Pancreatic Cancer Data Classification with Quantum Machine Learning

Quantum computing is a promising new approach to tackle the complex real-world computational problems by harnessing the power of quantum mechanics principles.The inherent parallelism and exponential computational power of quantum systems hold the potential to outpace classical counterparts in solving complex optimization problems,which are pervasive in machine learning.Quantum Support Vector Machine(QSVM)is a quantum machine learning algorithm inspired by classical Support Vector Machine(SVM)that exploits quantum parallelism to efficiently classify data points in high-dimensional feature spaces.We provide a comprehensive overview of the underlying principles of QSVM,elucidating how different quantum feature maps and quantum kernels enable the manipulation of quantum states to perform classification tasks.Through a comparative analysis,we reveal the quantum advantage achieved by these algorithms in terms of speedup and solution quality.As a case study,we explored the potential of quantum paradigms in the context of a real-world problem:classifying pancreatic cancer biomarker data.The Support Vector Classifier(SVC)algorithm was employed for the classical approach while the QSVM algorithm was executed on a quantum simulator provided by the Qiskit quantum computing framework.The classical approach as well as the quantum-based techniques reported similar accuracy.This uniformity suggests that these methods effectively captured similar underlying patterns in the dataset.Remarkably,quantum implementations exhibited substantially reduced execution times demonstrating the potential of quantum approaches in enhancing classification efficiency.This affirms the growing significance of quantum computing as a transformative tool for augmenting machine learning paradigms and also underscores the potency of quantum execution for computational acceleration.

High Oxygen Nanocomposite Barrier Films Based on Xylan and Nanocrystalline Cellulose

The goal of this work is to produce nanocomposite film with low oxygen permeability by casting an aqueous solution containing xylan,sorbitol and nanocrystalline cellulose.The morphology of the resulting nanocomposite films was examined by scanning electron microscopy and atomic force microscopy which showed that control films containing xylan and sorbitol had a more open structure as compared to xylan-sorbitol films containing sulfonated nanocrystalline cellulose.The average pore diameter,bulk density,porosity and tortuosity factor measurements of control xylan films and nanocomposite xylan films were examined by mercury intrusion porosimetry techniques.Xylan films reinforced with nanocrystalline cellulose were denser and exhibited higher tortuosity factor than the control xylan films.Control xylan films had average pore diameter,bulk density,porosity and tortuosity factor of 0.1730 μm,0.6165 g/ml,53.0161% and 1.258,respectively as compared to xylan films reinforced with 50% nanocrystalline cellulose with average pore diameter of 0.0581 μm,bulk density of 1.1513 g/ml,porosity of 22.8906% and tortuosity factor of 2.005.Oxygen transmission rate tests demonstrated that films prepared with xylan,sorbitol and 5%,10%,25% and 50% sulfonated nanocrystalline cellulose exhibited a significantly reduced oxygen permeability of 1.1387,1.0933,0.8986 and 0.1799 cm^3×μm/m^2×d×k Pa respectively with respect to films prepared solely from xylan and sorbitol with a oxygen permeability of 189.1665 cm^3×μm/m^2×d×k Pa.These properties suggested these nanocomposite films have promising barrier properties.

Effect of heterogeneity and injection rates on the recovery of oil from conventional sand packs:A simulation approach

The study of flow through porous media has been of cardinal gravity in oil and gas applications like enhanced oil recovery(EOR),acidizing,fracturing,etc.One of the most anticipated apprehensions is that the core flooding and simulation have become prevalent to understand the flow through porous media.This study aims at simulating and analyzing the effect of alkaline surfactant flooding through heterogeneous permeability conditions in the lab-scale methods.The conventional methods of core flood deal with the effective permeability of the system without considering the effect of heterogeneity within the core.The heterogeneous studies are conducted by simulating a finely meshed 2-D axisymmetric model of the sand pack.The novel Karanj oil surfactant extracted from Pongamia Pinnata,and partially hydrolyzed polyacryl amide polymer are considered for the physicochemical properties of displacing fluid used in the simulations.Different heterogeneity combinations and displacing fluid injection flow rates are introduced for a single absolute permeability system.Results indicate a trend of oil recovery upright with increasing vertical permeability.A lower areal sweep efficiency and early breakthrough are observed in models with high horizontal permeabilities.Further,the effect of heterogeneity on oil recovery is reduced with the increase in injection flow rates of displacing fluid.The present work utilizes computational fluid dynamics to model multiphase flow through a heterogeneous permeability media and its effect on oil recovery.