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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-F-I-66: The Effects of Nicotinic Agonists On Rat Hippocampal Glutamatergic Fluctuation by Using Proton Magnetic Resonance Spectroscopy at 9.4T

Purpose: Nicotine exerts its effects through the activation of nicotinic acetylcholine receptors (nAChRs). Varenicline, a smoking cessation aid, is a partial agonist acting at the α4β2 nAChRs. Although nicotine and varenicline contribute to the reward system at the same time, the influence of the substances on hippocampal neurochemical changes has not been investigated yet. We therefore studied the effects of repeated nicotine exposure and varenicline administration on hippocampus of rats by using in vivo proton magnetic resonance spectroscopy (1H MRS) at 9.4T. Methods: Male Wistar rats (n = 11; mean body weight, 304.9 ± 9.9 g) were divided into 3 groups: control rats (control, n = 3); nicotine-induced rats (nicotine, n = 4); and nicotine- and varenicline-induced rats (varenicline, n = 4). Acquisition of in vivo MRS was conducted by using 9.4 T Agilent Scanner. The linear combination of model spectra (LCModel, version 6.3, Stephen W. Provencher) fitting software was used to quantify the metabolites in the frequency domain, using the basis metabolites. Results: In this study, the results show the tendency of increased Glu level in nicotine group than in the control and varenicline groups. Moreover, GSH and NAA levels tended to decrease in the nicotine group in comparison with those in the control and varenicline groups. Conclusion: These findings indicate that the hippocampus is integrally linked to the brain reward sensitization involved in addiction and glutamate release through mobilization of intracellular calcium stores. Further, oxidative stress and toxicity of nicotine on brain would cause the decline of GSH and NAA. In conclusion, we found that varenicline effectively inhibits the reward cycle.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data

Abstract Two of the central challenges of using mathematical models for predicting the spatiotemporal development of tumors is the lack of appropriate data to calibrate the parameters of the model, and quantitative characterization of the uncertainties in both the experimental data and the modeling process itself. We present a sequence of experiments, with increasing complexity, designed to systematically calibrate the rates of apoptosis, proliferation, and necrosis, as well as mobility, within a phase-field tumor growth model. The in vitro experiments characterize the proliferation and death of human liver carcinoma cells under different initial cell concentrations, nutrient availabilities, and treatment conditions. A Bayesian framework is employed to quantify the uncertainties in model parameters. The average difference between the calibration and the data, across all time points is between 11.54% and 14.04% for the apoptosis experiments, 7.33% and 23.30% for the proliferation experiments, and 8.12% and 31.55% for the necrosis experiments. The results indicate the proposed experiment-computational approach is generalizable and appropriate for step-by-step calibration of multi-parameter models, yielding accurate estimations of model parameters related to rates of proliferation, apoptosis, and necrosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

The ORNL Overhead Vehicle Dataset (OOVD)

Roadways are critical to meeting the mobility and economic needs of the nation. The United States uses 28% of its energy in moving goods and people, with approximately 60% of that utilized by cars, light trucks, and motorcycles. Thus, improved transportation efficiency is vital to America’s economic progress. The increasing congestion and energy resource requirements of transportation systems for metropolitan areas require research in methods to improve and optimize control methods. Coordinating and optimizing traffic in urban areas may introduce hundreds of thousands of vehicles and traffic management systems, which can require high performance computing (HPC) resources to model and manage. This data set was created to understand the potential for machine learning, computer vision, and HPC to improve the energy efficiency aspects of traffic control by leveraging GRIDSMART traffic cameras as sensors for adaptive traffic control, with a sensitivity to the fuel consumption characteristics of the traffic in the camera’s visual field. GRIDSMART cameras—an existing, fielded commercial product—sense the presence of vehicles at intersections and replace more conventional sensors (such as inductive loops) to issue calls to traffic control. These cameras, which have horizon-to-horizon view, offer the potential for an improved view of the traffic environment which can be used to generate better control algorithms.

