Performance Assessment of a Coupled Circulation–Wave Modelling System for the Northwest Atlantic by Colin J. Hughes, Jinyu Sheng, William Perrie, Guoqiang Liu

“…The performance of the modified version of CWMS-NWA during Hurricane Arthur in 2014 is assessed using in situ measurements and satellite data. Several error statistics are used to evaluate the model performance, including correlation (R), root mean square error (RMSE), normalized model variance of model errors (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>γ</mi><mn>2</mn></msup></semantics></math></inline-formula>) and relative bias (RB). …”
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Optimizing HVAC energy efficiency in low-energy buildings: a comparative analysis of reinforcement learning control strategies under Tehran climate conditions by Mohammad Anvar Adibhesami, Amir Hassanzadeh

“…Model B (DRL) demonstrated a 50 percent improvement in prediction accuracy over Model A, with a mean absolute error of 0.579366 compared to 1.140008 and a root mean square error of 0.689770 versus 1.408069. …”
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Data-Driven Analysis of Ocean Fronts’ Impact on Acoustic Propagation: Process Understanding and Machine Learning Applications, Focusing on the Kuroshio Extension Front by Weishuai Xu, Lei Zhang, Ming Li, Xiaodong Ma, Maolin Li

“…Utilizing marine big data statistics and machine learning evaluation metrics such as out-of-bag (OOB) error and Shapley values, this study quantitatively assesses the variations in sound speed structures across the KEF and their effects on acoustic propagation shifts. …”
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I2DNet - Design and real-time evaluation of an appearance-based gaze estimation system by L R D Murthy, Siddhi Brahmbhatt, Somnath Arjun, Pradipta Biswas

“…Model-based approaches rely on features extracted from eye images to fit a 3D eye-ball model to obtain gaze point estimate while appearance-based methods attempt to directly map captured eye images to gaze point without any handcrafted features. …”
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Effective Formalization of Design Processes as a Key Factor in Achieving Optimal Solutions When Creating the Final Stages of Steam Turbines by Anatolii O. Tarelin, Iryna Ye. Annopolska

“…This makes it possible to take into account the influence of manufacturing errors and specific features of machine equipment on the blade strength indicators, its gas-dynamic characteristics, and also on the efficiency of the stage operation at the design stage. …”

Occupational Therapy Practice Based on New-Generation Information Technology for Employee Emotion Analysis and Management by Yueyuan Cheng

“…Secondly, based on the convolutional neural network technology, the facial feature recognition and the body feature recognition are combined, and the employees’ emotions can be accurately identified by establishing a multilearning emotion recognition network. …”
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A finger on the pulse of cardiovascular health: estimating blood pressure with smartphone photoplethysmography-based pulse waveform analysis by Ivan Shih-Chun Liu, Fangyuan Liu, Qi Zhong, Shiguang Ni

“…Data collected from 127 participants demonstrated strong correlations between smartphone-derived digital waveform features and those from reference BP devices. The mean absolute errors (MAE) for systolic BP (SBP), diastolic BP (DBP), and pulse pressure (PP) using multiple linear regression models were 7.75, 6.35, and 4.49 mmHg, respectively. …”
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Reliable estimation via hybrid gradient boosting machine for mud loss volume in drilling operations by Xiaozhi Lu, Farag M. A. Altalbawy, Tarak Vora, R. Manjunatha, Debasish Shit, Shirin Shomurotova, Akshay Kumar, Atreyi Pramanik, Ajay Sharma, Raed H. C. Alfilh, Samim Sherzod, Mohammad Mahtab Alam

“…Model performance was evaluated using k-fold cross-validation, with metrics including R², mean squared error and average absolute relative error percentage. …”
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A Robust Approach to Vision-Based Terrain-Aided Localization by Dan Navon, Ehud Rivlin, Hector Rotstein

“…The resulting algorithm consistently demonstrated high precision and statistical independence in the presence of initial errors across various simulations. …”
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Navigating cognitive boundaries: the impact of CognifyNet AI-powered educational analytics on student improvement by Mrim M. Alnfiai, Faiz Abdullah Alotaibi, Mona Mohammed Alnahari, Nouf Abdullah Alsudairy, Asma Ibrahim Alharbi, Saad Alzahrani

