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The purpose of the study is to investigate the impact of implementing anti-dumping duties on imported Australian wine to China in the short- and long-run, respectively.

First, the Difference-in-Differences (DID) method is used in this study to evaluate the short-run causal effect of implementing anti-dumping duties on imported Australian wine to China. Second, a Bayesian ensemble method is used to predict 2023–2025 wine exports from Australia to China. The disparity between the forecasts and counterfactual prediction which assumes no anti-dumping duties represents the accumulated impact of the anti-dumping duties in the long run.

The anti-dumping duties resulted in a significant decline in red and rose, white and sparkling wine exports to China by 92.59%, 99.06% and 90.06%, respectively, in 2021. In the long run, wine exports to China are projected to continue this downward trend, with an average annual growth rate of −21.92%, −38.90% and −9.54% for the three types of wine, respectively. In contrast, the counterfactual prediction indicates an increase of 3.20%, 20.37% and 4.55% for the respective categories. Consequently, the policy intervention is expected to result in a decrease of 96.11%, 93.15% and 84.11% in red and rose, white and sparkling wine exports to China from 2021 to 2025.

The originality of this study lies in the creation of an economic paradigm for assessing policy impacts within the realm of wine economics. Methodologically, it also represents the pioneering application of the DID and Bayesian ensemble forecasting methods within the field of wine economics.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

Power plant projects are very complex and encounter serious cost overruns worldwide. Their cost overrun risks are not independent but interrelated in many cases, having structural relationships among each other. The purpose of this study is, therefore, to establish the complex structural relationships of risks involved.

In total, 76 published articles from the previous literature are reviewed using the content analysis method. Three risk networks in different phases of power plant projects are depicted based on literature review and case studies. The possible methods of solving these risk networks are also discussed.

The study finds critical cost overrun risks and develops risk networks for the procurement, civil and mechanical works of power plant projects. It identifies potential models to assess cost overrun risks based on the developed risk networks. The literature review also revealed some research gaps in the cost overrun risk management of power plants and similar infrastructure projects.

This study will assist project risk managers to understand the potential risks and their relationships to prevent and mitigate cost overruns for future power plant projects. It will also facilitate decision-makers developing a risk management framework and controlling projects’ cost overruns.

The study presents conceptual risk networks in different phases of power plant projects for comprehending the root causes of cost overruns. A comparative discussion of the relevant models available in the literature is presented, where their potential applications, limitations and further improvement areas are discussed to solve the developed risk networks for modeling cost overrun risks.

The purpose of this study is to unfold the existing information channel in the higher moments of currency futures for different time horizons.

The authors use a quasi-Bayesian local likelihood approach within a time-varying parameter vector autoregression (TVP-VAR) framework and a dynamic connectedness measure to study the volatility, skewness and kurtosis of most traded currency futures.

The authors’ results suggest a time-varying presence of dynamic connectedness within higher moments of currency futures. Most spillovers pertain to shorter time horizons. The authors find that in net terms, CHF, EUR and JPY are the most important contributors to the system, while the authors emphasize that the role of being a transmitter or a receiver varies for pairwise interactions and time windows.

To the best of the authors’ knowledge, this is the first study that looks upon the connectivity vis-á-vis uncertainty, asymmetry and fat tails in currency futures within a dynamic Bayesian paradigm. The authors extend the current literature by proposing new insights into asset distributions.

This study aims to evaluate the usage of partial least squares structural equation modeling (PLS-SEM) in journals related to logistics and supply chain management (LSCM).

Based on a structured literature review approach, the authors reviewed 401 articles in the field of LSCM applying PLS-SEM published in 15 major journals between 2014 and 2022. The analysis focused on reasons for using PLS-SEM, measurement model and structural model evaluation criteria, advanced analysis techniques and reporting practices.

LSCM researchers sometimes did not clarify the reasons for using PLS-SEM, such as sample size, complex models and non-normal distributions. Additionally, most articles exhibit limited use of measurement models and structural model evaluation techniques, leading to inappropriate use of assessment criteria. Furthermore, progress in the practical implementation of advanced analysis techniques is slow, and there is a need for improved transparency in reporting analysis algorithms.

This study contributes to the field of LSCM by providing clear criteria and steps for using PLS-SEM, enriching the understanding and advancement of research methodologies in this field.

With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

This paper aims to propose a new deep learning technique to detect the type of material to improve automated construction quality monitoring.