Karnowski, Thomas P.↗
DOE OSTI2019

Reinforcement Learning-based Traffic Control to Optimize Energy Usage and Throughput (CRADA report)

The US roadways are critical to meeting the mobility and economic needs of the nation. The United States uses 28% of its energy in moving goods and people, with approximately 60% of that utilized by cars, light trucks, and motorcycles. Thus, improved transportation efficiency is vital to America’s economic progress. The increasing congestion and energy resource requirements of transportation systems for metropolitan areas require research in methods to improve and optimize control methods. Coordinating and optimizing traffic in urban areas may introduce hundreds of thousands of vehicles and traffic management systems, which can require high performance computing (HPC) resources to model and manage. In this work, we seek to use machine learning, computer vision, and HPC to improve the energy efficiency aspects of traffic control by leveraging GRIDSMART traffic cameras as sensors for adaptive traffic control, with a sensitivity to the fuel consumption characteristics of the traffic in the camera’s visual field. Traffic control use cases using reinforcement learning have been published and achieved good results. Surveys from DOE national laboratories estimate that the fuel cost of idling is 6 billion gallons wasted annually [Argonne National Laboratory, 2019]. GRIDSMART cameras—an existing, fielded commercial product—sense the presence of vehicles at intersections and replace more conventional sensors (such as inductive loops) to issue calls to traffic control. These cameras, which have horizon-to-horizon view, offer the potential for an improved view of the traffic environment which can be used to generate better control algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Water effects on NH 3 -SCR over Cu-based small-pore zeolite catalysts: A review

The selective catalytic reduction (SCR) of NO x with NH 3 as the reductant over small-pore Cu-zeolite catalysts is a leading strategy for controlling emissions from diesel engines and mobile sources. While the hydrothermal stability of Cu-zeolites such as Cu-SSZ-13, Cu-SAPO-34, and Cu-SSZ-39 has been extensively studied, the multifaceted role of water vapor under operating conditions has only recently received systematic attention. Water impacts multiple aspects of SCR catalysis, including NO x conversion, N 2 O formation, Cu ion speciation, redox kinetics, and surface reactions. This review comprehensively summarizes recent advances in understanding the effect of water on NH 3 -SCR over Cu-based catalysts. For Cu-SSZ-13, water vapor induces both inhibitory and promotive effects depending on temperature, Cu loading, and Si/Al ratio by altering NH 3 adsorption, Cu mobility, and redox half-cycle kinetics. Studies have revealed that water can suppress ammonium nitrate formation at low temperatures and enhance nitrate pathways at higher temperatures. Over Cu-SAPO-34, water promotes NO conversion by improving Cu(II) reducibility and strengthening Brønsted acidity. First-principles calculations confirm that water-coordinated Cu species lower O 2 activation barriers. In Cu-SSZ-39, water was found to facilitate Cu reduction, migration, and O 2 activation, resulting in superior water tolerance and unexpected catalytic promotion. Both experimental and computational studies highlight that water vapor exerts complex, catalyst-dependent effects on NH 3 -SCR performance, underscoring the need for tailored catalyst designs that leverage beneficial water interactions while minimizing inhibitory impacts.

Ammonia selective catalytic reduction↗
DOE OSTI2015

Mobile platform for radiological characterization of sites under or after decommissioning - 15139

Nowadays, nuclear industry is facing a crucial need in establishing radiological characterization for the assessment and the monitoring of any remediation work. Regarding its experience in this domain, the French Commission for Atomic Energy and Alternative Energies (CEA) of Fontenay-aux-Roses, established an important feedback and developed a sound methodology for radiological characterization. A clean-up project is managed from the initial surface characterization to the final one, once volumes of contaminated soil have been removed. Samples are collected via recognized methods and data is gathered by real-time acquisition with different conveyance such as all-in-vehicles, cart, walking, helicopter or drone. Accurate radiological analyses are performed on site thanks to the mobile laboratories cluster, called SMaRT. It enables to realize long-time gamma spectroscopy, chemical preparation for alpha spectroscopy or beta liquid scintillation counting. Data is processed with geo-statistics via the software Kartotrak, 2D or 3D mapping are created with the associated uncertainty and probability map. Drilling profiles with geological data (layers composition, particles size, etc.) are studied in order to understand the behavior of the contamination in depth. Source term, contaminated volumes and sanitary impact are calculated in order to determine the optimum excavation depth. Finally, data is stored in an online site that insures the traceability of the whole process. This just-in-time production model allows a fast response and control of costs and avoids the disadvantages of radioactive material transport. This whole innovative process, developed inside the mobile platform, requires all these tools that have been used in France and continuously evolving for more than 10 years thanks to a learning organization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Health intelligence: how artificial intelligence transforms population and personalized health

