Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms

Abstract : In today’s grid, the technological based cyber-physical systems have continued to be plagued with cyberattacks and intrusions. Any intrusive action on the power system’s Optimal Power Flow (OPF) modules can cause a series of operational instabilities, failures, and financial losses. Real time intrusion detection has become a major challenge for the power community and energy stakeholders. Current conventional methods have continued to exhibit shortfalls in tackling these security issues. In order to address this security issue, this paper proposes a hybrid Support Vector Machine and Multilayer Perceptron Neural Network (SVMNN) algorithm that involves the combination of Support Vector Machine (SVM) and multilayer perceptron neural network (MPLNN) algorithms for predicting and detecting cyber intrusion attacks into power system networks. In this paper, a modified version of the IEEE Garver 6-bus test system and a 24-bus system were used as case studies. The IEEE Garver 6-bus test system was used to describe the attack scenarios, whereas load flow analysis was conducted on real time data of a modified Nigerian 24-bus system to generate the bus voltage dataset that considered several cyberattack events for the hybrid algorithm. Sising various performance metricion and load/generator injections, en included in the manuscriptmulation results showed the relevant influences of cyberattacks on power systems in terms of voltage, power, and current flows. To demonstrate the performance of the proposed hybrid SVMNN algorithm, the results are compared with other models in related studies. The results demonstrated that the hybrid algorithm achieved a detection accuracy of 99.6%, which is better than recently proposed schemes

Computational and experimental study for the desalination of petrochemical industrial effluents using direct contact membrane distillation

Abstract The petrochemical, mining and power industries have reacted to the recent South African water crisis by focussing on improved brine treatment for water and salt recovery with the aim of achieving zero liquid effluent discharge. The purpose of this novel study was to compare experimentally obtained results from the treatment of synthetic NaCl solutions and petrochemical industrial brines such as spent ion exchange regenerant brines and reverse osmosis (RO) brines to the classical well-known Knudsen diffusion, molecular diffusion and transition predictive models. The predictive models were numerically solved using a developed mathematical algorithm that was coded using MATLAB® software. The impact of experimentally varying the inlet feed temperature on process performance of the system is presented here and compared to simulated results. It was found that there was good agreement between the experimentally obtained results, for both the synthetic NaCl solution and the industrial brines. The mean average percentage error (MAPE) was found to be 7.9% for the synthetic NaCl solutions when compared to the Knudsen model. The Knudsen/molecular diffusion transition theoretical model best predicted the performance of the membrane for the industrial spent ion exchange regenerant brine with a mean absolute percentage error (MAPE) of 13.3%. The Knudsen model best predicted the performance of the membrane (MAPE of 10.5%) for the industrial RO brine. Overall, the models were able to successfully predict the water flux and can be used as potential process design tools

Blockchain-Enabled Vaccination Registration and Verification System in Healthcare Management

Client-server-based healthcare systems are unable to manipulate a high data volume, prone to a single failure point, limited scalability, and data integrity. Particularly, several measures introduced to help curb the spread of Covid-19 were not effective and patient records were not adequately managed and maintained. Most vaccination-proof certificates were forged by unauthorized parties and no standard verification medium exists. Therefore, this paper proposes a blockchain-enabled vaccination management system (VMS). VMS utilizes smart contracts to store encrypted patients record, generate vaccination certificates, and verify the legitimacy of the certificate using a QR code. VMS prototype is implemented using Ethereum, a public blockchain and simulations performed based on Apache JMeter and Hyperledger Caliper to assess its performance in terms of throughput, latency and response time, and the average time per transaction. Results show VMS achieved an average: response time of 132.24 ms, the throughput of 379.89 tps, latency of 204.60 ms, and time of transactions is 10s-12s for 1000 transactions. Also, its comparison with the centralized database shows the traditional database’s effectiveness in transaction processing but lacks data privacy and security strengths. We, therefore, recommend the use of blockchain in the healthcare system and other related sectors such as elections, and student records management to ensure data privacy and security and rid the system of a single point of failure

Overlay virtualized wireless sensor networks for application in industrial internet of things : a review

Abstract: In recent times, Wireless Sensor Networks (WSNs) are broadly applied in the Industrial Internet of Things (IIoT) in order to enhance the productivity and efficiency of existing and prospective manufacturing industries. In particular, an area of interest that concerns the use of WSNs in IIoT is the concept of sensor network virtualization and overlay networks. Both network virtualization and overlay networks are considered contemporary because they provide the capacity to create services and applications at the edge of existing virtual networks without changing the underlying infrastructure. This capability makes both network virtualization and overlay network services highly beneficial, particularly for the dynamic needs of IIoT based applications such as in smart industry applications, smart city, and smart home applications. Consequently, the study of both WSN virtualization and overlay networks has become highly patronized in the literature, leading to the growth and maturity of the research area. In line with this growth, this paper provides a review of the development made thus far concerning virtualized sensor networks, with emphasis on the application of overlay networks in IIoT. Principally, the process of virtualization in WSN is discussed along with its importance in IIoT applications. Different challenges in WSN are also presented along with possible solutions given by the use of virtualized WSNs. Further details are also presented concerning the use of overlay networks as the next step to supporting virtualization in shared sensor networks. Our discussion closes with an exposition of the existing challenges in the use of virtualized WSN for IIoT applications. In general, because overlay networks will be contributory to the future development and advancement of smart industrial and smart city applications, this review may be considered by researchers as a reference point for those particularly interested in the study of this growing field

