Gokul Global University

This study introduces an AI-driven integrated framework for predicting and optimizing the performance of turbo air classifiers, addressing the limited application of advanced intelligence techniques in fine-particle processing. A turbo air classifier was examined using three operational inputs, rotor speed (561–1739 rpm), primary air flow (98.87–351.13 m3/h), and secondary air flow (6–74 m3/h), to predict two key performance indicators: cut size (CS) and classification accuracy index (CAI). Multilayer perceptron neural networks (MLPNNs) were optimized using modified particle swarm optimization (MPSO), marine predators algorithm (MPA), and gray wolf optimizer (GWO). MPSO-MLPNN yielded the best CS predictions (R > 0.999), while GWO-MLPNN achieved the most accurate CAI predictions (R > 0.99). Pareto-based multi-objective bat algorithm (MOBA) was then applied to minimize CAI while constraining CS within 15–18 μm and 18–21 μm. The Pareto results revealed a clear trade-off: CAI decreased from ∼2.30 to ∼1.65 as CS increased slightly in the fine separation regime and stabilized at ∼1.58–1.60 for coarser separation. Optimal conditions showed that fine separation requires high rotor speed with moderate–high airflow, whereas coarser, energy-efficient operation is achievable with lower rotor speeds and high airflow.

The persistent global burden of tuberculosis (TB) and the context-dependent efficacy of the Bacillus Calmette–Guérin (BCG) vaccine necessitate the development of innovative prophylactic strategies. mRNA vaccine platforms have emerged as a transformative toolkit, offering unprecedented versatility in antigen design and manufacturing scalability. This inclusive innovation review synthesizes the molecular engineering and immunological mechanisms of mRNA TB vaccines, evaluating their capacity to address the unique challenges posed by the intracellular lifestyle of Mycobacterium tuberculosis (Mtb). mRNA platforms realistically offer superior endogenous antigen production for CD8⁺ T-cell activation and the flexibility to encode multi-stage fusion antigens targeting both active and latent bacilli. However, significant constraints remain; mRNA technology alone cannot resolve the spatial sequestration of Mtb within necrotic granulomas or the "recruitment lag" of systemic immunity to the lung parenchyma. Achieving sterile protection requires a transition toward mucosal delivery systems capable of inducing lung TRM cells. Furthermore, translational success must be measured beyond classical interferon-gamma (IFN-γ) readouts, prioritizing correlates of protection that reflect site-specific immunity, safety in latently infected populations, and the deployment of thermostable formulations in endemic regions. By integrating mRNA constructs into heterologous prime-boost regimens and host-directed therapies, the field moves toward a precision vaccinology framework capable of curtailing the TB epidemic.

Microsphere-based technologies have increasingly gained attention as adaptable and multifunctional tools in breast cancer research and clinical care. Traditional approaches, including two‑dimensional cell cultures, systemic drug delivery, and broad diagnostic methods, often fail to accurately mimic tumor behavior or deliver therapeutics efficiently. Microspheres, with their customizable size, composition, mechanical characteristics, and surface chemistry, offer a platform that can overcome many of these limitations. Their ability to modulate cell–material interactions and control local drug release positions them as valuable components in modern cancer modeling, diagnosis, and therapy. The primary aim of this review is to synthesize current advancements in microsphere technologies as they apply to breast cancer. The review seeks to evaluate how microsphere-based systems contribute to improved tumor modeling, enhanced diagnostic accuracy, and more effective therapeutic strategies. Additionally, it identifies existing challenges and defines future directions needed to translate these technologies into routine clinical use. This review explores microsphere technologies in breast cancer research and clinical applications. It highlights their roles in three‑dimensional tumor modeling, advanced diagnostic platforms integrating imaging, electrochemical sensors, and microfluidics, and controlled drug delivery systems for chemotherapeutic and endocrine therapies. The review also discusses stimuli‑responsive microspheres enabling targeted release and clinical uses such as transarterial chemoembolization and yttrium‑90 radioembolization. Evidence from experimental, preclinical, and clinical studies is synthesized to evaluate current progress and future potential. Findings indicate that microsphere-enabled three‑dimensional tumor culture systems better replicate key hallmarks of breast cancer biology compared to traditional two‑dimensional platforms. These 3D systems capture tumor architecture, mechanobiology, metabolic diversity, and drug resistance behavior more accurately, improving the predictive value of drug screening. Diagnostic innovations demonstrate that functionalized microspheres significantly enhance analytical sensitivity, allow dynamic monitoring of tumor biomarkers, and improve cell tracking capabilities across imaging and sensor-based platforms. Therapeutic applications show that microspheres provide sustained and localized drug delivery, reducing systemic toxicity and enhancing treatment efficacy. Stimuli-responsive microspheres allow precisely targeted and temporally coordinated release, enabling more personalized and adaptive treatment strategies. Microsphere-based technologies represent a powerful and multifaceted toolkit for advancing breast cancer research, diagnosis, and therapy. Their versatility enables improved disease modeling, more sensitive and real-time diagnostics, and effective localized treatment strategies. Despite substantial progress, persistent challenges, such as standardization, biological complexity, reproducibility, scalability, and integration into routine clinical workflows, continue to hinder widespread adoption.

In pursuit of precise thermoelastic dissipation (TED) modeling in advanced small-scale devices, this article establishes a novel scale-dependent approach for TED in nanobeams based on shear deformation beam theories, integrating the nonlocal theory (NT) and Moore-Gibson-Thompson (MGT) heat equation to reflect both mechanical and thermal scale dependencies. Initially, the NT is employed to construct the non-classical equations of motion, after which the MGT-based heat conduction equation is formulated to extract the corresponding temperature profile. The next step involves calculating the real and imaginary parts of the frequency and applying the complex frequency (CF) method to derive the scale-dependent TED response governed by shear deformation formulations. Once the model's accuracy is confirmed, a detailed set of numerical results is presented to elucidate how TED responds to variations in principal parameters. It is apparent from the numerical findings that shear deformation theories significantly influence the TED behavior of nanobeams, particularly in cases involving low aspect ratios.

We report the rational design, synthesis, and comprehensive evaluation of a novel heterogeneous copper(I) nanocatalyst, designated GO/Fe₃O₄–Mel/Tet–CuI, that can be readily recovered by magnetic separation. The material arises from a stepwise functionalization of graphene oxide with Fe₃O₄ nanoparticles, melamine, and tetrazole ligands, followed by immobilization of CuI species, yielding a hybrid organic–inorganic nanocomposite with a high surface area, a nitrogen-rich coordination environment, and superparamagnetic behavior. Extensive characterization by FT-IR, XRD, SEM, TEM, EDX, ICP-OES, BET, TGA, and VSM confirmed successful construction, preserved structural integrity, thermal stability, and robust magnetic properties. The catalyst was then applied to the three-component synthesis of N-arylsulfonamides via sulfur dioxide insertion, using potassium metabisulfite as a safe and economical SO₂ surrogate. Under optimized conditions, a broad array of aryl iodides and amines were converted to the corresponding sulfonamides in high yields (83–98