Enhancing the Knowledge of Cervical Cancer Screening among Female Nursing Students: An Interventional Educational Program

Background: Cervical cancer is a growing health risk facing women worldwide with the human papillomavirus (HPV) as the primary underlying cause. Pap smear is a simple screening test that can detect early changes in cervical cells, which might develop into cancer cells. Raising awareness of cervical cancer prevention has a significant impact on decreasing the burden of the disease. The aim of the study is to assess female nursing students' knowledge on early detection and screening of cervical cancer, and to determine the effectiveness of an educational program. Methods: A quasi-experimental research design (one group for pre- and post-tests) was utilized with a convenience sample of 130 female nursing students in one of the nursing colleges in Saudi Arabia. The study’s educational intervention included information about anatomy of genital tract and the importance of regular check-ups. The pre- and post-tests were applied to identify changes after intervention measures. Results: The mean age of the participants were 21.32 years (SD: 1.34). The findings revealed a significant improvement of post-test students’ knowledge in all items related to risk factors, signs and symptoms, occurrence, identification of HPV as causative agent, vaccination against HPV, and finally Pap smear for early detection and screening of cervical cancer. Conclusion: The study results support implementing educational intervention to improve nursing students' knowledge and awareness about cervical cancer prevention. Furthermore, it is imperative that cervical cancer awareness education modules should be developed and integrated within the nursing curriculum. Further studies with large sample size are recommended to increase generalization of the results.  Key words: cervical cancer, education program, primary prevention, nursing students, Saudi Arabi

Measuring Social Media Activity of Scientific Literature: An Exhaustive Comparison of Scopus and Novel Altmetrics Big Data

This paper measures social media activity of 15 broad scientific disciplines indexed in Scopus database using Altmetric.com data. First, the presence of Altmetric.com data in Scopus database is investigated, overall and across disciplines. Second, the correlation between the bibliometric and altmetric indices is examined using Spearman correlation. Third, a zero-truncated negative binomial model is used to determine the association of various factors with increasing or decreasing citations. Lastly, the effectiveness of altmetric indices to identify publications with high citation impact is comprehensively evaluated by deploying Area Under the Curve (AUC) - an application of receiver operating characteristic. Results indicate a rapid increase in the presence of Altmetric.com data in Scopus database from 10.19% in 2011 to 20.46% in 2015. A zero-truncated negative binomial model is implemented to measure the extent to which different bibliometric and altmetric factors contribute to citation counts. Blog count appears to be the most important factor increasing the number of citations by 38.6% in the field of Health Professions and Nursing, followed by Twitter count increasing the number of citations by 8% in the field of Physics and Astronomy. Interestingly, both Blog count and Twitter count always show positive increase in the number of citations across all fields. While there was a positive weak correlation between bibliometric and altmetric indices, the results show that altmetric indices can be a good indicator to discriminate highly cited publications, with an encouragingly AUC= 0.725 between highly cited publications and total altmetric count. Overall, findings suggest that altmetrics could better distinguish highly cited publications.Comment: 34 Pages, 3 Figures, 15 Table

Evaluation of convergence, accommodation and fusional vergence in pre-presbyopes with asthenopia

Background: Pre-presbyopes may suffer from ocular symptoms such as asthenopia of near work. Aim: This study aimed to evaluate near points of convergence, amplitudes of accommodation, and fusional vergence among pre-presbyopes with asthenopia symptoms. Setting: The study was conducted at El-Walidain Eye Hospital, Khartoum, Sudan in 2022. Methods: The study was a hospital-based prospective, including 107 pre-presbyopes aged 35–40 years who complained of asthenopia symptoms. Clinical examinations included an assessment of amplitude of accommodation, near point of convergence and fusional vergence. Results: The findings showed receded in near points of convergence and a decrease in the accommodation was highly significantly associated with increased age among emerging presbyopes with asthenopia symptoms (p = 0.0001). Conversely, positive and negative fusional vergence amplitudes were not significantly correlated with age with p = 0.109 and p = 0.355, respectively. Positive and negative fusional amplitudes were not significantly different between males and females (p ˃ 0.05). Esophoria was more common in pre-presbyopia 62 (57.4%) and exophoria 45 (43.6) with p = 0.503. Conclusion: The pre-presbyopes presented with low accommodation amplitude and receded near point of convergence, but without significant changes in positive and negative fusional vergence amplitudes. Contribution: This study added by demonstrating how early presbyopia altered accommodation amplitude and near point of convergence significantly while having no significant impact on amplitudes of positive and negative fusional vergence

Impact of Room Size on WDM Optical Wireless Links with Multiple Access Points and Angle Diversity Receivers

Optical wireless communication (OWC) systems have been the subject of attention as a promising wireless communication technology that can offer high data rates and support multiple users simultaneously. In this paper, the impact of room size is investigated when using wavelength division multiple access (WDMA) in conjunction with an angle diversity receiver (ADR). Four wavelengths (red, yellow, green and blue) can be provided in this work based on the RYGB LDs transmitter used. Three room sizes are considered with two 8-user scenarios. A mixed-integer linear programming (MILP) model is proposed for the purpose of optimising the resource allocation. The optical channel bandwidth, SINR and data rate have been calculated for each user in both scenarios in all rooms. Room A, which is the largest room, can provide a higher channel bandwidth and SINR compared to the other rooms. However, all rooms can provide a data rate above 5 Gbps in both scenarios

What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter

Demonstrating and negotiating the adoption of web design technologies : Cascading Style Sheets and the CSS Zen Garden