Advances in computational and data sciences for data management, integration, mining, classification, filtering, visualization along with engineering innovations in medical devices have prompted demands for more comprehensive and coherent strategies to address the most fundamental questions in health care and medicine. Theory, methods, and models from artificial intelligence (AI) are changing the health care landscape in clinical and community settings and have already shown promising results in multiple applications in healthcare including, integrated health information systems, patient education, geocoding health data, social media analytics, epidemic and syndromic surveillance, predictive modeling and decision support, mobile health, and medical imaging (e.g. radiology and retinal image analyses). Health intelligence uses tools and methods from artificial intelligence and data science to provide better insights, reduce waste and wait time, and increase speed, service efficiencies, level of accuracy, and productivity in health care and medicine.

Shaban-Nejad, Arash↗
DOE OSTI2018

Bimodal hole transport in bulk BiVO 4 from computation

We report first principles and mesoscale kinetic simulations of intrinsic electron and hole transport in stoichiometric monoclinic BiVO 4 (BVO). A main finding is that hole transport is bimodal: there are fast hole hops within VO 4 tetrahedra that are not ‘transport-efficient’ and slower hole hops across VO 4 tetrahedra that are ‘transport-efficient’. This bimodality in hole transport may explain the slow transport/high recombination characteristics of BVO. In this work, polaron hops were described via the two-state Marcus/Holstein model of V V –V IV and O I –O II interchanges for electron and hole polarons respectively. The relevant parameters in the theory were obtained using the density functional theory DFT+U method. Hopping activation energies were combined with Einstein diffusion theory to yield mobility as well as with kinetic Monte Carlo (KMC) modeling. All unique nearest neighbor electron and hole hopping processes were characterized. For V-to-V hops of electron polarons, we obtained activation energies of ~0.37 eV that compare well with the experimental activation energy of ~0.30 eV for drift mobility. Electron polarons remain localized on single sites along the hopping pathways before hopping via tunneling, suggestive of small non-adiabatic coupling and a diabatic character consistent with V-to-V electron transfer through space (no bridge linkers). These simulations yield a small electron transport anisotropy, with transport in the (a,b) plane being faster than along the c direction by a factor of ~2. For hole polarons our calculations support the character of small polarons localized on O atoms as well. Some nearest neighbor hole hops have a diabatic character, while other nearest neighbor hops are adiabatic and phonon-assisted with an Arrhenius thermal dependence. Hops through O–V–O bridges and through space have low activation barriers (~0.17 eV and ~0.25 eV) while hops through O–Bi–O bridges have higher activation barriers (~0.37 eV and higher). The mobility is gated by the slower transport across VO 4 tetrahedra through O–Bi–O bridges owing to the underlying BVO lattice network. Hole mobility is determined to be about one order of magnitude larger than electron mobility consistent with THz spectroscopy measurements. Here, this work sets the foundation to address facet selectivity of charge carriers in shape and size-tailored crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Robot Adaptation to Unstructured Terrains by Joint Representation and Apprenticeship Learning

When a mobile robot is deployed in a field environment, e.g., during a disaster response application, the capability of adapting its navigational behaviors to unstructured terrains is essential for effective and safe robot navigation. In this paper, we introduce a novel joint terrain representation and apprenticeship learning approach to implement robot adaptation to unstructured terrains. Different from conventional learning-based adaptation techniques, our approach provides a unified problem formulation that integrates representation and apprenticeship learning under a unified regularized optimization framework, instead of treating them as separate and independent procedures. Our approach also has the capability to automatically identify discriminative feature modalities, which can improve the robustness of robot adaptation. In addition, we implement a new optimization algorithm to solve the formulated problem, which provides a theoretical guarantee to converge to the global optimal solution. In the experiments, we extensively evaluate the proposed approach in real-world scenarios, in which a mobile robot navigates on familiar and unfamiliar unstructured terrains. Experimental results have shown that the proposed approach is able to transfer human expertise to robots with small errors, achieve superior performance compared with previous and baseline methods, and provide intuitive insights on the importance of terrain feature modalities.

