26 research outputs found
On Case Marking in Assamese Bengali and Oriya
No full textCase is a grammatical category determined by the syntactic or semantic function of a noun or pronoun. Trask (1997) said that “Any one of the forms which a noun or noun phrase may assume in order to represent its grammatical and semantic relation to the rest of the sentence” (p.35).The present paper aims to explore the case marking in Assamese, Bengali spoken in Assam and Oriya in Orissa. In all three languages case is realized in the form of postpositions, when these postpositions take nouns structurally form phrases. Therefore, they are called postpositional phrases. Postpositional phrases are made up of a noun phrase followed by a postposition.
A Study on Rongmei Syllable Structure
No full textA syllable is a sound or succession of a sounds uttered within a single breath-impulse. Syllable is a unit of pronunciation consisting of a vowel alone or of a vowel with one or more consonants. Phonologically, the syllable is “a unit containing one and only one vowel either alone or surrounded by consonants in certain arrangements”. (O’Connor 1973). It is generally accepted that nucleus is obligatory in all languages, thus, the same is true in case of Rongmei. Rongmei is one of the schedule tribe of Northeast India, mainly concentrated in Assam (Barak Valley), Manipur and Nagaland. Ethnically, Rongmeis are Mongloids and their language belongs to Kuki-Naga section of the Kamarupan group of the Baric sub-division of Tibeto-Burman family of languages (Matisoff, 2001). The analysis indicates that Rongmei treats both onset and coda as optional. Besides, the clustering phenomenon is absent at both onset position coda position. This paper is an effort to look into the possible syllable structure in Rongmei Naga language spoken in Barak Valley of South Assam. Keywords: Syllable, Syllable Tree, Heavy Syllable, Light Syllable, Syllable Structure, DOI: 10.7176/JLLL/56-07 Publication date:May 31st 201
Forest structure and soil properties of mangrove ecosystems under different management scenarios: Experiences from the intensely humanized landscape of Indian Sunderbans
No full text− 0.05% Paraffin Wax Nanocomposite: The Role of Pinning Center at Intergrain Defect Site
No full textOptical, electrical properties and structural characterization of ZnO:rGO based photodetector
No full textEnhanced room temperature magneto resistance in (1-x) % La0.7Sr0.3MnO3-x %WAX (x=0, 0.1, 0.2 and 1.0) nanocomposites
No full textModeling of Spin Transport in Hybrid Magnetic Tunnel Junctions for Magnetic Recording Applications
No full textWe have demonstrated modeling of phonon and defect-induced spin relaxation length (LS) in Fe3O4 and organic semiconductor (OSC) Alq3. LS of Alq3 decreases with enhanced disorder and film thickness at a low film width regime. Exponential change of LS at low width regime is found for Alq3 which is, however, absent for Fe3O4 indicating comparable spin-dependent scattering and LS in Fe3O4. LS also decreases with spin-flip probability both for Alq3 and Fe3O4. Voltage-dependent tunnel magnetoresistance (TMR) response in Fe3O4/Alq3/Co and La0.7Sr0.3MnO3 (LSMO)/Alq3/Co hybrid magnetic tunnel junction (MTJ) devices has been attributed to modified spin filter effect across magnetic/OSC junction at high bias regime. TMR reduction with Alq3 thickness for Fe3O4 device has been attributed to spin relaxation at the organic spacer layer. A low bias peak from differential TMR indicates spin-polarized injection for both MTJ devices. Enhanced in-plane spin transfer torque for both MTJ is associated with modified spin filtering at magnetic/OSC junctions. Lower TMR signal for LSMO device indicates reduced tunneling and enhanced carrier injection across the OSC, which is also supported by the band structure profile. The TMR response observed from simulation results matches well with previously reported experimental results. Higher TMR response for Fe3O4 device indicates the possibility of device employment in room temperature magnetic recording applications
Organic semiconductor spacer thickness-dependent interface defect state spin injection across tunnel magnetoresistance devices
No full textThis study investigates the effect of organic spacer layer thickness on spin transport in magnetic tunnel junctions (MTJs) of the form /x/Co (x=Rubrene, ) with Rubrene and as organic spacer layers. The simulation uses a nonequilibrium Green’s function, assuming spin precession at defect states at the ferromagnet/organic semiconductor interface. Parallel and antiparallel resistances have been observed to be thickness-independent at low thicknesses due to excellent magnetic coupling and minimal interfacial imperfections that eventually increased at high thicknesses. Drastic reduction of parallel and antiparallel currents at high-thickness regime has been attributed to the trapping of spins in deeper pinning wells with strong pinning strengthNotably, the rise in tunnel magnetoresistance with thickness is high in O/C/Co device compared to the O/Rubrene/Co device and has been attributed to the change in defect state depth with thickness and electron-predominant nature of O/C/Co device. Therefore, engineering of spacer layer thickness-dependent spin transport in MTJs resulted in successful implementation of organic spacer MTJs for high-performance spintronic memory applications
Generative AI in Spectroscopy
No full textGenerative AI with spectroscopy is an innovative approach that combines artificial intelligence techniques with spectroscopic analysis, enabling researchers to extract valuable insights from complex spectral data more efficiently and accurately. AI can create high-precision synthetic spectra, enhance signal-to-noise ratios, and support spectral reconstruction by employing deep learning techniques like Generative Adversarial Networks (GANs), Graph Neural Networks (GNN), and Variational Autoencoders (VAEs). GenAI models facilitate the simulation of spectra from molecular structures, solving the forward design, whereas reasoning-driven models address the inverse design by predicting molecular structures with greater accuracy. GenAI can be integrated with spectroscopy to achieve various tasks such as improvement of spectral data analysis, rapid advancement in material discovery, automation, closed-loop optimization, etc. These innovations allow faster and more precise material identification, and real-time spectral prediction across diverse fields such as pharmaceuticals, environmental monitoring, and materials science. Despite challenges related to data quality, model interpretability, and computational demands, integrating Generative AI with spectroscopy offers significant potential for driving advancements in scientific research and industrial applications
