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This study aims to address the challenge of the real-time state of charge (SOC) estimation for lithium-ion batteries in robotic systems, which is critical for monitoring remaining battery power, planning task execution, conserving energy and extending battery lifespan.

The authors introduced an optimal observer based on adaptive dynamic programming for online SOC estimation, leveraging a second-order resistor–capacitor model for the battery. The model parameters were determined by fitting an exponential function to the voltage response from pulse current discharges, and the observer's effectiveness was verified through extensive experimentation.

The proposed optimal observer demonstrated significant improvements in SOC estimation accuracy, robustness and real-time performance, outperforming traditional methods by minimizing estimation errors and eliminating the need for iterative steps in the adaptive critic and actor updates.

This study contributes a novel approach to SOC estimation using an optimal observer that optimizes the observer design by minimizing estimation errors. This method enhances the robustness of SOC estimation against observation errors and uncertainties in battery behavior, representing a significant advancement in battery management technology for robotic applications.

The purpose of this study was to understand the impact of the coronavirus disease (COVID-19) pandemic on the global textile and apparel supply chain from the perspectives of industry professionals within the two leading production countries: Bangladesh and China.

Applying the theory of dynamic capabilities, a qualitative research design was employed using interviews with a total of 33 apparel industry practitioners, 15 in Bangladesh and 18 in China.

Findings indicate that four factors primarily impacted the extent to which COVID-19 disrupted a firm's supply chain: firm size, channel diversification, sourcing method and product type. Viewed through the lens of the theory of dynamic capabilities, findings point to the need for firms to address the challenges brought about by supply chain disruptions by creating opportunities.

It is important to understand the impact of COVID-19 in real time and within the two largest textile and apparel–producing countries globally, as they have borne much of the brunt of the supply chain disruptions brought on by COVID-19. This empirical study makes contributions to the apparel supply chain literature as it provides an in-depth investigation of what textile and apparel firms in China and Bangladesh have learned from the COVID-19 experience to better prepare for future unexpected global events.

In modern China, sports and nationalism always have close connection, and nationalism is the important reason for the promotion of Chinese sports. However, the relationship between Chinese sports and nationalism in globalised China could be much more examined by academics, as well as its influencing factors. This chapter selects the Beijing 2008 Olympic Games as the context and representative three Chinese sports heroes in the period of globalisation to study. The findings show that in some extent, Beijing 2008 Olympic Games and three Chinese sports heroes represent the national image of China in the globalised world, also bearing the burden of washing away historical humiliation and pursuing national glory. Furthermore, it is manifested that China have a complex nationalism in the process of hosting the 2008 Olympic Games. Under the influence of mass media, market economy and sports professionalisation, nationalism still exists in Chinese sports, but people gradually start to reflect on the ‘Juguo Tizhi’, the traditional Chinese sports system and the concept of ‘winning glory for the nation’. The relationship between Chinese nationalism and sports shows the important implications of rapid Chinese sports development.

This study aims to examine the research trends in fashion industry during the coronavirus disease 2019 (COVID-19) pandemic. Besides, it also provides an overview on the new marketing and operational strategies, and reveals the corresponding business challenges of a footwear enterprise in Hong Kong during the COVID-19 pandemic.

A comprehensive literature review is first conducted to identify the research trends in fashion industry during the COVID-19 pandemic. A qualitative exploratory case study is then used to illustrate how a footwear enterprise has coped with the COVID-19 pandemic.

The case study has showed that omni-channel retailing, collaboration with e-tailers, quick response system and mixed production strategy are adopted in the targeted case during the COVID-19 pandemic. Besides, the targeted case has also faced the challenges in the areas of sales, customer relationship management, and demand forecasting and inventory planning during the COVID-19 pandemic.

This study provides managerial insights on the real practices used to deal with the COVID-19 pandemic and proposes various academic future research directions in fashion industry based on the real-world observations.

The purpose of the study is to investigate the effect of terminal and instrumental values on the attitude and brand desirability for upcycled luxury designer facemasks, in relation with generic luxury designer facemasks.

A quantitative approach was adopted in this study, and data were collected via an online consumer panel from 390 existing Australian luxury consumers, aged 18 and above. Stimuli that represented the two upcycling conditions (remnants and past collections) and a control condition (generic product) from an actual Burberry-branded facemask were designed for the study. The collected data were analysed using the least partial square and multi-group analysis of the structural equation model

The findings indicate that consumers do not perceive Burberry facemasks made from upcycled remnant materials and previous collections have superior aesthetic or self-expressive benefits to them when compared to the generic Burberry masks. In the same vein, both upcycled categories do not provide superior instrumental values through economic benefits or safety when compared to the generic Burberry masks. Hence, terminal and instrumental values had no influence on the attitude and brand desirability for upcycled luxury designer facemasks, in relation with generic luxury designer facemasks.

