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The purpose of this paper is to develop a hybrid‐Trefftz (HT) finite element model (FEM) for simulating heat conduction in nonlinear functionally graded materials (FGMs) which can effectively handle continuously varying properties within an element.

In the proposed model, a T‐complete set of homogeneous solutions is first derived and used to represent the intra‐element temperature fields. As a result, the graded properties of the FGMs are naturally reflected by using the newly developed Trefftz functions (T‐complete functions in some literature) to model the intra‐element fields. The derivation of the Trefftz functions is carried out by means of the well‐known Kirchhoff transformation in conjunction with various variable transformations.

The study shows that, in contrast to the conventional FEM, the HT‐FEM is an accurate numerical scheme for FGMs in terms of the number of unknowns and is insensitive to mesh distortion. The method also performs very well in terms of numerical accuracy and can converge to the analytical solution when the number of elements is increased.

The value of this paper is twofold: a T‐complete set of homogeneous solutions for nonlinear FMGs has been derived and used to represent the intra‐element temperature; and the corresponding variational functional and the associated algorithm has been constructed.

The purpose of this paper is to present a new special element model for thermal analysis of composites.

A hybrid finite element formulation taking the fundamental solution as kernel function is presented in this work for analyzing the thermal behavior and predicting the effective thermal conductivity of fiber‐reinforced composites. A representative volume cell containing single or multiple fibers (or inclusions) is considered to investigate the overall temperature distribution affected by the inclusions and the interactions among them, and to evaluate the effective thermal conductivity of the composites using the presented algorithm with special‐purpose inclusion elements. Numerical examples are presented to demonstrate the accuracy and applicability of the proposed method in analyzing fiber‐reinforced composites.

The independent intra‐element field and frame field, as well as the newly‐developed hybrid functional, make the algorithm versatile in terms of element construction, with the result that the related variational functional involves the element boundary integral only. All numerical results are compared with the solutions from ABAQUS and good agreement is observed for all cases, clearly demonstrating the potential applications of the proposed approach to large‐scale modeling of fiber‐reinforced composites. The usage of special inclusion element can significantly reduce model meshing effort and computing cost, and simultaneously avoid mesh regeneration when the fiber volume fraction is changed.

Due to the fact that the established special elements exactly satisfy the interaction of matrix and fiber within the element, only element boundary integrals are involved, thus the algorithm can significantly reduce modeling effort and computing cost with less elements, and simultaneously avoid mesh regeneration when the fiber volume fraction is changed.

Based on the special fundamental solution, a newly‐constructed inclusion element is applied to a number of test problems involving unit RVCs with multiple fibers to access the accuracy of the model. The effective thermal conductivity of the composites is evaluated for cases of single and multiple fibers using the average temperatures at certain points on a data‐collection surface. A new algorithm for evaluating effective properties with special elements is presented.

The bisection inverse search bow height control interpolation (BIS-BHCI) method for nonuniform rational B-splines (NURBS) curve is proposed to accomplish the serial robotic plasma cladding of planar complex curve coating with high precision.

A plasma–computer integrated cladding system is constructed based on a Motoman-UP6 serial robot and a plasma power. Based on the BIS-BHCI method, combining the serial robotic kinematics with the NURBS curve model, an offline plasma cladding software is developed for Motoman-UP6. Before plasma cladding, a planar NURBS curve coating is designed and defined and its BIS-BHCI is carried out with proper parameters. Then, the cladding programs are generated using the BIS-BHCI results and the robotic kinematics and inputted into the serial robotic controller. After that, the plasma cladding of the planar NURBS curve coating is implemented based on the Motoman-UP6 serial robot.

The simulation and plasma cladding for the NURBS curve coating shows that the BIS-BHCI method is feasible and effective. Plasma cladding of complex NURBS curve coating based on serial robot is feasible and effective.

The complex NURBS curve coating is prepared based on a serial robot platform for the first time. It provides a theoretical and technical basis for plasma cladding to produce surface coatings of industrial complex parts. With the increasing application of complex parts, the plasma cladding process of complex NURBS curve coatings has a broad application prospect.

