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We report gamma radiation-induced effects on the electrical properties of low-cost and easily scalable synthesis of TiO 2 nanorods of diameter ~ 20–80 nm on fluorine-doped tin oxide (FTO)-coated glass substrates using the hydrothermal method. X-ray diffraction studies show a rutile crystallographic phase which is highly textured along the (002) crystallographic plane. These nanorods are uniformly grown on FTO/glass substrate and nearly aligned normally to the surface plane. Raman studies confirm the observed rutile phase, showing all probable Raman vibrational modes. The bandgap of the synthesized TiO 2 nanorods is around ~ 3.14 eV. 60 Co gamma radiation source is used for irradiating gamma doses from 375 mGy to 3000 mGy. We observed that the leakage current increases linearly with increasing gamma radiation dose. These findings suggest that TiO 2 nanorods on FTO/glass substrates can be used for efficient gamma radiation dosimetry.

Introduction Chronic liver disease (CLD) frequently causes systemic complications, including acid-base disturbances, significantly influencing patient prognosis. Arterial blood gas (ABG) analysis is traditionally utilized to monitor these disturbances, but presents procedural risks, especially in patients with coagulopathies, which is a well-known complication of chronic liver disease (CLD). Venous blood gas (VBG) analysis has emerged as a safer alternative, yet its prognostic significance in CLD requires further investigation. Aim & objective This study aimed to evaluate the prognostic significance of venous blood gas parameters in chronic liver disease patients. The objectives included assessing VBG parameters in CLD patients, correlating these parameters with disease severity using the Child-Turcotte-Pugh (CTP) score, and determining their predictive role in patient outcomes and mortality. Methodology A hospital-based observational cross-sectional study was conducted from November 2022 to January 2025 at S. N. Medical College, Agra, including 253 patients diagnosed with CLD. VBG parameters such as pH, pCO₂, bicarbonate (HCO₃⁻), base excess, and serum lactate were measured upon admission. Clinical severity was classified using the CTP score, and outcomes including complications, hospital stay, and mortality were analyzed. Data analysis employed descriptive and inferential statistical methods using SPSS version 26 (IBM Corp., Armonk, NY, USA). Results Among participants, 63.24% were male, with alcohol-related liver disease being the most common etiology (47.8%). Significant differences were observed in VBG parameters across CTP classes, with lower pH (7.21±0.09) and bicarbonate (16.3±4.1 mmol/L) and elevated lactate (4.9±1.7 mmol/L) in CTP Class C (p<0.001). Strong correlations existed between lactate levels and CTP scores (r=0.82, p<0.001). Elevated lactate (>4.5 mmol/L) was the most potent independent mortality predictor (adjusted HR=7.1). Kaplan-Meier analysis showed significantly decreased 30-day survival rates with increased lactate and worsening CTP class (p<0.001). Conclusion VBG parameters, particularly elevated lactate and decreased pH, provide critical prognostic information in CLD patients, correlating strongly with disease severity and mortality. Incorporating VBG analysis in routine clinical practice enhances risk stratification and management of hospitalized CLD patients, offering a safer, reliable, and practical alternative to arterial blood gas analysis.

In the present work, we propose a novel hybrid explicit jump immersed interface approach in conjunction with a higher order compact (HOC) scheme for simulating transient complex flows governed by the streamfunction-vorticity (\\(\\psi\\)-\\(\\zeta\\)) formulation of the Navier-Stokes (N-S) equations for incompressible viscous flows. A new strategy has been adopted for the jump conditions at the irregular points across the interface using Lagrangian interpolation on a Cartesian grid. This approach, which starts with the discretization of parabolic equations with discontinuities in the solutions, source terms and the coefficients across the interface, can easily be accommodated into simulating flow past bluff bodies immersed in the flow. The superiority of the approach is reflected by the reduced magnitude and faster decay of the errors in comparison to other existing methods. It is seen to handle several fluid flow problems having practical implications in the real world very efficiently, which involves flows involving multiple and moving bodies. This includes the flow past a stationary circular and a twenty-four edge cactus cylinder, flows past two tandem cylinders, where in one situation both are fixed and in another, one of them is oscillating transversely with variable amplitude in time. To the best of our knowledge, the last two examples have been tackled for the first time by such an approach employing the \\(\\psi\\)-\\(\\zeta\\) formulation in finite difference set-up. The extreme closeness of our computed solutions with the existing numerical and experimental results exemplifies the accuracy and the robustness of the proposed approach.

