B-Spline Boundary Element Method for Ships

The development of a three dimensional B-Spline based method, which is suitable for the steady-state potential flow analysis of free surface piercing bodies in hydrodynamics, is presented. The method requires the B-Spline or Non Uniform Rational B-Spline (NURBS) representation of the body as an input. In order to solve for the unknown potential, the source surface, both for the body as well as the free surface, is represented by NURBS surfaces. The method does not require the body surface to be discritized into flat panels. Therefore, instead of a mere panel approximation, the exact body geometry is utilized for the computation. The technique does not use a free surface Green\u27s function, which already satisfies the linear free surface boundary conditions, but uses a separate source patch for the free surface. By eliminating the use of a free surface Green\u27s function, the method can be extended to considering non-linear free surface conditions, thus providing the possibility for wave resistance calculations. The method is first applied to the double body flow problem around a sphere and a Wigley hull. Some comparisons are made with exact solutions to validate the accuracy of the method. Results of linear free surface conditions are then presented

Influence of arbuscular mycorrhizal fungi and Trichoderma viride on growth performance of Salvia officinalis Linn.

Salvia officinalis (Sage) is a popular kitchen herb, member of mint (Lamiaceae) family has been cultivated for its wide range of medicinal values. Arbuscular mycorrhizae (AM) are beneficial symbionts for plant growth and development and offer a viable replacement of high input agricultural technology employed for production of environmentally hazardous fertilizers. Therefore, the present study was focused to analyze the effect of two AM fungi (Acalospora laevis and Glomus mosseae) along with Trichoderma viride, alone and in combination, on different growth parameters of S.officinalis in a green house pot experiment with sterilized soil. AM inoculum and T.viride showed significant increase of different growth parameters after 45 and 90 days of inoculation. Among all treatments, dual combination of A.laevis plus T.viride was most effective in increasing shoot length, leaf area, root length, root weight, AM spore number and percent root colonization. Moreover, maximum increase in shoot biomass was found in plant treated with T.viride

Response of Strawberry plant (Fragaria ananassa Duch.) to inoculation with arbuscular mycorrhizal fungi and Trichoderma viride

The present paper represents the positive role of Arbuscular Mycorrhizal (AM) fungi as biofertilizers in strawberry. Experiments were carried out to assess the effectiveness of Trichoderma viride and AM fungi (Glomus mosseae and Acaulospora laevis) alone or in combination, on the growth and biomass production of strawberry. After 120 days, dual inoculation of A. laevis + T. viride showed maximum increase in plant height (30.5±0.3), fresh shoot weight (10.16±0.20), dry shoot weight (2.82±0.02), fresh root weight (6.70±0.10), total chlorophyll (0.841±0.05) and phosphorus content in root (1.13±0.02) as compared to control. However root colonization and AM spore number were maximum in G. mosseae + A. lavies (90.76±1.32) and in G. mosseae (211.16±2.56) respectively as compared to uninoculated plants. Triple inoculation of G. mosseae + A. laevis + T. viride (12.33± 057) was effective in increasing the leaf area

Subtractive genomics approach for in silico identification and characterization of novel drug targets in Neisseria meningitides serogroup B

Meningococcal disease is a life-threatening illness with annual incidence rates varying from 1 to 1000 per 100 000 persons in different parts of the world. Effective polysaccharide and polysaccharide-protein conjugate vaccines that offer protection against infection with meningococcal serogroups A, C, Y and W-135 have been licensed and are available worldwide. Serogroup B remains the most prevalent cause of meningococcal disease responsible for 32% of all meningococcal disease in the United States, 45 to 80% of the cases in Europe, and for the majority of cases in the rest of the world. The development of a vaccine against serogroup B poses the biggest problem due to the similarity between the B capsular polysaccharide structure and a polysialic acid containing glycopeptides that are a part of human brain tissue. Prevention of meningococcal disease will require the development of an effective vaccine to combat serogroup B, which is the cause of most meningococcal cases in developed countries. The availability of the complete sequence information of Neisseria meningitides serogroup B proteome has made it possible to carry out the in silico analysis of its genome for identification of potential vaccine and drug targets. Our study revealed 1413 proteins which are non-homologous to human genome. Screening these proteins using the Database of Essential Genes (DEG) resulted in the identification of 362 proteins as essential proteins of the bacterium. Analysis of the identified essential proteins, using the KEGG Automated Annotation Server (KAAS) housed at Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways database, revealed 35 enzymes of N. Meningitides that may be used as potential drug targets, as they belongs to pathways present only in the bacterium and not present in humans. Subcelluler localization prediction of these essential proteins revealed that 9 proteins lie on the outer membrane of the pathogen which could be potential vaccine targets. Screening of the functional inhibitors against these novel targets may result in discovery of novel therapeutic compounds that can be effective against Neisseria meningitides Serogroup B

