1,240 research outputs found
sj-docx-1-pie-10.1177_09544089231151865 - Supplemental material for Application of Taguchi coupled with genetic algorithm (GA) for optimizing surface quality in drilling of duplex stainless steel 2205
No full textSupplemental material, sj-docx-1-pie-10.1177_09544089231151865 for Application of Taguchi coupled with genetic algorithm (GA) for optimizing surface quality in drilling of duplex stainless steel 2205 by Rajeev Sharma, Vipin Pahuja and Binit Kumar Jha in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering</p
sj-docx-2-pie-10.1177_09544089231151865 - Supplemental material for Application of Taguchi coupled with genetic algorithm (GA) for optimizing surface quality in drilling of duplex stainless steel 2205
No full textSupplemental material, sj-docx-2-pie-10.1177_09544089231151865 for Application of Taguchi coupled with genetic algorithm (GA) for optimizing surface quality in drilling of duplex stainless steel 2205 by Rajeev Sharma, Vipin Pahuja and Binit Kumar Jha in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering</p
High Strain Rate Behavior of Stir Cast Hybrid Al-Si Matrix Composites Using Split Hopkinson Pressure Bar
No full textAluminum alloy based metal matrix composites are widely used in different engineering applications that are subjected to dynamic loading conditions. In the present study, aluminum alloy Al-Si7Cu3Mn0.5(LM27) composites are manufactured by a stir casting route with two different weight percentages and different size of SiC and TiO2. The reinforcement particles of 15 μm and 115 μm sizes are reinforced in a concentration of 3wt. % and 12wt. %. Split Hopkinson pressure bar is used to evaluate dynamic compressive behavior of the composites at the strain rate of 700, 1500 and 2500 s−1. Microstrucutral examination of fine size reinforced composites exhibited the formation of globular silicon that is arranged around the particles. Micro-hardness of the particle–matrix interface of the fine particle reinforced composite is higher in comparison to composite reinforced with coarse particles. At the strain rate of 700 s−1, at higher concentration of reinforcement particles the fine particles reinforced composites exhibit maximum strength whereas lower concentration of fine particle reinforced composite showed the maximum strain. Strain sensitivity is exhibited by all the composites and strength shows an increasing trend with an increase in the strain rate. The fine particles reinforced composites exhibited maximum flow stress at higher weight percent of reinforcement particles whereas maximum strain is found at lower weight percent of fine particles. The dynamic compressive behavior of composite is found dependent on the degradation of elastic modulus, stress localization phenomena and debonding characteristics
Story of the Story-Teller: A Conversation with Ramendra Kumar
Get PDFRamendra 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.
 
Interview with Lakshmi Raj Sharma, Author of The Tailor’s Needle
Get PDFInterview with Indian writer Lakshmi Raj Sharma, author of 'The Tailor's needle
Geostatistical modelling of soil properties towards long-term ecological sustainability of agroecosystems
Get PDFA profound grasp of the quantitative spatial heterogeneity and distribution of the soil physicochemical attributes is crucial in understanding agricultural landscapes for ensuring the provisioning of soil ecosystem services. However, the analysis of data from remote sensing, like NDVI, can be of help in analysing the capacity of the landscape to provide supporting ecosystem services such as primary productivity. The research investigated and addressed the dispersion of important soil physico-chemical attributes in agricultural lands of the temperate Himalayan region of India using a geostatistical method and combining normalized difference vegetation index (NDVI) time-series data and the regression Kriging method. A 206 soil samples were gathered and assessed for soil parameters like pH, EC, OC, and available N, P, K, Ca, and Mg from Kishtwar district of Jammu. The coefficient of variation (CV) for pH and electrical conductivity (EC) ranged notably from 8.75 % to 118.98 %, highlighting diverse soil characteristics critical for local management practices. Mean elevation averaged 2743.32 m (m), with a moderate NDVI of 0.15, indicating dynamics in vegetation cover. Soil pH ranged from intensely acidic to marginally alkaline, with varying EC levels. Seemingly high organic carbon (OC), nitrogen (N), and potassium (K) levels, accompanied by medium phosphorus (P), calcium (Ca), and magnesium (Mg) levels were found in the region. The study employed ordinary kriging (OK) to map the spatial distribution of soil parameters, utilizing mean square error (MSE), root mean square error (RMSE), and the Moran’s I index. Exponential models were the best fit models for OC, while spherical models were fit for pH, EC, N, P, and Ca. Mathematical models were best fit for K and Mg. Spatial analysis using spherical and exponential models revealed distinct distribution patterns for pH, N, P, Ca, and Mg. The results of the degree of spatial dependence from the semi-variogram analyses indicated a strong (0.06 %) to moderate (0.51 %) to weak (2.81 %) dependence. The interpolated maps showed a