अंतर्स्थलीय मात्स्यिकी का दायित्वपूर्ण विकास एवं मात्स्यिकी अनुसंधान

कृपया पूरा लेख पढे

Using in-situ microLaue diffraction to understand plasticity in MgO

The present study investigates the micromechanical modes of deformation in MgO prior to cracking at room temperature. A combination of time resolved white beam Laue diffraction technique and in-situ nano-indentation of large single crystal micropillars provides a unique method to study the operating mechanisms of deformation in this otherwise brittle oxide ceramic. Upon indenting an [100]-oriented MgO micropillar, rotation and streaking of Laue spots were observed. From the streaking of the Laue spots, differential slip on orthogonal {110} slip planes was inferred to take place in adjacent areas under the indent - this was consistent with the results from the transmission electron microscopy studies. Upon cyclic loading of the pillar, subsequent stretching and relaxation of peaks was hypothesised to happen due to pronounced mechanical hysteresis commonly observed in MgO. Also, time-resolved spatial mapping of the deformation gradients of the area under the indent were obtained from which the strain and rotation components were identified

Herbicide resistance in turfgrass: a chance to change the future?

Herbicide resistance has for decades been an increasing problem of agronomic crops such as corn and soybean. Several weed species have evolved herbicide resistance in turfgrass systems such as golf courses, sports fields, and sod production—particularly biotypes of annual bluegrass and goosegrass. Consequences of herbicide resistance in agronomic cropping systems indicate what could happen in turfgrass if herbicide resistance becomes broader in terms of species, distribution, and mechanisms of action. The turfgrass industry must take action to develop effective resistance management programs while this problem is still relatively small in scope. We propose that lessons learned from a series of national listening sessions conducted by the Herbicide Resistance Education Committee of the Weed Science Society of America to better understand the human dimensions affecting herbicide resistance in crop production provide tremendous insight into what themes to address when developing effective resistance management programs for the turfgrass industry

Using coupled micropillar compression and micro-Laue diffraction to investigate deformation mechanisms in a complex metallic alloy Al13Co4

In this investigation, we have used in-situ micro-Laue diffraction combined with micropillar compression of focused ion beam milled Al13Co4 complex metallic alloy to study the evolution of deformation in Al13Co4. Streaking of the Laue spots showed that the onset of plastic flow occured at stresses as low as 0.8 GPa, although macroscopic yield only becomes apparent at 2 GPa. The measured misorientations, obtained from peak splitting, enabled the geometrically necessary dislocation density to be estimated as 1.1 x 1013 m-2

Interpretable Machine Learning for Materials Design

Fueled by the widespread adoption of Machine Learning (ML) and the high-throughput screening of materials, the data-centric approach to materials design has asserted itself as a robust and powerful tool for the in-silico prediction of materials properties. When training models to predict material properties, researchers often face a difficult choice between a model's interpretability or its performance. We study this trade-off by leveraging four different state-of-the-art ML techniques: XGBoost, SISSO, Roost, and TPOT for the prediction of structural and electronic properties of perovskites and 2D materials. We then assess the future outlook of the continued integration of ML into materials discovery and identify key problems that will continue to challenge researchers as the size of the literature's datasets and complexity of models increases. Finally, we offer several possible solutions to these challenges with a focus on retaining interpretability and share our thoughts on magnifying the impact of ML on materials design

Segmental K-Means Learning with Mixture Distribution for HMM Based Handwriting Recognition

This paper investigates the performance of hidden Markov models (HMMs) for handwriting recognition. The Segmental K-Means algorithm is used for updating the transition and observation probabilities, instead of the Baum-Welch algorithm. Observation probabilities are modelled as multi-variate Gaussian mixture distributions. A deterministic clustering technique is used to estimate the initial parameters of an HMM. Bayesian information criterion (BIC) is used to select the topology of the model. The wavelet transform is used to extract features from a grey-scale image, and avoids binarization of the image.</p

Small Quadrupole Deformation for the Dipole Bands in 112In

High spin states in $^{112}$In were investigated using $^{100}$Mo($^{16}$O, p3n) reaction at 80 MeV. The excited level have been observed up to 5.6 MeV excitation energy and spin $\sim$ 20$\hbar$ with the level scheme showing three dipole bands. The polarization and lifetime measurements were carried out for the dipole bands. Tilted axis cranking model calculations were performed for different quasi-particle configurations of this doubly odd nucleus. Comparison of the calculations of the model with the B(M1) transition strengths of the positive and negative parity bands firmly established their configurations.Comment: 10 pages, 11 figures, 2 table