Behavior-change trials to assess the feasibility of improving complementary feeding practices and micronutrient intake of infants in rural Bangladesh

This study used simple rapid-assessment techniques to test the feasibility of increasing the consumption of complementary foods by infants by asking mothers to increase meal quantity or frequency or by altering the viscosity/energy density of the food. The feasibility of using micronutrient supplements either added directly to food or administered as liquid drops was also examined. The study was conducted in rural Bangladesh and involved four separate short-term behavioral change trials. Depending on the trial, fieldworkers recruited 30 to 45 infants 6 to 12 months of age. Following recommendations to increase the amount of food provided to infants, the mean intakes from single meals increased from 40 +/- 23 g on day 1 to 64 +/- 30 g on day 7 (p < 0.05). In a second trial, the mean meal frequency increased from 2.2 +/- 1.3 on day 1 to 4.1 +/- 1.3 on day 7 (p < 0.05). Provision of high-energy-density diets, prepared by decreasing viscosity with alpha-amylase or by hand-mashing rice and dhal into a paste before feeding, increased single-meal energy consumption from 54 +/- 35 kcal to 79 +/- 52 kcal or 75 +/- 37 kcal (p < 0.05), respectively. Both types of micronutrient supplements were well accepted and used according to recommendations. In conclusion, it was possible to change short-term child-feeding behaviors to promote increased food intake, mealfrequency, energy density, and micronutrient consumption. Because each of these interventions lasted for only about 1 week, however, the long-term sustainability of these changes is not known. Moreover, the effect of increased feeding of complementary foods on intakes of breastmilk and total daily consumption of energy and nutrients requires further study.     

A higher-order spectral element for wave propagation analysis in functionally graded materials

Summary. A new higher-order spectral element (SE) is developed for wave propagation analysis of a functionally graded material (FGM) beam in the presence of thermal and mechanical loading. The element is based on first order shear deformation theory (FSDT) and takes into account the depthwise contraction due to Poissons ratio. A new method of element formulation is employed, which is the most general one and devoid of all previous cumbersome wavenumber and wave amplitude computation. The beam can be subjected to temperature variation in depth direction. This variation is found by solving the one-dimensional heat conduction equation uncoupled from the elasticity equation. The effect of the computed temperature field is subsequently superimposed on the mechanical loading in the form of an equivalent nodal load. Numerical examples are directed towards highlighting the effect of the Poissons contraction on the structural response and stress wave. The spectrum and the dispersion relation are studied in detail. The stress field generated by the element and its difference from the FSDT stress field is outlined. The response of an FGM beam to thermo-mechanical loading is analysed and the effect of thermal loading on the overall response is elicited.     

Polarization-based compensation of astigmatism

One approach to aberration compensation of an imaging system is to introduce a suitable phase mask at the aperture plane of an imaging system. We utilize this principle for the compensation of astigmatism. A suitable polarization mask used on the aperture plane together with a polarizer-retarder combination at the input of the imaging system provides the compensating polarization-induced phase steps at different quadrants of the apertures masked by different polarizers. The aberrant phase can be considerably compensated by the proper choice of a polarization mask and suitable selection of the polarization parameters involved. The results presented here bear out our theoretical expectation.     

Evaluation of the performance of backpropagation and radial basis function neural networks in predicting the drill flank wear

This study compares the performance of backpropagation neural network (BPNN) and radial basis function network (RBFN) in predicting the flank wear of high speed steel drill bits for drilling holes on mild steel and copper work pieces. The validation of the methodology is carried out following a series of experiments performed over a wide range of cutting conditions in which the effect of various process parameters, such as drill diameter, feed-rate, spindle speed, etc. on drill wear has been considered. Subsequently, the data, divided suitably into training and testing samples, have been used to effectively train both the backpropagation and radial basis function neural networks, and the individual performance of the two networks is then analyzed. It is observed that the performance of the RBFN fails to match that of the BPNN when the network complexity and the amount of data available are the constraining factors. However, when a simpler training procedure and reduced computational times are required, then RBFN is the preferred choice.     

Distributed Intelligent Energy Management System for a Single-Phase High-Frequency AC Microgrid

In this paper, a single-phase high-frequency AC (HFAC) microgrid is shown as a novel solution towards integrating renewable energy sources in a distributed generation system. Better utilization of the Microgrid is achieved by solving power flow and power quality issues using p-q theory-based active filtering called universal active power line conditioner and unified power quality conditioner, respectively. A distributed intelligent energy management system (DIEMS) is implemented to optimize operating costs. As the optimization greatly depends on the power generation and the power output from renewable sources strongly depends on the weather, the forecast of power generation is required for DIEMS. A Fuzzy ARTMAP neural network is used to predict hourly day-type outputs based on which generation can be forecasted. Depending on the forecast, an optimization scheme is developed utilizing linear programming along with heuristics. The results obtained show the successful implementation of HFAC Microgrid with adequate power flow and power quality control, as well as the optimization of operation cost by the DIEMS with Fuzzy ARTMAP-based day-type forecasting. The improvement in the battery life is also achieved due to optimization of storage charge states using the proposed DIEMS     

A Novel Approach to Improve the Speech Intelligibility Using Fractional Delta-amplitude Modulation Spectrogram

Abstract

Speech enhancement is an interesting research area that aims at improving the quality and intelligibility of the speech that is affected by the additive noises, such as airport noise, train noise, restaurant noise, and so on. The presence of these background noises degrades the comfort of listening of the end user. This article proposes a speech enhancement method that uses a novel feature extraction which removes the noise spectrum from the noisy speech signal using a novel fractional delta-AMS (amplitude modulation spectrogram) feature extraction and the D-matrix feature extraction method. The fractional delta-AMS feature extraction strategy is the modification of the delta-AMS with the fractional calculus that increases the sharpness of the feature extraction. The extracted features from the frames are used to determine the optimal mask of all the frames of the noisy speech signal and the mask is employed for training the deep belief neural networks (DBN). The two metrics root mean square error (RMSE) and perceptual evaluation of speech quality (PESQ) are used to evaluate the method. The proposed method yields a better value of PESQ at all level of noise and RMSE decreases with increased noise level.     

