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Apart from, the smart edge computing (EC) robot (SECR) provides the tools to manage Internet of things (IoT) services in the edge landscape by means of real-world test-bed designed in ECR. Eventually, based on the results from two experiments held in little constrained condition, such as the maximum data size is 2GB, the performance of the proposed techniques demonstrate the effectiveness, scalability and performance efficiency of the proposed IoT model.

Certainly, the proposed SECR is trying primarily to take over other traditional static robots in a centralized or distributed cloud environment. One aspect of representation of the proposed edge computing algorithms is due to challenge to slow down the consumption of time which happened in an artificial intelligence (AI) robot system. Thus, the developed SECR trained by tiny machine learning (TinyML) techniques to develop a decentralized and dynamic software environment.

Specifically, the waste time of SECR has actually slowed down when it is embedded with Edge Computing devices in the demonstration of data transmission within different paths. The TinyML is applied to train with image data sets for generating a framework running in the SECR for the recognition which has also proved with a second complete experiment.

The work presented in this paper is the first research effort, and which is focusing on resource allocation and dynamic path selection for edge computing. The developed platform using a decoupled resource management model that manages the allocation of micro node resources independent of the service provisioning performed at the cloud and manager nodes. Besides, the algorithm of the edge computing management is established with different path and pass large data to cloud and receive it. In this work which considered the SECR framework is able to perform the same function as that supports to the multi-dimensional scaling (MDS).

This study aims to explore how a buyer's perceived buyer-seller (B-S) guanxi facets (i.e. ganqing, renqing and mianzi) and guanxi positions (i.e. zi-ji-ren, shou-ren and sheng-ren) affect the seller's influence effectiveness (SIE) and purchase intention (PI) in social commerce.

This study conducted an online survey in three cities of Taiwan and collected a total of 364 data. The structural equation modeling and cluster analysis were used to test research hypotheses.

The results indicate that (1) each guanxi facet exerts a different and positive impact on SIE, but only one guanxi facet – renqing – helps improve PI, (2) guanxi facets can be used to predict the buyer's perceived guanxi position toward the seller, (3) the effect of guanxi facets on SIE and PI varies across B-S guanxi positions and (4) the SIE positively mediates effects of guanxi facets on the PI.

This study demonstrates the individual effect of each guanxi facet on SIE and PI and affirms the implicit guanxi position features guanxi facets and determines the buyer's perceived SIE and PI as well. To the best of our knowledge, these findings are rarely proposed in previous research and are beneficial for understanding the guanxi mechanism in social commerce.

This study aims to examine whether and how the effect of intimate relationships with micro-influencers on customer behaviour is interrupted by external cues such as sponsorship disclosures and negative electronic word-of-mouth (eWOM).

The study worked with Instagram micro-influences to conduct a vignette survey with four experimental scenarios.

The benefits of parasocial relationships (PSR) in enhancing customer engagement (CE), brand preference (BP) and purchase intention (PI) cannot be sustained in the presence of external interruptive cues. For micro-influencers, whilst sponsorship disclosures do not moderate the influence of PSR, customers are considerably sensitive to negative eWOM or when the two cues co-occur.

This study focusses on micro-influencers and investigates whether the follower–micro-influencer bond can be moderated by external cues including sponsorship disclosure and negative eWOM.

Damage due to delamination is an important issue during drilling in polymer-matrix composites (PMCs). It depends on thrust force and torque which are functions of feed rate. Transfer function of thrust force with feed rate and torque with feed rate is constructed through experiments. These transfer functions are then combined in state-space to formulate a sixth-order model. Then thrust force and torque are controlled by using optimal controller. The paper aims to discuss these issues.

A glass fiber reinforced plastic composite is drilled at constant feed rate during experimentation. The corresponding time response of thrust force and torque is recorded. Third-order transfer functions of thrust force with feed rate and torque with feed rate are identified using system identification toolbox of Matlab®. These transfer functions are then converted into sixth-order combined state-space model. Optimal controller is then designed to track given reference trajectories of thrust force/torque during drilling in composite laminate.

Optimal control is used to simultaneously control thrust force as well as torque during drilling. There is a critical thrust force during drilling below which no delamination occurs. Therefore, critical thrust force profile is used as reference for delamination free drilling. Present controller precisely tracks the critical thrust force profile. Using critical thrust force as reference, high-speed drilling can be done. The controller is capable of precisely tracking arbitrary thrust force and torque profile simultaneously. Findings suggest that the control mechanism is efficient and can be effective in minimizing drilling induced damage in composite laminates.

