Amrita Vishwa Vidyapeetham is a private deemed-to-be-university and Institute of Eminence based in Coimbatore, Tamil Nadu, India. The multi-campus, multi-disciplinary university currently has 6 campuses with 15 constituent schools across Tamil Nadu, Kerala and Karnataka. It offers a total of 207 undergraduate, postgraduate, integrated-degree, dual-degree, doctoral, and certification programs in Engineering, Medicine, Business, Arts & Sciences, Biotechnology, Mass Communication and Social Work. The university was founded with the opening of Amrita School of Engineering, Coimbatore in 1994 by Mata Amritanandamayi Devi, and is managed by her international humanitarian organisation Mata Amritanandamayi Math. In 2003, it became one of the youngest institutes to be deemed a university, when it was conferred this status by the UGC.The university has been re-accredited by NAAC with 'A' grade in 2014, and has been ranked 4th among the Indian universities by NIRF in 2020. It continuously collaborates with top American & European universities including Ivy league universities for regular student exchange programs, and has been emerging as one of the top ranked private university in India, according to the QS World University Rankings and the Times Higher Education Rankings.
This research confronts the pressing problem of plastic waste within construction by exploring the potential of discarded polypropylene cement bag waste (PPCBW) fragment usage in concrete. The sustainability of the PPCBW concrete is evaluated through environmental impact assessment. Different dosages of PPCBW fragments (such as 0
Conventional micromixing approaches are complex, time-consuming, and require large quantities of reagents, resulting in higher costs and complications. Although microfluidic technology holds significant potential, the development of a compact, modest and economical microfluidic mixing device that ensures complete sample mixing and easy integration remains a major hurdle. This study presents a novel passive micromixer design that employs Koch snowflake fractal obstacles (KSFO) through computational fluid dynamics (CFD) simulations. This research uniquely integrates Koch snowflake geometry as an obstacle, utilizing fractal principles within the micromixer design strategy. To optimize the mixing quality of the KSFO micromixer, by adjusting a range of geometric and operational parameters, with the mixing index serving as the primary criterion. Within the Reynolds number range of 0.1 <= Re <= 100, the mixing effectiveness of the KSFO micromixer improves as the fractal iteration order (n) is increased, more KSFOs are incorporated, and increased spacing between the KSFOs (Dksfo). The results indicate that the micromixer configured with the most effective wavy KSFO layout boosts chaotic advection and significantly enhances mixing efficiency than the oblique and centered configurations. At Re = 100, the mixing efficiency of n = 2, and 6-wavy-KSFO micromixer with Dksfo of 175 mu m reaches 98.99%, outperforming the simple T micromixer. In contrast, an Dksfo = 125 mu m provided the highest Performance Parameter (Pp), representing the best compromise between mixing enhancement and hydraulic penalty among the investigated configurations. This research delves into the intricacies of microscale mixing, offering crucial insights and practical strategies. This improved mixing efficiency of the KSFO micromixer holds significant potential for varied applications, including chemical reactors, biological analysis, drug delivery systems, fuel cells, and integration with mu-total analysis system (mu TAS) or lab-on-a-chip (LOC) devices, offering simpler fabrication processes and greater compatibility compared to conventional micromixers.
Speech Emotion Recognition (SER) in low-resource languages is hindered by scarce labeled data, speaker variability and prosodic diversity. Existing few-shot learning models underperform in such settings due to weak generalization and inadequate exploitation of emotional cues in speech. This study proposes SegTM-SGD, a novel meta-learning framework that integrates Whisper-derived acoustic representations with a lightweight Transformer encoder optimized using Meta-SGD. Unlike prior approaches, SegTM-SGD employs segment-level temporal modeling with learnable per-parameter adaptation, enabling rapid task transfer with minimal supervision. The framework adopts episodic N-way K-shot learning to simulate real-world data scarcity. Experiments conducted on Tamil, Malayalam, and Indian-English emotional speech datasets demonstrate significant performance gains over state-of-the-art metric- and gradient-based baselines. SegTM-SGD achieves macro F1-scores of 0.90, 0.83, and 0.92 in 1-shot settings, improving to 0.95, 0.94, and 0.96 under 10-shot conditions, respectively. These results highlight the effectiveness of segment-aware transformer refinement with adaptive meta-learning for few-shot SER in multilingual low-resource environments.
A steadily growing need for efficient energy storage systems has positioned supercapacitors as an innovative technology. Conventional supercapacitor materials have low energy density compared to batteries, limited electrolyte stability and capacitance loss over prolonged operation. Polymer nanocomposites have gained attention as a solution to these drawbacks to a certain extent but still face challenges like poor filler dispersion and weak interfacial bonding. One efficient way to get around these obstacles is to employ functionalized nanofillers in polymer nanocomposites. This review briefly discusses various classes of nanofillers, their functionalization, along with fabrication techniques of functionalized polymer nanocomposites. A detailed discussion is presented on the effect of functionalized nanofillers in polymer matrices on supercapacitor performance parameters, such as specific capacitance, power density, and long-term cyclic stability. Among the reported nanofiller systems, graphene and its derivatives were the most extensively investigated, followed by carbon nanotubes and carbon quantum dots, while polyaniline and polypyrrole dominate as the preferred polymer matrices. A wide range of emerging nanomaterials and polymer matrices have received comparatively limited attention. This review provides a comprehensive overview of the performance of functionalized polymer nanocomposites for advanced supercapacitor applications and provide an outline for future research and development strategies.
This study presents an anisotropic strain-gradient elasticity framework in which direction-dependent microstructural interactions arise from a geometrically defined nonlocal domain. The resulting characteristic-length tensor comes from the second moments of a three-dimensional ellipsoidal averaging domain, giving it a direct geometric meaning similar to a fabric tensor. A positive-definite strain-gradient energy is discretized using smooth, continuously differentiable Hermite finite elements in one, two, and three dimensions, together with a corrected consistent mass formulation. For an axial characteristic length of 0.20 m and transverse lengths of 0.02 m, rotating the microstructural orientation produces a monotonic response. Static displacement increases by about 4%, while the fundamental frequency decreases from 186.3 Hz to 173.1 Hz. The normalized frequency ranges from 1.053 to 0.979 relative to a volume-equivalent isotropic reference of 176.8 Hz. The axial length dominates the transverse contribution, producing a 5.8% stiffness increase compared with only 0.5% in the transverse-dominant case. A Directional Stiffening Index, measured against a volume-equivalent isotropic reference, is predominantly positive, ranging from 0% to about 30% across the anisotropy ratios and orientations studied. Only a few high-angle configurations show small negative values. These trends allow the stiffness and vibration frequency of anisotropic microstructured materials to be tailored through controlled variation of characteristic length and orientation.
