Solar Assisted Water Purification System Analysis

A theoretical analysis of solar assisted water purification is presented in this research. A system is designed in which Maximum heat energy is absorbed by collector, the heated primary fluid is transferred to heat exchanger consists of salt water. Vapourization of salt water takes place and pure water vapour is taken out by vaccum pump and further condensed to get drinkable water. The remaining salt is flushed out from heat exchanger. The components that system consisted are parabolic troughs, heat exchanger, vaccum pump, primary fluid and the pump which circulates the primary fluid.  The designed collector is parabolic which helps to take more amount of solar energy and transmit to pipe consisting primary fluid. The primary fluid used is Duratherm 450 because it is economically and thermally stable, it gives high performance, it is environmentally friendly and cost effective. Parametric analysis carried out with help of computer model which simulated experimental results by solving  equations and calculations, based on this graphical analysis was done which concluded our experiment

The role of the NMD factor UPF3B in olfactory sensory neurons.

The UPF3B-dependent branch of the nonsense-mediated RNA decay (NMD) pathway is critical for human cognition. Here, we examined the role of UPF3B in the olfactory system. Single-cell RNA-sequencing (scRNA-seq) analysis demonstrated considerable heterogeneity of olfactory sensory neuron (OSN) cell populations in wild-type (WT) mice, and revealed that UPF3B loss influences specific subsets of these cell populations. UPF3B also regulates the expression of a large cadre of antimicrobial genes in OSNs, and promotes the selection of specific olfactory receptor (Olfr) genes for expression in mature OSNs (mOSNs). RNA-seq and Ribotag analyses identified classes of mRNAs expressed and translated at different levels in WT and Upf3b-null mOSNs. Integrating multiple computational approaches, UPF3B-dependent NMD target transcripts that are candidates to mediate the functions of NMD in mOSNs were identified in vivo. Together, our data provides a valuable resource for the olfactory field and insights into the roles of NMD in vivo

LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology

International audienceThis paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for implementing various neural architectures, training schema, priors, and density estimators in a manner easily adaptable to any research workflow. It includes comprehensive validation metrics to assess posterior estimate coverage, enhancing the reliability of inferred results. Additionally, the pipeline is easily parallelizable, designed for efficient exploration of modeling hyperparameters. To demonstrate its capabilities, we present real applications across a range of astrophysics and cosmology problems, such as: estimating galaxy cluster masses from X-ray photometry; inferring cosmology from matter power spectra and halo point clouds; characterising progenitors in gravitational wave signals; capturing physical dust parameters from galaxy colors and luminosities; and establishing properties of semi-analytic models of galaxy formation. We also include exhaustive benchmarking and comparisons of all implemented methods as well as discussions about the challenges and pitfalls of ML inference in astronomical sciences. All code and examples are made publicly available at https://github.com/maho3/ltu-ili

LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology

International audienceThis paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for implementing various neural architectures, training schema, priors, and density estimators in a manner easily adaptable to any research workflow. It includes comprehensive validation metrics to assess posterior estimate coverage, enhancing the reliability of inferred results. Additionally, the pipeline is easily parallelizable, designed for efficient exploration of modeling hyperparameters. To demonstrate its capabilities, we present real applications across a range of astrophysics and cosmology problems, such as: estimating galaxy cluster masses from X-ray photometry; inferring cosmology from matter power spectra and halo point clouds; characterising progenitors in gravitational wave signals; capturing physical dust parameters from galaxy colors and luminosities; and establishing properties of semi-analytic models of galaxy formation. We also include exhaustive benchmarking and comparisons of all implemented methods as well as discussions about the challenges and pitfalls of ML inference in astronomical sciences. All code and examples are made publicly available at https://github.com/maho3/ltu-ili