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1 – 10 of over 3000Ann G. Green and Myron P. Gutmann
In developing and debating digital repositories, the digital library world has devoted more attention to their missions and roles in supporting access to and stewardship of…
Abstract
Purpose
In developing and debating digital repositories, the digital library world has devoted more attention to their missions and roles in supporting access to and stewardship of academic research output than to discussing discipline, or domain, specific digital repositories. This is especially interesting, given that in social science these domain‐specific repositories have been in existence for many decades. The goal of this paper is to juxtapose these two kinds of repositories and to suggest ways that they can help build partnerships between themselves and with the research community.
Design/methodology/approach
The approach taken in the paper is based on the fundamental idea that all the parties involved share important goals, and that by working together these goals can be advanced successfully.
Findings
The key message is that by visualizing the role of repositories explicitly in the life cycle of the social science research enterprise, the ways that the partnerships work will be clear. These workings can be seen as a sequence of reciprocal information flows between parties to the process, triggers that signal that one party or another has a task to perform, and hand‐offs of information from one party to another that take place at crucial moments. This approach envisions both cooperation and specialization.
Practical implications
If followed, the recommendations offered in the paper will allow those implementing various kinds of repositories to work together with others in new ways, thus both enhancing the amount of information preserved and its value for the community.
Originality/value
This is one of the first times that the mutual possibilities of institutional and domain‐specific repositories have been brought together.
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Imdadullah Hidayat-ur-Rehman and Md Nahin Hossain
The global emphasis on sustainability is driving organizations to embrace financial technology (Fintech) solutions as a means of enhancing their sustainable performance. This…
Abstract
Purpose
The global emphasis on sustainability is driving organizations to embrace financial technology (Fintech) solutions as a means of enhancing their sustainable performance. This study seeks to unveil the intermediary role played by green finance and competitiveness, along with the moderating impact of digital transformation (DT), in the intricate relationship between Fintech adoption and sustainable performance.
Design/methodology/approach
Drawing on existing literature, we construct a comprehensive conceptual framework to thoroughly analyse these interconnected variables. To empirical validate of our model, a dual structural equation modelling–artificial neural network) SEM–ANN approach was employed, adding a robust layer of validation to our study’s proposed framework. A sample of 438 banking employees in Pakistan was collected using a simple random sampling technique, with 411 samples deemed suitable for subsequent analysis. Initially, data scrutiny and hypothesis testing were carried out using Smart-PLS 4.0 and SPSS-23. Subsequently, the ANN technique was utilized to assess the importance of exogenous factors in forecasting endogenous factors.
Findings
The findings from this research underscore the direct and significant influence of Fintech adoption and DT on the sustainable performance of banks. Notably, green finance and competitiveness emerge as pivotal mediators, bridging the gap between Fintech adoption and sustainable performance. Moreover, DT emerges as a critical moderator, shaping the relationships between Fintech adoption and both green finance and competitiveness. The integration of the ANN approach enhances the SEM analysis, providing deeper insights and a more comprehensive understanding of the subject matter.
Originality/value
This study contributes to the enhanced comprehension of Fintech, green finance, competitiveness, DT and the sustainable performance of banks. Recognizing the importance of amalgamating Fintech adoption, green finance and transformational leadership becomes essential for elevating the sustainable performance of banks. The insights garnered from this study hold valuable implications for policymakers, practitioners and scholars aiming to enhance the sustainable performance of banks within the competitive business landscape.
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Yasser Mater, Mohamed Kamel, Ahmed Karam and Emad Bakhoum
Utilization of sustainable materials is a global demand in the construction industry. Hence, this study aims to integrate waste management and artificial intelligence by…
Abstract
Purpose
Utilization of sustainable materials is a global demand in the construction industry. Hence, this study aims to integrate waste management and artificial intelligence by developing an artificial neural network (ANN) model to predict the compressive strength of green concrete. The proposed model allows the use of recycled coarse aggregate (RCA), recycled fine aggregate (RFA) and fly ash (FA) as partial replacements of concrete constituents.
