The growing interest in renewable energy and the falling prices of solar panels place solar electricity in a favourable position for adoption. However, the high-rate adoption of intermittent renewable energy introduces challenges and the potential to create power instability between the available power generation and the load demand. Hence, accurate solar Photovoltaic (PV) power forecasting is essential to maintain system reliability and maximize renewable energy integration. The current solar PV power forecasting approaches are an essential tool to maintain system reliability and maximize renewable energy integration. This paper presents a comprehensive and comparative review of existing Machine Learning (ML) based approaches used in PV power forecasting, focusing on short-term horizons. We provide an overview of factors affecting solar PV power forecasting and an overview of existing PV power forecasting methods in the literature, with a specific focus on ML-based models. To further enhance the comparison and provide more insights into the advancement in the area, we simulate the performance of different ML methods used in solar PV power forecasting and, finally, a discussion on the results of the work.

Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, divided into training and validation subsets and augmented before training. The model achieved a remarkable accuracy of 94%, with 95.12% of cancerous cases correctly identified and 93.02% of healthy cells accurately classified. The significance of this study lies in overcoming the challenges associated with the human expert examination, such as higher misclassification rates, inter-observer variability, and extended analysis times. This study presents a more accurate, efficient, and reliable approach to predicting and diagnosing ovarian cancer. Future research should explore recent advances in this field to enhance the effectiveness of the proposed method further.

The rapid development of wireless technology has sparked interest in multi-band reconfigurable antennas as devices and satellites are innovating toward miniaturization. With limited space, reliable and efficient high bandwidth antenna systems are needed for current and next-generation wireless technology as well as for the revolutionary small satellites. The fifth generation of mobile communication technology promises high data rates, low latency and good spectrum efficiency. One of the key enablers of this technology is the integration of satellite technology-particularly CubeSats with terrestrial communication technologies. Next-generation antennas that can meet functional requirements for 5G and CubeSat applications are therefore of fundamental importance. These antenna systems should have large bandwidth, high gain and efficiency and be compact in size. Reconfigurable antennas can provide different configurations in terms of the operating frequency, radiation pattern and polarization. Tuning reconfigurable antennas can be done by changing the physical parameters of the antenna elements through electronic switches, optical switches and the use of meta-materials. The most popular implementation method for reconfigurable antennas for wireless and satellite communication is the electronic switching method due to its high reliability, efficiency, and ease of integration with microwave circuitry. In this article, different techniques for implementing reconfigurable and multi-band antennas are reviewed, with emphasis on two main application areas; 5G wireless communication and CubeSat application. Different reconfiguration techniques have been studied for application in various wireless communication systems such as satellite communication, multiple-input multiple-output (MIMO) systems, cognitive radio and 5G communication. It has been found that reconfigurable antennas have favourable properties for next-generation wireless technology and small satellites. These properties include low cost, less volume requirements and good isolation between wireless standards.