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This study provides a comprehensive overview of the research landscape of Kansei engineering (KE) within the domain of emotional clothing design. It explores the pivotal technologies, challenges and potential future directions of KE, offering application methodologies and theoretical underpinnings to support emotional clothing design.

This study briefly introduces KE, outlining its overarching research methodologies and processes. This framework lays the groundwork for advancing research in clothing Kansei. Subsequently, by reviewing literature from both domestic and international sources, this research initially explores the application of KE in the design and evaluation of clothing products as well as the development of intelligent clothing design systems from the vantage point of designers. Second, it investigates the role of KE in the customization of online clothing recommendation systems and the optimization of retail environments, as perceived by consumers. Finally, with the research methodologies of KE as a focal point, this paper discusses the principal challenges and opportunities currently confronting the field of clothing Kansei research.

At present, studies in the domain of clothing KE have achieved partial progress, but there are still some challenges to be solved in the concept, technical methods and area of application. In the future, multimodal and multisensory user Kansei acquisition, multidimensional product deconstruction, artificial intelligence (AI) enabling KE research and clothing sales environment Kansei design will become new development trends.

This study provides significant directions and concepts in the technology, methods and application types of KE, which is helpful to better apply KE to emotional clothing design.

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.

Drawing upon socio-technical system theory, this study intends to investigate the effects of the congruence and incongruence between artificial intelligence (AI) and explorative learning on supply chain resilience as well as the moderating role of organizational inertia.

Using survey data collected from 170 Chinese manufacturing firms, we performed polynomial regression and response surface analyses to test our hypotheses.

We find that the congruence between AI and explorative learning enhances firms’ supply chain resilience, while the incongruence between these two factors impairs their supply chain resilience. In addition, compared with low–low congruence, high–high congruence between AI and explorative learning improves supply chain resilience to a greater extent. Moreover, organizational inertia attenuates the positive influence of the congruence between AI and explorative learning on supply chain resilience, while it aggravates the negative influence of the incongruence between these two factors on supply chain resilience.

Our study expands the literature on supply chain resilience by demonstrating that the congruence between a firm’s AI (i.e. technical aspect) and explorative learning (i.e. social aspect) boosts its supply chain resilience. More importantly, our study sheds new light on the role of organizational inertia in moderating the congruent effect of AI and explorative learning, thereby extending the boundary condition for socio-technical system theory in the supply chain resilience literature.

Many factors affect the emergence and development of crop diseases and insect pests. Traditional methods for investigating this subject are often difficult to employ and produce limited data with considerable uncertainty. The purpose of this paper is to predict the annual degree of cotton spider mite infestations by employing grey theory.

The authors established a GM(1,1) model to forecast mite infestation degree based on the analysis of historical data. To improve the prediction accuracy, the authors modified the grey model using Markov chain and BP neural network analyses. The prediction accuracy of the GM(1,1), Grey-Markov chain, and Grey-BP neural network models was 84.31, 94.76, and 96.84 per cent, respectively.

Compared with the single grey forecast model, both the Grey-Markov chain model and the Grey-BP neural network model had higher forecast accuracy, and the accuracy of the latter was highest. The improved grey model can be used to predict the degree of cotton spider mite infestations with high accuracy and overcomes the shortcomings of traditional forecasting methods.

The two new models were used to estimate mite infestation degree in 2015 and 2016. The Grey-Markov chain model yielded respective values of 1.27 and 1.15, whereas the Grey-BP neural network model yielded values 1.4 and 1.68; the actual values were 1.5 and 1.8.

The improved grey model can be used for medium- and long-term predictions of the occurrence of cotton spider mites and overcomes problems caused by data singularity and fluctuation. This research method can provide a reference for the prediction of similar diseases.

This quantitative study aims to examine the determinants that impact the behavioral intention to use mobile payment (m-payment) among Generation Z (Gen Z) customers in Indonesia.

The theoretical model comprises seven latent variables: effort expectancy, performance expectancy, social influence, facilitating conditions, promotional activities, perceived security and behavioral intention. In addition, the two moderating factors of education and gender are used to investigate the significant effect of the determinants on intention to adopt m-payment. This study obtained the final data size of 430 respondents. The data analysis is conducted using structural equation modeling.

The results substantiate the significance of promotional activities, perceived security, performance expectancy, effort expectancy and social influence, on the behavioral intention to accept m-payment systems. Gender is revealed to significantly moderate two constructs: social influence and promotional activities, on the m-payment usage intention. Meanwhile, education moderates the effect of perceived security on behavioral intention.

This research is expected to fill the gap because only a few studies discuss the determinants affecting m-payment usage in Indonesia, especially among Gen Z-ers. Furthermore, the new findings associated with the role of two moderating factors become important practical implications because most of the prior studies often ignore the moderating factors.

This study aims to characterize the mechanical properties of sintered nano-silver under various sintering processes by nano-indentation tests.

Through microstructure observations and characterization, the influences of sintering process on the microstructure evolutions of sintered nano-silver were presented. And, the indentation load, indentation displacement curves of sintered silver under various sintering processes were measured by using nano-indentation test. Based on the nano-indentation test, a reverse analysis of the finite element calculation was used to determine the yielding stress and hardening exponent.

The porosity decreases with the increase of the sintering temperature, while the average particle size of sintered nano-silver increases with the increase of sintering temperature and sintering time. In addition, the porosity reduced from 34.88%, 30.52%, to 25.04% if the ramp rate was decreased from 25°C/min, 15°C/min, to 5°C/min, respectively. The particle size appears more frequently within 1 µm and 2 µm under the lower ramp rate. With reverse analysis, the strain hardening exponent gradually heightened with the increase of temperature, while the yielding stress value decreased significantly with the increase of temperature. When the sintering time increased, the strain hardening exponent increased slightly.

