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The present study analyzed ROH and consensus ROH regions in 102 animals of eleven diverse Indian goat (Capra hircus) breeds using whole genome sequencing. A total of 51,705 ROH and 21,271 consensus regions were identified. The mean number of ROH per animal was highest in the meat breed, Jharkhand Black (2693) and lowest in the pashmina breed, Changthangi (60). The average length of ROH (ALROH) was maximum in Kanniadu (974.11 Kb) and minimum in Tellicherry (146.98 Kb). Long ROH is typically associated with more recent inbreeding, whereas short ROH is connected to more ancient inbreeding. The overall ROH-based genomic inbreeding (FROH) was highest for Jharkhand Black (0.602) followed by Kanniadu (0.120) and Sangamneri (0.108) among all breeds. FROH of Jharkhand Black was higher than Kanniadu up to 5 Mb ROH length category. However, in > 20 Mb ROH length category, Kanniadu (0.98) exhibited significantly higher FROH than Jharkhand Black (0.46). This implies that Kanniadu had higher levels of recent inbreeding than Jharkhand Black. Despite this, due to the presence of both recent and ancient inbreeding, Jharkhand Black demonstrated higher overall FROH compared to Kanniadu. ROH patterns revealed dual purpose (meat and dairy) and pashmina breeds as less consanguineous while recent inbreeding was apparent in meat breeds. Analysis of ROH consensus regions identified selection sweeps in key genes governing intramuscular fat deposition, meat tenderisation, lean meat production and carcass weight (CDK4, ALOX15, CASP9, PRDM16, DVL1) in meat breeds; milk fat percentage and mammary gland development (POLD1, NOTCH2, ARHGAP35) in dual purpose (meat and dairy) breeds; while cold adaptation and hair follicle development (APOBEC1, DNAJC3, F2RL1, FGF9) in pashmina breed. MAPK, RAS, BMP and Wnt signaling pathways associated with hair follicle morphogenesis in Changthangi were also identified. PCA analysis based on ROH consensus regions revealed that meat breeds are more diverse than other goat breeds/populations. [ABSTRACT FROM AUTHOR]

Among many applications of DC–DC isolated converters, power supplies, renewable energy sources, and electric vehicles are prominent. These converters are the basic, easy to use, low component count and being used in industry from a few decades. In this study, a unique operation mode of DC–DC isolated converter called “green mode” is proposed to increase the efficiency of converter under light load conditions. Switching losses of the converter can be reduced by using the converter’s secondary-side control method, which enables a reduction in the primary-side switches' switching frequency and turn-off duration. The converter also uses a soft switching mechanism to further minimize the switching losses. The converter achieves the output voltage of 24 V under different load conditions. Under No load condition the efficiency of the converter has significantly improved, which is 82.3%, due to reduced power losses under light load conditions. It is demonstrated through experimental data that validates the green-mode operation. Although the efficiency under full load conditions is around 79.7%. These DC–DC converters shall enhance the range of electric vehicle (EV) and shall be a boon to EV technology. [ABSTRACT FROM AUTHOR]

India being an agrarian economy, rainfall is an essential component that directly or indirectly influences agricultural produce. With the increasing impacts of the changing climate scenario, it is anticipated that in the near future, frequent and extreme rainfall episodes will trigger events like severe floods, landslides, etc. Therefore, it is extremely important to make precise predictions so that the intensity of the impacts on life and property can be reduced. In recent times, with the advancement of AI/ML applications, it has become popular in weather and climate sciences. The current work uses 121 years of rainfall data for analysis and prediction purposes, where deep learning (DL) approaches like LSTM (Long Short Term Memory), BiLSTM (Bi-directional LSTM) and GRU (Gated Recurrent Unit) have been adopted. The long-term rainfall analysis and prediction over selected smart cities of India is based on their location in the homogenous monsoon regions. The results obtained from the models indicated that for univariate forecasting, the overall performance of BiLSTM is better than others for most cities considered, while GRU predicted well for places with higher rainfall variability. In the multivariate approach, LSTM's performance is superior. Therefore, a combination of BiLSTM and GRU might offer a better result in the univariate approach, or an advanced version of LSTM might enrich the outcomes in the multivariate approach for rainfall analysis and forecasting. [ABSTRACT FROM AUTHOR]