Comparative Soil Organic Carbon Dynamics in Tropical and Subtropical Grassland Ecosystems

Grassland ecosystems play significant role in mitigating the climate change by sequestering atmospheric CO2. One fifth of the total terrestrial C is stored in the root zone of grasslands as soil organic carbon. However, because of lack of proper management, overgrazing, and conversion to crop lands, these grasslands are becoming a source of CO2 emissions. It has been observed that in Imperata grasslands of Northeast India, a third of total C captured annually is lost though CO2 emissions. In the absence of intensified grazing and burning, these grasslands exhibit significantly high capacity to store SOC stocks. On the other hand, Southern grasslands of China inherently have a weak C sink. Grazing and burning together significantly increased CO2 fluxes as observed in Andean grasslands. With the introduction of high yielding grass species and with liberal use of chemical fertilizers, grazing land intensification has been found to rather promote SOC sequestration. It has been observed that in C4 grass species dominated tropical and sub-tropical grasslands; there occurs a rapid transfer of plant C into mineral-dominated C pools. With change of C3 to C4 grass species, the grazer effects rather shift from negative to positive even under decreasing precipitation conditions. Similarly, rise in atmospheric temperatures due to climate change affects grasslands differently depending on the dominating grass species. Graminoids and shrubs appear to benefit from elevated temperatures while forbs are likely to decrease in abundance through competitive elimination. Extreme heat waves and frequent drought events is decreasing the extent and capacity of forests as C sink as compared to grasslands. Grasslands have been shown to be comparatively more resilient to changes in climate. The resilience of grasslands to rising temperatures, drought and fire events helps to preserve sequestered terrestrial C in the root-zone of grassland soil and prevent it from re-entering atmosphere

Drought Mitigation in Bundelkahand Grassland Ecosystem for Improving Livelihood of Farming Community-A Case Study

Bundelkhand grassland ecosystem (23º20´ and 26°20´N latitude and 78°20´ and 81°40´E longitude) is an undulating rain fed region (annual rain fall, 768-1087 mm) spread over an area of 7.08 m ha in central India in the states of Madhya Pradesh and Uttar Pradesh. This region has to support 16 million human and 8.5 million animal populations. Area is prone to surface run off losses, severe soil erosion and increasingly more drought events, leading to only mono-cropping. Lively-hood of the people, which is mainly the live-stock rearing and marginal agriculture, is at stake. In-situ conservation of rain water, forage management, and environmental services are the main issues to be addressed at priority to enhance productivity per unit area of this biome. Development funds amounting to about 1000 million US$, provided for constructing sustainable infrastructure like check-dams, dug-wells, embankments, rising of crest height etc. has significantly improved the surface and ground water resources. Impact evaluation as discussed in this paper includes water resource development, watershed management, crop and live-stock productivity, and rural drinking water. A robust and resilient management system has been developed through farmer’s participatory integrated watershed management program. Major aim is in-situ conservation of the rain water and recharging the dug wells, open wells, village ponds, and farm ponds. It has remarkably improved the financial condition of farming community. Initiation of restoration process of this grassland biome has increased its carrying capacity by 41%. An additional 25% land area has come under irrigation resulting in increase of net-sown area by 11%, cropping intensity by 6%, and farm income by 35%

Compressive Properties of Carbon Fibre Reinforced Plastic (CFRP) at Low Strain Rate

An experimental programme was carried out for testing and characterising the mechanical properties of carbon fibre reinforced plastic (CFRP) at low strain rates ranging from 10-' to 10 Is. Transverse compressive properties were obtained by carrying a series of quasi-static and dynamic tests on filament wound CFRP tubes with winding angle of ± 90° (the angle is relative to the tube axis). The quasi-static test were carried out using an Instron and RDP machines whereas dynamic tests on drop hammer rig. Axial and hoop strains were measured by foil strain gauges bonded to the specimen inside and outside surfaces. Load-displacement, load time and strain-time signals recorded during relevant tests are used to produce stresss train curves. The transverse compressive strength and ultimate failure strain increases with increasing strain rate. The modulus and Poisson's ratio are independent of strain rate. The stress strain curves at different strain rates exhibit a degree of non-linearity. No rate effect is observed on the mode of failure

BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning

Training deep learning networks involves continuous weight updates across the various layers of the deep network while using a backpropagation algorithm (BP). This results in expensive computation overheads during training. Consequently, most deep learning accelerators today employ pre-trained weights and focus only on improving the design of the inference phase. The recent trend is to build a complete deep learning accelerator by incorporating the training module. Such efforts require an ultra-fast chip architecture for executing the BP algorithm. In this article, we propose a novel photonics-based backpropagation accelerator for high performance deep learning training. We present the design for a convolutional neural network, BPLight-CNN, which incorporates the silicon photonics-based backpropagation accelerator. BPLight-CNN is a first-of-its-kind photonic and memristor-based CNN architecture for end-to-end training and prediction. We evaluate BPLight-CNN using a photonic CAD framework (IPKISS) on deep learning benchmark models including LeNet and VGG-Net. The proposed design achieves (i) at least 34x speedup, 34x improvement in computational efficiency, and 38.5x energy savings, during training; and (ii) 29x speedup, 31x improvement in computational efficiency, and 38.7x improvement in energy savings, during inference compared to the state-of-the-art designs. All these comparisons are done at a 16-bit resolution; and BPLight-CNN achieves these improvements at a cost of approximately 6% lower accuracy compared to the state-of-the-art

One-step synthesis of hydrophobized gold nanoparticles of controllable size by the reduction of aqueous chloroaurate ions by hexadecylaniline at the liquid-liquid interface

Vigorous stirring of a biphasic mixture containing hexadecylaniline in chloroform and aqueous chloroauric acid results in the formation of gold nanoparticles of controllable size in the organic phase

Lymphocytic Esophagitis: An Emerging Clinicopathologic Disease Associated with Dysphagia

Lymphocytic Esophagitis (LyE) is a recently described clinicopathological condition, but little is known about its features and clinical associations