Effect of source and methods of zinc application on corn productivity, nitrogen and zinc concentrations and uptake by high quality protein corn (Zea mays)

Results of a field study conducted at the Indian Agricultural Research Institute, New Delhi, India showed that the combined application of soil + foliar (in two sprays at tasseling and initiation of flowering) produced significantly more grain and stover yields than either soil or foliar applications alone. Application of Zn-coated urea was better than soil application of Zn sulphate with regard to grain and stover yields. The combined application also recorded the highest Zn concentration in corn grain as well as in stover, with the treatments falling in the following order: combined ˃ foliar ˃ soil through Zn-coated urea ˃ soil. This is an important finding for the agronomic biofortification of Zn in corn.Keywords: Crude protein; foliar application of zinc, zinc biofortification, zinc-coated urea, zinc sulphat

Implementation of optimal solution for network lifetime and energy consumption metrics using improved energy efficient LEACH protocol in MANET

In current scenarios MANET mainly focus on low power battery operated devices. Moreover in a MANET transmission of large data consumes more energy that affects the performance of network, energy consumption, throughput, end to end delay, and packet overhead. The sum of these parameter metrics measure must be taken into account to increase the life-time and network energy efficiency. The main constraint in WSN is due to the restricted power in a node, which cannot be substituted. The node senses the data and it is moved towards the sink. This action of data movement needs to be efficient and the usage of battery in the sensor node requires to be efficiently employed to improve the network lifetime. The development of the energy efficient algorithms is of primary concern in the research arena of MANET. In any network, most of the routing protocols are focused directly to collect and bifurcate the large data for long distance communication. The prime goal of this research focused to identifies and survey more suitable routing protocol for MANET. That consumed less energy and increase life time of network. In this paper the author made on attempt on improved energy efficient LEACH protocol for MANET to reduce the energy dissipation that to life time of the network during the data transmission between source nodes and destination nodes

Analytical Model of Adaptive CSMA-CA MAC for Reliable and Timely Clustered Wireless Multi-Hop Communication

Reliability and delay of a single cluster wireless network is well analysed in the literature. Multi-hop communication over the number of clusters is essential to scale the network. Analytical model for reliability and end-to-end delay optimization for multi-hop clustered network is presented in this paper. Proposed model is a three dimensional markov chain. Three dimensions of markov model are the adaptable mac parameters of CSMA-CA. Model assumes wakeup rates for each cluster. Results show that reliability and delay are significantly improved than previous analytical models proposed. It has been observed that overall reliability of multi-hop link is improved, with reduction in end-to-end delay is reduced even at lower wakeup rates of a cluste

Characteristics of superplasticity domain in the processing map for hot working of as-cast Mg–11.5Li-1.5Al alloy

Processing map for hot working of as-cast Mg-11.5Li-1.5Al alloy has been developed in the temperature range 200–450°C and strain rate range 0.001-100 s−1. The map exhibited a single domain with a peak efficiency of 65% occurring at 400°C and 0.001 s−1. Under these conditions, the material exhibited abnormal elongation. On the basis of the elongation, the grain structure, the apparent activation energy for hot deformation (95 kJ mole−1) and the peak efficiency of power dissipation (65% corresponding to a strain rate sensitivity of about 0.5), the domain is interpreted to represent superplasticity. At strain rates higher than about 10 s−1, the material exhibits microstructural instability, while at temperatures of 450°C and a strain rate of 0.001 s−1, grain boundary cracking is observed

