Abstract The fluctuating production of renewable energy constraints the operation of Power-to-X processes such that steady-state conditions are unattainable without energy storage. It seems eminent to establish operation strategies considering significant disturbances along the process and to determine those scenarios where the operation becomes unfeasible. In this work, an industrial methanol Lurgi-type reactor, embedded in a Power-to-Jet process (Figure 1), is evaluated under fluctuating feed conditions. The simulated scenarios consist of step functions up to 20 % (w/w) increments in the feed flowrate as consequence of the fluctuating power input on the electrolysis stage. A one-dimensional dynamic model for a multi-tubular fixed bed reactor is implemented, considering both the gas and catalyst phase. The mathematical model is solved numerically using orthogonal collocation at the spatial domain and backward differences at the time domain. The system shows rapid response to disturbances, reaching steady state conditions in 1.5 minutes. Furthermore, it is evidenced that the feasible region to increase the production of methanol is narrowed down by rises of carbon dioxide feed flowrate up to 5 % (w/w).

The fifth generation (5G) of wireless communications has led to many advancements in technologies such as large and distributed antenna arrays, ultra-dense networks, software-based networks and network virtualization. However, the need for a higher level of automation to establish hyper-low latency, and hyper-high reliability for beyond 5G applications requires extensive research on machine learning with applications in wireless communications. Thereby, learning techniques will take a central stage in the sixth generation of wireless communications to cope up with the stringent application requirements. This paper studies the practical limitations of these learning methods in the context of resource management in a non-stationary radio environment. Based on the practical limitations we carefully design and propose supervised, unsupervised, and reinforcement learning models to support rate maximization objective under user mobility. We study the effects of practical systems such as latency and reliability on the rate maximization with deep learning models. For common testing in the non-stationary environment, we present a generic dataset generation method to benchmark across different learning models versus traditional optimal resource management solutions. Our results indicate that learning models have practical challenges related to training limiting their applications. These models need an environment-specific design to reach the accuracy of an optimal algorithm. Such an approach is practically not realistic due to the high resource requirement needed for frequent retraining.

In fifth generation and beyond Internet of things (5G-IoT), the buffer-aided relaying network provide an efficient way to maintain the coverage area and enhance the edge user’s transmission experience. However, the effect of non-ideal factor to the buffer-aided relaying network, such as the signaling overhead, have not been investigated. In this paper, we propose a practical hybrid bit-level network coding (BNC) for the buffer-aided relaying network while taking the unreliable transmission, limited relay-buffer size and signaling overhead into consideration. Afterward, we derive its concise closed-form expressions and the associated upper bound expressions for the throughput, queuing delay and overflow probability. Monte-Carlo simulation proves the validness of our analysis. Simulation results also demonstrate that compared to existing studies, our proposal can enhance the system energy efficiency (EE) performance, and buffer size has a positive effect on the EE performance.

Organophosphorus (OP) compounds have been used as pesticides and have been developed as warfare nerve agents such as tabun, soman, sarin, VX, and others. Exposure to even small amounts of an OP compound can be fatal and death is usually caused by respiratory failure resulting from paralysis of the diaphragm and intercostal muscles, depression of the brain respiratory center, bronchospasm, and excessive bronchial secretions. The mechanism of OP poisoning involves phosphorylation of the serine hydroxyl group in the active site of acetylcholinesterase (AChE), leading to the inactivation of this essential enzyme, which has an important role in neurotransmission. AChE inhibition results in the accumulation of acetylcholine at cholinergic receptor sites, producing continuous stimulation of cholinergic fibers throughout the central and peripheral nervous systems. Presently, a combination of an antimuscarinic agent (e.g., atropine), AChE reactivator such as one of the recommended pyridinium oximes (PAM-2, TMB-4, HI-6, LuH-6), and diazepam are used for the treatment of organophosphate poisoning in humans. In this chapter, we review the mechanisms of action of OP and the role of pyridinium oximes used as AChE reactivators in the treatment of OP poisoning.

This study reviews the current understanding of chemical and biochemical mechanisms of metabolism of warfare nerve agents and describes the role of the enzymes involved in this process. Among enzymes participating in the metabolism of nerve agents, the roles of A-esterases, serum cholinesterase, carboxylesterase, and prolidase are discussed.