Design/methodology/approach
The model is constructed, trained and validated using python through a set of experimental data collected from the literature. The model’s architecture comprises an input layer containing seven neurons representing concrete constituents and two neurons as the output layer to represent the 7- and 28-days compressive strength. The model showed high performance through multiple metrics, including mean squared error (MSE) of 2.41 and 2.00 for training and testing data sets, respectively.
Findings
Results showed that cement replacement with 10% FA causes a slight reduction up to 9% in the compressive strength, especially at early ages. Moreover, a decrease of nearly 40% in the 28-days compressive strength was noticed when replacing fine aggregate with 25% RFA.
Research limitations/implications
The research is limited to normal compressive strength of green concrete with a range of 25 to 40 MPa.
Practical implications
The developed model is designed in a flexible and user-friendly manner to be able to contribute to the sustainable development of the construction industry by saving time, effort and cost consumed in the experimental testing of materials.
Social implications
Green concrete containing wastes can solve several environmental problems, such as waste disposal problems, depletion of natural resources and energy consumption.
Originality/value
This research proposes a machine learning prediction model using the Python programming language to estimate the compressive strength of a green concrete mix that includes construction and demolition waste and FA. The ANN model is used to create three guidance charts through a parametric study to obtain the compressive strength of green concrete using RCA, RFA and FA replacements.
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Sun-Hwa Kim, Kiwon Lee and Ann Fairhurst
Green practices have been of increasing interest to both practitioners and researchers in the hospitality context. To understand how green practices have been adopted in the…
Abstract
Purpose
Green practices have been of increasing interest to both practitioners and researchers in the hospitality context. To understand how green practices have been adopted in the industry, a systematic review of recent hospitality literature is essential. The purpose of this paper is to identify research domains and formulate a definition of green practices that accurately reflects the current hospitality context.
Design/methodology/approach
The authors reviewed 146 articles on green practices published between 2000 and 2014 in eight hospitality journals. Using content analysis, multiple researchers coded the articles using a standardized coding scheme.
Findings
The number of articles on green practices in the hospitality context has been growing. Most studies focus on managers and the lodging sector. The authors identify three research domains for green practices in the hospitality literature: organizational, operational and strategic. They define a green practice as a value-added business strategy that benefits hospitality operations that engage in environmental protection initiatives.
Research limitations/implications
This framework may help practitioners develop green practice strategies and governments develop effective green policies and reinforce activities aimed at environmental protection. It provides theoretical foundation for future research related to green practices in the hospitality industry. Overall, hospitality stakeholders can use this framework to understand the implementation and effects of green practices.
Originality/value
The authors create an organizational framework for a fragmented body of literature by identifying three research domains for green practices based on a systematic review of recently published hospitality articles (2000-2014). They challenge existing definitions of green practices and propose an accurate definition tailored to the hospitality context.
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This paper gives a bibliographical review of the finite element methods (FEMs) applied to the analysis of ceramics and glass materials. The bibliography at the end of the paper…
Abstract
This paper gives a bibliographical review of the finite element methods (FEMs) applied to the analysis of ceramics and glass materials. The bibliography at the end of the paper contains references to papers, conference proceedings and theses/dissertations on the subject that were published between 1977‐1998. The following topics are included: ceramics – material and mechanical properties in general, ceramic coatings and joining problems, ceramic composites, ferrites, piezoceramics, ceramic tools and machining, material processing simulations, fracture mechanics and damage, applications of ceramic/composites in engineering; glass – material and mechanical properties in general, glass fiber composites, material processing simulations, fracture mechanics and damage, and applications of glasses in engineering.
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Miguel Ángel Caminero, Ana Romero Gutiérrez, Jesús Miguel Chacón, Eustaquio García-Plaza and Pedro José Núñez
The extrusion-based additive manufacturing method followed by debinding and sintering steps can produce metal parts efficiently at a relatively low cost and material wastage. In…
Abstract
Purpose
The extrusion-based additive manufacturing method followed by debinding and sintering steps can produce metal parts efficiently at a relatively low cost and material wastage. In this study, 316L stainless-steel metal filled filaments were used to print metal parts using the extrusion-based fused filament fabrication (FFF) approach. The purpose of this study is to assess the effects of common FFF printing parameters on the geometric and mechanical performance of FFF manufactured 316L stainless-steel components.
