Towards Next Generation Teaching, Learning, and Context-Aware Applications for Higher Education: A Review on Blockchain, IoT, Fog and Edge Computing Enabled Smart Campuses and Universities

[Abstract] Smart campuses and smart universities make use of IT infrastructure that is similar to the one required by smart cities, which take advantage of Internet of Things (IoT) and cloud computing solutions to monitor and actuate on the multiple systems of a university. As a consequence, smart campuses and universities need to provide connectivity to IoT nodes and gateways, and deploy architectures that allow for offering not only a good communications range through the latest wireless and wired technologies, but also reduced energy consumption to maximize IoT node battery life. In addition, such architectures have to consider the use of technologies like blockchain, which are able to deliver accountability, transparency, cyber-security and redundancy to the processes and data managed by a university. This article reviews the state of the start on the application of the latest key technologies for the development of smart campuses and universities. After defining the essential characteristics of a smart campus/university, the latest communications architectures and technologies are detailed and the most relevant smart campus deployments are analyzed. Moreover, the use of blockchain in higher education applications is studied. Therefore, this article provides useful guidelines to the university planners, IoT vendors and developers that will be responsible for creating the next generation of smart campuses and universities.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work

Exploring the risk and protective factors associated with obesity amongst Libyan adults (20 -65 years)

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyBackground Obesity is a highly complex, chronic disorder with a multifactorial aetiology that includes biological, psychosocial and cultural factors. Since the discovery of oil in 1959, Libya has been undergoing a nutrition transition. Despite obesity reaching epidemic proportions in Libya, there is a lack of information about obesity in Libyan adults. Aims The aims of this study were to investigate the risks and protective factors associated with obesity among adult men and women in Libya; to estimate gender differences in the prevalence of obesity among Libyan adults; and to explore key informants’ views about obesity within the context of Libyan culture. Design of study An adapted mixed-methods sequential explanatory design was used, consisting of two phases: a quantitative study in the form of a cross-sectional design, and a qualitative study in the form of semi-structured interviews, which followed up on the findings of the first phase. Method A multi-stage cluster sampling technique was used to select participants from the Benghazi electoral register. With a response rate of 78%, the sample consisted of 401 Libyan adults, aged 20-65 years, who have lived in Benghazi for over ten years; 63% were female. A survey questionnaire was used to examine the relationship between Body Mass Index (BMI) and the following four-predictor variables, derived from the Socio-Ecological Model (SEM): socioeconomic status; unhealthy eating habits; physical activities and sedentary lifestyle; and neighbourhood environment. Anthropometric measurements were collected from participants in their homes. For the qualitative phase, 9 Libyan healthcare professionals and 12 Libyan community leaders (key informants) were individually interviewed. A mixed-methods approach to study obesity has not previously been used in Libya. Results The prevalence of obesity among Libyan adults was found to be 42.4%, whereas that of being overweight was 32.9%. A significant positive association was found between obesity and two SES components (education level and income) in Libyan adults of both genders, while occupational status was significantly positively associated with obesity in women only. Obesity was significantly positively associated with fast-food consumption, and the consumption of large food portion sizes, in Libyan adults of both genders. In contrast, the consumption of sugar-sweetened beverages was significantly positively associated with obesity in Libyan women but not in men. A significant inverse association was found between breakfast consumption and obesity in Libyan adults. Obesity was significantly negatively associated with physical activity in Libyan women, while significantly positively associated with sedentary behaviour in Libyan women but not in Libyan men. Finally, a significant association exists between the BMI of Libyan adults in 6 of the 12 neighbourhood environment attributes. For Libyan men and women these were: street connectivity, ‘unsafe environment and committing crimes at night’, and neighbourhood aesthetics. For men only, these were: access to public transport, access to recreational facilities, and ‘unsafe environment and committing crimes during the day’. Finally, ‘residential density zones’ was significant for women but not for men. Three main risk factors were identified from the qualitative study. The first concerned the heavy subsidisation of staple food commodities in Libya; the second is Libya’s deteriorating health sector performance; and the third is the effect of the neighbourhood environment on physical activity and food, including the current political and economic instability in Benghazi which is potentially fuelling the obesity epidemic. These themes and additional sub-themes were categorised as belonging to one of the five spheres of the SEM (individual; interpersonal; institutional and organisational; community and physical environment; and public policy), resulting in the final conceptual framework of this study. Some of the qualitative results contradicted the quantitative results, resulting in some inconclusive findings. Conclusion These findings could inform Libyan health policies and the interventions that are urgently needed for preventing or controlling the obesity epidemic in Libya. Key recommendations are that an electronic health information system needs to be implemented and awareness about obesity and its causes and consequences needs to be raised among the public in order to dispel the many myths and misconceptions held by Libyans about obesity