distinct gradient in elevation (1053–4413 m), OC (0.13–2.80 %), NDVI (−0.16–0.54), pH (4.80–8.00), EC (0.03–9.80 dS m−1), N (201.15–993.19 kg ha−1), P (3.00–96.00 kg ha−1), K (124.88–1110.71 kg ha−1), Ca (7.00–46.00 meq 100 g soil−1), and Mg (2.30–21.50 meq 100 g soil−1) at the regional scale, indicating a wide range of spatial soil heterogeneity. The heterogeneity maps of soil parameters generated by this research can be effectively used by land planners and farm managers at a regional scale for crop nutrient management to reduce soil contamination risk. These maps serve as baseline materials and effective tools for suitable land management strategies such as conservation-effective tillage, integrated nutrient management, and organic farming based on the spatial distribution of soil properties and they can significantly enhance the long-term ecological sustainability of agro-ecosystems’ management
The Federal Approach to FiscalDecentralisation: Conceptual Contours for Policy Makers
Get PDFChanchal Kumar Sharma,in his paper demonstrates that in order for fiscal decentralisation to be effective, it must be approached federally. A federal approach is not a decentralised approach but a dynamically balanced approach; one that constantly keeps on adjusting the contrasting forces of centralisation and decentralisation to create a system that can ensure good governance in accordance with the rapidly changing global and local scenario. According to the author, the good governance of the present time has to be federally flexible and dynamically decentralised and institutions of fiscal federalism are crucial for achieving such a dynamic equilibrium. Fiscal decentralisation cannot be detached from the broader principles of fiscal federalism if it is to be successful, irrespective of the fact of whether it is being carried out in a federal or non-federal country. He argues that too much decentralisation or an overly strong central federal government precludes the survival of a constitutional federal state.Federalism; Fiscal Decentralization; centralization
The Federal Approach to FiscalDecentralisation: Conceptual Contours for Policy Makers
Get PDFChanchal Kumar Sharma,in his paper demonstrates that in order for fiscal decentralisation to be effective, it must be approached federally. A federal approach is not a decentralised approach but a dynamically balanced approach; one that constantly
keeps on adjusting the contrasting forces of centralisation and
decentralisation to create a system that can ensure good governance in
accordance with the rapidly changing global and local scenario.
According to the author, the good governance of the present time has to
be federally flexible and dynamically decentralised and institutions of
fiscal federalism are crucial for achieving such a dynamic equilibrium.
Fiscal decentralisation cannot be detached from the broader principles
of fiscal federalism if it is to be successful, irrespective of the fact of
whether it is being carried out in a federal or non-federal country. He
argues that too much decentralisation or an overly strong central federal
government precludes the survival of a constitutional federal state
Knowledge‐Guided Machine Learning for Operational Flood Forecasting
No full textWe present a knowledge-guided machine learning framework for operational hydrologic forecasting at the catchment scale. Our approach, a Factorized Hierarchical Neural Network (FHNN), has two main components: inverse and forward models. The inverse model uses observed precipitation, temperature, and streamflow data to generate a representation of the current underlying catchment state. The forward model predicts streamflow using the learned catchment state. The FHNN architecture is designed to model multi-scale processes and capture their interactions, a critical ability for flood modeling. FHNN also improves forecasts based on real-time data through an inference-based data integration approach using inverse modeling. FHNN's data integration approach improves forecasts in response to observed data more efficiently than data assimilation methods that require computationally intensive optimization. We compare the FHNN to a leading deep learning alternative (autoregressive LSTM) on the large-sample CAMELS-US data set, and operational flood forecast data from the US National Weather Service (NWS). Official NWS flood forecasts are generated by expert human forecasters using a physics-based model, in a human-in-the-loop process. Thus, we assess the flood forecast ability of FHNN by directly comparing its performance against these NWS expert-derived forecasts. The human forecaster creates a more accurate forecast within the first 12–18 hr of a forecast's issuance, but FHNN has significantly better predictions thereafter. This research lays the groundwork for leveraging the predictive performance of AI-based models with the expertise in forecasting agencies to produce better river forecasts.McEachran, Zac; Ghosh, Rahul; Renganathan, Arvind; Sharma, Somya; Lindsay, Kelly; Steinbach, Michael; Nieber, John; Duffy, Christopher; Kumar, Vipin. (2025). Knowledge‐Guided Machine Learning for Operational Flood Forecasting. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/278843
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