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A dynamically crosslinked, self-adapting, injectable gelatin-chondroitin sulfate hydrogel with antibacterial and antioxidant properties for treatment of deep and irregular wounds
No full textChronic, deep, and irregularly shaped wounds often infected with bacteria are considered a major clinical concern. The overproduction of reactive oxygen species (ROS) and disruption of the balance between pro-inflammatory and anti-inflammatory cytokines delay the healing process. Traditionally used dressings are unable to address these multiple issues. We present a multifunctional, self-adaptable, injectable hydrogel composed of gelatin (G) and chondroitin sulfate (CS) containing borate-crosslinked tannic acid (TA), enriched with in situ synthesized silver nanoparticles (AgNPs), which eliminates the necessity of any secondary dressing. The dynamically crosslinked hydrogel demonstrates efficient self-healing, adhesiveness, antioxidant properties, and potential antibacterial activity (E. coli and S. aureus). The injectable hydrogels also exhibit sustained release of TA and AgNPs. The in vitro cytotoxicity reveals the excellent cytocompatibility of the hydrogel with HDF-N fibroblast cells and red blood cells. In vivo studies confirm that the injectable hydrogel demonstrates self-adaptability in irregularly shaped wounds and accelerates the healing process in terms of healing percentage, fibroblast generation, neovascularization, and hair follicle development. Additionally, the in vivo application of the fabricated hydrogels does not produce any significant systemic toxicity. This study demonstrates that the dynamically crosslinked, multifunctional, injectable hydrogel is a promising candidate for treating irregular deep penetrating wounds
Characteristics of Haze Pollution Events During Biomass Burning Period at an Upwind Site of Delhi
No full textThe National Capital Region (NCR) of Delhi frequently experiences severe haze episodes during the post-monsoon and winter months, driven by long-range transport of biomass burning aerosols, local emissions, and unfavorable meteorological conditions. However, observational studies tracing these pollution episodes along the pathway to Delhi are lacking. This study investigates haze pollution at an upwind site in Sonipat using advanced instrumentation during October 25 to 15 November 2023, encompassing biomass burning and Diwali events. Sudden spikes in pollutants caused severe haze, temporary reductions in pollution due to rainfall, and a resurgence of haze during Diwali. Two major haze episodes were identified, with particulate matter (PM2.5) levels exceeding 300 μg/m3. Organics dominated composition based PM2.5 (C-PM2.5) followed by Black Carbon (BC), jointly accounting for ∼80% of total mass during all the episodes, with secondary inorganics contributed minimally. Limited day-night variations and low inorganics contribution suggested minimal photochemical activity and secondary formation. Elevated levels of biomass burning tracers and emission ratios indicated aged, oxidized aerosols from crop residue burning in Punjab and Haryana, supported by fire count data and 72-hr backward trajectory analysis. Regional meteorology, including a shallow atmospheric boundary layer (ABL) and low wind speeds, hindered pollutant dispersion, leading to accumulation and prolonged haze. By integrating emission analysis, meteorological factors, and transport dynamics, this study provides critical insights into haze formation, emphasizing the need for targeted mitigation strategies, such as stricter crop residue burning controls and improved emission management, to address haze pollution and its health risks effectively
Lightweight multimodal techniques for improving Alt-Text generation in low-resource web environments
No full textElectrophoresis of non-uniformly charged particles in viscoelastic fluids: The weak field limit
No full textElectrophoresis of charged particles in polymeric (viscoelastic) fluids remains important in various separation processes, although their theoretical analysis is rather scarce in the literature. The ones which do investigate this topic use simplifying assumptions, especially that of thin Electrical Double Layers (EDLs) and weak surface charge on the particles, which are often assumed to be uniform in nature. In contrast, this article seeks to move beyond such conventional analytical boundaries, by probing the electrophoretic motion of a non-uniformly (but axisymmetrically) charged particle in an Oldroyd-B fluid, accounting for arbitrary EDL thickness and surface potential. The only restriction is that of a weak external electric field (the so-called “weak field limit”), which enables us to use regular perturbation expansions to deduce the particle's electrophoretic velocity. Our results reveal that the excess polymeric stresses in a viscoelastic medium tend to significantly impact the particle's velocity only when the EDL is sufficiently thin. At the same time, increasing the magnitude of the surface potential (or charge) tends to augment the impact of viscoelasticity. We find that depending on the precise distribution of the particle's surface potential, the medium's viscoelasticity may either speed up or slow down the particle, when compared to a Newtonian fluid. Overall, the inhomogeneity in the surface potential enhances the influence of viscoelasticity, and this enhancement is more pronounced for smaller particles as compared to larger ones
