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The purpose of this study is to propose a fast method for predicting flow fields with periodic behavior with verification in the context of a radial turbine to meet the urgent requirement to effectively capture the unsteady flow characteristics in turbomachinery. Aiming at meeting the urgent requirement to effectively capture the unsteady flow characteristics in turbomachinery, a fast method for predicting flow fields with periodic behavior is proposed here, with verification in the context of a radial turbine (RT).

Sparsity-promoting dynamic mode decomposition is used to determine the dominant coherent structures of the unsteady flow for mode selection, and for flow-field prediction, the characteristic parameters including amplitude and frequency are predicted using one-dimensional Gaussian fitting with flow rate and two-dimensional triangulation-based cubic interpolation with both flow rate and rotation speed. The flow field can be rebuilt using the predicted characteristic parameters and the chosen model.

Under single flow-rate variation conditions, the turbine flow field can be recovered using the first seven modes and fitted amplitude modulus and frequency with less than 5% error in the pressure field and less than 9.7% error in the velocity field. For the operating conditions with concurrent flow-rate and rotation-speed fluctuations, the relative error in the anticipated pressure field is likewise within an acceptable range. Compared to traditional numerical simulations, the method requires a lot less time while maintaining the accuracy of the prediction.

It would be challenging and interesting work to extend the current method to nonlinear problems.

The method presented herein provides an effective solution for the fast prediction of unsteady flow fields in the design of turbomachinery.

A flow prediction method based on sparsity-promoting dynamic mode decomposition was proposed and applied into a RT to predict the flow field under various operating conditions (both rotation speed and flow rate change) with reasonable prediction accuracy. Compared with numerical calculations or experiments, the proposed method can greatly reduce time and resource consumption for flow field visualization at design stage. Most of the physics information of the unsteady flow was maintained by reconstructing the flow modes in the prediction method, which may contribute to a deeper understanding of physical mechanisms.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

Within the current institutional landscape, characterized by increased societal and governmental emphasis on environmental preservation, there is growing interest in the potential of digital transformation (DT) to advance the circular economy (CE). Nonetheless, the empirical substantiation of the connection between DT and CE remains limited. This study seeks to investigate the impact of DT on CE at the organizational level and examine how various institutional factors may shape this relationship within the Chinese context.

To scrutinize this association, we construct a research framework and formulate hypotheses drawing on institutional theory, obtaining panel data from 238 Chinese-listed high-tech manufacturing firms from 2006 to 2019. A regression analysis approach is adopted for the sample data.

Our regression analysis reveals a positive influence of DT on CE performance at the organizational level. Furthermore, our findings suggest that the strength of this relationship is bolstered in the presence of heightened regional institutional development and industry competition. Notably, we find no discernible effect of a firm’s political connections on the DT–CE performance nexus.

This study furnishes empirical evidence on the relationship between DT and CE performance. By elucidating the determinants of this relationship within the distinct context of Chinese institutions, our research offers theoretical and practical insights, thus laying the groundwork for subsequent investigations into this burgeoning area of inquiry.

It has been observed that, in contrast to other Asian and Southeast Asian polities, there are no records of monetary transactions in Angkor's 6th–14th century inscriptions, and no reference to a unit of account after the late 8th century. Explanations for this have been offered, but none of them have much support. In fact, a considerable range of monetary concepts are expressed throughout the study period, and it is unlikely that there was no unit of account. Differences between records of temple inventories and exchange transactions suggest that perhaps display was more important in temples, and that quantitative values such as weights were important in the exchanges. An explanation for the lack of monetary transactions may lie in the fact that the epigraphy is written by and for an elite seemingly concerned more with merit, hierarchy and display of wealth than bureaucratic detail.

To predict the price of battery-grade lithium carbonate accurately and provide proper guidance to investors, a method called MFTBGAM is proposed in this study. This method integrates textual and numerical information using TCN-BiGRU–Attention.

The Word2Vec model is initially employed to process the gathered textual data concerning battery-grade lithium carbonate. Subsequently, a dual-channel text-numerical extraction model, integrating TCN and BiGRU, is constructed to extract textual and numerical features separately. Following this, the attention mechanism is applied to extract fusion features from the textual and numerical data. Finally, the market price prediction results for battery-grade lithium carbonate are calculated and outputted using the fully connected layer.

Experiments in this study are carried out using datasets consisting of news and investor commentary. The findings reveal that the MFTBGAM model exhibits superior performance compared to alternative models, showing its efficacy in precisely forecasting the future market price of battery-grade lithium carbonate.

The dataset analyzed in this study spans from 2020 to 2023, and thus, the forecast results are specifically relevant to this timeframe. Altering the sample data would necessitate repetition of the experimental process, resulting in different outcomes. Furthermore, recognizing that raw data might include noise and irrelevant information, future endeavors will explore efficient data preprocessing techniques to mitigate such issues, thereby enhancing the model’s predictive capabilities in long-term forecasting tasks.

The price prediction model serves as a valuable tool for investors in the battery-grade lithium carbonate industry, facilitating informed investment decisions. By using the results of price prediction, investors can discern opportune moments for investment. Moreover, this study utilizes two distinct types of text information – news and investor comments – as independent sources of textual data input. This approach provides investors with a more precise and comprehensive understanding of market dynamics.

