Cross-sensor data reconstruction for optical remote sensing gap-filling with attention-enhanced multi-scale fusion network by Xi Wang, Songchao Chen, Chang Zhou, Si-Bo Duan, Zhou Shi

“…Moreover, using the reconstructed images to interpolate missing data produces a denser NDVI time series, offering great potential for downstream tasks such as crop growth monitoring, identification of key phenological stages, and enabling more precise monitoring and management in agricultural applications.…”
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Time-Series Data-Driven PM_2.5 Forecasting: From Theoretical Framework to Empirical Analysis by Chunlai Wu, Ruiyang Wang, Siyu Lu, Jiawei Tian, Lirong Yin, Lei Wang, Wenfeng Zheng

“…This review comprehensively explores the progress of PM_2.5 concentration prediction, covering bibliometric trends, time series data characteristics, deep learning applications, and future development directions. …”
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Hail Forecast and Estimation of Its Maximum Size on the Output Data of the Global Atmospheric Model with Tree-Day Lead Time by Artur Khasanbievich Kagermazov, Lezhinka Tanashevna Sozaeva

“…The indicators characterizing the statistical significance and practical applicability of the equation were calculated.The observation data on the fallout of hail and its size were provided by paramilitary services for active impact on meteorological and other geophysical processes located within the radius of the representativeness of the actual data of aerological sounding at the «MineralnyeVody» station.The results of the study and  their discussion. …”
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A Case Study in China to Determine Whether GPS Data and Derivative Indicator Can Be Used to Identify Risky Drivers by Rui Fu, Tong Liu, Yuxi Guo, Shiwei Zhang, Wendong Cheng

“…The main objective is to examine whether GPS data and derivative indicator can be used to identify risky drivers by means of factor analysis. …”
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Building end to end service guarantee system of IoT covering“terminal pipe and cloud” based on signaling big data by Xuli YE, Qiang ZENG, Liyu LIU, Yu SU, Meng KANG

“…With the evolution of wireless communication service types to massive,large connections,small traffic scenarios,mobile Internet has marched into the Internet of things.Carrying on business networking wireless network deployment in communication operators,which provides a wealth of industry applications also enable operators facing huge end-to-end backward challenges to business.An end to end service guarantee system based on the signaling big data and covering the “terminal pipe and cloud” was proposed and constructed,and the six network dimensions in the user,terminal,wireless,transmission,core network and digital network was solved,as well as the four stages of network access,service access,network switching,and service quality of six dimensions and four stages facing the Internet of things.In order to solve the problem of inter professional collaboration and demarcation,the assistant operator completes the transformation from the traditional KPI (key performance indicator) index mode to the service aware KQI (key quality indicator) end to end full process business guarantee mechanism,and provides the end to end full process one key positioning ability guarantee for the Internet of things applications.…”
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Integrating multi-source data for life-cycle risk assessment of bridge networks: a system digital twin framework by Ziheng Geng, Chao Zhang, Yishuo Jiang, Dora Pugliese, Minghui Cheng

“…However, existing works that incorporate data for probabilistic risk assessment typically focus on individual bridges and rely on single-source data, limiting their scope and applicability. …”
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Data-driven thrust prediction in applied-field magnetoplasmadynamic thrusters for space missions using artificial intelligence-based models by Tarik Pinaffo Almeida, Shahin Alipour Bonab, Mohammad Yazdani-Asrami

“…This data-driven approach can be helpful in upscaling or specially down-scaling AF-MPDTs to make them better suited for many lower power space applications.…”
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Development and evaluation of accurate 3D human models using scan data: a comparison with SMPL and CLO models by Gyungin Jung, Minjoo Kang, Sungmin Kim

“…Abstract Accurate digital reconstruction of the human body is critical for a wide range of applications, including apparel design, ergonomic evaluation, and biomedical engineering. …”
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MapReduce based big data framework using associative Kruskal poly Kernel classifier for diabetic disease prediction by R. Ramani, S. Edwin Raja, D. Dhinakaran, S. Jagan, G. Prabaharan

“…The computation process must be accelerated to achieve early disease prediction in order to accomplish the prospects of ML for big data applications. In this paper, a method named “Associative Kruskal Wallis and MapReduce Poly Kernel (AKW-MRPK)'' is presented for early disease prediction. …”
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Virtual reality in undergraduate and postgraduate nursing education: a scoping review protocol integrating data mining for topic discovery by Silvia Ronchi, Rosario Caruso, Diletta Fabrizi, Michela Luciani, Celeste M. Alfes, Noriyo Colley, Arianna Magon, Gianluca Conte, Anna Valli, Stefano Terzoni, Silvia Cilluffo, Maura Lusignani, Davide Ausili

