Individuals across different industries, including but not limited to agriculture, drones, pharmaceuticals and manufacturing, are increasingly using thermal cameras to achieve various safety and security goals. This widespread adoption is made possible by advancements in thermal imaging sensor technology. The current literature provides an in-depth exploration of thermography camera applications for detecting faults in sectors such as fire protection, manufacturing, aerospace, automotive, non-destructive testing and structural material industries. The current discussion builds on previous studies, emphasising the effectiveness of thermography cameras in distinguishing undetectable defects by the human eye. Various methods for defect

In this investigation, deep eutectic solvent based on propionic acid and choline chloride was used to prepare polyaniline, polypyrrole, and poly(aniline-co-pyrrole) samples using chemical oxidation and polymerization. Quantum mechanical calculations were conducted using time-dependent density functional theory to determine the electronic, spectroscopic properties and structure of homopolymers and the copolymer which were compared with the respective experimental data. Furthermore, the synthesized materials were characterized using Fourier transform IR spectroscopy, scanning electron microscopy (SEM), UV-visible spectroscopy, and thermogravimetric analysis. IR and UV-visible spectra confirmed copolymerization, and SEM revealed that the copol

A cost-effective and efficient detector was created to conduct thorough turbidimetric measurements by reaction of Co (II) ion with calcium ferro cyanide to form bright green particulate, using the method of continuous flow injection analysis, the use of NAG-5SX1-1D-SSP Analyzer in determining cobalt (II) ion in a test for the validity of the new design. The NAG-5SX1-1D-SSP Analyzer is composed of five irradiation sources of white snow leds having the diameter of 10 mm with one solar cell of 55 mm length, 13.5 mm width. Using a selector switch to select the optimum voltage to be used which was 2.7 VDC. Under conditions of optimization, cobalt (II) ion was determined at 0.005–20 mmol. L–1(n = 23) while linearity dynamic range 0.005–7 mm

ABSTRACT Background: This study aimed to study the effect of some acidic drinks (Vinegars and fresh Orange juice) and energy drinks (Red bull) on surface roughness of three types of bulkfill composite materials: Filtek posterior bulkfill (3M), Sonicfill (Kerr) and Filtek p60 (3M). Materials and Methods: Total number of 120 samples are prepared by using a mold of (12mm diameter and 3mm height), which were divided into three groups forty samples for each group: Group A: Filtek bulkfill posterior composite (3M), Group B: Sonicfill composite (Kerr), Group C: Filtek P60 (3 M) which then divided into four sub- groups (n=10) (1) samples were kept in distilled water as a control group (2) samples were immersed in Redbull (3) samples were immersed

Background: Cerium oxide and chitosan (Ce/CS) nanoparticles have garnered significant interest due to their remarkable properties, including antioxidant activity, biocompatibility, antibacterial effectiveness and antiinflammatory activities. This study evaluates the impact of incorporating a Ce/CS nanocomposite into polymethylmethacrylate (PMMA) resin on its mechanical properties. The composite consisted of 30% cerium oxide and 70% chitosan by weight (wt), with varying percentages incorporated into the PMMA resin selected according to a pilot study. Material and Methods: Ninety PMMA specimens were fabricated and categorized into three groups according to the Ce/CS nanocomposite percentage in the PMMA resin: control (0 wt%), group A

The research aims to evaluate the radioactivity in elected samples of cereals and legume which are wide human consumption in Iraq using Nuclear Track Detectors (NTDs) model CN-85.
The samples were prepared scientifically according to references in this field. After 150 days of exposure, the detector were collected and chemically treated according to scientific sources (etching chemical), nuclear effects have been calculated using the optical microscope.
Radon (222Rn) concentration and uranium (238U) were calculated in unit Bq/m3 and (ppm), the results indicate that the highest concentration of radon and uranium was in yellow corn where the concentration of radon was 137.17×102 Bq/m3 and uranium concentration 2.63 (ppm). The lowest

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet

Incorporating waste byproducts into concrete is an innovative and promising way to minimize the environmental impact of waste material while maintaining and/or improving concrete’s mechanical characteristics and strength. The proper application of sawdust as a pozzolan in the building industry remains a significant challenge. Consequently, this study conducted an experimental evaluation of sawdust as a fill material. In particular, sawdust as a fine aggregate in concrete offers a realistic structural and economical possibility for the construction of lightweight structural systems. Failure under four-point loads was investigated for six concrete-filled steel tube (CFST) specimens. The results indicated that recycled lightweight co

Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security