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1.
NiO nanostructure was synthesized using a simple co-precipitation method and was embedded on reduced graphene oxide surface via ultrasonication. Structural investigations were made through X-ray diffraction (XRD) and functional groups were confirmed by Fourier transform infrared spectroscopy (FTIR). XRD analysis revealed the grain size reduction with doping. Fourier transform infrared spectroscopy confirmed the presence of metal-oxygen bond in pristine and doped NiO nanostructure as well as the presence of carbon containing groups. Scanning electron microscopy (SEM) indicated that the particle size decreased when NiO nanostructure was doped with copper. BET surface area was found to increase almost up to 43 m2/g for Cu doped NiO nanostructure/rGO composite. Current-voltage measurements were performed using two probe method. UV–Visible spectroscopic profiles showed the blue and red shift for Cu doped NiO nanostructure and Cu doped NiO Nanostructure/rGO composite respectively. Rate constant for Cu doped NiO nanostructure/rGO composite found to increase 4.4 times than pristine NiO nanostructure.  相似文献   
2.
Abstract

Model order reduction is a common practice to reduce large order systems so that their simulation and control become easy. Nonlinearity aware trajectory piecewise linear is a variation of trajectory piecewise linearization technique of order reduction that is used to reduce nonlinear systems. With this scheme, the reduced approximation of the system is generated by weighted sum of the linearized and reduced sub-models obtained at certain linearization points on the system trajectory. This scheme uses dynamically inspired weight assignment that makes the approximation nonlinearity aware. Just as weight assignment, the process of linearization points selection is also important for generating faithful approximations. This article uses a global maximum error controller based linearization points selection scheme according to which a state is chosen as a linearization point if the error between a current reduced model and the full order nonlinear system reaches a maximum value. A combination that not only selects linearization points based on an error controller but also assigns dynamic inspired weights is shown in this article. The proposed scheme generates approximations with higher accuracies. This is demonstrated by applying the proposed method to some benchmark nonlinear circuits including RC ladder network and inverter chain circuit and comparing the results with the conventional schemes.  相似文献   
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In the last two decades, global environmental change has increased abiotic stress on plants and severely affected crops. For example, drought stress is a serious abiotic stress that rapidly and substantially alters the morphological, physiological, and molecular responses of plants. In Arabidopsis, several drought-responsive genes have been identified; however, the underlying molecular mechanism of drought tolerance in plants remains largely unclear. Here, we report that the “domain of unknown function” novel gene DUF569 (AT1G69890) positively regulates drought stress in Arabidopsis. The Arabidopsis loss-of-function mutant atduf569 showed significant sensitivity to drought stress, i.e., severe wilting at the rosette-leaf stage after water was withheld for 3 days. Importantly, the mutant plant did not recover after rewatering, unlike wild-type (WT) plants. In addition, atduf569 plants showed significantly lower abscisic acid accumulation under optimal and drought-stress conditions, as well as significantly higher electrolyte leakage when compared with WT Col-0 plants. Spectrophotometric analyses also indicated a significantly lower accumulation of polyphenols, flavonoids, carotenoids, and chlorophylls in atduf569 mutant plants. Overall, our results suggest that novel DUF569 is a positive regulator of the response to drought in Arabidopsis.  相似文献   
5.
Lakes play a vital role in regulating water storage, flow of river water, and ultimately maintaining a balanced ecosystem. Spatial and temporal variations in physicochemical parameters of water in Harike Wetland, a Ramsar site in the northwestern state of Punjab, India, were studied. This study was conducted on a monthly basis from January to December 2015. The water quality was studied at ten locations from sites 1 to 10 upstream, central and downstream from Harike Lake for ten physicochemical parameters, including temperature, pH, electrical conductivity, turbidity, dissolved oxygen concentration biological oxygen demand, nitrate and phosphate concentrations and salinity. The findings of this study revealed that, except for temperature and pH, all parameters exhibited relatively higher values for the Sutlej River, compared with the Beas River, with sampling sites 5 to site 7 exhibiting intermediate results. The mean seasonal temperature variations ranged from 16.9 to 26.6 °C, the pH from 7.7 to 8.2, electrical conductivity from 223 to 303 μS cm?1 and TDS concentration from 148.7 to 180.4 ppm. Correlation analysis was conducted to assess the relations between the variables. The electrical conductivity exhibited a high positive correlation with salinity and biological oxygen demand, whereas it correlated negatively with the dissolved oxygen concentration. Box and whisker plots were also plotted for the study results to better examine the data distribution.  相似文献   
6.
