Development and Evaluation of Game-Based Learning System Using the Microsoft Kinect Sensor by Chih-Hsiao Tsai, Yin-Hao Kuo, Kuo-Chung Chu, Jung-Chuan Yen

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HSFMamba: Hierarchical Selective Fusion Mamba Network for Optics-Guided Joint Super-Resolution and Denoising of Noise-Corrupted SAR Images by Zhicheng Zhao, Qing Gao, Jinquan Yan, Chenglong Li, Jin Tang

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中心蜗杆范成法数控加工廓面误差综合研究 by 胡自化, 陈生墨, 陈小告, 张华

“…In order to explore the influence law of original errors included machine tool kinematic errors,tool wear errors on the profile machining accuracy of sun-worm,based on the multi-body system error-modeling method and the conjugate meshing theory of spatial mechanism,the sunworm profile equation included these errors is deduced.The normal error between worm theoretical profile and error profile is calculated by using Newton iteration method and the result is analyzed to describe the effect of each error and comprehensive error on sun-worm profile accuracy.The main research conclusion can effectively disclosure and predict the law of influence that the various errors for sun-worm profile normal error.Consequently,a scientific proof is supplied to compensate machine geometric errors and improve the precision and quality of the sun-worm.…”
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A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data by Kai Du, Jingni Song, Dan Chen, Ming Li, Yadi Zhu

“…First, individual spatiotemporal travel patterns are mapped and discretized in both the spatial and temporal dimensions. Travel characteristic data are then extracted for spatial grid units. …”
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PFRNet: A Small Object Detection Method Based on Parallel Feature Extraction and Attention Mechanism by Hai Lin, Ji Wang, Jingguo Li

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A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation by Jonathan Schmidt, Luca Schmidt, Felix M. Strnad, Nicole Ludwig, Philipp Hennig

“…As this predictive task is inherently uncertain, we leverage the probabilistic nature of diffusion models and sample multiple trajectories. …”
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Urban subsidence zones prone to flooding: mitigated deformation trends post-2024 Guilin megaflood by Pinglang Kou, Xu Dong, Qiang Xu, Ali P. Yunus, Ying Xia, Yuxiang Tao, Rui Liu, Qin Ji, Ruikun Chen, Zhengwu Yuan

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Machine learning approaches for imputing missing meteorological data in Senegal by Mory Toure, Nana Ama Browne Klutse, Mamadou Adama Sarr, Md Abul Ehsan Bhuiyan, Annine Duclaire Kenne, Wassila Mamadou Thiaw, Daouda Badiane, Amadou Thierno Gaye, Ousmane Ndiaye, Cheikh Mbow

“…XGB consistently outperformed all methods across variables and scenarios, achieving the highest average predictive accuracy with R2 values up to [95 % CI: 0.82–0.88], along with lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). …”
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Marker-Less Navigation System for Anterior Cruciate Ligament Reconstruction with 3D Femoral Analysis and Arthroscopic Guidance by Shuo Wang, Weili Shi, Shuai Yang, Jiahao Cui, Qinwei Guo

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Using random forests to forecast daily extreme sea level occurrences at the Baltic Coast by K. Bellinghausen, B. Hünicke, E. Zorita

“…<p>We have designed a machine learning method to predict the occurrence of daily extreme sea level at the Baltic Sea coast with lead times of a few days. …”
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Spatiotemporal Forecasting of Traffic Flow Using Wavelet-Based Temporal Attention by Yash Jakhmola, Madhurima Panja, Nitish Kumar Mishra, Kripabandhu Ghosh, Uttam Kumar, Tanujit Chakraborty

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Computational exploration of structural hypotheses for an additional sequence in a mammalian mitochondrial protein. by Aymen S Yassin, Rajendra K Agrawal, Nilesh K Banavali

“…<h4>Conclusions</h4>By hypothesizing that the insert sequence occupies the IF1 binding site, continuous IF2(mt) models that occupy both the IF2 and IF1 binding sites can be predicted computationally. …”
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Less Is More: Brain Functional Connectivity Empowered Generalizable Intention Classification With Task-Relevant Channel Selection by Haowei Lou, Zesheng Ye, Lina Yao, Yu Zhang

“…Inevitably, the sensory electrodes on the entire scalp would collect signals irrelevant to the particular BCI task, increasing the risks of overfitting in machine learning-based predictions. While this issue is being addressed by scaling up the EEG datasets and handcrafting the complex predictive models, this also leads to increased computation costs. …”
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A photogrammetric approach to the estimation of distance to animals in camera trap images by B. Mirka, C.D. Lippitt, G.M. Harris, R. Converse, M. Gurule, S.E. Sesnie, M.J. Butler, D.R. Stewart, Z. Rossman

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Forecasting Stock Market Volatility Using Housing Market Indicators: A Reinforcement Learning-Based Feature Selection Approach by Pourya Zareeihemat, Samira Mohamadi, Jamal Valipour, Seyed Vahid Moravvej

“…We propose a sophisticated Early Warning System (EWS) designed to forecast stock market instability by leveraging the predictive power of housing market bubbles. Current EWS methods often face significant hurdles, including model generalization, feature selection, and hyperparameter optimization challenges. …”
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Ocean Currents Velocity Hindcast and Forecast Bias Correction Using a Deep-Learning Approach by Ali Muhamed Ali, Hanqi Zhuang, Yu Huang, Ali K. Ibrahim, Ali Salem Altaher, Laurent M. Chérubin

“…However, numerical models are often unable to accurately model and predict real ocean dynamics, leading to a lack of fulfillment of a range of services that require reliable predictions at various temporal and spatial scales. …”
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Statistical Analysis of High-Resolution Coherent Monopulse Radar Sea Clutter by Hong Zhu, Qingping Wang, Ning Tai, Jingjian Huang, Naichang Yuan

“…Finally, we perform a spectral analysis, highlighting the temporal and spatial variabilities of Doppler spectra. It is found that the individual Doppler spectra in all three channels can be represented by Gaussian-shaped power spectral densities, and their centroid and width can be modeled as two separate stage linear functions of spectrum intensity.…”
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Flood Risk Forecasting: An Innovative Approach with Machine Learning and Markov Chains Using LIDAR Data by Luigi Bibbò, Giuliana Bilotta, Giuseppe M. Meduri, Emanuela Genovese, Vincenzo Barrile

“…These extreme events are often unpredictable and pose considerable challenges for spatial planning and risk management. This study explores an innovative approach that employs machine learning and Markov chains to enhance spatial planning and predict flood risk areas. …”
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BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data by Abdullah Alourani, Farzeen Ashfaq, N. Z. Jhanjhi, Navid Ali Khan

“…Attention modules are added to the GNN and BLSTM to find high-impact attention weight values for the chosen road section. Our model offers the best prediction accuracy with a mean absolute percentage error of 5.21% and a root mean squared error of 4. …”
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Explaining drivers of housing prices with nonlinear hedonic regressions by Heng Wan, Pranab K. Roy Chowdhury, Jim Yoon, Parin Bhaduri, Vivek Srikrishnan, David Judi, Brent Daniel

“…We then conduct sensitivity and Partial Dependence Plot (PDP) analyses to interpret the fitted ANN model. We find that the ML model achieves higher predictive accuracy and explains 16 % more of housing price variance than a traditional linear regression model. …”
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