Phase change materials are extensively studied for use in low-, mid-, and high-temperature applications due to their melting and solidification temperatures, latent heat, and thermophysical properties. This work aims to explore the energy stored, or released and their duration for the energy storage unit formed of a phase change material surrounding a tube within which a hot or cold, single or Two-Phase fluid flows, serving as a heat source or sink. The 3D axial transient thermal analysis of the energy storage unit is performed using the finite element method via a MATLAB-developed computer program. The effects of single- or Two-Phase fluid flow on temperature distribution, solidification, melting duration, and energy stored within phase ch

2-amino-4-(4-chloro phenyl)-1,3-thiazole (1) was synthesized by refluxing thiourea with para-chloro phenacyl bromide in absolute methanol. The condensation of amine compound (1)  with phenylisothiocyanate in the presence of pyridine will  produce 1-(4-(4-chlorophenyl)thiazol-2-yl)-3-phenylthiourea(2), which is  upon treatment with 2,4 dinitrophenyl hydrazine by conventional method, afforded 1- ( 4 - ( 4 – chlorophenyl ) thiazol – 2 – yl ) – 3 - phenylhydrazonamide,N' - ( 2 , 4 -dinitrophenyl) ,(3).The characterization of the titled compounds were performed utilizing FTIR spectroscopy, ¹HNMR and CHNS elemental analysis, and by me

Background/purpose: Dental implantology involves different treatments that have been used in conjunction with dental implant surgery to increase implant stability and bone regeneration process. Photobiomodulation( PBM) can be one of these techniques. The objective of this study was to evaluate the bone density around implants. Materials and methods: in this study, 10 individuals had 20 implants inserted in the posterior of their mandibles. each patient received two implants the left side served as the control whereas the right side served as the study group with a diode laser (same patients). measurements were made for each implant. Measurements were obtained using cone-

Digital tampering identification, which detects picture modification, is a significant area of image analysis studies. This area has grown with time with exceptional precision employing machine learning and deep learning-based strategies during the last five years. Synthesis and reinforcement-based learning techniques must now evolve to keep with the research. However, before doing any experimentation, a scientist must first comprehend the current state of the art in that domain. Diverse paths, associated outcomes, and analysis lay the groundwork for successful experimentation and superior results. Before starting with experiments, universal image forensics approaches must be thoroughly researched. As a result, this review of variou

Community detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can.Therefore,we utilized two objective

Early and accurate detection of COVID-19 from chest computed tomography (CT) scans are becoming essential for effective clinical decision-making and disease control. This study is proposing a robust deep learning framework that integrates a convolutional self-attention network (CSAN), gamma correction for image enhancement, and a voting-based ensemble classifier to improving diagnostic performance. The model is being evaluated on a dataset of 2,271 CT images and is achieving an accuracy of 95.12%, sensitivity of 97.25%, specificity of 98.11%, F1-score of 96.46%, and area under the curve (AUC) of 0.977. Experimental results are demonstrating that the proposed method significantly surpasses baseline models, including standalone CSAN,