SAPO-34 supported Pt–Sn-based novel catalyst for propane dehydrogenation to propylene

The Pt–Sn-based catalyst was intensified using SAPO-34 as support for direct propane dehydrogenation to propylene. The catalyst was prepared by sequential impregnation method and characterized by XRF, BET, XRD, NH₃-IR, NH₃-TPD, H₂-TPR, HR-TEM and O₂-pulse coke analysis. NH₃-TPD, IR spectra and XRD results suggested that the doping of metals on SAPO-34 did not affect its acidic strength and structural topology of support, respectively. Propylene selectivity of 94% and total olefins selectivity greater than 97% was achieved using Pt–Sn/SAPO-34. The results were compared with Pt–Sn/ZSM-5 under identical conditions. The possible reasons for improvement were the larger surface area, shape selectivity and particular by suitable acidity of SAPO-34.     

Evaluation of process parameter space of bulk FinFETs using 3D TCAD

Using full 3D TCAD, an evaluation of process parameter space of bulk FinFET is presented from the point of view of DRAM, SRAM and I/O applications. Process and device simulations are performed with varying uniform fin doping, anti-punch implant dose and energy, fin width, fin height and gate oxide thickness. Bulk FinFET architecture with anti-punch implant is introduced beneath the channel region to reduce the punch-through and junction leakage. For 30 nm bulk FinFET, anti-punch implant with low energy of 15 to 25 keV and dose of 5.0 × 10¹³ to 1.0 × 10¹⁴ cm⁻² is beneficial to effectively suppress the punch-through leakage with reduced GIDL and short channel effects. Our simulations show that bulk FinFETs are approximately independent of back bias effect. With identical fin geometry, bulk FinFETs with anti-punch implant show same I_ON-I_OFF behavior and approximately equal short channel effects like SOI FinFETs.     

Facile modification of polysulfone hollow-fiber membranes via the incorporation of well-dispersed iron oxide nanoparticles for protein purification

Protein existence in wastewater is an important issue in wastewater management because proteins are generally present as contaminants and foulants. Hence, in this study, we focused on designing a polysulfone (PSf) hollow-fiber membrane embedded with hydrophilic iron oxide nanoparticles (IONPs) for protein purification by means of ultrafiltration. Before membrane fabrication, the dispersion stability of the IONPs was enhanced by the addition of a stabilizer, namely, citric acid (CA). Next, PSf–IONP–CA nanocomposite hollow-fiber membranes were prepared via a dry–wet spinning process and then characterized in terms of their hydrophilicity and morphology. Ultrafiltration and adsorption experiments were then conducted with bovine serum albumin as a model protein. The results that an IONP/CA weight ratio of 1:20 contributed to the most stable IONP dispersion. It was also revealed that the membrane incorporated with IONP–CA at a weight ratio of 1:20 exhibited the highest pure water permeability (58.6 L m⁻² h⁻¹ bar⁻¹) and protein rejection (98.5%) while maintaining a low protein adsorption (3.3 μg/cm²). The addition of well-dispersed IONPs enhanced the separation features of the PSf hollow-fiber membrane for protein purification. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47502.     

Prediction of postpartum depression using machine learning techniques from social media text

Early screening of mental disorders plays a crucial role in diagnosis and treatment. This study explores how data‐driven methods can leverage the information available on social media platforms to predict postpartum depression (PPD). A generalized approach is proposed where linguistic features are extracted from user‐generated textual posts on social media and categorized as general, depressive, and PPD representative using multiple machine learning techniques. We find that techniques used in our study exhibit strong predictive capabilities for PPD content. Holdout validation showed that multilayer perceptron outperformed other techniques such as support vector machine and logistic regression used in this study with 91.7% accuracy for depressive content identification and up to 86.9% accuracy for PPD content prediction. This work adopts a hierarchical approach to predict PPD. Therefore, the reported PPD accuracy represents the performance of the model to correctly classify PPD content from non‐PPD depressive content.     

