Ghulam Ishaq Khan Institute of Engineering Sciences and Technology

About: Ghulam Ishaq Khan Institute of Engineering Sciences and Technology is a education organization based out in Topi, Pakistan. It is known for research contribution in the topics: Quantum efficiency & Diode. The organization has 618 authors who have published 940 publications receiving 10674 citations.

Detection of subclinical rheumatic heart disease in children using a deep learning algorithm on digital stethoscope: a study protocol.

Abstract: Introduction Rheumatic heart diseases (RHDs) contribute significant morbidity and mortality globally. To reduce the burden of RHD, timely initiation of secondary prophylaxis is important. The objectives of this study are to determine the frequency of subclinical RHD and to train a deep learning (DL) algorithm using waveform data from the digital auscultatory stethoscope (DAS) in predicting subclinical RHD. Methods and analysis We aim to recruit 1700 children from a group of schools serving the underprivileged over a 12-month period in Karachi (Pakistan). All consenting students within the age of 5–15 years with no underlying congenital heart disease will be eligible for the study. We will gather information regarding sociodemographics, anthropometric data, history of symptoms or diagnosis of rheumatic fever, phonocardiogram (PCG) and electrocardiography (ECG) data obtained from DAS. Handheld echocardiogram will be performed on each study participant to assess the presence of a mitral regurgitation (MR) jet (>1.5 cm), or the presence of aortic regurgitation (AR) in any view. If any of these findings are present, a confirmatory standard echocardiogram using the World Heart Federation (WHF) will be performed to confirm the diagnosis of subclinical RHD. The auscultatory data from digital stethoscope will be used to train the deep neural network for the automatic identification of patients with subclinical RHD. The proposed neural network will be trained in a supervised manner using labels from standard echocardiogram of the participants. Once trained, the neural network will be able to automatically classify the DAS data in one of the three major categories—patient with definite RHD, patient with borderline RHD and normal subject. The significance of the results will be confirmed by standard statistical methods for hypothesis testing. Ethics and dissemination Ethics approval has been taken from the Aga Khan University, Pakistan. Findings will be disseminated through scientific publications and to collaborators. Article focus This study focuses on determining the frequency of subclinical RHD in school-going children in Karachi, Pakistan and developing a DL algorithm to screen for this condition using a digital stethoscope.

Post-Forming Mechanical Properties of a Polymer Sheet Processed by Incremental Sheet Forming: Insights into Effects of Plastic Strain, and Orientation and Size of Specimen.

Abstract: The innovative Incremental Sheet Forming (ISF) process affects the post-forming properties of thermoplastic polymers. However, the effects of degree of plastic strain, and the orientation and size of specimen on the mechanical properties are still unknown. In the present study, therefore, the ISF process is performed on a polymer sheet by varying the plastic strain ranging from 6% to 108%. The corresponding effects on the properties and associated polymer structure are quantified by conducting a variety of mechanical and structural tests. The results reveal that the post-ISF tensile properties like yield stress, ultimate stress, drawing stress, elastic modulus and elongation decrease from 26.6 to 10 MPa, 30.5 to 15.4 MPa, 18.9 to 9.9 MPa, 916 to 300 MPa and 1107% to 457%, respectively, as the strain increases in the investigated range. The value of post-ISF relaxation properties, contrary to the tensile properties, increases with increasing strain up to 62%. Particularly, reductions in stress, strain and modulus increase from 41% to 202%, 37% to 51%, and 41% to 202%. As regard the orientation effect, the sheet in the feed direction shows greater strength than the transverse direction (up to 142% in yield stress and 72% in ultimate stress). Moreover, the smaller sample offers greater strength than the larger one (up to 158% in yield stress and 109% in ultimate stress). The analysis of the post-ISF tensile properties and structural results lead us to conclude that the drop in the tensile properties due to increasing strain occurs due to corresponding increase in the voids area fraction (1.25% to 31%) and a reduction in the crystallinity (38% to 31%).