Design/methodology/approach
The microstructural characteristics of the metal filled filament, three-dimensional (3D) printed green parts and final sintered parts were analysed. In addition, the dimensional accuracy of the green parts was evaluated, as well as the hardness, tensile properties, relative density, part shrinkage and the porosity of the sintered samples. Moreover, surface quality in terms of surface roughness after sintering was assessed. Predictive models based on artificial neural networks (ANNs) were used for characterizing dimensional accuracy, shrinkage, surface roughness and density. Additionally, the response surface method based on ANNs was applied to represent the behaviour of these parameters and to identify the optimum 3D printing conditions.
Findings
The effects of the FFF process parameters such as build orientation and nozzle diameter were significant. The pore distribution was strongly linked to the build orientation and printing strategy. Furthermore, porosity decreased with increased nozzle diameter, which increased mechanical performance. In contrast, lower nozzle diameters achieved lower roughness values and average deviations. Thus, it should be noted that the modification of process parameters to achieve greater geometrical accuracy weakened mechanical performance.
Originality/value
Near-dense 316L austenitic stainless-steel components using FFF technology were successfully manufactured. This study provides print guidelines and further information regarding the impact of FFF process parameters on the mechanical, microstructural and geometric performance of 3D printed 316L components.
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Sadia Samar Ali, Rajbir Kaur and Jose Antonio Marmolejo Saucedo
Manpreet Kaur, Amit Kumar and Anil Kumar Mittal
In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered…
Abstract
Purpose
In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.
Design/methodology/approach
To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.
Findings
The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.
Originality/value
To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.
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Vinicius Luiz Pacheco, Lucimara Bragagnolo and Antonio Thomé
The purpose of this article is to analyze the state-of-the art in a systematic way, identifying the main research groups and their related topics. The types of studies found are…
Abstract
Purpose
The purpose of this article is to analyze the state-of-the art in a systematic way, identifying the main research groups and their related topics. The types of studies found are fundamental for understanding the application of artificial neural networks (ANNs) in cemented soils and the potential for using the technique, as well as the feasibility of extrapolation to new geotechnical or civil and environmental engineering segments.
Design/methodology/approach
This work is characterized as being bibliometric and systematic research of an exploratory perspective of state-of-the-art. It also persuades the qualitative and quantitative data analysis of cemented soil improvement, biocemented or microbially induced calcite precipitation (MICP) soil improvement by prediction/modeling by ANN. This study sought to compile and study the state of the art of the topic which possibilities to have a critical view about the theme. To do so, two main databases were analyzed: Scopus and Web of Science. Systematic review techniques, as well as bibliometric indicators, were implemented.
Findings
This paper connected the network between the achievements of the researches and illustrated the main application of ANNs in soil improvement prediction, specifically on cemented-based soils and biocemented soils (e.g. MICP technique). Also, as a bibliometric and systematic review, this work could achieve the key points in the absence of researches involving soil-ANN, and it provided the understanding of the lack of exploratory studies to be approached in the near future.
Research limitations/implications
Because of the research topic the article suggested other applications of ANNs in geotechnical engineering, such as other tests not related to geomechanical resistance such as unconfined compression test test and triaxial test.
Practical implications
This article systematically and critically presents some interesting points in the direction of future research, such as the non-approach to the use of ANNs in biocementation processes, such as MICP.
Social implications
Regarding the social environment, the paper brings approaches on methods that somehow mitigate the computational use, or elements necessary for geotechnical improvement of the soil, thereby optimizing the same consequently.
Originality/value
Neural networks have been studied for a long time in engineering, but the current computational power has increased the implementation for several engineering applications. Besides that, soil cementation is a widespread technique and its prediction modes often require high computational strength, such parameters can be mitigated with the use of ANNs, because artificial intelligence seeks learning from the implementation of the data set, reducing computational cost and increasing accuracy.