Thermal-mechanical coupled stress prediction of printed circuit heat exchanger in the supercritical CO2 Brayton cycle by Junlin Chen, Wenhai Du, Keyong Cheng, Xunfeng Li, Xiulan Huai, Jiangfeng Guo, Pengfei Lv, Hongsheng Dong

“…The results reveal that mechanical stress is most sensitive to cold-side pressure, while thermal stress correlates linearly with temperature gradients. Dimensional analysis yielded predictive formulas for thermal stress (±13.3 % error) and mechanical stress (±14.3 % error), validated against finite element method results. …”
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Winter Wheat Canopy Height Estimation Based on the Fusion of LiDAR and Multispectral Data by Hao Ma, Yarui Liu, Shijie Jiang, Yan Zhao, Ce Yang, Xiaofei An, Kai Zhang, Hongwei Cui

“…The mean absolute error values were 0.006 m, 0.011 m, and 0.011 m, respectively. …”
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Development of an Artificial Neural Network Model to Predict the Tensile Strength of Friction Stir Welding of Dissimilar Materials Using Cryogenic Processes by Mingoo Cho, Jinsu Gim, Ji Hoon Kim, Sungwook Kang

“…The ANN was optimized using the Hyperband tuner and validated against experimental results. …”
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Research on Automatic Tracking and Size Estimation Algorithm of “Low, Slow and Small” Targets Based on Gm-APD Single-Photon LIDAR by Dongfang Guo, Yanchen Qu, Xin Zhou, Jianfeng Sun, Shengwen Yin, Jie Lu, Feng Liu

“…Among them, the fitting error of the target is always less than 2 pixels, while the size calculation error of the target is less than 2.5 cm. …”
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Multi-agent maglev harmonic prediction algorithm based on integrated attention mechanism by WANG Zongyan, MAO Zhongya, HUANG Shize, GUO Qiyi

“…Theoretical analysis and simulations of Shanghai high-speed maglev traction system were conducted, with the grid-side current data collected for experiment and analysis using the proposed algorithm model. …”
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The Spacecraft Parabolic Antenna Payload Orientation Estimation Method Based on the Step Effect of Measured Radar Cross Section Sequences by Junzhi Li, Xin Ning

“…The analysis and processing of active radar image information is an important method for determining the payload orientation of non-cooperative targets. …”
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Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML by Mahdi Zeynali, Khalil Alipour, Bahram Tarvirdizadeh, Mohammad Ghamari

“…The results showed an average root mean squared error (RMSE) of 19.7 mg/dL, with 76.6% accuracy within the A zone and 23.4% accuracy within the B zone of the Clarke Error Grid Analysis (CEGA), indicating a 100% clinical acceptance. …”
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Predicting pull-out strength and failure modes of metal anchors embedded in masonry structures using explainable machine learning models and empirical equations by Aryan Baibordy, Mohammad Yekrangnia

“…To achieve this, 13 distinct ML models, including both individual and ensemble models, were implemented for predicting each target variable. Optimization techniques were then used to fine-tune the hyperparameters, improving the performance of each ML model. …”
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The Establishment of a Discrete Element Model of Wheat Grains with Different Moisture Contents: A Research Study by He Li, Guangmeng Guo, Lu Xun, Junhao Lu, Huanhuan Chen, Gongpei Cui

“…The high moisture content of wheat grains in extreme weather, such as continuous rain, can easily cause mildew, and we lack accurate discrete element parameters when conducting a simulation analysis of the rapid dehumidification of high-moisture grains. …”
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HEVERL – Viewport Estimation Using Reinforcement Learning for 360-degree Video Streaming by Nguyen Viet Hung, Pham Tien Dat, Nguyen Tan, Nguyen Anh Quan, Le Thi Huyen Trang, Le Mai Nam

“…Additionally, it reduces the Root Mean Square Error (RMSE) by 0.008 to 0.013, the Mean Absolute Error (MAE) by 0.012 to 0.018 and the F1-score by 0.017 to 0.028. …”
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Automated generation of discharge summaries: leveraging large language models with clinical data by Matthias Ganzinger, Nicola Kunz, Pascal Fuchs, Cornelia K. Lyu, Martin Loos, Martin Dugas, Thomas M. Pausch

