1,720,965 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Flood risk assessment and regional detailed spatial planning in Lagos State: A remote sensing perspective
Get PDFBackground: Lagos State, Nigeria, is increasingly confronting flooding as an aftermath of hasty urbanization, climate change, and inefficiency in land-use planning. This paper formally reviewed remote sensing technology application in flood hazard analysis and incorporating it into the Regional Detailed Spatial Planning (RDTR) framework. Methods: A systematic search of 40 peer-reviewed articles (2000–2023) was conducted following PRISMA guidelines to identify and analyze trends and patterns. Remote sensing technologies, including optical images and synthetic aperture radar (SAR), were used to monitor flood dynamics, evaluate vulnerability, and identify flood zones in near real-time. Indicators such as rainfall intensity, elevation, land use, and population density were also assessed. Findings: Although remote sensing provides actionable data for zoning and infrastructure planning in flood-prone areas, its application to RDTR planning is limited by insufficient high-resolution data, technical limitations, and stakeholders' coordination problems. The study also highlights the critical role of geospatial innovations in improving flood resilience and urban planning. Conclusion: Improved data access, technical capacity building, and multi-stakeholder collaboration are essential to address current limitations. Novelty/Originality of this article: This research bridges the gap between flood hazard mapping technologies and detailed spatial planning frameworks. It provides a framework that can guide policymakers and urban planners in Lagos and similar contexts toward sustainable flood risk management and urban development
Application of spectral indices and deep learning (convolutional neural network model) on land cover change analysis
Get PDFBackground: Understanding land cover change is crucial for sustainable urban development, particularly in rapidly growing coastal cities such as Semarang City, Central Java, Indonesia. Methods: This study investigates spatial and temporal patterns of land cover change from 2000 to 2025 by integrating multi-temporal Landsat satellite imagery, key spectral indices—namely the normalized difference vegetation index, normalized difference water index, and normalized difference built-up index—and a deep learning approach based on convolutional neural networks. Annual Landsat images were preprocessed for atmospheric correction, cloud masking, and spatial subsetting using Google Earth Engine. Adaptive thresholding was then applied to each spectral index to delineate vegetation, water bodies, and built-up areas. Findings: Quantitative analysis revealed a significant decline in vegetation cover, with the normalized difference vegetation index dropping from 53.66% (397.59 km²) in 2000 to 46.83% (346.98 km²) in 2025, driven by urban expansion and landscape conversion, especially in coastal and lowland areas. Normalized difference water index analysis indicated a reduction and fragmentation of water bodies after 2015, linked to reclamation, sedimentation, and urban encroachment. Conversely, built-up areas expanded steadily, confirming accelerated urbanization. Scatter plot and regression analyses showed strong inverse relationships among vegetation, water, and built-up land, emphasizing ecological trade-offs and the loss of green-blue infrastructure. Conclusion: To enhance classification accuracy, a convolutional neural network was trained and validated on image patches, achieving a validation accuracy of 60%—outperforming conventional threshold-based methods by better capturing complex spatial patterns. The integrated remote sensing and deep learning framework offers robust potential for long-term, large-area land cover monitoring. Novelty/Originality of this article: The novelty of this research lies in its combined use of spectral indices and deep learning for multi-decadal land cover change analysis, providing a transferable methodology for other rapidly urbanizing coastal cities
Land cover change analysis based on spectral indices and deep learning convolutional neural network model
No full textBackground: Coastal zones are undergoing rapid land cover changes due to urban development, land reclamation, and environmental degradation, threatening ecological stability and sustainable coastal management. This study investigates land cover changes in the coastal districts of Tangerang Regency, Indonesia, from 2020 to 2024 using an integrated remote sensing and deep learning approach. Methods: Three spectral indices—Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI)—were applied to Sentinel-2A imagery to monitor vegetation, water bodies, and built-up areas. Additionally, a Convolutional Neural Network (CNN) model was trained to enhance classification accuracy. Findings: The results showed a sharp decline in vegetated areas from 195.91 km² in 2020 to 118.32 km² in 2023, followed by a partial recovery to 157.81 km² in 2024. Water bodies consistently decreased from 122.35 km² in 2020 to 110.62 km² in 2024, suggesting intensified coastal modifications. Built-up areas displayed fluctuating patterns, with a peak of 8.88 km² in 2021 and a significant drop to 1.01 km² in 2024. The CNN model achieved a 60% validation accuracy, indicating its capacity to detect complex land cover features, despite challenges related to data imbalance and class similarity. Conclusion: This study demonstrates that combining spectral indices and deep learning provides a robust framework for detecting and analyzing coastal land cover change. The findings highlight the need for integrated methods in environmental monitoring and support sustainable planning efforts in dynamic coastal regions. Novelty/Originality of this article: This study uniquely integrates spectral indices with deep learning to accurately detect and analyze dynamic coastal land cover changes, providing a robust tool for sustainable coastal management
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