Smartphone LIDAR can measure tree cavity dimensions for wildlife studies by Jessica M. Stitt, Leona K. Svancara, Lee A. Vierling, Kerri T. Vierling

“…Correlations between Spike measurements and cavity dimensions were high (r > 0.91 across 3 dimensions; n = 294). Measurement error for both vertical and horizontal diameters of cavity entrances was <1 cm on average. …”
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Motion correction of simultaneous brain PET/MR images based on tracer uptake characteristics by Sheng-Chieh Chiu, Jose Angelo U. Perucho, Yu-Hua Dean Fang

“…Clinical trial number Not applicable.…”
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Assessment of a Hyperspectral Remote Sensing Model Performance for Particulate Phosphorus in Optically Shallow Lake Water by Banglong Pan, Wuyiming Liu, Zhuo Diao, Qianfeng Gao, Lanlan Huang, Shaoru Feng, Juan Du, Qi Wang, Jiayi Li, Jiamei Cheng

“…The results indicate that the CNN-RF model has the best prediction performance, with a coefficient of determination (R2) of 0.91, a residual prediction deviation (RPD) of 3.42, a root mean square error (RMSE) of 0.0155 mg/L, and a percentage bias (PBIAS) of 5.01%, which is better than the BP model by 42%, 106%, 51%, and 81%, the RF model by 25%, 77%, 44%, and 59%, and the CNN model by 9.6%, 42%, 30%, and 49%, respectively, and 59% better than the CNN model. …”
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Dose-dependent myopia-suppressing effect of 4-phenlybutyric acid eye drops in a mouse myopia model under masked condition by Shin-ichi Ikeda, Kohei Sone, Yoshihiro Takai, Takayuki Miyano, Kazuno Negishi, Kazuo Tsubota, Toshihide Kurihara

“…Changes in refractive error and axial length were measured to assess myopia progression. …”
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Bringing Machine Learning Classifiers Into Critical Cyber-Physical Systems: A Matter of Design by Burcu Sayin, Tommaso Zoppi, Nicolo Marchini, Fahad Ahmed Khokhar, Andrea Passerini

“…We validate our approach through experiments on tabular datasets related to failure prediction, intrusion detection, and error detection&#x2014;common use cases for classifiers in CPSs. …”
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Psychometric properties of the Chinese version physical literacy assessment questionnaire among high school students in Gansu, China by Zilu Qu, Jiarun Wu, Yee Cheng Kueh, Dongqing Ye, Garry Kuan

“…Confirmatory factor analysis (CFA) assessed model fit using standardized root mean square residual (SRMR), root mean square error of approximation (RMSEA), Tucker-Lewis index (TLI), and comparative fit index (CFI) indices. …”
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A Novel Approach to Generate Large-Scale InSAR-Derived Velocity Fields: Enhanced Mosaicking of Overlapping InSAR Data by Xupeng Liu, Guangyu Xu, Yaning Yi, Tengxu Zhang, Yuanping Xia

“…In some tracks, there are overlapping areas on the east and west sides, and the line-of-sight (LOS) value can be effectively corrected by using these overlapping areas with similar size for two cross-track mosaics. The root mean square error (RMSE) of these tracks was reduced by about 4% to 8% on average when verified using true values of GNSS data compared to no cross-track mosaic. …”
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Using new control strategies to improve the effectiveness and efficiency of the hybrid power system based on the battery storage system by Karima Boutaghane, Habib Benbouhenni, Nedjoua Bennecib, Ilhami Colak, Z. M. S. Elbarbary, Saad F. Al-Gahtani

“…These results make the designed approach of great importance in the future in other industrial applications.…”
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Development of the nursing practice scale to enhance resilience in family caregivers and evaluation of its reliability and validity by Ruxin Lei, Kazuaki Tanabe, Chie Teramoto, Hiroyuki Sawatari, Mikako Yoshihara, Hisae Nakatani

“…Clinical trial number Not applicable.…”
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Rethinking Macramé Instruction: A Mathematical Approach to Sustainable Cord Usage in Textile Education by Fatimatu Hajia Ibrahim, Boakye Antwi, Karim Azumah, Issah Mohammed Seini