“…After de-identifying 25 patient datasets, we optimized the output of the LLaMA3 model through prompt engineering and evaluated it using error analysis, as well as quantitative and qualitative metrics. …”
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An Efficient SM9 Aggregate Signature Scheme for IoV Based on FPGA by Bolin Zhang, Bin Li, Jiaxin Zhang, Yuanxin Wei, Yunfei Yan, Heru Han, Qinglei Zhou

“…The experimental results and analysis indicate that under error-free conditions, the proposed non-fault-tolerant aggregate mode reduces the verification time by up to 97.1% compared to other schemes. …”
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Physics-Informed Neural Networks for Advanced Thermal Management in Electronics and Battery Systems: A Review of Recent Developments and Future Prospects by Zichen Du, Renhao Lu

“…Notably, research combining PINNs with LSTM networks for battery thermal management at a 2.0 C charging rate has achieved impressive results—an R² of 0.9863, a mean absolute error (MAE) of 0.2875 °C, and a root mean square error (RMSE) of 0.3306 °C—demonstrating high predictive accuracy. …”
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The Jackknife method as a new approach to validate strong lens mass models by Shun Nishida, Masamune Oguri, Yoshinobu Fudamoto, Ayari Kitamura

“…The accuracy of a mass model in the strong lensing analysis is crucial for unbiased predictions of physical quantities such as magnifications and time delays. …”
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KDFE: Robust KNN-Driven Fusion Estimator for LEO-SoOP Under Multi-Beam Phased-Array Dynamics by Jiaqi Yin, Ruidan Luo, Xiao Chen, Linhui Zhao, Hong Yuan, Guang Yang

“…Both simulations and field tests demonstrate KDFE’s dual superiority: Doppler estimation errors were reduced by 26.3% (vs. Rife–Vincent) and 67.9% (vs. …”
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Research on Carbonation Characteristics and Frost Resistance of Iron Tailings Powder Concrete under Low-Cement Clinker System by Ruidong Wu, Juanhong Liu, Guangtian Zhang, Yueyue Zhang, Shuhao An

“…According to the pore structure analysis, the introduction of iron tailings powder can optimize the pore structure, improve the porosity of harmless and less harmful pores, and thus improve the frost resistance.…”
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Artificial Intelligence in the Identification of Germinated Soybean Seeds by Hiago H. R. Zanetoni, Lucas G. Araujo, Reynaldo P. Almeida, Carlos E. A. Cabral

“…The results derived from the analysis of the graphs and comparisons to the conventional methodology of seed classification showed the effectiveness of YOLO for classifying seeds as germinated or nongerminated, reaching 95% accuracy in seed classification, beyond the range of 0–0.110 of the prediction errors, determined by the application of the methodology of mean square error, highlighting the efficiency of YOLO.…”
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Detecting Botrytis Cinerea Control Efficacy via Deep Learning by Wenlong Yi, Xunsheng Zhang, Shiming Dai, Sergey Kuzmin, Igor Gerasimov, Xiangping Cheng

“…It aims to address the limitations of traditional statistical analysis methods in capturing non-linear relationships and multi-factor synergistic effects. …”
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The Intervened effect of place attachment on the relationship between community attachment and environmental behaviors (case study: Tajrish neighborhood) by Atousa Soleimani, Ahmad Nohegar

“…The obtained data were statistically analyzed using the correlation analysis and structural equation modeling in SPSS-21 and AMOS-21. …”
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Mental and Emotional Experiences among Registered Nurses during SARS COV-2 Pandemic: A Meta-Synthesis by Manish Kumar Balai, V. A. Raghu, Dutt Avasthi Rishi, Ram Bishnoi Hanuman

“…The meta-synthesis reported data from 10 phenomenological studies with 198 nurses as informants of which 122 were female and 40 were male and the remaining 36 sample categories were not reported and their mean age was 29.62 years. The thematic analysis was performed to derive the six major themes which include negative emotion, optimism, adaptation to the COVID-19 pandemic, health-care concern, somatic experience, and professional obligation with 12 subthemes. …”
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