Biological assessment of water pollution: A study of the river Kapila

An attempt has been made to assess the feasibility of application of biological data to evaluate and monitor water pollution of the river Kapila, near Nanjangud, Karnataka. Two pollution index factors, one at the generic level and another at species level of the Algae, have been computed. Significant correlation between biological and some physico-chemical factors has been established. The theme that algae serve as tools of pollution and that their index scores at the species level is a more reliable parameter for the evaluation of water quality has been established. © 1984, Taylor & Francis Group, LLC. All rights reserved

Evaluation of soil physico-chemical properties, growth and yield of pigeonpea as influenced by method of planting and integrated nutrient management in vertisols of Karnataka

The present investigation entitled "Evaluation of soil physico-Chemical properties, growth and yield of pigenonpea as influenced by method of planting and integrated nutrient management in vertisols of Karnataka" was conducted at farmer's fieldi i.e. in three location of the same village Kasbe camp, District: Raichur (Karnataka)under the project of Bhoo Samruddhi, ICRISAT (International Crop Research Institute for Semi-Arid Tropics Agriculture), Patancheru,Hyderabad during kharif season 2016. The soil of the experiment plots was clayey. Low available nitrogen in all the three farmers plot phosphorus was high in two plots except one plot, while potassium was high in all plots. The experiment was laid out in factorial randomized block design (FRBD) with three replications comprising ten treatment combination. Treatment combination consisting of two factor, factor-1 at two levels viz., methods of planting (dibbling and transplanting), and factor-2 at five levels viz., N1-control (Farmers practice) N2- FYM @ 5 t ha-1, N3-vermicompost @ 5 t ha-1, N4-neem cake @ 250 kg ha-1, N5- green leaf manure (Gliricidia) @ 5 t ha-1. Sowing was done on July 14, 2016 harvesting was done on January 28, 2017.The transplanted pigeonpea (M2) recorded the maximum growth parameters viz., plant height, number of leaves, number of primary and secondary branches plant-1, leaf area plant-1, leaf area index, total dry matter plant-1 as well as yield and yield attributing characters viz., number of pods plant-1, weight of pods plant-1, seed yield plant-1, grain yield ha-1, stalk yield ha-1 and quality attributes of pigeonpea crop viz., protein yield ha-1 . The nutrient content and uptake by seed and stalk, N in seed, content of P, K, and S in seed and stalk were found higher. In case of micronutrients Fe and Cu in seed, B and Zn in stalk and Mn in both was recorded higher nutrient content. Whereas concerned to uptake all the nutrients i.e. primary (N, P, K), secondary (Ca, Mg, S) and micronutrients (Fe, Zn, Cu, Mn and B) were recorded higher uptake in the transplanted pigeonpea. However, there was no effect on physico-chemical properties due to the method of planting

SARAS 2: A Spectral Radiometer for probing Cosmic Dawn and the Epoch of Reionization through detection of the global 21 cm signal

The global 21 cm signal from Cosmic Dawn (CD) and the Epoch of Reionization (EoR), at redshifts $z \sim 6-30$, probes the nature of first sources of radiation as well as physics of the Inter-Galactic Medium (IGM). Given that the signal is predicted to be extremely weak, of wide fractional bandwidth, and lies in a frequency range that is dominated by Galactic and Extragalactic foregrounds as well as Radio Frequency Interference, detection of the signal is a daunting task. Critical to the experiment is the manner in which the sky signal is represented through the instrument. It is of utmost importance to design a system whose spectral bandpass and additive spurious can be well calibrated and any calibration residual does not mimic the signal. SARAS is an ongoing experiment that aims to detect the global 21 cm signal. Here we present the design philosophy of the SARAS 2 system and discuss its performance and limitations based on laboratory and field measurements. Laboratory tests with the antenna replaced with a variety of terminations, including a network model for the antenna impedance, show that the gain calibration and modeling of internal additives leave no residuals with Fourier amplitudes exceeding 2~mK, or residual Gaussians of 25 MHz width with amplitudes exceeding 2~mK. Thus, even accounting for reflection and radiation efficiency losses in the antenna, the SARAS~2 system is capable of detection of complex 21-cm profiles at the level predicted by currently favoured models for thermal baryon evolution.Comment: 44 pages, 17 figures; comments and suggestions are welcom

Predictive Analysis of Tuberculosis Treatment Outcomes Using Machine Learning: A Karnataka TB Data Study at a Scale

Tuberculosis (TB) remains a global health threat, ranking among the leading causes of mortality worldwide. In this context, machine learning (ML) has emerged as a transformative force, providing innovative solutions to the complexities associated with TB treatment.This study explores how machine learning, especially with tabular data, can be used to predict Tuberculosis (TB) treatment outcomes more accurately. It transforms this prediction task into a binary classification problem, generating risk scores from patient data sourced from NIKSHAY, India's national TB control program, which includes over 500,000 patient records. Data preprocessing is a critical component of the study, and the model achieved an recall of 98% and an AUC-ROC score of 0.95 on the validation set, which includes 20,000 patient records.We also explore the use of Natural Language Processing (NLP) for improved model learning. Our results, corroborated by various metrics and ablation studies, validate the effectiveness of our approach. The study concludes by discussing the potential ramifications of our research on TB eradication efforts and proposing potential avenues for future work. This study marks a significant stride in the battle against TB, showcasing the potential of machine learning in healthcare

Effects of age on female reproductive success in Drosophila bipectinata

Female age influence on mating success, courtship activities, mating latency, copulation duration, fecundity, ovarioles number, and wing length has been studied using isofemale lines of Drosophila bipectinata collected at three different localities. It was observed that in all localities, middle-aged D. bipectinata females had significantly greater mating success, showed less rejection responses to courting male, mated faster, copulated longer, and had greater fecundity and ovariole number than young and old-aged females. Further, old-aged females had comparatively less fitness traits than young age females. This research suggests the occurrence of age specific female reproductive success as follows: middle-aged > young > old-aged