A new data augmentation approach that has improved the model robustness against different illumination conditions and overfitting is proposed. This study uses data augmentation at test time and adds outlier samples to training set to prevent over-fitted network training. For data augmentation at test time, five segments are extracted from each sample image and fed to the network. For these images, the network outputting average values is used as the final prediction. Then, the proposed approach is evaluated on multiple deep networks used as material classifiers. The fully connected layers are removed from the end of the networks, and only convolutional layers are retained.

The proposed method is evaluated on recognizing 11 types of building materials which include 1,231 images taken from several construction sites. Each image resolution is 4,000 × 3,000. The images are captured with different illumination and camera positions. Different illumination conditions lead to trained networks that are more robust against various environmental conditions. Using VGG16 model, an accuracy of 97.35% is achieved outperforming existing approaches.

It is believed that the proposed method presents a new and robust tool for detecting and classifying different material types. The automated detection of material will aid to monitor the quality and see whether the right type of material has been used in the project based on contract specifications. In addition, the proposed model can be used as a guideline for performing quality control (QC) in construction projects based on project quality plan. It can also be used as an input for automated progress monitoring because the material type detection will provide a critical input for object detection.

Several studies have been conducted to perform quality management, but there are some issues that need to be addressed. In most previous studies, a very limited number of material types were examined. In addition, although some studies have reported high accuracy to detect material types (Bunrit et al., 2020), their accuracy is dramatically reduced when they are used to detect materials with similar texture and color. In this research, the authors propose a new method to solve the mentioned shortcomings.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

The imperative to conserve resources and minimize operational expenses has spurred a notable increase in the adoption of lean manufacturing within the context of the circular economy across diverse industries in recent years. However, a notable gap exists in the research landscape, particularly concerning the implementation of lean practices within the pharmaceutical industry to enhance circular economy performance. Addressing this void, this study endeavors to identify and prioritize the pivotal drivers influencing lean manufacturing within the pharmaceutical sector.

The outcome of this rigorous examination highlights that “Continuous Monitoring Process for Sustainable Lean Implementation,” “Management Involvement for Sustainable Implementation” and “Training and Education” emerge as the most consequential drivers. These factors are deemed crucial for augmenting circular economy performance, underscoring the significance of management engagement, training initiatives and a continuous monitoring process in fostering a closed-loop practice within the pharmaceutical industry.

The findings contribute valuable insights for decision-makers aiming to adopt lean practices within a circular economy framework. Specifically, by streamlining the process of developing a robust action plan tailored to the unique needs of the pharmaceutical sector, our study provides actionable guidance for enhancing overall sustainability in the manufacturing processes.

This study represents one of the initial efforts to systematically identify and assess the drivers to LM implementation within the pharmaceutical industry, contributing to the emerging body of knowledge in this area.

Acquainting organisations regarding the concepts of Total Quality Management (TQM) and its implementation is one measure that effectively improves their global position and performance. Kaizen is one of the concepts of TQM, which focuses on low-cost organisational transformational methods and often saves consuming significant resources (time, capital, etc.). Using Kaizen in organisational transformation sets efficient guidelines to improve processes agility and leanness and increase manufacturing productivity. Hence, this study aims to identify the key success factors in Kaizen projects and presents a score function that measures the readiness level of organisations to implement Kaizen projects.

A literature review first extracts the key success factors in Kaizen projects. Afterwards, the selected factors are screened via the fuzzy Delphi method using expert opinions from the manufacturing sector of an emerging economy. Subsequently, their importance is cross-examined by the Bayesian best–worst Method (BBWM). The BBWM is one of the most recent multiple criteria decision-making (MCDM) methods that lead to stable, dynamic and robust pairwise comparisons. After analysing the weights of the key factors, a score function is designed so that organisations can understand how much they are ready to launch Kaizen projects.

According to the findings, “Training and education” and “Employee attitude” played an important role in the success of Kaizen projects. The literature extracted 22 success factors of Kaizen projects, and 10 factors were eliminated through the fuzzy Delphi method. Twelve success factors in Kaizen projects were evaluated and investigated through the BBWM. Matching to this method, “Training and education” and “Employee attitude” weighed 0.119 and 0.112, relatively. Furthermore, “Support from senior management” was the least important factor.

To the best knowledge of the authors, this is the first research in which the success factors of Kaizen projects have been identified and analysed through an integrated multi-layer decision-making framework. Although some studies have investigated the key success factors of Kaizen projects and analysed them through statistical approaches, research that examines the success factors of Kaizen projects through MCDM methods is yet to be reported. Moreover, the score function that measures the level of readiness of each organisation for the successful implementation of Kaizen projects is a unique contribution to this research.