The mechanical properties of sintered nano-silver under different sintering processes are clearly understood.

This paper could provide a novel perspective on understanding the sintering process effects on the mechanical properties of sintered nano-silver.

The purpose of this study was to develop a measurement instrument for organizational learning capability (OLC) in a Chinese management context. Previous research has indicated a need for measurement instruments with proven ecological validity in China, because the learning capability of organizations is influenced by the organization’s external environment.

The authors followed a consequent inductive procedure from item sampling through exploratory factor analysis (EFA) to confirmatory factor analysis (CFA) and nomological validation. The initial part sampled relevant descriptors from a diverse sample of 159 employees from heterogeneous backgrounds in China. After sorting by an expert panel, EFA of data from a sample of 161 executive students yielded a three-dimensional construct comprising knowledge acquisition, knowledge sharing and knowledge utilization. These three constructs were again tested in CFA using a sample of 357 employees from five companies.

The findings across the three samples resulted in a three-dimensional measurement scale that is called as the organizational learning capability questionnaire (OLCQ). The OLCQ displayed high internal consistency, reliability and nomological validity.

This focus of this study has only been to establish a measurement instrument that allows indigenous research on organizational learning in China. The approach was statistically driven grounded approach, not a theoretical assumption of learning mechanisms special to the Chinese culture. Further research is needed to estimate how this approach yields results that are different from other cultures or the extent to which our findings can be explained by features of the Chinese culture or business environment.

This study offers a practical measurement instrument to assess practical and scientific problems of organizational learning in China.

The work here emphasizes the necessity of a knowledge sharing community for organizational learning to appear. It addresses a call for more indigenous Chinese management research.

The authors provide a measurement instrument for OLC with proven ecological validity and with promising consequences for research and practice in China. The instrument is empirically grounded in the practices and behaviors of Chinese managers, avoiding biases that stem from previously identified shortcomings in cross-cultural management research. To the knowledge, it is the first of its kind and a contribution to a call for indigenous management theories with contextual validity.

This paper aims to investigate the relationship between tourism efficiency at the city level and the quality of life (QOL) of residents. It focuses on assessing whether more efficient tourism cities in China, from an economic standpoint, also offer a higher quality of life for their residents.

A sample of 40 Chinese cities from 2010 to 2019 is analyzed. The study first employs Data Envelopment Analysis to construct a production technology and estimate the technical tourism efficiency of each city. Subsequently, a nonparametric statistical test of independence is applied yearly to explore potential relationships between the cities’ tourism efficiency rankings and their residents’ QOL. This latter is measured by constructing an index for each city following the OECD framework.

The findings of the study are mixed, revealing no clear relationship between tourism efficiency and residents’ quality of life within the analyzed period. This suggests a complex interplay between economic efficiency in tourism and the broader social and environmental factors contributing to QOL.

This study enhances the literature on tourism efficiency by investigating the relationship between tourism efficiency and QOL, an aspect frequently overlooked in efficiency evaluations. Our approach offers a comprehensive understanding of the interplay between economic performance in the tourism sector and the social well-being of city populations. To the best of the authors’ knowledge, this is the first instance where such a relationship has been explored at the city level within the Chinese context.

The purpose of this paper is to enhance the compatibility of titanium dioxide in epoxy resins and thus the corrosion resistance of the coatings.

In this work, TiO₂ was modified by the mechanochemistry method where mechanical energy was combined with thermal energy to complete the modification. The stability of modified TiO₂ in epoxy was analyzed by sedimentation experiment. The modified TiO₂-epoxy coating was prepared, and the corrosion resistance of the coating was analyzed by open circuit potential, electrochemical impedance spectroscopy and neutral salt spray test.

High-temperature mechanical modification can improve the compatibility of TiO₂ in epoxy resin. At the same time, the modified TiO₂-epoxy coating showed better corrosion resistance. Compared to the unmodified TiO₂-epoxy coating, the coating improved the dry adhesion force by 61.7% and the adhesion drop by 33.3%. After 2,300 h of immersion in 3.5 Wt.% NaCl solution, the coating resistance of the modified TiO₂ coating was enhanced by nearly two orders of magnitude compared to the unmodified coating.

The authors have grafted epoxy molecules onto TiO₂ surfaces using a high-temperature mechanical force modification method. The compatibility of TiO₂ with epoxy resin is enhanced, resulting in improved adhesion of the coating to the substrate and corrosion resistance of the coating.

The purpose of this study was to examine how data from the World Health Organization, United States Environmental Protection Agency and Center for Disease Control have evolved with relation to engineering controls for heating, ventilation and air-conditioning (HVAC) systems to mitigate the spread of spread of aerosols (specifically related to the COVID-19 pandemic) in occupied buildings.

A document analysis of the pandemic-focused position documents from the aforementioned public health agencies and national HVAC authorities was performed. This review targeted a range of evidence from recommendations, best practices, codes and regulations and peer-reviewed publications and evaluated how they cumulatively evolved over time. Data was compared between 2020 and 2021.

This research found that core information provided early in the pandemic (i.e. early 2020) for engineering controls in building HVAC systems did not vary greatly as knowledge of the pandemic evolved (i.e. in June of 2021). This indicates that regulating agencies had a good, early understanding of how airborne viruses spread through building ventilation systems. The largest evolution in knowledge came from the broader acceptance of building ventilation as a transmission route and the increase in publications and ease of access to the information for the general public over time.

The promotion of the proposed controls for ventilation in buildings, as outlined in this paper, is another step toward reducing the spread of COVID-19 and future aerosol spread viruses by means of ventilation.