بحوث سكوبس — احمد عدنان هادي

This paper presents a multifunctional optical sensor that exploits the excitation of Tamm plasmon polaritons (TPPs) in a Fabry-Perot-like structure. The proposed sensor can be used in various applications and, most importantly, biological and high-temperature applications in the THz range. The sensor consists of a one-dimensional dielectric-metallic photonic crystal (DM-PC), an analyte detection region, a spacer layer followed by a graphene sheet. The sensor's performance is analyzed utilizing the transfer matrix method under different conditions, such as the graphene sheet's existence, replacing the metallic layers with a dielectric material, graphene's chemical potential variations, the thickness of the layers, as well as the polarization and angle of the incident light. The numerical findings indicate that TPPs are stimulated by using the graphene sheet and PC. Furthermore, using the DM-PC results in sensor thinness and high absorptivity. In addition to achieving perfect absorptions of 100 %, the sensor's sensitivity can reach up to 1.2275 THz/RIU (equivalent to 240.449 μm/RIU) for the analyte of air by controlling the geometrical parameters of the structure. The sensor can detect the blood components of water, plasma, white and red blood cells, and hemoglobin with a sensitivity of 0.6975 THz/RIU (equivalent to 240.913 μm/RIU). Furthermore, the sensor acts as a temperature sensor, measuring temperature changes with a temperature sensitivity of 0.0607 THz/°C (equivalent to 15.03 nm/°C). © 2024

COVID-19 gains from the research and technology component's establishment of information science, artificial intelligence, and computer understanding. The article aims to discuss the numerous facets of today's modern technology utilized to combat COVID-19 emergencies on various scales, such as medicinal picture handling, illness tracking, expected outcomes, computational science, and medications. Techniques: A complex search of the knowledge base associated with existing COVID-19 innovation is conducted. Furthermore, a concise survey of the excluded data is conducted, analyzing the various aspects of current developments for dealing with the COVID-19 pandemic. The below are the outcomes: We have a window of musings on the audit of the tech propellers used to mitigate and mask the significant impact of the upheaval. Even though several investigations into current innovation in COVID-19 have surfaced, there are still required implementations and contributions of innovation in this war. Consequently, a thorough presentation of the available data is given, and several modern technology implementations for combating the pandemic of COVID-19. Continuous advancements of advanced technologies have aided in improving the public's lives, and there is a strong belief that proven study plans utilizing AI would be of great benefit in assisting people in combating this infection. © 2021

The volatility and complexity of the stock market make it difficult to predict stock values accurately. The primary goal of this paper is to overcome some of these difficulties by training the data to anticipate stock prices based on sentiment analysis of tweets. Using natural language processing (NLP) technology, the tweet sentiments were categorized into (positive-neutral-negative). The stock price was predicted using deep learning algorithms (CNNs, RNNs, LSTMs, BiLSTMs). Among the algorithms, (BiLSTM) achieved the best results in terms of accuracy (94%) and the others (CNN=90%, RNN=91%, LSTM=92%). The paper also confirms that the average MSE and RMSE (MSE=0.03552, RMSE=0.1882064) for the BiLSTM algorithm are achieved (MSE=0.03552, RMSE=0.1882064). As a result, the obtained results were better than previous studies. © 2023, Penerbit Akademia Baru. All rights reserved.