Showing papers in "Knowledge in 2022"
TL;DR: In this paper , the authors present a systematic literature review of the environmental impacts at the building-construction stage with the objective of identifying the current findings, gaps and future research scopes.
Abstract: The building-and-construction industry has been researched extensively over its life cycle regarding green and sustainable processes and techniques due to its major contributions towards energy consumption and its environmental impacts. Over the past decade, the construction stage of a building is often criticized for overlooking or approximating the environmental impacts as compared to other life-cycle stages of a building. This is evident through strong research findings regarding other building life-cycle stages in building-emission-assessment studies. With the drive towards digitization, the construction industry is receiving significant research attention in order to minimize environmental impacts at the construction stage. Despite these research initiatives, only a handful of recent review studies have systematically furnished current advances, gaps and future directions in environmentally sustainable building-construction techniques. The current study represents a systematic literature review of the environmental impacts at the building-construction stage with the objective of identifying the current findings, gaps and future research scopes. A bibliometric assessment revealed key author contributions, key research areas and collaboration aspects of research works related to environmental impacts of construction in building projects. Four major barriers and knowledge gaps in conducting a comprehensive assessment at the construction stage of a building were identified, including the lack of definition of a generic system boundary, difficulties in data collection, complex modeling issues and complications in the classification and analysis of emissions. The findings would provide key knowledge for passionate construction-industry stakeholders who are keen to benchmark green and sustainable construction practices in the building industry.
31 citations
TL;DR: In this paper , the authors proposed a data-driven methodology that is capable of providing a reliable assessment schema regarding LAMs Facebook performance page that involves several variables, examining a more extended set of LAM social media pages compared to other prior investigations with limited samples as case studies, and understanding which are the administrators' actions that increase the engagement of users.
Abstract: Social media platforms can be used as a tool to expand awareness and the consideration of cultural heritage organizations and their activities in the digital world. These platforms produce daily behavioral analytical data that could be exploited by the administrators of libraries, archives and museums (LAMs) to improve users’ engagement with the provided published content. There are multiple papers regarding social media utilization for improving LAMs’ visibility of their activities on the Web. Nevertheless, there are no prior efforts to support social media analytics to improve users’ engagement with the content that LAMs post to social network platforms. In this paper, we propose a data-driven methodology that is capable of (a) providing a reliable assessment schema regarding LAMs Facebook performance page that involves several variables, (b) examining a more extended set of LAMs social media pages compared to other prior investigations with limited samples as case studies, and (c) understanding which are the administrators’ actions that increase the engagement of users. The results of this study constitute a solid stepping-stone both for practitioners and researchers, as the proposed methods rely on data-driven approaches for expanding the visibility of LAMs services on the Social Web.
18 citations
TL;DR: Analysis with a free and open source python script to collate all journals with impact factors with the now more than 12,000 OA journals that are truly platinum OA (neither the author nor the readers pay for the peer-reviewed work).
Abstract: It appears that open access (OA) academic publishing is better for science because it provides frictionless access to make significant advancements in knowledge. OA also benefits individual researchers by providing the widest possible audience and concomitant increased citation rates. OA publishing rates are growing fast as increasing numbers of funders demand it and is currently dominated by gold OA (authors pay article processing charges (APCs)). Academics with limited financial resources perceive they must choose between publishing behind pay walls or using research funds for OA publishing. Worse, many new OA journals with low APCs did not have impact factors, which reduces OA selection for tenure track professors. Such unpleasant choices may be dissolving. This article provides analysis with a free and open source python script to collate all journals with impact factors with the now more than 12,000 OA journals that are truly platinum OA (neither the author nor the readers pay for the peer-reviewed work). The results found platinum OA is growing faster than both academic publishing and OA publishing. There are now over 350 platinum OA journals with impact factors over a wide variety of academic disciplines, giving most academics options for OA with no APCs.
18 citations
TL;DR: In this article , the authors present a bibliometric analysis of the literature focusing on knowledge dynamics in managerial decision making, showing that there is a consistent link between knowledge dynamics and the managerial decision-making process.
