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Environment fluctuations have become the greatest threat to global food security. Of various abiotic stress factors, aridity hampers the most yield contributing attributes. In the context of agriculture, term “aridity” refers to a protracted period of insufficient precipitation, having detrimental influence on crop development and overall biological output. A sustained drought has considerable negative effects on crops and livestock, including the reduced production, destruction of property, and livestock sell-offs. Consequently, plants themself exert various kinds of defensive mechanisms to combat the ill effects of climate change. For example, plants with small leaves, benefit from aridity as part of their strategy for modifying the soil to water shortages and nutrient restrictions. Furthermore, low genetic diversity among significant crop species, together with ecological productivity limits, must be addressed in order to adapt crops to episodic drought spells in the coming days. A deeper understanding of the molecular and genetic underpinnings of the most important intrinsic adaptation responses to drought stress seems to be beneficial for gene engineering as well as gene-based expression investigations in plant systems under hostile environment. Recently, molecular markers and “omics” have opened a huge opportunity to identify and develop specific gene constructs governing plant adaptation to environmental stress.

Solvated electrons are powerful reducing agents capable of driving some of the most energetically expensive reduction reactions. Their generation under mild and sustainable conditions remains challenging though. Using near-ultraviolet irradiation under low-intensity one-photon conditions coupled with electrochemical and optical detection, we show that the yield of solvated electrons in water is increased more than 10 times for nanoparticle-decorated electrodes compared to smooth silver electrodes. Based on the simulations of electric fields and hot carrier distributions, we determine that hot electrons generated by plasmons are injected into water to form solvated electrons. Both yield enhancement and hot carrier production spectrally follow the plasmonic near-field. The ability to enhance solvated electron yields in a controlled manner by tailoring nanoparticle plasmons opens up a promising strategy for exploiting solvated electrons in chemical reactions.

The Jhalida porphyritic granitoid pluton is exposed in a regional shear zone belonging to the Chhotanagpur Gneissic Complex of the Satpura Orogen (c. 1.0 Ga), regarded as the collisional suture between the South and North Indian blocks. The pluton intruded the migmatitic gneisses, metapelites, calc-silicate rocks and amphibolites belonging to the amphibolite facies. The mineral assemblage indicates the calc-alkaline nature of the granitoids. Mafic (Pl–Qz–Bt±Hbl) schists occur as xenoliths within the pluton. The granitoids are classified as alkali-calcic to alkalic, dominantly magnesian grading to ferroan, metaluminous to slightly peraluminous, and shoshonitic to ultrapotassic. Geochemically, the granitoids are enriched in large-ion lithophile elements (LILE), particularly K, and light rare earth elements (LREE), but are comparatively depleted in Nb, Ta, and heavy rare earth elements (HREE). The strong negative correlation between SiO2and P2O5, metaluminous to weakly peraluminous character, high liquidus temperature (798–891°C) and high fO2(ΔQFM +0.8 to +1.6) of the melt suggest their I-type nature. Field relations and tectonic discrimination diagrams imply their post-collisional emplacement. Low Nb/U (average 8.5), Ce/Pb (average 9.0), and Al2O3/(Al2O3+ FeO(t) + MgO + TiO2) ratios and relatively low Mg number (average 0.15) of these granitoids indicate a crustal mafic source. Batch melting (at 825–950°C) of 10–20% of an old, incompatible elements-rich high-K high-alumina hornblende granulite can generate the porphyritic granite melt. The heat source for melting was an upwelling of the asthenospheric mantle in the post-collisional set-up. Textural and chemical characteristics of the mafic xenoliths show that invading porphyritic granitoid magma metasomatized the amphibolite protoliths.

Nanoscale assemblies of amphiphiles have been vividly explored in pharmaceutical formulations as drug nanocarriers. Aqueous interfaces of liquid crystals (LCs) are known to direct the self-assembly of a range of amphiphiles. These amphiphile-decorated interfaces of LCs have evoked interest for applications as diverse as the detection of disease markers, screening of toxins, mimicking complex biomolecular interactions, and cell-based sensing. Aiming to explore these interfaces for encapsulation and enzyme-triggered release, we report a simple and rational design of enzyme-responsive LC interfaces programmed with a cleavable non-ionic surfactant. We encapsulated a hydrophobic dye within the surfactant micelles and investigated the enzyme-triggered dye release. Interestingly, we found that LC droplets, when decorated with the dye-loaded micelles, offer significant advantages over the conventional micellar nanocarriers. The LC droplets showed controlled release features which weren't affected at high dilutions. Our work, although exploratory in nature, provides fresh approaches for tailoring LC interfaces as vehicles for drug delivery.

This work presents detailed small-signal-modeling and closed-loop controller design of novel Cuk-based PFC converter for on-board EV charger. Since the converter is designed to operate in discontinuous conduction mode (DCM) for attaining power factor correction (PFC) naturally on the input side. Thus, only the output voltage sensing is required as the input voltage and current sensing is not necessary. Thereby, making the operation of the converter less-costly, reliable, and intensifying the robustness towards higher frequency noise. The control strategy is straightforward requiring one control loop and sensor. This article describes the complete derivation of control-to-output transfer function along with optimal selection of control parameters. Finally, to validate the controller design, simulation and hardware results are provided.

The lack of familiarity with sign language among non-users and the absence of a universal method for communication poses major problems for the deaf community. The digitization of communication channels can greatly benefit society. Previous attempts to develop sign language recognition (SLR) systems have posed disadvantages of low feasibility, portability, and accuracy which hamper widespread use. In this paper, a vision-based sign language recognition system using convolutional neural networks (CNN) is proposed that can efficiently and accurately recognize the American Sign Language (ASL) alphabet. The proposed method is implemented using the Keras library on Python. It is easy to implement and has attained high accuracies of over 99% with low bias and variance. The implementation has been validated on the ASL MNIST dataset and its performance is established through simulation studies.

This work proposes a Fourier-EWT based methodology for tracking the modal parameters of low-frequency range electromechanical oscillations of the power system. It is essential to monitor the low-frequency electromechanical oscillations as poorly damped uncorrected modes adversely affect the power system reliability leading to generator rotor fatigue. The proposed Fourier-EWT method uses EWT technique for the extraction of monocomponent and an FFT based technique to estimate the modal parameters of each monocomponent. The algorithm has been tested for both synthetic and real-time signals and the results are compared with that of TLS-ESPRIT based method and an FFT based method in the literature. Results analysis show that the proposed method is more effective than the other two methods even under high levels of noise.

Contactless energy transfer (CET) systems transfer electric energy without any wired connection. General CET system mainly consists of three parts: coil system, compensation system and power electronics system. This Paper describes design procedure of CET systems for e-scooter. Theoretical findings are validated through computer assisted softwares i.e. Finite Element Method and Power Simulation. Further, it is observed that the magnetic properties of the coils i.e. self-inductance, mutual inductance and coupling coefficient are improving while using different types of cores.

This paper addresses the problem of text normalization, an often overlooked problem in natural language processing, in code-mixed social media text. The objective of the work presented here is to correct English spelling errors in code-mixed social media text that contains English words as well as Romanized transliteration of words from another language, in this case Bangla. The targeted research problem also entails solving another problem, that of word-level language identification in code-mixed social media text. We employ a CRF based machine learning approach followed by post-processing heuristics for the word-level language identification task. For spelling correction, we used the noisy channel model of spelling correction. In addition, the spell checker model presented here tackles wordplay, contracted words and phonetic variations. Overall, the word-level language identification achieved 90.5% accuracy and the spell checker achieved 69.43% accuracy on the detected English words.