Densely connected convolutional networks-based COVID-19 screening model

The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been implemented in the literature for the early-stage classification of suspected subjects. To handle the sensitivity issue associated with RT-PCR, chest CT scans are utilized to classify the suspected subjects as COVID-19 (+), tuberculosis, pneumonia, or healthy subjects. The extensive study on chest CT scans of COVID-19 (+) subjects reveals that there are some bilateral changes and unique patterns. But the manual analysis from chest CT scans is a tedious task. Therefore, an automated COVID-19 screening model is implemented by ensembling the deep transfer learning models such as Densely connected convolutional networks (DCCNs), ResNet152V2, and VGG16. Experimental results reveal that the proposed ensemble model outperforms the competitive models in terms of accuracy, f-measure, area under curve, sensitivity, and specificity.

     

Reactive pozzolana from Indian clays—their use in cement mortars

Studies were undertaken to produce reactive pozzolana i.e. metakaolin from four kaolinitic clays collected from different sources in India. The metakaolin produced from these clays at 700-800 °C show lime reactivity in between 10.5 to 11.5 N/mm² which is equivalent to commercially available calcined clay Metacem-85. The microstructure of the metakaolin has been reported. The effect of addition of metakaolin up to 25% in the Portland cement mortars was investigated. An increase in compressive strength and decrease of porosity and pore diameter of cement mortars containing metakaolin (10%) was noted over the cement mortars without metakaolin. The hydration of metakaolin blended cement mortars was investigated by differential thermal analysis (DTA) and scanning electron microscopy (SEM). The major hydraulic products like C-S-H and C₄AH₁₃ have been identified. Durability of the cement mortars with and without metakaolin was examined in different sulphate solutions. Data show better strength achievement in cement mortars containing 10% MK than the OPC mortars alone.     

Studies on the Extraction of Manganese from Sea Nodule Residue as Ferrosilicomanganese

Most of the extraction processes developed for the polymetallic sea nodules concentrated mainly on the recovery of strategically important metals viz. Cu, Ni and Co. The residue generated in such processes is quite high enough to upset the environmental balance for industrial scale operation. The economics of the process too cannot be favourable if manganese in the residue is not recovered. In this study an attempt has been made to utilize this residue, containing appreciable amount of manganese (about 20%) for producing ferrosilicomanganese, an important ferroalloy primarily used for deoxidation in the steel industry, through smelting process. In order to have an alloy of standard grade, the residue is enriched in its manganese content, by blending it with ferromanganese slag. Bench scale studies indicate that it is possible to produce ferrosilicomanganese, of the grade required by the steel industry, by reduction smelting of the sea nodule residue with ferromanganese slag. Metal recovery, however, was less because of metal entrapment.     

Biogenic synthesis,optimisation and antibacterial efficacy of extracellular silver nanoparticles using novel fungal isolate Aspergillus fumigatus MA

To eliminate the elaborate processes employed in other non‐biological‐based protocols and low cost production of silver nanoparticles (AgNPs), this study reports biogenic synthesis of AgNPs using silver salt precursor with aqueous extract of Aspergillus fumigates MA. Influence of silver precursor concentrations, concentration ratio of fungal extract and silver nitrate, contact time, reaction temperature and pH are evaluated to find their effects on AgNPs synthesis. Ultraviolet–visible spectra gave surface plasmon resonance at 420 nm for AgNPs. Fourier transform infrared spectroscopy and X‐ray diffraction techniques further confirmed the synthesis and crystalline nature of AgNPs, respectively. Transmission electron microscopy observed spherical shapes of synthesised AgNPs within the range of 3–20 nm. The AgNPs showed potent antimicrobial efficacy against various bacterial strains. Thus, the results of the current study indicate that optimisation process plays a pivotal role in the AgNPs synthesis and biogenic synthesised AgNPs might be used against bacterial pathogens; however, it necessitates clinical studies to find out their potential as antibacterial agents.Inspec keywords: nanoparticles, microorganisms, cellular biophysics, silver, antibacterial activity, pH, surface plasmon resonance, ultraviolet spectra, visible spectra, X‐ray diffraction, Fourier transform infrared spectra, optimisation, nanomedicine, nanofabricationOther keywords: biogenic synthesis, optimisation, antibacterial efficacy, extracellular silver nanoparticles, fungal isolate Aspergillus fumigatus MA, nonbiological‐based protocols, silver salt precursor, fungal extract, silver nitrate, pH, ultraviolet‐visible spectra, surface plasmon resonance, Fourier transform infrared spectroscopy, X‐ray diffraction, crystalline nature, transmission electron microscopy, spherical shapes, potent antimicrobial efficacy, bacterial strains, optimisation process, bacterial pathogens, antibacterial agents, wavelength 420 nm, size 3 nm to 20 nm, Ag     

