Influence of thermomechanical ageing on tensile properties of 2014 aluminium alloy

2014 aluminium alloy was subjected to various thermomechanical ageing (TMA) treatments which included partial peak ageing (25% and 50%), warm rolling (10% and 20%) and further ageing to peak hardness level at 160 ° C. The tensile tests reveal that TMA treatments cause a substantial improvement in tensile properties and thermal stability. The electron microscopic studies reveal that the TMA treatments affect substantially the ageing characteristics. The TMA Ib treatment yields the finest needles having longitudinal dimensions of 40nm. The TMA treatments also lead to precipitate-dislocation networks of different densities. It is observed that TMA IIb treatment results in the densest precipitate-dislocation tangles of all the TMA treatments. As a result, a significant improvement in the tensile properties of 2014 aluminium alloy has been observed.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

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.

     

Hierarchical clustering and routing protocol to ensure scalability and reliability in large-scale wireless sensor networks

The Journal of Supercomputing - Cluster-based routing protocols have been proven efficient in prolonging the life cycle of wireless sensor networks (WSNs). Periodic and multi-hop clustering are the...     

Predator–prey optimization based clustering algorithm for wireless sensor networks

Neural Computing and Applications - Grouping the sensor nodes into clusters is an effective way to organize wireless sensor networks and to prolong the networks’ lifetime. This paper presents...     

Investigation of rectangular solid-core photonic crystal fiber as temperature sensor

Microsystem Technologies - In this article, a rectangular solid-core photonic crystal fiber (PCF) is proposed as temperature sensor. The air-holes of the PCF have been filled with Ethyl alcohol...     

Estimation of elastic constant of rocks using an ANFIS approach

The engineering properties of the rocks have the most vital role in planning of rock excavation and construction for optimum utilization of earth resources with greater safety and least damage to surroundings. The design and construction of structure is influenced by physico-mechanical properties of rock mass. Young's modulus provides insight about the magnitude and characteristic of the rock mass deformation due to change in stress field. The determination of the Young's modulus in laboratory is very time consuming and costly. Therefore, basic rock properties like point load, density and water absorption have been used to predict the Young's modulus. Point load, density and water absorption can be easily determined in field as well as laboratory and are pertinent properties to characterize a rock mass. The artificial neural network (ANN), fuzzy inference system (FIS) and neuro fuzzy are promising techniques which have proven to be very reliable in recent years. In, present study, neuro fuzzy system is applied to predict the rock Young's modulus to overcome the limitation of ANN and fuzzy logic. Total 85 dataset were used for training the network and 10 dataset for testing and validation of network rules. The network performance indices correlation coefficient, mean absolute percentage error (MAPE), root mean square error (RMSE), and variance account for (VAF) are found to be 0.6643, 7.583, 6.799, and 91.95 respectively, which endow with high performance of predictive neuro-fuzzy system to make use for prediction of complex rock parameter.     

Phase transformations in Mn-Al permanent magnet alloys

Phase transformation studies have been made of the Mn-Al alloys with compositions near the equiatomic range with or without small amounts of carbon, copper and nickel, using differential thermal analysis, X-ray diffraction and optical and electron microscopy. The high temperature hexagonal phase obtained by quenching, transforms to the ferromagnetic phase between 500 and 550° C and on further heating transforms back to the hexagonal phase between 750 and 950° C. Also, on controlled cooling of the phase from about 900° C, the ferromagnetic phase is formed between 800 and 670° C. TEM studies have shown the presence of the B19 ordered phase, ferromagnetic phase and Mn₅Al₈ precipitates even in quenched alloys.     

An infrared study of the structure of GeO2-CeO2 thin films

The infrared spectra of amorphous thin films consisting of GeO₂ co-evaporated with CeO₂ are presented and interpreted in relation to the spectrum of the pure amorphous GeO₂ film. The lower frequency side of the broad absorption band within this spectrum peaking at 730 cm^–1 is believed to be due to defect centres similar to the O ₁ ^– and O ₃ ⁺ centres found in a-SiO₂. Absorption at higher frequencies within this band is due to the O stretch vibrations of the Ge-O-Ge linkage. After considering the vibrations of the O ₁ ^– and O ₃ ⁺ centres in detail, it is shown that the band at 495 cm^–1 cannot be due to either of these centres and must therefore by caused by some other reactive defect centres. The variation of the position of the 730 cm^–1 peak within the series of spectra is noted and probable explanations are offered. The optical absorption edge of a-GeO₂ thin film is compared with that of a-SiO and a possible explanation of the basic differences is proposed.