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Privacy-preserving and secure data sharing are critical for medical image analysis while maintaining accuracy and minimizing computational overhead are also crucial. Applying existing deep neural networks (DNNs) to encrypted medical data is not always easy and often compromises performance and security. To address these limitations, this research introduces a secure framework consisting of a learnable encryption method based on the block-pixel operation to encrypt the data and subsequently integrate it with the Vision Transformer (ViT). The proposed framework ensures data privacy and security by creating unique scrambling patterns per key, providing robust performance against leading bit attacks and minimum difference attacks., Comment: 2 pages, 2 figures

Double perovskite halides are promising materials for renewable energy production, meeting the criteria to address energy scarcity issues. As a result, studying these halides could be useful for optoelectronic and solar cell applications. In this study, we investigated the structural, mechanical, thermodynamic, electronic, and optical properties of A2AgIrCl6 (A = Cs, Rb, K) double perovskite halides using density functional theory calculations with the full-potential linearized augmented plane-wave (FP-LAPW) approach, aiming to evaluate their suitability for renewable energy devices. The Goldsmith tolerance factor, octahedral factor, and new tolerance factor have confirmed the cubic stability of the predicted compounds. We have also verified the thermodynamic stability of these compounds by calculating the formation enthalpy, binding energy, and phonon dispersion curves. Additionally, Born-Huang stability requirements on stiffness constants confirmed the mechanical stability of the titled compounds. To predict the accurate optoelectronic properties, we employed the TB-mBJ potential. The electronic band structure calculations revealed that the titled halides exhibit a direct band gap semiconducting nature with values of 1.43 eV, 1.50 eV, and 1.55 eV for Cs2AgIrCl6, Rb2AgIrCl6, and K2AgIrCl6, respectively. Besides, all these compounds showed remarkably low effective electron masses, indicating their potential for high carrier mobility. Furthermore, the optical properties of A2AgIrCl6 (A = Cs, Rb, K) compounds demonstrated very low reflectivity and excellent light absorption coefficients (105 cm-1) in the visible light spectrum, suggesting their suitability as an absorbing layer in solar cells. The photoconductivity and absorption spectra of these compounds validate the accuracy of our band structure results., Comment: 38 pages

Texture image generation has been studied for various applications, including gaming and entertainment. However, context-specific realistic texture generation for industrial applications, such as generating defect textures on railway components, remains unexplored. A mobile-friendly, LLM-based tool that generates fine-grained defect characteristics offers a solution to the challenge of understanding the impact of defects from actual occurrences. We introduce TextureMeDefect, an innovative tool leveraging an LLM-based AI-Inferencing engine. The tool allows users to create realistic defect textures interactively on images of railway components taken with smartphones or tablets. We conducted a multifaceted evaluation to assess the relevance of the generated texture, time, and cost in using this tool on iOS and Android platforms. We also analyzed the software usability score (SUS) across three scenarios. TextureMeDefect outperformed traditional image generation tools by generating meaningful textures faster, showcasing the potential of AI-driven mobile applications on consumer-grade devices., Comment: 6 Pages, 8 figures

Scaling laws dictate that the performance of AI models is proportional to the amount of available data. Data augmentation is a promising solution to expanding the dataset size. Traditional approaches focused on augmentation using rotation, translation, and resizing. Recent approaches use generative AI models to improve dataset diversity. However, the generative methods struggle with issues such as subject corruption and the introduction of irrelevant artifacts. In this paper, we propose the Automated Generative Data Augmentation (AGA). The framework combines the utility of large language models (LLMs), diffusion models, and segmentation models to augment data. AGA preserves foreground authenticity while ensuring background diversity. Specific contributions include: i) segment and superclass based object extraction, ii) prompt diversity with combinatorial complexity using prompt decomposition, and iii) affine subject manipulation. We evaluate AGA against state-of-the-art (SOTA) techniques on three representative datasets, ImageNet, CUB, and iWildCam. The experimental evaluation demonstrates an accuracy improvement of 15.6% and 23.5% for in and out-of-distribution data compared to baseline models, respectively. There is also a 64.3% improvement in SIC score compared to the baselines., Comment: 19 pages, 15 figures, 4 tables