“…Two independent reviewers will assess study eligibility and extract data using a standardized form. Additionally, data mining techniques, including Latent Dirichlet Allocation enhanced by Bayesian optimization, will be employed to identify trends and emerging topics in the field, providing valuable insights for educators, researchers, and policymakers. • This scoping review protocol outlines the methodology for systematically mapping the existing literature on VR in nursing education, providing a comprehensive overview of its applications at undergraduate and postgraduate levels. • Advanced data mining techniques will be applied to uncover emerging trends and key topics, enhancing the understanding of VR’s evolving role in nursing education. • Findings will offer methodological and practical insights, supporting educators, researchers, and policymakers in optimizing and expanding VR-based learning strategies in nursing.…”
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Integrating single-cell and bulk RNA sequencing data to characterize the heterogeneity of glycan-lipid metabolism polarization in hepatocellular carcinoma by Peng Lin, Qiong Qin, Xiang-yu Gan, Jin-shu Pang, Rong Wen, Yun He, Hong Yang

“…Abstract Background Hepatocellular carcinoma (HCC) is high heterogeneity and remains an unmet medical challenge, but their metabolic heterogeneity has not been fully uncovered and required clinical applicable translational strategies. Methods By analyzing the RNA sequencing data in the in-house cohort and public HCC cohorts, we identified a metabolic subtype of HCC associated with multi-omics features and prognosis. …”
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A novel fixed-point based two-step inertial algorithm for convex minimization in deep learning data classification by Kobkoon Janngam, Suthep Suantai, Rattanakorn Wattanataweekul

“…We then demonstrate its versatility by applying it to convex minimization problems and extending it to data classification tasks, specifically through a multihidden-layer extreme learning machine (MELM). …”
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ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data by Rebecca K. Lindsey, Sorin Bastea, Sebastien Hamel, Yanjun Lyu, Nir Goldman, Vincenzo Lordi

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A Sensor Data Prediction and Early-Warning Method for Coal Mining Faces Based on the MTGNN-Bayesian-IF-DBSCAN Algorithm by Mingyang Liu, Xiaodong Wang, Wei Qiao, Hongbo Shang, Zhenguo Yan, Zhixin Qin

“…By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. …”
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Data-Driven Motion Planning: A Survey on Deep Neural Networks, Reinforcement Learning, and Large Language Model Approaches by Gabriel Peixoto de Carvalho, Tetsuya Sawanobori, Takato Horii

“…Our work is the first to integrate all three major data-driven approaches, discussing their applications and future research directions.…”
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Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series Data by David Orlando Salazar Torres, Diyar Altinses, Andreas Schwung

“…In time series analysis, the ability to effectively handle data with varying sampling rates is crucial for accurate modeling and analysis. …”
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Evaluation of the Efficacy of a Nomogram to Predict Multidrug-Resistant Pulmonary Infections Based on Data from Neurosurgery Ward Patients by Zhou R, Chen X, Jia H, Duan W

“…DCA confirmed the clinical applicability of this nomogram.Conclusion: We propose for the first time that augmented renal clearance (ARC) is an independent risk factor for the occurrence of MDR pneumonia in neurosurgical patients. …”
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A novel approach for graph-based real-time anomaly detection from dynamic network data listened by Wireshark by Muhammet Onur Kaya, Mehmet Ozdem, Resul Das

“… This paper presents a novel approach for real-time anomaly detection and visualization of dynamic network data using Wireshark, globally's most widely utilized network analysis tool. …”
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Photothermal Integration of Multi-Spectral Imaging Data via UAS Improves Prediction of Target Traits in Oat Breeding Trials by David Evershed, Jason Brook, Sandy Cowan, Irene Griffiths, Sara Tudor, Marc Loosley, John H. Doonan, Catherine J. Howarth

“…The modelling and prediction of important agronomic traits using remotely sensed data is an evolving science and an attractive concept for plant breeders, as manual crop phenotyping is both expensive and time consuming. …”
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FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting by He Zhu, Jun Bai, Na Li, Xiaoxiao Li, Dianbo Liu, David L. Buckeridge, Yue Li

“…Abstract Federated learning (FL) enables collaborative analysis of decentralized medical data while preserving patient privacy. However, the covariate shift from demographic and clinical differences can reduce model generalizability. …”
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