Resource augmentation for uniprocessor and multiprocessor partitioned scheduling of sporadic real-time tasks

Although the earliest-deadline-first (EDF) policy is known to be optimal for preemptive real-time task scheduling in uniprocessor systems, the schedulability analysis problem has recently been shown to be $\mathit{co}\mathcal{NP}$ -hard. Therefore, approximation algorithms, and in particular, approximations based on resource augmentation have attracted a lot of attention for both uniprocessor and multiprocessor systems. Resource augmentation based approximations assume a certain speedup of the processor(s). Using the notion of approximate demand bound function (dbf), in this paper we show that for uniprocessor systems the resource augmentation factor is at most $\frac{2e-1}{e} \approx1.6322$ , where e is the Euler number. We approximate the dbf using a linear approximation when the analysis interval length of interest is larger than the relative deadline of the task. For identical multiprocessor systems with M processors and constrained-deadline task sets, we show that the deadline-monotonic partitioning (that has been proposed by Baruah and Fisher) with the approximate dbf leads to an approximation factor of $\frac{3e-1}{e}-\frac{1}{M} \approx 2.6322-\frac{1}{M}$ with respect to resource augmentation. We also show that the corresponding factor is $3-\frac{1}{M}$ for arbitrary-deadline task sets. The best known results so far were $3-\frac{1}{M}$ for constrained-deadline tasks and $4-\frac {2}{M}$ for arbitrary-deadline ones. Our tighter analysis exploits the structure of the approximate dbf directly and uses the processor utilization violations (which were ignored in all previous analysis) for analyzing resource augmentation factors. We also provide concrete input instances to show that the lower bound on the resource augmentation factor for uniprocessor systems—using the above approximate dbf—is 1.5, and the corresponding bound is 2.5 for identical multiprocessor systems with an arbitrary order of fitting and a large number of processors. Further, we also provide a polynomial-time approximation scheme (PTAS) to derive near-optimal solutions under the assumption that the ratio of the maximum relative deadline to the minimum relative deadline of tasks is a constant, which is a more relaxed assumption compared to the assumptions required for deriving such a PTAS in the past.     

Delineation of glacial zones of Gangotri and other glaciers of Central Himalaya using RISAT-1 C-band dual-pol SAR

The Himalayan glaciers, being unique in nature, need more detailed study over their evolution in the Himalayan glacial zones. A methodology has been developed using two-dimensional signatures from synthetic aperture radar (SAR) C-band dual-polarized data. A linear decision rule-based model has been generated using the signatures and the result further filtered by the use of a digital elevation model (DEM) to delineate glacial zones in the Himalayas. The advantage of using cross-polarized data is the addition of extra information from the volume of the glacial mass. Some important prerequisites for the analysis are SAR image ortho-rectification and calibration, glacier boundary delineation, and the development of sites for collecting SAR backscattering signatures from glaciers along the profile. The study deals with the evolution of glacial snow cover and glacial zones/facies in the Himalayan region under a subtropical humid climate from the ablation to the accumulation season. SAR images from 15 July 2012 to 30 June 2013 over the Gangotri and Mana glaciers were evaluated with the developed model. The identification of a superimposed zone during the ablation season is among the key results. The identified snowlines and other boundaries of glacial facies are studied on a temporal scale. The highest snowline altitude of Mana was recorded at 5768 and 5194 m for the Gangotri glacier in 2012. SAR data are also important in identifying glacial zones buried under winter snow cover. The results obtained are useful in regard to further glaciological studies of the Himalayan glaciers.     

Adsorption Desalination Quenches Global Thirst

The search for fresh or potable water remains a pressing concern throughout many regions of the world. Although most of the Earth's surface is covered by oceans, the effort to provide for the world's inhabitants with fresh or potable water seems to be insurmountable; at least one billion people do not have access to clean and fresh water. The World Health Organization (WHO) reported that about 41% of the Earth's population lives in water-stressed areas, and the number of people in the water scarce regions may climb to 3.5 billion by the year 2025. Thus, innovative and cost-effective desalination technologies for producing fresh water are needed to provide practical solutions.     

Automatic determination of parting directions,parting lines and surfaces for two-piece permanent molds

Minimization of surface area/volume of undercuts, flatness of parting surface and minimization of draw depth play a major role to determine the best pair of parting directions for two-piece permanent molds. In most of the earlier attempts a combined effect of the three factors was not considered. Moreover with the increasing complexity of molded product geometry, heuristic and feature-based approaches may prove inefficient. In this paper the combined affect of minimized surface area of undercut, flatness of parting surface and minimized draw depth is considered for the determination of best parting direction, parting line and surface for a two-piece permanent molded component. Tessellated geometry of the component is used as input.