Influence of alloying elements and grain refiner on microstructure,mechanical and wear properties of Mg-Al-Zn alloys

In the present investigation, the effects of alloying elements (Sn, Pb) and grain refiner (Ag, Zr) on microstructure, mechanical and wear properties of as-cast Mg-Al-Zn alloys were studied. The alloys were prepared through melting-casting route under a protective atmosphere and cast into a permanent mould. The microstructure of the base alloy consisted of α-Mg, Mg₁₇Al₁₂ continuous eutectic phase at the grain boundary and Mg-Zn phase was distributed within the grains. Addition of Sn and Pb suppressed the formation of continuous Mg₁₇Al₁₂ eutectic phase and formed Pb enriched Mg₂Sn precipitates at the grain boundary as well as inside the grain. The Ag and Zr addition to Mg-Al-Zn-Sn-Pb alloy suppressed the Mg₁₇Al₁₂ phase formation and refined the grains leading to improve mechanical properties. Addition of Sn, Pb and grain refiner (Ag, Zr) significantly enhanced the tensile strength and elongation but reduced hardness. The Ag addition imparted best tensile properties, where ultimate tensile strength (UTS) and elongation are 205?MPa and 8.0%, respectively. The fracture surfaces were examined under SEM which revealed cleavage facets and dimple formation. Therefore, the cleavage fracture and dimple rupture were considered as the dominant fracture mechanisms for developed Mg alloys. The cumulative volume loss of Mg alloys increased with sliding distance and applied load. The coefficient of friction decreased with sliding distance. The microscopic observation, analysis of the wear surface and coefficient of friction revealed that the wear mechanism of developed Mg alloys changes from abrasion oxidation to delamination wear.     

Adaptive machine learning classification for diabetic retinopathy

Multimedia Tools and Applications - Diabetic retinopathy is the main cause of the blindness worldwide. However, the diabetic retinopathy is hard to be detected in the initial stages, and the...     

Structure–property correlation of silicone hydrogels based on 3-[tris(trimethylsilyloxy)silyl]propyl methacrylate monomer

We have investigated silicone-based hydrogels highlighting the effect of silyl comonomer, method of polymerization and nature of cross-linker on their bulk as well as surface properties. Transmission electron microscopy results showed that solution thermal polymerization mitigated phase segregation of hydrophobic siloxane components over bulk thermal polymerization. 2-hydroxyethyl methacrylate cannot provide transparent hydrogels with 3-[tris(trimethylsilyloxy)silyl]propyl methacrylate alone and requires equal weight percent of N, N-dimethylacrylamide to compatibilize the comonomers to yield a silicone hydrogel network showing no microphase separation. The differentiated surface properties of silicone hydrogels containing varying amount of siloxane comonomers were also unraveled and rationally supported by surface infrared spectroscopic results. Different molecular states of water entrapped within hydrogel network were identified to understand the nature of gel-mesh functionality. The effect of silicone pendant groups on the gel elasticity was also explored.     

Phase-selective micro-structural effects on rheological-networks,segmental relaxation,and electrical conductivity behavior of melt-mixed polyamide-12/polypropylene-multi walled carbon nanotubes ternary nanocomposites

Morphological interpretations and their correlation with biphasic rheological networks and subsequent segmental relaxation, and electrical conductivity were comprehensively addressed for polyamide-12/polypropylene-multi-walled carbon nanotubes (PA-12/PP/MWNT) based ternary nanocomposites fabricated by melt mixing route. The partial migration of MWNT from PP to PA-12 phase is evident from the spreading coefficient estimations based on interfacial dynamics and transmission electron microscopy (TEM) analysis. Melt rheology measurements based on scaling parameters associated with various viscosity models such as, Cross model, Carreau-Yasuda model, and Berzin model indicated systematic variation in network rigidity that is in tune with dispersion-selective nano-morphology of the nanocomposites. The phase inversion was attained for composition in the range of 50 to 60 wt% of PP-MWNT content as indicated by Han plot and van-Gurp Palmen plots which is in direct correspondence to dispersed-phase-volume-fraction range of ~0.3-0.36. Broadening of loss-peaks vis-a-vis enhanced storage moduli in dynamic mechanical analysis (DMA) signifies the reduced mobility (of polyamide chains) and hence the enhanced stiffness. The electrical conductivity of the nanocomposites post-annealing decreased at temperatures above 100°C demonstrating the temperature-sensitive morphology disruption (of the conductive PP-MWNT channels) in the nanocomposites.     

