Birla Institute of Technology and Science

About: Birla Institute of Technology and Science is a education organization based out in Pilāni, Rajasthan, India. It is known for research contribution in the topics: Computer science & Population. The organization has 8897 authors who have published 13947 publications receiving 170008 citations.

A Review on Chemical Methodologies for Preparation of Mesoporous Silica and Alumina Based Materials

Abstract: The discovery of novel family of molecular sieves called M41S aroused a worldwide resurgence in the field of porous materials. According to IUPAC definition inorganic solids that contain pores with diameter in the size range of 20-500 A are considered mesoporous materials. Mesoporous silica and alumina based materials find applications in catalysis, adsorption, host- guest encapsulation etc. This article reviews the current state of art and outline the recent patents in mesoporous materials research in three general areas: Synthesis, various mechanisms involved for porous structure formation and applications of silica and alumina based mesoporous materials.

Irrigation planning using Genetic Algorithms

Abstract: The present study deals with the application of Genetic Algorithms(GA) for irrigation planning. The GA technique is used to evolve efficient cropping pattern for maximizing benefits for an irrigation project in India. Constraints include continuity equation, land and water requirements, crop diversification and restrictions on storage. Penalty function approach is used to convert constrained problem into an unconstrained one. For fixing GA parameters the model is run for various values of population, generations, cross over and mutation probabilities. It is found that the appropriate parameters for number of generations, population size, crossover probability, and mutation probability are 200, 50, 0.6 and 0.01 respectively for the present study. Results obtained by GA are compared with Linear Programming solution and found to be reasonably close. GA is found to be an effective optimization tool for irrigation planning and the results obtained can be utilized for efficient planning of any irrigation system.

Nanoparticulate drug delivery systems for cancer chemotherapy.

Abstract: Nanoparticles (NPs) are, in general, colloidal particles, less than 1000 nm, that can be used for better drug delivery and prepared either by encapsulating the drug within a vesicle and or by dispersing the drug molecules within a matrix. Nanoparticulate drug delivery systems have been extensively studied in recent years for spatial and temporal delivery, especially in tumour and brain targeting. NPs have great promise for better drug delivery as found in both pharmaceutical and clinical research. As a drug carrier, NPs have significant advantages like better bioavailability, systemic stability, high drug loading, long blood circulation time and selective distribution in the organs/tissues with longer half life. The selective targeting of NPs can be achieved by the enhanced permeability and retention effect (EPR-effect), attaching specific ligands, or by making selective distribution due to change of the physiological conditions of specific systems like nature, pH, temperature, etc. It has been observed that drug-loaded NPs can have selective distribution to organs/tissues using different types of and proportions of polymers. The current aim of researchers is to prepare NPs that are long-lived with and that demonstrate the appropriate selective distribution for better therapy and thus improved clinical outcomes. Nanoparticulate drug delivery systems have the potential to deliver a drug to the target site with specificity and to maintain the desired concentration at the site for the intended time without untoward effects. In this review article, the methods for the preparation of NPs, their characterization, biodistribution, and pharmacokinetic characteristics are discussed.

Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation

Abstract: We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total We refer to our collection as the DNC: Diverse Natural Language Inference Collection The DNC is available online at https://wwwdecompnet, and will grow over time as additional resources are recast and added from novel sources