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This paper aims to investigate the effect of ultrasonic vibration (USV) on the evolution of intermetallic compounds (IMCs), grain morphology and shear strength of soldered Ni/Sn/Ni samples.

The Ni/Sn/Ni joints were obtained through ultrasonic-assisted soldering. The formation of IMCs, their composition, grain morphology and the fractured-surface microstructures from shear tests were characterized using scanning electron microscopy and energy-dispersive x-ray spectroscopy.

Without USV, a planar interfacial Ni₃Sn₄ layer was formed at the Ni/Sn interface, and a few Ni₃Sn₄ grains were distributed in the soldered joint. The morphology of these grains was needle-shaped. With USV, several grooves were formed at the interfacial Ni₃Sn₄ layer due to ultrasonic cavitation. Some deepened grooves led to “neck” connections at the roots of the Ni₃Sn₄ grains, which accelerated the strong detachment of Ni₃Sn₄ from the substrate. In addition, two types of Ni₃Sn₄ grains, needle-shaped and granular-shaped, were observed at the interface. Furthermore, the shear strength increased with longer USV time, which was attributed to the thinning of the interfacial IMC layers and dispersion strengthening from the Ni₃Sn₄ particles distributed evenly in the joint.

The novelty of the paper is the detailed study of the effect of USV on the morphology, size changes of interfacial IMC and joint strength. This provides guidance for the application of ultrasonic-assisted soldering in electronics packaging.

Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent errors of LIDAR, conventional methods typically emphasize on a single obstacle-avoidance algorithm and neglect the limitation of sensors and safety in a local region. Conventional methods also fail in seamlessly integrating local and global obstacle avoidance algorithms. This paper aims to present a cooperative manoeuvring approach including both local and global obstacle avoidance.

The global algorithm used in our USV is the Artificial Potential Field-Ant Colony Optimization (APF-ACO) obstacle-avoidance algorithm, which plans a relative optimal path on the specified electronic map before the cruise of USV. The local algorithm is a multi-layer obstacle-avoidance framework based on a single LIDAR to present an efficient solution to USV path planning in the case of sensor errors and collision risks. When obstacles are within a layer, the USV uses a corresponding obstacle-avoidance algorithm. Then the USV moves towards the global direction according to fuzzy rules in the fuzzy layer.

The presented method offers a solution for obstacle avoidance in a complex environment. The USV follows the global trajectory planed by the APF-ACO algorithm. While, the USV can bypass current obstacle in the local region based on the multi-layer method effectively. This fact was validated by simulations and field trials.

The method presented in this paper takes advantage of algorithm integration that remedies errors of obstacle detection. Simulation and experiments were also conducted for performance evaluation.

This paper aims to propose a new method for combining global path planning with local path planning, to provide an efficient solution for unmanned surface vehicle (USV) path planning despite the changeable environment. Path planning is the key issue of USV navigation. A lot of research works were done on the global and local path planning. However, little attention was given to combining global path planning with local path planning.

A search of shortcut Dijkstra algorithm was used to control the USV in the global path planning. When the USV encounters unknown obstacles, it switches to our modified artificial potential field (APF) algorithm for local path planning. The combinatorial method improves the approach of USV path planning in complex environment.

The method in this paper offers a solution to the issue of path planning in changeable or unchangeable environment, and was confirmed by simulations and experiments. The USV follows the global path based on the search of shortcut Dijkstra algorithm. Both USV achieves obstacle avoidances in the local region based on the modified APF algorithm after obstacle detection. Both the simulation and experimental results demonstrate that the combinatorial path planning method is more efficient in the complex environment.

This paper proposes a new path planning method for USV in changeable environment. The proposed method is capable of efficient navigation in changeable and unchangeable environment.

Trajectory tracking is an important issue to underactuated unmanned surface vehicles (USVs). However, parametric uncertainties and environmental disturbances bring great challenges to the precise trajectory tracking control of USVs. This paper aims to propose a robust trajectory tracking control algorithm with exponential stability for underactuated USVs with parametric uncertainties and unknown environmental disturbances.