In this paper we carry out a numerical investigation of forced convection heat transfer from a heated elliptical cylinder in a uniform free stream with angle of inclination \\(\\theta^{\\circ}\\). Numerical simulations were carried out for \\(10 \\leq Re \\leq 120\\), \\(0^{\\circ} \\leq \\theta \\leq 180^{\\circ}\\), and \\(Pr = 0.71\\). Results are reported for both steady and unsteady state regime in terms of streamlines, vorticity contours, isotherms, drag and lift coefficients, Strouhal number, and Nusselt number. In the process, we also propose a novel method of computing the Nusselt number by merely gathering flow information along the normal to the ellipse boundary. The critical \\(Re\\) at which which flow becomes unsteady, \\(Re_c\\) is reported for all the values of \\(\\theta\\) considered and found to be the same for \\(\\theta\\) and \\(180^\\circ -\\theta\\) for \\(0^\\circ \\leq \\theta \\leq 90^\\circ\\). In the steady regime, the \\(Re\\) at which flow separation occurs progressively decreases as \\(\\theta\\) increases. The surface averaged Nusselt number (\\(Nu_{\\text{av}}\\)) increases with \\(Re\\), whereas the drag force experienced by the cylinder decreases with \\(Re\\). The transient regime is characterized by periodic vortex shedding, which is quantified by the Strouhal number (\\(St\\)). Vortex shedding frequency increases with \\(Re\\) and decreases with \\(\\theta\\) for a given \\(Re\\). \\(Nu_{\\text{av}}\\) also exhibits a time-varying oscillatory behaviour with a time period which is half the time period of vortex shedding. The amplitude of oscillation of \\(Nu_{\\text{av}}\\) increases with \\(\\theta\\).

We present a new higher-order accurate finite difference explicit jump Immersed Interface Method (HEJIIM) for solving two-dimensional elliptic problems with singular source and discontinuous coefficients in the irregular region on a compact Cartesian mesh. We propose a new strategy for discretizing the solution at irregular points on a nine point compact stencil such that the higher-order compactness is maintained throughout the whole computational domain. The scheme is employed to solve four problems embedded with circular and star shaped interfaces in a rectangular region having analytical solutions and varied discontinuities across the interface in source and the coefficient terms. We also simulate a plethora of fluid flow problems past bluff bodies in complex flow situations, which are governed by the Navier-Stokes equations; they include problems involving multiple bodies immersed in the flow as well. In the process, we show the superiority of the proposed strategy over the EJIIM and other existing IIM methods by establishing the rate of convergence and grid independence of the computed solutions. In all the cases our computed results extremely close to the available numerical and experimental results.

This work introduces a novel Lagrangian-based framework to analyze forced convective heat transfer in the unsteady wake of a heated elliptical cylinder inclined at angles ranging from \\(\\theta = 0^\\circ\\) to \\(90^\\circ\\), in \\(15^\\circ\\) increments with \\(Pr = 0.71\\) at a fixed Reynolds number of \\(Re = 100\\). The framework correlates the temporal evolution of the surface-averaged Nusselt number with the dynamic behavior of Lagrangian saddle points, formed at the intersection of repelling and attracting Lagrangian Coherent Structures (LCSs) extracted via Finite-Time Lyapunov Exponent (FTLE) fields.The study is carried out within a precisely constructed observational domain, a previously unreported influential region in the near-wake, where the trajectory analysis of the newly defined key saddle points (active saddle points) consistently aligns with the trends in surface heat transfer. This domain enables predictive identification of key transitional events in the Nusselt number profile, including local extrema and slope inflections, across all inclination angles. The analysis reveals that oblique displacement of active saddle points enhances heat transfer by promoting the shedding of repelling LCSs, while parallel displacement leads to weakened heat transfer due to the delayed detachment of repelling coherent structures. The proposed framework enables the construction of a temporal function that closely replicates the monotonicity and transitional features of the Nusselt number evolution. Furthermore, threshold displacement metrics are defined for dominant repelling LCSs to correspond with peak heat transfer efficiency. The proposed methodology not only generalizes across a wide range of inclination angles but also provides a physically interpretable framework for predicting heat transfer enhancement based on coherent structure evolution in unsteady flows.

