Yousef Jaradat

As the global demand for clean and sustainable energy grows, photovoltaics (PVs) have become an important technology in this industry. Thin-film and flexible PV modules offer noticeable advantages for irregular surface mounts and mobile applications. This study investigates the use of four machine learning models to detect different flexible PV module geometries based on power output data. Three identical flexible PV modules were mounted in flat, concave, and convex configurations and connected to batteries via solar chargers. The experimental results showed that all geometries fully charged their batteries within 6–7 h on a sunny day with the flat, concave-, and convex-shaped modules achieving a peak power of 95 W. On a cloudy day, the concave and convex modules recorded peak outputs of 72 W and 65 W, respectively. Simulation results showed that the XGBoost model delivered the best classification performance, showing 93% precision with the flat-mounted module and 98% recall across all geometries. In comparison, the KAN model recorded the lowest precision (78%) with the curved geometries. A calibration analysis on the ML models showed that Random Forest and XGBoost were well calibrated for the flat-mounted module. However, they also showed overconfidence and underconfidence issues with the curved module geometries.

Quantum computing applies quantum physics ideas to problems that traditional computers cannot address. The qubit, or quantum equivalent of the classical bit, is fundamental to this paradigm shift. Unlike its classical equivalent, a qubit can exist in a superposition of states, representing both 0 and 1. This superposition is defined not only by magnitudes, but also by important phase variables. These phases have a significant impact on qubit behavior and quantum computation outputs. This work conducts a thorough investigation of qubit phases, exploring their tremendous impact on the efficacy and capabilities of quantum algorithms. We investigate how constructive and destructive interference caused by phase interactions provides the foundation of quantum algorithms. Furthermore, we look into the intricate role of phases in establishing and managing entanglement, a unique quantum phenomenon that allows tremendous interactions between qubits. Our investigation includes the effects of numerous quantum operations on qubit phases. We present a thorough mathematical framework for describing how typical quantum gates, such as Hadamard, Pauli, and phase-shift gates, change the phase and thus the overall state of a qubit. We show these concepts through actual implementations of the Qiskit library. Finally, we discuss the intrinsic difficulty of managing and monitoring qubit phases, particularly the negative impacts of decoherence, which disrupts the delicate phase relationships. We describe tactics for mitigating these obstacles and investigate techniques for extracting phase information indirectly, such as quantum state tomography and interferometry. This comprehensive study seeks to provide a better understanding of the critical role phases play in quantum computing, paving the way for advances in algorithm design, quantum control, and the development of fault-tolerant quantum computers.

Bifacial photovoltaics have gained a lot of popularity in recent years given their ability to utilize scattered and reflected solar radiation from both sides of the panel. Although the price of a Bifacial module is generally higher than conventional mono-photovoltaic panel, it compensates for the higher energy generation per unit area. Even though the potential of Bifacial photovoltaics market is promising, their applications are still limited compared to mono-photovoltaics. However, researchers have been experimenting with Bifacial photovoltaics to exploit their capabilities in different applications and working scenarios, especially with Artificial Intelligence (AI) models. This study will focus on reviewing different Machine Learning (ML) algorithms that have been exploited and modified in order to be used with Bifacial system applications in the last three years of literature. Moreover, most popular ML algorithms are presented and discussed with respect to different Bifacial system parameters. Finally, a conclusion of future prospects and the potential of ML in bifacial photovoltaic industry and applications is presented.

This review paper offers a thorough analysis of different data imputation methods that can be applied to time series data. Time series data is an essential element in various analytical and predictive models used in different domains. Time series data frequently experiences missing values as a result of diverse factors, such as system errors, human influences, or inherent gaps in data collection. The presence of these missing values significantly undermines the precision and dependability of models constructed using this data. This paper classifies imputation methods into basic and advanced techniques, providing a comprehensive examination of each. The simplicity and effectiveness of basic techniques, such as mean or median imputation and linear interpolation, are discussed in specific scenarios. The study investigates the efficacy of advanced techniques, such as ARIMA statistical models, K-Nearest Neighbors machine learning approaches, and Long Short-Term Memory networks deep learning techniques, in managing intricate and extensive time series datasets. The paper emphasizes a comparative approach, assessing each method's complexity, accuracy, and computational demands. The paper concludes by emphasizing the need for continuous innovation in imputation techniques to meet the growing complexity of time series data across various domains. It advocates for a collaborative approach that combines domain expertise with advanced data science methods to develop tailored, efficient, and accurate imputation strategies.

This paper explains the roadmap for running quantum circuit programs based on the Qiskit library in quantum simulators as well as real cloud-based IBM quantum computers. Qiskit is a free and open-source software development platform based on the Python programming language that is used in quantum programming. Qiskit acts as a link between quantum computing’s theoretical foundations and the practical aspects of programming and experimentation. It also allows users to experiment with and develop quantum algorithms, as well as simulate and execute them on simulators and real-world cloud-based quantum devices. It also simplifies the quantum programming process and allows a diverse range of people to participate in the exciting world of quantum computing. The paper, on the other hand, provides the mathematical foundation for analyzing quantum circuits and algorithms using linear algebra principles, as they provide the tools needed to describe and manipulate quantum states and operations. Furthermore, the paper shows quantum circuit design and implementation using real Qiskit codes.