Characterization of temporary and permanent 3D-printed crown and bridge resins by Roope Salonen, Sufyan Garoushi, Pekka Vallittu, Lippo Lassila

“…Disk-shaped specimens (n = 5/material) were prepared to measure translucency parameter, gloss and light penetration. For gloss measurement, specimens underwent laboratory-machine polishing (4,000-grit abrasive paper) and chairside two-step hand polishing (Top Dent DiaComposite). …”
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State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms by Romel Carrera, Leonidas Quiroz, Cesar Guevara, Patricia Acosta-Vargas

“…The novelty of this approach lies in the integration of machine learning and physical modeling, optimized via evolutionary algorithms, to address limitations of standalone methods in real-time applications. …”
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From Meta SAM to ArcGIS: A Comparative Analysis of Image Segmentation Methods for Monitoring Refugee Camp Transitions by Noor Marji, Michal Kohout

“…The study establishes specific parameter optimization guidelines for humanitarian contexts, with spectral detail values of 3.0–7.0 and spatial detail values of 14.0–18.0, yielding optimal results for refugee settlement analysis. …”
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Identifying Effective Rock-Breaking Ratio Based on Rock Chip Information for Rock-Breaking Efficiency Evaluation of TBM by Chuigang Zeng, Changbin Yan, Gaoliu Li, Xiao Xu, Fengwei Yang, Weilin Su

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Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data by Xin Dang, Congcong Hua, Chuitian Rong

“…Due to the unique industry characteristics of QAR data and the requirements of FOQA, feature engineering and hyper-parameter optimization techniques are utilized to enhance the machine learning model. …”
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Developing an Integrative Data Intelligence Model for Construction Cost Estimation by Zainab Hasan Ali, Abbas M. Burhan, Murizah Kassim, Zainab Al-Khafaji

“…Several input predictors were used, and XGBoost highlighted inflation as the most crucial parameter. The results indicated that the best prediction was attained by XGBoost-RF using six input parameters, with r-squared and the mean absolute percentage error equal to 0.87 and 0.25, respectively. …”
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On the effects of tool nose radius in alleviating parasitic mechanisms for cutting of aluminum alloy by Sweta Baruah, James B. Mann, Srinivasan Chandrasekar, Balkrishna C. Rao

“…This study focuses on the cutting tool nose radius – a critical geometric parameter – as a lever to mitigate these issues. Through a systematic series of experiments on the machining of Al 6061-T6, the influence of tool nose radius on key machining parameters, including cutting forces, specific energy, chatter stability, tool wear, and surface finish, was investigated. …”
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Stochastic Block-Coordinate Gradient Projection Algorithms for Submodular Maximization by Zhigang Li, Mingchuan Zhang, Junlong Zhu, Ruijuan Zheng, Qikun Zhang, Qingtao Wu

“…We consider a stochastic continuous submodular huge-scale optimization problem, which arises naturally in many applications such as machine learning. Due to high-dimensional data, the computation of the whole gradient vector can become prohibitively expensive. …”
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Optimization of Surface Roughness of AISI 1040 Stainless Steel in Milling Process Using Taguchi Method by Neslihan Özsoy, Murat Özsoy

“…Surfaceroughness significantly affects the work efficiency and life of machine partsinteracting with each other. There are many parameters that affect surfaceroughness such as processed material, cutting tool, cutting parameters, coolingtype. …”
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Research on the Parametric Design and Application of Ceramic Modeling Based on Python by Zhenjie Wang, Longyao Xu, Jing Dai, Chengqian Zeng, Yu Zhong, Liyan Liu, Shuanghua Wang

“…In response to the growing diverse needs of the ceramic industry, this paper presents a creative approach employing the ErgoLAB human–machine environment synchronization platform to identify key parameters for ceramic shape parametric design. …”
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Prediction of High-ozone Events Using GAM, SMOTE, and Tail Dependence Approaches in Texas (2005–2019) by Benjamin Brown-Steiner, Xiong Zhou, Matthew J. Alvarado, Brook T. Russell

“…Finally, the feature selection via the tail dependence method performs comparably to other forms of machine learning-based feature selection and we find that there are multiple parameter sets that can predict MDA8 O3 with equal success.…”
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Design and experiment of spiral conveying pipe in pneumatic centralized fertilizer discharge system. by Longmei Zhang, Wensheng Yuan, Yugang Feng, Chengqian Jin, Gangwei Liu, Shuangcheng Xie

