A Study on Rupture Resistance of Groundnut (cv. SAMNUT 22) Kernel

This study was done to assess the influence of compression loading rate and kernel size on the rupture resistance of groundnut (cv. SAMNUT 22) kernel. These groundnut kernel mechanical parameters (rupture force, deformation at rupture, rupture power, firmness and toughness) were evaluated under three loading rates (15 mm min-1, 20 mm min-1 and 25 mm min-1), and three size categories (small, medium and large). The groundnut kernels were harvested at peak maturity stage, and tested in accordance to ASTM International standards. Results obtained from the tests showed that the rupture resistance of SAMNUT 22 kernel was highly dependent on its size and the loading rate. Generally, as the loading rate increases, the mechanical parameters values declined significantly (p ≤ 0.05). Rupture force, deformation at rupture point, rupture power and the firmness increased as the kernel size increases; but in contrast, the kernel toughness decreases as its size increased. An average force of 57.96 N ruptured the large kernel, while a lower force of 27.35 N ruptured the small kernel. Moreover, the large kernel recorded the highest firmness (59.03 N mm-1), when compared to the medium (51.69 N mm-1) and small (44.98 N mm-1) size kernel. In terms of rupture power, the small kernel power ranged from 0.1002 W (15 mm min-1) to 0.084 W (25 mm min-1); medium size kernel ranged from 0.115 W (15 mm min-1) to 0.074 W (25 mm min- 1); while the large size kernel ranged from 0.135 W (15 mm min-1) to 0.104 W (25 mm min-1). These results portrayed importance of sorting of the groundnut kernels before processing unit operation, as it will help to conserve power and energy during the processing operation.

The study of collection durum winter wheat samples according to grain quality in the Rostov region

The success of any agricultural crop breeding, including winter durum wheat primarily depends on the initial material at the breeder’s disposal, its value, and the degree of study. The purpose of the current study was to evaluate collection samples of winter durum wheat according to quality indicators and to select the best ones for use in breeding programs. In the Rostov region there were studied 159 winter durum wheat samples of different ecological and geographical origin according to grain quality (protein percentage, gluten content, amount of carotenoid pigments, kernel hardness, nature weight). The winter durum wheat samples had a high protein percentage and belonged to the 1-st quality class. According to gluten content in grain there were identified 17 (10.7%) samples. The following samples had the maximum values of trait ‘SDS-sedimentation’: ‘588/15’ (Russia) with 50 ml; ‘SAHINBEY’ (Turkey), ‘SARI BUGDAY 2’ (Turkey), ‘543/15’ (Russia) with 49 ml; ‘ANKARA 98’ (Turkey) with 48 ml. The following 43 winter durum wheat samples (more than 85%) had large kernel hardness in the trial: ‘663/17’, ‘1121/12’, ‘Novinka 4’, ‘Alena’ (Russia), ‘C1252’ (Turkey), ‘SN TURK MI 82-83 90 / GUTROS-2’, ‘DF 28.82.84 / DAB-18’, ‘P 1290493 // HUI // AV79’ (Mexico), ‘K-61869’ (Moldova). Over the years of study, a large amount of carotenoid pigments was identified in the following samples: ‘Novinka 4’ with 705 μg /%, ‘535/17’ with 689 μg /%, ‘543/15’ with 664 μg /% (Russia), ‘OSU-3880001 / 4AOS / SNIP / 3 / MEDIUM / KIF // SAPI’ with 704 μg /% (Mexico), ‘Winter Gold’ with 697 μg /% (Germany). According to the complex of qualitative indicators, there were identified 5 winter durum wheat samples, which are recommended to be included in the breeding programs of the Rostov region.

A Kernel-Width Adaption Diffusion Maximum Correntropy Algorithm

In this paper, a diffusion maximum correntropy criterion (DMCC) algorithm with adaption kernel width is proposed, denoting as DMCC$_{\rm adapt}$ algorithm, to find out a solution for dynamically choosing the kernel width. The DMCC$_{\rm adapt}$ algorithm chooses small kernel width at initial stage to improve its convergence speed rate, and uses large kernel width at completion stage to reduce its steady-state error. To render the proposed DMCC$_{\rm adapt}$ algorithm suitable for sparse system identifications, the DMCC$_{\rm adapt}$ algorithm based on proportional coefficient adjustment is realized and named as diffusion proportional maximum correntropy criterion (DPMCC$_{\rm adapt}$). The theoretical analysis and simulation results are presented to show that the DPMCC$_{\rm adapt}$ and DMCC$_{\rm adapt}$ algorithms have better convergence than the traditional diffusion AF algorithms under impulse noise and sparse systems.

A Kernel-Width Adaption Diffusion Maximum Correntropy Algorithm

In this paper, a diffusion maximum correntropy criterion (DMCC) algorithm with adaption kernel width is proposed, denoting as DMCC$_{\rm adapt}$ algorithm, to find out a solution for dynamically choosing the kernel width. The DMCC$_{\rm adapt}$ algorithm chooses small kernel width at initial stage to improve its convergence speed rate, and uses large kernel width at completion stage to reduce its steady-state error. To render the proposed DMCC$_{\rm adapt}$ algorithm suitable for sparse system identifications, the DMCC$_{\rm adapt}$ algorithm based on proportional coefficient adjustment is realized and named as diffusion proportional maximum correntropy criterion (DPMCC$_{\rm adapt}$). The theoretical analysis and simulation results are presented to show that the DPMCC$_{\rm adapt}$ and DMCC$_{\rm adapt}$ algorithms have better convergence than the traditional diffusion AF algorithms under impulse noise and sparse systems.