The results indicate that strategies will have to be better designed to have a balance between safety features (as opposed to fashion) and luxury desirability of the brand to better capture the market for difference consumer values for the facemasks. As this is a relatively low involvement product, the pricing strategies must be re-evaluated.

This study offers empirical support for the proposition that different upcycling methods in the name of sustainable practices may have different functions for different consumer values in luxury marketing implementations. For the choice of facemasks during the COVID-19 pandemic, it provides empirical evidence for consumer choice for the different types and how it can be used to elevate luxury brand desirability.

The COVID-19 pandemic has caused irrefutable devastation globally. Yet, academic and trade commentators have claimed that this disruption could have had a silver lining by presenting the fashion industry with the opportunity to reassess and rebuild in a slower, more considered way. Part of this reassessment, some have argued, may have been allowing the industry's pre-COVID sustainability buzz to come to fruition by nudging the fashion industry to go circular. This paper explores if, and how, the COVID-19 pandemic was (not) nudging the industry towards circularity using the case study of circular textiles.

Serial, semi-structured, qualitative interviews were conducted with three buyers and sourcers working for three UK-based fashion retailers. Each participant was involved in three interviews in June 2020 following the UK's first national lockdown.

The research findings suggest that, at the time this research was undertaken, these retailers were focussed on regaining profit, protecting their supply chains and producing “safe” designs whilst cutting back and becoming more risk averse. These actors suggested that, in contrast to the suppositions made by academic and media commentators, the COVID-19 pandemic was acting as a hindrance to circularity, not a helping hand, as retailers were less willing to invest in circular textiles at that time than they were pre-pandemic.

This paper offers valuable insight into the COVID-19 pandemic's impact on circular innovation within fashion retail whilst contributing to broader understandings of the principles of the circular economy within textiles and design.

Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.

An extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.

The results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.

This study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.

Despite the knowledge transfer in project contexts which has been extensively studied by scholars, the study on inter-organizational knowledge transfer in international construction projects is still scattered and unsystematic. This research aims to explore the key factors influencing inter-organizational knowledge transfer of international construction projects and analyze how these factors interact to create a synthetic framework that enhances the effectiveness of knowledge transfer (EKT).

At first, eight factors influencing inter-organizational knowledge transfer within international construction projects were identified, which were from the four dimensions of subject, relationship, channel and context, namely cultural distance, connection strength, organizational climate, intercultural competence, information technology capability, transmit willingness, receive willingness and richness of transfer channels. Then, a conceptual model was developed and 13 hypotheses were formulated, which were derived from a literature review and in-depth survey. After that, data from 353 respondents were collected and analyzed, and the hypotheses were tested by structural equation modeling analysis and bootstrapping test.

The results suggest that cultural distance hinders transfer willingness, which further affects EKT. Connection strength affects EKT by positively influencing transfer willingness and channel. Besides, organizational climate and intercultural competence positively influence transfer willingness and further affect EKT, while information technology capability affects the richness of transfer channels.

This research gives a thorough examination of the determinants influencing inter-organizational knowledge transfer of international construction projects, thus formulating available approaches that project managers and personnel can employ to effectively facilitate EKT.

This paper aims to investigate the differences in hot corrosion behavior of the GTD222 superalloy and TiC/GTD222 composite in a mixed salt of 75% Na₂SO₄ and 25% K₂SO₄ at 900°C.

The GTD222 superalloy and TiC/GTD222 nickel-based composite were prepared using selective laser melting (SLM). Subsequently, the hot corrosion behavior of the two alloys was systematically investigated in a salt mixture consisting of 75% Na₂SO₄ and 25% K₂SO₄ (Wt.%) at 900°C.

The TiC/GTD222 composite exhibited better hot corrosion resistance compared to the GTD222 superalloy. First, the addition of alloying elements led to the formation of a protective oxide film on the TiC/GTD222 composites 20 h before hot corrosion. Second, TiC/GTD222 composite corrosion surface has a higher Ti content, after 100 h of hot corrosion, the composite corrosion surface Ti content of 10.8% is more than two times the GTD222 alloy 4% Ti. The Ti and Cr oxides are tightly bonded, effectively resisting the erosion of corrosive elements.

The hot corrosion behavior of GTD222 superalloy and TiC/GTD222 composites prepared by SLM in a mixed salt of 75% Na₂SO₄ and 25% K₂SO₄ was studied for the first time. This study provides insights into the design of high-temperature alloys resistant to hot corrosion.