case study draws from participant observation and document analysis to describe and examine the endeavors of a community office operating within ISC. Through this reflective analysis, the authors illustrate how a social change organization can effectively tackle public issues like illiteracy within informal settlements.

This paper applies complexity leadership theory to a social context. The study illustrates how social change organizations can support the transformation of informal spaces into adaptive spaces to enact social change.

This paper reflects on engagement activity near the insecure borders of Iran, Afghanistan and Pakistan. By extending an organizational-level theory to the public sphere, this paper contributes theoretically to the complexity theory literature. Moreover, it provides a practical insight for community development and slum upgrading projects.

Component positioning is an important part of aircraft assembly, aiming at the problem that it is difficult to accurately fall into the corresponding ball socket for the ball head connected with aircraft component. This study aims to propose a ball head adaptive positioning method based on impedance control.

First, a target impedance model for ball head positioning is constructed, and a reference positioning trajectory is generated online based on the contact force between the ball head and the ball socket. Second, the target impedance parameters were optimized based on the artificial fish swarm algorithm. Third, to improve the robustness of the impedance controller in unknown environments, a controller is designed based on model reference adaptive control (MRAC) theory and an adaptive impedance control model is built in the Simulink environment. Finally, a series of ball head positioning experiments are carried out.

During the positioning of the ball head, the contact force between the ball head and the ball socket is maintained at a low level. After the positioning, the horizontal contact force between the ball head and the socket is less than 2 N. When the position of the contact environment has the same change during ball head positioning, the contact force between the ball head and the ball socket under standard impedance control will increase to 44 N, while the contact force of the ball head and the ball socket under adaptive impedance control will only increase to 19 N.

In this paper, impedance control is used to decouple the force-position relationship of the ball head during positioning, which makes the entire process of ball head positioning complete under low stress conditions. At the same time, by constructing an adaptive impedance controller based on MRAC, the robustness of the positioning system under changes in the contact environment position is greatly improved.

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service.

An adaptive optimal service resource management strategy was developed considering a value co-creation model in health information service with a focus on collaborative and interactive with users. The deep reinforcement learning algorithm was embedded in the Internet of Things (IoT)-based health information service system (I-HISS) to allocate service resources by controlling service provision and service adaptation based on user engagement behaviour. The simulation experiments were conducted to evaluate the significance of the proposed algorithm under different user reactions to the health information service.

The results indicate that the proposed service resource management strategy, considering user co-creation in the service delivery, process improved both the service provider’s business revenue and users' individual benefits.

The findings may facilitate the design and implementation of health information services that can achieve a high user service experience with low service operation costs.

This study is amongst the first to propose a service resource management model in I-HISS, considering the value co-creation of the user in the service-dominant logic. The novel artificial intelligence algorithm is developed using the deep reinforcement learning method to learn the adaptive service resource management strategy. The results emphasise user engagement in the health information service process.

Bayesian cubature (BC) has emerged to be one of most competitive approach for estimating the multi-dimensional integral especially when the integrand is expensive to evaluate, and alternative acquisition functions, such as the Posterior Variance Contribution (PVC) function, have been developed for adaptive experiment design of the integration points. However, those sequential design strategies also prevent BC from being implemented in a parallel scheme. Therefore, this paper aims at developing a parallelized adaptive BC method to further improve the computational efficiency.

By theoretically examining the multimodal behavior of the PVC function, it is concluded that the multiple local maxima all have important contribution to the integration accuracy as can be selected as design points, providing a practical way for parallelization of the adaptive BC. Inspired by the above finding, four multimodal optimization algorithms, including one newly developed in this work, are then introduced for finding multiple local maxima of the PVC function in one run, and further for parallel implementation of the adaptive BC.

The superiority of the parallel schemes and the performance of the four multimodal optimization algorithms are then demonstrated and compared with the k-means clustering method by using two numerical benchmarks and two engineering examples.

Multimodal behavior of acquisition function for BC is comprehensively investigated. All the local maxima of the acquisition function contribute to adaptive BC accuracy. Parallelization of adaptive BC is realized with four multimodal optimization methods.

