Introduction The Hybrid Gamma Camera (HGC) is being developed to enhance the localisation of radiopharmaceutical uptake in targeted tissues during surgical procedures such as sentinel lymph node (SLN) biopsy. Purpose To assess the capability of the HGC, a lymph-node-contrast (LNC) phantom was constructed for an evaluative study simulating medical scenarios of varying radioactivity concentration and SLN size. Materials and methods The phantom was constructed using two methyl methacrylate PMMA plates (8 mm thick). The SLNs were simulated by drilling circular wells of diameters ranging between 10 mm and 2.5 mm (16 wells in total) in one plate. These simulated SLNs were placed underneath scattering material with thicknesses ranging between 5 mm and 40 mm. The second plate contains four rectangular wells to simulate background activity uptake surrounding the SLNs. The activity used ranged between 4 MBq and 0.025 MBq for the SLNs. The background activity was 1/10 of the SLNs activity. The collimator to source distance was 120 mm. Results Signal to Noise Ratio (SNR) analysis and spatial resolution measurements of the simulated SLN were used to compare the imaging sets over acquisition times ranging between 60s and 240s. The HGC successfully detected 87.5% to 100% of the SLNs through 20mm of scattering material, and it could detect 75% to 93.75% of the SLNs through 40mm of scattering material. Measurement of Full-Width-at-Half-Maximum (FWHM) for the detected SLNs ranged between 9.5 mm and 12 mm. Conclusion The HGC is capable of detecting low activity uptake in small SLNs indicating its usefulness as an intraoperative imaging system during surgical SLN procedures.
Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
... Show MoreIn this research was conducted to provide a product to analyze the performance sensor fiber optic used to measure and feel the intensity of the electric field results showed obtained that use sensor long gives reactive high electric field strength and a high value for allergic sensor, but that is at the expense of reducing the intensity of the electric field that is detected
Background: Diagnosis and treatment planning can be difficult with conventional radiographic methods as the orthodontic-surgical management of impacted canines requires accurate diagnosis and precise localization of the impacted canine and the surrounding structures. This study was aimed to localize and evaluate weather there is any differences in the diagnostic information provided by multi-slice computed tomography three dimensional volumetric CT images and two dimensional reconstructed panorama images (derived from CT) in subjects with impacted maxillary canines. Materials and Methods: Thirty patients including 24 female and 6 male with mean age of 18 years with suspected unilaterally or bilaterally impacted maxillary canines were evalu
... Show MoreWireless sensor applications are susceptible to energy constraints. Most of the energy is consumed in communication between wireless nodes. Clustering and data aggregation are the two widely used strategies for reducing energy usage and increasing the lifetime of wireless sensor networks. In target tracking applications, large amount of redundant data is produced regularly. Hence, deployment of effective data aggregation schemes is vital to eliminate data redundancy. This work aims to conduct a comparative study of various research approaches that employ clustering techniques for efficiently aggregating data in target tracking applications as selection of an appropriate clustering algorithm may reflect positive results in the data aggregati
... Show MoreYY Lazim, NAB Azizan, 2nd International Conference on Innovation and Entrepreneurship, 2014
A mathematical model has been formulated to predict the influence of high outdoor air temperature on the performance of small scale air - conditioning system using R22 and alternative refrigerants R290, R407C, R410A. All refrigerants were investigated in the cooling mode operation. The mathematical model results have been validated with experimental data extracted from split type air conditioner of 2 TR capacity. This entailed the construction of an experimental test rig which consists of four main parts. They are, the refrigeration system, psychrometric test facility, measuring instrumentation, and auxiliary systems. The conditioned air was maintained at 25 0C dry bulb and 19 0C wet bulb for all tests. The outdoor ambient air temperatur
... Show MorePlumbago (Plumbaginaceae) is a genus of 10-20 species of flowering plants used in traditional Indian medicine, native to warm temperature to tropical regions of the world. The roots of Plumbago europaea, the Iraqi species of Plumbago, have been used for the treatment of cancer, rheumatoid arthritis, and dysmenorrhea. The main active constituents from dried powdered leaves and roots of Plumbago europaea were extracted by Soxhlet apparatus using ethyl acetate, the main active constituent was characterized by spectroscopic analysis (IR, 1H NMR, and 13C NMR) as plumbagin. Quantitative and qualitative study of plumbagin in the roots and leaves extracts was carried out by HP
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