Search results

This study firstly aims to prepare a hierarchical structure of the barriers contributing to digital exclusion of women. Secondly, the study aims to understand the inter-relationship of the barriers by converting them into a cyclic flow.

Systematic review was conducted through Web of Science and EBSCO Host Discovery Services with keywords: “digital divide” OR “digital gap” OR “digital inequality” OR “online difference” AND gender AND wom?n AND factors OR “demographic factors” OR “socio-economic” OR determinants OR reasons OR causes OR influences. Review was conducted of articles published in English language after 2012. Systematic literature review was conducted in line with Preferred Reporting Items for Systematic Reviews and Meta-Analyses Framework 2020. Authors reviewed 21 reports.

This research found absence of education as the root of digital exclusion of women in the hierarchy of barriers. The cyclic flow of barriers displayed that how ill education of women lead to financial and psychological dependence of women on men, creating a hurdle in women empowerment. To break the cycle of digital exclusion, women education is must not only on primary basis but their tertiary education is important too.

This study makes an important contribution by ranking those barriers in a pyramid hierarchy and depicting their inter relation by framing a cyclic structure. This study provides suggestions to remove those barriers and ultimately break their chain to provide digital inclusive society for women.

The purpose of this study is to systematically examine the existing literature published on the circulation methods used in academic libraries and to discuss a proposed model for the self-check-in and check-out methods using quick response (QR) codes.

A systematic literature review (SLR) provided a complete overview of circulation systems used in academic libraries for the last more than 140 years. Preferred reporting items for systematic reviews and meta-analyses (PRISMA) method for SLR was used. Several databases such as ProQuest, Emerald, Library and Information Science Abstracts, EBSCO and Google Scholar were searched. Out of 277 papers retrieved in the search, 43 most relevant papers were taken up for the SLR. These were classified into four themes based on the systems of circulation used, namely, manual (5), mechanized (13), automated (5) and networked system (20). Based on the findings of the SLR, a model of circulation system in which QR code technology has been used.

The result of SLR identified 33 systems used in above mentioned four groups. Among this, a large number 48.48% of mechanized systems were found. Each system has limitations either because of devices used or of technology. The present study proposes a QR code-based model at the circulation desk, for which a prototype of Android app has been designed. This would help to initiate the new “self-services” facility to users at the circulation desk.

The proposed model, after its successful implementation, can be adopted by academic libraries. Guidelines and a graphical representation of this study can be used by any researcher for further experimentation.

To the best of the authors’ knowledge, this is the first kind of study in which a QR code-based Android app model has been proposed for library circulation records.

Despite the growing attention on the relevance of improved building management systems with cognition in recent years in the architecture, engineering, construction and operation (AECO) community, no review has been conducted to understand the human-environment interaction features of cyber-physical systems (CPS) and digital twins (DTs) in developing the concept of a cognitive building (CB). Thus, this paper aims to review existing studies on CPS and DTs for CB to propose a comprehensive system architecture that considers human-environment interactions.

Scientometric analysis and content analysis were adopted for this study.

The scientometric analysis of 1,042 journal papers showed the major themes of CPS/DTs for CB, and these can be categorized into three key technologies to realize CB in the AECO community: CPS, DTs and cognitive computing (CC). Content analysis of 44 relevant publications in the built environment assisted in understanding and evidently confirming the claim of this study on the integration of CPS and DTs for CB in construction by also involving the CC. It is found and confirmed that CB can be realized with CPS and DTs along with the CC. A CB system architecture (CBSA) is proposed from the three key technologies considering the human-environment interactions in the loop. The study discovered the potential applications of the CBSA across the building lifecycle phases, including the design, construction and operations and maintenance, with the potential promise of endowing resilience, intelligence, greater efficiency and self-adaptiveness. Based on the findings of the review, four research directions are proposed: human-environment interactions, CB for sustainable building performance, CB concept for modular buildings and moving beyond CB.

This study stands out for comprehensively surveying the intellectual core and the landscape of the general body of knowledge on CPS/DTs for CB in the built environment. It makes a distinctive contribution to knowledge as it does not only propose CBSA by integrating CPS and DTs along with CC but also suggests some potential practical applications. These may require expert judgments and real case examples to enhance reproducibility and validation.

