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The purpose of this paper is to present a general formulation of the quadrature element method (QEM). The method is then used to investigate the free vibration of functionally graded (FG) beams with general boundary conditions and different variations of material properties.

The quadrature elements with arbitrary number of nodes and nodal distributions are established on the basis of two types of FG Timoshenko beam theories. One called TBT-1 takes the cross-sectional rotation as the unknown function and the other called TBT-2 uses the transverse shear strain as the unknown function. Explicit formulas are provided via the help of the differential quadrature (DQ) rule and thus the elements can be implemented adaptively with ease.

The suitability and computational efficiency of the proposed quadrature elements for the vibration analysis of FG beams are demonstrated. The convergence rate of the proposed method is high. The elements are shear-locking free and can yield accurate solutions with a small number of nodes for both thin and moderately thick beams. The performance of the element based on TBT-1 is better than the one based on TBT-2.

The present QEM is different from the existing one which exclusively uses Gauss–Lobatto–Legendre (GLL) nodes and GLL quadrature and thus is more general. The element nodes can be either the same or different from the integration points, making the selection of element nodes more flexible. Presented data are accurate and may be a reference for other researchers to develop new numerical methods. The QEM may be also useful in multi-scale modeling and in the analysis of civil infrastructures.

The purpose of this paper is to present an analytical modelling approach to predict engineering elastic constants of 3D woven orthogonal composites (3DWOCs) at representative volume element (RVE) level using a volume averaging method.

The present analytical modeling approach uses a more realistic internal geometry representation of the 3DWOC. A RVE which is a repeating element at macroscopic level is first identified and volume proportions and engineering elastic constants of various constituents (matrix and family of constituent tows; warp, weft and z‐fibers) which contribute to the global stiffness of RVE/composite are next determined. For accurate predictions, emphasis is given to accurate measurements of micro‐structural data such as fiber volume fraction and fiber volume proportion of constituent tows and internal geometry of the 3DWOC structure‐in particular, undulation of z‐fibers. Microscopic study has been conducted in this regard.

The predicted engineering elastic constants are compared against experimental data, showing a strong correlation between predicted and experimental values.

The methodology presented in the paper can be used for accurate computation of the engineering elastic constants of 3DWOCs. The method can also be used for other types of 2D or 3D textile composites such as 2D woven composites, 2D braided composites, 3D woven angle interlock composites and 3D braided composites.

The paper succeeds in relating the micro‐structural properties and analytical model for the accurate prediction of engineering elastic constants.

Driven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.

Drawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.

The findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.

The findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.

The research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.

Compared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.

This study aims to improve the corrosion resistance of TA2-welded joints by superhydrophobic surface modification using micro-arc oxidation technology and low surface energy substance modification.

The microstructure and chemical state of the superhydrophobic film layer were analyzed using scanning electron microscopy, energy dispersive X-ray spectroscopy, three-dimensional morphology, X-ray diffraction, X-ray photoelectron spectroscopy and Fourier transform infrared absorption spectroscopy. The influence of the superhydrophobic film layer on the corrosion resistance of TA2-welded joints was investigated using classical electrochemical testing methods.

The characterization results showed that the super hydrophobic TiO₂ ceramic membrane was successfully constructed on the surface of the TA2-welded joint, and the construction of the super hydrophobic film greatly improved the corrosion resistance of the TA2-welded joint.

The superhydrophobic TiO₂ ceramic membrane has excellent corrosion resistance. The micro nanostructure in the superhydrophobic film can intercept air to form an air layer to prevent the corrosion medium from contacting the surface, thus, improving the corrosion resistance of the sample.

This paper aims to focus on the preparation of Kevlar fiber (KF) and alkaline hydrolyzed KF (KF-H) to improve the dispersed condition of polyaniline (PAn), as the aggregation of PAn would lead to some adsorption sites buried. And then the materials were used to enrich anionic dye Congo red (CR) from aqueous solution.

The materials (KF@PAn and KF-H@PAn) were designed by means of “diffusion-interfacial-polymerization” under mild condition as high affinity due to the structural properties of PAn, KF and KF-H. The dispersed degree of PAn on the surface of KF and KF-H was validated according to adsorption efficiency for CR.