42 ENGINEERING↗
DOE OSTI2024

Machine-learning-assisted deciphering of microstructural effects on ionic transport in composite materials: A case study of Li 7 La 3 Zr 2 O 12 -LiCoO 2

The effective diffusivity of ionic species in multiphase materials is critical for the design and function of composite materials for electrochemical energy storage. In practice, effective diffusivity depends sensitively not only on the intrinsic diffusivities of constituting materials but also on their topological arrangement; nevertheless, these coupled contributions are oversimplified in most analytical models. Here, we combine atomistically informed mesoscale modeling and machine learning (ML) analysis to unravel how such features affect effective diffusivity in two-phase composites. Using the Li 7 La 3 Zr 2 O 12 -LiCoO 2 composite solid-state battery cathode as a model system, we compute effective diffusivity for 600 distinct dense polycrystalline microstructures with different topological configurations of grains, grain boundaries, and heterointerfaces. We verify that in addition to atomic-scale variabilities, microstructural feature diversity can significantly impact effective transport properties. Across the ensemble of test microstructures, this often results in bimodal distributions of effective diffusivity that encompass two qualitatively distinct operating mechanisms, which we identify via flux analysis. An ML approach reveals that the most critical determining factors for effective diffusivity are the connectivity of bulk phases and their heterointerfaces. The role of ionic mobility at the heterointerfaces is also discussed. These insights highlight the combined importance of microstructure and interface engineering in tuning the transport properties of ionic species in composite materials. In conclusion, our framework can also be extended for understanding generic microstructure-property relationships in other complex multiphase materials.

25 ENERGY STORAGE↗
DOE OSTI2020

CasCollect: targeted assembly of CRISPR-associated operons from high-throughput sequencing data

Abstract CRISPR arrays and CRISPR-associated (Cas) proteins comprise a widespread adaptive immune system in bacteria and archaea. These systems function as a defense against exogenous parasitic mobile genetic elements that include bacteriophages, plasmids and foreign nucleic acids. With the continuous spread of antibiotic resistance, knowledge of pathogen susceptibility to bacteriophage therapy is becoming more critical. Additionally, gene-editing applications would benefit from the discovery of new cas genes with favorable properties. While next-generation sequencing has produced staggering quantities of data, transitioning from raw sequencing reads to the identification of CRISPR/Cas systems has remained challenging. This is especially true for metagenomic data, which has the highest potential for identifying novel cas genes. We report a comprehensive computational pipeline, CasCollect, for the targeted assembly and annotation of cas genes and CRISPR arrays—even isolated arrays—from raw sequencing reads. Benchmarking our targeted assembly pipeline demonstrates significantly improved timing by almost two orders of magnitude compared with conventional assembly and annotation, while retaining the ability to detect CRISPR arrays and cas genes. CasCollect is a highly versatile pipeline and can be used for targeted assembly of any specialty gene set, reconfigurable for user provided Hidden Markov Models and/or reference nucleotide sequences.

