Earticle

The rapid growth of electric vehicles and portable electronics has led to a significant increase in the consumption of lithium-ion batteries (LIBs). Consequently, a large number of spent batteries are being generated, posing serious environmental and resource challenges. Among the components of LIBs, separators—typically composed of polyolefin materials—are difficult to degrade and contribute to microplastic pollution when improperly disposed. However, these separators still retain a relatively intact physical structure after use, making them valuable candidates for recycling and reuse in next-generation energy storage systems. To address both environmental concerns and resource efficiency, this study explores a high-performance recycling strategy for spent LIB separators using electrospinning-based surface modification. Electrospinning offers several advantages, including low cost, high controllability of fiber morphology, and strong adhesion to substrate surfaces. It enables the fabrication of nanofiber layers with tunable porosity, mechanical strength, and wettability, which are crucial for restoring or even enhancing the performance of used separators. Experimental results show that spent separators exhibit a significant decline in high-rate (C-rate) discharge performance compared to fresh ones. Long-term usage appears to reduce the thickness of the separators, and their surfaces show signs of corrosion due to prolonged contact with battery electrolytes. FE-SEM analysis further reveals that the used separators consist of randomly distributed fibers with non-uniform diameters and surface contamination from particulate debris, indicating the necessity of targeted surface modification. Moreover, TGA results suggest that while thermal degradation is minimal below 100°C, decomposition begins at temperatures exceeding 100°C, highlighting the importance of future thermal resistance enhancement strategies. Based on our investigation of the fundamental physicochemical properties of spent separators, we propose an eco-friendly surface modification strategy utilizing electrospinning techniques. Specifically, we aim to coat the degraded separators using a composite nanofiber layer composed of biodegradable polyvinyl alcohol (PVA), recycled carbon fiber-reinforced polymer (rCFRP), and thermally stable aluminum oxide (Al₂O₃) additives. This surface treatment will be applied via electrostatic spraying methods, which allow uniform deposition and strong interfacial bonding. The incorporation of rCFRP enhances mechanical robustness, while Al₂O₃ improves thermal resistance, addressing the degradation issues observed in spent separators. This approach is expected to significantly improve the safety, durability, and reusability of separators in regenerated lithium-ion batteries, and contributes to the advancement of carbon-neutral and circular energy storage technologies.

Lithium-ion secondary batteries(LIBs) are currently the most significant commercially accessible battery type for applications i.e., electric devices, e-mobility, energy storage systems. Despite the great advantages of LIBs, numerous limitations still exist such as high cost, safety risks, limited lifespan, environmental ethical issues, and so on. Therefore, the recycling technologies of the LIBs are strongly demanded. In this review paper, the working processes, metal recovery, disadvantages, advantages of current recycling technologies for LIBs are examined via the previous research papers. Additionally, a future developments of the recycling technology are discussed. This review paper will be advantages for the researchers in terms of the LIBs recycling.

This study utilized first-principles calculations to evaluate the d-electron energy levels and bond orders of binary Ti-XM titanium alloys for potential application as dental implant materials. For the Ti-Zr alloy, the Md values range from 2.80631 to 3.03427, and the bond order Bo values range from 2.47383 to 2.84890. In the Ti-Cu alloy, the nMd values range from 2.73779 to 2.20090, and the Bo values range from 2.30180 to 0.80866. For the Ti-Au alloy, the Md values range from 2.76799 to 2.21561, and the Bo values range from 2.38945 to 0.94418. In the Ti-Ag alloy, the Md values range from 2.89325 to 11.1530, and the Bo values range from 2.45666 to 3.22978.

This study examines the effects of propeller pitch angle variation on noise and thrust performance for three composite propellers commonly used in Korean light training aircraft. With increased training operations, noise complaints around airfields have become a growing issue. Experimental results show that each propeller exhibits unique characteristics depending on pitch angle. The “W” propeller generated the lowest noise at 17°–18°, while the “K” propeller performed best at 19°. The “D” propeller consistently produced the highest thrust across all angles. Findings indicate that adjusting pitch angle can effectively balance thrust and noise. In particular, a 19° pitch is recommended for the “D” and “K” propellers to achieve better noise-thrust trade-offs. These results suggest that optimal pitch angle selection can reduce noise impact while maintaining operational efficiency in flight training environments.