We propose a novel price prediction method based on TCN-BiGRU Attention for “text-numerical” information fusion. We separately use two types of textual information, news and investor comments, for prediction to enhance the model's effectiveness and generalization ability. Additionally, we utilize news datasets including both titles and content to improve the accuracy of battery-grade lithium carbonate market price predictions.

Forecasting of stock indices is a challenging issue because stock data are dynamic, non-linear and uncertain in nature. Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices. The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices.

A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine (QSOS-ELM) is proposed to forecast the next-day closing prices effectively. Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases. This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms.

Simulation is carried out on seven stock indices, and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error, mean absolute percentage error, accuracy and paired sample t-test. Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study.

The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices. The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.

The purpose of this study is to examine the effects of consumer ethnocentrism and animosity on the importance of country-of-origin in food product evaluation. It also tested the moderating effect of purchase frequency.

Data were collected from dairy consumers residing in China. The research model was tested using structural equation modelling with AMOS.

The results indicated that the importance of country-of-origin in product evaluation is not necessarily driven by consumer ethnocentrism or animosity. Only among frequent purchasers, a higher level of consumer ethnocentrism or animosity can be associated with more importance of country-of-origin in product evaluation.

Despite the significant role of purchase frequency, this factor has been less considered in the existing literature on consumer ethnocentrism and animosity. This study represented an initial attempt to the role of purchase frequency in the effects of consumer ethnocentrism and animosity on food product evaluation. It revealed that purchase frequency should be adopted as a moderating factor in future studies in this field.

The purpose of this paper is to examine the current state of corporate entrepreneurship (CE) of emerging market firms (EMFs) and provide direction for future research on the topic.

The authors specifically review the recent literature between the years 2000 and 2019 on CE with the keywords “corporate entrepreneurship,” “emerging economies” and “emerging countries” published in the Australian Business Deans Council list journals. The authors review the existing literature about CE in emerging markets, summarize current achievements and present an agenda for future research.

Based on the review, the authors categorized the macro and micro contexts of CE and summarized the current articles on CE in emerging markets within each macro and micro context. The authors conclude that despite the abundance of research on CE that investigates the three prongs of CE in terms of innovation, strategic renewal and new venturing in developed market contexts, there is a scarcity of literature that focuses on CE in emerging markets from a holistic perspective.

While there is an abundance of literature review on CE in general in terms of the drivers of the construct, the contexts contributing to it and the outcomes, the reviews are lacking about CE specifically within the context of emerging markets. Emerging markets vary from developed markets institutionally, economically, culturally, socially and technologically. However, the questions of how these differences impact the CE activities, as it relates to innovation, venturing and strategic renewal in EMFs, and how these differences provide incentives or hinder the activities that contribute to CE remain mostly unanswered. This paper reviewed the research on CE and emerging market contexts from 2000 to present. It targets to provide a better understanding of the current achievement on this topic and what to be done in the future.

Circular economy (CE) has gained considerable attention from researchers and practitioners over the past few years because of its potential social and environmental benefits. However, limited attention has been given in the literature to explore the drivers and barriers in CE implementation in emerging and developing countries besides China. Therefore, the purpose of this paper is to identify the drivers and barriers to implementing a CE in Pakistan’s automobile manufacturing industry.

This study adopts an explorative approach to understand the drivers and barriers at the micro-level CE implementation in Pakistan’s automobile industry. The research design includes both qualitative and quantitative methods using a survey instrument and interviews to gather data. The use of the two main sources of data provides the opportunity for triangulation of the data to improve the validity of the findings, and enables greater inferences from the results.

This study shows that “profitability/market share/benefit” (30 percent), “cost reduction” (22 percent) and “business principle/concern for environment/appreciation” (19 percent) are the top three drivers. Similarly, “unawareness” (22 percent), “cost and financial constraint” (20 percent) and “lack of expertise” (17 percent) are the top three barriers in implementing CE principles in Pakistan automobiles industry.

This study considers only Pakistan automobiles industry, and the practical implications potentially limit to emerging Asian economies.

This study is the first of its kind that has investigated the drivers and barriers of CE at the organizational level in the automobile industry of Pakistan. Thus, it helps to advance the understanding of the subject matter and enables the formulation of effective policies and business strategies by practitioners for upscaling CE and sustainability.

The circular economy (CE) has become increasingly popular in recent years due to its potential in combining the economic growth positively with businesses, society and the environment simultaneously. The purpose of this review is to provide a concise summary of the existing literature regarding how CE strategies can help mitigate climate change.

A comprehensive review of extant literature is undertaken on the topic of CE and climate change. Six sectors are explored in this chapter; although these sectors are different, but still, they are interdependent and are not completely separate.

Thorough study of literature reveals that the energy, transport and manufacturing sectors have the greatest potential for reducing greenhouse gas emissions, while the waste and building sector have a moderate potential for savings. The agriculture sector, on the other hand, is predicted to have the lowest potential for savings.

This chapter provides implications for achieving a sustainable future in terms of economic growth, social harmony and environment conditions by developing efficient and affordable methods to achieve the goal of CE.

This chapter is a unique endeavour to explore the intricate relationship between CE and climate change. Although both concepts have been explored individually by various studies, but our study is one of the few attempts made to emphasise the crucial role of CE to mitigate the climate change.