A Digital Twin (DT) replicates objects, processes, or systems for real-time monitoring, simulation, and predictive maintenance. Recent advancements like Large Language Models (LLMs) have revolutionized traditional AI systems and offer immense potential when combined with DT in industrial applications such as railway defect inspection. Traditionally, this inspection requires extensive defect samples to identify patterns, but limited samples can lead to overfitting and poor performance on unseen defects. Integrating pre-trained LLMs into DT addresses this challenge by reducing the need for vast sample data. We introduce DefectTwin, which employs a multimodal and multi-model (M^2) LLM-based AI pipeline to analyze both seen and unseen visual defects in railways. This application enables a railway agent to perform expert-level defect analysis using consumer electronics (e.g., tablets). A multimodal processor ensures responses are in a consumable format, while an instant user feedback mechanism (instaUF) enhances Quality-of-Experience (QoE). The proposed M^2 LLM outperforms existing models, achieving high precision (0.76-0.93) across multimodal inputs including text, images, and videos of pre-trained defects, and demonstrates superior zero-shot generalizability for unseen defects. We also evaluate the latency, token count, and usefulness of responses generated by DefectTwin on consumer devices. To our knowledge, DefectTwin is the first LLM-integrated DT designed for railway defect inspection., Comment: 12 pages, 10 figures, IEEE transaction on consumer electronics

In video analysis, understanding the temporal context is crucial for recognizing object interactions, event patterns, and contextual changes over time. The proposed model leverages adjacency and semantic similarities between objects from neighboring video frames to construct context-aware temporal object embeddings. Unlike traditional methods that rely solely on visual appearance, our temporal embedding model considers the contextual relationships between objects, creating a meaningful embedding space where temporally connected object's vectors are positioned in proximity. Empirical studies demonstrate that our context-aware temporal embeddings can be used in conjunction with conventional visual embeddings to enhance the effectiveness of downstream applications. Moreover, the embeddings can be used to narrate a video using a Large Language Model (LLM). This paper describes the intricate details of the proposed objective function to generate context-aware temporal object embeddings for video data and showcases the potential applications of the generated embeddings in video analysis and object classification tasks.

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape. The advancement of generative artificial intelligence has positioned Multi-modal Large Language Models (MLLMs) as crucial tools for managing healthcare data. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including healthcare data security and freshness issues, affecting the output quality of MLLMs. In this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLMs framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share fresh data, mitigating information asymmetry in data sharing. Finally, we utilize a generative diffusion model-based reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management., Comment: 12 pages, 6 figures

We report the $p+p$ and $p+d$ differential cross sections measured in the SeaQuest experiment for $J/\psi$ and $\psi\left(2S\right)$ production at 120 GeV beam energy covering the forward $x$-Feynman ($x_F$) range of $0.5 < x_F <0.9$. The measured cross sections are in good agreement with theoretical calculations based on the nonrelativistic QCD (NRQCD) using the long-distance matrix elements deduced from a recent global analysis of proton- and pion-induced charmonium production data. The $\sigma_{\psi\left(2S\right)} / \sigma_{J/\psi}$ cross section ratios are found to increase as $x_F$ increases, indicating that the $q \bar{q}$ annihilation process has larger contributions in the $\psi\left(2S\right)$ production than the $J/\psi$ production. The $\sigma_{pd}/2\sigma_{pp}$ cross section ratios are observed to be significantly different for the Drell-Yan process and $J/\psi$ production, reflecting their different production mechanisms. We find that the $\sigma_{pd}/2\sigma_{pp}$ ratios for $J/\psi$ production at the forward $x_F$ region are sensitive to the $\bar{d}/ \bar{u}$ flavor asymmetry of the proton sea, analogous to the Drell-Yan process. The transverse momentum ($p_T$) distributions for $J/\psi$ and $\psi\left(2S\right)$ production are also presented and compared with data collected at higher center-of-mass energies., Comment: 8 pages, 7 figures, with 1 page of supplementary material which has 2 figures