This study investigates the effects of offset distance and rotational speed differences between the upper and lower rotors on the aerodynamic performance of coaxial co‑rotating propellers. The test propeller is an 18‑inch diameter, pitch‑6.5‑inch, two‑bladed tapered configuration. Numerical simulations were performed using ANSYS Fluent V2023R1 by solving the incompressible Reynolds‑Averaged Navier–Stokes equations with the Shear Stress Transport (SST) k–ω turbulence model and the Multiple Reference Frame (MRF) approach based on steady-state assumption. The results show that, regardless of rotational speed configuration, increasing the offset distance between the propellers consistently improves the Figure of Merit (FoM). For different speed combinations, increasing the upper rotor speed led to higher inflow velocity into the lower rotor, reducing its thrust and increasing its torque, which in turn decreased the overall FoM. Conversely, when the lower rotor rotated faster, interference effects were mitigated, resulting in improved thrust and torque distribution and a higher FoM. These findings confirm the existence of an optimal rotational speed ratio that balances thrust and efficiency, providing valuable insights for performance optimization in unmanned aerial vehicles (UAVs) and rotorcraft systems.

This study focuses on developing a system that uses drones equipped with Laser Range Finders (LRF) to obtain precise ground coordinates in real time. To meet the requirements for speed and precision in various applications such as surveillance and reconnaissance, search for missing persons, and disaster response, an integrated development approach was taken. This includes drone hardware, position calculation software (S/W), and a user interface (UI) for ground control systems. This technology is expected to maximize the utility of drones in both civilian and military sectors and contribute to enhancing the global competitiveness of domestic drone technology.

This study proposes the development of an AI-based smart desk-and-chair system designed to enhance students’ posture, health, and learning efficiency. In modern smart classrooms, physical environments are optimized, but poor posture still leads to health issues and reduced concentration. The proposed system uses AI technologies such as posture recognition via vision sensors and voice-controlled mode switching. The hardware includes motorized desk and chair adjustments, LED lighting, and a liftable bookshelf, all controlled by Arduino and Raspberry Pi. Each learning mode (focus, rest, reading, sleep) dynamically adjusts furniture position and lighting color temperature. A lightweight AI model enables real-time posture analysis, while voice commands trigger environment changes. The system is tested for accuracy, response speed, and usability. Results show posture recognition accuracy above 90%, confirming its effectiveness. This integrated solution contributes to personalized learning environments and promotes healthier study habits for students.

This study analyzes the concept, policy background, corporate responses, and legal issues surrounding the extension of the retirement age through 14 Korean literature and draws implications. The research results show that, firstly, while extending the retirement age contributes to employment stability for older adults, it also causes problems such as a decrease in youth employment, a decline in labor market flexibility, and limitations in social acceptance. Secondly, it was confirmed that a flexible and differentiated policy design is needed rather than a uniform policy, depending on the differences in effects by industry, company size, and employment type. Based on this study, there is a need to structurally understand the multifaceted impacts of extending the retirement age.

This study explored non-cognitive factors affecting early stroke symptom recognition in Korean working-class males with diabetes (n=4125) using machine learning on KNHANES data (2016-2022). Younger age, higher BMI, hypertension, elevated cholesterol and triglycerides, depression, stress, smoking, and alcohol consumption were significant predictors of non-recognition. Regular physical activity and diabetes education participation were positive factors. These findings highlight the multifactorial nature of stroke symptom recognition, suggesting targeted interventions addressing physiological, psychological, and lifestyle aspects are crucial for improving outcomes in this population.

This study was initiated in response to an incident that occurred at Muan Airport, with the goal of finding a more effective and faster method for aircraft deceleration in emergency situations. While conventional spoilers are primarily designed to assist deceleration during landing, their simple structure limits the amount of drag they can generate in unexpected situations. To address this limitation, we designed a curved, deployable spoiler system that can normally adjust drag but additionally deploy in four extra stages during emergencies to significantly increase aerodynamic resistance. The curved geometry was modeled using CATIA and converted into an STL file for 3D printing to create a prototype. The initial mechanical reliability of the actuating components was verified through physical testing. The curved structure is advantageous for generating high drag coefficients by intentionally disturbing airflow, causing flow separation and increasing resistance. Future work includes wind tunnel testing and CFD analysis to quantitatively evaluate the increase in drag coefficient and determine how effectively the aircraft can be decelerated and stabilized across different speeds and flight attitudes. If successfully implemented, this system could contribute significantly to enhancing flight safety by ensuring rapid deceleration and attitude stability during emergency landings or other critical scenarios.

This study aims to design a three-point hitch system attachable to a compact electric cart to improve the efficiency of small-scale farming. The system is configured to control agricultural implements using a linear actuator and an angle adjustment bracket. Structural analysis using ANSYS and dynamic simulation using ADAMS verified the strength and stability of the system. S50C material was found to be suitable for high-load agricultural tools such as plows.

