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The purpose of this paper is to determine: how the thermal cycling aging affects the ratcheting behavior of anisotropic conductive adhesive film (ACF); how the loading conditions and loading history affect the ratcheting strain and strain rate of ACF with different thermal cycling aging histories.

The ACF of CP6920F was cured at 190°C in an electro-thermal vacuum drying apparatus for 30 s. The cured specimens were put into the thermal cycling chamber (−40-150°C) for aging to 25, 50, 100, 200 and 500 cycles. A series of uniaxial ratcheting tests of aged ACF after different thermal cycles was carried out under stress control at 80°C.

The ACF subjected to larger number of thermal aging cycles exhibits less ratcheting strain under the same loading conditions. The ACF with the same thermal cycling aging history shows more ratcheting strain and a higher ratcheting strain rate when loaded under a larger mean stress or stress amplitude or a lower loading rate. The ratcheting behavior of aged ACF is found to be more sensitive to the lower loading rate. The higher mean stress (or stress amplitude) enhances the deformation resistance and consequently restrains the ratcheting strain of subsequent cycling with a lower mean stress (or stress amplitude). The prior lower loading rate accelerates the plastic deformation more significantly than the higher one.

The influencing trends of thermal cycling aging, loading condition and loading history on ratcheting behavior of ACF are obtained, which is important for the design and safety assessment of ACF joints.

This research aims to investigate the influence of consumers' anticipated trust in service providers on brand switching intention and its underlying psychological mechanism. More importantly, this study explores the moderating role of type of service providers (human staff/humanoid robots/nonhumanoid robots).

This study adopted two single-factor between-subjects experimental designs and tested the hypotheses in two typical service failure scenarios: Study 1, a hotel scenario (N = 403); and Study 2, a restaurant scenario (N = 323).

The results suggest that there is an inverted U-shaped relationship between consumers' anticipated trust and tolerance of service failure and that such tolerance has a mediating effect on the relationship between anticipated trust and brand switching intention. Moreover, when service failure is caused by a humanoid service robot, a moderate anticipated trust level of consumers is most conducive to increasing tolerance, which in turn reduces their propensity to switch brands.

This study examines the nature of the relationship between anticipated trust and tolerance in a service failure context, revealing an inverted U-shaped relationship. More importantly, the boundary conditions under which different service provides have an influence on this relationship are incorporated. Finally, this study explores the influence of service failure tolerance on brand switching intentions in a technological context, enriching consumer–brand relationship research.

Assessing the failure probability of engineering structures is still a challenging task in the presence of various uncertainties due to the involvement of expensive-to-evaluate computational models. The traditional simulation-based approaches require tremendous computational effort, especially when the failure probability is small. Thus, the use of more efficient surrogate modeling techniques to emulate the true performance function has gained increasingly more attention and application in recent years. In this paper, an active learning method based on a Kriging model is proposed to estimate the failure probability with high efficiency and accuracy.

To effectively identify informative samples for the enrichment of the design of experiments, a set of new learning functions is proposed. These learning functions are successfully incorporated into a sampling scheme, where the candidate samples for the enrichment are uniformly distributed in the n-dimensional hypersphere with an iteratively updated radius. To further improve the computational efficiency, a parallelization strategy that enables the proposed algorithm to select multiple sample points in each iteration is presented by introducing the K-means clustering algorithm. Hence, the proposed method is referred to as the adaptive Kriging method based on K-means clustering and sampling in n-Ball (AK-KBⁿ).

The performance of AK-KBⁿ is evaluated through several numerical examples. According to the generated results, all the proposed learning functions are capable of guiding the search toward sample points close to the LSS in the critical region and result in a converged Kriging model that perfectly matches the true one in the regions of interest. The AK-KBⁿ method is demonstrated to be well suited for structural reliability analysis and a very good performance is observed in the investigated examples.