“…Methodology/Design: Research suggests that traditional Macramé teaching methods have remained largely unchanged, often relying on estimation and trial-and-error techniques for determining cord lengths. This lack of standardisation contributes to inefficiencies in material use and instructional delivery. …”
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Deep learning for sunflower (Helianthus annuus L.) mapping and stand counting: Trade-offs between closed vs. open-access methods by Maria Villamil-Mahecha, Harsh Pathak, Nitin Rai, Paul Overby, Xin Sun

“…The root mean squared error (RMSE) for stand count was 26.7 sunflowers per tile further confirmed the model’s accuracy. …”
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Modeling and simulation of optical wireless communication channels in IoUT considering water types turbulence and transmitter selection by M. Mokhtar Zayed, Mona Shokair

“…Performance metrics, including received optical power, signal-to-noise ratio (SNR), and bit error rate (BER), are evaluated to provide in-depth insights into system behavior. …”
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Object-Based Downscaling Method for Land Surface Temperature with High-Spatial-Resolution Multispectral Data by Siyao Wu, Shengmao Zhang, Fei Wang

“…A comparison with PBA methods for pixel downscaling also indicated that the OBD method achieves the lowest root mean square error (RMSE) across different landcovers, including urban areas, water bodies, and natural terrain. …”
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Evaluation of LSTM, GRU, and ANFIS Models for Ankle Angle and Ankle Moment Prediction Using Biomechanical Data by T. S. Sangeetha, S. N. Muralikrishna, Advay Mandar Naik, Arun P. Parameswaran

“…The performance of the models were assessed using Root Mean Square Error (RMSE) and Coefficient of Determination (R2) metrics, under both subject-specific and Leave-One-Subject-Out (LOSO) cross-subject validation frameworks. …”
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Predicting indoor temperature of solar green house by machine learning algorithms: A comparative analysis and a practical approach by Wenhe Liu, Tao Han, Cong Wang, Feng Zhang, Zhanyang Xu

“…This performance significantly exceeded that of LSTM, Random Forest (RF), Support Vector Regression (SVR), and Multiple Linear Regression (MLR), with GRU reducing the root mean squared error (RMSE) by 12.3 %–27.5 % compared to LSTM in long-term predictions. …”
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Research on dynamic prediction and optimization of high altitude photovoltaic power generation efficiency using GVSAO-CNN Model under 8-climate modes by Xiaoming Xiong, Heng Hu, Qiangfu Jia, Rongjian Zhang, Chongan Huang, Qingyuan Lu

“…Specifically, it has been demonstrated to achieve a 22.6% reduction in root-mean-square error (RMSE) and a 16.7% decrease in training time when compared to traditional models. …”
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A Two-Step Framework for Generating 0.01&#x00B0;, Hourly, and Gapless Land Surface Temperature by Jun Ma, Jingping Guo, Jingan Wu, Huanfeng Shen

“…The results reveal that the daily LST reconstruction model performs well, with a Pearson correlation coefficient (R) of 0.97&#x2013;0.98 and a root-mean-square error (RMSE) of 3.01&#x2013;3.6 K in cloudy conditions. …”
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A Learning-Based Dual-Scale Enhanced Confidence for DSM Fusion in 3-D Reconstruction of Multiview Satellite Images by Shuting Yang, Hao Chen, Fachuan He, Wen Chen, Ting Chen, Jianjun He

“…The proposed method achieves an average MAE of 1.14 m, RMSE of 2.16 m, median height error of 0.47 m, and COMP of 75.13&#x0025;, outperforming several mainstream methods.…”
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Addressing the dynamic nature of reference data: a new nucleotide database for robust metagenomic classification by Jose Manuel Martí, Car Reen Kok, James B. Thissen, Nisha J. Mulakken, Aram Avila-Herrera, Crystal J. Jaing, Jonathan E. Allen, Nicholas A. Be

“…This new resource demonstrably reduces errors and improves the reliability of microbial identification across diverse taxonomic groups. …”
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Revolutionizing Clear-Sky Humidity Profile Retrieval with Multi-Angle-Aware Networks for Ground-Based Microwave Radiometers by Yinshan Yang, Zhanqing Li, Jianping Guo, Yuying Wang, Hao Wu, Yi Shang, Ye Wang, Langfeng Zhu, Xing Yan

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