Although m-blackboard has an extensive role in the educational context, the adoption of m-learning in higher education institutes is still in its infancy. However, m-blackboard faces various types of challenges that could affect its acceptance and usage. The previous studies on m-blackboard generated general findings, and studied frequently common factors, did not adopt a focused view of organizational and technology infrastructural factors. These studies investigated the adoption of m-learning in general and did not focus on M-LMS in particular, which is different to m-learning because m-learning is more personal, where learning content is personalized for the individual learner. These promising findings call for more focus from the perspective of the authors of this study. This study used a mixed research methods (i.e., qualitative and quantitative methods). For the qualitative method, researchers interviewed eight staff from the University of Ha'il. As for the quantitative method, a survey questionnaire was employed. This paper contributes to enrich the literature by reviewing, comparing, and analyzing previous works that have examined m-blackboard systems. The respondents using m-blackboard report significant benefits; e.g., it provides easier access to information, increases productivity, makes wise use of time and money, and is accessible anywhere and anytime. On the other hand, some challenges remain; these include the weakness of mobile network signals, the small size of mobile device screens, the costs of connecting mobile devices to the internet, and the time needed to download the m-blackboard application. Researchers recommended that the University of Ha'il should move forward with the m-blackboard platform; and to examine the challenges and benefits in different settings, technologies, and countries.

the extended coastlines of oman have been forced to change in the last few decades because of urbanization development or by natural disasters. Recently, Oman has suffered from a couple of tornados and cyclones, e.g. Cyclone Gonu on June 1, 2007, making the changes even much more dynamic. in order to protect the coastal regions infrastructure, an accurate estimation of shoreline erosion is required. this research paper presents an assessment of shoreline erosion magnitudes using field measurements coupled with Multiple Linear Regressions Models (MLR) to predict future changes. inverse Distance Weighing and Kriging interpolation methods have been applied in order to visualize shoreline variations from gathered data prospective. The field measurements for the shoreline were taken at 19 different points, the space between the points in a range of 500-700 m approximately. The first field measurements were taken on 19 th 20 th 21 st of June, 2016 while the second field measurements were taken on 14 th 15 th 16 th of November 2016. Pearson correlation shows a strong relationship between the first and the second field trips with an average of 0.83. This significant relationship ensures the applicability of MLRs to project future changes on the shorelines. The results of the MLRs showed severe negative volumetric shoreline erosion with an average of 5.2 m/year with some exceptions at the catchment outlets.

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Artificial intelligence (AI) is simulating human intelligence processes by machines and software simulators to help humans in making accurate, informed, and fast decisions based on data analysis. The medical field can make use of such AI simulators because medical data records are enormous with many overlapping parameters. Using in-depth classification techniques and data analysis can be the first step in identifying and reducing the risk factors. In this research, we are evaluating a dataset of cardiovascular abnormalities affecting a group of potential patients. We aim to employ the help of AI simulators such as Weka to understand the effect of each parameter on the risk of suffering from cardiovascular disease (CVD). We are utilizing seven classes, such as baseline accuracy, naïve Bayes, <em>k</em>-nearest neighbor, decision tree, support vector machine, linear regression, and artificial neural network multilayer perceptron. The classifiers are assisted by a correlation-based filter to select the most influential attributes that may have an impact on obtaining a higher classification accuracy. Analysis of the results based on sensitivity, specificity, accuracy, and precision results from Weka and Statistical Package for Social Sciences (SPSS) is illustrated. A decision tree method (J48) demonstrated its ability to classify CVD cases with high accuracy 95.76%.