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The fictional future home, workspace or city, as predicted by science TV shows of the 1960s, is now a reality. Modern microelectronics and communication technologies offer the type of smart living that looked practically inconceivable just a few decades ago. The Internet of Things (IoT) is one of the main drivers of the future smart spaces. It enables new operational technologies and offers vital financial and environmental benefits. With IoT, spaces are evolving from being just 'smart' to become intelligent and connected. This survey paper focuses on how to leverage IoT technologies to build a modular approach to smart campuses. The paper identifies the key benefits and motivation behind the development of IoT-enabled campus. Then, it provides a comprehensive view of general types of smart campus applications. Finally, we consider the vital design challenges that should be met to realise a smart campus. 1

In recent years, and also due to the COVID-19 pandemic, education institutions worldwide have changed their education paradigm from a traditional to an online system. These institutions have rapidly accomplished their educational programs and activities by supporting various web applications, allowing the completion of the education process remotely. This motivated us to investigate the general perceptions of the faculty members who are teaching different courses for undergraduate students using the distance education system. The proposed technique is based on the use of advanced analysis methods to test the hypothesis of instructors' perceptions of online teaching effectiveness, compared with traditional teaching, will not be affected by the following eight factors, including gender, academic major, age, academic degree, country of teaching, teaching experience in traditional classes, teaching experience in virtual classes (VCs), and whether or not the department/ faculty provided e-learning courses before the COVID-19 pandemic. A total of 187 lecturers from 71 departments in 24 educational institutions located in 11 countries participated in this research. Results indicate that the perceptions of instructors regarding the online teaching system generally do not change based on the studied factors. Furthermore, the quantitative analyses illustrate that no significant differences exist in all dimension scales in terms of gender, major specification, age, country of teaching, and virtual experience. Significant differences are found in the dimension scale of distance education effectiveness in terms of academic degree and the educator status in terms of supporting VCs and traditional experience dimension scales. Additionally, the study provides various recommendations, including that the departments should encourage instructors to positively adapt with online teaching by developing the required skills and knowledge. Moreover, departments and institutions should support the teaching staff with specialized education tools. In addition, instructors should strive to decrease the communication and interaction gap between distance education and traditional in-class teaching.

Bat algorithm (BA) is one of the promising metaheuristic algorithms. It proved its efficiency in dealing with various optimization problems in diverse fields, such as power and energy systems, economic load dispatch problems, engineering design, image processing and medical applications. Thus, this review introduces a comprehensive and exhaustive review of the BA, as well as evaluates its main characteristics by comparing it with other optimization algorithms. The review paper highlights the performance of BA in different applications and the modifications that have been conducted by researchers (i.e., variants of BA). At the end, the conclusions focus on the current work on BA, highlighting its weaknesses, and suggest possible future research directions. The review paper will be helpful for the researchers and practitioners of BA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

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Due to current technology enhancement, molecular databases have exponentially grown requesting faster efficient methods that can handle these amounts of huge data. Therefore, Multi-processing CPUs technology can be used including physical and logical processors (Hyper Threading) to significantly increase the performance of computations. Accordingly, sequence comparison and pairwise alignment were both found contributing significantly in calculating the resemblance between sequences for constructing optimal alignments. This research used the Hash Table-NGram-Hirschberg (HT-NGH) algorithm to represent this pairwise alignment utilizing hashing capabilities. The authors propose using parallel shared memory architecture via Hyper Threading to improve the performance of molecular dataset protein pairwise alignment. The proposed parallel hyper threading method targeted the transformation of the HT-NGH on the datasets decomposition for sequence level efficient utilization within the processing units, that is, reducing idle processing unit situations. The authors combined hyper threading within the multicore architecture processing on shared memory utilization remarking performance of 24.8% average speed up to 34.4% as the highest boosting rate. The benefit of this work improvement is shown preserving acceptable accuracy, that is, reaching 2.08, 2.88, and 3.87 boost-up as well as the efficiency of 1.04, 0.96, and 0.97, using 2, 3, and 4 cores, respectively, as attractive remarkable results.

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