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Highly sophisticated digital technologies have distanced architects and designers from intimate and immediate hand-drawing practices. Meanwhile the changes they rapidly bring come with undetected changes in cultural and social norms regarding the built environment. The growing dependence on computers calls for a more holistic, socially inclusive and place-responsive design practice. This paper aims to shed light on what we are losing in the design process as we rapidly transition to communicate architecture using digital media. The authors contemplate the paradigms in which the human body and physical objects still play an important role in today's design environment.

The paper looks at current trends in developing and establishing “computer imaging” within architectural education, and the architectural profession through parametric design and the area of sustainability. In order to reveal novel and hybrid ways of architectural image-making, it also looks into art forms that already experiment with bodily practices in design by taking an artisanal animation project as a case study.

The renewed longing for craft, haptic environments, tactile experiences and hand-crafted artifacts and artworks that engage the senses can be exemplified with the success of the documentary Last Dance on the Main, an animated film on the endangered layers of human presence in one of Montreal's downtown neighborhoods. The open possibilities for creative hybridizations between the handmade and the digital in architecture practice and education are exposed.

The influence of film on the perception and consequent design of cities is well documented. There is little literature, however, on how the materiality and process of artisanal film animation can provide alternative, if not additional, insights on how to communicate various aspects of the built environment, particularly those rooted in the human body. Furthermore, handmade film explores a broader understanding of sustainability, which includes considerations for social and cultural contexts.

Cost overrun in infrastructure projects is a constant concern, with a need for a proper solution. The current estimation practice needs improvement to reduce cost overruns. This study aimed to find possible statistical modelling techniques that could be used to develop cost models to produce more reliable cost estimates.

A bibliographic literature review was conducted using a two-stage selection method to compile the relevant publications from Scopus. Then, Visualisation of Similarities (VOS)-Viewer was used to develop the visualisation maps for co-occurrence keyword analysis and yearly trends in research topics.

The study found seven primary techniques used as cost models in construction projects: regression analysis (RA), artificial neural network (ANN), case-based reasoning (CBR), fuzzy logic, Monte-Carlo simulation (MCS), support vector machine (SVM) and reference class forecasting (RCF). RA, ANN and CBR were the most researched techniques. Furthermore, it was observed that the model's performance could be improved by combining two or more techniques into one model.

The research was limited to the findings from the bibliometric literature review.

The findings provided an assessment of statistical techniques that the industry can adopt to improve the traditional estimation practice of infrastructure projects.

This study mapped the research carried out on cost-modelling techniques and analysed the trends. It also reviewed the performance of the models developed for infrastructure projects. The findings could be used to further research to develop more reliable cost models using statistical modelling techniques with better performance.

With hybrid simulation techniques getting popular for systems improvement in multiple fields, this study aims to provide insight on the use of hybrid simulation to assess the effect of lean manufacturing (LM) techniques on manufacturing facilities and the transition of a mass production (MP) facility to incorporating LM techniques.

In this paper, the authors apply a hybrid simulation approach to improve an educational automotive assembly line and provide guidelines for implementing different LM techniques. Specifically, the authors describe the design, development, verification and validation of a hybrid discrete-event and agent-based simulation model of a LEGO® car assembly line to analyze, improve and assess the system’s performance. The simulation approach examines the base model (MP) and an alternative scenario (just-in-time [JIT] with Heijunka).

The hybrid simulation approach effectively models the facility. The alternative simulation scenario (implementing JIT and Heijunka LM techniques) improved all examined performance metrics. In more detail, the system’s lead time was reduced by 47.37%, the throughput increased by 5.99% and the work-in-progress for workstations decreased by up to 56.73%.

This novel hybrid simulation approach provides insight and can be potentially extrapolated to model other manufacturing facilities and evaluate transition scenarios from MP to LM.

This study aims to thoroughly examine the trends and developments of crane layout planning (CLP) in the construction field and reveal future research directions for modular integrated construction (MiC).

Through a rigorous systematic mixed-review methodology that integrates bibliometric, scientometric and qualitative analysis, this study explored the crane layout research trend; the scientometric analysis of journal sources and keywords occurrence network; the research contributions and links between influential countries; the classification of research articles based on the type of problems and solution approaches; the qualitative analysis of existing findings and research gaps; and the future research direction for CLP in MiC.

This study found five categories under the CLP domain, namely, crane selection, crane location, integrated crane selection and location, integrated crane location and allocation of supply points and hybrid problems. The major research approaches used to solve CLP is optimization (43%), visualization (23%), decision support systems (16%), simulation (11%) and qualitative techniques (7%). The possible future research directions include artificial intelligence-based models, multi-crane locations, CLP for MiC re-use, dynamic models representing real-life scenarios and building information modeling-based virtual reality models.

Through a mixed-review methodology, this study provides a comprehensive analysis of problem settings and solution methods of CLP while mitigating the subjectivity of traditional review methods. Also, it presents a repertoire on CLP and illuminates future directions for seasoned researchers in the context of MiC.

The purpose of this paper is to propose visualization techniques as a new representation for privacy policies instead of traditional textual representation and to examine empirically their effects on users’ information privacy awareness level.

The authors selected as a case the privacy policy of Instagram and conducted two empirical investigations, each one with three interventions and each representing a different version of the Instagram privacy policy to users. Through a pre- and a post-questionnaire, the authors examined the effects that each representation technique had on the users’ privacy awareness level.

