Search results

This study aims to solve the problem of repair path planning between multiple small-size defects in the field of additive manufacturing (AM) repair by using Python-based ant colony algorithm (ACO). The optimal parameter combination scheme is obtained by discussing the influencing factors of parameters in the ACO.

The effects of the information heuristic factor α, the expected heuristic factor ß and the pheromone volatile factor ρ on the simulation results were investigated by designing a three-factor and three-level orthogonal experiment. The fast convergence of ACO in finding the optimal solution of multiple small-size defect repair path problem is proved by comparing the simulation results with those of genetic algorithm (GA) on the same data set.

The ACO can effectively solve the repair path planning problem between multiple small-size defects by optimizing the parameters. In the case of 50 defect locations, the simulation results of the ACO with optimized parameters are 159.8 iterations and 3,688 average path lengths, while the GA has 4,027.2 average path lengths under the same data set and the same number of iterations, and by comparison, it is proved that the ACO can find the optimal solution quickly in the small-size defects repair path planning problem, which greatly improves the efficiency of defect repair.

The parameter-optimized ACO can be quickly applied to the planning problem of repair paths between multiple small-size defects in the field of AM repair, which can better improve the defect repair efficiency and reduce the waste of resources.

Based on cognitive evaluation theory and gamification affordances, this study aims to understand how gamification affordances influence users’ intention to engage in sustainable behaviour and how new trends in Ant Forest influence its impact on green intrinsic motivation to support sustainable behaviours.

The authors developed a research model to explore the mechanisms underlying gamification affordances, psychological needs and green intrinsic motivation. Partial least squares structural equation modelling was used to assess the survey data (n = 393) and test the research model.

The results show that different gamification affordances can satisfy users’ needs for autonomy, competence and relatedness, which positively influences their green intrinsic motivation and engagement in sustainable behaviours. However, some affordances, such as competition, might negatively impact these psychological needs.

This research updates information system research on environmental sustainability and the Ant Forest context. The authors provide a new framework that links gamification affordances, psychological needs and sustainable behaviour. The study also examines changing trends in Ant Forest and their implications.

Educators have had good reason to be concerned with social justice in a context where diversity has become more pronounced in both our schools and communities, with widening divisions between the advantaged and the disadvantaged. Internationally, increasing emphasis has been placed on utilizing the role of school leadership to address issues of social justice and equality, within a scenario where comparative studies of the performance of educational systems dominate the policy imagination globally, thus leading to increased pressure on school systems. This chapter presents a problematization of the social justice concept within education as presented in the literature, while setting out to critique this concept as an educational goal, as well as the role educational leadership is expected to play in the promotion of equity and social justice discourses through the lens of Actor-Network Theory (ANT). This theoretical chapter has implications for theory, policy, and practice.

With the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.

An improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.

Experimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.

The proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.

To solve the problem that the underwater vehicles is difficult to turn and exit in a small range in the face of complex marine environment such as concave and ring under the limitation of its limitation of its shape and maximum steering angle, this paper aims to propose an improved ant colony algorithm based on trap filling strategy and energy consumption constraint strategy.

Firstly, on the basis of searching the global path, the disturbed terrain was pre-filled in the complex marine environments. Based on the energy constraint strategy, the ant colony algorithm was improved to make the search path of the underwater vehicle meet the requirements of the lowest energy consumption and the shortest path in the complex obstacle environment.

The simulation results showed that the modified grid environment diagram effectively reduced the redundancy search and improved the optimization efficiency. Aiming at the problem of “the shortest distance is not the lowest energy consumption” in the traditional path optimization algorithm, the energy consumption level was reduced by 26.41% after increasing the energy consumption constraint, although the path length and the number of inflection points were slightly higher than the shortest path constraint, which was more conducive to the navigation of underwater vehicles.

The method proposed in this paper is not only suitable for trajectory planning of underwater robots but also suitable for trajectory planning of land robots.

This article discusses the methodological implications of a recent study on Luxembourg's offshore financial center. Insight from actor-network theory was essential in undertaking its ethnographic research with elites from the country's state and financial institutions. My intention in documenting this approach is to provide a template for ethnographers studying other localized contexts of global politico-economic significance, in which elite actors usually seek to curtail the enquiries of investigators. With this actor-network from Luxembourg as an example, I demonstrate how elite and difficult-to-access milieus can be entered via “networking” coupled with outreach via interviews and email correspondence. As I show, by initiating various modalities of entry into the context in question, ethnographers can establish themselves within an actor-network for the purposes of conducting interviews and participant observation with elite interlocutors.

Adsorption parameters (e.g. Langmuir constant, mass transfer coefficient and Thomas rate constant) are involved in the design of aqueous-media adsorption treatment units. However, the classic approach to estimating such parameters is perceived to be imprecise. Herein, the essential features and performances of the ant colony, bee colony and elephant herd optimisation approaches are introduced to the experimental chemist and chemical engineer engaged in adsorption research for aqueous systems. Key research and development directions, believed to harness these algorithms for real-scale water treatment (which falls within the wide-ranging coverage of the Sustainable Development Goal 6 (SDG 6) ‘Clean Water and Sanitation for All’), are also proposed. The ant colony, bee colony and elephant herd optimisations have higher precision and accuracy, and are particularly efficient in finding the global optimum solution. It is hoped that the discussions can stimulate both the experimental chemist and chemical engineer to delineate the progress achieved so far and collaborate further to devise strategies for integrating these intelligent optimisations in the design and operation of real multicomponent multi-complexity adsorption systems for water purification.

This paper aims to present a set of processes for obtaining the global spraying trajectory of a cold spraying robot on a complex surface.

The complex workpiece surfaces in the project are first divided by triangular meshing. Then, the geodesic curve method is applied for local path planning. Finally, the subsurface trajectory combination optimization problem is modeled as a GTSP problem and solved by the ant colony algorithm, where the evaluation scores and the uniform design method are used to determine the optimal parameter combination of the algorithm. A global optimized spraying trajectory is thus obtained.

The simulation results show that the proposed processes can achieve the shortest global spraying trajectory. Moreover, the cold spraying experiment on the IRB4600 six-joint robot verifies that the spraying trajectory obtained by the processes can ensure a uniform coating thickness.

The proposed processes address the issue of different parameter combinations, leading to different results when using the ant colony algorithm. The two methods for obtaining the optimal parameter combinations can solve this problem quickly and effectively, and guarantee that the processes obtain the optimal global spraying trajectory.

The purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.

This method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.

The algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.

This paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.