The rapid proliferation of devices in the Internet of Things (IoT) has ushered in a transformative era of data-driven connectivity across various domains. However, this exponential growth has raised pressing concerns about environmental sustainability and data privacy. In response to these challenges, this paper introduces One-shot Federated Learning (OSFL), an innovative paradigm that harmonizes sustainability and machine learning within IoT ecosystems. OSFL revolutionizes the traditional Federated Learning (FL) workflow by condensing multiple iterative communication rounds into a single operation, thus significantly reducing energy consumption, communication overhead, and latency. This breakthrough is coupled with the strategic integration of generative learning techniques, ensuring robust data privacy while promoting efficient knowledge sharing among IoT devices. By curtailing resource utilization, OSFL aligns seamlessly with the vision of green and sustainable IoT, effectively extending device lifespans and mitigating their environmental footprint. Our research underscores the transformative potential of OSFL, poised to reshape the landscape of IoT applications across domains such as energy-efficient smart cities and groundbreaking healthcare solutions. This contribution marks a pivotal step towards a more responsible, sustainable, and technologically advanced future., Comment: This paper is a correction of the published version, in which we corrected the grammatical errors between contexts and highlighted the relationship with "Federated generative learning with foundation models"

This paper presents the phase stability, opto-electronic and thermo-electric behavior of X2AuYZ6 (X = Cs, Rb; Z = Cl/Br/I) double perovskite halides by using the DFT method. The compounds belong to the cubic arrangement and are verified by the tolerance and octahedral factor. Formation enthalpy and binding energy meet the requirements of structural stability. The ductility behavior was also confirmed by the Cauchy pressure, Pugh's ratio, and Poisson's ratio. The positive frequency of phonon dispersion except Rb2AuYI6 compound shows the dynamical stability and the negative formation energy of each identified competing phase confirms the thermo-dynamic equilibrium of all compounds. The band gap values of 2.85(2.91), 2.35(2.40), and 1.74(1.78) eV of Cs2AuYZ6 (Rb2AuYZ6) [Z = Cl, Br, I) double perovskites has been explored in the context of optoelectronic properties, and the results show that these materials might be useful in such devices. The spectral optical response covers the visible-to-UV area, which governs the solar cell and thermo-electric device applications. A comprehensive study of thermo-electric properties such as the thermal conductivity (electrical and electronic part), carrier concentration, thermo-power, and figure of merit was also observed. The investigated compounds [Cs (Rb)-based] exhibit ZT values of 0.51(0.55), 0.53(0.62), and 0.58(0.75) at room temperature with Cl, Br, and I respectively. Additional routine work was also done on the thermo-mechanical characteristics. These studies provide in-depth knowledge of these materials in preparation for their future use.

Recent advancements in cognitive computing, with the integration of deep learning techniques, have facilitated the development of intelligent cognitive systems (ICS). This is particularly beneficial in the context of rail defect detection, where the ICS would emulate human-like analysis of image data for defect patterns. Despite the success of Convolutional Neural Networks (CNN) in visual defect classification, the scarcity of large datasets for rail defect detection remains a challenge due to infrequent accident events that would result in defective parts and images. Contemporary researchers have addressed this data scarcity challenge by exploring rule-based and generative data augmentation models. Among these, Variational Autoencoder (VAE) models can generate realistic data without extensive baseline datasets for noise modeling. This study proposes a VAE-based synthetic image generation technique for rail defects, incorporating weight decay regularization and image reconstruction loss to prevent overfitting. The proposed method is applied to create a synthetic dataset for the Canadian Pacific Railway (CPR) with just 50 real samples across five classes. Remarkably, 500 synthetic samples are generated with a minimal reconstruction loss of 0.021. A Visual Transformer (ViT) model underwent fine-tuning using this synthetic CPR dataset, achieving high accuracy rates (98%-99%) in classifying the five defect classes. This research offers a promising solution to the data scarcity challenge in rail defect detection, showcasing the potential for robust ICS development in this domain., Comment: 26 pages, 13 figures, Springer Journal

