7,569 research outputs found
Bibliographics for the 983 eprints in the live archives of E-LIS : trends and status report up to 7th July 2004, based on author-self-archiving metadata
No full textThe priority for ideas and philosophy related to "Network Theory" have been traced back and documented by Braun(2004),and credit goes to Karinthy(1929).The IT has empowered to realise it, as the most practical phenomena and it is no more a humour. The OAI (Open Archives Initiatives)and ACIS (Academic Contributor Information System)are progressive in the direction ,which may lead to realise the "Collective Genius" at global level. Focus of present study is on Author-Self-Archiving (A-S-A)Metadata of the 983 Eprints in the Live Archives of the E-LIS (EPrints of Library and Information Science),which were approved till 7th July 2004.The A-S-A Metadata was used for librametric analysis. Self-explanatory bibliographics are illustrated.The highlights include: Conference papers (34%); highest approval, June 2004 (28%); published archives (76%);not refereed (52%); not in public domain (60%); highest self-archiving-author (De Robbio, Antonella).The Nos. of EPrints having single JITA domain specifications were: Theoretical and general aspects of libraries and information(27); Information use and sociology of information(80);Users,literacy and reading(13);Libraries as physical collections(30);Publishing and legal issues(57);Management(13);Industry, profession and education(36);Information sources, supports, channels(113) ; Information treatment for information services, Information functions and techniques (101); Technical services libraries, archives and museums(25); Housing technologies(1); Information technology and library technology(92); and Inter-domainery (395) i.e. having specifications of two or more than two JITA classes
Forecasting of Time-Dependent Scour Depth based on Bagging and Boosting Machine Learning Approaches
No full textForecasting the time-dependent scour depth (dst) is very important for the protection of bridge structures. Since scour is the result of a complicated interaction between structure, sediment, and flow velocity, empirical equations cannot guarantee an advanced accuracy, although they would preserve the merit of being straightforward and physically inspiring. In this article, we propose three ensemble machine learning methods to forecast the time-dependent scour depth at piers: extreme gradient boosting regressor (XGBR), random forest regressor (RFR), and extra trees regressor (ETR). These models predict the scour depth at a given time, dst, based on the following main variables: the median grain size, d50, the sediment gradation, σg, the approach flow velocity, U, the approach flow depth y, the pier diameter Dp, and the time t. A total of 555 data points from different studies have been taken for this research work. The results indicate that all the proposed models precisely estimate the time-dependent scour depth. However, the XGBR method performs better than the other methods with R = 0.97, NSE = 0.93, AI = 0.98, and CRMSE = 0.09 at the testing stage. Sensitivity analysis exhibits that the time-dependent scour depth is highly influenced by the time scale
A Unified Shell model for Buoyancy-Driven Turbulence
No full textWe construct a unified shell model for stably stratified and convective turbulence. Shell model simulation of stably stratified flow in turbulent regime exhibit Bolgiano-Obukhbov (BO) scaling in which the kinetic energy spectrum varies as . However, simulation of convective turbulence shows Kolmogorov's spectrum. These results are consistent with the direct numerical simulations of Kumar {\em et al.} [Phys. Rev. E {\bf 90}, 023016 (2014)]. We also observe a dual scaling ( and ) for a limited range of parameters in stably stratified flow
Experimental investigation and comparative machine learning prediction of the compressive strength of recycled aggregate concrete incorporated with fly ash, GGBS, and metakaolin
No full textRecycled aggregates (RA) can provide a sustainable solution for replacing natural aggregates (NA) in the concrete mix. However, the stakeholders and inspection professionals lack confidence in predicting their compressive strength (CS) due to limited databases. Most of them solely focus on the concrete mix with natural aggregates only. Even though numerous researchers have proposed alternative mix designs for recycled aggregate concrete (RAC), utilizing RA is still not practicable. One of them is the lack of a simple and effective compressive strength prediction that uses RAC. This study focuses on the application of six different machine learning (ML) techniques: XG Boost, K-nearest neighbors (KNN), artificial neural network (ANN), support vector machine (SVM), linear regression, decision tree (DT), and random forest (RF), for predicting the CS of concrete mixed with RA. The input variables are weights of coarse RA, Portland cement, fly ash, ground granulated blast furnace slag, and metakaolin. The database is prepared by experimental testing of concrete cube specimens for 188 mixes in the concrete technology laboratory of IIT Bhubaneswar. For most of the mixes, coarse RA was the only coarse aggregate to get the compressive strength. It includes variations in water/binder from 0.25 to 0.75. It was observed that the addition of flyash, GGBS, and MK significantly impacted the CS at a later age. The ML model indicates that an accuracy of 0.95 was achieved on the current test database for predicting CS. Among all the machine-learning algorithms, XG Boost can be used for forecasting compressive strength since it provides excellent accuracy with minimal computation. This research can be used as a data-driven novel solution for developing concrete mixes to achieve a specified CS. However, this work employs only experimental data as a machine learning input, which can be improved further by including databases from the literature
Hydrothermal liquefaction of biomass for production of diluents for bitumen transport
