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Our analysis is targeted at researchers in the fields of economics and finance, and we place emphasis on the incremental contributions of each paper, key research questions, study methodology, main conclusions and data and identification tactics. By focusing on these critical areas, our review seeks to provide valuable insights and guidance for future research in this rapidly evolving and complex field.

This paper conducts a structured literature review (SLR) of Bitcoin-related articles published in the leading finance, economics and accounting journals between 2018 and 2023. Following Massaro et al. (2016), SLR is a method for examining a corpus of scholarly work to generate new ideas, critical reflections and future research agendas. The goals of SLR are congruent with the three outcomes of critical management research identified by Alvesson and Deetz (2000): insight, critique and transformative redefinition.

The present state of research on Bitcoin lacks coherence and interconnectedness, leading to a limited understanding of the underlying mechanisms. However, certain areas of research have emerged as significant topics for further exploration. These include the decentralized payment system, equilibrium price, market microstructure, trading patterns and regulation of Bitcoin. In this context, this review serves as a valuable starting point for researchers who are unacquainted with the interdisciplinary field of bitcoin and blockchain research. It is essential to recognize the potential value of research in Bitcoin-related fields in advancing knowledge of the interaction between finance, economics, law and technology. Therefore, future research in this area should focus on adopting innovative and interdisciplinary methods to enhance our comprehension of these intricate and evolving technologies.

Our review encompasses the latest research on Bitcoin, including its market microstructure, trading behavior, price patterns and portfolio analysis. It explores Bitcoin's market microstructure, liquidity, derivative markets, price discovery and market efficiency. Studies have also focused on trading behavior, investors' characteristics, market sentiment and price volatility. Furthermore, empirical studies demonstrate the advantages of including Bitcoin in a portfolio. These findings enhance our understanding of Bitcoin's potential impact on the financial industry.

Green building education, an important aspect of sustainability in higher education, has rapidly expanded across the world. Yet, a bespoke pedagogical model integrating the essential elements of green building knowledge into a university course is lacking. To plug this deficiency, this study aims to develop an innovative pedagogical model that incorporates four types of teaching activities, namely, lecture, virtual reality (VR)-aided site visit, physical site visit and practicum-based project.

Based on an extensive review of the relevant literature and course materials, a pedagogical model was constructed for application to the teaching and learning activities of a university’s hospitality and real-estate programme. Using a case study approach involving in-depth interviews with green building professionals and a workshop coupled with an online survey on building professionals, the model’s transformative effectiveness was evaluated.

The study finds that the pedagogical model was able to effectively equip students with the essential green building knowledge pertinent to the different stages of a building life cycle. Concerns about wider applications of the model, including barriers to implementation in other academic programmes and resources for updating the VR platform, were identified.

The VR-aided and project-based pedagogy model is novel and effective in delivering green building education. Future work, particularly expanding the VR platform to cover more green building cases, thereby allowing multiple case studies to be conducted, is recommended for illustrating further contributions and implications of the model.

This paper aims to inspect the defects of solder joints of printed circuit board in real-time production line, simple computing and high accuracy are primary consideration factors for feature extraction and classification algorithm.

In this study, the author presents an ensemble method for the classification of solder joint defects. The new method is based on extracting the color and geometry features after solder image acquisition and using decision trees to guarantee the algorithm’s running executive efficiency. To improve algorithm accuracy, the author proposes an ensemble method of random forest which combined several trees for the classification of solder joints.

The proposed method has been tested using 280 samples of solder joints, including good and various defect types, for experiments. The results show that the proposed method has a high accuracy.

The author extracted the color and geometry features and used decision trees to guarantee the algorithm's running executive efficiency. To improve the algorithm accuracy, the author proposes using an ensemble method of random forest which combined several trees for the classification of solder joints. The results show that the proposed method has a high accuracy.

The authors propose a solder joint recognition method based on eigenspace technology.

The original solder joint image is transformed into a small set of feature subspace called “eigensolder”, which is the eigenvector of the training set and can represent a solder joint well. Then, the eigensolder feature is extracted by projecting the new solder joint image into the subspace, and the Euclidean distance measure is used to classify the solder joint.

The experimental results show that the proposed method is superior to the traditional classification method in solder joint recognition, and it can achieve 96.43 per cent recognition rate using only 15 eigenvalue images. It is suitable for the classification with small samples.

Traditional classification method like neural network and statistical method cost long time. Here, Eigensolder method is used to extract feature. Eigensolder method is more efficient, as it uses the principal component analysis method to reduce the feature dimension of input image and only measure the distance to classify.

Foreign firms entering into the domestic real estate industry and foreign investment control are significant in global hot markets such as Australia. Despite their market impact and policy sensitivity, developer choice is rarely studied. The purpose of this paper is to study domestic and overseas property developers for their motive and preference in response to market growth and market barriers including regulatory constraint.

International trade theory suggests local and overseas firms can vary significantly for their risk profile when engaging in location-specific development opportunities. Using a comprehensive decision factor system for the residential development process, the authors conducted an experimental survey to collect the prime data to measure stated preference of domestic and overseas developers in the context of the Melbourne residential market.

Results suggest high consistency between the samples of domestic and overseas developers. Possible explanations include vertical integration by innovative contracting, strict regulatory constraint dictates domestic and overseas firms’ preference or sample selection bias. This micro-analysis of developer stated preference highlights their entrepreneurial ability to combine substitution and integration for innovative contractual strategy. This ability to join asset holding and project management enables firm flexibility to mitigate business risk in rapidly globalising capital and factor markets.

