We provide here structural features and the optical properties of Niobium (V) Oxide nanoparticles (NPs) synthesized by thermolysis method. It A quick and affordable technique to produce NPs has been found, and it was utilized to create Niobium (V) Oxide NPs using a set amount of precursor Niobium pentachloride and a variable amount of surfactant Polyvinylpyrrolidone (PVP). Utilizing X-ray diffraction (XRD), Field Emission Scanning Electron microscopy (FESEM), Fourier Transform Infrared Spectroscopy (FT-IR), and Ultraviolet-Visible (UV-Vis) spectroscopic techniques and Raman Spectroscopy, NPs were thoroughly studied. XRD and Raman Spectroscopy were employed to verify the crystalline quality and stoichiometry of these nanoparticles. The average crystallite size of nanoparticles calculated from XRD analysis was observed to be 18-30nm. Measurements of diffuse reflectance spectroscopy in the ultraviolet-visible region indicate that the optical band gap values (similar to 3.10eV, 3.11eV, 3.13eV, and 3.14eV) varied during the crystal formation process. The orthorhombic phase of the material is shown by the Raman vibrational modes. The material's vibrational information is collected by FT-IR, which identifies the various modes of stretching vibration of oxygen and niobium in the spectrum. It was found that all samples exhibited the fingerprint stretching vibrations within a particular range. The work identifies the characteristics of Niobium (V) Oxide nanoparticles that make the material adaptable, enabling its use in high-power batteries, sensors, and supercapacitors. PVP's effectiveness as a capping agent for nanoparticle formation is also described.
A K-tapered two-frequency undulator configuration is proposed to explore its potential advantages over conventional helical wiggler-based inverse free-electron lasers. The conventional helical wiggler based inverse free electron configuration provides efficient acceleration in the optical regime at low electron energies, it becomes fundamentally limited at high electron beam energy due to radiation losses and resonance detuning. The two-frequency tapered undulator overcomes these limitations through dynamic resonance control, leading to sustained acceleration even at high electron beam energies. We presents an analytically derived dual-frequency resonance tapered inverse free electron laser configuration that modifies the longitudinal phase dynamics and enhances energy extraction compared to conventional helical wiggler inverse free electron laser configurations. The theoretical research incorporates the Lorentz force equation, generalized Bessel functions, and energy exchange relations to derive analytical expressions for electron trajectories, acceleration gradient. This study provides for a comprehensive evaluation of how tapering with a dual-frequency undulator affects beam-wave interaction. The results demonstrate that the resonance tapered two-frequency undulator scheme provides a significantly higher acceleration gradient than conventional configurations, thereby enabling more efficient energy transfer between the electron beam and the radiation field. In addition, the design offers an alternative to the helical wiggler inverse free electron laser, providing a simpler geometry for the inverse free electron laser accelerator. This analytical treatment underscores the capability of the proposed approach to advance IFEL accelerator technology by offering enhanced efficiency and performance compared with conventional wiggler-based systems.
Medical science has worked proactively for long years—sees symptoms, treat them. Yet, the deployment of AI is changing this to a creative predictive environment [1]. This review questions that transition, from being physician subjective to highly-precision data based diagnosis [2]. We review the literature in the field on Machine Learning (ML) and Deep Learning (DL) which suggests that models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks are obtaining diagnostic accuracies of 90% and up in areas such as oncology and neurology[3]. But beyond the technical marvels we dive into the uncomfortable fault lines: A vast "black box" of algorithmic decision-making The danger of data-bias Why there’s a desperate need for XAI (Explainable AI)[4]. Finally, this article envisioned one future in which rather than replacing the clinician, AI helps them via precision medicine and real-time surveillance[5].
Purpose: This paper seeks to reframe the ongoing scholarly and policy discussions surrounding the societal obligations associated with artificial intelligence (AI). While governments, regulatory bodies, researchers, media institutions, and end-users have been repeatedly examined as responsible actors in the AI ecosystem, the corporate sector has received comparatively limited conceptual attention. This study, therefore, advances a rearticulated understanding of corporate responsibility by proposing a dedicated principle that anchors how businesses ought to confront the ethical and societal implications of AI technologies. Design/methodology/approach:The paper develops a conceptual argument structured around two analytical moves. First, it positions AI governance concerns within the broader evolution of corporate social responsibility (CSR), arguing that established CSR frameworks are no longer sufficient in the AI-driven business landscape. Second, it theorizes what an updated CSR principle-one explicitly oriented toward the development and use of AI—should encompass, drawing on emerging ethical norms, socio-technical debates, and global policy trends. Findings: CSR principles have historically been shaped by prevailing ethical priorities and societal expectations. As AI systems increasingly mediate decision-making, operations, and stakeholder relationships, corporations must account for the preservation of meaningful human participation and value in AI-infused environments. This paper conceptualizes this obligation as the principle of human relevance, a more pragmatic and future-oriented alternative to the frequently invoked-but often impractical-ideal of human centrality. Similar to other CSR norms (e.g., integrity initiatives, anti-corruption measures, and transparency commitments), this AI-related principle may begin as a voluntary corporate standard but could crystallize into firmer regulatory expectations as societal demands evolve. Originality/value: This study is among the first to argue systematically for the expansion of CSR principles to explicitly address AI. The formulation of human relevance as the normative core of such a principle offers a fresh intellectual lens, challenging existing assumptions and providing a more actionable orientation for corporate AI responsibility.
This paper presents a Deep Neural Network (DNN)-based modeling framework for accurately predicting the current–voltage (I–V) characteristics of AlInGaN/GaN High Electron Mobility Transistors (HEMTs). The proposed model leverages the nonlinear learning capability of DNNs to capture complex relationships between fabrication parameters. These include aluminum (Al) and indium (In) mole fractions, barrier thickness, and gate dimensions. The quaternary AlInGaN barrier layer improves polarization control and the formation of a two-dimensional electron gas (2DEG), which requires robust modeling for performance optimization. A three-layer DNN architecture was trained on experimental I–V datasets and validated against multiple published device structures. The model demonstrates excellent agreement with measured data, significantly outperforming conventional compact models in both accuracy and adaptability. This approach provides a cost-effective, scalable, and data-driven alternative for modeling advanced gallium nitride (GaN) HEMTs, offering improved convergence, reduced development time, and potential for broader application in GaN-based power and RF electronics. The model achieves an RMSE of 0.004 and an MAE of 0.011, outperforming previously reported machine learning and hybrid models.