Synthesis, computational study and characterization of a 3-{[2,3-diphenylquinoxalin-6-yl]diazenyl}-4-hydroxy-2H-chromen-2- one azo dye for dye-sensitized solar cell applications

Abstract: A solution-processable 3-{[2,3-diphenylquinoxalin-6-yl]diazenyl}-4-hydroxy-2H-chromen-2-one azo dye was synthesized. Analysis of measured UV-visible absorption spectrum and frontier orbitals computed using simplified time dependent density functional theory (sTDDFT) revealed its suitability for optoelectronic applications. A dye-sensitized solar cell (DSSC) was fabricated using this metal-free organic dye as a sensitizer. The photovoltaic parameters of the cell were studied under simulated AM 1.5 illumination (100 mWcm $$^{-2}$$ ). Comparing the photovoltaic data with a DSSC using a different member of the azo family of dyes, open circuit voltage and fill factor of the device studied in this work were found higher by 33 and 104%, respectively. The performance was also compared with the DSSCs fabricated using 49 commercial mordant dyes and open circuit photovoltage of the device studied in this work was found higher. To gain insight into its charge transport, impedance spectroscopy was performed. Impedance spectra were observed both voltage and frequency dependent.

Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net

Abstract: MRI images are visually inspected by domain experts for the analysis and quantification of the tumorous tissues. Due to the large volumetric data, manual reporting on the images is subjective, cumbersome, and error prone. To address these problems, automatic image analysis tools are employed for tumor segmentation and other subsequent statistical analysis. However, prior to the tumor analysis and quantification, an important challenge lies in the pre-processing. In the present study, permutations of different pre-processing methods are comprehensively investigated. In particular, the study focused on Gibbs ringing artifact removal, bias field correction, intensity normalization, and adaptive histogram equalization (AHE). The pre-processed MRI data is then passed onto 3D U-Net for automatic segmentation of brain tumors. The segmentation results demonstrated the best performance with the combination of two techniques, i.e., Gibbs ringing artifact removal and bias-field correction. The proposed technique achieved mean dice score metrics of 0.91, 0.86, and 0.70 for the whole tumor, tumor core, and enhancing tumor, respectively. The testing mean dice scores achieved by the system are 0.90, 0.83, and 0.71 for the whole tumor, core tumor, and enhancing tumor, respectively. The novelty of this work concerns a robust pre-processing sequence for improving the segmentation accuracy of MR images. The proposed method overcame the testing dice scores of the state-of-the-art methods. The results are benchmarked with the existing techniques used in the Brain Tumor Segmentation Challenge (BraTS) 2018 challenge.

Physico-mechanical properties of emulsion paint embedded with silver nanoparticles

Abstract: This study reports the influence of silver nanoparticles (AgNPs) on some selected physical and mechanical properties of emulsion paint. AgNPs solution of different concentrations (0, 0.15, 0.175, 0.20 and 0.35 wt% AgNPs) were prepared via green synthesis involving reduction of silver nitrate using extract of cobwebs. Each paint sample contained 0.35 wt% of combined AgNPs and biocide acticide (benzimidazole carbamate, EPW) at different proportions. The samples were then characterized for structural, physical and mechanical properties. Transmission electron microscopy images showed that AgNPs are generally spherical in shape with size in the range of 3–50 nm. X-ray diffraction spectra indicate that the formulation contained Ag, TiO2 and CaCO3. Extensive characterization indicates that the paint containing equal fraction (0.175 wt%) of AgNPs and EPW gave the optimal mix for all physical and mechanical properties examined. The specific gravity was reduced by 16%, the hiding power/opacity increased by 30% while the abrasion strength was enhanced by 236%. Our results were compared with other nanoparticles, especially ZnO and Fe2O3, and found to require lower concentration while creating greater effects. The results of this study will assist paint industries to maximize the benefits of nanotechnology.