Impact of Zinc oxide nanoparticles on eggplant (S. melongena): studies on growth and the accumulation of nanoparticles

The increasing use of nanoparticles and their occurrence in the environment has made it imperative to elucidate their impact on the environment. Although several studies have advanced the authors’ understanding of nanoparticle–plant interactions, their knowledge of the exposure of plants to nanoparticles and their effects on edible crop plants remain meager and is often paradoxical. The aim of this study was to increase their knowledge on the effect of zinc oxide (ZnO) nanoparticles on eggplant seed germination and seedling growth. ZnO nanoparticles had a negative effect on the growth of eggplant in plant tissue‐culture conditions, as the growth of seedlings decreased with the increase in the concentration of ZnO nanoparticles. In contrast, ZnO nanoparticles enhanced eggplant growth under greenhouse conditions. The accumulation of ZnO nanoparticles in various parts of eggplant was observed through scanning electron microscopy of both plant tissue‐culture and greenhouse‐raised eggplant seedlings. To the best of their knowledge, this is the first study to report on ZnO nanoparticle accumulation in eggplant and its effect on seed germination and seedling growth.Inspec keywords: crops, zinc compounds, scanning electron microscopy, II‐VI semiconductors, nanoparticles, agriculture, cellular biophysics, nanofabricationOther keywords: plant tissue‐culture, greenhouse‐raised eggplant seedlings, ZnO nanoparticle accumulation, seedling growth, ZnO nanoparticles, nanoparticle–plant interactions, zinc oxide nanoparticles, eggplant seed germination, eggplant growth, ZnO     

Synthesis and properties analysis of char-reinforced Al–13.5Si–2.5Mg alloy composites

The re-evaluation of previous and existing methods in materials processing is becoming ever more critical because of processing and starting materials cost factors. A study on the synthesis and properties investigation of hypereutectic Al–13.5Si–2.5Mg alloy reinforced with carbon chars using coconut shell as the organic precursor has been carried out. The low-cost, double compaction solid-state technique was used. Reinforcing the hypereutectic alloy with coconut shell char particles (size:<140 m) at 2 vol % and consolidating by reaction sintering at 600 °C in vacuum for 15 min, followed by near net-shape compaction at 250 MPa, increased the hardness of the alloy 6% while reducing its strength (UTS) by only 3%. The use of palm kernel shell char as the dispersed phase was found to yield identical results. At 2 vol % char, the mechanical properties, sintered density and dimensional changes were optimally found to be suitable for lightweight anti-friction electromechanical applications. Attempts to reinforce the alloy with 2 vol % coconut shell chars activated in CO2 reduced its strength in the range of 19 to 26% at different burn-off percentages. This is attributed to the higher amount of oxide products formed during the activation process. At 600 °C, formation of the brittle Al4C3 phase in the different sintered composites containing activated and unactivated chars was identified by X-ray studies. © 1998 Chapman & Hall     

Electrical properties of ZnSe-CdS alloy films

Thin films of ZnSe _x CdS_1–x (t 0.6 m) over the entire range of x, were deposited on glass substrates at two temperatures, T _s (350 and 470 K) by vacuum evaporation. X-ray diffraction studies showed that all the films were polycrystalline in nature. Films prepared at 470 K were nearly stoichiometric. Grain size increased with substrate temperature, T _s. The electrical conductivity and Hall measurements were carried out by d.c. van der Pauw technique. Hall effect studies/hot probe test showed that all the films were of n-type conductivity. Hall mobility increased with T _s. In addition, mobilities increased with temperature in films of all compositions, indicating the dominance of grain-boundary scattering. Grain-boundary potentials were in range 0.03–0.06 eV.     

Helicobacter pylori does not migrate from the antrum to the corpus in response to omeprazole

BACKGROUND: Omeprazole is known to have an effect on Helicobacter pylori in vivo. One opinion is that H. pylori "migrates" from the antrum to the corpus in response to omeprazole therapy. METHODS: To determine whether H. pylori migrates in response to omeprazole, we assessed the presence of H. pylori in the antrum and corpus in duodenal ulcer patients receiving omeprazole for 4 wk. Culture and histological examination of antral biopsies (Genta stain) were performed before patients received omeprazole, at the end of therapy, and 4-6 wk later. The end points were presence or absence of H. pylori and the number of H. pylori colonies per biopsy. RESULTS: Seventy-two patients had H. pylori in both the antrum and corpus at entry and 4-6 wk after ending therapy. Three general patterns were prevalent at the end of omeprazole therapy: antrum- and corpus-positive (54%), antrum-negative and corpus-positive (24%), both antrum- and corpus-negative (21%), and one patient had antrum-positive with corpus-negative (1%). Evaluation of the number of colonies per biopsy in those who remained H. pylori-positive in both the antrum and corpus throughout showed that the number of H. pylori decreased in both the antrum and corpus during therapy (507 +/- 60 vs. 225 +/- 51, p < 0.01 and 415 +/- 58 vs. 290 +/- 46 0.1) for antrum and corpus, respectively, and tended to return to pre-therapy levels 4-6 wk later. The number of H. pylori in the corpus also decreased in the antrum-negative and corpus-positive group during therapy with omeprazole (433 +/- 87 vs. 185 +/- 61, p < 0.05). In most of the patients studied, the number of H. pylori in the corpus was less posttreatment than it was pretreatment. The decrease in H. pylori load was also reflected in the development of false-negative urea breath tests. CONCLUSIONS: Omeprazole is detrimental to H. pylori in both the antrum and the corpus; migration from the antrum to the corpus in response to omeprazole is a myth.     