The content of PAn introduced was not beyond 20 wt.%, while adsorption capacity for CR was significantly enhanced by 4–8 times (on the basis of kinetic data) according to the calculation only by the content of PAn due to KF and alkaline hydrolyzed KF exhibited almost no adsorption for CR, indicating dispersed situation of PAn coating was greatly enhanced and more active sites exposed, which was favorable for the adsorption process. Presence of NaCl would exhibit a more or less positive effect on CR uptake, suggesting the materials could be used for high salt environment.

The investigated means of dispersed degree of PAn on the surface of KF and KF-H are the further and future investigation.

This study will provide a method to improve the dispersed situation of PAn and a theoretical support to treat anionic dyes from aqueous solution especially for salt environment.

The results showed that the dispersed condition of PAn on the surface of KF and KF-H was greatly improved. According to the adsorption capacities for CR, it can be concluded that part of adsorption sites were buried due to the aggregation of PAn, and introduction of KF and KF-H, buried adsorption sites decreased greatly. This study will provide a method to decrease buried adsorption sites of PAn and a contribution for their convenient application in wastewater treatment especially for high salt environment.

Currently, the vision and depth information obtained from the eye-to-hand RGB-D camera can apply to the reconstruction of the three-dimensional (3D) environment for a robotic operation workspace. The reconstructed 3D space contributes to a symmetrical and equal observation view for robots and humans, which can be considered a digital twin (DT) environment. The purpose of this study is to enhance the robot skill in the physical workspace, although the artificial intelligence (AI) technique has high performance of the robotic operation in the known environments.

A multimodal interaction framework is proposed in DT operation environments.

A fast image-based target segmentation technique is combined in the 3D reconstruction of the robotic operation environment from the eye-to-hand camera, thus expediting the 3D DT environment generation without accuracy loss. A multimodal interaction interface is integrated into the DT environment.

The users are supported to operate the virtual objects in the DT environment using speech, mouse and keyboard simultaneously. The humans’ operations in 3D DT virtual space are recorded, and cues are provided for the robot’s operations in practice.

Marketing research has focussed more on in-group favoritism and out-group derogation (i.e. ethnocentrism) than out-group favoritism and in-group derogation (i.e. xenocentrism). The purpose of this paper is to explore the xenocentric behavior in the consumer sphere to explain why some consumers have a bias for foreign products even when domestic ones are qualitatively similar or better. As the Chinese economy has experienced more than three decades of near double-digit growth and increased openness to foreign products, it is important to examine phenomena related to the formation of Chinese attitudes toward foreign products with the rising tensions between the seemingly irreversible globalization and Chinese re-awakening nationalism.

This study is based on a review of the extant literature and focus groups in three cities in China.

This study has found that consumer xenocentrism (CX) is prevalent in China, especially among the new emerging wealthy classes, younger consumers, and the local elite. It appears that Chinese consumers are psychologically or sociologically orientated or predisposed toward foreign (Western) goods. The findings from this study suggest that both consumer ethnocentrism and CX are possible or even expected. The short review of Chinese history presented here has shown that these phenomena can be explained by traditional in-group/out-group theories. Specifically, when there are too many xenocentrics, national esteem is threatened and this prompts many individuals to become more ethnocentric.

This study is based on the literature and focus groups data, hence, the findings are not intended to be generalizable.

The findings from this study should be of interest to business practitioners and policy makers.

The historical and cultural perspectives taken in this study indicate that understanding consumers’ xenocentric behavior entails knowledge and deep understanding of how cultural values and contemporary social-political forces interplay within consumers’ formation and change of attitudes toward the choice of domestic and foreign products.

This study shows that the ability of foreign products to meet the individual’s need or enhance his/her self-esteem more so than domestic products is indicative of something more than simply an international, cosmopolitan, or modern orientation. The fact that consumer foreign bias is found with both mundane and widely available products, expensive and inexpensive products, and conspicuous and non-conspicuous goods challenges the assumption that this phenomenon is simply traditional prestige-consumption behavior. Future research needs to be directed at measuring the CX construct and examine potential antecedents of such a behavior.

This study aims to assess the default risk of borrowers in peer-to-peer (P2P) online lending platforms. The authors propose a novel default risk classification model based on data cleaning and feature extraction, which increases risk assessment accuracy.

