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This article involves the design of a novel path tracking control technique for the self-driving car. Fixed settling time sliding mode control (FSTSMC) with barrier Lyapunov function is implemented to deal with the non-linear lateral dynamics and to ensure stable standard double-lane-change (DLC) maneuver of self-driving cars in the presence of unknown lateral tire forces and parametric uncertainties. A two-time scale-based approach is employed to deal with the slow and fast dynamics of the car separately. The proposed control scheme efficacy is investigated using simulations based on CarSim and Simulink. The simulation results validate the path-tracking ability of the proposed controller for self-driving cars while accomplishing the stable double-lane-change maneuver at various forward speeds.

Fifth-generation (5G) cellular networks are a promising technology to meet the rapid growth in wireless traffic. Small cells are critical in fulfilling the requirements of 5G networks. A heterogeneous ultra-dense network (HUDN) is an enabling technology consisting of several types of small cells to enhance the performance of 5G networks effectively. A critical issue of HUDN is the cell selection method because the traditional technique for cell selection is inapplicable in such a network. This study proposes a novel adaptive cell selection (ADA-CS) scheme. It adapts to various characteristics of HUDNs and vehicle movements. It performs six phases to select the best base station with which to be associated. Simulation results show that, with low- and medium-speed vehicles, the ADA-CS scheme outperforms the traditional protocol in terms of the average number of handovers by 42.39%. In addition, it is superior to some relevant recent schemes by up to 36.53%. The adaptation feature of the proposed protocol provides additional improvements regarding the average number of handovers with high-speed vehicles. Therefore, it achieves superiority in terms of the average number of handover failures and unnecessary handovers. In addition, the ADA-CS scheme enhances the average achievable downlink data rates and spectral efficiency per vehicle by 3.98% and 2.79% compared with the traditional and the relevant recent schemes.

Two-terminalt and tandem solar-cells have a high efficiency of power conversion. One of their main limitations is the operating current density as the two-terminal tandem solar-cell is equivalent to electrically connected series subcells. Increasing the top absorber layer’s thickness will lead to an increase in the top subcell current and a decrease in the bottom subcell’s current. The subcell with the minimum current forces the tandem cell to operate at its value, limiting the overall performance. In this paper, a proposed solution for such a problem is introduced using a bottom subcell consisting of germanium-telluride (GeTe), which gives a high current and matches the top subcell at a thicker absorber layer. A proposal of three different tandem cells with perovskite (MAPbI3)/CIGS, perovskite (MAPbI3)/GeTe, and perovskite (MAPbI3-xClx)/GeTe have been presented. The proposed perovskite (MAPbI3)/CIGS has an efficiency of 30.52%, whereas the replacement of the CIGS bottom subcell by GeTe led to a significant enhancement of the efficiency to reach 35.9%. High efficiency of 41.7% is obtained by replacing the perovskite (MAPbI3) top subcell with perovskite (MAPbI3-xClx). A modified numerical algorithm is proposed to obtain the optimum thickness of the top subcell to achieve higher power-conversion efficiency. The performance evaluation and simulation of the designed tandem cells were carried out using SCAPS-1D. The temperature effects on the proposed cells have been encountered in simulation. The results show that the proposed tandem solar-cells have comparable performance and higher efficiencies relative to the published works.

There is an increasing awareness that predictive analytics helps universities to evaluate students’ performances. Big data analytics, such as student demographic datasets, can provide insight that helps to support academic success and completion rates. For example, learning analytics is an essential component of big data in universities that can provide strategic decision makers with the opportunity to perform a time series analysis of learning activities. A two-year retrospective analysis of student learning data from the University of Ha’il was conducted for this study. Predictive deep learning techniques, the bidirectional long short term model (BLSTM), were utilized to investigate students whose retention was at risk. The model has diverse features which can be utilized to assess how new students will perform and thus contributes to early prediction of student retention and dropout. Further, the condition random field (CRF) method for sequence labeling was used to predict each student label independently. Experimental results obtained with the predictive model indicates that prediction of student retention is possible with a high level of accuracy using BLSTM and CRF deep learning techniques.

In this article, we present an encryption technique that can encrypt any digital data. The proposed scheme basically depends on a substitution-permutation network. Two separate bijective maps are used in the proposed algorithm. Firstly, the substitution boxes are used to perform the substitution process. This technique uses elliptic curves (ECs) to create several substitution boxes with good cryptographic properties. The generated substitution boxes are utilized to replace the arrange data that produces the most uncertainty in the plain image data. Further, we used the permutation process to generate strong randomness in the proposed technique. For the evaluation of the stability of the proposed algorithm; nonlinearity, linear approximation probability, bit independence criterion, strict avalanche criterion and differential approximation probability were performed on the substitution boxes. Differential attacks (Number of Changing Pixel Rate (NPCR), Uniform Average Change Intensity (UACI)) and statistical tests (Entropy, Correlation) were performed on the encrypted images to check their resistance against different attacks. The execution and image analysis reveals that the proposed scheme attains good encryption results while requiring minimal computing ability and has efficient potential in real-time image encryption applications.