A Real-Time Energy Consumption Minimization Framework for Electric Vehicles Routing Optimization Based on SARSA Reinforcement Learning

A real-time, metadata-driven electric vehicle routing optimization to reduce on-road energy requirements is proposed in this work. The proposed strategy employs the state–action–reward–state–action (SARSA) algorithm to learn the EV’s maximum travel policy as an agent. As a function of the received reward signal, the policy model evaluates the optimal behavior of the agent. Markov chain models (MCMs) are used to estimate the agent’s energy requirements on the road, in which a single Markov step represents the average energy consumption based on practical driving conditions, including driving patterns, road conditions, and restrictions that may apply. A real-time simulation in Python with TensorFlow, NumPy, and Pandas library requirements was run, considering real-life driving data for two EVs trips retrieved from Google’s API. The two trips started at 4.30 p.m. on 11 October 2021, in Los Angeles, California, and Miami, Florida, to reach EV charging stations six miles away from the starting locations. According to simulation results, the proposed AI-based energy minimization framework reduces the energy requirement by 11.04% and 5.72%, respectively. The results yield lower energy consumption compared with Google’s suggested routes and previous work reported in the literature using the DDQN algorithm

Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning

The simultaneous charging of many electric vehicles (EVs) stresses the distribution system and may cause grid instability in severe cases. The best way to avoid this problem is by charging coordination. The idea is that the EVs should report data (such as state-of-charge (SoC) of the battery) to run a mechanism to prioritize the charging requests and select the EVs that should charge during this time slot and defer other requests to future time slots. However, EVs may lie and send false data to receive high charging priority illegally. In this paper, we first study this attack to evaluate the gains of the lying EVs and how their behavior impacts the honest EVs and the performance of charging coordination mechanism. Our evaluations indicate that lying EVs have a greater chance to get charged comparing to honest EVs and they degrade the performance of the charging coordination mechanism. Then, an anomaly based detector that is using deep neural networks (DNN) is devised to identify the lying EVs. To do that, we first create an honest dataset for charging coordination application using real driving traces and information revealed by EV manufacturers, and then we also propose a number of attacks to create malicious data. We trained and evaluated two models, which are the multi-layer perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the GRU detector gives better results. Our evaluations indicate that our detector can detect lying EVs with high accuracy and low false positive rate

Hybrid microgrid energy management and control based on metaheuristic-driven vector-decoupled algorithm considering intermittent renewable sources and electric vehicles charging lot

Energy management and control of hybrid microgrids is a challenging task due to the varying nature of operation between AC and DC components which leads to voltage and frequency issues. This work utilizes a metaheuristic-based vector-decoupled algorithm to balance the control and operation of hybrid microgrids in the presence of stochastic renewable energy sources and electric vehicles charging structure. The AC and DC parts of the microgrid are coupled via a bidirectional interlinking converter, with the AC side connected to a synchronous generator and portable AC loads, while the DC side is connected to a photovoltaic system and an electric vehicle charging system. To properly ensure safe and efficient exchange of power within allowable voltage and frequency levels, the vector-decoupled control parameters of the bidirectional converter are tuned via hybridization of particle swarm optimization and artificial physics optimization. The proposed control algorithm ensures the stability of both voltage and frequency levels during the severe condition of islanding operation and high pulsed demands conditions as well as the variability of renewable source production. The proposed methodology is verified in a state-of-the-art hardware-in-the-loop testbed. The results show robustness and effectiveness of the proposed algorithm in managing the real and reactive power exchange between the AC and DC parts of the microgrid within safe and acceptable voltage and frequency levels

Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization

The optimal reactive power dispatch (ORPD) problem represents a noncontinuous, nonlinear, highly constrained optimization problem that has recently attracted wide research investigation. This paper presents a new hybridization technique for solving the ORPD problem based on the integration of particle swarm optimization (PSO) with artificial physics optimization (APO). This hybridized algorithm is tested and verified on the IEEE 30, IEEE 57, and IEEE 118 bus test systems to solve both single and multiobjective ORPD problems, considering three main aspects. These aspects include active power loss minimization, voltage deviation minimization, and voltage stability improvement. The results prove that the algorithm is effective and displays great consistency and robustness in solving both the single and multiobjective functions while improving the convergence performance of the PSO. It also shows superiority when compared with results obtained from previously reported literature for solving the ORPD problem

Feelings, Stress, and Coping of Nurses Amidst COVID-19 Outbreak in Saudi Arabia

Background: A year after the COVID-19 pandemic spread around the world, the pandemic is still affecting healthcare systems with an increasing number of infected healthcare workers. Such a unique situation may often result in emotional turmoil, anxiety, depression, and fear, which could lead to resignation and burnout. The study intended to assess the feelings of nurses toward the COVID-19 outbreak; ascertain the factors that cause stress; and determine their coping strategies and factors contributing to coping. Methods: A descriptive cross-sectional design was utilized to recruit 313 nurses working in the Ministry of Health (Saudi Arabia) hospitals that accommodate COVID-19 patients. The study instrument was adapted and modified from the ”MERS-CoV Staff Questionnaire” and the Brief COPE. Results: The results showed that female, married, those with a bachelor’s degree, and aged 25–34 years had higher significant coping strategies. On the other hand, Filipino nurses assigned in the Outpatient Department and COVID-19 Isolation Ward had more negative feelings and encountered several factors causing stress but were coping in a better way than others. Conclusion: Nurses’ commitment to their profession appears to be an intrinsic motivation to continue caring for COVID-19 patients despite the risk of infection. Comfort with religion, spiritual beliefs, and the presence of a support system were the coping strategies used by nurses to ameliorate the stress and negative feelings during the COVID-19 outbreak

A valid treatment option for isolated congenital microgastria

Congenital microgastria (CM) is an extremely rare anomaly of the caudal part of the foregut. Treatment of CM has not yet been standardized. We present the case of a 34-monthold girl with an isolated CM complicated by nasogastric tube-related gastric perforation. During the definitive reconstructive surgery, a scarred structure (1.5 1.5 cm) was found to follow a dilated esophagus. The scarred microstomach was resected, and a Roux-en-Y esophagojejunostomy was performed. The patient has been followed for 6 months. She tolerates a regular oral diet and has reached acceptable growth parameters. We describe the first case of CM to be treated with resection of the microstomach and with a Roux-en-Y esophagojejunostomy.Keywords: congenital microgastria, gastrectomy, Roux-en-Y esophagojejunostom

Sialidase Inhibitors with Different Mechanisms

Sialidases, or neuraminidases, are enzymes that catalyze the hydrolysis of sialic acid (Sia)-containing molecules, mostly removal of the terminal Sia (desialylation). By desialylation, sialidase can modulate the functionality of the target compound and is thus often involved in biological pathways. Inhibition of sialidases with inhibitors is an important approach for under-standing sialidase function and the underlying mechanisms and could serve as a therapeutic approach as well. Transition-state analogues, such as anti-influenza drugs oseltamivir and zanamivir, are major sialidase inhibitors. In addition, difluoro-sialic acids were developed as mechanism-based sialidase inhibitors. Further, fluorinated quinone methide-based suicide substrates were reported. Sialidase product analogue inhibitors were also explored. Finally, natural products have shown competitive inhibiton against viral, bacterial, and human sialidases. This Perspective describes sialidase inhibitors with different mechanisms and their activities and future potential, which include transition-state analogue inhibitors, mechanism-based inhibitors, suicide substrate inhibitors, product analogue inhibitors, and natural product inhibitors