Abstract: The purpose of this paper is to present a bibliometric analysis of the literature, focusing on knowledge dynamics in managerial decision making. The motivation of our research is based on the new theory of knowledge fields and knowledge dynamics and its influence on decision making in business and management. The methodology used is based on a bibliometric analysis performed with the specialized software VOSviewer. The analysis graphically presents a series of semantic clusters which show the co-citation distances between different concepts related to the search expressions used like “knowledge dynamics”, “managerial decision”, and “decision making”. As a database, we used the papers published in journals indexed in Web of Science. The outcomes of our analysis are some graphical representations of semantic clusters for the expressions “knowledge dynamics” and “managerial decision making”, and a series of tables with the content analysis of the clusters and some other data concerning publications and authors. The findings demonstrate that there is a consistent link between knowledge dynamics and the managerial decision making process. The contribution of the paper comes from the fact that it is a first bibliometric analysis of the correlations between knowledge dynamics and managerial decision making as reflected in papers indexed in Web of Science. Also, the analysis includes for the first time the topic of entropic knowledge dynamics as reflected in papers indexed in Web of Science.
7 citations
TL;DR: In this paper , an expedient method, the generalization of the Thurstone method for more than two options, is applied to evaluate the results of the played matches without requiring equal matches' numbers.
Abstract: Due to the non-played matches on the grounds of COVID-19 pandemics, the usual evaluation of the results of tournaments is biased. Matches won by default may cause unrealistic results. In this paper, an expedient method, the generalization of Thurstone method for more than two options, is applied. It is able to evaluate the results of the played matches without requiring equal matches’ numbers. This method takes the strength of the opposer into consideration as well. We apply the method for evaluating Handball Champions’ League’s results. We illustrate that it efficiently predicts the results in the future.
7 citations
TL;DR: This study developed, implemented, and evaluated a multiview conversational image search system, and explored the potential for reinforcement learning to learn from user search behaviour and support the user in the complex information seeking process.
Abstract: In the realm of information, conversational search is a relatively new trend. In this study, we have developed, implemented, and evaluated a multiview conversational image search system to investigate user search behaviour. We have also explored the potential for reinforcement learning to learn from user search behaviour and support the user in the complex information seeking process. A conversational image search system may mimic a natural language discussion with a user via text or speech, and then assist the user in locating the required picture via a dialogue-based search. We modified and improved a dual-view search interface that displays discussions on one side and photos on the other. Based on the states, incentives, and dialogues in the initial run, we developed a reinforcement learning model and a customized search algorithm in the back end that predicts which reply and images would be provided to the user among a restricted set of fixed responses. Usability of the system was validated using methodologies such as Chatbot Usability Questionnaire, System Usability Scale, and User Experience Questionnaire, and the values were tabulated. The result of this usability experiment proved that most of the users found the system to be very usable and helpful for their image search.
6 citations
TL;DR: An integrated approach combining word and contextual embedding models based on deep learning with a rule-based algorithm based on wild cards and truncation operators is suggested for correcting the query, automatically suggesting the most consistent misspellings, thus achieving a more accurate and reliable result.
Abstract: Among all sources of technical information, patent information is one of the richest and most comprehensive. Knowing how to search in this mass of documents is becoming increasingly crucial. However, many users have limited knowledge of patents and search strategies, so they must use intuitive, often approximate approaches that can lead to highly inaccurate searches and be time-consuming. To address this problem, there are tools that help expand queries to increase recall so as not to miss good documents, however, it remains an open problem dealing with misspellings-based strategies. Typically, the problem of the presence of misspellings in patent text is underestimated even by experts in the field, and there is no specific functionality to handle it in the tools available, both free and paid. The goal of the article is to raise awareness about the difficulties in making a proper patent strategy that also takes into account the possible presence of misspellings. It is important to know where we expect to find them and how much these may affect the final result. In particular, it is chosen to divide misspellings into categories, distinguishing between misspellings associated with a generic keyword or multiword from misspellings in acronyms, chemical formulas, names of applicants, inventors, or names of specific formulas or theorems. At least one example case is given for each category, showing when and how it may affect the result. Finally, an integrated approach combining word and contextual embedding models based on deep learning with a rule-based algorithm based on wild cards and truncation operators is suggested for correcting the query, automatically suggesting the most consistent misspellings, thus achieving a more accurate and reliable result.