A Jacobian free Newton Krylov (JFNK) method with a globalization scheme is introduced to solve large and complex nonlinear systems of equations that arise in groundwater flow models of multi-layer aquifer systems. We explore the advantages of the JFNK method relative to the Newton-Krylov (NK) method and identify the circumstances in which the JFNK method demonstrates computing efficiency. We perform the validation and efficiency of the JFNK method on various test cases involving an unconfined single-layer aquifer and a two-layer aquifer with both confined and unconfined conditions. The results are validated by the NK method. The JFNK method is incorporated in Integrated Water Flow Model (IWFM), an integrated hydrologic model developed and maintained by California Department of Water Resources. We examine the determinacy of the JFNK's adaptability on practical models such as the California Central Valley Groundwater-Surface Water Simulation Model (C2VSim).

Low-Rank Adaptation (LoRA) is a popular technique for efficient fine-tuning of foundation models. However, applying LoRA in federated learning environments, where data is distributed across multiple clients, presents unique challenges. Existing methods rely on traditional federated averaging of LoRA adapters, resulting in inexact updates. To address this, we propose Federated Exact LoRA, or FedEx-LoRA, which adds a residual error term to the pretrained frozen weight matrix. Our approach achieves exact updates with minimal computational and communication overhead, preserving LoRA's efficiency. We evaluate the method on various models across arithmetic reasoning, commonsense reasoning, natural language understanding and natural language generation tasks, showing consistent performance gains over state-of-the-art methods across multiple settings. Through extensive analysis, we quantify that the deviations in updates from the ideal solution are significant, highlighting the need for exact aggregation. Our method's simplicity, efficiency, and broad applicability position it as a promising solution for accurate and effective federated fine-tuning of foundation models. Our code is publicly available at https://github.com/RaghavSinghal10/fedex-lora.

Low-Rank Adaptation (LoRA) is a popular technique for efficient fine-tuning of foundation models. However, applying LoRA in federated learning environments, where data is distributed across multiple clients, presents unique challenges. Existing methods rely on traditional federated averaging of LoRA adapters, resulting in inexact updates. To address this, we propose Federated Exact LoRA, or FedExLoRA, which adds a residual error term to the pretrained frozen weight matrix. Our approach achieves exact updates with minimal computational and communication overhead, preserving LoRA's efficiency. We evaluate the method on various models across arithmetic reasoning, commonsense reasoning, natural language understanding and natural language generation tasks, showing consistent performance gains over state-of-the-art methods across multiple settings. Through extensive analysis, we quantify that the deviations in updates from the ideal solution are significant, highlighting the need for exact aggregation. Our method's simplicity, efficiency, and broad applicability position it as a promising solution for accurate and effective federated fine-tuning of foundation models. Our code is publicly available at https://github.com/RaghavSinghal10/fedex-lora.

Reversing a diffusion process by learning its score forms the heart of diffusion-based generative modeling and for estimating properties of scientific systems. The diffusion processes that are tractable center on linear processes with a Gaussian stationary distribution. This limits the kinds of models that can be built to those that target a Gaussian prior or more generally limits the kinds of problems that can be generically solved to those that have conditionally linear score functions. In this work, we introduce a family of tractable denoising score matching objectives, called local-DSM, built using local increments of the diffusion process. We show how local-DSM melded with Taylor expansions enables automated training and score estimation with nonlinear diffusion processes. To demonstrate these ideas, we use automated-DSM to train generative models using non-Gaussian priors on challenging low dimensional distributions and the CIFAR10 image dataset. Additionally, we use the automated-DSM to learn the scores for nonlinear processes studied in statistical physics.