No full textThis study explores the hydrothermal liquefaction (HTL) of wood chips to bio-crude followed by upgrading to diluents, which are used to transport bitumen through pipelines. In this study, we considered a 2000 dry t day-1 plant capacity with two scenarios. The first scenario uses hydrogen for upgrading from the on-site hydrogen production plant (i.e., the hydrogen production scenario) and the other relies on procuring hydrogen from an external source (i.e., the hydrogen purchase scenario). We developed a data-intensive process model for HTL and used it to estimate plant capital costs. Project investment costs for the hydrogen production and hydrogen purchase scenarios are 559.67 and 429.13 M L-1, respectively, at a 95% confidence interval. The sensitivity analysis shows that diluent yield and internal rate of return (IRR) have the highest impact on the PV of the diluent, followed by capital cost and biomass cost. The optimum plant size at which the cost of production is lowest is 4000 dry t day-1 for PVs of 0.82 L-1 for the hydrogen production and purchase scenarios, respectively. This study offers insights into the techno-economic feasibility of producing diluents from HTL. The results of the study could help in the production of diluents for bitumen transportation for the oil sands industry and help reduce the overall greenhouse gas (GHG) footprint of the oil and gas sector
Story of the Story-Teller: A Conversation with Ramendra Kumar
No full textRamendra Kumar (Ramen) is an award-winning writer, storyteller and inspirational speaker with 42 books to his name. Ramen’s writings have been published by many of the leading publishers in the county and translated into 30 languages. They have found a place in several textbooks and anthologies. He has written across all genres ranging from picture books to adult fiction, satire, poetry, travelogues, biographies and on issues related to parenting and relationships. He has been invited to literary festivals held in Denmark, Greece, Sharjah, Sri Lanka as well Indian events including the prestigious Jaipur Litfest to conduct storytelling sessions and creative writing workshops. He has also been empanelled by Pearson India Education Services as well as several schools to conduct workshops. He was nominated as a Jury Member for the Best Children’s Author Category of The Times of India’s ‘Women AutHer’ Awards 2020. Many of his stories have been showcased by popular audio streaming, apps both within and outside the country, such as Spotify, Gaatha, Talking Stories Radio – London et al.
An Engineer & an MBA, Ramen was serving as the General Manager (Corporate Communications), SAIL, Rourkela Steel Plant, when he took Voluntary Retirement to pursue his passion, in August 2020. To know more about the writer, you can visit his website www.ramendra.in & his page on Wikipedia. Dr. Sagar Kumar Sharma interviews the author and unfolds the pages of his life.
 
Torsional springback analysis in thin tubes with non-linear work hardening / Vikas Kumar Choubey, Mayank Gangwar and J. P. Dwivedi
No full textA theoretical analysis of the springback of thin tubular sections of non linear work-hardening materials under torsional loading has been carried out. The non-linear behavior of the material is approximated by using Modified Ludwik type stress-strain relation. The theoretical analysis is supported by experimental results for different tubular section viz. square, triangular and rectangular
sections of different thicknesses. Finally analytical generalized expressions relating angle of twist to twisting moment and residual/springback angle of twist per unit length for thin tubular bars under plastic torsion are obtained in non-dimensionalized form. A comparison between the results obtained for thin tubes on non-linear and linear work-hardening material loaded under torsion is also made
Springback analysis of thin tubes under torsional loading / Vikas Kumar Choubey, Mayank Gangwar and J. P. Dwivedi
No full textSpringback, the elastic recovery of material on the release of applied load, is the major factor in obtaining the accurate and consistent dimensions of the final parti The mechanics of springback is essential for its effective prediction and compensation. The aim of the paper is to present a theoretical analysis of the torsional springback in thin tubes ofbi-linear work hardening material. The
bi-linear behavior of the material is approximated by using modified Ludwik type stress-strain relation. The theoretical analysis is based on membrane and sand heap analogies. The analytical calculations establishes relationship for angle of twist to twisting moment and residual/springback angle of twist per unit length for thin tubes under plastic torsion in non-dimensionalized form.
The theoretical analysis is supported by experiments performed on thin tubes of mild steel and aluminium with different geometry and mechanical properties. A comparison between the results obtained for thin tubes on bi-linear and nonlinear work-hardening material loaded under torsion is also made
Monitoring sugar release during pipeline hydro-transport of wheat straw
No full textPipeline transport of biomass is an economically viable and technically feasible approach to replace conventional truck delivery approach and make the biomass-based energy industry more competitive with fossil fuel-based plants. A 25 m long and 50 mm diameter closed-circuit pipeline facility was fabricated to experimentally investigate the mechanical and chemical feasibility of transporting agricultural residue biomass-water mixtures (slurries) through pipelines. This research used the pipeline facility to study the loss of sugars (glucose and xylose) while pipelining wheat straw-water mixtures. The release of similar sugars was also measured in shake-flask cultures under controlled conditions. The output of this research is important for bio-processing facilities as a high sugar content slurry would improve the yield of biofuels produced from pipelined lignocellulosic materials. After several hours of recirculating throughout the pipeline, as well as shaking in the flask, a drop in sugar concentration was detected. A microbiological analysis performed on both slurries proved the decline to be due to microbial proliferation. Accordingly, diethyl pyrocarbonate oxidizing antimicrobial agent and glutaraldehyde and bronopol non-oxidizing agents were alternatively tested to restrict microbial proliferation. These agents demonstrated reduced sugar loss and, in turn, showed an enhancement in the yield of glucose and xylose. This research aims at maximizing possible sugar release through mechanical action throughout the pipeline in the presence of antimicrobial compounds, which would increase the yield of biofuel produced from pipelined agricultural residue biomass
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