Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, ...and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).
Plant diseases are a severe cause of crop losses in the agriculture globally. Detection of diseases in plants is difficult and challenging due to the lack of expert knowledge. Deep learning-based ...models provide promising ways to identify plant diseases using leaf images. However, need of larger training sets, computational complexity, and overfitting, etc. are the major issues with these techniques that still need to be addressed. In this work, a convolutional neural network (CNN) is developed that consists of smaller number of layers leading to lower computational burden. Some augmentation techniques such as shift, shear, scaling, zoom, and flipping are applied to generate additional samples increasing the training set without actually capturing more images. The CNN model is trained for apple crop using a publicly available dataset PlantVillage to identify Scab, Black rot, and Cedar rust diseases in apple leaves. The rigorous experimental results revealed that the proposed model is well fit to identify apple leaf diseases and achieves 98% classification accuracy. It is also evident from the results that it needs lesser amount of storage and takes smaller execution time than several existing deep CNN models. Although, there exist several CNN models for crop disease detection with comparable accuracy, but the proposed model needs lower storage and computational resources. Therefore, it is highly suitable for deploying in handheld devices.
Migmatites and partial melts are exposed in both the lower and upper package of the Higher Himalayan Crystallines (HHC) thrust sheet within the Sikkim Himalayas. Zircon monazite and quartz oxygen ...isotopic ratios from Yumthang Valley, North Sikkim, and Rathong Chuu, West Sikkim, have been used to identify their sources and equilibrium conditions. Monazites show homogeneous growth, whereas zircons show growth rings. U-Th-Pb data on monazite only indicate the latest metamorphic event. However, zircons show metamorphic rim growth between 36 and 24 Ma over their detrital core with trailing growth from 22 Ma to 15 Ma. Pervasive fluids have been interpreted in coeval development during metamorphism, as shown by monazite and zircon c. 30 Ma. The Th/U ratio of zircon is higher and variable with weak residual zoning in the samples from higher elevations. Quartz–metamorphic zircon oxygen fractionation suggests Teq > 600 °C, while quartz–monazite fractionation shows the same or lower temperatures. Multiple sources of melts in the HHC (even along a single valley) have been observed by δ18O of 7‰ to 10‰ in zircon and 5‰ to 9‰ in monazite. Zircon and monazite generated in the same rock have similar δ18O values. Monazite grown ~20 Ma in the lower elevation sample had a low δ18O, suggesting interaction with an external fluid.
An attempt has been made to evaluate crustal melting evidence through textural studies within a narrow zone of migmatite present in NW Himalaya along Bhagirathi and Dhauliganga valleys and two zones ...close to MCT and throughout within in the Sikkim Himalayas, NE Himalaya. It appears that partial melting was initiated by muscovite dehydration melting with a positive volume change driving melt segregation and discontinuous crystallization of peritectic biotite in the leucosome. Further, during retrogression due to cooling, a certain amount of melt was consumed. The occurrence of isolated pseudomorphosed melt pockets and lack of euhedral magmatic flow textured feldspar further indicates that the melt fraction was low for the alignment of crystals. The processes of melt segregation and migration could have been limited. The migmatite leucosomes and a small volume of
in situ
tourmaline-bearing leucogranite along extensional crenulation cleavages and melt pods indicate water-saturated melting of pelitic metasedimentary rocks. The formation of migmatites happened at around 46 Ma, corresponding to a peak metamorphic event due to collisional tectonics of the Himalayan orogeny. The presence of feeder dikes for main tourmaline-bearing leucogranite indicates that the source for the main body could be migmatite which is also supported by the similarity in REE patterns of the main body and
in situ
tourmaline-bearing leucogranite.
We develop a spatial electricity planning model to guide grid expansion in countries with low pre-existing electricity coverage. The model can be used to rapidly estimate connection costs and compare ...different regions and communities. Inputs that are modeled include electricity demand, costs, and geographic characteristics. The spatial nature of the model permits accurate representation of the existing electricity network and population distribution, which form the basis for future expansion decisions. The methodology and model assumptions are illustrated using country-specific data from Kenya. Results show that under most geographic conditions, extension of the national grid is less costly than off-grid options. Based on realistic penetration rates for Kenya, we estimate an average connection cost of $1900 per household, with lower-cost connection opportunities around major cities and in denser rural regions. In areas with an adequate pre-existing medium-voltage backbone, we estimate that over 30% of households could be connected for less than $1000 per connection through infilling. The penetration rate, an exogenous factor chosen by electricity planners, is found to have a large effect on household connection costs, often outweighing socio-economic and spatial factors such as inter-household distance, per-household demand, and proximity to the national grid.
Crop yield predictions are important for crop monitoring and agronomic management. The traditional methods for yield predictions are complicated and resource consuming. With the availability of ...affordable handheld imaging and computing devices, the image processing-based yield prediction methods are gaining popularity. In this work, RGB images of
rice
panicles are captured using DSLR camera with simple background and processed to determine the panicle area in terms of number of pixels. A machine learning-based model is developed to make predictions for rice yield. The model is trained to make predictions on unseen data. Various machine learning-based regression algorithms including decision tree, random forest, support vector machine, and convolution neural network are tested. The experiments are performed on a publically available dataset from China as well as on a self-acquired dataset in India. The results have shown that image processing and machine learning-based methods can make yield predictions satisfactorily as evident from the coefficient of determination (
R
2
) that ranges 0.80–0.97 for different cultivars. The prediction error is determined in terms of root mean square error (RMSE) and mean absolute error (MAE). RMSE for different methods lies between 0.14 and 0.40, whereas MAE varies from 0.11 to 0.30. Among the tested algorithms, linear regression achieved the best precision with
R
2
= 0.97, RMSE = 0.14, and MAE = 0.11.
The present study deals with the development of a combined reactor involving an ultrasonic reactor (UR) and a mechanical stirrer (MS) for the transesterification of vegetable oil, extracted from ...semal (Bombax Ceiba). Reaction variables such as reaction time, methanol to oil molar ratio, catalyst concentration, and ultrasonic irradiation power were investigated to find the optimal parameters for maximizing biodiesel yield. The optimum conditions with the combined process reactor (CPR) are: 30 min reaction time, 4.5/1 molar ratio, 0.5% catalyst, and 40% of the maximum ultrasonic power with a maximum yield of 96.4% as compared to 110 min, 6/1 molar ratio, and 1% catalyst with a maximum yield of 90.7% for the MS, and 40 min, 4.5/1 molar ratio, 0.75% catalyst, and 50% of the maximum ultrasonic power with a maximum yield of 92.1% in a UR.
PDF
