Abstract
Battery degradation remains a major challenge in electric vehicle (EV) adoption, directly affecting long-term performance, cost, and user satisfaction. This paper proposes a data-driven charging strategy that reduces battery wear while meeting the user's daily range needs. By integrating manufacturer guidelines, battery aging models, and thermal dynamics, the proposed optimization algorithm dynamically adjusts the charging current and timing to minimize stressors, such as high temperatures and prolonged high state of charge (SoC). The methodology is responsive to user inputs such as departure time and required driving range, enabling personalized charging behavior. Simulation results show that this approach can reduce battery degradation by up to 2.7% over a 30-day period compared to conventional charging habits, without compromising usability. The framework is designed for integration into Battery Management Systems (BMS), with applications for both private EV users and fleet operators. We address EV battery aging driven by high core temperature and prolonged high state of charge (SoC) during overnight/home charging. Given a user-specified departure time and required driving range, we schedule charging power over time to minimize predicted degradation exposure while still meeting the range requirement. The scheduler optimizes charging timing/current under SoC dynamics, thermal constraints, and charger/ BMS limits.

Abstract
This study investigates the effectiveness of different hyperparameter tuning strategies for peer-to-peer risk management. Ensemble learning techniques have shown superior performance in this field compared to individual classifiers and traditional statistical methods. However, model performance is influenced not only by the choice of algorithm but also by hyperparameter tuning, which impacts both predictive accuracy and computational efficiency. This research compares the performance and efficiency of three widely used hyperparameter tuning methods, Grid Search, Random Search, and Optuna, across XGBoost, LightGBM, and Logistic Regression models. The analysis uses the Lending Club dataset, spanning from 2007 Q1 to 2020 Q3, with comprehensive data preprocessing to address missing values, class imbalance, and feature engineering. Model explainability is assessed through feature importance analysis to identify key drivers of default probability. The findings reveal comparable predictive performance among the tuning methods, evaluated using metrics such as G-mean, sensitivity, and specificity. However, Optuna significantly outperforms the others in computational efficiency; for instance, it is 10.7 times faster than Grid Search for XGBoost and 40.5 times faster for LightGBM. Additionally, variations in feature importance rankings across tuning methods influence model interpretability and the prioritization of risk factors. These insights underscore the importance of selecting appropriate hyperparameter tuning strategies to optimize both performance and explainability in peer-to-peer risk management models.

Abstract
Obstructive sleep apnea is a heterogeneous sleep disorder with varying phenotypes. Several studies have already performed cluster analyses to discover various obstructive sleep apnea phenotypic clusters. However, the selection of the clustering method might affect the outputs. Consequently, it is unclear whether similar obstructive sleep apnea clusters can be reproduced using different clustering methods. In this study, we applied four well-known clustering methods: Agglomerative Hierarchical Clustering; K-means; Fuzzy C-means; and Gaussian Mixture Model to a population of 865 suspected obstructive sleep apnea patients. By creating five clusters with each method, we examined the effect of clustering methods on forming obstructive sleep apnea clusters and the differences in their physiological characteristics. We utilized a visualization technique to indicate the cluster formations, Cohen's kappa statistics to find the similarity and agreement between clustering methods, and performance evaluation to compare the clustering performance. As a result, two out of five clusters were distinctly different with all four methods, while three other clusters exhibited overlapping features across all methods. In terms of agreement, Fuzzy C-means and K-means had the strongest (kappa = 0.87), and Agglomerative hierarchical clustering and Gaussian Mixture Model had the weakest agreement (kappa = 0.51) between each other. The K-means showed the best clustering performance, followed by the Fuzzy C-means in most evaluation criteria. Moreover, Fuzzy C-means showed the greatest potential in handling overlapping clusters compared with other methods. In conclusion, we revealed a direct impact of clustering method selection on the formation and physiological characteristics of obstructive sleep apnea clusters. In addition, we highlighted the capability of soft clustering methods, particularly Fuzzy C-means, in the application of obstructive sleep apnea phenotyping.

Abstract
This paper explores the integration of SolidWorks, LabVIEW, and Arduino as a comprehensive and cost-effective approach to teaching robotics to undergraduate students. In scenarios where real hardware is unavailable or prohibitively expensive, this methodology offers significant advantages. SolidWorks enables students to design and simulate robotic components in a virtual environment, fostering a deep understanding of mechanical design and engineering principles. LabVIEW provides an intuitive graphical interface for programming and control, allowing students to develop and test their algorithms. Finally, Arduino, as an open-source hardware platform, bridges the gap between virtual simulations and physical implementation, offering a hands-on experience with minimal financial investment. Together, these tools create a robust educational framework that enhances theoretical knowledge through practical application, encourages innovation, and prepares students for real-world engineering challenges. The paper concludes that this integrated approach not only mitigates the limitations of resource constraints but also enriches the learning experience by providing a versatile and accessible platform for robotics education.

Abstract

Abstract

A novel technique based on multiple amplitude wavelength modulation spectroscopy (MA-WMS) for simultaneous measurement of CH4 gas concentration and pressure was developed and validated both through simulation and experiment, showing good agreement. To capture the spectrum broadening caused by increasing pressure and concomitantly obtain the concentration at the sensor’s location, a laser centered at 1650.9 nm was subjected to multiple amplitude modulation depths while the 2fm signal, normalized by the DC component (an invariant quantity under optical loss), was recorded. While the use of a single and fixed modulation can introduce an ambiguity, as different pairs of pressure and concentration can yield the same value, this ambiguity is eliminated by employing multiple amplitude modulations. In this approach, the intersection point of the three level curves can provide the local pressure and concentration. The proposed system was able to measure concentrations from a few percentage points up to 50% and pressure from 0.02 atm up to 2 atm, with a maximum error of 2% in concentration and 0.06 atm in pressure, respectively. The system was also tested for attenuation insensitivity, demonstrating that measurements were not significantly affected for up to 10 dB applied optical loss.