The Harike Wetland situated in Punjab is a Ramsar site and a wetland of national importance. The present study was undertaken to assess the spatiotemporal dynamics of the wetland on the basis of geospatial technology and ground‐based studies. Landsat images for the years 2002 and 2014 were acquired from the United States Geological Survey and classified digitally to generate landuse/land cover maps involving four classes (water, grassland (including water hyacinth), agriculture, built‐up (settlement), barren land). The total area of the Harike Wetland was found to be 8023.68 ha. Water sampling at eleven sites was carried out and evaluated for physicochemical parameters. The water quality at several sampling points was found to be severely degraded. Change detection analysis revealed the submerged area (area under water) and grassland (including water hyacinth) had decreased over the past 12 years, whereas that area under agriculture and built‐up land has increased, indicating a shrinkage in the total wetland area. The present study also indicated that the near‐infrared band is a good indicator of water quality parameters, as indicated by the significant positive correlation between the near‐infrared band and relevant water parameters. Because the wetland is important from both an ecological perspective and economic perspective, regular monitoring is recommended, for which geospatial technology has proven to be very useful.  相似文献   
7.
Structural changes in β-isotactic polypropylene (β-iPP) during the heating were studied by means of differential scanning calorimetry and real-time in situ X-ray diffraction using a synchrotron source. Crystalline phase transformation and the memory effect caused by residual nuclei of α-iPP were observed during the heating of β-iPP. The memory effect observed in β-iPP during heating and crystallization is believed to be due to the existence of locally ordered α-from in the melt. The effect of local α-form order was probed by studying the behavior under heating of samples with a range of thermal histories. Samples were heated above the equilibrium melting temperature of iPP to remove all residual local order and the memory effect associated with this local order. The samples crystallized isothermally at different temperatures exhibited a significantly different melting and phase transformation behavior during heating. β-iPP is found to be an excellent material for the study of polymorphism, phase transformations, and characteristic memory effects in semicrystalline polymers.  相似文献   
8.
Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To deal with this problem we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of the best variables to train the model. A simple ANN model with one input, one output and two hidden layers was used for the training instead of a very deep and complex model. AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model. At first, variables were narrowed down to a smaller number using correlation values. Then subsets for all the possible variable combinations were formed. In the end, an artificial neural network (ANN) model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value. It was found that combination of only two variables’ ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values. While, nm and npt is the worst combination and gives maximum AIC and BIC values.  相似文献   
9.
One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness. The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary, for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis. Fourier Transform Infrared Spectroscopy (FTIR) is used in this work to identify lard adulteration in cow, lamb, and chicken samples. A simplified extraction method was implied to obtain the lipids from pure and adulterated meat. Adulterated samples were obtained by mixing lard with chicken, lamb, and beef with different concentrations (10%–50% v/v). Principal component analysis (PCA) and partial least square (PLS) were used to develop a calibration model at 800–3500 cm−1. Three-dimension PCA was successfully used by dividing the spectrum in three regions to classify lard meat adulteration in chicken, lamb, and beef samples. The corresponding FTIR peaks for the lard have been observed at 1159.6, 1743.4, 2853.1, and 2922.5 cm−1, which differentiate chicken, lamb, and beef samples. The wavenumbers offer the highest determination coefficient R2 value of 0.846 and lowest root mean square error of calibration (RMSEC) and root mean square error prediction (RMSEP) with an accuracy of 84.6%. Even the tiniest fat adulteration up to 10% can be reliably discovered using this methodology.  相似文献   
10.
Two proposed techniques let microprocessors operate at low voltages despite high memory-cell failure rates. They identify and disable defective portions of the cache at two granularities: individual words or pairs of bits. Both techniques use the entire cache during high-voltage operation while sacrificing cache capacity during low-voltage operation to reduce the minimum voltage below 500 mV.  相似文献   
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