In the maritime and shipping industries, there is a growing demand for environment friendly ships due to increasingly stringent environmental regulations. Hybrid ships, which alter propulsion methods to achieve eco-friendliness, are one such solution. These ships integrate auxiliary power sources with conventional mechanical propulsion systems to optimize engine operating points and enhance propulsion performance. However, hybrid electric propulsion systems are mechanisms that maximize performance by assisting in the efficiency of the main power source through an auxiliary power source, and the efficiency can vary greatly depending on the operation of each power source. Therefore, an optimal power control strategy is needed to maximize efficiency. Heuristic-based control strategies are widely used in hybrid electric vehicles and various industries due to their simplicity and computational efficiency. However, they rely on experiential rule-based definitions, which can lead to suboptimal performance. In this study, to overcome these limitations and increase the energy efficiency of hybrid electric propulsion ships, a global optimization algorithm called Dynamic Programming (DP) is applied to the power control problem to derive a global optimal solution, and an improved heuristic control strategy is established through regression using sigmoid and polynomial functions. The improved control strategy ensures that hybrid power distribution comes closer to global optimality under various SOC conditions, thereby enhancing overall energy efficiency. To evaluate the proposed strategy, a simulator developed for performance assessment of hybrid electric propulsion ships was used. A comparison with the results of the existing heuristic-based strategy confirmed a significant improvement in energy efficiency.

Due to tightening regulations on exhaust emissions from ships, there is a growing need to develop electric or hybrid electric propulsion systems to replace conventional diesel-based ship power systems. The hybrid electric propulsion system is suitable for small and medium-sized vessels and its energy efficiency significantly depends on the arrangement of different power sources, power control strategies for energy sources, and energy storage system (ESS). Therefore, an analytical simulation to evaluate the energy efficiency of ships with their structure and control strategies is needed. In this study, a back–forward approach-based efficiency performance analysis model was developed using the Holtrop–Mennen resistance model to calculate ship resistance and power demand based on a given ship’s speed profiles. This model has the advantages of using easily obtainable ship speed profiles as the input and can be modularized for each power source and ESS, incorporating mechanical performance limitations, and allows for rapid analysis. The developed analytical model was applied to a hybrid electric propulsion system in a marine support vessel and its energy efficiency was evaluated by establishing rule-based power control strategies. As a result, the engine efficiency of the hybrid electric propulsion system increased from about 27% to 30% compared to the existing system, and the final effect of reducing fuel consumption by about 10% compared to the existing system was confirmed through the developed simulator. In the future, this analytical model could be utilized to derive the optimal layout of hybrid electric propulsion systems, and to formulate power control strategies.

Keloids are a common form of pathologic wound healing and are characterized by an excessive production of extracellular matrix. This study examined the major contributing mechanism of human keloid pathogenesis using transcriptomic analysis. We identified the upregulation of mitochondrial oxidative stress response, protein processing in the endoplasmic reticulum, and TGF-β signaling in human keloid tissue samples compared to controls, based on ingenuity pathway and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Electron microscopic examinations revealed an increased number of dysmorphic mitochondria and expanded endoplasmic reticulum (ER) in human keloid tissue samples than that in controls. Western blot analysis performed using human tissues suggested noticeably higher ER stress signaling in keloids than in normal tissues. Treatment with tauroursodeoxycholic acid (TUDCA), an ER stress inhibitor, significantly decreased scar formation in rabbit models, compared to normal saline and steroid injections. In summary, our findings demonstrate the contributions of mitochondrial dysfunction and dysregulated ER stress signaling in human keloid formation and the potential of TUDCA in the treatment of keloids.

The accurate assessment of wound size is a critical step in advanced wound care management. This study aims to introduce and validate a Light Detection and Ranging (LiDAR) technique for measuring wound size. Twenty-eight wounds treated from December 2022 to April 2023 at the Chungnam National University Hospital were analyzed. All the wounds were measured using three techniques: conventional ruler methods, the LiDAR technique, and ImageJ analysis. Correlation analysis, linear regression, and Bland–Altman plot analysis were performed to validate the accuracy of the novel method. The measurement results (mean ± standard deviation) obtained using the ruler method, LiDAR technique, and ImageJ analysis were 112.99 ± 110.07 cm², 73.59 ± 72.97 cm², and 74.29 ± 72.15 cm², respectively. The Pearson correlation coefficient was higher for the LiDAR application (0.995) than for the conventional ruler methods (mean difference, −5.0000 cm²), as was the degree of agreement (mean difference, 38.6933 cm²). Wound size measurement using LiDAR is a simple and reliable method that will enable practitioners to conveniently assess wounds with a flattened and irregular shape with higher accuracy. However, non-flattened wounds cannot be assessed owing to the technical limitations of LiDAR.