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Motivated by the significant role of uncertainty in affecting investment decisions and China's economic leadership in Asia, this paper investigates the predictive role of exposure to Chinese economic policy uncertainty at the individual stock level in large Asian markets.

We estimate the monthly uncertainty exposure (beta) for each stock and then employ the portfolio-level sorting analysis to investigate the relationship between the China’s uncertainty exposure and the future returns of major Asian markets over multiple trading horizons. The raw returns of the high-minus-low portfolios are then adjusted using conventional asset pricing models to investigate whether the relationship is explained by common risk factors. Finally, we check the robustness of the portfolio-level results through firm-level Fama and MacBeth (1973) regressions.

Applying portfolio-level sorting analysis, we reveal that exposure to Chinese uncertainty is negatively related to the future returns of large stocks over multiple trading horizons in Japan, Hong Kong and India. We discover this is unexplained by common risk factors, including market, size, value, profitability, investment and momentum, and is robust to the specification of stock-level Fama and MacBeth (1973) regressions.

Our analysis demonstrates the spillover effects of Chinese economic policy uncertainty across the region, provides evidence of China's emerging economic leadership, and offers trading strategies for managing uncertainty risks.

The findings of the study significantly improve our understanding of stock return predictability in Asian markets. Unlike previous studies, our results challenge the leading role of the US by providing a new intra-regional return predictor, namely, China’s uncertainty exposure. These results also evidence the continuing integration of the Asian economy and financial markets. However, contrary findings for some Asian markets point toward certain market-specific features. Compared with market-level research, our analysis provides deeper insights into the performance of individual stocks and is of particular importance to investors and other market participants.

The impact on both the environment and operator health is significant. As high-alumina silica glass finds applications in smart devices such as curved mobile phone screens, the grinding of complex curved surfaces necessitates cleaner and more efficient cooling and lubrication methods to enhance processing quality and improve grinding yield rates. This study aims to focus on grinding high-alumina silica glass using micro-lubrication technology and compares its performance with traditional cutting fluid cooling methods.

In the fabrication of mobile phone cover plates composed of high-alumina silicon glass, the incorporation of micro-lubrication grinding technology was undertaken, with the conventional cutting fluid cooling approach serving as the benchmark control group for comparative analysis.

The results indicate that increasing the spray pressure of micro-lubrication within a specific range contributes to reducing grinding surface roughness. At a grinding speed ranging from 25 to 35 m/s, using micro-lubrication can effectively replace the traditional cutting fluid cooling method, resulting in glass surfaces with roughness levels between 0.22 and 0.26. However, at grinding speeds exceeding 35 m/s, the insufficient pressure of the micro-lubricant mist hinders most of the oil mist from entering the grinding zone, leading to inferior cooling performance compared to cutting fluid cooling. Notably, at a grinding speed of 35 m/s, micro-lubrication demonstrates better effectiveness in suppressing chipping during glass grinding compared to traditional cutting fluid cooling methods.

Through the application of micro-lubrication grinding technology, a marked improvement in the grinding quality of high-alumina silicon mobile phone cover plate glass can be achieved, leading to a reduction in surface roughness, a decrease in processing defects and ultimately satisfying the demands for high-precision and high-quality fabrication of such cover plates.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0297

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.

The proliferation of non-fungible token (NFT)-based crypto-art platforms has transformed how creators manage, own and earn money through the creation, assets and identity of their digital works. Despite this, no studies have examined the drivers of continuous content contribution behavior (CCCB) toward NFTs. Hence, this study draws on the theory of relational bonds to examine how various relational bonds affect feelings of psychological ownership, which, in turn, affects CCCB on metaverse platforms.

Using structural equation modeling and importance-performance matrix analysis, an online survey of 434 content creators from prominent NFT platforms empirically validated the research hypotheses.

Financial, structural, and social bonds positively affect psychological ownership, which in turn encourages CCCBs. The results of the importance-performance matrix analysis reveal that male content creators prioritized virtual reputation and social enhancement, whereas female content creators prioritized personalization and monetary gains.

We examine Web 3.0 and the NFT creators’ network that characterizes the governance practices of the metaverse. Consequently, the findings facilitate a better understanding of creator economy and meta-verse commerce.

The balance between power supply and demand gets more challenging when electrical networks switch from centralized thermal power plants to distributed renewable energy sources for power generation. Such problems present a diverse set of challenges that require a solution through system and control methods. Hence, the purpose of this study is to understand the issues faced by each actor in the power sector’s supply chain, which would restrict the stability of the power supply and quality of service.

This study provides a conceptual model, soft system methodology (SSM), for power supply management or grid integration issues through the mapping of identified issues with their possible solutions.

This study offers an analysis that uses methods of problem structuring to construct the major issues and measure technological advancements in the energy sector. This research highlights the need to integrate energy storage systems with the grid for the effective operation of the system to manage various power supply issues.

SSM is used to establish a mechanism to manage grid integration problems by comparing established problems with their potential solutions. The resulting framework would help managers, researchers, policymakers, engineers and smart grid professionals to make the required and informed decisions on the management of grid integration issues and to form strategies fostering efficient and secure energy network.

The research is based on a conceptual framework for enhancing energy efficiency and integrated smart grid technology, which would contribute to a better supply of electricity and a more environmentally sustainable future.

This exploratory study aims to integrate both quantitative and qualitative methods to examine the influence of contemporary university dining attributes and practices on student patronage.

First, a review of the extant literature on-campus dining in universities was conducted. Second, innovative practices of on-campus dining facilities of a large public university were identified. Finally, student perceptions of those practices were examined using a mixed method approach.

The review of literature uncovered 49 articles across 35 years on key topics such as food waste, healthy eating, and service evaluation. From site tours and interviews with related personnel, 40 innovative on-campus dining practices were identified.

Importance ratings revealed cleanliness of the environment, fresh fruit and vegetables, and digitally enabled ordering, as the top three highest rated practices. Factor analysis unveiled six factors that students find important: food diversity, good standards, innovativeness, quick options, menu variety, and fish and seafood. The thematic analysis further revealed four overarching themes (convenience, familiarity, food offerings, and value) and 13 subthemes which complemented the quantitative results.

In addition to shedding post-pandemic light on students’ dining needs, it highlights the paucity of theory used to support extant studies and suggests a novel theoretical underpinning.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.