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A-Kinase anchoring protein 1 (AKAP1) is a multifunctional mitochondrial scaffold protein that regulates mitochondrial dynamics, bioenergetics, and calcium homeostasis by anchoring several proteins, including protein kinase A, to the outer mitochondrial membrane. Glaucoma is a complex, multifactorial disease characterized by a slow and progressive degeneration of the optic nerve and retinal ganglion cells (RGCs), ultimately resulting in vision loss. Impairment of the mitochondrial network and function is linked to glaucomatous neurodegeneration. Loss of AKAP1 induces dynamin-related protein 1 dephosphorylation-mediated mitochondrial fragmentation and loss of RGCs. Elevated intraocular pressure triggers a significant reduction in AKAP1 protein expression in the glaucomatous retina. Amplification of AKAP1 expression protects RGCs from oxidative stress. Hence, modulation of AKAP1 could be considered a potential therapeutic target for neuroprotective intervention in glaucoma and other mitochondria-associated optic neuropathies. This review covers the current research on the role of AKAP1 in the maintenance of mitochondrial dynamics, bioenergetics, and mitophagy in RGCs and provides a scientific basis to identify and develop new therapeutic strategies that could protect RGCs and their axons in glaucoma.

Cyber–physical systems have become critical across industries. They have driven investments in education services to develop well-trained engineers. Education services for cyber–physical systems require the hiring of expert tutors with multidisciplinary knowledge, as well as acquiring expensive facilities/equipment. In response to the challenges posed by the need for the equipment and facilities, a metaverse-based education service that incorporates digital twins has been explored as a solution. However, the issue of recruiting expert tutors who can enhance students' achievements remains unresolved, making it difficult to effectively cultivate talent. This paper proposes a reference architecture for a learner-centric educational metaverse with an intelligent tutoring framework as its core feature to address these issues. We develop a novel explainable artificial intelligence scheme for multi-class object detection models to assess learners' achievements within the intelligent tutoring framework. Additionally, a genetic algorithm-based improvement search method is applied to the framework to derive personalized feedback. The proposed metaverse architecture and framework are evaluated through a case study on drone education. The experimental results show that the explainable AI scheme demonstrates an approximately 30% improvement in the explanation accuracy compared to existing methods. The survey results indicate that over 70% of learners significantly improved their skills based on the provided feedback. [ABSTRACT FROM AUTHOR]

This study aims to accurately estimate the emissions of seven air pollutants (NOX, SOX, CO2, CO, PM2.5, PM10, and NMVOC) from ships for one year (2020) in the busiest port in Korea, Busan Port. To achieve this, a more detailed method for calculating emissions was developed, taking into account factors such as the type of fuel used, mode of operation, and engine output of each ship that entered and left the port. In cases where information on the ship's main engine was not available, a regression analysis was performed to estimate the engine output based on the ship's tonnage. In addition, the output of auxiliary engines was estimated based on the ratio of the output of the main engine to that of the auxiliary engine for recently built ships. Finally, emissions from fishing vessels were also included in the calculations. Thus, in Busan port, the total estimated fuel consumption from 44,315 cases in 2020 was 252,519 t; the consumption during hoteling accounted for 87%. In detail, the emissions of seven air pollutants reflecting the emission factors were analyzed as follows: NOX, 18,323 t; SOX, 16,924 t; CO2, 790.383 t; CO, 714 t; PM2.5, 1484 t, PM10, 1614 t; and NMVOC, 772 t. In addition, this study provides an estimation equation to estimate engine output based on a ship's tonnage, which showed the highest fitness when compared to actual engine output. This methodology can be useful for calculating emissions of air pollutants at the port in detail and promoting marine environment policies. [ABSTRACT FROM AUTHOR]