Prakash Kumar

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.

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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.

The transition from food waste to food security is a critical component of sustainability efforts. This approach focuses on repurposing organic waste products generated throughout the food supply chain into valuable resources. Food waste, encompassing everything from agricultural residues to post-consumer waste, represents a significant untapped potential that can be harnessed to enhance food security. By implementing strategies such as composting, bioconversion, and innovative recycling technologies, biowastes can be transformed into fertilizers, animal feed, and even new food products, thus closing the loop in the food system and aiding sustainable solutions for waste valorization. This transition not only addresses environmental concerns by reducing landfill waste and greenhouse gas emissions but also contributes to economic sustainability by creating new opportunities within the food production and waste management sectors. Ultimately, transforming food waste into a resource aligns with the broader goals of a circular economy, ensuring a sustainable, resilient, and food-secure future.

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Physical Layer Security (PLS) in Cognitive Radio Networks (CRN) improves the confidentiality, availability, and integrity of the external communication between the devices/ users. The security models for sensing and beamforming reduce the impact of adversaries such as eavesdroppers in the signal processing layer. To such an extent, this article introduces a Secure Channel Estimation Model (SCEM) using Channel State Information (CSI) and Deep Learning (DL) to improve the PLS. In this proposed model, the CSI is exploited to evaluate the channel utilization and actual capacity availability throughout the allocation intervals. The change in channel capacity and utilization augments the need for security through 2-level key shared authentication. The deep learning algorithm verifies the authentication completeness for maximum channel capacity utilization irrespective of adversary interference. This verification follows mutual authentication between the primary and secondary users sharing the maximum capacity channel with high secrecy. The learning monitors the outage secrecy rates to verify failed allocations such that the replacement for allocation is pursued. Thus, the physical layer security between different user categories is administered through maximum CSI exploitation with high beamforming abilities. The proposed model leverages the secrecy rate by 10.77% and the probability of detection by 15.01% and reduces the interference rate by 11.07% for the varying transmit powers.