Voluntary Health Association Of India

Abstract The present research focuses on the hot deformation behavior, microstructure, texture evolution, and fracture mechanisms of wire arc additively manufactured Inconel 625 alloy. The deposited material exhibits columnar, cellular, and dendritic grain structures, with grain widths ranging between 10 to 40 μm and lengths extending up to 150 μm. Deformation characteristics were studied across a temperature range of 700 to 900°C. At medium temperatures, distinct serrations were observed, transitioning from B-type to C-type as the temperature increased. At elevated temperatures, B-type serrations reappeared, attributed to interactions between C14-Ni2Nb Laves phases and mobile dislocations, as revealed by TEM analysis. Conversely, C-type serrations were associated with the nucleation and growth of deformation twins. Grain size and boundaries under varying deformation temperatures were examined using EBSD and TEM, which revealed occurrence of dynamic recrystallization (DRX). It was observed that preferred orientation for DRX nucleation in the Inconel 625 alloy is along the <001> direction. At 900°C, recrystallized grains were prominent, with EBSD results confirming both discontinuous dynamic recrystallization (DDRX) and continuous dynamic recrystallization (CDRX), where CDRX acted as a secondary nucleation mechanism. Below 900°C, cracks primarily nucleate due to stress concentrations near Nb-rich phases. At 900°C, crack initiation was influenced by slip band impingement at grain boundaries combined with stress concentrations around Nb-rich phases. The high-temperature deformation behavior of WAAM-deposited Inconel 625 is strongly influenced by the interplay of DDRX and CDRX mechanisms.

In current days, sandwich structures have become popular due to their flexibility with design requirements and excellent performance under extreme loads, such as blast. There are different strategies for enhancing the blast resistance of such sandwich structures. Including an additional layer of polyurea and stiffeners are widely used techniques that may enhance the performance of the panels under high-rate loadings. In this study, the effects of polyurea and stiffeners on the protection of a steel and aluminum foam sandwich panels is studied. Effective configuration of the panels with both polyurea and stiffeners are investigated. Here, different configuration cases of the sandwich panels: (a) panel without polyurea and stiffeners, (b) with polyurea applied on the rear face, (c) with stiffeners applied on the rear face, and (d) with stiffeners and polyurea on the rear face are investigated and compared. The finite element models of sandwich panels are developed, where steel facesheets, steel stiffeners, and polyurea are modeled with shell elements, and aluminum foam core is modeled with solid elements. Elastic-plastic, crushing foam, and hyperelastic material behaviors are implemented for steel, aluminum, and polyurea layers of the sandwich panels, respectively. The performance of the different configurations of the panels are compared in terms of the response quantities, i.e., deformation, equivalent von-Mises stresses, and energy absorption. Moreover, the damage patterns with fragmentation effect are depicted for all the considered sandwich panels. The results of the study show that both polyurea and stiffeners are the most effective configurations in protecting the sandwich structures; however, with the same thickness of polyurea and stiffener, the stiffeners show better performance than polyurea against blast load. Furthermore, it is observed that the deflection values across the configurations follow a logarithmic decay pattern.

<title>Abstract</title> Gas sensors have gain attention with the technological advancement as they are widely used in industries, health care, agriculture and environmental monitoring. The MOSFET based sensor are the mostly preferred in the sensing application due to their reduced power consumption, lower cost and higher sensitivity. The Nano wire with gate stacked gate all around configuration is estimated to dominate the modern on chip transistors with lower leakage current. The inhalation of ethanol vapor can cause several health risks i.e. respiratory irritation and intoxication. In this study, we have investigated a novel ethanol gas sensor using gate stack Junction-less Gate All Around (GS-JL-GAA) MOSFET with varying ethanol gas concentration as 1 ppm, 10 ppm, 50 ppm and 100 ppm. The operation of proposed gas sensor will be based on the change in the work function of palladium gate electrode with the variation in the concentration of ethanol gas. The ethanol vapor dehydrogenation takes place in presence of palladium catalyst at room temperature. Due to this the released hydrogen molecule forms a dipole at the palladium oxide interface and the work function of gate electrode alters. A rigorous simulation study on electrostatic, analog, radio frequency and linearity analysis of GS-JL-GAA MOSFET has been performed using ATLAS device simulator. It is observed that with the variation of ethanol gas concentration, the characteristics of GS-JL-GAA MOSFET varies. Further, the sensitivity of all these parameters has been investigated to access the impact of gas concentration on GS-JL-GAA MOSFET based gas sensor. The result reveals that the proposed gas sensor exhibits superiority in terms of sensitivity and improved sensor performance.

