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Electricity is a critical component of many everyday activities, demanding continuing research to create new or improved techniques for generating electrical power. Thermoelectric generators (TEGs), which work basically on the Seebeck effect can successfully transform input heat from numerous applications into valuable electrical energy, as well as power electronic devices and sensors on their own. However, obstacles include increasing the temperature difference and creating novel materials to improve electrical output and efficiency. Accordingly, this paper discusses these problems by providing a thorough examination of available strategies to enhance the thermoelectric performance. In this study, a variety of materials is presented, starting by the standard used conventional organic and inorganic thermoelectric (TE) materials. Organic materials, such as polyaniline and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT) composites, acquired ZT values ranging from 0.5 to 1.0, demonstrating their promise for versatile and low-cost applications. At extreme temperatures (∼500 K), inorganic materials such as bismuth telluride (Bi2Te3) and lead telluride (PbTe) achieved ZT values around 2.0, indicating great efficiency in power production. Bi2Te3/PEDOT, a hybrid material with organic and inorganic components, demonstrated improved performance with ZT values of 1.5–2.0 due to the synergistic effects of its constituents. Novel composite materials, such as Bi2Te3-carbon nanotube (CNT) composites and using graphene, developed to optimize thermal and electrical characteristics, enhanced device performance by up to 25% over standard materials, with ZT values ranging from 1.8 to 2.2. In addition, in the present study the new recent materials after applying enhancement methods will be presented. These new materials are developed by different methods and synthesis such as doping, superlattice and heterostructure materials and other methods will be discussed. The main findings indicate that the strategic use of these advanced materials may significantly increase the efficiency and output power of TEG devices, making them more practical for a wide variety of applications. As an examples, 2.8 for (GeTe)0.95(Sb2Te3)0.05 alloy, 2.4 for Chalcogenide, ZrS2, Bismuth telluride thin film (p-type Bi2Te3/Sb2Te3 superlattices) and 2.75 for Bismuth telluride thin film (Bi2Se1.2Te1.8). Finally, the present paper investigates on the newest technology and strategies that are applied in this research area in order to enhance the TEG performance enhancement.

Several researchers have studied how impact loads from impact hazards affect reinforced concrete (RC) slabs. There is relatively little research on impact loading effects on pre-stressed structures. The usage of fibers in structural elements intrigued researchers. In this paper, impact-loaded post-tensioned (PT) slabs with and without Forta-Ferro fibers were compared to post-tensioned slabs with plain concrete and conventional shear reinforcement. Forta-Ferro is a lightweight, low-cost fiber, and hence its effects on slab structural response under impact load deserve to be explored. Post-tensioned slabs’ impact resistance and energy absorption were tested using real-world situations of rapid and severe loads. Four identical 3.3 by 1.5 m concrete slabs were utilized in the experiment. The experiment involved dropping a 600 kg iron ball from 8 m onto each slab’s center of gravity. The slabs’ responses were investigated. The four slab configurations were tested for displacement, energy absorption, and cracking. Forta-Ferro fiber reinforcement is understudied, making this study significant. The study’s findings may help us comprehend fiber-reinforced concrete PT slabs’ impact resistance and structural performance. Engineers and designers of impact-prone buildings like slabs and bridges will benefit from the findings. The study also suggests adding Forta-Ferro fibers to post-tensioned slabs to improve durability and resilience against unanticipated impact hazards.

This research focuses on the ride comfort of passengers in vehicles equipped with passive suspension systems. To ensure ride comfort, the paper proposes predicting the vehicle's appropriate velocity that drivers should sustain before approaching speed humps. Support Vector Machines, a powerful Machine Learning tool, is used to predict the appropriate vehicle velocity. A simulation technique was used to build the dataset for the learning process; an adequate velocity was found for each hump shape. The K-folds cross validation technique was applied to 80% of the dataset using three types of Support Vector Machine kernel functions: linear, polynomial, and Gaussian kernels. The results of the validation showed very good performance of the three kernel functions. However, Gaussian kernel function performed best, with a correlation coefficient (R) of 0.998, a mean absolute error (MAE) of 0.383, and a root mean square error (RMSE) of 0.247. The final test was performed on the remaining 20% of the dataset and the predicted velocity was compared with the simulated one. The results showed excellent performance, with an error that is less than 3% for velocity greater than 6 Km/h.

This paper presents a proposed design of a mechatronics system for autonomous vehicles. The proposed design is able to memorize a route based on Global Positioning System (GPS) rather than using pre-saved maps that are infrequently updated and do not include all roads of all countries. Moreover, it can autonomously avoid obstacles and detect bumps. Experimental tests are conducted using a small-scale car equipped with the proposed mechatronics system. The results show that the proposed system operates with minor errors and slips. The proposed autonomous vehicle can serve normal, disabled, and elderly people. It can be used on roads and even inside facilities like campuses, airports, and factories to transport passengers or loads thus reducing workmanship and costs. Keywords: Autonomous vehicles, GPS, Collision avoidance, Tracking system