Podlevsky, Joshua D.↗
DOE OSTI2014

Geoelectrical Measurement of Multi-Scale Mass Transfer Parameters

Mass transfer affects contaminant transport and is thought to control the efficiency of aquifer remediation at a number of sites within the Department of Energy (DOE) complex. An improved understanding of mass transfer is critical to meeting the enormous scientific and engineering challenges currently facing DOE. Informed design of site remedies and long-term stewardship of radionuclide-contaminated sites will require new cost-effective laboratory and field techniques to measure the parameters controlling mass transfer spatially and across a range of scales. In this project, we sought to capitalize on the geophysical signatures of mass transfer. Previous numerical modeling and pilot-scale field experiments suggested that mass transfer produces a geoelectrical signature—a hysteretic relation between sampled (mobile-domain) fluid conductivity and bulk (mobile + immobile) conductivity—over a range of scales relevant to aquifer remediation. In this work, we investigated the geoelectrical signature of mass transfer during tracer transport in a series of controlled experiments to determine the operation of controlling parameters, and also investigated the use of complex-resistivity (CR) as a means of quantifying mass transfer parameters in situ without tracer experiments. In an add-on component to our grant, we additionally considered nuclear magnetic resonance (NMR) to help parse mobile from immobile porosities. Including the NMR component, our revised study objectives were to: 1. Develop and demonstrate geophysical approaches to measure mass-transfer parameters spatially and over a range of scales, including the combination of electrical resistivity monitoring, tracer tests, complex resistivity, nuclear magnetic resonance, and materials characterization; and 2. Provide mass-transfer estimates for improved understanding of contaminant fate and transport at DOE sites, such as uranium transport at the Hanford 300 Area. To achieve our objectives, we implemented a 3-part research plan involving (1) development of computer codes and techniques to estimate mass-transfer parameters from time-lapse electrical data; (2) bench-scale experiments on synthetic materials and materials from cores from the Hanford 300 Area; and (3) field demonstration experiments at the DOE’s Hanford 300 Area. In a synergistic add-on to our workplan, we analyzed data from field experiments performed at the DOE Naturita Site under a separate DOE SBR grant, on which PI Day-Lewis served as co-PI. Techniques developed for application to Hanford datasets also were applied to data from Naturita.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Mobile Spectrometer Comparison to TCCON Field Campaign Report

During the summer of 2018, a field campaign took place to help characterize off-the-shelf portable solar-viewing Fourier-transform spectroscope (FTS) instruments (EM27/SUN). These instruments retrieve greenhouse gas (GHG) abundances from direct solar spectra (Wunch et al. 2011). The campaign included assistance and instrumentation from the University of Toronto, Environment and Climate Change Canada, and the California Institute of Technology and took place at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory from July 22 to July 28, 2018. The instruments were sited on the outside landing of the guest trailer (Figure 1) and were set up and taken down each morning and afternoon. During the campaign, we had minor inconveniences like overheating computers, but the experience involving the ARM site itself held nothing out of the ordinary for July in Oklahoma.

greenhouse gases, Southern Great Plains, Total Col↗
DOE OSTI2020

Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials

Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors, which is known to be correlated to valley degeneracy in the electronic band structure. However, there are over 50 known semiconducting half-Heusler phases, and it is not clear how the chemical composition affects the electronic structure. While all the n-type electronic structures have their conduction band minimum at either the Γ - or X -point, there is more diversity in the p-type electronic structures, and the valence band maximum can be at either the Γ -, L -, or W -point. Here, we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W -point to the valence band maximum. We do this by constructing an “orbital phase diagram” to cluster the variety of electronic structures expressed by these phases into groups, based on the atomic orbitals that contribute most to their valence bands. Then, with the aid of machine learning, we develop new chemical rules that predict the location of the valence band maximum in each of the phases. These rules can be used to engineer band structures with band convergence and high valley degeneracy.

36 MATERIALS SCIENCE↗
DOE OSTI2019

High-frequency, high-power performance of AlGaN-channel high-electron-mobility transistors: an RF simulation study [High-frequency high power performance of AlGaN channel HEMTs: an RF simulation study]

The emerging Al-rich AlGaN-channel Al x Ga 1–x N/Al y Ga 1–y N high-electron-mobility transistors (HEMTs) with 0.7 ≤ y < x ≤ 1.0 have the potential to greatly exceed the power handling capabilities of today's GaN HEMTs, possibly by five times. This projection is based on the expected 4× enhancement of the critical electric field, the 2× enhancement of sheet carrier density, and the parity of the electron saturation velocity for Al-rich AlGaN-channel HEMTs relative to GaN-channel HEMTs. In this paper, the expected increased RF power density in Al-rich AlGaN-channel HEMTs is calculated by theoretical analysis and computer simulations, based on existing data on long-channel AlGaN devices. It is shown that a saturated power density of 18 W mm –1 , a power-added efficiency of 55% and an output third-order intercept point over 40 dB can be achieved for this technology. Furthermore, the method for large-signal RF performance estimation presented in this paper is generic and can be applied to other novel high-power device technologies at the early stages of development.

