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Gerbera (Gerbera jamesonii) is a popular ornamental plant cultivated all over the world. It is grown in beds, pots and used as cut-flower in making bouquets and for decoration in ceremonial functions. Gerbera has been commercially cultivated by a large number of growers in India as a primary source of income, therefore, has high socioeconomic impact in floriculture industry. The gerbera cultivation areas in India are increasing day by day due to its uses and the market demands. Its cultivation has been hampered by a variety of diseases that affect its flower quality and quantity. Of them, the viral and phytoplasma diseases cause considerable losses in gerbera cultivation. In this review, we have described about the disease symptoms, detection methods and identification of causal virus and phytoplasma pathogens affecting gerbera production worldwide and their disease management strategies opted by the researchers for production of pathogen-free plants.

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A stability-indicating gradient reverse-phase liquid chromatographic method was developed for the quantitative determination of process-related impurities and forced degradation products of oxcarbazepine in pharmaceutical formulation. The method was developed by using Inertsil cyano (250 × 4.6 mm) 5 μm column with mobile phase containing a gradient mixture of solvent A (0.01 M sodium dihydrogen phosphate, pH adjusted to 2.7 with orthophosphoric acid and acetonitrile in the ratio of 80:20 v/v) and B (50:40:10 v/v/v mixture of acetonitrile, water, and methanol). The flow rate of mobile phase was 1.0 mL min−1. Column temperature was maintained at 25°C and detection wavelength at 220 nm. Developed reverse-phase high-performance liquid chromatography (RP-HPLC) method can adequately separate and quantitate five impurities of oxcarbazepine, namely imp-A, imp-B, imp-C, imp-D, and imp-E. Oxcarbazepine was subjected to the stress conditions of oxidative, acid, base, hydrolytic, thermal, and photolytic degradation. Oxcarbazepine was found to degrade significantly in acid, base, and oxidative stress conditions. The degradation products were well resolved from oxcarbazepine and its impurities. The developed method was validated as per International Conference on Harmonization (ICH) guidelines with respect to specificity, linearity, limit of detection and quantification, accuracy, precision, and robustness.

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In this paper, advanced DC-Link (DCL) based reversing voltage type Multilevel Inverter (MLI) topologies by compensating the difficulties in the conventional MLIs are reviewed. These topologies consist of less switching components and driver circuits when compared with conventional MLIs predominantly in higher levels. Consequently, installation area, total cost and hardware difficulties are reduced by increasing the voltage levels. The unipolar based Pulse Width Modulation Schemes (PWMS) will improve DCL inverters performance. This paper presents unipolar Multi-Reference (MR) based sine and space vector PWMS with single triangular carrier wave for generating required levels in output voltage. Comparison between UMR sine and space vector PWMS for DCL inverter topologies is presented in terms of Fundamental Output Voltage (FOV) and Total Harmonic Distortion (THD). The research tries to establish the survey analysis for single-phase 7-level DCL based reversing voltage type MLI topologies with UMR based sine and space vector PWMs. Finally, to confirm the feasibility of proposed DCL-MLIs in terms of FOV and THD the simulation results are incorporated. Further, the prototype model is developed for single-phase 7-level DCL inverter with Field Programmable Gate Array (FPGA) based UMR sine and space vector PWMS to authenticate simulation results. The efficiency of the proposed cascaded MLI achieves the value of 99.003%.

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Global warming is rising as a serious concern affecting agricultural production worldwide. Rice is a staple food crop and the threshold temperature for its pollination is 35 °C. A rise in temperature above this value can cause pollen sterility and may severely affect fertilization. Therefore, a study emphasizing the rise in temperature with respect to pollen viability was conducted with eleven rice genotypes during kharif seasons of 2010 and 2011 in indigenous field conditions. Increasing mean temperature by 12 °C at full flowering was found to severely affect the spikelet attributes of the crop. All genotypes showed spikelet sterility above 90% during both seasons. The study indicated that increased temperature may limit rice yield by affecting spikelet fertility and grain filling. The net reduction in grain yield was 30.4% and 27.6% in 2010 and 2011, respectively. A clear reduction in pollen size under high temperature was shown by scanning electron microscopy.