Vehicular Metaverses represent emerging paradigms arising from the convergence of vehicle road cooperation, Metaverse, and augmented intelligence of things. Users engaging with Vehicular Metaverses (VMUs) gain entry by consistently updating their Vehicular Twins (VTs), which are deployed on RoadSide Units (RSUs) in proximity. The constrained RSU coverage and the consistently moving vehicles necessitate the continuous migration of VTs between RSUs through vehicle road cooperation, ensuring uninterrupted immersion services for VMUs. Nevertheless, the VT migration process faces challenges in obtaining adequate bandwidth resources from RSUs for timely migration, posing a resource trading problem among RSUs. In this paper, we tackle this challenge by formulating a game-theoretic incentive mechanism with multi-leader multi-follower, incorporating insights from social-awareness and queueing theory to optimize VT migration. To validate the existence and uniqueness of the Stackelberg Equilibrium, we apply the backward induction method. Theoretical solutions for this equilibrium are then obtained through the Alternating Direction Method of Multipliers (ADMM) algorithm. Moreover, owing to incomplete information caused by the requirements for privacy protection, we proposed a multi-agent deep reinforcement learning algorithm named MALPPO. MALPPO facilitates learning the Stackelberg Equilibrium without requiring private information from others, relying solely on past experiences. Comprehensive experimental results demonstrate that our MALPPO-based incentive mechanism outperforms baseline approaches significantly, showcasing rapid convergence and achieving the highest reward.

In this study, the effects of sulfur substitution on the structural, mechanical, electronic, optical, and thermodynamic properties of RbTaO3-xSx have been investigated using the WIEN2k code in the framework of density functional theory (DFT). The cubic phase of RbTaO3 transforms to tetragonal for RbTaO2S and RbTaOS2, the later transforms again to a cubic phase with added sulfur for RbTaS3. The results showed that substituting S for O anions in RbTaO3 effectively decreased the band gap from 2.717 eV to 1.438 eV, 0.286 eV, and 0.103 eV for the RbTaO3,RbTaO2S, RbTaOS2, and RbTaS3 compounds, respectively. The optical constants such as dielectric constants, refractive index, absorption coefficient, photoconductivity, reflectivity and loss function have been calculated and analyzed. The elastic constants and moduli, and their anisotropic nature were also investigated. Finally, the Debye temperature, thermal conductivity, melting temperature, specific capacities and thermal expansion coefficients were computed and analyzed using established formalisms. The reduced band gap (1.438 eV) and high absorption coefficient (~106 cm-1) of RbTaO2S makes it suitable for solar cell applications and for other visible light devices. Reduction of the band gap and phonon thermal conductivity owing to Ssubstitution is expected to enhance thermoelectric performances of the S-containing phases, Comment: 41 pages

In this work, density functional theory (DFT) is used to find out the ground state structures of A2AuScX6 (A= Cs, Rb; X= Cl, Br, I) double Perovskite (DP) halides for the first time. The DP A2AuScX6 halides were studied for their structural phase stability and optoelectronic properties in order to identify potential materials for energy harvesting systems. The stability criteria were verified by computing the formation energy, binding energy, phonon dispersion curve, stiffness constants, tolerance, and octahedral factors. The electronic band structure, carrier effective mass, density of states (DOS), and charge density distribution were calculated to reveal the nature of electronic conductivity and the chemical bonding nature present within them. For Cs2AuScX6 (Rb2AuScX6) [X = Cl, Br, I], the corresponding values of band gap [using TB-mBJ] are 1.88 (1.93), 1.68 (1.71), and 1.30 (1.32) eV. The optical constants (dielectric function, absorption coefficient, refractive index, energy loss function, photoconductivity, and reflectivity) were also calculated to get more insights into their electronic nature. For Cs2AuScX6 (Rb2AuScX6) [X = Cl, Br, I], the absorption coefficient in the visible range are 3.33 (3.45) 105 cm-1, 2.70 (2.81) 105 cm-1, and 2.13 (2.18) 105 cm-1, respectively. We also investigated the thermoelectric properties to predict promising applications in thermoelectric devices. Our calculations revealed high ZT values of 0.92, 1.07, and 1.06 for Cs2AuScX6 (X = Cl, Br, I) and 0.97, 0.99, and 1.01 for Rb2AuScX6 (X = Cl, Br, I) at 300 K. To further aid in predicting any novel materials, routine research was also done on the thermo-mechanical characteristics. The results suggest the compounds considered as potential candidates for use in solar cells and/or thermoelectric devices., Comment: 38