This study focuses on the development of a modular robotic arm system, with particular emphasis on the design of the robotic arm and grippers, as well as AI-based control. The research aims to automate crop harvesting tasks by developing a robotic arm and gripper system designed with a modular structure to adapt to various operational environments and targets. The robotic arm is designed to be compatible with different platforms, and the end-effectors (grippers) are replaceable depending on the task requirements. To this end, three types of grippers were developed: a mechanical (claw-type) gripper, a spiral continuum gripper, and an electromagnetic gripper. The motion characteristics and structural features of each gripper were analyzed using ADAMS software to evaluate their feasibility in performing actual harvesting tasks and their structural responses. The proposed system is applicable in a modular form to various platforms, including drones. It is expected to serve as foundational research for the development of robotic systems capable of performing diverse functions in agricultural automation beyond harvesting.

This study aims to develop an autonomous driving-based system for the automatic monitoring and enforcement of illegal parking in urban areas. The system is built on a small, four-wheel-drive autonomous robot that navigates by recognizing road-edge lines. Using line sensors mounted on its underside, the robot detects lane markings and controls its steering mechanism to perform smooth, vehicle-like turns, allowing for natural, curved path adjustments instead of abrupt right-angle rotations. In addition, the system integrates ultrasonic sensors for obstacle avoidance and an ESP32-CAM-based camera module for license plate recognition, enabling real-time detection of illegally parked vehicles. The detected information is transmitted to a server for recording and further response, thereby overcoming the limitations of conventional manpower-based enforcement and contributing to a more efficient and sustainable urban traffic management system.

This research proposes a React-ROS2 interactive navigation system for indoor delivery robots. Traditional ROS2 interfaces require complex commands and specialized knowledge, creating barriers for non-expert users. Our system addresses this issue by providing intuitive button-based destination selection using React, enabling autonomous navigation through simple clicks instead of complex parameter settings. The system implements efficient communication between React frontend and ROS2 backend using Rosbridge websocket protocol, allowing real-time data exchange. We validated the system in a Gazebo virtual environment, demonstrating successful integration of user-friendly interface with ROS2 navigation stack. This methodology enhances operational efficiency of indoor delivery robots and improves accessibility for users without specialized knowledge, contributing to broader adoption of robotic technology.

This study proposes a system that non-invasively predicts electrocardiogram (ECG) and photoplethysmography (PPG) biosignals through a multimodal deep learning model utilizing driver image information and time-series characteristics. As the first step toward responding to cardiac emergencies while driving, we developed a multimodal architecture combining Vision Transformer (ViT) and LSTM. The proposed system optimizes prediction performance through feature engineering that includes facial region analysis, RGB and HSV color feature extraction, and utilization of time-series patterns in biosignals. Experimental results demonstrate that the proposed model achieves excellent performance with R² of 0.998 and MAE of 0.014 for ECG prediction, and R² of 0.992 and MAE of 0.018 for PPG prediction. These results suggest that the proposed multimodal approach is effective in predicting driver biosignals. This study establishes the foundation for a non-invasive monitoring system that can provide notifications for cardiac emergencies while driving and suggests the possibility of expansion into a real-time anomaly detection and early warning system in the future.

This study proposes an environment-adaptive sensor weight fusion model for autonomous driving systems that automatically optimizes the weights between LiDAR and camera sensors based on environmental conditions. Using an F1TENTH autonomous driving platform, we implemented a dual-branch neural network that processes data from 2D LiDAR and monocular camera sensors and combines their outputs with learnable weights. Through extensive experiments in various indoor environments, we found that the optimal sensor weight ratio in general conditions was approximately 64% for LiDAR and 36% for camera sensors. However, these weights dynamically adjusted based on environmental changes, with LiDAR reliance increasing in low-light conditions and camera influence growing in well-lit environments. Our results highlight the importance of dynamic sensor fusion in autonomous driving systems and provide insights into optimal weight distributions across different driving scenarios. Future work will extend this approach to larger-scale platforms with additional sensors such as GPS.

This study proposes a structural improvement to the overhead baggage compartments in commercial aircraft cabins by changing the baggage insertion direction from vertical to horizontal, while maintaining the original upper installation position. The existing vertical-insertion design poses difficulties for elderly, short-statured, or physically limited passengers and often causes aisle congestion and boarding delays. To address these issues, a horizontal-insertion model was designed using CATIA V5 and manufactured prototype using an 3D printer. The proposed design enhances accessibility, improves storage capacity, reduces boarding time, and decreases crew workload. It also offers practical benefits for airlines by enabling quicker passenger flow and potentially increasing turnaround efficiency. Since the modified structure retains the original mounting points, it minimizes certification and implementation costs. Overall, this design presents a user-friendly and operationally efficient alternative for aircraft interior layouts, especially we expect in line with universal design and next-generation cabin standards.