Evaluating ephemeral gullies with a process-based topographic index model

Soil conservation practices have been implemented to control soil degradation from sheet and rill erosion, but excessive sediment runoff remains among the most prevalent water quality problems in the world. Ephemeral gully (EG) erosion has been recognized as a major source of sediment in agricultural watersheds; thus, predicting location and length of EGs is important to assess sediment contribution from EG erosion. Geomorphological models are based on topographic information and ignore other important factors such as precipitation, soil, topography, and land use/land management practices, whereas physically based models are complex, require detailed input information, and are difficult to apply to larger areas. In this study, an approach was developed to incorporate a process-based Overland Flow-Turbulent (OFT) EG model that contained factors accounting for drainage area, surface roughness, slope, soil critical shear stress, and surface runoff in the ArcGIS environment. Two hydrologic models, Soil Water Assessment Tool (SWAT) and ArcCN-Runoff (ACR), were adopted to simulate precipitation excess in Goose Creek watershed in central Kansas, USA. These two realizations of the OFT model were compared with the Slope-Area (SA) topographic index model for accuracy of EG location identification and length calculation. The critical threshold index in the SA model was calibrated in a single field in the watershed prior to EG identification whereas the OFT models were uncalibrated. Results demonstrated overall similar performance between calibrated SA model and uncalibrated OFT-SWAT model, and both outperformed the uncalibrated OFT-ACR model. In simulation of EG location, the OFT-SWAT model resulted in 12% fewer false negatives but 8% more false positives than the SA model, compared with 19% fewer false positive and 6% more false negatives than the OFT-ACR model. Greater errors in runoff estimation by ACR translated directly into errors in EG simulation. All models over-predicted EG lengths compared with observed data, though OFT-SWAT and SA models did so with better fit exceedance probability curves, about zero Nash-Sutcliff model efficiency and ≤40% bias compared to -3 model efficiency and >100% bias for OFT-ACR. Success of the uncalibrated OFT-SWAT model in producing satisfactory predictions of EG location and EG length shows promise for process-based EG simulation. The OFT-SWAT model used data and parameters also commonly used for SWAT model development, which should simplify its adoption to other watersheds and regions. Further testing is needed to determine the robustness of the OFT-SWAT model to dissimilar field and hydrologic conditions. It is expected that inclusion of more site-specific physical properties in OFT-SWAT would improve model performance in predicting location and length of EGs, which is essential for accurate estimation of EG sediment erosion rates

Isolation, identification, synthesis, and bioefficacy of female Diacrisia obliqua (Arctiidae) sex pheromone blend. An Indian agricultural pest

Diacrisia obliqua is a polyphagous pest especially on oil seed crops. Adult female sex pheromone blend consists of five pheromone components, which include (3Z,6Z)-cis-9,10-epoxyl,3,6-henicosatriene and (3Z,6Z)-cis-9,10-epoxy3,6-henicosadiene. Synthesis of these enantiomers was achieved through alkylative epoxide rearrangement and stereoselective Wittig olefination reactions as key steps. Bioefficacy experiments both at laboratory and minifield were very positive

LATENTIATED PRODRUG APPROACH OF DRUGS: AN OVERVIEW

Prodrugs, with their capability of declining the adverse events and elevating the bioavailability of certain drugs, have captured enormous attention throughout the world since the 20th century. The versatility of the prodrugs that are inert and after administration releasing the parent moiety for the desired effect has become a major criterion for the scientists to incorporate this to alleviate the undesired effects of a conventional drug. About 10% of the prevailing drugs are prodrugs and their usage is being amplified owing to its critical application in cancer therapy, toxicity alleviation, and specificity. The purpose of this review is to understand the prodrugs, strategies incorporated in designing the prodrugs, applications, their crucial benefits in targeted action at a specific site of the body, their advantageous effects in chemotherapy. Also, to be acknowledged with the ongoing clinical trials and researches on prodrugs and some notable marketed prodrugs in a depth manner

Optimization of Deep CNN Techniques to Classify Breast Cancer and Predict Relapse

Breast cancer is a fatal disease that has a high rate of morbidity and mortality. Finding the right diagnosis is one of the most crucial steps in breast cancer treatment. Doctors can use machine learning (ML) and deep learning techniques to aid with diagnosis. This work makes an effort to devise a methodology for the classification of Breast cancer into its molecular subtypes and prediction of relapse. The objective is to compare the performance of Deep CNN, Tuned CNN and Hypercomplex-Valued CNN, and infer the results, thus automating the classification process. The traditional method used by doctors to detect is tedious and time consuming. It employs multiple methods, including MRI, CT scanning, aspiration, and blood tests as well as image testing. The proposed approach uses image processing techniques to detect irregular breast tissues in the MRI. The survivors of Breast Cancer are still at risk for relapse after remission, and once the disease relapses, the survival rate is much lower. A thorough analysis of data can potentially identify risk factors and reduce the risk of relapse in the first place. A SVM (Support Vector Machine) module with GridSearchCV for hyperparameter tuning is used to identify patterns in those patients who experience a relapse, so that these patterns can be used to predict the relapse before it occurs. The traditional deep learning CNN model achieved an accuracy of 27%, the tuned CNN model achieved an accuracy of 92% and the hypercomplex-valued CNN achieved an accuracy of 98%. The SVM model achieved an accuracy of 89% and on tuning the hyperparameters by using GridSearchCV it achieved and accuracy of 98%