Visual aesthetics are a vital aspect of environmental quality. The objective of this study is to demonstrate the implementation of visibility analysis and visual quality standards on a campus to enhance productivity and effectiveness.

This study has identified the most crucial and valuable metrics for evaluating the visual quality of open spaces through an analysis of theoretical foundations and relevant background information. To achieve research goal, a multi-method approach was employed, incorporating a survey, user satisfaction ratings and ISOVIST simulation techniques. Specifically, this study focused on assessing the quality of open spaces in three open areas located on the campus of the Iran University of Science and Technology.

Based on the study’s findings, the most significant factors that students considered when evaluating the visual quality of open spaces on the Iran University of Science and Technology campus were green areas, gathering spaces and architectural elements such as furniture, color and texture. Among the three open areas examined, “Open Space One” was identified as the most satisfactory location for students. According to the study, “sensory richness,” “complexity” and “mystery” were significant indicators of students' satisfaction in this area. This area also had the widest radius and field of view feasible, which gave it a feeling of openness and spaciousness.

This study explores the influence of students' experiences, behavioral patterns and visual analyses on their use of open spaces on university campuses, with a focus on the Iran University of Science and Technology. By assessing students' satisfaction levels with these spaces, this research provides valuable insights that can guide the initial analysis stage before the design process and facilitate design optimization during the development stages. The results highlight the importance of considering user experiences and visual analysis when planning and creating open spaces on university campuses.

Highlights

1. Conducting an initial analysis before developing a design plan can be very helpful in understanding how users think and behave.

2. The three criteria of visual quality that have the strongest correlation with students' satisfaction with “open space” are “mystery,” “sensory richness” and “complexity.”

3. Two factors, namely the “radius of vision” and the “area” index, significantly influence students' satisfaction with open spaces.

4. Outdoor designers should incorporate “green space” and “gathering spaces” into their designs since the presence of these is effective in attracting and satisfying students.

5. The number of people using an open space has little to do with how satisfied students are with it.

6. Half of the students use open areas between 11:00 and 14:00, so the provision of “canopy” and “shelter” in these spaces is essential.

Conducting an initial analysis before developing a design plan can be very helpful in understanding how users think and behave.

The three criteria of visual quality that have the strongest correlation with students' satisfaction with “open space” are “mystery,” “sensory richness” and “complexity.”

Two factors, namely the “radius of vision” and the “area” index, significantly influence students' satisfaction with open spaces.

Outdoor designers should incorporate “green space” and “gathering spaces” into their designs since the presence of these is effective in attracting and satisfying students.

The number of people using an open space has little to do with how satisfied students are with it.

Half of the students use open areas between 11:00 and 14:00, so the provision of “canopy” and “shelter” in these spaces is essential.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

Deep learning (DL) is a new and relatively unexplored field that finds immense applications in many industries, especially ones that must make detailed observations, inferences and predictions based on extensive and scattered datasets. The purpose of this paper is to answer the following questions: (1) To what extent has DL penetrated the research being done in finance? (2) What areas of financial research have applications of DL, and what quality of work has been done in the niches? (3) What areas still need to be explored and have scope for future research?

This paper employs bibliometric analysis, a potent yet simple methodology with numerous applications in literature reviews. This paper focuses on citation analysis, author impacts, relevant and vital journals, co-citation analysis, bibliometric coupling and co-occurrence analysis. The authors collected 693 articles published in 2000–2022 from journals indexed in the Scopus database. Multiple software (VOSviewer, RStudio (biblioshiny) and Excel) were employed to analyze the data.

The findings reveal significant and renowned authors' impact in the field. The analysis indicated that the application of DL in finance has been on an upward track since 2017. The authors find four broad research areas (neural networks and stock market simulations; portfolio optimization and risk management; time series analysis and forecasting; high-frequency trading) with different degrees of intertwining and emerging research topics with the application of DL in finance. This article contributes to the literature by providing a systematic overview of the DL developments, trajectories, objectives and potential future research topics in finance.

The findings of this paper act as a guide for literature review for anyone interested in doing research in the intersection of finance and DL. The article also explores multiple areas of research that have yet to be studied to a great extent and have abundant scope.