In this study, the statistical information of Kriging prediction, the relative contribution of the sample points to the failure probability and the distances between the candidate samples and the existing ones are all integrated into the proposed learning functions, which enables effective selection of informative samples for updating the Kriging model. Moreover, the number of required iterations is reduced by introducing the parallel computing strategy, which can dramatically alleviate the computation cost when time demanding numerical models are involved in the analysis.

This study aims to find a novel solution to the calibration of three-axis magnetometers to suppress errors of sensors. The nature of the calibration process is parameter estimation and hence the purpose of the paper is to calculate the error parameters and eliminate sensor errors and obtain the true value of the pure magnetic field.

The paper puts forward a calibration method using an alternative iteration looping optimization (AILO) to estimate the parameters. The proposed method divided the parameters to be estimated into two parts: a portion less than one and the other greater than one. Parameters with different orders of magnitude are calculated respectively, which let one part to be a known quantity and the other part is derived by the known quantity; the derived quantity is used to calculate the known quantity again, and looping iteration multiple times until the iteration termination condition is satisfied.

The simulation and experimental results indicate that the calibration accuracy is improved at least by two orders by the proposed method compared to the two-step method and the linear decreasing weight particle swarm optimization (LDW-PSO) algorithm which proves the validity of the proposed method.

The proposed method can improve the calibration accuracy of total magnetic field, which provides a reference to the calibration of three-axis magnetometers.

A calibration method based on the AILO is proposed in this paper, which is used to improve the calibration accuracy of the three-axis magnetometer.

This paper aims to provide a comprehensive review of uncertainty quantification methods supported by evidence-based comparison studies. Uncertainties are widely encountered in engineering practice, arising from such diverse sources as heterogeneity of materials, variability in measurement, lack of data and ambiguity in knowledge. Academia and industries have long been researching for uncertainty quantification (UQ) methods to quantitatively account for the effects of various input uncertainties on the system response. Despite the rich literature of relevant research, UQ is not an easy subject for novice researchers/practitioners, where many different methods and techniques coexist with inconsistent input/output requirements and analysis schemes.

This confusing status significantly hampers the research progress and practical application of UQ methods in engineering. In the context of engineering analysis, the research efforts of UQ are most focused in two largely separate research fields: structural reliability analysis (SRA) and stochastic finite element method (SFEM). This paper provides a state-of-the-art review of SRA and SFEM, covering both technology and application aspects. Moreover, unlike standard survey papers that focus primarily on description and explanation, a thorough and rigorous comparative study is performed to test all UQ methods reviewed in the paper on a common set of reprehensive examples.

Over 20 uncertainty quantification methods in the fields of structural reliability analysis and stochastic finite element methods are reviewed and rigorously tested on carefully designed numerical examples. They include FORM/SORM, importance sampling, subset simulation, response surface method, surrogate methods, polynomial chaos expansion, perturbation method, stochastic collocation method, etc. The review and comparison tests comment and conclude not only on accuracy and efficiency of each method but also their applicability in different types of uncertainty propagation problems.

The research fields of structural reliability analysis and stochastic finite element methods have largely been developed separately, although both tackle uncertainty quantification in engineering problems. For the first time, all major uncertainty quantification methods in both fields are reviewed and rigorously tested on a common set of examples. Critical opinions and concluding remarks are drawn from the rigorous comparative study, providing objective evidence-based information for further research and practical applications.

