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© Copyright 2021, A. O. Nyanteh et al. Software-Defined Networking (SDN) is an innovative technology which provides a programmable network control which is decoupled from the physical infrastructure. Network Virtualization (NV) is the phenomenon where a given physical network infrastructure and its resources are abstracted to create multiple logical virtual network slices of the underlying substrate. NV enables independent virtual networks to co-exist on one or more shared physical network infrastructure. Edge computing makes use of the edge resources in close proximity to end-users to reduce service delay and the network traffic volume in the end-to-end networks. Similarly, network slicing which is a key enabling technology for 5G networks is designed to support different services from different platforms at different scales enables sharing of physical network infrastructure on many different virtual network layers. These innovative technologies and strategies have gained significant attention from both academia and industry as they have the potential to maximize network resource utilization and optimize end–to–end network service delivery in 5G solutions deployment. To enable continuous simulation and development of applicable 5G networking concepts using these technologies, there is a need for an accessible and easy-to-learn testbed which is able to efficiently measure the performance of physical and virtual network capacities, provisioning approaches and management of multiple architectural models using large-scale network slicing configurations in a repeatable and controllable manner. These tools and toolkits provide scalable, lightweight and controlled cloud simulation environments necessary to analyse network traffic flows, allocation capacities and policies and the behaviour of multiple heterogeneous networks at an extremely low cost as compared to the huge financial commitments involved in conducting similar experiments in a real-life event. Existing solutions do not support Network Slicing and end- to -end heterogenous network automation which are key enablers of 5G network implementation. Hence in this paper, the CloudSimHypervisor framework is developed in this based on CloudSimSDN-NFV. The complete architecture and features of the CloudSimHypervisor framework and some used cases are presented in this paper. We validate the CloudSimHypervisor framework with two use case experiments in the cloud computing environment: Joint compute and network resource utilization and network traffic prioritization. Results from these experiments display the efficiency of the CloudSimHypervisor in estimating and measuring processing speed, transmission speed, compute and network usage efficiency and energy consumption.

One of the most challenging predictive data analysis efforts is accurate prediction of depth of anesthesia (DOA) indicators which has attracted a growing attention since it provides patients a safe surgical environment in case of secondary damage caused by intraoperative awareness or brain injury. However, many researchers put heavily handcraft feature extraction or carefully tailored feature engineering to each patient to achieve very high sensitivity and low false prediction rate for a particular dataset. This limits the benefit of the proposed approaches if a different dataset is used. Recently, representations learned using deep convolutional neural network (CNN) for object recognition are becoming widely used model of the processing hierarchy in the human visual system. The correspondence between models and brain signals that holds the acquired activity at high temporal resolution has been explored less exhaustively. In this paper, deep learning CNN with a range of different architectures, is designed for identifying related activities from raw electroencephalography (EEG). Specifically, an improved short-time Fourier transform (STFT) is used to stand for the time-frequency information after extracting the spectral images of the original EEG as input to CNN. Then CNN models are designed and trained to predict the DOA levels from EEG spectrum without handcrafted features, which presents an intuitive mapping process with high efficiency and reliability. As a result, the best trained CNN model achieved an accuracy of 93.50%, interpreted as CNN’s deep learning to approximate the DOA by senior anesthesiologists, which highlights the potential of deep CNN combined with advanced visualization techniques for EEG-based brain mapping. This research was financially supported by Lenovo Technology B.V. Taiwan Branch. Also, it was supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant no. 51475342).

Phase-rectified signal averaging (PRSA) has been known to be a useful method to detect periodicities in non-stationary biological signals. Determination of quasi-periodicities in electroencephalogram (EEG) is a candidate for quantifying the changes of depth of anesthesia (DOA). In this paper, DOA monitoring capacity of periodicities detected using PRSA were quantified by assessing EEG signals collected from 56 patients during surgery. The method is compared to sample entropy (SampEn), detrended fluctuation analysis (DFA) and permutation entropy (PE). The performance of quasi-periodicities defined by acceleration capacity (AC) and deceleration capacity (DC) was tested using the area under the receiver operating characteristic curve (AUC) and Pearson correlation coefficient. During the surgery, a significant difference (p < 0.05) in the quasi-periodicities was observed among three different stages under general anesthesia. There is a larger mean AUC and correlation coefficient of quasi-periodicities compared to SampEn, DFA and PE using expert assessment of conscious level (EACL) and bispectral index (BIS) as the gold standard, respectively. Quasi-periodicities detected using PRSA in EEG signals are powerful monitor of DOA and perform more accurate and robust results compared to SampEn, DFA and PE. The results do provide a valuable reference to researchers in the filed of clinical applications. 10.13039/501100003711-Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by the Ministry of Science and Technology; 10.13039/501100001809-National Natural Science Foundation of China