Very few studies have explored the applications of machine learning (ML), namely, DL in finance, which is a much more specialized subset of ML. The authors look at the problem from the aspect of different techniques in DL that have been used in finance. This is the first qualitative (content analysis) and quantitative (bibliometric analysis) assessment of current research on DL in finance.

The cardiomegaly can be determined by the cardiothoracic ratio (CTR) which can be measured in a chest x-ray image. It is calculated based on a relationship between a size of heart and a transverse dimension of chest. The cardiomegaly is identified when the ratio is larger than a cut-off threshold. This paper aims to propose a solution to calculate the ratio for classifying the cardiomegaly in chest x-ray images.

The proposed method begins with constructing lung and heart segmentation models based on U-Net architecture using the publicly available datasets with the groundtruth of heart and lung masks. The ratio is then calculated using the sizes of segmented lung and heart areas. In addition, Progressive Growing of GANs (PGAN) is adopted here for constructing the new dataset containing chest x-ray images of three classes including male normal, female normal and cardiomegaly classes. This dataset is then used for evaluating the proposed solution. Also, the proposed solution is used to evaluate the quality of chest x-ray images generated from PGAN.

In the experiments, the trained models are applied to segment regions of heart and lung in chest x-ray images on the self-collected dataset. The calculated CTR values are compared with the values that are manually measured by human experts. The average error is 3.08%. Then, the models are also applied to segment regions of heart and lung for the CTR calculation, on the dataset computed by PGAN. Then, the cardiomegaly is determined using various attempts of different cut-off threshold values. With the standard cut-off at 0.50, the proposed method achieves 94.61% accuracy, 88.31% sensitivity and 94.20% specificity.

The proposed solution is demonstrated to be robust across unseen datasets for the segmentation, CTR calculation and cardiomegaly classification, including the dataset generated from PGAN. The cut-off value can be adjusted to be lower than 0.50 for increasing the sensitivity. For example, the sensitivity of 97.04% can be achieved at the cut-off of 0.45. However, the specificity is decreased from 94.20% to 79.78%.

The locational disadvantage of a health-care centre often restricts adequate delivery of health-care services in an area. The purpose of this study is to examine the status of primary health-care services in such a geographically disadvantageous area which is confined by forests, tea gardens and undulating topography.

Necessary secondary data of 13 primary health centres and 236 sub-centres has been collected from the Office of the Chief Medical Officer of Health. Based on obtained data, Health-care Infrastructure Index has been prepared which has been validated by an expert panel, and subsequently, the Thiessen Polygon method has been applied through Arc GIS software to show spatial variation of health-care services delivered by different health-care centres.

In the study area, there is wide variation found in the case of physical facilities, caregivers and connectivity of road networks, which altogether affect the overall status of health-care services. Among all the indicators, some health-care centres experience staff shortages for prolonged non-recruitment, inaccessibility and inconsistent patient load in different health centres.

In spite of the unfavourable geographical landscape, health-care centres have to be set up wherever possible. There is a need to make new roads and simultaneously the existing road connectivity should be improved so that patients and caregivers can move quickly whenever required. Existing physical facilities need to be renewed or redeveloped along with increasing the number of doctors and other health-care providers as per the need of people with an adequate and optimum level of services.

The role of Technology Transfer Units, examined in this study, was found to be vital to expedite the process of disseminating new varieties and their production technology.

A total of 1,000 households were surveyed in the sampled states. A probit model was used to analyse.

Age, education, land holding, tractor use and number of working family members in agriculture were found to significantly affecting adoption of the new seed varieties. Technology transfer through licensing has impacted the adoption of new seed varieties positively by highlighting Punjab possessing the highest adoption and western Uttar Pradesh was majorly adopting the old variety.

The authors believed in farmers’ memory to recall the varietal information of wheat.

The study recommended various incentives to attract the seed industry in UP to minimize the economic loss potentially suffered by them.

Quality seeds are germane to increase the productivity of crops, and it is paramount to disburse the seed varieties to the end users in an efficient way to achieve the overall objective of productivity enhancement.

In this context, a study was conducted in three states of India, namely, Punjab, Haryana and Uttar Pradesh (UP) to find out the adoption rate of newly developed varieties of wheat, HD 3086 after three years (2014–2015) of its commercialization by IARI as well as HD 2967, which was released in 2011.