In this chapter, we present our ongoing efforts in developing and sustaining interdisciplinary STEM undergraduate programs at North Carolina A&T State University (NCA&T) – a state-supported HBCU and National Science Foundation (NSF) Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) Institutional Implementation Project grantee. Through three rounds of NSF HBCU-UP implementation grants, a concerted effort has been made in developing interdisciplinary STEM undergraduate research programs in geophysical and environmental science (in round 1), geospatial, computational, and information science (in round 2), and mathematical and computational biology (in round 3) on NCA&T campus. We first present a brief history and background information about the interdisciplinary STEM undergraduate research programs developed and sustained at NCA&T, giving rationales on how these programs had been conceived, and summarizing what have been achieved. Next we give a detailed description on the development of undergraduate research infrastructure including building research facilities through multiple and leveraged funding sources, and engaging a core of committed faculty mentors and research collaborators. We then present, as case studies, some sample interdisciplinary research projects in which STEM undergraduate students were engaged and project outcomes. Successes associated to our endeavor in developing undergraduate research programs as well as challenges and opportunities on implementing and sustaining these efforts are discussed. Finally, we discuss the impact of well-structured undergraduate research training on student success in terms of academic performance, graduation rate and continuing graduate study, and summarize many of the learnings we have gained from implementation and delivery of undergraduate research experiences at HBCUs.

This study aims to make full use of the advantages of connected and autonomous vehicles (CAVs) and dedicated CAV lanes to ensure all CAVs can pass intersections without stopping.

The authors developed a signal coordination model for arteries with dedicated CAV lanes by using mixed integer linear programming. CAV non-stop constraints are proposed to adapt to the characteristics of CAVs. As it is a continuous problem, various situations that CAVs arrive at intersections are analyzed. The rules are discovered to simplify the problem by discretization method.

A case study is conducted via SUMO traffic simulation program. The results show that the efficiency of CAVs can be improved significantly both in high-volume scenario and medium-volume scenario with the plan optimized by the model proposed in this paper. At the same time, the progression efficiency of regular vehicles is not affected significantly. It is indicated that full-scale benefits of dedicated CAV lanes can only be achieved with signal coordination plans considering CAV characteristics.

To the best of the authors’ knowledge, this is the first research that develops a signal coordination model for arteries with dedicated CAV lanes.

Because of the popularity of digital cameras, the number of personal photographs is increasing rapidly. In general, people manage their photos by date, subject, participants, etc. for future browsing and searching. However, it is difficult and/or takes time to retrieve desired photos from a large number of photographs based on the general personal photo management strategy. In this paper the authors aim to propose a systematic solution to effectively organising and browsing personal photos.

In their system the authors apply the concept of content‐based image retrieval (CBIR) to automatically extract visual image features of personal photos. Then three well‐known clustering techniques – k‐means, self‐organising maps and fuzzy c‐means – are used to group personal photos. Finally, the clustering results are evaluated by human subjects in terms of retrieval effectiveness and efficiency.

Experimental results based on the dataset of 1,000 personal photos show that the k‐means clustering method outperforms self‐organising maps and fuzzy c‐means. That is, 12 subjects out of 30 preferred the clustering results of k‐means. In particular, most subjects agreed that larger numbers of clusters (e.g. 15 to 20) enabled more effective browsing of personal photos. For the efficiency evaluation, the clustering results using k‐means allowed subjects to search for relevant images in the least amount of time.

CBIR is applied in many areas, but very few related works focus on personal photo browsing and retrieval. This paper examines the applicability of using CBIR and clustering techniques for browsing personal photos. In addition, the evaluation based on the effectiveness and efficiency strategies ensures the reliability of our findings.

The purpose of this paper is to find out the problems in the contemporary development of Chinese research universities and think about relevant measures and policy for promotion.

After over 20 years practice, China has made quite significant achievements in developing research universities, yet there are still some issues that exist. Analyzing the issues and the effects of existing policies for construction and development of China's research universities, and looking back at practices guided by these polices, the study proposes new dimensions in the measures and policies, on the bases of the achievements that have been made.

This paper analyzes the current situation of China's research university development and reveals that the critical issues, such as faculty qualities, academic freedom, post‐graduate education, initiative research outcome, and internationalization, etc. must be highlighted and solved.

To further progress it is important to analyze the problems of developing research university development in the existing higher education system. The policy implication of this paper lies in that the results can be a reference for policy makers and the HEI administrations.