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Featured Application: Thermal Material Characterisation. Determining thermal material properties such as thermal diffusivity can provide valuable insights into a material's thermal characteristics. A well-established method for this purpose is flash thermography using Parker's half-rise equation. It assumes one-dimensional heat transfer for thermal diffusivity estimation through the thickness of the material. However, research evidence suggests that the technique has not developed as much as the reflection mode over the last decade. This systematic review explores the current state-of-the-art in through-transmission thermography. The methodology adopted for this review is the SALSA framework that seeks to Search, Appraise, Synthesise, and Analyse a selected list of papers. It covers the fundamental physics behind the technique, the advantages/limitations it has, and the current state-of-the-art. Additionally, based on the Population, Intervention, Comparison, Outcome, and Context (PICOC) framework, a specific set of inclusion and exclusion criteria was determined. This resulted in a final list of 81 journal/conference papers selected for this study. These papers were analysed both quantitatively and quantitatively to identify and address the current knowledge gap hindering the further development of through-transmission thermography. The findings from the review outline the current knowledge gap in through-transmission thermography and the challenges hindering the development of the technique, such as depth quantification in pulsed thermography and the lack of a standardised procedure for conducting measurements in the transmission mode. Overcoming some of these obstacles can pave the way for further development of this method to aid in material characterisation. [ABSTRACT FROM AUTHOR]

The use of generative learning models in natural language processing (NLP) has significantly contributed to the advancement of natural language applications, such as sentimental analysis, topic modeling, text classification, chatbots, and spam filtering. With a large amount of text generated each day from different sources, such as web-pages, blogs, emails, social media, and articles, one of the most common tasks in NLP is the classification of a text corpus. This is important in many institutions for planning, decision-making, and creating archives of their projects. Many algorithms exist to automate text classification tasks but the most intriguing of them is that which also learns these tasks automatically. In this study, we present a new model to infer and learn from data using probabilistic logic and apply it to text classification. This model, called GenCo, is a multi-input single-output (MISO) learning model that uses a collaboration of partial classifications to generate the desired output. It provides a heterogeneity measure to explain its classification results and enables a reduction in the curse of dimensionality in text classification. Experiments with the model were carried out on the Twitter US Airline dataset, the Conference Paper dataset, and the SMS Spam dataset, outperforming baseline models with 98.40%, 89.90%, and 99.26% accuracy, respectively. [ABSTRACT FROM AUTHOR]

Many researchers in the construction field have explored the utilization of augmented reality (AR) and its impact on the industry. Previous studies have shown potential uses for AR in the construction industry. However, a comprehensive critical review exploring the ways in which AR supports synchronized communication is still missing. This paper aims to fill this gap by examining trends identified in the literature and by analyzing both beneficial and challenging attributes. This work was performed by collecting numerous journal and conference papers, using keywords including "augmented reality", "construction", and "synchronous communication". The papers were then categorized based on the reported attributes that were indicated to be challenges or benefits. Throughout the analysis, several benefits were consistently reported, including training, visualization, instantly sharing information, decision making, and intuitive interaction. Similarly, several challenges were consistently reported, such as difficulty in manipulation, unfriendly interface, device discomfort, and sun brightness. Regarding other attributes, such as field of view, cost, safety hazards, and hands-free mode, researchers provided divergent reports regarding whether they were beneficial or detrimental to AR communication. These findings provide valuable guidance for future researchers and practitioners, enabling them to leverage AR for synchronized communication in ways that consistently offer value. [ABSTRACT FROM AUTHOR]

Hyper-heuristics have arisen as methods that increase the generality of existing solvers. They have proven helpful for dealing with complex problems, particularly those related to combinatorial optimization. Their recent growth in popularity has increased the daily amount of text in the related literature. This information is primarily unstructured, mainly text that traditional computer data systems cannot process. Traditional systematic literature review studies exhibit multiple limitations, including high time consumption, lack of replicability, and subjectivity of the results. For this reason, text mining has become essential for researchers in recent years. Therefore, efficient text mining techniques are needed to extract meaningful information, patterns, and relationships. This study adopts a literature review of 963 journal and conference papers on hyper-heuristic-related works. We first describe the essential text mining techniques, including text preprocessing, word clouds, clustering, and frequent association rule learning in hyper-heuristic publications. With that information, we implement visualization tools to understand the most frequent relations and topics in the hyper-heuristic domain. The main findings highlight the most dominant topics in the literature. We use text mining analysis to find widespread manifestations, representing the significance of the different areas of hyper-heuristics. Furthermore, we apply clustering to provide seven categories showing the associations between the topics related to hyper-heuristic literature. The vast amount of data available that we find opens up a new opportunity for researchers to analyze the status of hyper-heuristics and help create strategic plans regarding the scope of hyper-heuristics. Lastly, we remark that future work will address the limitations of collecting information from multiple data sources and analyze book chapters related to hyper-heuristics. [ABSTRACT FROM AUTHOR]

In this state-of-the-art review, the authors explore the recent advancements in the topics of learning curve models and their estimation methods for manual operations and processes as well as the data collection and monitoring technologies used for supporting these. This objective is achieved by answering the following three research questions: (RQ1) What calculation methods for estimating the learning curve of a worker exist in the recent scientific literature? (RQ2) What other usages are manufacturing enterprises giving to the modern learning curve prediction models according to the recent scientific literature? and (RQ3) What data collection and monitoring technologies exist to automatically acquire the data needed to create and continuously update the learning curve of an assembly operator? To do so, the PRISMA methodology for literature reviews was used, only including journal articles and conference papers referencing the topic of manual operations and processes, and to fulfil the criteria of a state-of-the-art review, only the literary corpus generated in the last five years (from 2017 to 2022) was reviewed. The scientific databases where the explorative research was carried out were Scopus and Web of Science. Such research resulted in 11 relevant journal articles and international conference papers, which were first reviewed, synthesized, and then compared. Four estimating methods were found for learning curves, and one recently developed learning curve model was found. As for the data collection and monitoring technologies, six frameworks were found and reviewed. Lastly, in the discussion, different areas of opportunity were found in the current state-of-the-art, mainly by combining the existing learning curve models and their estimation methods and feeding these with modern real-time data collection and monitoring frameworks. [ABSTRACT FROM AUTHOR]

The shift to e-commerce has changed many business areas. Real estate is one of the applications that has been affected by this modern technological wave. Recommender systems are intelligent models that assist users of real estate platforms in finding the best possible properties that fulfill their needs. However, the recommendation task is substantially more challenging in the real estate domain due to the many domain-specific limitations that impair typical recommender systems. For instance, real estate recommender systems usually face the clod-start problem where there are no historical logs for new users or new items, and the recommender system should provide recommendations for these new entities. Therefore, the recommender systems in the real estate market are different and substantially less studied than in other domains. In this article, we aim at providing a comprehensive and systematic literature review on applications of recommender systems in the real estate market. We evaluate a set of research articles (13 journal and 13 conference papers) which represent the majority of research and commercial solutions proposed in the field of real estate recommender systems. These papers have been reviewed and categorized based on their methodological approaches, the main challenges that they addressed, and their evaluation procedures. Based on these categorizations, we outlined some possible directions for future research. [ABSTRACT FROM AUTHOR]