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The practical purpose of this research is to propose a candidate for post-quantum signature standard that is free of significant drawback of the finalists of the NIST world competition, which consists in the large size of the signature and the public key. The practical purpose is to propose a fundamentally new method for development of algebraic digital signature algorithms.

The proposed method is distinguished by the use of two different finite commutative associative algebras as a single algebraic support of the digital signature scheme and setting two different verification equation for a single signature. A single public key is computed as the first and the second public keys, elements of which are computed exponentiating two different generators of cyclic groups in each of the algebras.

Additionally, a scalar multiplication by a private integer is performed as final step of calculation of every element of the public key. The same powers and the same scalar values are used to compute the first and the second public keys by the same mathematic formulas. Due to such design, the said generators are kept in secret, providing resistance to quantum attacks. Two new finite commutative associative algebras, multiplicative group of which possesses four-dimensional cyclicity, have been proposed as a suitable algebraic support.

The introduced method is novel and includes new techniques for designing algebraic signature schemes that resist quantum attacks. On its base, a new practical post-quantum signature scheme with relatively small size of signature and public key is developed.

Design is an important construct in the retail environment literature. Yet, the measures used for design have not followed appropriate scale development procedures. The purpose of this study is to provide a conceptual definition and then develop a scale for retail environment design (RED).

Interviews with both consumers and marketing researchers are used to generate a potential list of items. Using four different studies, these items are refined, and the RED scale is offered.

This study develops and validates the four-dimensional RED scale to measure the design of retail environments. The dimensions are functional, aesthetic, lighting and signage.

The newly developed RED scale will allow retailing researchers to measure lighting and signage qualities as part of retail design, measure design of retail environments more accurately and allow different studies to be compared.

The newly developed RED scale will allow retailers to better understand customers’ perceptions of the four dimensions of design. Retailers spend significant time and money designing and redesigning retail environments. The RED scale will enable managers to ensure these significant investments create competitive advantages and an appropriate return on investment.

A scale to measure retail environment design is developed. The scale includes two dimensions (lighting and signage) that are not typically investigated.

Several studies have addressed the use of four-dimensional (4D) building information modeling (BIM) for construction management. However, the automation of the processes for generating 4D models and their integrated use with Location-Based Planning and the Last Planner® System is not well discussed. Therefore, this paper aims to develop a method for automating the generation and use of 4D BIM models integrated with Location-Based Planning and Last Planner® System supporting project control cycles.

The research strategy adopted was Design Science Research. The automated method for using the 4D models was developed and refined in two residential building projects in Brazil, along with 31 meetings and involving 11 direct users. The assessment of the proposed method focuses on four constructs: the impact of process automation, the impact on the identification and assessment of site progress and the planning process, ease of adoption and utility of the proposed method.

The results of this paper indicated increased adherence between planned and executed through an automated method for using the 4D models. The established routines enabled automating the link between the planning levels and the three-dimensional (3D) model, providing a more agile and updated data source and achieving 92.8% of user satisfaction regarding the deadline and frequency of delivery of the 4D model reports. Moreover, this study identified the relationships between the processes of the method proposed and Digital Models.

The primary scientific value achieved in this study is creating a method for automating processes and simplifying steps for the generation and use of 4D BIM models in the production planning and control cycles during the construction phase.

Promoting positive mental health is increasingly being encouraged as the focus of research and policies on the mental health of construction personnel. Most measures of mental health, however, typically use negative indicators such as depression and anxiety and are not specifically developed for the construction workforce, especially those with a Global South background. These limitations have made it challenging to measure construction personnel’s positive mental health. The purpose of this study was, therefore, to develop a scale for measuring the positive mental health of construction personnel with a Global South background.

Guided by Keyes’ two-continua model of mental health, the study objectives were addressed through a mixed-methods study using the case of Ghana. Qualitative data collected from eight key stakeholder groups using 16 interviews and two rounds of focus group discussions were analysed thematically. Quantitative data were obtained through a survey of 425 construction personnel and analysed using confirmatory factor analysis and correlation analysis.

Thematic analysis revealed a four-dimensional structure of positive mental health, namely, emotional, psychological, social and spiritual. Confirmatory factor analysis and correlation analysis of the results indicated good instrument validity and reliability.

Existing measures of positive mental health are based on a three-dimensional model, i.e. emotional, social and psychological well-being. By including spiritual well-being, this study proposes a four-dimensional measurement model as a more comprehensive and promising measure to use in surveys of positive mental health among the construction workforce, especially those with a Global South background, and to develop suitable interventions for them.

Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.

This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).

In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.

(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.

The purpose of this study is to revisit brand performance metrics (BPMs) (brand affinity, brand content and knowledge, brand image, brand ethics and brand value) and evaluate the moderated mediation effect of relationship quality (mediator) and relationship duration (moderator) in brand performance and customer loyalty relationship in an Indian banking context.

The research model was tested in the Indian banking sector. The primary data was collected from the 1,000 account holders of five Indian public and private banks. The data was analysed and validated using exploratory factor analysis and confirmatory factor analysis. Structural equation modelling and the Hayes process were used for testing the hypotheses.

The study results established BPMs as a four-dimensional structure comprising brand affinity, brand content and knowledge, brand image, brand ethics and brand value. The BPMs significantly positively impact relational quality (RQ) and customer loyalty. Further results also prove the existence of moderated mediation effect on BPMs and customer loyalty link and portray that the impact of BPMs on customer loyalty is mediated by the RQ and influenced by relationship duration.

The study is confined to the Indian banking sector. It did not examine the dimension-wise impact of brand performance indicators on RQ and customer loyalty. Future research is required to explore their influence in banking and other sectors.

The study findings suggest that to enhance brand performance, banks need to follow excellence in every conduct, take immediate actions against inappropriate behaviour, consistently update their relevant and valuable contents (news, videos, white papers, e-books, case studies, FAQ’s, photos, etc.) on their websites and also introduce loyalty schemes to reimburse customers’ interests with some substantial benefits such as rebates, discounts, annual gifts and extraordinary or additional services. These strategies can pave the way for enhancing long-term quality relationships between customers and their service providers and increasing customer loyalty.

To the best of the authors’ knowledge, the study is a maiden attempt to assess the effect of BPMs on customer loyalty in the presence of RQ and at the value of relationship duration/length. Besides, the study results also prove the existence of moderated mediation effect and portray that the impact of customer equity and relational benefits on customer loyalty is influenced by relationship duration and mediated by RQ.

The purpose of this study is to develop a framework for the perceived intelligence of VAs and explore the mechanisms of different dimensions of the perceived intelligence of VAs on users’ exploration intention (UEI) and how these antecedents can collectively result in the highest level of UEI.

An online survey on Amazon Mechanical Turk is employed. The model is tested utilizing the structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) approach from the collected data of VA users (N = 244).

According to the SEM outcomes, perceptual, cognitive, emotional and social intelligence have different mechanisms on UEI. Findings from the fsQCA reinforce the SEM results and provide the configurations that enhanced UEI.

This study extends the conceptual framework of perceived intelligence and enriches the literature on anthropomorphism and users’ exploration. These findings also provide insightful suggestions for practitioners regarding the design of VA products.

This paper aims to explore the potential of implementing an action competence teaching model as a framework for achieving sustainable development goals (SDGs) in higher education. The paper seeks to draw insights from the students on the potential of this teaching model.

The study adopted a quantitative research design in exploring the potential of an action competence teaching model. This study used self-report measures to obtain insights into the objective of the study.

The action competence teaching model was seen as useful in equipping students with knowledge about a problem, confidence and willingness to act. However, some issues like the design of the projects, teamwork and instructional practices were identified and discussed.

To the best of the authors’ knowledge, this study is the first to implement an action competence teaching model to help draw insights from students on its potential. This paper documents certain aspects of action competence that require attention before being implemented in higher education. This information so far lacking in scientific literature contributes to ongoing discussions on SDGs while unveiling strengths and weaknesses to be considered.

This study aims to produce textile-like surfaces using fused deposition modelling (FDM) 3D printers and create a garment collection.

Experiments were conducted using different types of materials in FDM 3D printers until the sufficient flexibility was achieved to create textile-like structures. During the research, properties of polylactic acid (PLA), acrylonitrile butadiene styrene (ABS) and thermoplastic polyurethane (TPU) were observed. Geometrical patterns were printed and each of them gave a different result depending on the pattern. Based on the information obtained from the experiments, a garment collection with four total looks was designed inspired by Vivaldi’s “Four Seasons”.

Among the materials used, TPU, a flexible filament, yielded the best results. Because of the rigid properties of PLA and ABS, chain-like structures were printed to create relatively flexible surfaces, but the results were still not successful enough to create a clothing material. Therefore, TPU was preferred for the garment material selection.

In this study, combinations of 3D printed flexible structures and different types of fabrics were used to create a garment collection. It was concluded that, with the right material selection, 3D printing can be used as an alternative method to create a new aesthetic language in fashion 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.