42 ENGINEERING↗
DOE OSTI2024

Evaluation of a Reference-Free Collision Cross Section Calibration Strategy for Proteomics Using SLIM-Based High-Resolution Ion Mobility Spectrometry–Mass Spectrometry

Ion mobility spectrometry (IMS) is a gas-phase analytical technique that separates ions with different sizes and shapes and is compatible with mass spectrometry (MS) to provide an additional separation dimension. The rapid nature of the IMS separation combined with the high sensitivity of MS-based detection and the ability to derive structural information on analytes in the form of the property collision cross section (CCS) makes IMS particularly well-suited for characterizing complex samples in -omics applications. In such applications, the quality of CCS from IMS measurements is critical to confident annotation of the detected components in the complex -omics samples. However, most IMS instrumentation in mainstream use requires calibration to calculate CCS from measured arrival times, with the most notable exception being drift tube IMS measurements using multifield methods. The strategy for calibrating CCS values, particularly selection of appropriate calibrants, has important implications for CCS accuracy, reproducibility, and transferability between laboratories. The conventional approach to CCS calibration involves explicitly defining calibrants ahead of data acquisition and crucially relies upon availability of reference CCS values. In this work, we present a novel reference-free approach to CCS calibration which leverages trends among putatively identified features and computational CCS prediction to conduct calibrations post-data acquisition and without relying on explicitly defined calibrants. We demonstrated the utility of this reference-free CCS calibration strategy for proteomics application using high-resolution structures for lossless ion manipulations (SLIM)-based IMS-MS. In conclusion, we first validated the accuracy of CCS values using a set of synthetic peptides and then demonstrated using a complex peptide sample from cell lysate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Dislocation dynamics in polycrystalline materials

An extension of the dislocation dynamics method to polycrystalline materials is presented with emphasis on handling interactions between dislocations and grain boundaries. The key new features of the method include: (1) a grain boundary detection algorithm, (2) new dislocation emission criteria based on power dissipation and area growth, (3) mobility of dislocations at grain boundaries, and (4) extension of topological operations e.g. mesh adaption, collision, and dissociation to handle dislocations interacting with grain boundaries. Furthermore, verification and validation examples are executed to compare our calculations with existing analytic methods, molecular dynamics simulations, and experimental observations.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Cyber-Infrastructure for Data-Intensive Geospatial Computing

With the recent advent of heterogeneous High-performance Computing (HPC) to handle EO “Big Data” workloads, there is a need for a unified Cyber-infrastructure (CI) platform that can bridge the best of many HPC worlds. In this chapter, we discuss such a CI platform being developed at Geographic Information Science and Technology (GIST) group using novel and innovative techniques, and emerging technologies that are scalable to large-scale supercomputers. The CI platform utilizes a wide variety of computing such as GPGPU, distributed, real-time and cluster computing, which are being brought together architecturally to enable data-driven analysis, scientific understanding of earth system models, and research collaboration. This development addresses the need for close integration of EO and other geospatial information in the face of growing volumes of the data, and facilitates spatio-temporal analysis of disparate and dynamic data streams. Horizontal scalability and linear throughput are supported in the heart of the platform itself. It is being used to support very broad application areas, ranging from high-resolution settlement mapping, national bioenergy infrastructure to urban information and mobility systems. The platform provides spatio-temporal decision support capabilities in planning, policy and operational missions for US federal agencies. Also, the platform is designed to be functionally and technologically sustainable for continued support of the US energy and environment mission for the coming decades.

Karthik, Rajasekar↗