CARDINAL WTRL: TECHNOLOGY MATURITY, SCHEDULE SLIPPAGE AND TREND FORECASTING.

Maturity assessments of technology is a crucial process to identify and acquire compatible technologies for a system’s development. However, being a complex and highly subjective process, the Government Accountability Office (GAO) has reported cost overruns and schedule slippages through the years. This study provides a unique Weighted Technology Readiness Level (WTRL) framework which utilizes cardinal factors to ascertain the maturity, schedule and trend of NASA’s 7 Technologies based on their maturity time. The framework utilizes MCDM methods to determine the cardinal complexity of each TRL. It allows the assimilation of other cardinal factors using a simple, open structure to track the overall technology maturity and readiness. Furthermore, this study highlights the importance of tailored TRL frameworks to determine the accurate cardinal coefficient of the said technology and the inferences derived otherwise. It eliminates several limitations of previous frameworks and compares against their performance using a verified statistical representation of processed data.

Automated pill quality inspection using deep learning

The pill manufacturing process accrues substantial financial costs due to quality. Pill quality inspection is laborious, time-consuming and subjective, resulting in poor statistical representation and inconsistent results. In this study, we developed an approach that integrates deep learning algorithms and computer-vision-based processing with an optimization algorithm to fully automate the image analysis of internal crack/contamination detection. This approach exploits the features learned by convolutional neural network using various sub-processing techniques and Adam optimization. It achieves robust quantification of internal pill defects with an average accuracy of 95%.

Reduced Statistical Representation of Crystallographic Textures Based on Symmetry-Invariant Clustering of Lattice Orientations

As proven in numerous experimental and theoretical studies, physical and mechanical properties of materials are determined by their internal structure. In the particular case of polycrystalline metals and alloys, an important role is given to the orientation distributions of crystalline lattices, or, in other words, crystallographic textures. Physically reasonable models of texture formation are highly demanded in modern Material Science and Engineering since they can provide an efficient tool for designing polycrystalline products with improved operational characteristics. Models of interest can be obtained on the basis of statistical formulations of multilevel approaches and crystal elasto–visco–plasticity theories (in particular, Taylor–Bishop–Hill models and their various modifications are appropriate here). In such a framework, a representative volume element of a polycrystal is numerically implemented as a finite aggregate of crystallites (grains or subgrains) with a homogenized response at the macro-scale. Quantitative texture analysis of this aggregate requires estimating statistically stable features of the orientation distribution. The present paper introduces a clustering-based approach for executing this task with regard to preferred orientations. The proposed procedure operates with a weighted sample of orientations representing the aggregate and divides it into clusters, i.e., disjoint subsets of close elements. The closeness criterion is supposed to be defined with the help of a special pseudometric distance, which takes rotational symmetry of the crystalline lattice into account. A specific illustrative example is provided for better understanding the developed procedure. The texture in the clustered aggregate can be described reductively in terms of effective characteristics of distinguished clusters. Several possible reduced-form representations are considered and investigated from the viewpoint of aggregating elastic properties in application to some numerically simulated textures.

Brief stimuli cast a long-term trace in visual cortex

Sensory processing and perception are strongly influenced by recent stimulus history – a phenomenon termed adaptation. While perception can be influenced even by brief stimuli presented dozens of seconds ago, neural adaptation to brief stimuli has not been observed beyond time lags of a few hundred milliseconds. Here, using an openly available dataset from the Allen Brain Observatory, we show that neurons in the early visual cortex of the mouse exhibit remarkably long timescales of adaptation in response to brief visual stimuli, persisting over dozens of seconds, despite the presentation of several intervening stimuli. Long-term adaptation was selectively expressed in cortical, but not in thalamic neurons, which only showed short-term adaptation. Visual cortex thus maintains concurrent stimulus-specific memory traces of past input that enable the visual system to build up a statistical representation of the world over multiple timescales, to efficiently represent information in a changing environment.

Effect of body weight at first mating on reproductive performance of rabbit does

Eighteen mature nulliparous rabbit does with initial body weight between 1600g-2500g were used to evaluate the effect of body weight at first mating on reproductive performance of rabbit does. These rabbits were assigned into three treatments, with T1, T2 and T3 weighing (1600-1900), (1901-2200) and (2201-2500) respectively. The study lasted for 5weeks. The does were mated with buck at ratio 1:3. The reproductive parameters evaluated were number of does that kindled, litter size at birth, breeding efficiency, litter size at weaning, pre-weaning loss. All data collected were subjected to descriptive statistical representation. The highest number of kindled does was 5 in T2 and T3; T1 had the lowest number (3). Breeding efficiency of 83.33% was recorded in T1 and T2 while T3 had 50.00%. T3 had highest average birth weight (49.51g) and T2 had the least (41.51g); litter size at birth and weaning ranged from 18 -28kits and 15-24kits, respectively. T2 does recorded highest value in both parameters, T3 does recorded same value (28kits) for litter size at birth. Highest pre-weaning loss of 7kits was recorded from does in T3 while T1 and T2 recorded 4kits. It was therefore concluded that does with weight range 1901g – 2200g had better reproductive performance with highest breeding efficiency and lower pre-weaning loss.