A nonthermal plasma reactor (NTPR) using two 2.45-GHz microwave (MW) generators for the abatement of nitrogen oxides ( ${\text {NO}}_{\text {x}}$ ) and sulfur ( ${\text {SO}}_{\text {x}}$ ) contained in the exhaust gas of a 200-kW marine diesel engine was built and tested. Numerical analysis based on a nonthermal plasma kinetics model for the abatement of ${\text {NO}}_{\text{x}}$ and ${\text{SO}}_{\text{x}}$ from marine diesel engine exhaust gas was performed. A generic kinetic model that implements electron collisions and plasma chemistry has been developed for applications involving low-temperature (50–100 K) nonthermal plasma. Abatement efficiencies of ${\text {NO}}_{\text{x}}$ and ${\text {SO}}_{\text{x}}$ were investigated for a range of mean electron energies, which directly impact on the rate constants of electron collisions. The simulation was conducted using the expected composition of exhaust gas from a typical two-stroke, slow-speed marine diesel engine. The simulation results predict that mean electron energy of 0.25–3.2 eV gives abatement efficiency of 99% for ${\text {NO}}_{\text{x}}$ and ${\text {SO}}_{\text{x}}$ . The minimum residence time required was found to be 80 ns for the mean electron energy of 1 eV. Multimode cavity was designed using COMSOL multiphysics. The NTPR performance in terms of ${\text {NO}}_{\text{x}}$ and ${\text {SO}}_{\text{x}}$ removal was experimentally tested using the exhaust from a 2-kW lab scale, two-stroke diesel engine. The experimental results also show that the complete removal of NO is possible with the MW plasma (yellow color) generated. However, it was found that generating required MW plasma is a challenging task and requires further investigation.

Electrostatic properties of formulation component materials and blends play an important role in dry powder inhalation (DPI) products. Valid measurement of charge distribution will therefore lead to better control of powder behavior in DPI manufacturing processes. Ultrafine powders are known to have bipolar charge, have nonspherical shapes, and tend to be highly cohesive. Real-time noninvasive techniques need to be developed to obtain a precise and accurate measurement of electrically charged powders, as they aerosolize from a DPI product. How this measure relates to materials behavior throughout the various steps of a manufacturing process, e.g., from drug micronization and blending with lactose to filling dose units, also needs to be addressed. A novel noninvasive technique, which employs the phase Doppler anemometry (PDA) system for simultaneous measurement of size and charge of pharmaceutical powders, is currently being considered. Previous research demonstrated the advantages of this technique in measuring the bipolar charge distribution on a population of liquid aerosols. These findings led to significant improvements in understanding the performance of inhaler formulations, manufacturing processes, and development of new devices for inhaled drug delivery. This paper presents an investigation of electrostatic properties of lactose materials (typically used as a DPI excipient) using the PDA system. PDA calibration was checked using dry polystyrene microspheres, followed by an investigation of different grades of lactose. The PDA technique was used to track the motion of charged particles in the presence of an electric field. The magnitude, as well as the polarity of the particle charge, can be obtained by solving the equation of particle motion combined with the simultaneous measurement of its size and velocity. Results show the capability of the technique to allow real-time charge distribution and size measurement in the control of dry powder attributes that are critical to a better understanding of the manufacturing design space.

Many synergies have been proposed between soft-computing techniques, such as neural networks (NNs), fuzzy logic (FL), and genetic algorithms (GAs), which have shown that such hybrid structures can work well and also add more robustness to the control system design. In this paper, a new control architecture is proposed whereby the on-line generated fuzzy rules relating to the self-organizing fuzzy logic controller (SOFLC) are obtained via integration with the popular generalized predictive control (GPC) algorithm using a Takagi-Sugeno-Kang (TSK)-based controlled autoregressive integrated moving average (CARIMA) model structure. In this approach, GPC replaces the performance index (PI) table which, as an incremental model, is traditionally used to discover, amend, and delete the rules. Because the GPC sequence is computed using predicted future outputs, the new hybrid approach rewards the time-delay very well. The new generic approach, named generalized predictive self-organizing fuzzy logic control (GPSOFLC), is simulated on a well-known nonlinear chemical process, the distillation column, and is shown to produce an effective fuzzy rule-base in both qualitative (minimum number of generated rules) and quantitative (good rules) terms.