“…By leveraging the combined effects of high-speed swirling airflow and a spiral conveying pipe, the system generates a high-speed rotating air-fertilizer mixed flow, mitigating the negative effects of ground unevenness and machine vibration on fertilizer performance. Through multi-parameter coupled simulation experiments, the optimal working parameters for the spiral conveying pipe were identified as follows: a spiral pipe length of 444.35 mm, a cross-sectional slope angle of 26°, an airflow velocity of 35 m s-1, and a screw pitch of 105 mm, achieving a coefficient of variation of 4.61%. …”
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An enhanced time efficient technique for image watermarking using ant colony optimization and light gradient boosting algorithm by Vipul Sharma, Roohie Naaz Mir

“…This paper proposes a time efficient optimization method based on machine learning algorithms to detect the best embedding parameter for image watermarking with both robustness and imperceptibility. …”
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Research Progress and Current Status of Metal Cutting Fluid Technology by LUO Xiuyu, XIAO Shaowen, ZHAN Wen, LI Yingpeng

“…In recent years， with the rapid development of industries such as new energy vehicles， aerospace， and high-end equipment manufacturing， along with the integration of technologies like artificial intelligence and big data， machine tools are advancing toward higher levels of high-end，intelligent，and green technologies.Cutting fluids are essential for achieving efficient，precise，and low-cost metal cutting processes.However， with the rapid growth of the machinery manufacturing industry， the conflict between the low performance of traditional cutting fluids and the high-quality manufacturing requirements of core components has become increasingly evident.On the other hand， the green application and safe disposal of cutting fluids have become major obstacles to the green development of the industry.Therefore， addressing the series of issues associated with traditional cutting fluids is a critical research topic faced by both the machinery manufacturing industry and the environmental protection field.This paper took the limitations of traditional cutting fluids as the starting point， and organized the current research on cutting fluids from three dimensions: environmental protection， economy， and functionality.The latest research progress on lubrication and cooling mechanisms， process effects， and parameter optimization was reviewed， and the advantages and disadvantages of different cutting fluids， along with the issues encountered during their development， were analyzed.Finally， from the perspective of industrial practical applications， the paper provided an outlook on metal cutting fluids and offered theoretical insights and research directions for the functionalization and greenization of cutting fluid technology， aiming to promote the domestic substitution of cutting fluid technologies in China.…”
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Exploring quantum control landscape and solution space complexity through optimization algorithms and dimensionality reduction by Haftu W. Fentaw, Steve Campbell, Simon Caton

“…Our results provide insights into effective quantum control strategies, emphasizing the significance of parameter selection and algorithm optimization.…”
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Emotion Recognition Based on Handwriting Using Generative Adversarial Networks and Deep Learning by Hengnian Qi, Gang Zeng, Keke Jia, Chu Zhang, Xiaoping Wu, Mengxia Li, Qing Lang, Lingxuan Wang

“…The TabNet (a neural network designed for tabular data) with SimAM (a simple, parameter-free attention module) was employed and compared with the original TabNet and traditional machine learning models; the incorporation of the SimAm attention mechanism led to a 1.35% improvement in classification accuracy. …”
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Challenges and Opportunities in Remote Sensing-Based Fuel Load Estimation for Wildfire Behavior and Management: A Comprehensive Review by Arnick Abdollahi, Marta Yebra

“…Fuel load is a crucial input in wildfire behavior models and a key parameter for the assessment of fire severity, fire flame length, and fuel consumption. …”
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Flexible Adaptive Marine Predator Algorithm and Its Application in Fault Detection for Wind Turbines by WANG Wen, YI Jiabiao

“…To solve these issues, this paper proposes a flexible adaptive marine predator algorithm (FAMPA) by introducing an adaptive parameter mechanism to enhance the original MPA. FAMPA allows for flexibly adjusting population location changes to optimize the balance between global exploration and local exploitation, thus significantly improving both convergence rates and optimization accuracy. …”
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Disentangling autoencoders and spherical harmonics for efficient shape classification in crystal growth simulations by Jaehoon Cha, Steven Tendyra, Alvin J. Walisinghe, Adam R. Hill, Susmita Basak, Peter R. Spackman, Michael W. Anderson, Jeyan Thiyagalingam

“…Despite machine learning advances in crystal growth for structure-property relationships, applications targeting morphological control remain underdeveloped. …”
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Quantization-based chained privacy-preserving federated learning by Ya Liu, Shumin Wu, Yibo Li, Fengyu Zhao, Yanli Ren

“…Abstract Federated Learning (FL) is an advanced distributed machine learning framework crucial in protecting data privacy and security. …”
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