Integrating IoT devices in SCADA systems has provided efficient and improved data collection and transmission technologies. This enhancement comes with significant security challenges, exposing traditionally isolated systems to the public internet. Effective and highly reliable security devices, such as intrusion detection system (IDSs) and intrusion prevention systems (IPS), are critical. Countless studies used deep learning algorithms to design an efficient IDS; however, the fundamental issue of imbalanced datasets was not fully addressed. In our research, we examined the impact of data imbalance on developing an effective SCADA-based IDS. To investigate the impact of various data balancing techniques, we chose two unbalanced datasets, the Morris power dataset, and CICIDS2017 dataset, including random sampling, one-sided selection (OSS), near-miss, SMOTE, and ADASYN. For binary classification, convolutional neural networks were coupled with long short-term memory (CNN-LSTM). The system’s effectiveness was determined by the confusion matrix, which includes evaluation metrics, such as accuracy, precision, detection rate, and F1-score. Four experiments on the two datasets demonstrate the impact of the data imbalance. This research aims to help security researchers in understanding imbalanced datasets and their impact on DL SCADA-IDS.

Accessing healthcare services by several stakeholders for diagnosis and treatment has become quite prevalent owing to the improvement in the industry and high levels of patient mobility. Due to the confidentiality and high sensitivity of electronic healthcare records (EHR), the majority of EHR data sharing is still conducted via fax or mail because of the lack of systematic infrastructure support for secure and reliable health data transfer, delaying the process of patient care. As a result, it is critically essential to provide a framework that allows for the efficient exchange and storage of large amounts of medical data in a secure setting. The objective of this research is to develop a Patient-Centered Blockchain-Based EHR Management (PCEHRM) system that allows patients to manage their healthcare records across multiple stakeholders and to facilitate patient privacy and control without the need for a centralized infrastructure by means of granting or revoking access or viewing one’s records. We used an Ethereum blockchain and IPFS (inter-planetary file system) to store records because of its advantage of being distributed and ensuring the immutability of records and allowing for the decentralized storage of medical metadata, such as medical reports. To achieve secure a distributed, and trustworthy access control policy, we proposed an Ethereum smart contract termed the patient-centric access control protocol. We demonstrate how the PCEHRM system design enables stakeholders such as patients, labs, researchers, etc., to obtain patient-centric data in a distributed and secure manner and integrate utilizing a web-based interface for the patient and all users to initiate the EHR sharing transactions. Finally, we tested the proposed framework in the Windows environment by compiling a smart contract prototype using Truffle and deploy on Ethereum using Web3. The proposed system was evaluated in terms of the projected medical data storage costs for the IPFS on blockchain, and the execution time for a different number of peers and document sizes. The findings of the study indicate that the proposed strategy is both efficient and practicable.

Consensus protocols stand behind the success of blockchain technology. This is because parties that distrust each other can make secure transactions without the oversight of a central authority. The first consensus protocol emerged with Bitcoin. Since then, many others have appeared. Some of them have been implemented by official blockchain platforms, whereas others, for the time being, remain as proposals. A blockchain consensus is a trade-off. The new solutions promise to overcome the known drawbacks of blockchain, but they may also bring new vulnerabilities. Moreover, blockchain performance metrics are not clearly defined, as some metrics, such as delay and throughput, which are key factors for the efficiency of standard networks, are purposely constrained by most mainstream blockchain platforms. The main body of this paper consolidates knowledge of blockchains, focusing on the seminal consensus protocols in large-scale market capitalization platforms, and how consensus is achieved for large-scale, decentralized, blockchain architectures. The benefits, limitations, and tradeoffs, as well as the subsequent trend in current consensus development, and its limitations as a general paradigm, are highlighted. The paper also sheds light on overlooked potential performance metrics, and it proposes some novel solutions to some of the identified problems.

This article presents a numerical simulation model using COMSOL software to study breast thermophysical properties. It analyzes tumor heat at different locations within the breast, records breast surface temperatures, investigates the effects of factors such as blood perfusion, size, depth, and thermal conductivity on breast size, and applies Pennes’ bioheat formula to illustrate thermal distribution on the breast skin surface. An analysis was conducted to examine how changes in tumor location depth, size, metabolism, blood flow, and heat conductivity affect breast skin surface temperature. The simulation model results showed that the highest variations in skin temperatures for breasts with tumors and without tumors can range from 2.58 °C to 0.274 °C. Further, large breast size with a large surface area consistently reduces the temperature variations on the skin and might have difficulty in yielding observable temperature contrast. For small breast sizes, however, heat from tumor sizes below 0.5 cm might be quite difficult to detect, while tumors located deep within the breast layers could not produce observable temperature variations. Motivated by the above interesting results, an emulation experiment was conducted to enhance the observable heat and temperature background contrast, using situ-cooling gel applied to silicon breasts, while the tumor source was emulated using LEDs. The experiment was used to evaluate the effectiveness of adding situ-cooling to the breast surface area, and to study the modulated effect of tumor size and depth. Experimental results showed that situ-cooling enhances thermal contrast in breast thermal images. For example, for a tumor location at a depth of 10 cm, a difference of 6 °C can still be achieved with situ-cooling gel applied, a feat that was not possible in the simulation model. Furthermore, changes in tumor size and location depth significantly impacted surface temperature distribution.

This paper introduces a blockchain-based P2P energy trading platform, where prosumers can trade energy autonomously with no central authority interference. Multiple prosumers can collaborate in producing energy to form a single provider. Clients’ power consumption is monitored using a smart meter that interfaces with an IoT node connected to a blockchain private network. The smart contracts, invoked on the blockchain, enable the autonomous trading interactions between parties and govern accounts behavior within the Ethereum state. The decentralized P2P trading platform utilizes autonomous pay-per-use billing and energy routing, monitored by a smart contract. A Gated Recurrent Unit (GRU) deep learning-based model, predicts future consumption based on past data aggregated to the blockchain. Predictions are then used to set Time of Use (ToU) ranges using the K-mean clustering. The data used to train the GRU model are shared between all parties within the network, making the predictions transparent and verifiable. Implementing the K-mean clustering in a smart contract on the blockchain allows the set of ToU to be independent and incontestable. To secure the validity of the data uploaded to the blockchain, a consensus algorithm is suggested to detect fraudulent nodes along with a Proof of Location (PoL), ensuring that the data are uploaded from the expected nodes. The paper explains the proposed platform architecture, functioning as well as implementation in vivid details. Results are presented in terms of smart contract gas consumption and transaction latency under different loads.

Utilizing renewable energy (RE) sources can provide a realistic answer to the problem of many nations’ energy needs. This paper reviews the current status of using RE to produce electricity in the Kingdom of Saudi Arabia (KSA). The primary aim of the review is to identify and analyze the solar- and wind-energy utilization, problems, and future trends in KSA while taking into account all relevant aspects, associated development hurdles, and suggested solutions. All involved parties will receive useful results and conclusions from a thorough examination and analysis of the pertinent literature that can assist them in understanding the potential of solar and wind energy, considering all the linked issues.