In this method, the backstepping method and sliding mode control method are combined to ensure that the underactuated USV can track and maintain the desired trajectory. In addition, a modified switching-gain adaptation algorithm is adopted to enhance the robustness and reduce chattering. Besides, the global exponential stability of the closed-loop system is proved by Lyapunov’s direct method.

The proposed method in this paper offers a robust trajectory tracking solution to underactuated USVs and it is verified by simulations and experiments. Compared with the traditional proportion-integral-derivative method and several state-of-the-art algorithms, the proposed method has superior performance in simulation and experimental results.

This paper proposes a robust trajectory tracking control algorithm with exponential stability for underactuated USVs. The proposed method achieves exponential stability with better robustness and transient performance.

Since many global path planning algorithms cannot achieve the planned path with both safety and economy, this study aims to propose a path planning method for unmanned vehicles with a controllable distance from obstacles.

First, combining satellite image and the Voronoi field algorithm (VFA) generates rasterized environmental information and establishes navigation area boundary. Second, establishing a hazard function associated with navigation area boundary improves the evaluation function of the A^* algorithm and uses the improved A^* algorithm for global path planning. Finally, to reduce the number of redundant nodes in the planned path and smooth the path, node optimization and gradient descent method (GDM) are used. Then, a continuous smooth path that meets the actual navigation requirements of unmanned vehicle is obtained.

The simulation experiment proved that the proposed global path planning method can realize the control of the distance between the planned path and the obstacle by setting different navigation area boundaries. The node reduction rate is between 33.52% and 73.15%, and the smoothness meets the navigation requirements. This method is reasonable and effective in the global path planning process of unmanned vehicle and can provide reference to unmanned vehicles’ autonomous obstacle avoidance decision-making.

This study establishes navigation area boundary for the environment based on the VFA and uses the improved A* algorithm to generate a navigation path that takes into account both safety and economy. This study also proposes a method to solve the redundancy of grid environment path nodes and large-angle steering and to smooth the path to improve the applicability of the proposed global path planning method. The proposed global path planning method solves the requirements of path safety and smoothness.

The purpose of this paper is to provide a more capable and holistic adjustable autonomy system, involving situation reasoning among all involved information sources, to make an adjustable autonomy system which knows what the situation is currently, what needs to be done in the present situation, and how risky the task is in the present situation. This will enhance efficiency for calculating the level of autonomy.

Situation reasoning methodologies are present in many autonomous systems which are called situation awareness. Situation awareness in autonomous systems is divided into three levels, situation perception, situation comprehension and situation projection. Situation awareness in these systems aims to make the tactical plans cognitive, but situation reasoning in adjustable autonomous systems aim to communicate mission assessments to unmanned vehicle or humans. Thus, in solving this problem, it is important to design a new situation reasoning module for the adjustable autonomous system.

The contribution of this paper is presenting the Situation Reasoning Module (SRM) for an adjustable autonomous system, which encapsulates event detection, cognitive situations, cognitive tasks, performance capacity assessment and integrated situation reason. The paper concludes by demonstrating the benefits of the SRM in a real‐world scenario, a situation reasoning simulation in unmanned surface vehicles (USV) while performing a navigation mission.

The method presented in this paper represents a new SRM to reason the situation for adjustable autonomous system. While the results presented in the paper are based on fuzzy logic and Bayesian network methodology. The results of this paper can be applicable to land, sea and air robotics in an adjustable autonomous system.

Hydrodynamic parameter estimation is significant for the velocity prediction of unmanned surface vehicles. Considering the field data’s uncertain nonlinearities (environmental disturbances and measurement noise), this paper aims to propose a hybrid adaptive parameter estimation (HAPE) strategy.

