Identification of microstructural zones and thermal cycles in a weldment of modified 9Cr-1Mo steel

This paper presents the results of a study on the microstructural and microchemical variations in a multipass Gas Tungsten Arc weld (GTAW) of modified 9Cr-1Mo steel. The changes brought about in the steel due to the heating and cooling cycles during welding and the subsequent effects due to reheating effects during multipass welding are described. Detailed analytical transmission electron microscopy has been carried to study the type and composition of the primary and secondary phases in this steel. The systematic changes in microstructural parameters such as Prior Austenite Grain Size, martensite lath size, number density, size and microchemistry of carbides, have been understood based on the different transformations that the steel undergoes during the heating and cooling process. Based on the observed microstructure, an attempt has been made to identify distinct microstructural zones and possible thermal cycles experienced by different regions of the weldment.     

A VLSI architecture for a half-edge-based corner detector

Corner detection is a low-level feature detection operator that is of great use in image processing applications, for example, optical flow and structure from motion by image correspondence. The detection of corners is a computationally intensive operation. Past implementations of corner detection techniques have been restricted to software. In this paper we propose an efficient very large-scale integration (VLSI) architecture for detection of corners in images. The corner detection technique is based on the half-edge concept and the first directional derivative of Gaussian. Apart from the location of the corner points, the algorithm also computes the corner orientation and the corner angle and outputs the edge map of the image. The symmetrical properties of the masks are utilized to reduce the number of convolutions effectively, from eight to two. Therefore, the number of multiplications required per pixel is reduced from 1800 to 392. Thus, the proposed architecture yields a speed-up factor of 4.6 over conventional convolution architectures. The architecture uses the principles of pipelining and parallelism and can be implemented in VLSI.     

Useful NMR solvent mixture for polyesters: Trifluoroacetic acid-d/chloroform-d

Summary NMR spectra of high molecular weight polyesters such as polyglycolide (PG) and poly(ethylene terephthalate) (PET) have not been available because of low solubility in suitable NMR solvents. Although PG is not soluble enough at ambient temperatures for NMR spectra in either chloroform-d or trifluoroacetic acid-d alone, it dissolves readily to over 0.9% in a mixture of the two. At this concentration, ¹H and ¹³C spectra can be obtained on samples where end group signals are visible from which molecular weights can readily be determined. The solutions are stable up to at least four days when moisture is excluded. NMR spectra of PET can likewise be obtained at ambient temperatures in the solvent mixture which dissolves up to at least 0.6% of PET.     

Microstructural development in microsecond pulsed laser melted polycrystalline nickel

The microsecond Nd glass pulsed laser interaction with polycrystalline nickel was studied with particular emphasis on melting and the nature of the microstructural development following irradiation. The change in the melt pool shape and the melting efficiency as a function of the total laser power has been determined. Evidence is presented to show that for low energy pulse, the microstructural development accompanies fresh nucleation of grains and leads to a finer grain size than that of the substrate. At a higher energy level, growth competition leads to a coarser grain size. We also report a banded feature roughly parallel to the melt substrate interface that develops in the resolidified region during high energy interaction. These results are discussed in the light of our present understanding of rapid solidification processes.     

AHW-BGOA-DNN: a novel deep learning model for epileptic seizure detection

Neural Computing and Applications - “Brain–Computer Interface” (BCI)—a real-life support system provides a way for epileptic patients to improve their quality of life. In...     

Quasicrystalline phases and their approximants in AlMnZn alloys

Two AlMnZn alloys in melt-spun condition have been studied by transmission electron microscopy. The Al₂₄Mn₅Zn alloy was found to be fully icosahedral, while the Al₁₂Mn_2.9Zn alloy gave rise to decagonal quasicrystal. The decagonal phase grew in clusters with an orientation relationship between the grains suggesting nucleation on an icosahedral seed. On annealing at 500–600°C, the quasicrystalline phase transformed to a body centered orthorhombic phase L (a = 1.24, b = 1.26 andc = 3.05nm) with a high density of planar defects. This phase transforms to an ordered and defect free monoclinic phase M, a superlattice structure of L (a = c = 1.77, b = 3.05nm and β = 89.1°). The L phase is shown to be a rational approximant of the icosahedral phase. The interrelationship among quasicrystalline phases and their rational approximants in AlMnZn system are highlighted.     

A mathematical model for predicting the soil nitrogen status under varying fertilizer practices in a continuous cropping sequence

A mathematical model has been proposed for predicting the changes in soil nitrogen status due to continuous fertilization in a continuous cropping sequence. The model also enabled the prediction of the steady state of soil nitrogen for a specified fertilizer practice.The model was applied to six years nitrogen availability data of four fertilizer practices in finger millet-maize-cowpea sequence followed in the Long Term Fertilizer Experiments conducted at Tamil Nadu Agricultural University, Coimbatore, India. The agreement between the predicted soil nitrogen status by the model and the actuals was proved by employing reliability index.