Functionally graded materials in medical applications
No full textFunctionally graded materials are engineered materials fabricated by systematically changing material composition, microstructure, or processing parameters that show gradual variations in the desired properties across their volume, unlike traditional homogeneous materials, where properties are consistent throughout. Functionally graded materials are inspired by natural structures like bones and teeth. Hence, functionally graded materials have recently garnered significant attention in implants, drug delivery, and tissue engineering applications due to their ability to mimic natural structures. Additionally, these materials allow for patient-specific customization, precisely matching individual anatomical requirements and providing tailored solutions for advanced implant technologies, personalized drug delivery systems, and tissue-engineered constructs that closely resemble native tissues. Therefore, this chapter instigates by providing a comprehensive overview of functionally graded materials, fabrication methods, and design principles. Then, explain their application in implants, drug delivery, tissue engineering, and many more, discussing how these materials can enhance performance, functionality, and biocompatibility. Furthermore, this chapter also discusses the challenges and future directions of functionally graded materials in clinical applications
Novel Reference Current Generation Technique for Shunt Active Power Filter Under Unbalanced and Distorted Grid
No full textThe prevalence of nonlinear loads has increased significantly due to technological advancements. As a consequence, harmonics are introduced into the current supplied by the grid. This leads to distortion in the voltage at the point of common coupling (PCC), an increase in transmission line losses, and a deterioration of the power factor. To address these issues, a Shunt Active Power Filter (SAPF) is integrated into the power system at the PCC to supply the harmonic, reactive, and neutral currents required by the load. This paper introduces a novel method that utilizes a Linear Kalman Filter (LKF) to generate a reference current for the SAPF, even in the presence of an unbalanced and distorted voltage at PCC. To keep the DC-link capacitor voltage constant, Proportional and Integral (PI) control is used. Notably, this method eliminates the need for a Phase-Locked Loop (PLL), reducing the computational burden on the controller. The proposed approach is robust and exhibits a favorable dynamic response. The MATLAB/Simulink environment is employed to verify the outcomes of the proposed control technique across various voltage and load scenarios
Radiative mass generation in gauged theories of flavour : a path to Fermion mass hierarchies
No full textFlood inundation management in the Narmada Basin: an AIML application for the upstream area of Sardar Sarovar Dam
No full textRecurrent flooding poses a significant threat to various sub-catchments of the Narmada River Basin, one of India's major river systems. This study focuses on the flood-prone sub-catchment area upstream of the Sardar Sarovar Dam, where impacts are particularly severe on tribal communities, forests, and the newly formed reservoir ecosystem. To enhance flood risk management, this research investigates the application of Artificial Intelligence and Machine Learning (AIML) for high-resolution flood inundation mapping. The primary objective is to generate high-resolution flood inundation maps that surpass hydrological modelling in accuracy and spatial detail, enabling precise identification of vulnerable areas within the sub-catchment. A comprehensive dataset, including historical rainfall data (1990-2024) from IMD gridded data and local rain gauges, river discharge records from various gauging stations and a 12.5m resolution Digital Elevation Model (DEM), is used to train and validate AIML models (Artificial Neural Network (ANN), Random Forests (RF), and K-Nearest Neighbor (KNN)). Beyond flood inundation, the models were employed to simulate the effects of various flood control measures, including optimized reservoir operation, embankment construction, and afforestation, to inform optimal implementation strategies. The results are expected to demonstrate the superior performance of AIML in capturing and predicting future flood inundations in the region. Based on error calculation, the performance of combined models is expected to be better than that of individual models. The findings will help develop targeted early warning systems, improved land-use planning, and evidence-based decision-making for sustainable flood risk management in the Narmada Basin and contribute to the broader application of AI for disaster risk reduction globally
