Low-temperature stratification, high-volumetric storage capacity, and less-complicated material processing make phase-changing materials (PCMs) very suitable candidates for solar energy storage applications. However, their poor heat diffusivities and suboptimal containment designs severely limit their decent storage capabilities. In these systems, the arrangement of tubes conveying the heat transport fluid (HTF) plays a crucial role in heat communication between the PCM and HTF during phase transition. This study investigates a helical coil tube-and-shell thermal storage system integrated with a novel central return tube to enhance heat transfer effectiveness. Three-dimensional computational fluid dynamics simulations compare the proposed d

This article presents the simultaneous adsorption of bimetal Cu²⁺ and Zn²⁺ from an aqueous solution using activated carbon synthesized from a plum seed precursor by sulfuric acid and microwave activation: plum seeds chemically activated by 45% (w/w) sulfuric acid with 2:1 ratio for 4 h, then carbonized for 2 h at 700 °C and the product obtained activated in a microwave oven for 20 min at 700 W for final of activation. Plum seeds and activated carbon produced were characterized in terms of their physical and chemical composition using Brunauer–Emmett–Teller measurements, field emission scanning electr

Designing high-performance solid polymer electrolytes (SPEs) remains a key challenge, primarily due to their inherently low ionic conductivity, which hampers the advancement of all-solid-state lithium-ion batteries (ASSLiBs). In this work, we address this limitation by developing a green, crosslinked polymer blend comprising carboxymethyl cellulose (CMC) - extracted and functionalized from palm frond fibers - and poly (vinyl alcohol) (PVA). Citric acid (CA) was utilized as a crosslinking agent, while lithium perchlorate (LiClO4) was incorporated as a dopant to enhance ionic conductivity. The SPE membranes were fabricated via solution casting, with the optimized formulation - designated CP-15X-Li25 - containing 15 wt.-% CA and 25 wt.-% LiClO

Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise