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>Luteolin, a naturally occurring flavonoid, exhibits potent anticancer activity but is limited by poor aqueous solubility and low oral bioavailability. This study aimed to develop and optimize a niosomal drug delivery system to enhance the solubility, stability, and therapeutic efficacy of luteolin against breast cancer. Luteolin-loaded niosomes were prepared using the ethanol injection method and lyophilized with 2.5
The current study investigates how the addition of zirconium (Zr) influences the mechanical and microstructural properties of composites consisting of aluminum and boron carbide (Al–B₄C) produced by powder metallurgy. To fabricate hybrid composites with improved mechanical properties, Al powder was reinforced with 7 wt% B₄C and additions of Zr in the range of 1–5 wt%. To achieve a uniform metallurgical bonding, the mixed powders were compacted and sintered for 2 h at 600 °C under an argon atmosphere. The influence exerted by Zr on impact energy, hardness, compressive strength, and densification has been investigated, and the results have been correlated to the microstructural features obtained using SEM. The results indicated that the addition of Zr significantly improved the densification and bonding characteristics between B₄C particles and the Al matrix. Indeed, porosity decreased from 2.5% (Al–7% B₄C) to 1.9% (Al– 7% B₄C–4% Zr), suggesting improved diffusion and wettability at the reinforcement–matrix interface. Grain refinement and dispersion strengthening were promoted by the formation of fine Al3Zr dispersoids, which favored a gradual enhancement of the mechanical properties. For 4 wt% Zr, Vickers hardness and compressive strength reached maximum values of 102 VHN and 285 MPa, respectively, and the impact energy also improved with 6.8 J, showing a good compromise between toughness and strength. Particle agglomeration and development of pores were held responsible for the slight deterioration of properties beyond 4 wt% Zr.
The present research examines the mechanical behavior and microstructural features of aluminum matrix composites containing Silicon Carbide (SiC) and boron carbide (B₄C) as reinforcing particles. The matrix material used is LM25 alloy with different SiC percentages (4–12
This study introduces an algorithm for solving fractional financial chaotic systems using Bernstein wavelets. Analyzing fractional-order systems is essential for capturing the complex dynamics of financial markets, as they account for memory effects and chaos, which are prevalent in real-world financial systems. This fractional-order financial chaotic model captures the interaction between memory effects and chaos, thereby providing a deeper understanding of the system's dynamics. The analysis of crucial elements within financial systems, such as interest rates, price indexes, and investment demand, can be effectively performed by converting fractional differential equations into algebraic equations by utilising wavelet approximation techniques, specifically Bernstein wavelets and associated fractional integral operators. Our study demonstrates the robustness of this approach through rigorous computation and a comparative analysis with the Toufik-Atangana method. We incorporated bifurcation maps to verify chaotic behaviors and introduced Lyapunov exponent graphs to gain deeper insights into the stability and dynamic characteristics of our system. Examining the Lyapunov exponents helps us better understand the system's responsiveness to initial conditions and its inherent chaotic dynamics. The key findings confirm the accuracy and efficiency of our method, underscoring its potential to significantly enhance financial modeling. This study's novelty lies in the application of Bernstein wavelets to fractional financial systems, offering a powerful alternative to traditional methods by more effectively capturing memory and chaos. Our work advances the field by not only improving the precision of fractional financial models but also opening new avenues for tackling complex financial challenges in the future.
This study introduces a comprehensive data-driven framework for predicting the compressive strength (CS) of Ultra-High-Performance Concrete (UHPC) through the application of three hybrid ensemble machine learning models; Random Forest-Particle Swarm Optimization (RF-PSO), Adaptive -Boosting PSO (AB-PSO), and Gradient Boosting-PSO (GB-PSO). A substantial dataset comprising 700 UHPC mix designs was employed, incorporating eleven critical input variables, including Cement, Slag, Silica Fume, Fly Ash, Limestone Powder, Water, Aggregate, Fiber, Superplasticizer, and Age. The RF-PSO model demonstrated the highest predictive accuracy, with R2 values of 0.9728 for training and 0.9584 for testing, alongside low error metrics: RMSE = 4.31 MPa, MAE = 3.09 MPa, and MAPE = 5.81