The paper finds that visualized privacy policies lead to higher privacy awareness levels than conventional textual ones, especially when icons are included.

The authors implemented two new representation techniques offering beneficial guidelines for designing more attractive privacy policy representations. However, the samples are rather limited for generalization to the wide population; nonetheless, they are significant to demonstrate the effect of visualized techniques. The findings might also be subject to bias (e.g. brand bias), although the authors took necessary methodological actions to prevent bias.

The results and the methodology of the paper could guide practitioners for the representation of a privacy policy, given that the authors provide systematic and concrete steps.

This paper examines the value of privacy policy visualization as a new approach for enabling user privacy awareness, as well as implements two visualization techniques for a given privacy policy. The paper and its findings should be useful for researchers, as well as for practitioners.

Hybrid systems as the next generation of intelligent applications within the field of mass appraisal and valuation are investigated. Motivated by the obvious limitations of paradigms that are being used in isolation or as stand‐alone techniques such as multiple regression analysis, artificial neural networks and expert systems. Clearly, there are distinct advantages in integrating two or more information processing systems that would address some of the discrete problems of individual techniques. Examines first, the strategic development of mass appraisal approaches which have traditionally been based on “stand‐alone” techniques; second, the potential application of an intelligent hybrid system. Highlights possible solutions by investigating various hybrid systems that may be developed incorporating a nearest neighbour algorithm (k‐NN). The enhancements are aimed at two major deficiencies in traditional distance metrics; user dependence for attribute weights and biases in the distance metric towards matching categorical variables in the retrieval of neighbours. Solutions include statistical techniques: mean, coefficient of variation and significant mean. Data mining paradigms based on a loosely coupled neural network or alternatively a tight coupling with genetic algorithms are used to discover attribute weights. The hybrid architectures developed are applied to a property data set and their performance measured based on their predictive value as well as perspicuity. Concludes by considering the application and the relevance of these techniques within the field of computer assisted mass appraisal.

This paper gives a bibliographical review of the finite element and boundary element parallel processing techniques from the theoretical and application points of view. Topics include: theory – domain decomposition/partitioning, load balancing, parallel solvers/algorithms, parallel mesh generation, adaptive methods, and visualization/graphics; applications – structural mechanics problems, dynamic problems, material/geometrical non‐linear problems, contact problems, fracture mechanics, field problems, coupled problems, sensitivity and optimization, and other problems; hardware and software environments – hardware environments, programming techniques, and software development and presentations. The bibliography at the end of this paper contains 850 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1996 and 2002.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

This paper aims to identify, evaluate and integrate the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. To achieve this aim, the authors use systematic literature review (SLR) as a powerful method to collect and critically analyze the research papers. Also, the authors discuss the selected recommender systems and its main techniques, as well as their benefits and drawbacks in general.

In this paper, the SLR method is utilized with the aim of identifying, evaluating and integrating the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. Also, the authors discussed recommender system and its techniques in general without a specific domain.

The major developments in categories of recommender systems are reviewed, and new challenges are outlined. Furthermore, insights on the identification of open issues and guidelines for future research are provided. Also, this paper presents the systematical analysis of the recommender system literature from 2005. The authors identified 536 papers, which were reduced to 51 primary studies through the paper selection process.

This survey will directly support academics and practical professionals in their understanding of developments in recommender systems and its techniques.

This paper aims to perform the lattice Boltzmann simulation of conjugate natural convection heat transfer, heat flow visualization via heatlines approach and entropy generation in a partitioned medium filled with Ag-MgO (15-85%)/water.

The lattice Boltzmann method (LBM) is used to predict the dynamic and thermal behaviors. Experimental correlations for dynamic viscosity and thermal conductivity versus solid volume fraction are used. The study is conducted for the ranges of Rayleigh number 10³ ≤ Ra ≤ 10⁶, the partitioner thickness 0.01 ≤ δ ≤ 0.9, its position 0.15 ≤ X_s ≤ 0.85 and the hybrid nano-suspensions volume fraction 0% ≤ ϕ ≤ 2%.

The effects of varying of controlling parameters on the convective flow patterns, temperature contours, heat transfers, the heatlines and the entropy generation are presented. It has been found that the maximum rate of heat transfer enhancement occurs for low Ra numbers (10³) and is close to 13.52%. The solid thickness d and its horizontal position X_s have a substantial influence on the heat transfer rate, flow structure, heatline, total entropy generation and Bejan number. Besides, the maximum heat transfer is detected for high Ra and δ ≈ 1 and the percentage of augmentation is equal to 65.55% for ϕ = 2%. According to the horizontal position, the heat transfer remains invariant for Ra = 10³ and takes a maximum value near the active walls for Ra ≥ 10⁴. The total entropy generation increases with Ra and decreases with ϕ for Ra = 10⁶. The increase of ϕ from 0 to 2% leads to a reduction in close to 40.76%. For this value of Ra, the entropy is the maximum for δ = 0.4 and X_s = 0.35 and X_s = 0.65%. Moreover, as the Ra increases the Bejan number undergoes a decrease. The Bejan number is the maximum for Ra = 10³ independently to δ and X_s. The superior thermal performance manifests at low Ra and high value of δ independently to the positions of the conducting body.

The originality of this paper is to analyze the hybrid nano-additive effects on the two-dimensional conjugate natural convection in a partitioned medium using the LBM. The experimental correlations used for the effective thermal conductivity and dynamic viscosity give credibility to our study. Different approaches such as heatlines and entropy generation are used.