This study aims to develop a green, economical and efficient ultrasonic-/microwave assisted extraction (UMAE) process for the extraction of anthocyanins.

After optimizing the extraction conditions by response surface methodology, three assays including DPPH, ABTS·+, FRAP were applied to analyze the antioxidant activity of the extracted anthocyanins. The stability under different temperatures, reductant concentrations and pHs was also discussed. The components of anthocyanins in blueberry were analyzed by HPLC-QTOF-MS2.

The optimal extraction parameters were ultrasonic power of 300 W, microwave power of 365.28 W and solid–liquid ratio of 30 (g/mL). The possible structures can be speculated as Delphinidin-3-O-galactoside, Delphinidin, Petunidin, Delphinidin-3-O-glucoside, Petunidin-3-O-glucoside, Cyanidin-3-O-glucoside. The results demonstrated that the UMAE can improve the yield of anthocyanins in shorter extraction time with higher activity.

The present study may provide a promising and feasible route for extracting anthocyanins from blueberries and studying their physicochemical properties, ultimately promoting the utilization of blueberry anthocyanins.

Purpose – This chapter gives an overview of methods for defining and analysing crash severity.

Methodology – Commonly used methods for defining crash severity are surveyed and reviewed. Factors commonly found to be associated with crash severity are discussed. Approaches for formulating and estimating models for predicting crash severity are presented and critiqued. Two examples of crash severity modelling exercises are presented and findings are discussed. Suggestions are offered for future research in crash severity modelling.

Findings – Crash severity is usually defined according to the outcomes for the persons involved. The definition of severity levels used by law enforcement or crash investigation professionals is less detailed and consistent than what is used by medical professionals. Defining crash severity by vehicle damage can be more consistent, as vehicle response to crash forces is more consistent than that of humans. Factors associated with crash severity fall into three categories – human, vehicle/equipment and environmental/road – and can apply before, during or after the crash event. Crash severity can be modelled using ordered, nominal or several different types of mixed models designed to overcome limitations of the ordered and nominal approaches. Two mixed modelling examples demonstrate better prediction accuracy than ordered or nominal modelling.

Research Implications – Linkage of crash, roadway and healthcare data sets could create a more accurate picture of crash severity. Emerging statistical analysis methods could address remaining limitations of the current best methods for crash severity modelling.

Practical Implications – Medical definitions of injury severity require observation by trained medical professionals and access to private medical records, limiting their use in routine crash data collection. Crash severity is more sensitive to human and vehicle factors than environmental or road factors. Unfortunately, human and vehicle factor data are generally not available for aggregate forecasting.

Entrepreneurial mentoring is widely regarded as an effective way to train novice entrepreneurs all over the world. However, the effectiveness of this approach and the determinants are not well understood under country-specific conditions. The purpose of this paper is to build a conceptual framework and use empirical analysis to explore the mentoring effect and its determinants, especially in the Chinese context.

The paper uses data from 172 young entrepreneurs which had been supported by Youth Business China, Mianyang Office since 2008. The factor analysis and structural equation model have been applied to analyze the data to investigate the quantitative relationship and path of the mentoring effect of entrepreneurship with mentor’s factors, young entrepreneurs (mentees’s factors) and their interrelationship.

The assessment scores of the entrepreneurship mentoring effect both in experience level and effectiveness level are relatively high, in the satisfactory range. The entrepreneurship mentoring effect, measured by the experience and the performance level, is determined by the coupling interaction of the mentor, the mentee and their interactive relationship. Among them, the mentor’s characteristic, the most important being his/her intention, has the biggest effect on the mentoring effect, significantly bigger than the mentor’s quality and skill. However, the mentor’s intention is not strong as expected. The interactive relationship between the mentor and the mentee also has a significant positive effect on entrepreneurial mentoring effect. The study also discovered the mentee factors have comparatively smaller effect on both the interactive relationship and the mentoring effect. Furthermore, it is found that the absorptive capability and learning intention of the young entrepreneur are relatively weak.