Power system events classification using genetic algorithm based feature weighting technique for support vector machine

Abstract: Currently, ensuring that power systems operate efficiently in stable and secure conditions has become a key challenge worldwide. Various unwanted events including injections and faults, especially within the generation and transmission domains are major causes of these instability menaces. The earlier operators can identify and accurately diagnose these unwanted events, the faster they can react and execute timely corrective measures to prevent large-scale blackouts and avoidable loss to lives and equipment. This paper presents a hybrid classification technique using support vector machine (SVM) with the evolutionary genetic algorithm (GA) model to detect and classify power system unwanted events in an accurate yet straightforward manner. In the proposed classification approach, the features of two large dimensional synchrophasor datasets are initially reduced using principal component analysis before they are weighted in their relevance and the dominant weights are heuristically identified using the genetic algorithm to boost classification results. Consequently, the weighted and dominant selected features by the GA are utilized to train the modelled linear SVM and radial basis function kernel SVM in classifying unwanted events. The performance of the proposed GA-SVM model was evaluated and compared with other models using key classification metrics. The high classification results from the proposed model validates the proposed method. The experimental results indicate that the proposed model can achieve an overall improvement in the classification rate of unwanted events in power systems and it showed that the application of the GA as the feature weighting tool offers significant improvement on classification performances

From Industry 4.0 to Agriculture 4.0 : current status, enabling technologies, and research challenges

Abstract: The three previous industrial revolutions profoundly transformed agriculture industry from indigenous farming to mechanized farming and recent precision agriculture. Industrial farming paradigm greatly improves productivity, but a number of challenges have gradually emerged, which have exacerbated in recent years. Industry 4.0 is expected to reshape the agriculture industry once again and promote the fourth agricultural revolution. In this article, first, we review the current status of industrial agriculture along with lessons learned from industrialized agricultural production patterns, industrialized agricultural production processes, and the industrialized agri-food supply chain. Furthermore, five emerging technologies, namely the Internet of Things, robotics, artificial intelligence, big data analytics, and blockchain, toward Agriculture 4.0 are discussed. Specifically, we focus on the key applications of these emerging technologies in the agricultural sector and corresponding research challenges. This article aims to open up new research opportunities for readers, particularly industrial practitioners

A review of research works on supervised learning algorithms for SCADA intrusion detection and classification

Abstract: Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Connect2NFT: A Web-Based, Blockchain Enabled NFT Application with the Aim of Reducing Fraud and Ensuring Authenticated Social, Non-Human Verified Digital Identity

As of 2022, non-fungible tokens, or NFTs, the smart contract powered tokens that represent ownership in a specific digital asset, have become a popular investment vehicle. In 2021, NFT trading reached USD 17.6 billion and entered mainstream media with several celebrities and major companies launching tokens within the space. The rapid rise in popularity of NFTs has brought with it a number of risks and concerns, two of which will be discussed and addressed in this technical paper. Data storage of the underlying digital asset connected to an NFT is held off-chain in most cases and is therefore out of the NFT holders’ control. This issue will be discussed and addressed using a theoretical workflow developed and presented for a system that converges NFTs and verifiable credentials with the aim of storing underlying NFT digital assets in a decentralized manner. The second issue focuses on the rise of NFT infringements and fraud within the overall NFT space. This will be discussed and addressed through the development of a practical application, named “Connect2NFT”. The main functionality of this practical application will enable users to connect their Twitter social media accounts to the NFTs they own, thus ensuring that potential buyers or viewers of the NFT can comprehensively conclude who is the authentic owner of a specific NFT. An individual performance analysis of the proposed solution will be conducted in addition to being compared and evaluated against similar applications. Thorough development, implementation, and testing has been performed in order to establish a practical solution that can be tested and applied to current NFT use cases. The theoretical NFT storage solution is a minor but equally important contribution in comparison

Connect2NFT: A Web-Based, Blockchain Enabled NFT Application with the Aim of Reducing Fraud and Ensuring Authenticated Social, Non-Human Verified Digital Identity

As of 2022, non-fungible tokens, or NFTs, the smart contract powered tokens that represent ownership in a specific digital asset, have become a popular investment vehicle. In 2021, NFT trading reached USD 17.6 billion and entered mainstream media with several celebrities and major companies launching tokens within the space. The rapid rise in popularity of NFTs has brought with it a number of risks and concerns, two of which will be discussed and addressed in this technical paper. Data storage of the underlying digital asset connected to an NFT is held off-chain in most cases and is therefore out of the NFT holders’ control. This issue will be discussed and addressed using a theoretical workflow developed and presented for a system that converges NFTs and verifiable credentials with the aim of storing underlying NFT digital assets in a decentralized manner. The second issue focuses on the rise of NFT infringements and fraud within the overall NFT space. This will be discussed and addressed through the development of a practical application, named “Connect2NFT”. The main functionality of this practical application will enable users to connect their Twitter social media accounts to the NFTs they own, thus ensuring that potential buyers or viewers of the NFT can comprehensively conclude who is the authentic owner of a specific NFT. An individual performance analysis of the proposed solution will be conducted in addition to being compared and evaluated against similar applications. Thorough development, implementation, and testing has been performed in order to establish a practical solution that can be tested and applied to current NFT use cases. The theoretical NFT storage solution is a minor but equally important contribution in comparison