Plastics, with their versatility and cost-effectiveness, have become indispensable materials across various industries. However, the improper disposal and mismanagement of plastic waste have led to significant environmental issues, including pollution, habitat destruction, and threats to wildlife. To address these challenges, numerous methods for plastic waste sorting and recycling have been developed. While conventional techniques like near-infrared spectroscopy (NIRS) have been effective to some extent, they face difficulties in accurately classifying chemically similar samples, such as polyethylene terephthalate (PET) and PET-glycol (PET-G), which have similar chemical compositions but distinct physical characteristics. This paper introduces an approach that adapts image sensors and deep learning object detection algorithms; specifically, the You Only Look Once (YOLO) model, to enhance plastic waste classification based on the shape of the waste. Unlike conventional methods that rely solely on spectral analysis, our methodology aims to significantly improve the accuracy and efficiency of classifying plastics, especially when dealing with materials having similar chemical compositions but differing physical attributes. The system developed using image sensors and the YOLO model proves to be not only effective but also scalable and adaptable for various industrial and environmental applications. In our experiments, the results are strikingly effective. We achieved a classification accuracy rate exceeding 91.7% mean Average Precision (mAP) in distinguishing between PET and PET-G, surpassing conventional techniques by a considerable margin. The implications of this research extend far and wide. By enhancing the accuracy of plastic waste sorting and reducing misclassification rates, we can significantly boost recycling efficiency. The proposed approach contributes to a more sustainable and efficient plastic waste management system, alleviating the strain on landfills and mitigating the environmental impact of plastic waste, contributing to a cleaner and more sustainable environment.

Accurate and rapid diagnosis of drug susceptibility of Mycobacterium tuberculosis is crucial for the successful treatment of tuberculosis, a persistent global public health threat. To shorten diagnosis times and enhance accuracy, this study introduces a fusion model combining laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. This model offers a rapid and accurate method for diagnosing drug-resistance. LIBS and Raman spectroscopy provide complementary information, enabling accurate identification of drug resistance in tuberculosis. Although individual use of LIBS or Raman spectroscopy achieved approximately 90% accuracy in identifying drug resistance, the fusion model significantly improved identification accuracy to 98.3%. Given the fast measurement capabilities of both techniques, this fusion approach is expected to markedly decrease the time required for diagnosis.

Janghee Choi. Med Lasers 2024;13:195-202. https://doi.org/10.25289/ML.24.030

This paper proposes a method to find a parameter that minimizes the average compressor power for R134a cycle using a univariate dynamic encoding algorithm for searches. optimizing modeling parameters for the refrigeration cycle is difficult because it has several parameters to conFigure such as the sizes of the compressor, the evaporator, the condenser, the cooler fan, and the internal heat exchange. The univariate dynamic encoding algorithm is a searching algorithm for the optimum point under a multivariate system variable with constraints to solve the aforementioned difficulty. The parameters are assigned to the simulation model and the compressor power is logged, then a univariate dynamic encoding algorithm for searches is applied to find the minimum point of the compressor power, corresponding to the modeling parameters. The refrigeration modeling parameters are optimized by the proposed method with the modeled specifications. The optimization results ensure the average compressor power of 28. 65W with system parameters related to the condenser, and evaporator.

Jong Hyun Kim, Hohyun Keum, Jinhwan Kim, Chang Gi Lee, Janghee Choi, Kwang Bok Kim, Hoon Jeong. Med Lasers 2026;15:1-12. https://doi.org/10.25289/ML.26.003