Collapse of conical cavities in aluminium metal under shock wave impact

Under shock wave impact the conical free surface of the metal collapses and a metallic jet with a velocity exceeding 10 km/s is squirted out of the metallic target. Variation in the jet tip velocity when the cavity half angle ranges from 60 down to 7.5° has been studied over three different shock pressures. The jet velocity has been found to increase with the decrease in the angle of the conical free surface and as the angle approaches zero the jet tip velocity attains a value near to the limit set by hydrodynamic theory for non-compressible fluids. The effect of aerodynamic drag on this type of jet has been studied and it has been found that the jet formed by the collapse of a small angle conical cavity quickly slows down while travelling in air, whereas the jet coming out of large angle cavity suffers a small retardation. Theoretical explanation and experimental evidence in support of this fact are also presented.     

Efficient prediction of drug–drug interaction using deep learning models

A drug–drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug–drug interaction is defined as an ill‐posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug–drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug–drug interaction score efficiently. However, these models suffer from the over‐fitting issue. Therefore, these models are not so‐effective for predicting the drug–drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.Inspec keywords: cancer, learning (artificial intelligence), drugs, recurrent neural nets, convolutional neural nets, drug delivery systemsOther keywords: drug synergy, drug–drug interaction score, drug–drug interaction prediction, deep learning, cancer treatment, machine learning, convolutional mixture density recurrent neural network     

Design and performance analysis of a CNFET-based TCAM cell with dual-chirality selection

The carbon nanotube field-effect transistor (CNFET) is emerging as one of the most promising alternatives to complementary metal–oxide–semiconductor (CMOS) transistors due to its one-dimensional (1-D) band structure, low off-current capability, near-ballistic transport operation, high stability, and low power consumption. This paper presents the design of a CNFET-based ternary content-addressable memory (TCAM) cell and rigorously analyzes its performance in terms of power–delay product (PDP) and static noise margin (SNM). The effect of variations of the chiral vector on the performance of the TCAM cell is also comprehensively investigated. While selecting the chirality, SNM, PDP, and search time are considered as figures of merit. In this TCAM cell design, we apply the same chirality for all CNFETs of the same type. Extensive HSPICE simulations have been performed for computation of performance parameters using the Stanford University CNFET model. Comparison of CNFET- and CMOS-based TCAM cells has been carried out at the 16-nm technology node. The results show that the CNFET-based TCAM cell exhibits significant improvements of PDP, i.e., by 38 % during write operation and 98 % during search operation, and 53 % in SNM, compared with its CMOS counterpart. It is also observed that the best chirality for the TCAM cell design is (22, 19, 0) or (10, 19, 0) from the point of view of SNM and PDP, respectively.     

Semantic Information Extraction from Multi-Corpora Using Deep Learning

Information extraction plays a vital role in natural language processing, to extract named entities and events from unstructured data. Due to the exponential data growth in the agricultural sector, extracting significant information has become a challenging task. Though existing deep learning-based techniques have been applied in smart agriculture for crop cultivation, crop disease detection, weed removal, and yield production, still it is difficult to find the semantics between extracted information due to unswerving effects of weather, soil, pest, and fertilizer data. This paper consists of two parts. An initial phase, which proposes a data preprocessing technique for removal of ambiguity in input corpora, and the second phase proposes a novel deep learning-based long short-term memory with rectification in Adam optimizer and multilayer perceptron to find agricultural-based named entity recognition, events, and relations between them. The proposed algorithm has been trained and tested on four input corpora i.e., agriculture, weather, soil, and pest & fertilizers. The experimental results have been compared with existing techniques and it was observed that the proposed algorithm outperforms Weighted-SOM, LSTM+RAO, PLR-DBN, KNN, and Naïve Bayes on standard parameters like accuracy, sensitivity, and specificity.     

Epitaxial film growth of chromium dioxide by low pressure chemical vapor deposition using chromium carbonyl

Epitaxial chromium dioxide (CrO₂) thin films have been deposited by low pressure chemical vapor deposition (LPCVD) on (100) TiO₂ substrates using the precursor chromium hexacarbonyl (Cr(CO)₆) within a narrow temperature window of 380-400 °C. Normal θ-2θ Bragg x-ray diffraction results show that the predominant phase is CrO₂ with only a small amount of Cr₂O₃ present, mostly at the film surface. The LPCVD films have a reasonably smooth surface morphology with a root mean square roughness of 4 nm on a scale of 5 μm. Raman spectroscopy confirms the existence of rutile CrO₂ in the deposited films, while transmission electron microscopy confirms the single-crystalline nature of the films. The LPCVD films showing a dominant CrO₂ phase exhibit clear uniaxial magnetic anisotropy with the easy axis oriented along the c direction.