Mapping Global Solar Radiation from Long-Term Satellite Data in the Tropics Using an Improved Model by S. Janjai, I. Masiri, S. Pattarapanitchai, J. Laksanaboonsong

“…This paper presents an improved model and its application for mapping global solar radiation from satellite data in the tropics. …”
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Shallow Water Bathymetry Inversion Based on Machine Learning Using ICESat-2 and Sentinel-2 Data by Mengying Ye, Changbao Yang, Xuqing Zhang, Sixu Li, Xiaoran Peng, Yuyang Li, Tianyi Chen

“…While traditional methods such as sonar and airborne LiDAR provide high accuracy, their high cost and time-consuming nature limit their application in remote and sensitive areas. Satellite remote sensing offers a cost-effective and rapid alternative for large-scale bathymetric inversion, but it still relies on significant in situ data to establish a mapping relationship between spectral data and water depth. …”
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Planning Flexible Bus Service as an Alternative to Suspended Bicycle-Sharing Service: A Data-Driven Approach by Yujia Wang, Yuan Tian, Binyu Yang, Jian Wang, Xiaowei Hu, Shi An

“…The generated planning results will cover ride-matching trips and high-frequency riding paths. Detailed application analysis and verification are carried out by using the real data from Tianjin, China. …”
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Using molecular data for epidemiological inference: assessing the prevalence of Trypanosoma brucei rhodesiense in tsetse in Serengeti, Tanzania. by Harriet K Auty, Kim Picozzi, Imna Malele, Steve J Torr, Sarah Cleaveland, Sue Welburn

“…Given the prohibitively high sample sizes required to obtain meaningful results by dissection/microscopy, PCR-based approaches offer the current best option for assessing trypanosome prevalence in tsetse but inconsistencies in relating PCR results to transmissibility highlight the need for a consensus approach to generate meaningful and comparable data.…”
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Support vector regression model for the prediction of buildings’ maximum seismic response based on real monitoring data by Dongwang Tao, Shizhe Fang, Haixu Liu, Jianqi Lu, Jiang Wang, Qiang Ma

“…The New Earthquake Data (NDE1.0) is the most comprehensive publicly available dataset of actual structural seismic response observations. …”
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Understanding Bio-Orthogonal Strain-Driven Sydnone Cycloadditions: Data-Assisted Profiles and the Search for Linear Relationships by Juan García de la Concepción, Pedro Cintas, Rafael Fernando Martínez

“…While there has been a significant accretion of experimental data, biological assays, and assessments of reaction mechanisms, some pieces of the tale are still missing. …”
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Evaluation of SDGSAT-1 MII data in total suspended matter estimation in clear to extremely turbid rivers by Shenglei Wang, Zichen Mao, Bing Zhang, Xiaolong Yu, Fenqi Zhao, Xiaofei Song, Changyong Dou, Fangfang Zhang, Junsheng Li

“…The study concludes by discussing the applicability of MII data in water quality mapping in nearshore waters, along with key considerations such as model applicability, atmospheric correction, and land adjacency.…”
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Translational analysis of data science and causal learning in real-world clinical evaluation of traditional Chinese medicine by Wei Yang, Danhui Yi, XiaoHua Zhou, Yuanming Leng

“…The methodology involves several key steps, including data integration and warehouse building, high-dimensional feature selection, the use of interpretable statistical machine learning algorithms, complex networks, and graph network analysis, knowledge mining techniques such as natural language processing and machine learning, observational study design, and the application of artificial intelligence tools to build an intelligent engine for translational analysis. …”
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Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review by Dhekre Saber Saleh, Mohd Shahizan Othman

“…This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid disease predictions. …”
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High-Quality Sample Generation for Power System Transient Stability Assessment Based on Data-Driven Methods by Baoqin Li, Pengfei Fan, Qixin Chen, Rong Li, Kaijun Lin

“…This paper proposes a high-quality sample generation framework based on data-driven methods to build a high-quality offline samples database for TSA model training and updating. …”
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Priority-Based Data Flow Control for Long-Range Wide Area Networks in Internet of Military Things by Rachel Kufakunesu, Herman C. Myburgh, Allan De Freitas

