Critical congenital heart disease screening by pulse oximetry in a neonatal intensive care unit.

ObjectiveCritical congenital heart disease (CCHD) screening is effective in asymptomatic late preterm and term newborn infants with a low false-positive rate (0.035%). (1) To compare 2817 neonatal intensive care unit (NICU) discharges before and after implementation of CCHD screening; and (2) to evaluate CCHD screening at <35 weeks gestation.Study designCollection of results of CCHD screening including pre- and postductal pulse oximetry oxygen saturation (SpO2) values.ResultDuring the pre-CCHD screen period, 1247 infants were discharged from the NICU and one case of CCHD was missed. After 1 March 2012, 1508 CCHD screens were performed among 1570 discharges and no CCHDs were missed. The pre- and postductal SpO2 values were 98.8 ± 1.4% and 99 ± 1.3%, respectively, in preterm and 98.9 ± 1.3% and 98.9 ± 1.4%, respectively, in term infants. Ten infants had false-positive screens (10/1508 = 0.66%).ConclusionPerforming universal screening in the NICU is feasible but is associated with a higher false-positive rate compared with asymptomatic newborn infants

Unconstrained Scene Text and Video Text Recognition for Arabic Script

Building robust recognizers for Arabic has always been challenging. We demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid architecture in recognizing Arabic text in videos and natural scenes. We outperform previous state-of-the-art on two publicly available video text datasets - ALIF and ACTIV. For the scene text recognition task, we introduce a new Arabic scene text dataset and establish baseline results. For scripts like Arabic, a major challenge in developing robust recognizers is the lack of large quantity of annotated data. We overcome this by synthesising millions of Arabic text images from a large vocabulary of Arabic words and phrases. Our implementation is built on top of the model introduced here [37] which is proven quite effective for English scene text recognition. The model follows a segmentation-free, sequence to sequence transcription approach. The network transcribes a sequence of convolutional features from the input image to a sequence of target labels. This does away with the need for segmenting input image into constituent characters/glyphs, which is often difficult for Arabic script. Further, the ability of RNNs to model contextual dependencies yields superior recognition results.Comment: 5 page

An empirical analysis of the impact of various dimensions of work-life balance on organizational commitment among service sector employees in India

The present study examined the relationships of the various facets of work-family balance with organisational commitment (OC) and its various dimensions among employees working in the service sector in India. Data were collected from 408 employees by means of questionnaires. Correlation and linear regression analysis of the collected data demonstrated that while one of the dimensions of work-life balance namely, work interferes with personal life (WIPL), acted as a significant negative predictor of OC, another dimension that is work enhancement/ personal enhancement (WE/PE) showed significant positive impact on OC. The third dimension, personal life interferes with work (PLIW) even though showed a negative correlation; the impact was generally not significant. The article concludes with the managerial implications of the study in service sector industries/institutions

Oxygen targeting in preterm infants: a physiological interpretation.

Randomized controlled trials evaluating low-target oxygen saturation (SpO2:85% to 89%) vs high-target SpO2 (91% to 95%) have shown variable results regarding mortality and morbidity in extremely preterm infants. Because of the variation inherent to the accuracy of pulse oximeters, the unspecified location of probe placement, the intrinsic relationship between SpO2 and arterial oxygen saturation (SaO2) and between SaO2 and partial pressure of oxygen (PaO2) (differences in oxygen dissociation curves for fetal and adult hemoglobin), the two comparison groups could have been more similar than dissimilar. The SpO2 values were in the target range for a shorter period of time than intended due to practical and methodological constraints. So the studies did not truly compare 'target SpO2 ranges'. In spite of this overlap, some of the studies did find significant differences in mortality prior to discharge, necrotizing enterocolitis and severe retinopathy of prematurity. These differences could potentially be secondary to time spent beyond the target range (SpO2 <85 or >95%) and could be avoided with an intermediate but wider target SpO2 range (87% to 93%). In conclusion, significant uncertainty persists about the desired target range of SpO2 in extremely preterm infants. Further studies should focus on studying newer methods of assessing oxygenation and strategies to limit hypoxemia (<85% SpO2) and hyperoxemia (>95% SpO2)

Fishery for the marine clam, Sunetta scripta at Vypin Island. Cochin

Sunetta scripta, the marine clam, locally known as "Kadal kakka" supports a well established commercial fishery in Vypin Island. Extensive beds of this clam occur in the subtidal region off Fort Cochin on either side of the Cochin bar mouth

Implicit self-consistent electrolyte model in plane-wave density-functional theory

The ab-initio computational treatment of electrochemical systems requires an appropriate treatment of the solid/liquid interfaces. A fully quantum mechanical treatment of the interface is computationally demanding due to the large number of degrees of freedom involved. In this work, we describe a computationally efficient model where the electrode part of the interface is described at the density-functional theory (DFT) level, and the electrolyte part is represented through an implicit solvation model based on the Poisson-Boltzmann equation. We describe the implementation of the linearized Poisson-Boltzmann equation into the Vienna Ab-initio Simulation Package (VASP), a widely used DFT code, followed by validation and benchmarking of the method. To demonstrate the utility of the implicit electrolyte model, we apply it to study the surface energy of Cu crystal facets in an aqueous electrolyte as a function of applied electric potential. We show that the applied potential enables the control of the shape of nanocrystals from an octahedral to a truncated octahedral morphology with increasing potential

Neural network based vehicle-following model for mixed traffic conditions

Car-following behaviour is well studied and analyzed in the last fifty years for homogeneous traffic. However in the mixed traffic, following behaviour is found to vary based on type of lead and following vehicles. In this study, a neural network based model is proposed to predict the following behaviour for different lead and following vehicle-type combinations. Performance of the model is studied using data collected for six vehicle-type combinations. A multi-layer feed-forward back propagation network is used to predict vehicle-type dependent following behaviour by incorporating the vehicle- type as input into the model. The neural network model is then integrated into a simulation program to study the macroscopic behaviour of the model. Performance of the proposed neural network model is compared with the conventional Gipps‟ model at microscopic and macroscopic level. This study prompts the need for considering vehicle-type dependent following behaviour and ability of neural networks to model this behaviour in mixed traffic conditions