Ground plane impact on quadrifilar helix antenna performance with respect to deployment heights

Quadrifilar helix antenna (QHA) has its applications in satellite communications. This paper presents the performance optimisation of input and radiation characteristics of QHA in the presence of infinite and finite metallic ground planes. For the infinite ground plane, it has been observed that input parameters such as impedance and voltage standing wave ratio (VSWR) are stable, and the antenna has broader half power beamwidth (HPBW). Smaller metallic platforms that act as finite ground planes produce better 3‐dB axial ratio beamwidth and boresight axial ratio. Deployment of QHA on smaller metallic platforms such as nanosatellites and CubeSats enhances the circularly polarised beamwidth of the antenna with improved boresight axial ratio. However, on large low earth orbit (LEO) satellites, stable input characteristics and broader HPBW have been achieved at the cost of narrow circularly polarised beamwidth and degraded boresight axial ratio.     

SPION@APTES@FA-PEG@Usnic Acid Bionanodrug for Cancer Therapy

In this work, we aimed to develop stable usnic acid (UA)-conjugated superparamagnetic iron oxide nanoparticles (SPIONs) as a potential drug carrier for in vitro analysis of MCF-7 (breast cancer cell line), HeLa (cervix cancer cell line), L929 (mouse fibroblast cell line), U87 (glioblastoma cell line, brain cancer), and A549 (human lung cancer cell line) cell lines. SPIONs were synthesized via the polyol method and functionalized with APTES using the Stöber method. Carboxylated polyethylene glycol (PEG-COOH), folic acid (FA), and carboxylated luteolin (CL) were conjugated on the surface via a carboxylic/amine group using the nanoprecipitation method, respectively. X-ray powder diffraction analysis confirmed the purity of the product with crystallite size of around 11 nm. Fourier-transformed infrared spectrophotometer (FT-IR) analyses explained the conjugation of all functional groups to the surface of SPIONs. The percentages of inorganic and organic content in the products were investigated via thermal gravimetric analyzer (TGA). For morphological analysis, a transmission electron microscope (TEM) was used. The superparamagnetic property of the product was also confirmed by vibrating sample magnetometer (VSM).     

A Framework for Systematic Classification of Assets for Security Testing

Over the last decade, a significant increase has been observed in the use of web-based Information systems that process sensitive information, e.g., personal, financial, medical. With this increased use, the security of such systems became a crucial aspect to ensure safety, integrity and authenticity of the data. To achieve the objectives of data safety, security testing is performed. However, with growth and diversity of information systems, it is challenging to apply security testing for each and every system. Therefore, it is important to classify the assets based on their required level of security using an appropriate technique. In this paper, we propose an asset security classification technique to classify the System Under Test (SUT) based on various factors such as system exposure, data criticality and security requirements. We perform an extensive evaluation of our technique on a sample of 451 information systems. Further, we use security testing on a sample extracted from the resulting prioritized systems to investigate the presence of vulnerabilities. Our technique achieved promising results of successfully assigning security levels to various assets in the tested environments and also found several vulnerabilities in them.     

Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists’ experience. Convolutional Neural Network (CNN—an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can negatively affect the performance of CNN. Therefore, an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance. In this process, a hierarchical CNN model is designed, in which the former model is non-regularized (due to dense architecture) and the later one is regularized, specifically designed for small histopathology images. Moreover, the regularized model is integrated with CNN’s basic architecture to reduce overfitting. Experimental results demonstrate that oversampling might be an effective way to address the imbalanced class problem during training. The training and testing accuracies of the non-regularized CNN model are 98% & 78% with an imbalanced dataset and 96% & 81% with a balanced dataset, respectively. The regularized CNN model training and testing accuracies are 84% & 75% for an imbalanced dataset and 87% & 86% for a balanced dataset.     

Automated Patient Discomfort Detection Using Deep Learning

The Internet of Things (IoT) has been transformed almost all fields of life, but its impact on the healthcare sector has been notable. Various IoT-based sensors are used in the healthcare sector and offer quality and safe care to patients. This work presents a deep learning-based automated patient discomfort detection system in which patients’ discomfort is non-invasively detected. To do this, the overhead view patients’ data set has been recorded. For testing and evaluation purposes, we investigate the power of deep learning by choosing a Convolution Neural Network (CNN) based model. The model uses confidence maps and detects 18 different key points at various locations of the body of the patient. Applying association rules and part affinity fields, the detected key points are later converted into six main body organs. Furthermore, the distance of subsequent key points is measured using coordinates information. Finally, distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions. The accuracy of the proposed system is assessed on various test sequences. The experimental outcomes reveal the worth of the proposed system’ by obtaining a True Positive Rate of 98% with a 2% False Positive Rate.