“…Existing LoRaWAN network simulation modules, such as those in ns-3, primarily support uniform periodic data transmissions, limiting their applicability in critical military and healthcare contexts that demand adaptive transmission rates, resource optimization, and prioritized data delivery. …”
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Detection of Geological Structures in the Gol Gohar Mining Iron Ore Area Using Airborne Magnetic Data by Abdullah Ezam Ahmady, Abdolhamid Ansari, Seyyed Hossein Mojtahedzadeh

“…This study focuses on the analysis of airborne magnetic data to identify surface and subsurface structures at various depths within the Gol Gohar mining area. …”
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Quantitative Detection Method for Surface Angled Cracks Based on Laser Ultrasonic Full-Field Scanning Data by Chenwei Wang, Rui Han, Yihui Zhang, Yuzhong Wang, Yanyang Zi, Jiyuan Zhao

“…To detect surface angled cracks on critical components, this paper proposes a “Quantitative Detection Method for Surface Angled Cracks Based on Full-field Scanning Data”. By analyzing different ultrasonic signals in the full-field scanning data from laser ultrasonics, the width, angle, and length of surface angled cracks can be determined. …”
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UPN-GCN: Update Positive-Negative Graph Convolution Neural Network in Non-Euclidean Structured Data by Lijun Fan, Shichao Yi, Wenrui Guan, Pingxin Wang

“…Graph Convolutional neural Networks (GCNs) demonstrate exceptional effectiveness when working with data that have non-Euclidean structures. In recent years, numerous researchers have utilized GCNs for data with Euclidean structures. such as image data and language data, and achieved good results, expanding the application scope of GCN. …”
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Fusion of UAV-Acquired Visible Images and Multispectral Data by Applying Machine-Learning Methods in Crop Classification by Zuojun Zheng, Jianghao Yuan, Wei Yao, Paul Kwan, Hongxun Yao, Qingzhi Liu, Leifeng Guo

“…This study explores the application of machine learning techniques to crop classification by integrating RGB images and multispectral data acquired by UAVs. …”
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Quantifying observer variance in expansive monitoring program indicator data with heterogeneous-variance mixed-effects models by Leah T. Dreesmann, Timothy R. Johnson, Jason W. Karl

“…In natural resource management, reliable monitoring data is necessary to make informed management decisions. …”
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Use of Smartphone-Based Experimental Data for the Calibration of Biodynamic Spring-Mass-Damper (SMD) Pedestrian Models by Chiara Bedon, Martina Sciomenta, Alessandro Mazelli

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Measurement Error Estimation Method of Field Service Electricity Energy Meters under the Condition of Big Data by DAI Yan-jie, DONG Xian-guang, LIU Ya-qi, LIANG Ya-jie, LIU Xiao, QI Jia, SUN Yong-quan

“…Finally, on the basis of field electric energy error data of electric energy meter, the calculation of engineering application example is carried out. …”
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On the Bayesian generalized extreme value mixture autoregressive model with adjusted SNR in non-standard actuarial data by Chrisandi R. Lande, Nur Iriawan, Dedy Dwi Prastyo

“…Some highlights of the proposed method are: • Development of a new model for the generalized extreme value mixture autoregressive. • Adjustment of SNR type 2 for the generalized extreme value mixture autoregressive model. • Application of the Bayesian GEVMAR (GEV type I) model to non-standard claim reserves data.…”
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Multimodal Fusion Mamba Network for Joint Land Cover Classification Using Hyperspectral and LiDAR Data by Haizhu Pan, Ruixiang Zhao, Haimiao Ge, Moqi Liu, Quanxiu Zhang

“…Recently, the emerging deep learning framework Mamba has shown superior performance over traditional architectures, including transformers and convolutional neural networks. However, its application to LCC faces challenges. First, existing methods typically use a unified feature extraction approach for heterogeneous data, which fails to capture the distinct characteristics of each data modality. …”
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