Developing Measures of Automation Implementation in Indian Industries

In the international business market, Automation has increased the competence of Indian Industry by making them fast, error free and providing them with greater customization option. This paper performs the review of automation and attempts to develop a framework for the implementation of automation by validating “IMPLAUT” (IMPLementing AUTomation) for Indian Industries. An exhaustive literature survey proceeded by simple meta-analysis have been carried out to find out various research gaps and further to address these gaps few objectives of this research study have been explored. For developing model for automation, the different variables are explored using ‘Churchill’s approach’ as may be applicable to Indian industrial scenario. It is evident from the model of “IMPLAUT” that automation will lead to the rise of competence in Indian industry provided the various input and output model suggested by the generic model are to be kept in view. It has been observed that the application of “IMPLAUT” reduces the manufacturing and downtime therefore increasing the overall efficiency of the industry. So “IMPLAUT” can be further researched and must be considered as an emerging field for research in engineering discipline. Keywords: Automation, IMPLAUT, classification schemes, Meta analysis, dimension

Mapping India's Energy Policy 2022

Carefully designed energy support measures—subsidies, public utilities' investments, and public finance institutions' lending—and government's energy revenues play a key role in India's transition to clean energy and reaching net-zero emissions by 2070. Looking at how the Government of India has supported different types of energy from FY 2014 to FY 2021, the study aims to improve transparency, create accountability, and encourage a responsible shift in support away from fossil fuels and toward clean energy.Mapping India's Energy Subsidies 2022 covers India's subsidies to fossil fuels, electricity transmission and distribution, renewable energy, and electric vehicles between fiscal year (FY) 2014 and FY 2021.We found that fossil fuels continue to receive far more subsidies than clean energy in India. This disparity became even more pronounced from FY 2020 to FY 2021, going from 7.3 times to 9 times the amount of subsidies to renewables

Node criticality assessment in a blockchain network

Blockchain systems are being rapidly integrated in various technologies, with limited work on the effect of the underlying network topology on the blockchain performance. In this work, we investigate the significance of each network node on the overall blockchain performance. This is assessed by selecting critical nodes according to different criticality metrics, and investigating, using simulations, the degradation in performance incurred upon removing these nodes. The most critical nodes are the ones that incur the greatest degradation in performance. The considered performance metrics are the blockchain size and the packet drop rate. Criticality metrics such as Betweennes Centrality, Closeness Centrality and Degree Centrality are compared. It is found that the Sign Change Spectral Partitioning approach, enhanced with Blockchain Specific traffic flow information, is able to identify critical nodes better in the sense that higher degradation in performance is reported upon their removal

AutoMix: Automatically Mixing Language Models

Large language models (LLMs) are now available in various sizes and configurations from cloud API providers. While this diversity offers a broad spectrum of choices, effectively leveraging the options to optimize computational cost and performance remains challenging. In this work, we present AutoMix, an approach that strategically routes queries to larger LMs, based on the approximate correctness of outputs from a smaller LM. Central to AutoMix is a few-shot self-verification mechanism, which estimates the reliability of its own outputs without requiring training. Given that verifications can be noisy, we employ a meta verifier in AutoMix to refine the accuracy of these assessments. Our experiments using LLAMA2-13/70B, on five context-grounded reasoning datasets demonstrate that AutoMix surpasses established baselines, improving the incremental benefit per cost by up to 89%. Our code and data are available at https://github.com/automix-llm/automix.Comment: The first two authors contributed equally. Work started and partly done during Aman's internship at Google. This version adds results on mixing 3 models, and will be presented at the workshop on robustness of zero/few-shot learning in foundation models, Neurips 202