4 citations
TL;DR: In this article , the authors identify the tacit or practical knowledge of an online community of practice (CoP) based on a content management system (CMS) technology and identify the organizational knowledge capital and functions embedded in a CoP using a CMS platform as a delivery mechanism.
Abstract: This study aimed to identify the tacit or practical knowledge of an online community of practice (CoP) based on a content management system (CMS) technology. The E-Learning Industry site is one of the most prominent news outlets that provides instructional design and technology (IDT) practitioners with insights into the field. Natural language processing (NLP) techniques were implemented to extract practical knowledge of publicly available and not password-protected text sources in seven news categories. First, the findings suggest emphasizing the production of online articles related to the production of e-learning materials in technology-enabled environments. Second, the results indicate the alternative uses of learning management systems to manage different aspects of the production of e-learning materials. Third, the findings show that the CoP’s main priority was to reference existing materials in the community and external resources. The results of this study have implications and provide recommendations for researchers, community leaders, and practitioners toward improving knowledge discovery mechanisms, increasing transparency and integrity in communities, and increasing practitioners’ ability to self-assess existing practical knowledge against competencies in the field. The present study takes an inventory of the organizational knowledge capital and functions embedded in a CoP using a CMS platform as a delivery mechanism for creating and sharing knowledge.
4 citations
TL;DR: In this paper , an innovative approach based on the implementation of a multi-level decision support system (DSS) modelling processes in the industry is introduced. But the authors focus on the application of the PM model to worker security systems characterized by the environment with a risk of emission of smoke and gases.
Abstract: The proposed paper introduces an innovative approach based on the implementation of a multi-level Decision Support System (DSS) modelling processes in the industry. Specifically, the work discusses a theoretical Process Mining (PM) DSS model gaining digital knowledge by means of logics that are able to select the best decisions. The PM model is applied to an open dataset simulating a working scenario and defining a possible safety control method based on the risk assessment. The application of the PM model provides automatic alerting conditions based on a threshold of values detected by sensors. Specifically, the PM model is applied to worker security systems characterized by the environment with a risk of emission of smoke and gases. The PM model is improved by Artificial Intelligence (AI) algorithms by strengthening information through prediction results and improving the risk analysis. An Artificial Neural Network (ANN) MultilaLayer Perceptron (MLP) algorithm is adopted for the risk prediction by achieving the good computational performance of Mean Absolute Error (MAE) of 0.001. The PM model is first sketched by the Business Process Modelling and Notation (BPMN) method, and successively executed by means of the Konstanz Information Miner (KNIME) open source tool, implementing the process-controlling risks for different working locations. The goal of the paper is to apply the theoretical PM model by means of open source tools by enhancing how the multi-level approach is useful for defining a security procedure to control indoor worker environments. Furthermore, the article describes the key variables able to control production and worker safety for different industry sectors. The presented DSS PM model also can be applied to industry processes focused on production quality.
TL;DR: The research showed that some phrases are key to describing particular types of accidents, as the overall predictability of the combined model improved from 71% to 73.28%.
Abstract: The mining industry is diligent about reporting on safety incidents. However, these reports are not necessarily analyzed holistically to gain deep insights. Previously, it was demonstrated that mine accident narratives at a partner mine site could be automatically classified using natural language processing (NLP)-based random forest (RF) models developed, using narratives from the United States Mine Safety and Health Administration (MSHA) database. Classification of narratives is important from a holistic perspective as it affects safety intervention strategies. This paper continued the work to improve the RF classification performance in the category “caught in”. In this context, three approaches were presented in the paper. At first, two new methods were developed, named, the similarity score (SS) method and the accident-specific expert choice vocabulary (ASECV) method. The SS method focused on words or phrases that occurred most frequently, while the ASECV, a heuristic approach, focused on a narrow set of phrases. The two methods were tested with a series of experiments (iterations) on the MSHA narratives of accident category “caught in”. The SS method was not very successful due to its high false positive rates. The ASECV method, on the other hand, had low false positive rates. As a third approach (the “stacking” method), when a highly successful incidence (iteration) from ASECV method was applied in combination with the previously developed RF model (by stacking), the overall predictability of the combined model improved from 71% to 73.28%. Thus, the research showed that some phrases are key to describing particular (“caught in” in this case) types of accidents.