Enhancing the knowledge level of dog owners using an electronic self-learning module

The present study was taken up to assess the knowledge about dog health and management among the pet owners and the effectiveness of a need based electronic self learning module “Dog Health Management Trainer” (DHMT) for enhancing their knowledge. The DHMT was developed and tested on 100 dog owners visiting the polyclinic at IVRI. Results revealed that dog owners were mainly facing problems related to diseases of skin, gastrointestinal system and parvoviral infection besides various other problems. Majority of the owners had medium knowledge about dog diseases while low knowledge about dog breeding and reproduction. Results revealed that DHMT was highly effective in enhancing the knowledge level and dog owners found it very much interesting and user friendly with an overall utility index of 0.87. The price proposed was negatively and significantly correlated with the pre-test knowledge scores for dog health indicating that those dog owners who were having low knowledge quoted higher price for procuring the DHMT

Search for flavor-changing neutral current interactions of the top quark and the Higgs boson decaying to a bottom quark-antiquark pair at √ s = 13 TeV

A preprint version of the article is available at arXiv (https://arxiv.org/abs/2112.09734).Copyright © CERN, for the benefit of the CMS Collaboration. A search for flavor-changing neutral current interactions of the top quark (t) and the Higgs boson (H) is presented. The search is based on a data sample corresponding to an integrated luminosity of 137 fb−1 recorded by the CMS experiment at the LHC in proton-proton collisions at s√ = 13 TeV. Events containing exactly one lepton (muon or electron) and at least three jets, among which at least two are identified as originating from the hadronization of a bottom quark, are analyzed. A set of deep neural networks is used for kinematic event reconstruction, while boosted decision trees distinguish the signal from the background events. No significant excess over the background predictions is observed, and upper limits on the signal production cross sections are extracted. These limits are interpreted in terms of top quark decay branching fractions (B) to the Higgs boson and an up (u) or a charm quark (c). Assuming one nonvanishing extra coupling at a time, the observed (expected) upper limits at 95% confidence level are B(t → Hu) < 0.079 (0.11)% and B(t → Hc) < 0.094 (0.086)%.SCOAP3

Short-term wind speed prediction at Bogdanci power plant in FYROM using an artificial neural network

The present study targets short-term wind speed prediction of the wind turbine station at Bogdanci in the Former Yugoslav Republic of Macedonia (FYROM), using artificial neural network (ANN) method. Wind directions and meteorological parameters (temperature, pressure, and humidity) measured at the interval of 10 min in between May 2015 and September 2015 have been used as the input of ANN to predict four kinds of wind speed (rotation mean, hub mean, tip low mean, and base mean). The best performance \lpar R^2 = 0.84 - 0.86\rpar of ANN method was achieved using wind direction base mean (WDBM) in September 2015, and using temperature \lpar {R^2 = 0.77 - 0.80} \rpar in May 2015. Reasonable performance of ANN method was achieved in the rest of the month