“…Evaluated through rigorous 5-fold cross-validation on a comprehensive dataset of 1200 anonymized student records and validated across multiple educational platforms, including UCI Student Performance and Open University Learning Analytics datasets, CognifyNet demonstrates superior performance over conventional approaches, achieving 10.5% reduction in mean squared error and 83% reduction in mean absolute error compared to baseline random forest models, with statistical significance confirmed through paired t-tests (p < 0.01). …”
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Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies. by Helian Feng, Nicholas Mancuso, Alexander Gusev, Arunabha Majumdar, Megan Major, Bogdan Pasaniuc, Peter Kraft

“…The gain in power is likely due to sCCA cross-tissue features being more likely to be detectably heritable. …”
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Enhancing the Recognition of Collinear Building Patterns by Shape Cognition Based on Graph Neural Networks by Fubing Zhang, Qun Sun, Wenjun Huang, Youneng Su, Jingzhen Ma, Ruixing Xing

“…Current building pattern recognition methods are often based on a shape index that can only characterize shape features from one aspect, resulting in significant errors. …”
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A hybrid deep learning framework for global irradiance prediction using fuzzy C-Means, CNN-WNN, and Informer models by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Wissem Dimassi, Salah Hannachi

“…The proposed CNN-WNN-Informer model achieved average reductions across all cities of 67.7% in t-statistic, 73.9% in Mean Absolute Percentage Error (MAPE), 82.5% in Mean Absolute Bias Error (MABE), and 59.0% in Root Mean Square Error (RMSE), underscoring its significant improvements. …”
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Bridging the Gap in Carbohydrate Counting With a Mobile App: Needs Assessment Survey by Asmaa Housni, Alexandra Katz, Lucien Junior Bergeron, Alain Simard, Ashley Finkel, Amélie Roy-Fleming, Meranda Nakhla, Anne-Sophie Brazeau

“…Descriptive statistics summarized barriers and app feature preferences, and statistical analyses identified differences by age, app use, and insulin modality. …”
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Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks by Pramit Pandit, Atish Sagar, Bikramjeet Ghose, Moumita Paul, Ozgur Kisi, Dinesh Kumar Vishwakarma, Lamjed Mansour, Krishna Kumar Yadav

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mbX: An R Package for Streamlined Microbiome Analysis by Utsav Lamichhane, Jeferson Lourenco

“…The results show that mbX ensures uniform taxonomic formatting, eliminates common manual errors, and quickly generates publication-ready figures, greatly facilitating downstream analysis. …”
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Research on Nutrition and Dietetic Undergraduates in Terms of Their Information Level and Views About Genetically Modified Organisms by Seydi Yıkmış, Başak Gökçe Çöl

“…students replied, “they cause cancer”. There is a statistical difference between the answers of first and third-year students. …”
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Experiment of Image Recognition of Railway Vehicle Brake Shoe Based on Edge Moment Invariants Matching Algorithm by 高向东, 伍世全, 王腾

“…The existing moment invariants algorithm was modified using the origin moment instead of central moments, and selected four lower order moments as the feature variable to reduce the calculation error. …”
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A novel fusion architecture for detecting Parkinson’s Disease using semi-supervised speech embeddings by Tariq Adnan, Abdelrahman Abdelkader, Zipei Liu, Ekram Hossain, Sooyong Park, Md Saiful Islam, Ehsan Hoque

“…Detailed error analysis revealed higher misclassification rates in specific age ranges for males and females, aligning with clinical insights. …”
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Characterization of RAP Signal Patterns, Temporal Relationships, and Artifact Profiles Derived from Intracranial Pressure Sensors in Acute Traumatic Neural Injury by Abrar Islam, Amanjyot Singh Sainbhi, Kevin Y. Stein, Nuray Vakitbilir, Alwyn Gomez, Noah Silvaggio, Tobias Bergmann, Mansoor Hayat, Logan Froese, Frederick A. Zeiler

“…Goal: Current methodologies for assessing cerebral compliance using pressure sensor technologies are prone to errors and issues with inter- and intra-observer consistency. …”
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