Concept-based evidential reasoning for multimodal fusion in human–computer interaction

Considerable research has been done on using information from multiple modalities, like hands, facial gestures or speech, for better interaction between humans and computers, and many promising human–computer interfaces (HCI) have been developed in recent years. However, most of the current HCI systems have a few drawbacks: firstly, they are highly dependent on the performance of individual sensors. S econdly, the information fusion process from these sensors tends to ignore the semantic nature of the modalities, which may reinforce or clarify each other over time. Finally, they are not robust enough at representing the imprecise nature of human gestures, since individual gestures are highly ambiguous in themselves. In this paper, we propose an approach for the semantic fusion of different input modalities, based on transferable belief models. We show that this approach allows for a better representation of the ambiguity involved in recognizing gestures. Ambiguity is resolved by combining the beliefs of the individual sensors on the input information, to form new extended concepts, based on a pre-defined domain specific knowledge base, represented by conceptual graphs. We apply this technique to a multimodal system consisting of a hand gesture recognition sensor and a brain computing interface. It is shown that the technique can successfully combine individual gestures obtained from the two sensors, to form meaningful concepts and resolve ambiguity. The advantage of this approach is that it is robust even if one of the sensors is inefficient or has no input. Another important feature is its scalability, wherein more input modalities, like speech or facial gestures, can be easily integrated into the system at minimal cost, to form a comprehensive HCI interface.     

Automatic joint classification and segmentation of whole cell 3D images

We present a machine learning tool for automatic texton-based joint classification and segmentation of mitochondria in MNT-1 cells imaged using ion-abrasion scanning electron microscopy (IA-SEM). For diagnosing signatures that may be unique to cellular states such as cancer, automatic tools with minimal user intervention need to be developed for analysis and mining of high-throughput data from these large volume data sets (typically ). Challenges for such a tool in 3D electron microscopy arise due to low contrast and signal-to-noise ratios (SNR) inherent to biological imaging. Our approach is based on block-wise classification of images into a trained list of regions. Given manually labeled images, our goal is to learn models that can localize novel instances of the regions in test datasets. Since datasets obtained using electron microscopes are intrinsically noisy, we improve the SNR of the data for automatic segmentation by implementing a 2D texture-preserving filter on each slice of the 3D dataset. We investigate texton-based region features in this work. Classification is performed by k-nearest neighbor (k-NN) classifier, support vector machines (SVMs), adaptive boosting (AdaBoost) and histogram matching using a NN classifier. In addition, we study the computational complexity vs. segmentation accuracy tradeoff of these classifiers. Segmentation results demonstrate that our approach using minimal training data performs close to semi-automatic methods using the variational level-set method and manual segmentation carried out by an experienced user. Using our method, which we show to have minimal user intervention and high classification accuracy, we investigate quantitative parameters such as volume of the cytoplasm occupied by mitochondria, differences between the surface area of inner and outer membranes and mean mitochondrial width which are quantities potentially relevant to distinguishing cancer cells from normal cells. To test the accuracy of our approach, these quantities are compared against manually computed counterparts. We also demonstrate extension of these methods to segment 3D images obtained using electron tomography.     

Hybrid signal processing and machine intelligence techniques for detection,quantification and classification of power quality disturbances

This paper presents an advanced signal processing technique known as S-transform (ST) to detect and quantify various power quality (PQ) disturbances. ST is also utilized to extract some useful features of the disturbance signal. The excellent time–frequency resolution characteristic of the ST makes it an attractive candidate for analysis of power system disturbance signals. The number of features required in the proposed approach is less than that of the wavelet transform (WT) for identification of PQ disturbances. The features extracted by using ST are used to train a support vector machine (SVM) classifier for automatic classification of the PQ disturbances. Since the proposed methodology can reduce the features of disturbance signal to a great extent without losing its original property, it efficiently utilizes the memory space and computation time of the processor. Eleven types of PQ disturbances are considered for the classification purpose. The simulation results show that the combination of ST and SVM can effectively detect and classify different PQ disturbances.     

MHD combined free and forced convection flow through two parallel porous walls

Summary The flow of a viscous conducting fluid between two parallel plates of infinite length (one of which is at rest and the other moving parallel to itself with a linear axial temperature variation) under the influence of a uniform transverse magnetic field is considered. It is assumed that the fluid is injected into the channel through the lower wall and sucked from the channel through the upper wall with the same velocityv ₀. The velocity and temperature distributions, the coefficient of skin friction and the rate of heat transfer coefficient are evaluated. The effects of magnetic parameterM, the suction Reynolds numberR, the Grashoff numberG and Binkmann numberB on the above mentioned physical quantities are investigated.With 21 Figures