This paper aims to explore the topic of adaptive reuse referring to urban open spaces into a more-than-human perspective. It underlines that dealing with heritage means being part of an inherent and ongoing process of transformation and so that reuse is inextricably an adaptive practice, constantly facing mutations, and that adaptation is a coral practice that involves different kinds of users and makers, inclusive of human and not human livings.

This paper looks at the lexicon of abandonment, in search of the more essential and intense meanings of words, and at some pioneering practices in Europe to comprehend the aesthetic and ethical implications of adaptive reuse of neglected landscapes.

Processes of reuse involve many different communities of users who in turn continuously redesign the site, into a comprehensive, coral and conflicting collaboration, whose results are never given once for all and are both uncanny and beautiful, scaring and marvellous, like a monster.

Accepting the idea that humans are not the only users and makers of urban sites can widen the range of tools, methods and values involved in heritage adaptive reuse.

This paper tries to widen the meanings of adaptation into a multispecies perspective. It intends to broaden the range of agents that can be involved as users and makers, assuming a more-than-human point of view that is not yet commonly applied.

The purpose of this paper is to develop and test an integrated model to examine the relationship between work engagement and three facets of perceived job performance (PJP). The authors argue that work engagement might not optimally improve PJP unless it is channelized through information and communication technology orientation.

Data for the present research were collected from higher educational institutes in the northern region of India by using a convenient sampling technique. Results of structural equation modeling (SEM) through AMOS 20 revealed that work engagement facilitates all three facets i.e. task performance, contextual performance and adaptive performance of teaching professionals. Furthermore, SEM results established the partial mediating effect of information and communication technology orientation between work engagement, task performance, contextual performance and adaptive performance.

Findings from present research contribute theoretically as well as practically to job performance and work engagement literature by giving insights to administrators and practitioners on how to improve the overall job performance of teaching professionals by enhancing their engagement and addressing their need for digital know-how.

To the best of the authors’ knowledge, this study is one of the first to study the impact of work engagement and information and communication technology on the three facets of PJP using a diverse sample of 1030 teachers from universities in North India.

In the final step, the trust model is applied to the on-demand federated multipath distance vector routing protocol (AOMDV) to introduce path trust as a foundation for routing selection in the route discovery phase, construct a trusted path, and implement a path warning mechanism to detect malicious nodes in the route maintenance phase, respectively.

A trust-based on-demand multipath distance vector routing protocol is being developed to address the problem of flying ad-hoc network being subjected to internal attacks and experiencing frequent connection interruptions. Following the construction of the node trust assessment model and the presentation of trust evaluation criteria, the data packet forwarding rate, trusted interaction degree and detection packet receipt rate are discussed. In the next step, the direct trust degree of the adaptive fuzzy trust aggregation network compute node is constructed. After then, rely on the indirect trust degree of neighbouring nodes to calculate the trust degree of the node in the network. Design a trust fluctuation penalty mechanism, as a second step, to defend against the switch attack in the trust model.

When compared to the lightweight trust-enhanced routing protocol (TEAOMDV), it significantly improves the data packet delivery rate and throughput of the network significantly.

Additionally, it reduces the amount of routing overhead and the average end-to-end delay.

Traditional management models are no longer viable in this complex era, with fast-pacing markets and various emergent properties affecting organizational success. The present study acknowledges the need for developing a coherent framework that leaders may use to evaluate their organizational adaptive ability.

This paper uses theoretical analysis.

This paper proposes the CAL-R framework, which identifies key characteristics of complex adaptive systems (CAS). These actionable elements, manifested in respective behaviors, will increase organizational adaptability. Adaptability is then suggested to be the mediating factor between complex adaptive leadership practice and organizational performance.

The suggested actionable elements are developed based on a theoretical, non-measurable approach; however, the need for statistical rigorousness is also acknowledged. Further research and a quantitative approach are also suggested for the development of a sound measurement scale.

Applying effective complex adaptive leadership will potentially provide organizations with a competitive advantage by making them more innovative, able to transform and adapt to environmental needs and changes and eventually meet their goals and reach their vision. Leaders may use the identified actionable elements as benchmarks against which they can evaluate their organizations' complex adaptive leadership readiness (CAL-R).

The CAL-R framework may be used by leaders in order to evaluate their organization’s current adaptive maturity level (ability) and identify the elements they need to focus on more and develop in order to become more adaptive and competitive.