The construction of the index system of this research reflects the overall characteristics of the research objects and their static relationships. Therefore, the dynamic change of the mentoring relationship in different phases of the mentorship has not been taken into account. Also, self-serving bias may exist as this research measures the mentoring effect by the feedback on the mentor’s perception, using surveys completed by the mentees to measure both the mentee’s traits and the mentor’s qualities.

This study provides guidance on how entrepreneurship could be promoted and on how educational institutions in China can make the mentoring process a positive and effective system in order to enhance the mentoring effect.

The empirical conclusions of the present study can be applied to other business incubator, entrepreneurship education institutions and for the improvement and promotion of entrepreneurial mentoring system in China.

This paper probes into the mentoring system in the context of China from a new perspective and proposes an original conceptual model to study the entrepreneurship mentoring effect and its determinant.

The goal of this study was to understand which employee-focused workplace practices and priorities – more formally known as human resource (HR) practices and priorities – employees with mental health and/or addiction challenges (MHAC) valued and how they perceived the day-to-day implementation of those practices and priorities in the workplace integration social enterprises (WISEs) that employed them.

Twenty-two WISE workers who self-identified as having serious MHAC participated in semi-structured interviews. Interviews were transcribed and coded to identify ways that employees did or did not feel supported in their WISEs.

Participants identified three HR practices and two HR priorities as important to establishing an inclusive workplace that accommodated their MHAC. The extent to which individual participants felt included and accommodated, however, was shaped by interactions with their supervisors and coworkers.

By evaluating the salience of WISEs’ employee-focused workplace practices and priorities through the lens of the employees themselves, our study articulates the critical role that interactions with coworkers and supervisors have in determining whether HR practices and priorities have the intended effect on worker experience.

Sensor arrays and pattern recognition-based electronic nose (E-nose) is a typical detection and recognition instrument for indoor air quality (IAQ). The E-nose is able to monitor several pollutants in the air by mimicking the human olfactory system. Formaldehyde concentration prediction is one of the major functionalities of the E-nose, and three typical machine learning (ML) algorithms are most frequently used, including back propagation (BP) neural network, radial basis function (RBF) neural network and support vector regression (SVR).

This paper comparatively evaluates and analyzes those three ML algorithms under controllable environment, which is built on a marketable sensor arrays E-nose platform. Variable temperature (T), relative humidity (RH) and pollutant concentrations (C) conditions were measured during experiments to support the investigation.

Regression models have been built using the above-mentioned three typical algorithms, and in-depth analysis demonstrates that the model of the BP neural network results in a better prediction performance than others.

Finally, the empirical results prove that ML algorithms, combined with low-cost sensors, can make high-precision contaminant concentration detection indoor.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.

Although user stickiness has been studied for several years in the field of live e-commerce, little attention has been paid to the effects of streamer attributes on user stickiness in this field. Rooted in the stimulus-organism-response (S-O-R) theory, this study investigated how streamer attributes influence user stickiness.

The authors obtained 496 valid samples from Chinese live e-commerce users and explored the formation of user stickiness using partial least squares-structural equation modeling (PLS-SEM). Artificial neural network (ANN) was used to capture linear and non-linear relationships and analyze the normalized importance ranking of significant variables, supplementing the PLS-SEM results.

The authors found that attractiveness and similarity positively impacted parasocial interaction (PSI). Expertise and trustworthiness positively impacted perceived information quality. Moreover, streamer-brand preference mediated the relationship between PSI and user stickiness, as well as the relationship between perceived information quality and user stickiness. Compared to PLS-SEM, the predictive ability of ANN was more robust. Further, the results of PLS-SEM and ANN both showed that attractiveness was the strongest predictor of user stickiness.

This study explained how streamer attributes affect user stickiness and provided a reference value for future research on user behavior in live e-commerce. The exploration of the linear and non-linear relationships between variables based on ANN supplements existing research. Moreover, the results of this study have implications for practitioners on how to improve user stickiness and contribute to the development of the livestreaming industry.