This study presents a novel hybrid approach integrating hydrodynamic cavitation (HC) with advanced oxidation processes (AOPs) like hydrogen peroxide (H2O2) and ozone (O3) to enhance the biodegradability of real dairy industry wastewater. The research focuses on optimizing HC with other oxidants and exploring the synergistic effects of oxidants on pretreatment efficacy. Experimental investigations were conducted using an orifice plate HC reactor at 2 bar inlet pressure, treating actual dairy effluent with high chemical oxygen demand (COD) and low initial biodegradability index (BI = 0.35). HC treatment alone improved the BI to 0.66, while combining HC with H2O2 (9 g/L) and combining with O3 (200 mg/h) further increased the BI to 0.74 and 0.81, respectively. Most notably, the ternary combination of HC + H2O2 + O3 achieved a substantial increase in BI to 0.89, demonstrating a strong synergistic effect. This enhancement is attributed to the intensified generation of reactive hydroxyl radicals (.OH) through cavitational and oxidative reactions, leading to effective degradation of complex organic pollutants and improved downstream biological treatment potential. These results underscore the viability of HC-AOPs hybrids as cost-effective and sustainable pretreatment strategies for dairy wastewater management.

ABSTRACTA lightweight encryption scheme has been proposed for the resource‐constrained devices in the HetNets, which is backed by re‐encryption for handling a massive amount of data, i.e., images and multimedia files in 5G. A comprehensive review on recent research, developments, and strategies in image encryption systems has been presented. Various underlined and exemplary security schemes that remain unexploited based on several statistical models are reviewed. Multiple hybrid keys have been employed that identify the data classes and subsequently encrypt as well as re‐encrypt the data by ensuring the security level does not fall below 40%. Both the gray scale and colored images have been encrypted, and subsequently, heat dissipation, encryption, and hacking time have also been recorded using FIS to depict the efficacy of the proposed scheme. Furthermore, these results have been compared with the recent encryption schemes to validate the superiority of the proposed scheme.

Sustainability metrics have increasingly become a crucial non-financial criterion in investment decision-making.Organizations worldwide are recognizing the importance of sustainability and are proactively highlighting their efforts through specialized sustainability reports.Unlike traditional annual reports, these sustainability disclosures are typically text-heavy and are often expressed as infographics, complex tables, and charts.The non-machine-readable nature of these reports presents a significant challenge for efficient information extraction.The rapid advancement of Vision Language Models (VLMs) has raised the question whether these VLMs can address such challenges in domain specific task.In this study, we demonstrate the application of VLMs for extracting sustainability information from dedicated sustainability reports.Our experiments highlight the limitations in the performance of several open-source VLMs in extracting information about sustainability disclosures from different type of pages.

This paper describes our submission to the NLP-AI4Health 2025 Shared Task on multilingual clinical dialogue summarization and structured information extraction.Our system is based on Qwen-1.5BInstruct fine-tuned with LoRA adapters for parameter-efficient adaptation.The pipeline produces (i) concise English summaries, (ii) schema-aligned JSON outputs, and (iii) multilingual Q&A responses.The Qwen-based approach substantially improves summary fluency, factual completeness, and JSON field coverage while maintaining efficiency within constrained GPU resources.