How infants' reaches reveal principles of sensorimotor decision making by Evelina Dineva, Gregor Schöner

“…The model predicts perseveration, but also a tendency to repeat spontaneous errors. To test the account, we pool data from several A-not-B experiments. …”
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SMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source data by Yuhan Liu, Yuanyuan Zha, Gulin Ran, Yonggen Zhang, Liangsheng Shi

“…SMRFR achieved an unbiased root mean square error of 0.0339 m3/m3 on the validation set. Our novel SM dataset offers a basis and valuable reference for agricultural, hydrological, and ecological research, enabling improved analysis and modelling of SM dynamics at regional to global scales.…”
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Monitoring Water Level Fluctuations of Reservoirs in the Krishna River Basin Using Sentinel-3 and ICESat-2 Altimetry Data by Ahalya Nalluri, H. Ramesh, Pankaj Ramji Dhote

“…The 2018-2022 time series was effectively built for all reservoirs after assessing sensor performance against Central Water Commission data. The study successfully generates water level time series for the reservoirs, with correlation coefficients above 0.96 and Mean Absolute Percentage Errors below 1%. …”
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A decentralized privacy-preserving XR system for 3D medical data visualization using hybrid biometric cryptosystem by Shreyansh Sharma, Debasis Das, Santanu Chaudhury

“…Abstract In the era of digital healthcare, accurate and secure 3D visualization of medical data is critical for collaborative surgical planning. …”
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Estimation of parameters for the modified Weibull distribution under step-stress accelerated life testing with application to aircraft windshields data by Mohammed M. A. Almazah, Mohammed A. Alshahrani, Qasim Ramzan, Irfan Ali Raza, Muhammad Tahir, Zahoor Ahmad

“…We compare Maximum Likelihood Estimators (MLE) and Bayesian estimators, utilizing gamma priors due to their suitability for capturing the positive, skewed nature of failure rate parameters, and evaluate these across balanced and unbalanced loss functions, such as Square Error, General Entropy, and Linear Exponential. Through simulation studies, we assess the performance of these estimators and validate our approach using failure times of aircraft windshield data. …”
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Conversion to Variable Flow Rate—Advanced Control of a District Heating (DH) System with a Focus on Operational Data by Stanislav Chicherin

“…The methodology integrates heat meter data, indoor temperature tracking, and Supervisory Control and Data Acquisition (SCADA)-like system inputs to dynamically adapt supply temperatures while avoiding overheating and reducing distribution losses. …”
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Online Banking Fraud Detection Model: Decentralized Machine Learning Framework to Enhance Effectiveness and Compliance with Data Privacy Regulations by Hisham AbouGrad, Lakshmi Sankuru

“…This research study explores a decentralized anomaly detection framework using deep autoencoders, designed to meet the dual imperatives of fraud detection effectiveness and user data privacy. Instead of relying on centralized aggregation or data sharing, the proposed model simulates distributed training across multiple financial nodes, with each institution processing data locally and independently. …”
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Molecular property prediction using pretrained-BERT and Bayesian active learning: a data-efficient approach to drug design by Muhammad Arslan Masood, Samuel Kaski, Tianyu Cui

“…Experiments on Tox21 and ClinTox datasets demonstrate that our approach achieves equivalent toxic compound identification with 50% fewer iterations compared to conventional active learning. Analysis reveals that pretrained BERT representations generate a structured embedding space enabling reliable uncertainty estimation despite limited labeled data, confirmed through Expected Calibration Error measurements. …”
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Detection and Prediction of Wind and Solar Photovoltaic Power Ramp Events Based on Data-Driven Methods: A Critical Review by Jie Zhang, Xinchun Zhu, Yigong Xie, Guo Chen, Shuangquan Liu

“…Future research should prioritize the development of adaptive, ramp-specific evaluation frameworks, the fusion of physical and data-driven modeling techniques, and the deployment of multi-modal systems capable of leveraging heterogeneous data sources for robust, actionable ramp event forecasting.…”
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Index Modulation OFDM Systems with Direct Pattern of Subcarrier Activation by Zainab Mohammed Abdulkareem, Abdulrahman Ikram Siddiq