First, a rough estimation of hydrodynamic parameters is used by the least squares method. Second, an improved adaptive parameter estimation algorithm is applied to compensate for the influence of uncertain nonlinearities and adjust the parameters within the rough range. Finally, it is proved that the calculated velocity asymptotically converges to the actual value during the parameter estimation procedure.

The numerical simulation and pool experiments are conducted in two scenarios of steady turning and sinusoidal thrust to verify the effectiveness of the proposed HAPE method. The results validate that the accuracy of the predicted velocity using the hydrodynamic model obtained by the HAPE strategy is better than the APE algorithm. In addition, the hydrodynamic parameters estimated with the sinusoidal thrust data are more applicable than the steady turning data.

This study proposes a HAPE strategy that considers the uncertain nonlinearities of the field data. This method provides a more accurate predicted velocity. Besides, as far as we know, it is the first time to analyze the influence of different test conditions on the accuracy of the predicted velocity.

The continuous evolution of e-commerce with young consumers’ growing interest in online shopping has transformed the retail landscape across the world. With the surge in online sales, counterfeits of luxury goods have also found themselves from brick-and-mortar shelves to online e-commerce sites. Against this backdrop, this study aims to understand and analyse young consumers’ online counterfeit purchase behaviour (OCPB). Additionally, it also aims at identifying the determinants that influence their purchase decisions.

Following an extensive review of the literature, the present study pursued a quantitative approach in exploring critical demographic, psychographic, behavioural and situational factors influencing OCPB. The study was conducted in India through an online survey using a structured questionnaire.

The findings indicate that young consumers’ OCPB is significantly related to influencing factors like brand consciousness, fashion involvement, face consciousness, impulsive buying tendency, acquisition centrality and utilitarian shopping values. Furthermore, moderating effects of perceived anonymity (PA) and moral disengagement (MD) on OCPB were also observed and validated.

The study examined the critical factors and their linkages while building upon a structural framework on OCPB, keeping India as a representative sample. The proposed framework will bring more clarity and further insights that will help scholars expand the research domain with more cross-cultural studies and aid brand e-marketers to strategize their action towards developing strong brand aesthetic values.

The study contributes towards the literature by introducing PA and MD vis-à-vis building a framework for studying young consumers’ OCPB.

The purpose of this paper is to offer an analysis of Mexican self-organized grassroots environmental groups’ activism.

Based on fieldwork in the State of Jalisco (Mexico), where the authors have carried out in-depth interviews with members of four self-organized collectives which are defending their territories, and following the sociological framework on emotions and protest.

The authors will show: how and where activism begins; the role of the emotions in various steps of mobilization (growth and decline) and in the construction of collective identity; as well as the role of emotions in the organizational and strategical choices.

The proposal aims to highlight the organizational forms of activism of self-organized grassroots environmental groups in Mexico through a sociological lens in which the emotional dimension of the protest matters to a great extent.

The purpose of this paper is to illustrate the growing role of robots in environmental monitoring.

Following an introduction, this first considers aerial robots for monitoring atmospheric pollution. It then discusses the role of aerial, surface and underwater robots to monitor aquatic environments. Some examples are then provided of the robotic monitoring of the terrestrial environment, and finally, brief conclusions are drawn.

Robots are playing an important role in numerous environmental monitoring applications and have overcome many of the limitations of traditional methodologies. They operate in all media and frequently provide data with enhanced spatial and temporal coverage. In addition to detecting pollution and characterising environmental conditions, they can assist in locating illicit activities. Drones have benefited from the availability of small and lightweight imaging devices and sensors that can detect airborne pollutants and also characterise certain features of aquatic and terrestrial environments. As with other robotic applications, environmental drone imagery is benefiting from the use of AI techniques. Ranging from short-term local deployments to extended-duration oceanic missions, aquatic robots are increasingly being used to monitor and characterise freshwater and marine environments.

This provides a detailed insight into the growing number of ways that robots are being used to monitor the environment.