Pasture BMP effectiveness using an HRU-based subarea approach in SWAT

Citation: Aleksey Y. Sheshukov, Kyle R. Douglas-Mankin, Sumathy Sinnathamby, Prasad Daggupati, Pasture BMP effectiveness using an HRU-based subarea approach in SWAT, Journal of Environmental Management, Volume 166, 2016, Pages 276-284, ISSN 0301-4797, http://dx.doi.org/10.1016/j.jenvman.2015.10.023.Many conservation programs have been established to motivate producers to adopt best management practices (BMP) to minimize pasture runoff and nutrient loads, but a process is needed to assess BMP effectiveness to help target implementation efforts. A study was conducted to develop and demonstrate a method to evaluate water-quality impacts and the effectiveness of two widely used BMPs on a livestock pasture: off-stream watering site and stream fencing. The Soil and Water Assessment Tool (SWAT) model was built for the Pottawatomie Creek Watershed in eastern Kansas, independently calibrated at the watershed outlet for streamflow and at a pasture site for nutrients and sediment runoff, and also employed to simulate pollutant loads in a synthetic pasture. The pasture was divided into several subareas including stream, riparian zone, and two grazing zones. Five scenarios applied to both a synthetic pasture and a whole watershed were simulated to assess various combinations of widely used pasture BMPs: (1) baseline conditions with an open stream access, (2) an off-stream watering site installed in individual subareas in the pasture, and (3) stream or riparian zone fencing with an off-stream watering site. Results indicated that pollutant loads increase with increasing stocking rates whereas off-stream watering site and/or stream fencing reduce time cattle spend in the stream and nutrient loads. These two BMPs lowered organic P and N loads by more than 59% and nitrate loads by 19%, but TSS and sediment-attached P loads remained practically unchanged. An effectiveness index (EI) quantified impacts from the various combinations of off-stream watering sites and fencing in all scenarios. Stream bank contribution to pollutant loads was not accounted in the methodology due to limitations of the SWAT model, but can be incorporated in the approach if an amount of bank soil loss is known for various stocking rates. The proposed methodology provides an adaptable framework for pasture BMP assessment and was utilized to represent a consistent, defensible process to quantify the effectiveness of BMP proposals in a BMP auction in eastern Kansas

Investigating Sources of Variability and Error in Simulations of Carbon Dioxide in an Urban Region

Greenhouse gas (GHG) emissions estimation methods that use atmospheric trace gas observations, including inverse modeling techniques, perform better when carbon dioxide (CO2) fluxes are more accurately transported and dispersed in the atmosphere by a numerical model. In urban areas, transport and dispersion is particularly difficult to simulate using current mesoscale meteorological models due, in part, to added complexity from surface heterogeneity and fine spatial/temporal scales. It is generally assumed that the errors in GHG estimation methods in urban areas are dominated by errors in transport and dispersion. Other significant errors include, but are not limited to, those from assumed emissions magnitude and spatial distribution. To assess the predictability of simulated trace gas mole fractions in urban observing systems using a numerical weather prediction model, we employ an Eulerian model that combines traditional meteorological variables with multiple passive tracers of atmospheric CO2 from anthropogenic inventories and a biospheric model. The predictability of the Eulerian model is assessed by comparing simulated atmospheric CO2 mole fractions to observations from four in situ tower sites (three urban and one rural) in the Washington DC/Baltimore, MD area for February 2016. Four different gridded fossil fuel emissions inventories along with a biospheric flux model are used to create an ensemble of simulated atmospheric CO2 observations within the model. These ensembles help to evaluate whether the modeled observations are impacted more by the underlying emissions or transport. The spread of modeled observations using the four emission fields indicates the model's ability to distinguish between the different inventories under various meteorological conditions. Overall, the Eulerian model performs well; simulated and observed average CO2 mole fractions agree within 1% when averaged at the three urban sites across the month. However, there can be differences greater than 10% at any given hour, which are attributed to complex meteorological conditions rather than differences in the inventories themselves. On average, the mean absolute error of the simulated compared to actual observations is generally twice as large as the standard deviation of the modeled mole fractions across the four emission inventories. This result supports the assumption, in urban domains, that the predicted mole fraction error relative to observations is dominated by errors in model meteorology rather than errors in the underlying fluxes in winter months. As such, minimizing errors associated with atmospheric transport and dispersion may help improve the performance of GHG estimation models more so than improving flux priors in the winter months. We also find that the errors associated with atmospheric transport in urban domains are not restricted to certain times of day. This suggests that atmospheric inversions should use CO2 observations that have been filtered using meteorological observations rather than assuming that meteorological modeling is most accurate at certain times of day (such as using only mid-afternoon observations)