In the last years, there is an increasing acknowledgment of our impact on the environment due to our lifestyle, while the need to adopt a more sustainable approach as to our consumption habits emerges as of particular significance. This trend regards industrial sectors affecting the consumption habits and, especially, electronic industry where the short life cycles and the rapidly developing technology have led to increased e-waste volumes, such as discarded electronic equipment. Waste Electric and Electronic Product or E-waste is referred to all kind of electric and electronic equipments and appliances that is thrown by users. The majority of such elements result in landfills because it is inexpensive disposal option. The E-waste has become a matter of concern because of toxic and hazardous present in electronic goods and if not properly managed. This equipments are a complicated assembly of thousand material, many of which one highly toxic such as brominates substances, toxic gases, toxic metals, biologically active material, acids, plastics and plastics additives. However, their partial recyclability, due to their material composition along with the unavoidable restrictions in landfills, has led to the development of retrieval techniques for their recycling and re-use, highlighting the significance of e-waste recycling, not only from a waste management aspect but also from a valuable materials' retrieval aspect. This paper provides an overview of E-waste generation and management in Malaysia, which, with rapid economic growth and urbanization, is becoming a major social and environmental issue. Thus, major concern for E-waste management in Malaysia has addressed to environmental protection, compared to quantity control. The challenge now is to make the practice effectively in the many different contexts in Malaysia.

Observing the vehicles movement becomes an urgent necessity due to exponentially increasing numbers of vehicles in the world. However, to this regard, a good deal of research had been presented to estimate the exact physical position of the vehicle. The major challenges faced vehicle localization systems are large coverage areas required, positioning at diverse environments and positioning during a high-speed movement. However, in this paper, the challenges of employing the vehicle localization techniques, which rely on the propagation signal properties, are discussed. Moreover, a comparison between these techniques, in terms of accuracy, responsiveness, scalability, cost, and complexity, is conducted. The presented positioning technologies are classified into five categories: satellite based, radio frequency based, radio waves based, optical based, and sound based. The discussion shows that, both of satellite-based technology and cellular-based technology are emerge solutions to overcome the challenges of vehicle positioning. Satellite-based can provide a high accurate positioning in open outdoor environment, whereas the cellular-based can provide accurate and reliable vehicle localization in urban environment, it can support non-line of sight (NLOS) positioning and provide large coverage and high data transmission. The paper also shows that, the standalone localization technology still has limitations. Therefore, we discussed how the presented techniques are integrated to improve the positioning performance.

This study aims to investigate the crashworthiness behavior of hollow Al-based functionally graded material (FGM) box. In order to introduce the concept of FGM, several holes are known as trigger points were drilled on a thin-walled Al column. The column was divided into four segments and each segment has a different number of trigger points. The difference number of trigger points on each segment produces the different structural strength. Quasi-static loading test was conducted in order to observe the deformation pattern occurred. The corresponding graph of force versus distance then was analyzed to obtain the value of energy absorption for each sample. Five samples of different segment arrangement were tested, namely, S1a, S1b, S2, S3 and S4. From the results obtained, the sample S1b give the best results with the highest value of energy absorption. This sample is designed with a large number of trigger points in the first segment, followed by a reduced number of trigger points until the end of the segment. The quality of crash box can be improved by introducing FGM concept. This type of crash box is able to produce a grade of absorption crushing energy. Thus it can reduce severity of injury during an accident.

In geometrical localization techniques, the propagated signal’s first-order multipath (FOMP) characteristics are used to calculate the location based on geometrical relationships. Utilizing the characteristics of higher order multipath (HOMP) results in a significant localization error. Therefore, distinguishing between FOMPs and HOMPs is an important task. The previous works used traditional methods based on a deterministic threshold to accomplish this task. Unfortunately, these methods are complicated and insufficiently accurate. This paper proposes an efficient method based on supervised learning to distinguish more accurately between the propagated FOMP and HOMP of millimeter-Wave Vehicle-to-Vehicle communication in an urban scenario. Ray tracing technique based on Shoot and Bounce Ray (SBR) is used to generate the dataset’s features including received power, propagation time, the azimuth angle of arrival (AAOA), and elevation angle of arrival (EAOA). A statistical analysis based on the probability distribution function (PDF) is presented first to study the selected features’ impact on the classification process. Then, six supervised classifiers, namely Decision Tree, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Random Forest, and artificial neural network, are trained and tested, and their performance is compared in terms of HOMP misclassification. The effect of the considered features on the classifiers’ performance is further investigated. Our results showed that all the proposed classifiers provided an acceptable classification performance. The proposed ANN showed the best performance, whereas the NB was the worst. In fact, the HOMP misclassification error varied between 2.3% and 16.7%. The EAOA exhibited the most significant influence on classification performance, while the AAOA was the least.