Performance of no-till maize under drip-fertigation in a double cropping system in semi arid Telangana state of India

Availability of water for Agriculture is becoming increasingly difficult, besides the cost of power for applying it. Improving the water and nitrogen use efficiency has become imperative in present day’s Agriculture. Drip irrigation and fertigation provides the efficient use of limited water with increased water and nutrient use efficiency, respec- tively. A field experiment was conducted during post rainy season of two consecutive years (2011 and 2012), in sandy loam soils of Warangal, Telangana State, India to study the response of no-till maize (Zea mays L) after aerobic rice (Oryza sativa L) to drip irrigation and nitrogen fertigation under semi-arid environment. The experiment was laid out in split plot design with four replications. Three irrigation schedules viz. drip irrigation at 75% Pan Evaporation (PE); 100% PE and 125% PE were taken as main plots and three nitrogen levels through fertigation viz. 120, 160, and 200 kg ha-1 as sub plots. The growth parameters (plant height, LAI, drymatter accumulation), root volume and dry weight, yield attributes (cobs plant-1, kernels cob-1, kernel weight cob-1) kernel yield, stover yield and nitrogen uptake of no till maize increased with increase in water input from 75% PE to 100% PE irrigation schedule in drip irrigation but could not reach the level of significance at 125% PE. Tasseling and silking was hastened in 125% PE schedule. Increase in the level of N application through fertigation from 120 to 160 kg N ha-1 resulted in the increase of all the growth parameters, yield attributes, kernel yield, stover yield and nitrogen uptake. Barrenness and test weight were unaffected by either the irrigation schedules or nitrogen levels. The economic indicators (gross returns, net returns and net benefit: cost ratio) were higher with the irrigation schedule of 125% PE and nitrogen dose of 200 kg N ha-1 applied through fertigation. Increased water input from 75 to 125% PE resulted in decreased water use efficiency but enhanced nitrogen use efficiency while the reverse trend was found with respect to N levels under fertigation

Exploring efficient seamless handover in VANET systems using network dwell time

Vehicular ad hoc networks are a long-term solution contributing significantly towards intelligent transport systems (ITS) in providing access to critical life-safety applications and services. Although vehicular ad hoc networks are attracting greater commercial interest, current research has not adequately captured the real-world constraints in vehicular ad hoc network handover techniques. Therefore, in order to have the best practice for vehicular ad hoc network services, it is necessary to have seamless connectivity for optimal coverage and ideal channel utilisation. Due to the high velocity of vehicles and smaller coverage distances, there are serious challenges in providing seamless handover from one roadside unit (RSU) to another. Though other research efforts have looked at many issues in vehicular ad hoc networks (VANETs), very few research work have looked at handover issues. Most literature assume that handover does not take a significant time and does not affect the overall VANET operation. In our previous work, we started to investigate these issues. This journal provides a more comprehensive analysis involving the beacon frequency, the size of beacon and the velocity of the vehicle. We used some of the concepts of Y-Comm architecture such as network dwell time (NDT), time before handover (TBH) and exit time (ET) to provide a framework to investigate handover issues. Further simulation studies were used to investigate the relation between beaconing, velocity and the network dwell time. Our results show that there is a need to understand the cumulative effect of beaconing in addition to the probability of successful reception as well as how these probability distributions are affected by the velocity of the vehicle. This provides more insight into how to support life critical applications using proactive handover techniques

Development of sesbania mosaic virus nanoparticles for imaging

AbstractThe capsids of viruses have a high degree of symmetry. Therefore, virus nanoparticles (VNPs) can be programmed to displaymany imaging agents precisely. Plant VNPs are biocompatible, biodegradable and non-infectious to mammals. We have carriedout bioconjugation of sesbania mosaic virus (SeMV), a well characterized plant virus, with fluorophores using reactivelysine-N-hydroxysuccinimide ester and cysteine-maleimide chemistries. Monitoring of cellular internalization of labelledSeMV nanoparticles (NPs) by confocal microscopy and flow cytometry showed that the particles have a natural preferencefor entry into MDA-MB-231 (breast cancer) cells, although they could also enter various other cell lines. The fluorescenceof SeMV NPs labelled via the cysteines with Cy5.5 dye was found to be more stable and was detectable with greater sensitivitythan that of particles labelled via the lysines with Alexa Fluor. Live-cell imaging using SeMV internally labelled withCy5.5 showed that it could bind to MDA-MB-231 cells in less than 5 minutes and enter the cells within 15 minutes. Theparticles undergo endolysosomal degradation by 6 h as evidenced by their co-localization with LAMP-1. Far-western blotanalysis with a HeLa cell membrane protein fraction showed that SeMV interacts with 54-, 35- and 33-kDa proteins, whichwere identified by mass spectrometry as vimentin, voltage-dependent anion-selective channel protein (VDAC1), and annexinA2 isoform 2 (ANXA2), respectively, suggesting that the particles may bind and enter the cell through these proteins. Theresults presented here demonstrate that the SeMV NPs provide a new platform technology that could be used to develop invivo imaging and targeted drug delivery agents for cancer diagnosis and therapy

ASTROMER

Taking inspiration from natural language embeddings, we present ASTROMER, a transformer-based model to create representations of light curves. ASTROMER was pre-trained in a self-supervised manner, requiring no human-labeled data. We used millions of R-band light sequences to adjust the ASTROMER weights. The learned representation can be easily adapted to other surveys by re-training ASTROMER on new sources. The power of ASTROMER consists in using the representation to extract light curve embeddings that can enhance the training of other models, such as classifiers or regressors. As an example, we used ASTROMER embeddings to train two neural-based classifiers that use labeled variable stars from MACHO, OGLE-III, and ATLAS. In all experiments, ASTROMER-based classifiers outperformed a baseline recurrent neural network trained on light curves directly when limited labeled data were available. Furthermore, using ASTROMER embeddings decreases the computational resources needed while achieving state-of-the-art results. Finally, we provide a Python library that includes all the functionalities employed in this work