Objectives: This study was designed to assess the effect of COVID-19 home quarantine and its lifestyle challenges on the sleep quality and mental health of a large sample of undergraduate University students in Jordan. It is the first study applied to the Jordanian population. The aim was to investigate how quarantine for several weeks changed the students' habits and affected their mental health.Methods: A cross-sectional study was conducted using a random representative sample of 6,157 undergraduate students (mean age 19.79 ± 1.67 years, males 28.7%) from the University of Jordan through voluntarily filling an online questionnaire. The Pittsburgh Sleep Quality Index (PSQI) and the Center for Epidemiologic Studies-Depression Scale (CES-D) were used to assess sleep quality and depressive symptoms, respectively.Results: The PSQI mean score for the study participants was 8.1 ± 3.6. The sleep quality of three-quarters of the participants was negatively affected by the extended quarantine. Nearly half of the participants reported poor sleep quality. The prevalence of poor sleep quality among participants was 76% (males: 71.5% and females: 77.8%). Similarly, the prevalence of the depressive symptoms was 71% (34% for moderate and 37% for high depressive symptoms), with females showing higher prevalence than males. The overall mean CES-D score for the group with low depressive symptoms is 9.3, for the moderate group is 19.8, while it is 34.3 for the high depressive symptoms group. More than half of the students (62.5%) reported that the quarantine had a negative effect on their mental health. Finally, females, smokers, and students with decreased income levels during the extended quarantine were the common exposures that are significantly associated with a higher risk of developing sleep disturbances and depressive symptoms.Conclusions: Mass and extended quarantine succeeded in controlling the spread of the COVID-19 virus; however, it comes with a high cost of potential psychological impacts. Most of the students reported that they suffer from sleeping disorders and had a degree of depressive symptoms. Officials should provide psychological support and clear guidance to help the general public to reduce these potential effects and overcome the quarantine period with minimum negative impacts.

To reduce the spread of COVID-19, Jordan enforced 10 weeks of home quarantine in the spring of 2020. A cross-sectional study was designed to assess this extended quarantine's effect on smartphone addiction levels among undergraduates. A random sample of 6,157 undergraduates completed an online questionnaire (mean age 19.79 ± 1.67 years; males 28.7%). The questionnaire contains different sections to collect socio-demographic, socio-economic, academic, quarantine-related information, and smartphone usage. The smartphone addiction scale-short version was used to assess the degree of addiction during the quarantine. The mean addiction score across the whole sample was 35.66 ± 12.08, while the prevalence of addiction among participants was 62.4% (63.5% in males and 61.9% in females). The majority of the participants (85%) reported that their smartphone usage during the quarantine increased or greatly increased (27.6 and 57.2%, respectively), with some 42% using their smartphones for more than 6 h a day. Nevertheless, three-quarters of the students wished to reduce their smartphone usage. Several demographic and quarantine factors have been assessed, and students' gender, the field of study, parental education, household income in addition to the location of quarantine (urban, rural) and the house specifications (apartment, independent house, with/without a garden) showed statistically significant associations with smartphone addiction during the quarantine. Female students, students studying scientific- and medical-related majors compared to those studying humanity majors, those with higher incomes, those who had been quarantined in an apartment without a garden, and those who lived in urban areas showed significantly higher addiction scores.

The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP URL' above for details on accessing the published version and note that access may require a subscription.

Original citation:Al Fayez, Reem Qadan and Joy, Mike (2015) Applying NoSQL databases for integrating web educational stores -an ontology-based approach. In: Maneth, Sebastian, (ed. Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.)Publisher's statement: "The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-20424-6_4 " A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. Abstract. Educational content available on the web is playing an important role in the teaching and learning process. Learners search for different types of learning objects such as videos, pictures, and blog articles and use them to understand concepts they are studying in books and articles. The current search platforms provided can be frustrating to use. Either they are not specified for educational purposes or they are provided as a service by a library or a repository for searching a limited dataset of educational content. This paper presents a novel system for automatic harvesting and connecting of medical educational objects based on biomedical ontologies. The challenge in this work is to transform disjoint heterogeneous web databases entries into one coherent linked dataset. First, harvesting APIs were developed for collecting content from various web sources such as YouTube, blogging platforms, and PubMed library. Then, the system maps its entries into one data model and annotates its content using biomedical ontologies to enable its linkage. The resulted dataset is organized in a proposed NoSQL RDF Triple Store which consists of 2720 entries of articles, videos, and blogs. We tested the system using different ontologies for enriching its content such as MeSH and SNOMED CT and compared the results obtained. Using SNOMED CT doubled the number of linkages built between the dataset entries. Experiments of querying the dataset is conducted and the results are promising compared with simple text-based search.

In recent years and with the advancement of semantic technologies, shared and published online data have become necessary to improve research and development in all fields. While many datasets are publicly available in social and economic domains, most lack standardization. Unlike the medical field, where terms and concepts are well defined using controlled vocabulary and ontologies, social datasets are not. Experts such as the National Consortium for the Study of Terrorism and Responses to Terrorism (START) collect data on global incidents and publish them in the Global Terrorism Database (GTD). Thus, the data are deficient in the technical modeling of its metadata. In this paper, we proposed GTD ontology (GTDOnto) to organize and model knowledge about global incidents, targets, perpetrators, weapons, and other related information. Based on the NeOn methodology, the goal is to build on the effort of START and present controlled vocabularies in a machine-readable format that is interoperable and can be reused to describe potential incidents in the future. The GTDOnto was implemented with the Web Ontology Language (OWL) using the Protégé editor and evaluated by answering competency questions, domain experts’ opinions, and running examples of GTDOnto for representing actual incidents. The GTDOnto can further be used to leverage the publishing of GTD as a knowledge graph that visualizes related incidents and build further applications to enrich its content.