“…Through the presented analysis and computer simulation results, it is demonstrated that the OFDM-IM system utilizing the proposed mapping scheme outperforms equivalent traditional OFDM-IM systems in both complexity and bit error rate (BER) performance. …”
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Piloting an automated query and scoring system to facilitate APDS patient identification from health systems by Amy M. FitzPatrick, Aaron T. Chin, Sharon Nirenberg, Charlotte Cunningham-Rundles, Keith Sacco, Jesse Perlmutter, Joseph F. Dasso, Athanasios Tsalatsanis, Jay Maru, Jessica Creech, Jolan E. Walter, Nicholas Hartog, Neema Izadi, Mandy Palmucci, Manish J. Butte, Klaus Loewy, Anurag Relan, Nicholas L. Rider

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Temporal expression-based analysis of metabolism. by Sara B Collins, Ed Reznik, Daniel Segrè

“…In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. …”
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A Video SAR Multi-Target Tracking Algorithm Based on Re-Identification Features and Multi-Stage Data Association by Anxi Yu, Boxu Wei, Wenhao Tong, Zhihua He, Zhen Dong

“…In this study, some key challenges in ViSAR systems are addressed, including the abundance of low-confidence shadow detections, high error rates in multi-target data association, and the frequent fragmentation of tracking trajectories. …”
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Modelling off-stream reservoir using rainfall-runoff transformation and satellite rainfall data in Liliba and Manikin watersheds of Indonesia

“…The statistical requirements for calibration and validation include the coefficient of determination (R2 > 0.40), Nash-Sutcliffe Efficiency (NSE >0.40), root mean square error (RMSE)-standard deviation ratio of research data, or RSR (<0.70), and percent bias (PBIAS <30). …”
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Advancing food security: Rice yield estimation framework using time-series satellite data &amp; machine learning. by Varun Tiwari, Kelly Thorp, Mirela G Tulbure, Joshua Gray, Mohammad Kamruzzaman, Timothy J Krupnik, A Sankarasubramanian, Marcelo Ardon

“…The results revealed a mean percentage root mean square error (RMSE) of 8.07% and 12.96% when validation was conducted using reported district yields and crop-cut yield data, respectively. …”
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Uncovering Diagnostic Correlations Between Traditional Chinese and Western Medicine Using Instrumental Variable Models in Cardiometabolic Patients: Evidence from 1.2 Million Record... by Zhou J, Zhou S, Chen H, Chen X, Zou G, Gao Y, Jing S, Bishai DM, Li J, Zhong A, Liu Z, Liu A

“…After correcting for diagnostic errors, significant associations were found between WM diagnoses and TCM syndromes. …”
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China Earth Observation Data Cube: The 30-m Seamless Annual Leaf-On Landsat Composites from 1985 to 2023 by Yaotong Cai, Xing Li, Peng Zhu, Sheng Nie, Cheng Wang, Xiaoping Liu, Yuhe Chen

“…The growing demand for high-quality, temporally consistent satellite imagery for environmental monitoring and land use research has exposed a substantial data gap in China. Unlike the United States, which provides Analysis Ready Data (ARD) for Landsat imagery, Chinese researchers currently lack an equivalent resource, resulting in time-intensive data processing and potential research inaccuracies. …”
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Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions by Jayaraman Kumarappan, Elakkiya Rajasekar, Subramaniyaswamy Vairavasundaram, Ketan Kotecha, Ambarish Kulkarni

“…These problems lead to frequent prediction errors and make the models difficult to implement in real-time trading systems. …”
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Experience of image-analysis of rye grain by P. Ä. Zykin, E. I. Utkina, A. V. Voylokov

“…It is shown that the individual analysis of size and color characteristics of the kernels give low values of standard errors. …”
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Leveraging Synthetic Data to Develop a Machine Learning Model for Voiding Flow Rate Prediction From Audio Signals by Marcos Lazaro Alvarez, Alfonso Bahillo, Laura Arjona, Diogo Marcelo Nogueira, Elsa Ferreira Gomes, Alipio M. Jorge

“…To evaluate the models in a real environment and assess the effectiveness of training with synthetic data, the best-performing models were retrained and validated using a real voiding sounds dataset. …”
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