Biomimetics

Osseointegration in dental implants involves the use of materials that mimic the bone tissue, with special properties such as biocompatibility and biodegradability. In this study, we describe the preparation and characterization of composites based on collagen, chondroitin sulfate, and sage oil obtained by freeze-drying method. Their morphological structures were determined by water uptake and scanning electron microscopy, the physical–chemical interactions between components by FT-IR, the stability by in vitro collagenase degradation, and the results indicate that the samples’ properties are highly influenced by the hydrophobic and hydrophilic character of sage essential oil and chondroitin sulfate, respectively, concluding that we can design a formulation with certain properties. The composite spongious forms were evaluated for cytocompatibility using the MG63 osteoblast cell line and subjected to histological observation. The results showed that the samples with sage essential oil were most resistant to enzymatic degradation, and the ones with chondroitin sulfate promoted the deposition of an abundant extracellular matrix. Taken together, the results suggest that incorporating chondroitin sulfate and sage oil in a controlled manner into collagen scaffolds represents a promising approach for enhancing bone tissue regeneration.

This paper proposes a Gudermannian function-based proportional–integral–derivative (G-PID) controller to enhance the transient performance of automatic voltage regulator (AVR) systems operating under highly dynamic conditions. By embedding the smooth and bounded nonlinear mapping of the Gudermannian function into the classical PID structure, the proposed controller improves adaptability to large signal variations while effectively suppressing overshoot. The controller parameters are optimally tuned using the starfish optimization algorithm (SFOA), which provides a robust balance between exploration and exploitation in nonlinear search spaces. Simulation results demonstrate that the SFOA-optimized G-PID controller achieves superior transient performance, with a rise time of 0.0551 s, zero overshoot, and a settling time of 0.0830 s. Comparative evaluations confirm that the proposed approach outperforms widely used optimization algorithms (particle swarm optimization, grey wolf optimizer, success history-based adaptive differential evolution with linear population size, and Kirchhoff’s law algorithm) and advanced AVR control schemes, including fractional-order and higher-order PID-based designs. These results indicate that the proposed SFOA optimized G-PID controller offers a computationally efficient and structurally simple solution for high-performance voltage regulation in modern power systems.

Global air transport has become the dominant mode of long-distance travel, carrying more than four billion passengers in 2019 and projected to exceed 8 billion by 2040. Nevertheless, limited demand and economic inefficiencies often make direct connections unfeasible, forcing many passengers to rely on transfers. In such cases, synchronizing arrivals and departures at hub airports is crucial to minimizing transfer times and maximizing passenger retention. This study investigates the synchronization problem at Istanbul Airport, one of the world’s largest hubs, using metaheuristic optimization. Three algorithms—Genetic Algorithms (GA), Modified Discrete Particle Swarm Optimization (MDPSO), and Evolutionary Strategies (ES)—were applied in parallel to optimize arrival and departure schedules for a major airline. The proposed chromosome-based framework was tested through parameter tuning and validated with statistical analyses, including ANOVA and Games–Howell pairwise comparisons. The results show that MDPSO achieved strong improvements, while ES consistently outperformed both GA and MDPSO, increasing successful passenger transfers by more than 200% compared to the original schedule. These findings demonstrate the effectiveness of evolutionary metaheuristics for large-scale airline scheduling and highlight their potential for improving hub connectivity. This framework is generalizable to other hub airports and airlines, and future research could extend it by integrating hybrid metaheuristics or applying enhanced forecasting methods and more dynamic scheduling approaches.