The authors use borrower data from the Lending Club and propose the risk assessment model based on low-rank representation (LRR) and discriminant analysis. Firstly, the authors use three LRR models to clean the high-dimensional borrower data by removing outliers and noise, and then the authors adopt a discriminant analysis algorithm to reduce the dimension of the cleaned data. In the dimension-reduced feature space, machine learning classifiers including the k-nearest neighbour, support vector machine and artificial neural network are used to assess and classify default risks.

The results reveal significant noise and redundancy in the borrower data. LRR models can effectively clean such data, particularly the two LRR models with local manifold regularisation. In addition, the supervised discriminant analysis model, termed the local Fisher discriminant analysis model, can extract low-dimensional and discriminative features, which further increases the accuracy of the final risk assessment models.

The originality of this study is that it proposes a novel default risk assessment model, based on data cleaning and feature extraction, for P2P online lending platforms. The proposed approach is innovative and efficient in the P2P online lending field.

Emerging research on education reform in Shanghai for the last decade or so has either focused on broad contexts and trends of the second-cycle curriculum reform or the professional development in response to the reform or a few detailed cases of teaching improvement to meet the reform demand. Little attention has been paid to how schools as institutions have been made to respond to and enact the reform. Through three detailed school cases, the purpose of this paper is to understand their distinctive responses to reform in terms of how they interpreted, enacted and sustained their reform efforts and how more importantly lesson-case study and multi-tiered research projects has become a reinvigorated form of Chinese lesson study and teaching research to significantly mediate the school’s curriculum reform efforts. Features of sustainable development behind these cases are conceptualized by Lave and Wenger’s notion of transparency of the mediating technology of a community of practice.

Drawing on master’s thesis reports of school leaders (2010-2016), school research publications and lesson cases as secondary data sources, an instrumental multi-case research design was adopted to build detailed case narratives and tease out cross-case comparisons.

Building on unique strengths and legacies to solve school problems, the three secondary schools responded to, enacted and sustained the reform in unique ways: case 1, a municipal key school, has focused on “three translations (of curriculum)” involving all teaching research groups (TRGs) in specifying broad curriculum standards and turning them into concrete, actionable designs and student tasks which are tested and refined through iterative cycles of lesson-case study, with the decision making for each translation informed by research projects studying problems arising. Case 2, a district key school, has capitalized on its strong TRGs and used research projects and lesson-case study to unite teaching, research and PD into a whole; and case 3, a regular neighborhood school, has aimed to build a structured PD system to tackle teacher stagnation by stressing the reflection components of each cycle of lesson-case study, challenging teachers to learn in the district-level curriculum integration experiment, and nudging them into their own research projects with well-staged support. In all the three cases, research projects have been networked connecting municipal, district, school and teachers in building a research climate. The lesson-case study has turned designs into refined actions to ensure quality of curriculum implementation and teacher growth.

This study yields insights into the inner workings of Shanghai’s recent curriculum reform. With strategic injection of research into the familiar institutional structures and organic cultural forms of collegiality, school innovations can be built on familiarity to create a sense of continuity, coherence and institutional identity so that teachers learn from doing with least disruption. The slow and steady work of sustaining innovations and reform goes beyond simple notions of scaling up and relies on building internal drive and institutional and teacher capacity for deep learning in responding to reform.

The purpose of this paper is to design a smart handheld device with force regulating function, which demonstrates the concept of patient-specialized tools.

This handheld device integrates an electrical bioimpedance (EBI) sensor for tissue measurement and a constant force regulation mechanism for ensuring stable tool–tissue contact. Particular focuses in this study are on the design of the constant force regulation mechanism whose design process is through genetic algorithm optimization and finite element simulation. In addition, the output force can be changed to the desired value by adjusting the cross-sectional area of the generated spring.

The following two specific applications based on ex vivo tissues are used for evaluating the designed device. One is in terms of safety of interaction with delicate tissue while the other is for compensating involuntary tissue motion. The results of both examples show that the handheld device is able to provide an output force with a small standard deviation.

In this paper, a handheld device with force regulation mechanism is designed for specific patients based on the genetic algorithm optimization and finite element simulation. The device can maintain a steady and safe interaction force during the EBI measurement on fragile tissues or moving tissues, to improve the sensing accuracy and to avoid tissue damage. Such functions of the proposed device are evaluated through a series of experiments and the device is demonstrated to be effective.