Simultaneous optimal control of thrust force and torque during drilling in composites is not available in literature. Feed rate corresponding to critical thrust force trajectory which can prevent delamination at fast speed also not available has been presented.

The purpose of this paper is to present a proportional-integral (PI) observer design on a linear system with stochastic noises.

The noised disturbances are modeled as independent Brownian motions for various affections, such as radiation, heat, and material fatigue. These phenomena are common in applications, such as biomolecules, nonlinear control, and biochemical networks. Under this framework, this paper proposes a new approach on a PI observer in terms of four crucial theorems, and an illustrative numerical example is given to verify the proposed design.

The results provide potential solutions for system fault tolerance and isolation.

This paper proposes a design, solvability, and controllability analysis on a PI observer in terms of four crucial theorems.

This paper aims to use the Matsuoka’s neural oscillators as the basic units of central pattern generator (CPG), and to offer a new CPG architecture consisting of a dual neural CPG of circular three links responsible for oscillator phase adjustment, to which an external neural oscillator is added, which is responsible for oscillator amplitude adjustment, to control foot depth to balance itself when treading on an obstacle.

It is equipped with a triaxial accelerometer and a triaxial gyroscope to obtain a real-time robot attitude, and to disintegrate the foot tilt in each direction as feedback signals to CPG to restore the robot’ horizontal attitude on an uneven terrain. The CPG controller is a distributed control method, with each foot controller consisting of a group of reciprocally coupling neural oscillators and sensors to generate different locomotion by different coupling patterns.

The experiment results indicated that the gait design method succeeded in enabling a steady hexapod walking on a rugged terrain, the mode of response is such that adjustments can only be made when the tilt occurs.

The overall control mechanism uses individual foot tilts as the feedback signal input to the neural oscillators to change the amplitude and compare against the reference oscillators of fixed amplitude to generate the foot height reference signals that can balance the body, and then convert the control signals, through a trajectory generator, to foot trajectories from which the actual rotation angle of servo motors can be obtained through inverse kinematics to achieve the effect of restoring the balance when traveling.

The controller design based on the bionic CPG model has the ability to restore its balance when its body tilts. In addition to the model’s ability to control locomotion, from the response waveforms of this experiment, it can also be noticed that it can control the foot depth to balance itself when treading on an obstacle, and it can adapt to a changing environment. When the obstacle is removed, the robot can quickly regain its balance.

The aim of this paper is to present a novel multifactor structural optimisation method incorporating reliability performance.

This research addresses structural optimisation problems in which the design is required to satisfy multiple performance criteria, such as strength, stiffness, mass and reliability under multiple loading cases simultaneously. A MOST technique is extended to accommodate the reliability‐related optimisation. Structural responses and geometrical sensitivities are analysed by a FE method, and reliability performance is calculated by a reliability loading‐case index (RLI). The evaluation indices of performances and loading cases are formulated, and an overall performance index is presented to quantitatively evaluate a design.

The proposed method is applicable to multi‐objective, multi‐loading‐case, multi‐disciplinary and reliability‐related optimisation problems. The applications to a star‐like truss structure and a raised‐access floor panel structure confirmed that the method is highly effective and efficient in terms of structural optimisation.

A systematic method is proposed. The optimisation method combines the MOST technique with a RLI (a new alternative route to calculate the reliability index at multiple loading cases) using a parametric FE model.

Transnational learning has become a mainstream issue in recent years due to the rise of global education. There are many kinds of overseas learning, including degree-seeking, joint/double degree, student exchange, internship, service learning and so on. The scope of learning may involve research, teaching/learning and community service. The purpose of the case study is to investigate how the Taiwanese students participating in an international internship project of the US–Taiwan Partnerships for International Research and Education (PIRE) acquire professional knowledge and soft skills, including cross-cultural awareness, interdisciplinary communication, skill development and social networking. It also explores how a joint research project contributes into a collaborative educational program.

To better understand participants' experiences in the PRIE, this study held three focus groups and seven in-depth interviews on the students, faculty members and project managers for data collection.

Three major findings are shown in the study. First, participants agreed that the overall learning experiences in knowledge acquisition or skill development have been positive. Second, participants obviously expressed their greater interests in intercultural interaction with the locals, which did not happen quite often during the internship. Third, the extent of interest in applying for the PIRE deg ree program after the internship program is escalating year by year.

More investigation into participants’ social and cultural engagement in similar project will be needed for future research.

The results will be implicated into other cross-border education project evaluation.

This study manages to investigate the cross-border research initiative from different participants' perspectives and received comprehensive feedbacks.