Here we report the structural, elastic, bonding, thermo-mechanical, optoelectronic and superconducting state properties of recently discovered XIr3 (X = La, Th) superconductors utilizing the density functional theory (DFT). The elastic, bonding, thermal and optical properties of these compounds are investigated for the first time. The calculated lattice and superconducting state parameters are in reasonable agreement to those found in the literature. In the ground state, both the compounds are mechanically stable and possess highly ductile character, high machinability, low Debye temperature, low bond hardness and significantly high melting point. The thermal conductivities of the compounds are found to be very low which suggests that they can be used for thermal insulation purpose. The population analysis and charge density distribution map confirm the presence of both ionic and covalent bonds in the compounds with ionic bond playing dominant roles. The calculated band structure and DOS profiles indicate metallic character. Unlike the significant anisotropy observed in elastic and thermal properties, all the optical constants of these compounds exhibit almost isotropic behavior. The optical constants correspond very well with the electronic band structure and DOS features. We have estimated the superconducting transition temperature of the compounds in this work.

We have looked into the structural, mechanical, optoelectronic, superconducting state and thermophysical aspects of intermetallic compound Zr$_3$Ir using the density functional theory (DFT). Many of the physical properties, including direction dependent mechanical properties, Vickers hardness, optical properties, chemical bonding nature, and charge density distributions, are being investigated for the first time. According to this study, Zr$_3$Ir exhibits ductile features, high machinability, significant metallic bonding, a low Vickers hardness with low Debye temperature, and a modest level of elastic anisotropy. The mechanical and dynamical stabilities of Zr$_3$Ir have been confirmed. The metallic nature of Zr$_3$Ir is seen in the electronic band structures with a high electronic energy density of states at the Fermi level. The bonding nature has been explored by the charge density mapping and bond population analysis. The tetragonal Zr$_3$Ir shows a remarkable electronic stability, as confirmed by the presence of a pseudogap in the electronic energy density of states at the Fermi level between the bonding and antibonding states. Optical parameters show very good agreement with the electronic properties. The reflectivity spectra reveal that Zr$_3$Ir is a good reflector in the infrared and near-visible regions. Zr$_3$Ir is an excellent ultra-violet (UV) radiation absorber. High refractive index at visible photon energies indicates that Zr$_3$Ir could be used to improve the visual aspects of electronic displays. All the optical constants exhibit a moderate degree of anisotropy. Zr$_3$Ir has a moderate melting point, high damage tolerance, and very low minimum thermal conductivity. The thermomechanical characteristics of Zr$_3$Ir reveal that it is a potential thermal barrier coating material. The superconducting state parameters of Zr$_3$Ir are also explored.

In this work, density functional theory (DFT) based calculations were performed to compute the physical properties (structural stability, mechanical behavior, electronic, thermodynamic, and optical properties) of synthesized MAX phases Hf2SB, Hf2SC, Hf2SeB, Hf2SeC, Hf2TeB, and the as-yet-undiscovered MAX carbide phase Hf2TeC. Calculations of formation energy, phonon dispersion curves, and elastic constants confirmed the stability of the aforementioned compounds. The obtained values of lattice parameters, elastic constants, and elastic moduli of Hf2SB, Hf2SC, Hf2SeB, Hf2SeC, and Hf2TeB showed fair agreement with earlier studies, whereas the values of the mentioned parameters for the predicted Hf2TeC exhibit a good consequence of B replacement by C. The anisotropic mechanical properties are exhibited by the considered MAX phases. The metallic nature and its anisotropic behavior were revealed by the electronic band structure and density of states. The analysis of the thermal properties Debye temperature, melting temperature, minimum thermal conductivity, and Gruneisen parameter confirmed that the carbide phases were more suited than the boride phases considered herein. The MAX phase response to incoming photons further demonstrated that they were metallic. Their suitability for use as coating materials to prevent solar heating was demonstrated by the reflectivity spectra. Additionally, this study demonstrated the impact of B replacing C in the MAX phases., Comment: 38 pages