These insights of firm-level decision making contribute to the decision literature of real estate developers and are relevant to the broader literature of industrial economics and international trade. Government may evaluate policy strategies based on the explicit entrepreneur (e.g. developer) preference for their “comparative advantage”.

This paper highlights developer’s ability to jointly consider investment and project management for decision making. It found that other than political cost such as national interest and domestic interest group pressure, domestic and overseas developers in the Melbourne residential market actually think quite alike. It suggests that irrespective of property ownership conditions, market integration occurs in the Melbourne residential sector.

This paper aims to propose a deep learning model that can be used to expand the number of samples. In the process of manufacturing and assembling electronic components on the printed circuit board in the surface mount technology production line, it is relatively easy to collect non-defective samples, but it is difficult to collect defective samples within a certain period of time. Therefore, the number of non-defective components is much greater than the number of defective components. In the process of training the defect detection method of electronic components based on deep learning, a large number of defective and non-defective samples need to be input at the same time.

To obtain enough electronic components samples required for training, a method based on the generative adversarial network (GAN) to generate training samples is proposed, and then the generated samples and real samples are used to train the convolutional neural networks (CNN) together to obtain the best detection results.

The experimental results show that the defect recognition method using GAN and CNN can not only expand the sample images of the electronic components required for the training model but also accurately classify the defect types.

To solve the problem of unbalanced sample types in component inspection, a GAN-based method is proposed to generate different types of training component samples and then the generated samples and real samples are used to train the CNN together to obtain the best detection results.

Data-driven models are increasingly being used to predict the fatigue life of many engineering components exposed to multiaxial loading. However, owing to their high data requirements, they are cost-prohibitive and underperforming for application scenarios with limited data. Therefore, it is essential to develop an advanced model with good applicability to small-sample problems for multiaxial fatigue life assessment.

Drawing inspiration from the modeling strategy of empirical multiaxial fatigue models, a modular neural network-based model is proposed with assembly of three sub-networks in series: the first two sub-networks undergo pretraining using uniaxial fatigue data and are then connected to a third sub-network trained on a few multiaxial fatigue data. Moreover, general material properties and necessary loading parameters are used as inputs in place of explicit damage parameters, ensuring the universality of the proposed model.

Based on extensive experimental evaluations, it is demonstrated that the proposed model outperforms empirical models and conventional data-driven models in terms of prediction accuracy and data demand. It also holds good transferability across various multiaxial loading cases.

The proposed model explores a new avenue to incorporate uniaxial fatigue data into the data-driven modeling of multiaxial fatigue life, which can reduce the data requirement under the promise of maintaining good prediction accuracy.

The traditional methods have difficulty to inspection various types of copper strips defects as inclusions, pits and delamination defects under uneven illumination. Therefore, this paper aims to propose an optimal real Gabor filter model for inspection; however, improper selection of Gabor parameters will cause the boundary between the defect and the background image to be not very clear. This will make the defect and the background cannot be completely separated.

The authors proposed an optimal Real Gabor filter model for inspection of copper surface defects under uneven illumination. This proposed method only requires a single filter by calculating the specific convolution energy of the Gabor filter with the image. The Real Gabor filter’s parameter is optimized by particle swarm optimization (PSO), which objective fitness function is maximization of the Gabor filter’s energy average divided by the energy standard deviation, the objective makes a distinction between the defect and normal area.

The authors have verified the effect with different iterations of parameter optimization using PSO, the effects with different control constant of energy and neighborhood window size of real Gabor filter, the experimental results on a number of metal surface have shown the proposed method achieved a well performance in defect recognition of metal surface.

The authors propose a defect detection method based on particle swarm optimization for single Gabor filter parameters optimization. This proposed method only requires a single filter and finds the best parameters of the Gabor filter. By calculating the specific convolution energy of the Gabor filter and the image, to obtain the best Gabor filter parameters and to highlight the defects, the particle swarm optimization algorithm’s fitness objective function is maximize the Gabor filter's average energy divided by the energy standard deviation.

The COVID-19 outbreak has brought serious disruptions worldwide and higher education has been at the forefront of this global pandemic. To adapt to the “new normal”, new technology-backed teaching mode emerges in universities as valued option to integrate face-to-face and remote teaching-learning activities. Blended synchronous learning (BSL) forms part of this new trial. This paper investigates the relevance and implications of BSL for university teaching and learning in the field of property and built environments in and beyond the transitional period of COVID disruptions and a time of global uncertainty.

This paper adopts case study approach to the understanding of BSL and its initial planning and design for property course delivery at the University of Melbourne. A review of literature helps formulate an analytical lens for the delivery mode and its significance and challenge in enhancing student learning experience. It also brings insights from the experience of participant observation.

This paper envisions new possibilities and challenges projecting the BSL as innovative and useful teaching-learning mode for property and built environments education in and beyond the pandemic. The analysis demonstrates the pedagogical values of BSL in facilitating supportive and equitable learning environment to achieve quality learning outcomes for property education. It identifies opportunities and challenges corresponding the underlying logic and practice of BSL.

This paper is the first to examine the use of BSL delivery and its pedagogical significance in post-pandemic property education. It sheds light on innovative pedagogical design for academic institutions to manage pandemic and technological disruptions to teaching-learning.