1,720,954 research outputs found
Sanitization and Sterilization Robot Ultra Violet Based
Full text linkIn recent years, the demand for effective and efficient sanitization and sterilization methods has surged due to the increasing concerns over infectious diseases and the need for maintaining clean environments. Ultraviolet (UV) based sanitization and sterilization robots have emerged as a promising solution to address this demand. These robots utilize UV-C light, which has germicidal properties, to eliminate harmful microorganisms on various surfaces. This abstract presents an overview of UV-based sanitization and sterilization robots, discussing their working principles, advantages, and limitations. The robots employ manually controlled-navigation systems with camera surveillance to detect and sanitize target areas efficiently. They offer several advantages, including non-toxicity, broad spectrum effectiveness, and time efficiency. Overall, UV-based sanitization and sterilization robots have the potential to significantly contribute to maintaining clean and pathogen-free environments, benefiting diverse industries such as healthcare, hospitality, and transportation.UV-based sanitization and sterilization robots have gained significant attention as an innovative solution for combatting the spread of infectious diseases and ensuring hygienic environments. This paper provides an in-depth analysis of UV-based sanitization and sterilization robots, focusing on their operational principles, key features, and potential applications. These robots employ UV-C light, a short-wavelength ultraviolet light, to disrupt the DNA and RNA of microorganisms, rendering them inactive and incapable of replication. They offer several advantages, including rapid disinfection, cost-effectiveness, and the ability to access hard-to-reach spaces. However, challenges such as shadowed areas, limited effectiveness on porous surfaces, and the need for proper safety precautions must be addressed. Further research and development efforts are essential to optimize their performance, address limitations, and ensure widespread adoption of this technology
Comparison of Implementation in Blood Cancer Causes and Diseases
Full text linkBlood malignancies are extremely dangerous for human life. Early and accurate detection is essential for efficient treatment and improved patient outcomes. Traditional diagnostic methods can be subjective and time-consuming. Delays in diagnosis can lead to life-threatening complications, as some blood cancers progress rapidly. This work explores the transformative potential of Machine Learning (ML) and Deep Learning (DL) in blood cancer detection. Support Vector Machine (SVM) and other machine learning methods and K Nearest Neighbour (KNN) analyze blood cell images and identify cancerous cell features, achieving high accuracy in leukemia detection. This allows for faster and more objective diagnoses, potentially leading to earlier interventions and improved patient outcomes. Deep Learning approaches, particularly Convolutional Neural Networks (CNNs), hold even greater promise. The requirement for manual feature extraction is eliminated by CNNs' ability to automatically learn features from images. The integration of ML and DL significantly improves blood cancer detection accuracy and efficiency. This paves the way for earlier diagnoses, improved patient care, and ultimately, saving lives. This work concludes by pointing forth possible directions for more study, such as improving these methods even more
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Full text linkWe 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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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