Cascading Style Sheets (CSS) express the visual design of a website through code and remain an understudied area of web history. Although CSS was proposed as a method of adding a design layer to HTML documents early on in the development of the web, they only crossed from a marginal position to mainstream usage after a long period of proselytising by web designers working towards “web standards”. The CSS Zen Garden grassroots initiative aimed at negotiating, mainstreaming and archiving possible methods of CSS web design, while dealing with varying levels of browser support for the technology. Using the source code of the CSS Zen Garden and the accompanying book, this paper demonstrates that while the visual designs were complex and sophisticated, the CSS lived within an ecosystem of related platforms, i.e., web browsers, screen sizes and design software, which constrained its use and required enormous sensitivity to the possibilities browser ecosystems could reliably provide. As the CSS Zen Garden was maintained for over ten years, it also acts as a unique site to trace the continuing development of web design, and the imaginaries expressed in the Zen Garden can also be related to ethical dimensions that influence the process of web design. Compared to Flash-based web design, work implemented using CSS required a greater willingness to negotiate source code configurations between browser platforms. Following the history of the individuals responsible for creating and contributing to the CSS Zen Garden shows the continuing influence of layer-based metaphors of design separated from content within web source code

Predicting Academic Performance of Students from VLE Big Data using Deep Learning Models

The abundance of accessible educational data, supported by the technology-enhanced learning platforms, provides opportunities to mine learning behavior of students, addressing their issues, optimizing the educational environment, and enabling data-driven decision making. Virtual learning environments complement the learning analytics paradigm by effectively providing datasets for analysing and reporting the learning process of students and its reflection and contribution in their respective performances. This study deploys a deep artificial neural network on a set of unique handcrafted features, extracted from the virtual learning environments clickstream data, to predict at-risk students providing measures for early intervention of such cases. The results show the proposed model to achieve a classification accuracy of 84%-93%. We show that a deep artificial neural network outperforms the baseline logistic regression and support vector machine models. While logistic regression achieves an accuracy of 79.82% - 85.60%, the support vector machine achieves 79.95% - 89.14%. Aligned with the existing studies - our findings demonstrate the inclusion of legacy data and assessment-related data to impact the model significantly. Students interested in accessing the content of the previous lectures are observed to demonstrate better performance. The study intends to assist institutes in formulating a necessary framework for pedagogical support, facilitating higher education decision-making process towards sustainable education

HTSS: A novel hybrid text summarisation and simplification architecture

Text simplification and text summarisation are related, but different sub-tasks in Natural Language Generation. Whereas summarisation attempts to reduce the length of a document, whilst keeping the original meaning, simplification attempts to reduce the complexity of a document. In this work, we combine both tasks of summarisation and simplification using a novel hybrid architecture of abstractive and extractive summarisation called HTSS. We extend the well-known pointer generator model for the combined task of summarisation and simplification. We have collected our parallel corpus from the simplified summaries written by domain experts published on the science news website EurekaAlert (www.eurekalert.org). Our results show that our proposed HTSS model outperforms neural text simplification (NTS) on SARI score and abstractive text summarisation (ATS) on the ROUGE score. We further introduce a new metric (CSS1) which combines SARI and Rouge and demonstrates that our proposed HTSS model outperforms NTS and ATS on the joint task of simplification and summarisation by 38.94% and 53.40%, respectively. We provide all code, models and corpora to the scientific community for future research at the following URL: https://github.com/slab-itu/HTSS/

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

In-text citation analysis is one of the most frequently used methods in research evaluation. We are seeing significant growth in citation analysis through bibliometric metadata, primarily due to the availability of citation databases such as the Web of Science, Scopus, Google Scholar, Microsoft Academic, and Dimensions. Due to better access to full-text publication corpora in recent years, information scientists have gone far beyond traditional bibliometrics by tapping into advancements in full-text data processing techniques to measure the impact of scientific publications in contextual terms. This has led to technical developments in citation classifications, citation sentiment analysis, citation summarisation, and citation-based recommendation. This article aims to narratively review the studies on these developments. Its primary focus is on publications that have used natural language processing and machine learning techniques to analyse citations

Webometrics: evolution of social media presence of universities

This is an accepted manuscript of an article published by Springer in Scientometrics on 03/01/2021, available online: https://doi.org/10.1007/s11192-020-03804-y The accepted version of the publication may differ from the final published version.This paper aims at an important task of computing the webometrics university ranking and investigating if there exists a correlation between webometrics university ranking and the rankings provided by the world prominent university rankers such as QS world university ranking, for the time period of 2005–2016. However, the webometrics portal provides the required data for the recent years only, starting from 2012, which is insufficient for such an investigation. The rest of the required data can be obtained from the internet archive. However, the existing data extraction tools are incapable of extracting the required data from internet archive, due to unusual link structure that consists of web archive link, year, date, and target links. We developed an internet archive scrapper and extract the required data, for the time period of 2012–2016. After extracting the data, the webometrics indicators were quantified, and the universities were ranked accordingly. We used correlation coefficient to identify the relationship between webometrics university ranking computed by us and the original webometrics university ranking, using the spearman and pearson correlation measures. Our findings indicate a strong correlation between ours and the webometrics university rankings, which proves that the applied methodology can be used to compute the webometrics university ranking of those years for which the ranking is not available, i.e., from 2005 to 2011. We compute the webometrics ranking of the top 30 universities of North America, Europe and Asia for the time period of 2005–2016. Our findings indicate a positive correlation for North American and European universities, but weak correlation for Asian universities. This can be explained by the fact that Asian universities did not pay much attention to their websites as compared to the North American and European universities. The overall results reveal the fact that North American and European universities are higher in rank as compared to Asian universities. To the best of our knowledge, such an investigation has been executed for the very first time by us and no recorded work resembling this has been done before.Published onlin