Barium titanate, BaTiO3 (BT), materials have been synthesized by two different routes: one ball-mill-derived (BMD) nanopowder and another precursor-derived (PCD) BT synthesis method were used separately to fabricate BT thin films on stainless steel (SS) and quartz substrates by spin coating. Then thin films from both synthesis routes were characterized by Ultraviolet-Visible-Near Infrared (UV-Vis-NIR) Spectroscopy, Field-Emission Scanning Electron Microscopy (FE-SEM), X-ray Diffractometry (XRD), Raman Spectroscopy, and Four-point collinear probe; all carried out at room temperature. Our studies revealed that the PCD synthesis process did not produce the BT phase even under the 900^0C air-annealing condition. In contrast, a homogeneous BT thin film has been formed from the BMD-BT nanopowder. The optical band gap of BMD-BT thin films was found in the 3.10 - 3.28 eV range. Finally, a Graphene-Barium Titanate-Graphene (G-BT-G) structure was fabricated on a SS substrate by spin coating at processing temperatures below 100^0C and characterized by two different pieces of equipment: a Potentiostat/Galvanostat (PG-STAT) and a Precision Impedance Analyzer (PIA). The G-BT-G structure exhibited a capacitance of 8 nF and 7.15 nF, a highest dielectric constant of 800 and 790, and a low dielectric loss of 4.5 and 5, investigated by PG-STAT and PIA equipment, respectively., Comment: 25 pages, 11 Figures

The rare-earth (RE) elements [Holmium (Ho) and Ytterbium (Yb)] doped vanadium pentoxide (V2O5) with a series of doping concentrations (1 mol.%, 3 mol.%, and 5 mol.%) have been successfully synthesized using environment-friendly facile hydrothermal method. The effect of RE ions on the photocatalytic efficiency of doped V2O5 has also been analyzed. The stable orthorhombic crystal structure of doped V2O5 confirms by the X-ray diffraction with no secondary phase, and high-stressed conditions are generated for the 3 mol.%. The crystallite size, strain, and dislocation density are calculated to perceive the doping effect on the bare V2O5. The optical characteristics have been measured using UV-vis spectroscopy. The absorptions are found to be increased with increasing doping concentrations; however, the bandgap remains in the visible range. The photocatalytic properties are examined for the compounds with varying pH, and it is observed that higher efficiency is exhibited for the pH 7 and catalyst concentration 500 ppm. The highest degradation efficiency is found to be 93% and 95% for the 3 mol.% of Ho and Yb-doped V2O5 samples within 2 hours, respectively. It is elucidated that the RE ions significantly impact the catalytic behavior of V2O5, and the mechanism behind these extraordinary efficiencies has been explained thoroughly.

Using density functional theory (DFT), the structural, mechanical, electronic, thermal, and optical properties of Hf2AB (A = Pb, Bi) borides were studied, considering the pressure effect up to 50 GPa. The lattice constants were found to be decreased with increasing pressure wherein the lattice constants at 0 GPa agree well with the reported values. The stability (mechanical and dynamical) of the titled compounds at different pressures was checked. The mechanical behavior was disclosed considering the bulk modulus, shear modulus, Youngs modulus, Pugh ratio, Poissons ratio, and hardness parameter at different pressures. Pugh and Poisson ratios were used to assess the brittleness and ductility of the titled borides. The anisotropic nature of mechanical properties was studied by calculating different indices and plotting 2D and 3D projections of the elastic moduli. The electronic properties were revealed by calculating the band structure, density of states, and charge density mapping at different pressures, wherein the anisotropic nature of the electronic conductivity was noted. We studied the Debye temperature, minimum thermal conductivity, Gruneisen parameter, and melting temperature of the titled borides at different pressures; the results revealed the improvement of the mentioned properties with rising pressure. The important optical constants to disclose the possible relevance in application purposes were investigated; a little pressure effect was noted. The thermal properties suggest that the titled borides could be used as thermal barrier coating (TBC) materials while the reflectivity spectra revealed their suitability to be used as cover materials for protection from solar heating.