Intelligent Methods for Smart Microgrids

This paper summarizes ongoing research in the application of intelligent methods to the design, modeling, simulation and control of microgrids including optimal design of microgrids, and centralized and decentralized control. © 2011 IEEE

Epigenetic profile of the euchromatic region of human Y chromosome

The genome of a multi-cellular organism acquires various functional capabilities in different cell types by means of distinct chromatin modifications and packaging states. Acquired during early development, the cell type-specific epigenotype is maintained by cellular memory mechanisms that involve epigenetic modifications. Here we present the epigenetic status of the euchromatic region of the human Y chromosome that has mostly been ignored in earlier whole genome epigenetic mapping studies. Using ChIP-on-chip approach, we mapped H3K9ac, H3K9me3, H3K27me3 modifications and CTCF binding sites while DNA methylation analysis of selected CpG islands was done using bisulfite sequencing. The global pattern of histone modifications observed on the Y chromosome reflects the functional state and evolutionary history of the sequences that constitute it. The combination of histone and DNA modifications, along with CTCF association in some cases, reveals the transcriptional potential of all protein coding genes including the sex-determining gene SRY and the oncogene TSPY. We also observe preferential association of histone marks with different tandem repeats, suggesting their importance in genome organization and gene regulation. Our results present the first large scale epigenetic analysis of the human Y chromosome and link a number of cis-elements to epigenetic regulatory mechanisms, enabling an understanding of such mechanisms in Y chromosome linked disorders

Hydrogeological typologies of the Indo-Gangetic basin alluvial aquifer, South Asia

The Indo-Gangetic aquifer is one of the world’s most important transboundary water resources, and the most heavily exploited aquifer in the world. To better understand the aquifer system, typologies have been characterized for the aquifer, which integrate existing datasets across the Indo-Gangetic catchment basin at a transboundary scale for the first time, and provide an alternative conceptualization of this aquifer system. Traditionally considered and mapped as a single homogenous aquifer of comparable aquifer properties and groundwater resource at a transboundary scale, the typologies illuminate significant spatial differences in recharge, permeability, storage, and groundwater chemistry across the aquifer system at this transboundary scale. These changes are shown to be systematic, concurrent with large-scale changes in sedimentology of the Pleistocene and Holocene alluvial aquifer, climate, and recent irrigation practices. Seven typologies of the aquifer are presented, each having a distinct set of challenges and opportunities for groundwater development and a different resilience to abstraction and climate change. The seven typologies are: (1) the piedmont margin, (2) the Upper Indus and Upper-Mid Ganges, (3) the Lower Ganges and Mid Brahmaputra, (4) the fluvially influenced deltaic area of the Bengal Basin, (5) the Middle Indus and Upper Ganges, (6) the Lower Indus, and (7) the marine-influenced deltaic areas

Detection of Parkinson’s Disease using Deep learning algorithms

Parkinson’s illness is an advancing genetic neurological chronic disease impacts people mostly in old age but still might infect very few young people. This disease slowly eats up a part of the brain which is responsible for body movement, resulting in a steady loss of muscle control of the entire body. For example, frequent hand and leg tremors, body stiffness, loss of speech, bradykinesia, and dystonia. The treatments available don’t entirely cure PD as there is no medication, but on the other side, clinicians are trying to improve the patient’s lifetime. As the pattern recognition region of the brain is related to PD, we are using a dataset with healthy and PD hand-drawn images from a small test conducted. Here we have proposed a combination of deep learning algorithms of ANN and CNN with a machine learning algorithm of Random Forest classifier to improve the accuracy rate by “74” in finding out the person with PD. Hence, it is inferred that the expected results benefit clinicians in identifying and treating patients with PD in an operative way

Change Point Detection of Temperature in Karnataka State in India During the Period 1979-2019

This paper deals with study of exposure of Karnataka state to climate change for a period 1979-2019. The Mann Whitney Pettit’s homogeneity test (MWP) was analysed for 240 data sets for monthly data of minimum (MTmin) and maximum temperature (MT max) across ten agro climatic zones) to estimate the year of structural break or year of shift in mean monthly temperature from one level to next higher level during the forty years of study period i.e., 1979-2019. About 77 data sets were identified to show year of structural break The annual mean temperature recorded anupward shift in all the agro climatic zones of Karnataka except in hilly zone. The break year was chosen based on its frequent occurrence in data sets of minimum and maximum temperature.  It is observed to be 1998 for North Eastern Transition Zone and is 1997, 1994, 1996, 1995, 1996, 1999, 1999, 1999 and 1997 for North Eastern Dry Zone, Northern Dry Zone, Central Dry Zone, Eastern Dry Zone, Southern Dry Zone, Southern Transition Zone, Northern Transition Zone, Hilly Zone and Coastal Zone respectively. Therefore, it is a evidential picture reflecting the increase in temperatures across the zones. Researchers should develop crop varieties that are insensitive to temperature changes and should develop packages of practices which will mitigate adverse effect of fluctuations in climate parameters on crop productivity

Farmer’s Perception and Efficacy of Adaptation Strategies to Climate Change in North Eastern Transition Zone of Karnataka State in India

A primary survey during the year 2021-22 was carried out among the 240 farmers of Bidar and Gulbarga districts of North Eastern Transition Zone in Karnataka to study the farmer’s perception on climate change for the period 1979 to 2019 and validate their opinions with the change in the meteorological indicators. About 74 percent of farmers expressed that there is a decline in crop yield while 83 percent of farmers opined that is a shift of employment from farm to non-farm activities during the period of forty years. Farmers practice various farm adaptation strategies activities to overcome the effect of climate change. Through Garatte ranking, it is revealed that most of the farmers prefer to practice alteration of sowing dates of crop (rank 1) as adaptation strategy followed by using of drought tolerant varieties (rank 2) and mixed cropping (rank3). Binary Logit analysis was used to identify the socio- economic attributes of households influencing the adoption of adaptation strategy to climate change. The results showed that, the variables like farm size, access to institutional credit, live stock ownership and climate information are the factors which positively shows significant influence on adoption. Economic incentives play an important role in the adoption of modern technologies. Access to institutional credit will support the farmers financially in adopting water conservation techniques like farm ponds, micro irrigation products