The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR) light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN) has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE-I) datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA) v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets.

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Existing iris recognition systems are heavily dependent on specific conditions, such as the distance of image acquisition and the stop-and-stare environment, which require significant user cooperation. In environments where user cooperation is not guaranteed, prevailing segmentation schemes of the iris region are confronted with many problems, such as heavy occlusion of eyelashes, invalid off-axis rotations, motion blurs, and non-regular reflections in the eye area. In addition, iris recognition based on visible light environment has been investigated to avoid the use of additional near-infrared (NIR) light camera and NIR illuminator, which increased the difficulty of segmenting the iris region accurately owing to the environmental noise of visible light. To address these issues; this study proposes a two-stage iris segmentation scheme based on convolutional neural network (CNN); which is capable of accurate iris segmentation in severely noisy environments of iris recognition by visible light camera sensor. In the experiment; the noisy iris challenge evaluation part-II (NICE-II) training database (selected from the UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE) dataset were used. Experimental results showed that our method outperformed the existing segmentation methods.

Theoretical calculations reveal that the quality of an aluminum-back-surface field ͑BSF͒ in a silicon solar cell can be improved by either increasing the thickness of the deposited aluminum ͑Al͒, peak alloying temperature, or both. However, this study shows that there is a critical temperature for a given screen-printed Al thickness, above which the BSF quality begins to degrade because of nonuniformity triggered by the agglomeration of Al-Si melt in combination with the bandgap narrowing resulting from the high doping effect in the agglomerated regions. It is found that this critical temperature decreases with the increase in the thickness of the deposited Al layer and, therefore, limits the quality and thickness of the Al-BSF that can be achieved before degradation sets in. This nonuniformity of Al-BSF is observed in the form of scattered Al bumps with thick and thin BSF regions. A combination of experimental results and model calculations is used to provide improved understanding and guidelines for choosing the optimal combination of Al thickness and alloying temperature.

Advanced maternal age (AMA) is a growing trend world-wide and is traditionally defined as childbearing in women over 35 years of age. The purpose of our study was to determine the maternal age group within the Korean population, in which the risk of early neonatal mortality is increased. Korean birth and mortality data from 2011 to 2015 were used to estimate the influence of maternal age on the risk of early neonatal mortality. A Poisson regression was used for the analysis of multiple clinical variables such as year of delivery, maternal age, gestational age, infant gender, birth weight, multiple birth, parity, and socioeconomic variables. Furthermore, a generalized additive model was used to determine the maternal age at which the risk for neonatal mortality increases. We included 2,161,908 participants and found that 49.4% of mothers were 30–34 years of age at delivery. The proportion of mothers aged 35 and above increased over the 5-year analysis period. A maternal age lower than 29 years or higher than 40 years was associated with a relatively higher risk of early neonatal mortality. The trend and magnitude of the age-related risk on early neonatal mortality were independent of maternal socioeconomic factors such as living in an obstetrically underserved area, education level, and employment status. Furthermore, we showed that the risk for early neonatal mortality was higher until the maternal age of 28. However, there were no significant changes in the risk between the age of 35 and 40 years. According to recent national-wide data, age-related risk for early neonatal mortality is only apparent for mothers ≥ 40 years old whereas, age between 35 and 39 are not at increased risk for early neonatal mortality, despite being classified as AMA.

The stable hydrogen isotope deuterium (D), which is released during the annealing of deuterated silicon nitride films, diffuses through the crystalline silicon and is captured by a thin, amorphous layer of silicon sputtered on the rear surface. We report on the measurement of the concentration of “penetrated” D by secondary ion mass spectrometry to monitor the flux of D diffusing through single-crystalline silicon wafers. The penetrated D content in the trapping layer increases with the annealing time. However, the flux of D injected into the silicon from the silicon nitride layer decreases as annealing time increases.

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