Excess heat significantly reduces the efficiency and lifespan of electrical and optoelectronic devices. While passive radiative cooling is becoming more common, achieving active thermal control without physical reconfiguration remains challenging. Unlike conventional static absorbers, we propose a broadband plasmonic solar absorber designed to regulate energy absorption in the near-infrared (NIR) region without modifying the geometrical parameters. The design utilizes a coaxial cylindrical metal-insulator-metal (MIM) configuration, combining refractory copper (Cu), silicon dioxide (SiO2), and a trilayer graphene (Gr) that allows electrical tuning in a broadband solar absorber. Simulation results show a maximum broadband absorption efficienc

Background: Normal thyroid function is essential for neonatal growth and brain development. In a newborn infant with severe disease, endocrine regulation of hormones can be affected by abnormal metabolism. The assessment of thyroid parameters results in the recognition of a dysfunction and its association with disease severity. Objective: This study aimed to assess thyroid function profiles in critically ill neonates in the neonatal intensive care unit (NICU) compared with healthy controls. Additionally, we aimed to detect the presence of TD and its possible association with critical illness. Methods: A case-control study was performed in 100 neonates, comprising 50 sick neonates and 50 healthy controls. We measured thyroid functio

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm

Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

An analysis of three selected wells in the Ain Zala oil field (AZ-16, AZ-17, and AZ-24) in the northwestern part of Mosul Governorate. In terms of biostratigraphy, it consists of planktonic and suspended foraminifera, which comprise 37 species belonging to 17 genera. Based on the recorded planktonic foraminifera, three main biozones were identified: Globotruncana aegyptiaca Partial Zone, which refers to the age of the Late Campanian, Gansserina gansseri Partial Range Zone, which indicates the age of the Early Maastrichtian, and Abathomphalus mayaroensis Interval Zone, which indicates the age of the Late Maastrichtian. The Abathomphalus mayaroensis Interval Zone is divided into four subzones: Racemiguembelina fructicosa Partial Range

African-American writers during the 19th century wrote in the shadow of the prominent romance, sentimental, and domestic fiction. Harriet Wilson’s Our Nig (1859) reflects an “alternative social character”, for the female protagonist suffers racism in the free North, because she is a mulatto child. Through depicting the life of free blacks, who supposedly lives a better life than Southern slaves, Wilson exposes how she has actually lived and sensed life in antebellum America. According to Raymond Williams (2011), there are two kinds of literary writings. The first represents the general tendency of the age, and he calls it “dominant social character”; representing the majority content of both the public writing and speaking. But, a

Blastocystosis is symptomatic infection caused by the protozoal parasite Blastocystis , which resides in the intestinal tract of its hosts and it is one of the most common parasites reported in humans. It’s prevalence ranges between (30 - 50%) of the population in developing countries. This genus has a worldwide distribution and often the most commonly reported human intestinal protozoan in children and adults, even infect infants

In this paper the variable structure control theory is utilized to derive a discontinuous controller to the magnetic levitation system. The magnetic levitation system model is considered uncertain, which subjected to the uncertainty in system parameters, also it is open-loop unstable and strongly nonlinear. The proposed variable structure control to magnetic levitation system is proved, and the area of attraction is determined. Additionally, the chattering, which induced due to the discontinuity in control law, is attenuated by using a non-smooth approximate. With this approximation the resulted controller is a continuous variable structure controller with a determined steady state error according to the selected control