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: Thin film & Quantum efficiency. The organization has 618 authors who have published 940 publications receiving 10674 citations.

A hybrid list-based task scheduling scheme for heterogeneous computing

Abstract: Efficient task scheduling is required to attain high performance in both homogeneous and heterogeneous computing systems. An application can be considered as a task grid represented using a Directed Acyclic Graph (DAG). Solving such DAG representing a scheduling problem is an NP-complete task. The primary concern in this problem domain is to reduce the schedule length with minimum complexity. This work presents a Hybrid List-based Task Scheduling using Duplication Scheme (HLTSD) algorithm for heterogeneous processors. The proposed HLTSD algorithm has the same time complexity as that of the recent state-of-the-art algorithms. However, it produces a minimum cost schedule in comparison with other related methods. This work also presents a mathematical formulation to find task priorities. The processor selection phase is improved by utilizing the techniques, like entry task duplication, insertion-based policy, duplication of parent task on other levels, and balancing the load on each processor. The current proposal minimizes the overall makespan of execution by reasonable levels. Performance of the proposed algorithm is evaluated using DAGs adopted from various state-of-the-art algorithms, real-world problems, like Gaussian elimination (GE) and fast Fourier transformation (FFT) task graph and randomly generated graphs with diverse characteristics. The proposed scheme is compared with four state-of-the-art list-based scheduling algorithms, namely Heterogeneous Earliest Finish Time (HEFT), Predict Earliest Finish Time (PEFT), Heterogeneous Scheduling with Improved task Priority (HSIP), and Task Scheduling for Heterogeneous Computing Systems (TSHCS). Based on the best quality schedule, the obtained results suggest that HLTSD has better results in 87% cases.

Application of MXenes in Perovskite Solar Cells: A Short Review.

Abstract: Application of MXene materials in perovskite solar cells (PSCs) has attracted considerable attention owing to their supreme electrical conductivity, excellent carrier mobility, adjustable surface functional groups, excellent transparency and superior mechanical properties. This article reviews the progress made so far in using Ti3C2Tx MXene materials in the building blocks of perovskite solar cells such as electrodes, hole transport layer (HTL), electron transport layer (ETL) and perovskite photoactive layer. Moreover, we provide an outlook on the exciting opportunities this recently developed field offers, and the challenges faced in effectively incorporating MXene materials in the building blocks of PSCs for better operational stability and enhanced performance.

ENHANCEMENT IN THE SENSING PROPERTIES OF METHYL ORANGE THIN FILM BY TiO2 NANOPARTICLES

Abstract: This paper reports the enhancement in the sensing properties of organic dye methyl orange (MO) by introducing TiO2 nanoparticles. For this purpose, two surface type Ag/MO/Ag and Ag/MO:TiO2/Ag multifunctional sensors were fabricated by spin coating a 3.0 wt.% solution of MO and 3.0:0.3 wt.% of MO:TiO2 composite on pre-patterned silver (Ag) electrodes. The gap between Ag electrodes was 40 μm. The Ag/MO/Ag and Ag/MO:TiO2/Ag structures were characterized to investigate their response towards humidity and temperature variations. The Ag/MO:TiO2/Ag sensor exhibited better sensitivity and response time than Ag/MO/Ag sensor. The large surface to volume ratio of TiO2 nanoparticles is the primary reason for the higher sensitivity of Ag/MO:TiO2/Ag sensor. The sensors can be used to detect humidity variations from 30% to 95% RH and temperature variation from 30°C to 200°C with good stability. Surface morphologies of the film were investigated by scanning electron microscope (SEM).

On the Effects of Process Parameters and Optimization of Interlaminate Bond Strength in 3D Printed ABS/CF-PLA Composite.

Abstract: The scope of additive manufacturing, particularly fused deposition modelling (FDM), can indeed be explored with the fabrication of multi-material composite laminates using this technology. Laminar composite structures made up of two distinct materials, namely acrylonitrile butadiene styrene (ABS) and carbon fiber reinforced polylactic acid (CF-PLA), were produced using the FDM process. The current study analyzes the effect of various printing parameters on the interfacial bond strength (IFBS) of the ABS/CF-PLA laminar composite by employing response surface methodology. The physical examination of the tested specimens revealed two failure modes, where failure mode 1 possessed high IFBS owing to the phenomenon of material patch transfer. Contrarily, failure mode 2 yielded low IFBS, while no patch transfer was observed. The analysis of variance (ANOVA) revealed that printing parameters were highly interactive in nature. After extensive experimentation, it was revealed that good quality of IFBS is attributed to the medium range of printing speed, high infill density, and low layer height. At the same time, a maximum IFBS of 20.5 MPa was achieved. The study presented an empirical relation between printing parameters and IFBS that can help in forecasting IFBS at any given printing parameters. Finally, the optimized printing conditions were also determined with the aim to maximize IFBS.

Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations.

Abstract: Understanding how a mutation might affect protein stability is of significant importance to protein engineering and for understanding protein evolution genetic diseases While a number of computational tools have been developed to predict the effect of missense mutations on protein stability protein stability upon mutations, they are known to exhibit large biases imparted in part by the data used to train and evaluate them Here, we provide a comprehensive overview of predictive tools, which has provided an evolving insight into the importance and relevance of features that can discern the effects of mutations on protein stability A diverse selection of these freely available tools was benchmarked using a large mutation-level blind dataset of 1342 experimentally characterised mutations across 130 proteins from ThermoMutDB, a second test dataset encompassing 630 experimentally characterised mutations across 39 proteins from iStable20 and a third blind test dataset consisting of 268 mutations in 27 proteins from the newly published ProThermDB The performance of the methods was further evaluated with respect to the site of mutation, type of mutant residue and by ranging the pH and temperature Additionally, the classification performance was also evaluated by classifying the mutations as stabilizing (∆∆G ≥ 0) or destabilizing (∆∆G < 0) The results reveal that the performance of the predictors is affected by the site of mutation and the type of mutant residue Further, the results show very low performance for pH values 6-8 and temperature higher than 65 for all predictors except iStable20 on the S630 dataset To illustrate how stability and structure change upon single point mutation, we considered four stabilizing, two destabilizing and two stabilizing mutations from two proteins, namely the toxin protein and bovine liver cytochrome Overall, the results on S268, S630 and S1342 datasets show that the performance of the integrated predictors is better than the mechanistic or individual machine learning predictors We expect that this paper will provide useful guidance for the design and development of next-generation bioinformatic tools for predicting protein stability changes upon mutations