Many environmental parameters affect the performance of solar photovoltaics (PV), such as dust and temperature. In this paper, three PV technologies have been investigated and experimentally analyzed (mono, poly, and flexible monocrystalline) in terms of the impact of dust and thermal energy on PV behavior. Furthermore, a modular neural network is designed to test the effects of dust and temperature on the PV power production of six PV modules installed at Sohar city, Oman. These experiments employed three pairs of PV modules (one cleaned daily and one kept dusty for 30 days). The performance of the PV power production was evaluated and examined for the three PV modules (monocrystalline, polycrystalline, and flexible), which achieved 30.24%, 28.94%, and 36.21%, respectively. Moreover, the dust reduces the solar irradiance approaching the PV module and reduces the temperature, on the other hand. The neural network and practical models’ performance were compared using different indicators, including MSE, NMSE, MAE, Min Abs Error, and r. The Mean Absolute Error (MAE) is used for evaluating the accuracy of the ANN machine learning model. The results show that the accuracy of the predicting power of the six PV modules was considerable, at 97.5%, 97.4%, 97.6%, 96.7%, 96.5%, and 95.5%, respectively. The dust negatively reduces the PV modules’ power production performance by about 1% in PV modules four and six. Furthermore, the results were evident that the negative effect of the dust on the PV module production based on the values of RMSE, which measures the square root of the average of the square’s errors. The average errors in predicting the power production of the six PV modules are 0.36406, 0.38912, 0.34964, 0.49769, 0.46486, and 0.68238.

Blockchain, a breakthrough technology, offers solutions to several healthcare challenges, includes Managing and sharing data securely, Making different systems work together seamlessly, Increasing transparency in the supply chain, Giving patients more control over their health information, and Adhering to regulations and guidelines. This paper offers an in-depth study of current literature on blockchain in healthcare. It covers areas that have been investigated, studied and implemented by previous research, provides valuable insights, and introduces practical suggestions. Research findings indicate that blockchain has the capacity to improve data security, privacy, and immutability. It can also facilitate effortless data sharing among different parties involved, enhance transparency in pharmaceutical supply chains, empower patients in managing their health information, and address regulatory concerns. The results of this study recommends Healthcare organizations to consider implementing secure transaction protocols to ensure the confidentiality and integrity of their data. Also to explore the use of Hyperledger Fabric architecture, a blockchain framework specifically designed for enterprise applications, Implementation of Secure Transaction Protocols, Utilization of Blockchain for Data Sharing, Adoption of Lightweight Consensus Protocols. By leveraging blockchain technology, healthcare businesses may securely share data with other trusted parties, improving interoperability and data exchange. Additionally, adopting lightweight consensus methods can help streamline decision-making processes within the organization. Future research shall involve the integration of blockchain technology with artificial intelligence and data analytics, the establishment of decentralized parallel healthcare organizations, the integration of blockchain with cloud computing, and the resolution of regulatory and standards difficulties

KSII Transactions on Internet and Information Systems TIIS Paper Details

KSII Transactions on Internet and Information Systems TIIS Paper Details

Using Unified Modeling Language (UML) notations and tools to represent structure, behavior, architecture, and business processes is crucial when specifying newly developing systems. UML has become the de facto standard in software engineering and development. Numerous tools support UML modeling, helping project managers, system analysts, business analysts, and architects create UML diagrams through both text-based instructions and visual drawing tools. These tools often have their own unique notations for text-based instructions. In this paper, we review a selection of popular UML textual notations and introduce a new formalized textual language called Standard Textual Unified Modeling Language (STUML). STUML is designed to standardize the way UML notations are expressed in text-based tools. It can be used directly or mapped to the notations of existing textual and graphical tools. This new language is intended for various types of diagrams, including class and use case diagrams, and is crafted to be easy to write, easy to read, and usable without requiring special editors. STUML has the potential to become the standard for textual UML notations.