Formal Probabilistic Risk Assessment using Theorem Proving with Applications in Power Systems

The central inquiry in many safety-critical systems is to assess the probability of all possible risk consequences that can occur in a system and its subsystems. In this research, we use theorem proving to formalize Event Trees (ET), Cause Consequence Diagrams (CCD) and Functional Block Diagrams (FBD), which are efficient techniques for probabilistic risk assessment at system and subsystem levels. Our approach provides the reasoning support with verified mathematical formulations that can analyze multi-level ETs, FBDs for complex systems, Cause Consequence Diagrams (CCD) based on Fault Trees (FT) as well as on Reliability Block Diagrams (RBD), as a novel approach. Also, the proposed formalizations of ETs/CCDs/FBDs allowed us to accurately determine of reliability indices, such as System/Customer Average Interruption Frequency and Duration (SAIFI, SAIDI and CAIDI) at system and subsystem levels. Moreover, we develop FBD and ET Modeling and Analysis (FETMA) software, which provides user-friendly features and graphical interfaces for industrial planners/designers. We applied our methods and tools on several realistic case studies from the power systems sector, i.e., the standard IEEE 3/39/118-bus electrical power generation/transmission/distribution networks, Quebec-New England High Voltage Direct Current (HVDC) transmission coupling system, multiple interconnected Micro-Grids, a nuclear power plant, transmission distance protection and a smart automated substation. Experimental results showed improvements compared to all existing reliability analysis methods in terms of scalability, expressiveness, accuracy and time

On the applicability of numerical tools for simulating wave-ports close to the cutoff frequency

This paper focuses on a common drawback in electromagnetic numerical computer aided design computer aided design (CAD) tools: high frequency structure simulator (HFSS), computer simulation technology (CST) and FEKO, where the excitation by using a wave-port below and close to the cutoff frequency has unreliable values for the reflection coefficient. An example for such problem is presented in the design of a dual horn antenna fed by two different waveguide sections. To overcome this numerical error in the results of these CAD tools, a tapered waveguide section is used in the simulation as an excitation mechanism to the feeding waveguide. The cross section of the input port at this tapered waveguide section is designed to have a cutoff frequency smaller than the lowest frequency under investigation for the original problem. Then, by extracting the effect of the tapered section from the obtained reflection coefficient, it would be possible to obtain the reflection coefficient of the original problem

Laparoscopic versus open splenectomy in children with benign hematological diseases in children: a single-center experience

Introduction: Splenectomy whether open or laparoscopic addresses the role of the spleen in the hematology disorders, particularly that of the cellular sequestration and destruction and antibody production. Laparoscopic splenectomy (LS) has been increasingly used for the removal of spleen in children. However, there are still certain questions as regards the feasibility, economic reflections, appropriate splenic size suitable for LS, vascular control of that organ, and final outcomes related to either procedure.Patients and methods: In the period between May 2007 and March 2017, 70 children with benign hematological diseases underwent splenectomy, either laparoscopic or open. They were divided into group A and group B. Group A included cases who had LS, and group B included cases who had open splenectomy (OS). We performed LS while the child was in the right lateral position. In cases of normal splenic size, we used three ports and four ports in cases of splenomegaly. OS was performed in supine position.Results: A total of 70 children were subjected to splenectomy, of whom 20 were boys and 50 were girls. Thalassemia was present in 36 cases, idiopathic thrombocytopenic purpura in 24 cases, and spherocytosis in 10 cases. Five cases were converted to the traditional approach, and three of them were because of huge splenomegaly and two cases were because of accidental bleeding. In the LS group, small-sized spleens were extracted using retrieval bags, whereas large spleens were extracted through Pfannenstiel incision. OS procedure was performed through midline incision. Cholecystectomy was performed in five cases during the original procedure because of gall bladder stones: three cases in group A and two cases in group B.Conclusion: Although both LS and OS achieved the same goal for the children with benign hematological disease, the advantages of minimal invasive surgery made LS the standard approach for treatment of children with benign hematological diseases. However, the main concern is the high economic burden of LS when compared with OS.Keywords: children, laparoscopic, open, splenectom

Influence of Channel Selection and Subject’s Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms

The electroencephalogram (EEG) signal is a key parameter used to identify the different sleep stages present in an overnight sleep recording. Sleep staging is crucial in the diagnosis of several sleep disorders; however, the manual annotation of the EEG signal is a costly and time-consuming process. Automatic sleep staging algorithms offer a practical and cost-effective alternative to manual sleep staging. However, due to the limited availability of EEG sleep datasets, the reliability of existing sleep staging algorithms is questionable. Furthermore, most reported experimental results have been obtained using adult EEG signals; the effectiveness of these algorithms using pediatric EEGs is unknown. In this paper, we conduct an intensive study of two state-of-the-art single-channel EEG-based sleep staging algorithms, namely DeepSleepNet and AttnSleep, using a recently released large-scale sleep dataset collected from 3984 patients, most of whom are children. The paper studies how the performance of these sleep staging algorithms varies when applied on different EEG channels and across different age groups. Furthermore, all results were analyzed within individual sleep stages to understand how each stage is affected by the choice of EEG channel and the participants’ age. The study concluded that the selection of the channel is crucial for the accuracy of the single-channel EEG-based automatic sleep staging methods. For instance, channels O1-M2 and O2-M1 performed consistently worse than other channels for both algorithms and through all age groups. The study also revealed the challenges in the automatic sleep staging of newborns and infants (1–52 weeks)

Modelling of Metaheuristics with Machine Learning-Enabled Cybersecurity in Unmanned Aerial Vehicles

The adoption and recent development of Unmanned Aerial Vehicles (UAVs) are because of their widespread applications in the private and public sectors, from logistics to environment monitoring. The incorporation of 5G technologies, satellites, and UAVs has provoked telecommunication networks to advance to provide more stable and high-quality services to remote areas. However, UAVs are vulnerable to cyberattacks because of the rapidly expanding volume and poor inbuilt security. Cyber security and the detection of cyber threats might considerably benefit from the development of artificial intelligence. A machine learning algorithm can be trained to search for attacks that may be similar to other types of attacks. This study proposes a new approach: metaheuristics with machine learning-enabled cybersecurity in unmanned aerial vehicles (MMLCS-UAVs). The presented MMLCS-UAV technique mainly focuses on the recognition and classification of intrusions in the UAV network. To obtain this, the presented MMLCS-UAV technique designed a quantum invasive weed optimization-based feature selection (QIWO-FS) method to select the optimal feature subsets. For intrusion detection, the MMLCS-UAV technique applied a weighted regularized extreme learning machine (WRELM) algorithm with swallow swarm optimization (SSO) as a parameter tuning model. The experimental validation of the MMLCS-UAV method was tested using benchmark datasets. This widespread comparison study reports the superiority of the MMLCS-UAV technique over other existing approaches

Smart Water Quality Prediction Using Atom Search Optimization with Fuzzy Deep Convolutional Network

Smart solutions for monitoring water pollution are becoming increasingly prominent nowadays with the advance in the Internet of Things (IoT), sensors, and communication technologies. IoT enables connections among different devices with the capability to gather and exchange information. Additionally, IoT extends its ability to address environmental issues along with the automation industry. As water is essential for human survival, it is necessary to integrate some mechanisms for monitoring water quality. Water quality monitoring (WQM) is an efficient and cost-effective system intended to monitor the quality of drinking water that exploits IoT techniques. Therefore, this study developed a new smart water quality prediction using atom search optimization with the fuzzy deep convolution network (WQP-ASOFDCN) technique in the IoT environment. The WQP-ASOFDCN technique seamlessly monitors the water quality parameters using IoT devices for data collection purposes. Data pre-processing is carried out at the initial stage to make the input data compatible for further processing. For water quality prediction, the F-DCN model was utilized in this study. Furthermore, the prediction performance of the F-DCN approach was improved by using the ASO algorithm for the optimal hyperparameter tuning process. A sequence of simulations was applied to validate the enhanced water quality prediction outcomes of the WQP-ASOFDCN method. The experimental values denote the better performance of the WQP-ASOFDCN approach over other approaches in terms of different measures

Modelling of Metaheuristics with Machine Learning-Enabled Cybersecurity in Unmanned Aerial Vehicles

The adoption and recent development of Unmanned Aerial Vehicles (UAVs) are because of their widespread applications in the private and public sectors, from logistics to environment monitoring. The incorporation of 5G technologies, satellites, and UAVs has provoked telecommunication networks to advance to provide more stable and high-quality services to remote areas. However, UAVs are vulnerable to cyberattacks because of the rapidly expanding volume and poor inbuilt security. Cyber security and the detection of cyber threats might considerably benefit from the development of artificial intelligence. A machine learning algorithm can be trained to search for attacks that may be similar to other types of attacks. This study proposes a new approach: metaheuristics with machine learning-enabled cybersecurity in unmanned aerial vehicles (MMLCS-UAVs). The presented MMLCS-UAV technique mainly focuses on the recognition and classification of intrusions in the UAV network. To obtain this, the presented MMLCS-UAV technique designed a quantum invasive weed optimization-based feature selection (QIWO-FS) method to select the optimal feature subsets. For intrusion detection, the MMLCS-UAV technique applied a weighted regularized extreme learning machine (WRELM) algorithm with swallow swarm optimization (SSO) as a parameter tuning model. The experimental validation of the MMLCS-UAV method was tested using benchmark datasets. This widespread comparison study reports the superiority of the MMLCS-UAV technique over other existing approaches