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Numerous agents with anxiolytic or stimulant effects have the potential to be overused, and their misuse is associated with serious side effects. In Saudi Arabia, the estimated percentage of Saudis who abuse drugs is around 7–8% and the age range is 12–22 years. Methamphetamine, captagon, tramadol, heroin, and cannabis/cannabinoids have been proven to be the most commonly abused drugs in Saudi Arabia, with methamphetamine being at the top of the list. The present study focuses on the chromatographic analytical methods used for the analysis of methamphetamine in combination with commonly abused drugs, aiming to point out the greenest among them. These mixtures have been chosen as they are analyzed periodically and frequently in criminal evidence and forensic medicine. Therefore, the chances of hazards for analysts and the environment are high if the mixtures are not handled appropriately. This study aims to compare 23 chromatographic methods used for the analysis of methamphetamine mixtures in four major combinations, and to assess their greenness by using three greenness assessment tools, namely, NEMI, ESA and AGREE, to recommend the greenest analytical method. The NEMI results were proven to have low discriminating abilities and, accordingly, the comparisons are based on ESA and AGREE scores. The analysis results show that the safest methods with the most eco-friendly results (based on ESA and AGREE) are the GC-MS method proposed by Mohammed et al. to analyze methamphetamine and captagon mixtures (ESA = 79 and AGREE = 0.57), the UHPLC–MS-MS method proposed by Busardò et al. to analyze methamphetamine and cannabis/cannabinoid mixtures (ESA = 78 and AGREE = 0.57), the LC-MS method proposed by Herrin et al. to analyze methamphetamine and tramadol mixtures (ESA = 81 and AGREE = 0.56), and the LC-MS method proposed by Postigo-et al to analyze methamphetamine and heroin mixtures (ESA = 76 and AGREE = 0.58).

The adoption of cooperative intelligent transportation systems (cITSs) improves road safety and traffic efficiency. Vehicles connected to cITS form vehicular ad hoc networks (VANET) to exchange messages. Like other networks and systems, cITSs are targeted by attackers intent on compromising and disrupting system integrity and availability. They can repeatedly spoof false information causing bottlenecks, traffic jams and even road accidents. The existing security infrastructure assumes that the network topology and/or attack behavior is static. However, the cITS is inherently dynamic in nature. Moreover, attackers may have the ability and resources to change their behavior continuously. Assuming a static IDS security model for VANETs is not suitable and can lead to low detection accuracy and high false alarms. Therefore, this paper proposes an adaptive security solution based on deep learning and contextual references that can cope with the dynamic nature of the cITS topologies and increasingly common attack behaviors. In this study, deep belief networks (DBN) modeling was used to train the detection model. Binary cross entropy was used as a loss function to measure the prediction error. Two activation functions were used, Relu and Softmax, for input–output mapping. The Relu was used in the hidden layers, while the Sigmoid was used in the last layer to map the real vector to output between 0 and 1. The adaptation mechanism was incorporated into the detection model using a moving average that monitors predicted values within a time window. In this way, the model can readjust the classification thresholds on-the-fly as appropriate. The proposed model was evaluated using the Next Generation Simulation (NGSIM) dataset, which is commonly used in such related works. The result is improved accuracy, demonstrating that the adaptation mechanism used in this study was effective.