Prakash Kumar

MSMEs form the backbone of the Indian economy and improving access to finance will be key in enabling them to become the cornerstone of a $5-trillion economy

This paper introduces a novel approach to neural networks: a Generalized Liquid Neural Network (GLNN) framework. This design excels at handling both sequential and non-sequential tasks. By leveraging the Runge Kutta DOPRI method, the GLNN enables dynamic simulation of complex systems across diverse fields. Our research demonstrates the framework's capabilities through three key applications. In predicting damped sinusoidal trajectories, the Generalized LNN outperforms the neural ODE by approximately 46.03% and the conventional LNN by 57.88%. Modelling non-linear RLC circuits shows a 20% improvement in precision. Finally, in medical diagnosis through Optical Coherence Tomography (OCT) image analysis, our approach achieves an F1 score of 0.98, surpassing the classical LNN by 10%. These advancements signify a significant shift, opening new possibilities for neural networks in complex system modelling and healthcare diagnostics. This research advances the field by introducing a versatile and reliable neural network architecture.

Investing in the stock market can be a powerful way to grow wealth, but it also comes with risks. For smart investors, navigating the complexities of the market requires more than just intuition—it demands expert guidance and insightful strategies. This is where stock tips advisory services come into play, providing investors with the tools and knowledge they need to make informed decisions.

M a t h J a x /jax/output/HTML-CSS/config.js

The growing challenge of food waste management presents a critical opportunity for advancing the circular bioeconomy, aiming to transform waste into valuable resources. This paper explores innovative strategies for converting food wastes into renewable food resources, emphasizing the integration of sustainable technologies and zero-waste principles. The main objective is to demonstrate how these approaches can contribute to a more sustainable food system by reducing environmental impacts and enhancing resource efficiency. Novel contributions of this study include the development of bioproducts from various food waste streams, highlighting the potential of underutilized resources like bread and jackfruit waste. Through case studies and experimental findings, the paper illustrates the successful application of green techniques, such as microbial fermentation and bioprocessing, in valorizing food wastes. The implications of this research extend to policy frameworks, encouraging the adoption of circular bioeconomy models that not only address waste management challenges but also foster economic growth and sustainability. These findings underscore the potential for food waste to serve as a cornerstone in the transition to a circular, regenerative economy.

This paper presents a deep-learning architecture for segmenting retinal fluids in patients with Diabetic Macular Oedema (DME) and Age-related Macular Degeneration (AMD). Accurate segmentation of multiple fluid types is critical for diagnosis and treatment planning, but existing techniques often struggle with precision. We propose an encoder–decoder network inspired by U-Net, processing enhanced OCT images and their edge maps. The encoder incorporates Residual and Inception modules with an autoencoder-based multiscale attention mechanism to extract detailed features. Our method shows superior performance across several datasets. On the RETOUCH dataset, the network achieved F1 Scores of 0.82 for intraretinal fluid (IRF), 0.93 for subretinal fluid (SRF), and 0.94 for pigment epithelial detachment (PED). The model also performed well on the OPTIMA and DUKE datasets, demonstrating high precision, recall, and F1 Scores. This architecture significantly enhances segmentation accuracy and edge precision, offering a valuable tool for diagnosing and managing retinal diseases. Its integration of dual-input processing, multiscale attention, and advanced encoder modules highlights its potential to improve clinical outcomes and advance retinal disease treatment.

Loading [MathJax]/extensions/MathEvents.js MathJax /extensions/tex2jax.js

Abstract This article presents a flexible four-element antenna for X-band applications.

Quick Takeaways:Elevated Lp(a) (≥50 mg/dL) significantly raises ASCVD risk, affecting around 25% of Indians.One-time Lp(a) screening in adulthood is recommended, especially for those with a family...

Salt stress is a major abiotic stressor that limits plant growth, development, and agricultural productivity, especially in regions with high soil salinity. With the increasing salinization of soils due to climate change, developing salt-tolerant crops has become essential for ensuring food security. This review consolidates recent advances in plant genetics, transcription factors (TFs), and next-generation sequencing (NGS) technologies that are pivotal for enhancing salt stress tolerance in crops. It highlights critical genes involved in ion homeostasis, osmotic adjustment, and stress signaling pathways, which contribute to plant resilience under saline conditions. Additionally, specific TF families, such as DREB, NAC (NAM, ATAF, and CUC), and WRKY, are explored for their roles in activating salt-responsive gene networks. By leveraging NGS technologies—including genome-wide association studies (GWASs) and RNA sequencing (RNA-seq)—this review provides insights into the complex genetic basis of salt tolerance, identifying novel genes and regulatory networks that underpin adaptive responses. Emphasizing the integration of genetic tools, TF research, and NGS, this review presents a comprehensive framework for accelerating the development of salt-tolerant crops, contributing to sustainable agriculture in saline-prone areas.