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Knowledge of the dynamics of HTO in leafy plant–soil system is required to verify models, such as the NORMTRI code, which predict environmental tritium following its release. Tritium concentrations in plants has been evaluated using the code NORMTRI and experimentally by collection of samples of different plants and their soils samples. In the present study, major seasonal crop plants i.e. wheat, mustard, sugar cane, coriander, spinach, potato, were collected beyond Narora Atomic Power Station site boundary and gular, arandi, neem, ashok, amaltas, csuarina leaf samples within NAPS site boundary for analysis of HTO content. Data analysis indicated that HTO in leaf is strongly influenced by atmospheric relative humidity and type of the plant.

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In this manuscript, the combination of IoT and Multilayer Hybrid Dropout Deep-learning Model for waste image categorization is proposed to categorize the wastes as bio waste and non-bio waste. The input captured images are pre-processed and remove noises in the captured images. Under this approach, a Nature inspired Multilayer Hybrid Dropout Deep-learning Model is proposed. Multilayer Hybrid Dropout Deep-learning Model is the consolidation of deep convolutional neural network and Dropout Extreme Learning Machine classifier. Here, deep convolutional neural network is used for feature extraction and Dropout Extreme Learning Machine classifier for categorizing the waste images. To improve the classification accurateness, Horse herd optimization algorithm is used to optimize the parameter of the Dropout Extreme Learning Machine classifier. The objective function is to maximize the accuracy by minimize the computational complexity. The simulation is executed in MATLAB. The proposed Multilayer Hybrid Dropout Deep-learning Model and Horse herd optimization algorithm attains higher accuracy 39.56% and 42.46%, higher Precision 48.74% and 34.56%, higher F-Score 32.5% and 45.34%, higher Sensitivity 24.45% and 34.23%, higher Specificity 31.43% and 21.45%, lower execution time 0.019(s) and 0.014(s) compared with existing waste management and classification using convolutional neural network with hyper parameter of random search optimization algorithm waste management and classification using clustering approach with Ant colony optimization algorithm. Finally, the proposed method categorizes the waste image accurately.

Open access

Two new pathotypes of Puccinia triticina , 121R60-1 and 377R60-1 the latter virulent on Lr28 , are described for the first time. Both the pathotypes are designated as MHTTS as per North American system of pathotype identification. Pathotype 377R60-1 appears to be the result of a single step mutation for virulence to Lr28 in pathotype 121R60-1. Both pathotypes appear closely related to the most prevalent virulent pathotype 121R63-1(THTTS). The avirulence/virulence profile, resistance sources and their possible relationship with other pathotypes are discussed. Lr28 is now included as one of the differentials for the identification of leaf rust pathotypes.

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The present paper reports the Differential Scanning Calorimetric (DSC) study of some Ag doped Se-Te chalcogenide glasses. DSC runs were taken at different heating rates. Well-defined endothermic and exothermic peaks were obtained at glass transition and crystallization temperatures. The variation of glass transition temperature T gwith Ag concentration has been studied. It has been found that T gdecreases with increase in Ag concentration. The heating rate dependence of T gis used to evaluate the activation energy of glass transition (DE t). The value ofDE thas been found to increase with increase in Ag concentration followed by nearly constant value at higher concentrations of Ag.

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A radiotracer study was carried out in a trickle bed reactor (TBR) independently filled with two different types of packing i.e., hydrophobic and hydrophilic. The study was aimed at to estimate liquid holdup and investigate the dispersion characteristics of liquid phase with both types of packing at different operating conditions. Water and H2 gas were used as aqueous and gas phase, respectively. The liquid and gas flow rates used ranged from 0.83 × 10−7–16.67 × 10−7 m3/s and 0–3.33 × 10−4 m3 (std)/s, respectively. Residence time distribution (RTD) of liquid phase was measured using 82Br as radiotracer and about 10 MBq activity was used in each run. Mean residence time (MRT) and holdup of liquid phase were estimated from the measured RTD data. An axial dispersion with exchange model was used to simulate the measured RTD curves and model parameters (Peclet number and MRT) were obtained. At higher liquid flow rates, the TBR behaves as a plug flow reactor, whereas at lower liquid flow rates, the flow was found to be highly dispersed. The results of investigation indicated that the dispersion of liquid phase is higher in case of hydrophobic packing, whereas holdup is higher in case of hydrophilic packing.

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N,N,N′,N′-Tetraoctyl-3-oxapentanediamide (TODGA) is a versatile extractant for partitioning of fission products from highly active raffinate wastes. Its PVT properties are not available in literature. In this work, PVT properties of TODGA, estimated using group contribution method, are reported. A corresponding-states based equation as well as Wagner constants were also reported in the range of 273.15 K to critical temperature.

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