Enhanced Archimedes Optimization Algorithm for Clustered Wireless Sensor Networks

Wireless sensor networks (WSN) encompass a set of inexpensive and battery powered sensor nodes, commonly employed for data gathering and tracking applications. Optimal energy utilization of the nodes in WSN is essential to capture data effectively and transmit them to destination. The latest developments of energy efficient clustering techniques can be widely applied to accomplish energy efficiency in the network. In this aspect, this paper presents an enhanced Archimedes optimization based cluster head selection (EAOA-CHS) approach for WSN. The goal of the EAOA-CHS method is to optimally choose the CHs from the available nodes in WSN and then organize the nodes into a set of clusters. Besides, the EAOA is derived by the incorporation of the chaotic map and pseudo-random performance. Moreover, the EAOA-CHS technique determines a fitness function involving total energy consumption and lifetime of WSN. The design of EAOA for CH election in the WSN depicts the novelty of work. In order to exhibit the enhanced efficiency of EAOA-CHS technique, a set of simulations are applied on 3 distinct conditions dependent upon the place of base station (BS). The simulation results pointed out the better outcomes of the EAOA-CHS technique over the recent methods under all scenarios.     

Nanoporous carbon materials with enhanced supercapacitance performance and non-aromatic chemical sensing with C1/C2 alcohol discrimination

We have investigated the textural properties, electrochemical supercapacitances and vapor sensing performances of bamboo-derived nanoporous carbon materials (NCM). Bamboo, an abundant natural biomaterial, was chemically activated with phosphoric acid at 400 °C and the effect of impregnation ratio of phosphoric acid on the textural properties and electrochemical performances was systematically investigated. Fourier transform-infrared (FTIR) spectroscopy confirmed the presence of various oxygen-containing surface functional groups (i.e. carboxyl, carboxylate, carbonyl and phenolic groups) in NCM. The prepared NCM are amorphous in nature and contain hierarchical micropores and mesopores. Surface areas and pore volumes were found in the range 218–1431 m² g^?1 and 0.26–1.26 cm³ g^?1, respectively, and could be controlled by adjusting the impregnation ratio of phosphoric acid and bamboo cane powder. NCM exhibited electrical double-layer supercapacitor behavior giving a high specific capacitance of c.256 F g^?1 at a scan rate of 5 mV s^?1 together with high cyclic stability with capacitance retention of about 92.6% after 1000 cycles. Furthermore, NCM exhibited excellent vapor sensing performance with high sensitivity for non-aromatic chemicals such as acetic acid. The system would be useful to discriminate C₁ and C₂ alcohol (methanol and ethanol).     

Analyzing performance and power efficiency of network processing over 10 GbE

Ethernet continues to be the most widely used network architecture today for its low cost and backward compatibility with the existing Ethernet infrastructure. Driven by increasing networking demands of cloud workloads, network speed rapidly migrates from 1 to 10 Gbps and beyond. Ethernet’s ubiquity and its continuously increasing rate motivate us to fully understand high speed network processing performance and its power efficiency.     

Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes. Much spatial information and spectral signatures of hyperspectral images (HSIs) present greater potential for detecting and classifying fine crops. The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging (RSI) has become an indispensable application in the agricultural domain. It is significant for the prediction and growth monitoring of crop yields. Amongst the deep learning (DL) techniques, Convolution Neural Network (CNN) was the best method for classifying HSI for their incredible local contextual modeling ability, enabling spectral and spatial feature extraction. This article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification (HMAODL-CTC) algorithm on HSI. The proposed HMAODL-CTC model mainly intends to categorize different types of crops on HSI. To accomplish this, the presented HMAODL-CTC model initially carries out image preprocessing to improve image quality. In addition, the presented HMAODL-CTC model develops dilated convolutional neural network (CNN) for feature extraction. For hyperparameter tuning of the dilated CNN model, the HMAO algorithm is utilized. Eventually, the presented HMAODL-CTC model uses an extreme learning machine (ELM) model for crop type classification. A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC algorithm. Extensive comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.     

Enzyme aided extraction of lycopene from tomato tissues

Lycopene is a natural carotenoid pigment and a high value nutraceutical having wide use. The objective of the present work was to obtain a good yield of lycopene from tomato tissues, using cellulase and pectinase enzymes. Various parameters such as concentration of enzymes and time of incubation were optimised, to improve the yield of lycopene from tomatoes. Enzyme aided extraction of lycopene from whole tomatoes under optimised conditions resulted in an increase in the lycopene yield by 132 μg/g (198%) in cellulase treated sample and 108 μg/g (224%) in case of pectinase treated sample. Extraction from tomato peel under optimised conditions showed a remarkable increase in the yield of lycopene by 429 μg/g (107%) and 1104 μg/g (206%), for cellulase and pectinase treated samples, respectively. Likewise, the enzyme aided extraction of lycopene from fruit pulper waste and industrial waste of tomatoes was done to determine the potential for recovering the natural pigment from tomato waste.