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Economic theories on structural change focus on factors such as fluctuations in relative prices and income growth. In addition, China’s reform and opening up has also been accompanied by increasing openness, significant fluctuations in investment rates, and frictions in the labor market. Existing literature lacks a unified theoretical framework to assess the relative importance of all these determinants. The paper aims to discuss these issues.

To incorporate all of the potential determinants of China’s structural change, the authors build a two-country four-sector neoclassical growth model that embeds the multi-sector Eaton and Kortum (2002) model of international trade, complete input-output structure, non-homothetic preference and labor market frictions. The authors decompose the sectoral employment shares into six effects: the Baumol, Engel, investment, international trade, factor intensity and labor market friction effects. Using the data of Chinese economy from 1978 to 2011, the authors perform a quantitative investigation of the six determinants’ effects through the decomposition approach and counterfactual exercises.

Low-income elasticity of demand, high labor intensity, and the existence of the switching costs are the reasons for the high employment share in the agricultural sector. Technological progress, investment and international trade have comparatively less influence on the proportion difference of employment in the three sectors.

Therefore, to examine the impact on China’s structural change, in addition to Baumol effect and the Engel effect, it is also necessary to consider the impact of three more factors: international trade, investment and switching costs. Therefore, the authors decompose the factors that may influence China’s structural change into the Baumol, Engel, investment, international trade, factor intensity effect and switching cost effects. The authors evaluate these six effects using the decomposition approach and counterfactual exercises.

The purpose of this paper is to explore the trajectories of the urbanization process in Saudi Arabia in its regional context from the unification of the country by King Abdul Aziz Al Saud in 1932 to the present time, and the urbanization impact on the status and management of cultural heritage in the Kingdom.

Our study design integrated a well-articulated theoretical frame of sustainability to gain a heuristical understanding of urbanization in Saudi Arabia, and its link to cultural heritage. The methodological approach was mixed in nature involving (1) literature search and review, (2) analysis of public documents and databases, (3) analysis of photographs and (4) expert interviews.

One of the most obvious findings reached in this study is that there is considerable trade-off between heritage site conservation, population and economic demand for increased urbanization. Hence, with increasing urbanization pressures, the value of the heritage site may be rethought based on Saudi Arabia's economic and cultural conservation perspectives.

Since our data are mostly of textual narrative in origin, precise predictions were difficult or impossible for many reasons such as non-linearity, and non-equilibrium dynamics, context and scale dependence as well as the historical exigency of urbanization. However, the same theoretical framework can be applied to appropriate longitudinal/ time series data for predictive analyses, which can be taken up as a future research agenda.

This paper analyzes the urbanization process and sustainability challenges of cultural heritage sites employing a mixed methodological approach, embedded in a holistic theoretical framework of sustainability.

MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity countermeasure domain, such as dynamic, emulated and file analysis. Those entities are linked by applying relationships such as analyze, may_contains and encrypt. A fundamental challenge for collaborative designers is to encode knowledge and efficiently interrelate the cyber-domain facts generated daily. However, the designers manually update the graph contents with new or missing facts to enrich the knowledge. This paper aims to propose an automated approach to predict the missing facts using the link prediction task, leveraging embedding as representation learning.

D3FEND is available in the resource description framework (RDF) format. In the preprocessing step, the facts in RDF format converted to subject–predicate–object triplet format contain 5,967 entities and 98 relationship types. Progressive distance-based, bilinear and convolutional embedding models are applied to learn the embeddings of entities and relations. This study presents a link prediction task to infer missing facts using learned embeddings.

Experimental results show that the translational model performs well on high-rank results, whereas the bilinear model is superior in capturing the latent semantics of complex relationship types. However, the convolutional model outperforms 44% of the true facts and achieves a 3% improvement in results compared to other models.

Despite the success of embedding models to enrich D3FEND using link prediction under the supervised learning setup, it has some limitations, such as not capturing diversity and hierarchies of relations. The average node degree of D3FEND KG is 16.85, with 12% of entities having a node degree less than 2, especially there are many entities or relations with few or no observed links. This results in sparsity and data imbalance, which affect the model performance even after increasing the embedding vector size. Moreover, KG embedding models consider existing entities and relations and may not incorporate external or contextual information such as textual descriptions, temporal dynamics or domain knowledge, which can enhance the link prediction performance.

Link prediction in the D3FEND KG can benefit cybersecurity countermeasure strategies in several ways, such as it can help to identify gaps or weaknesses in the existing defensive methods and suggest possible ways to improve or augment them; it can help to compare and contrast different defensive methods and understand their trade-offs and synergies; it can help to discover novel or emerging defensive methods by inferring new relations from existing data or external sources; and it can help to generate recommendations or guidance for selecting or deploying appropriate defensive methods based on the characteristics and objectives of the system or network.

The representation learning approach helps to reduce incompleteness using a link prediction that infers possible missing facts by using the existing entities and relations of D3FEND.

This research aims to investigate the impact of fear and perceived knowledge (PK) of Covid-19 on the sustainable consumption behaviour (SCB) of Muslim consumers and to test the mediating role of (intrinsic) religiosity.

A total of 417 responses were collected during Covid-19 lockdown through an online structured survey using the snowball technique. A two-step research approach was adopted. In Study 1, an exploratory factor analysis was performed on the SCB measurement scale through SPSS. In Study 2, hypothesised associations were analysed using SmartPLS-SEM.

PK of Covid-19 pandemic directly motivates SCB in Muslim consumers, whereas fear has no direct effect on any factor of SCB. Religiosity is found to be a significant driver of SCB. Indirect effects also depict that religiosity positively mediates the association between fear and SCB as well as PK and SCB.

The study may guide policymakers and marketers in using the current pandemic as a tool to inspire sustainable consumption. Religious values, teachings and knowledge about the pandemics can be publicised to create awareness and induce desired behaviour to cope with adverse events and adopt sustainable consumption patterns and lifestyles among Muslim consumers.

The article is the pioneer of its kind to present survey research about Covid-19 fear and PK’s impact on SCB through religiosity. It adds to the Islamic marketing literature about religiosity, coping theory, PK and fear of pandemics and their role in transitioning Muslim consumers towards SCB. Moreover, the use of partial least squares structural equation modelling in the context of Covid-19 research was extended.

The use of unmanned aerial vehicles (UAVs) has significantly increased in the past decade and nowadays is being used for various purposes such as image processing, cargo transport, archaeology, agriculture, manufacturing, health care, surveillance and inspections. For this reason, using the appropriate image processing method for the intended use of UAVs increases the study’s success. This study aims to determine the most suitable one among the innovative methods that constitute the image processing system for a UAV to be used for surveillance purposes.

Analytical hierarchy process has been used in the solution of the decision problem to be handled in three stages, namely, platform, architecture and method. The most suitable alternative and the effect weights of these criteria results were determined at each stage.

As a result of this study, Jetson TX2 was determined as the most suitable embedded platform, ResNet is the optimum architecture and Faster R-convolutional neural networks was the best method in the image processing layer for a system that will provide surveillance with image processing method using UAV.

In UAV designs, where multiple hardware and software choices and system combinations exist, multi-criteria decision-making (MCDM) approaches can be used as a system decision mechanism.

The novelty of this work comes from the application of MCDM methods that are used as a multi-layered decision mechanism in UAV design.

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.