Cardiac rhythm disorders can manifest in various ways, such as the heart rate being too fast (tachycardia) or too slow (bradycardia), irregular heartbeats (like atrial fibrillation-AF, ventricular fibrillation-VF), or the initiation of heartbeats in different areas from the norm (extrasystole). Arrhythmias can disrupt the balanced circulation, leading to serious complications like heart attacks, strokes, and sudden death. Medical devices like electrocardiography (ECG) and Holter monitors are commonly used for diagnosing and monitoring cardiac rhythm disorders. However, in recent years, the development of wearable devices has played a significant role in the detection and diagnosis of rhythm disorders through the use of photoplethysmography (PPG) signals. Wearable devices enable patients to continuously monitor their health status and allow doctors to provide earlier diagnoses and interventions. In this study, a 1D-CNN model is proposed to detect arrhythmias using PPG signals. A dataset prepared by the University of Massachusetts Medical Center (UMMC) containing both ECG and PPG signal data was utilized. In this dataset, ECG signals are filtered with a bandpass filter and raw PPG signals are divided into 30-second segments. Accuracy values were obtained by classifying ECG and PPG signals using a 1D CNN model. ECG signals were used as a reference. The proposed model achieved a 95.17% accuracy rate in detecting normal sinus rhythm (NSR), atrial fibrillation (AF), and premature atrial contractions (PAC) from PPG signals. Datasets are available for download on https://www.synapse.org/pulsewatch. The codes used in this study are available on the https://github.com/miraygunay/PPG-Code.git website.

One of the most important problems in the widespread use of electric vehicles is the lack of charging infrastructure. Especially in tourist areas where historical buildings are located, the installation of a power grid for the installation of electric vehicle charging stations or generating electrical energy by installing renewable energy production systems such as large-sized PV (photovoltaic) and wind turbines poses a problem because it causes the deterioration of the historical texture. Considering the need for renewable energy sources in the transportation sector, our aim in this study is to model an electric vehicle charging station using PVPS (photovoltaic power system) and FC (fuel cell) power systems by using irradiation and temperature data from historical regions. This designed charging station model performs electric vehicle charging, meeting the energy demand of a house and hydrogen production by feeding the electrolyzer with the surplus energy from producing electrical energy with the PVPS during the daytime. At night, when there is no solar radiation, electric vehicle charging and residential energy demand are met with an FC power system. One of the most important advantages of this system is the use of hydrogen storage instead of a battery system for energy storage and the conversion of hydrogen into electrical energy with an FC. Unlike other studies, in our study, fossil energy sources such as diesel generators are not included for the stable operation of the system. The system in this study may need hydrogen refueling in unfavorable climatic conditions and the energy storage capacity is limited by the hydrogen fuel tank capacity.