Trends of Obesity and Overweight among College Students in Oman : A cross sectional study

Objectives: Body mass index (BMI), total body fat (TBF), and physical activity in relation to obesity and overweight prevalence among Omani students were studied. Methods: A cross-sectional study of 202 Omani students (101 males and 101 females) from Sultan Qaboos University (SQU) was carried out. Data were collected by interview using a structured questionnaire. Weight, TBF and physical activity score (PAS) were measured using TANITA scales, and height measured using a standardised measuring tape. Results: Subjects were classified based on BMI as: underweight (2.48%), normal weight (69.31%), overweight (26.73%) and obese (1.49%). According to TBF, 32.67% of students had low body fat scores (BFS), 26.73% high BFS and 22.28% very high BFS. Low BFS was insignificantly less likely with the increase in the mean hours of weekly exercising, (odds ratio [OR] = 0.708; 95% confidence interval [CI] = 0.448, 1.119) and the PAS (OR = 0.728; 95% CI= 0.562, 0.944). Among high and very high BFS students, the mean hours of weekly exercising (6.73±1.20) and physical activity scores (7.51±1.67) were higher than those of healthy students. Nutrition knowledge was higher among healthy students compared to low BFS, and high and very high BFS subjects. Higher nutrition knowledge was associated with a non-significant lower risk of low BFS (OR = 0.986; 95% CI = 0.958, 1.015), high and very high BFS (OR = 0.984; 95% CI = 0.961, 1.008). Conclusion: High and very high BFS were prevalent among subjects with sedentary lifestyles. Nutritional and physical activity interventions should be introduced to combat the problem of overweight students

Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs

Different studies have recently emphasized the importance of deploying clustering schemes in Vehicular ad hoc Network (VANET) to overcome challenging problems related to scalability, frequent topology changes, scarcity of spectrum resources, maintaining clusters stability, and rational spectrum management. However, most of these studies addressed the clustering problem using conventional performance metrics while spectrum shortage, and the combination of spectrum trading and VANET architecture have not been tackled so far. Thus, this paper presents a new fuzzy logic based clustering control scheme to support scalability, enhance the stability of the network topology, motivate spectrum owners to share spectrum and provide efficient and cost-effective use of spectrum. Unlike existing studies, our context-aware scheme is based on multi-criteria decision making where fuzzy logic is adopted to rank the multi-attribute candidate nodes for optimizing the selection of cluster heads (CH)s. Criteria related to each candidate node include: received signal strength, speed of vehicle, vehicle location, spectrum price, reachability, and stability of node. Our model performs efficiently, exhibits faster recovery in response to topology changes and enhances the network efficiency life time

Optimal spectrum utilisation in cognitive network using combined spectrum sharing approach: overlay, underlay and trading

Cognitive radio technology enables unlicensed users (secondary users, SUs) to access the unused spectrum. In the literature, there are three spectrum sharing paradigms that enable SUs to access the licensed spectrum. These access techniques include underlay, overlay and spectrum trading, and have their own drawbacks. To combat these drawbacks, we propose a new approach for each of them and merge them into one combined system. Our overlay scheme provides quick access to the unused spectrum. We propose a new cooperative sensing protocol to reduce the likelihood of interfering with PUs. In order to enable SUs for transmitting simultaneously with PUs, we suggest using our underlay scheme. Our trading scheme allows PUs to trade the unused spectrum for the SUs that require better quality of service. The new combined scheme increases the size of spectrum in the cognitive network. Simulation results show the ability of the new scheme to serve extra traffic

IMECE2002-33401 FABRICATION OF NdFeB THIN FILM AND ITS APPLICATION IN MEMS

ABSTRACT The paper measures the magnetic properties of NdFeB thin films developed under the effects of magnetic field. The samples exhibited a larger residual inductance, saturation magnetization and energy product than those treated without field or with weaker field. Magnetic MEMS was introduced with application of the NdFeB film to micro device such as pumps and gear transmission system

The adoption of big data analytics in Jordanian SMEs: An extended technology organization environment framework with diffusion of innovation and perceived usefulness

While many small and medium enterprises (SMEs)recognize the benefits of Big Data Analytics (BDA) for digital transformation, they face challenges in implementing this technology, highlighting the need for more research on its adoption by SMEs. The objective of this study is to amalgamate the Technology Organization Environment (TOE) framework with the Diffusion of Innovation (DOI) theory, aiming to dissect the factors that sway BDA adoption in Jordanian SMEs. Additionally, the study delves into how perceived usefulness impacts this adoption process. Utilizing structural equation modeling, the study examined data from 388 managers in Jordan. The study validates all its hypotheses, revealing that variables like relative advantage, compatibility, complexity, top management support, competitive pressure, and security influence perceived usefulness, which subsequently has a positive impact on BDA adoption. This research presents a range of theoretical and practical insights

A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network

The continuous advancements in wireless network systems have reshaped the healthcare systems towards using emerging communication technologies at different levels. This paper makes two major contributions. Firstly, a new monitoring and tracking wireless system is developed to handle the COVID-19 spread problem. Unmanned aerial vehicles (UAVs), i.e., drones, are used as base stations as well as data collection points from Internet of Things (IoT) devices on the ground. These UAVs are also able to exchange data with other UAVs and cloud servers. Secondly, this paper introduces a new reinforcement learning (RL) framework for learning the optimal signal-aware UAV trajectories under quality of service constraints. The proposed RL algorithm is instrumental in making the UAV movement decisions that maximize the signal power at the receiver and the data collected from the ground agents. Simulation experiments confirm that the system overcomes conventional wireless monitoring systems and demonstrates efficiency especially in terms of flexible continues connectivity, line-of sight visibility, and collision avoidance

Improving Recommender Systems by a Further Factorization of the Factor Matrices

Due to the availability of massive numbers of items for any product on the Web, the burden of selecting an item is borne by the user. A Recommender System (RS) is a useful tool that has been employed to save the user’s time by recommending preferred items for him/her efficiently. Collaborative-based RS’s use the latent factor or/and the neighbourhood techniques including matrix factorization (MF), which is an efficient approach utilizing latent factors. The idea of the approach is based on calculating similarities through between users and items simultaneously, to predict appropriate recommendations. MF can be seen as mathematical model capable to split an entity into multiple smaller entries through an ordered rectangular array of functions, to discover the features or information underlying the interactions between users and items. The technique factorizes the user-item rating matrix ( $R$ ) (first-level factorization) into users ( $P$ ) and items ( $Q$ ) matrices. A recent RS system, named NLM, combines the neighbourhood and the latent factor techniques to produce attractive results. This paper proposes to improve the NLM method by proposing a more effective technique, named NLM+, by further factorizing $P$ and $Q$ using MF (second-level factorization). To the best of our knowledge, current studies in this field have not considered the use of the aforementioned two levels of factorizing. To validate our method, CiaoDVD, MovieLens, and FilmTrust datasets have been used and incorporated to demonstrate that NLM+ improves NLM by 48% and 42% in the recall and the precision values, respectively