A Robust Tool for 3D Rail Mapping Using UAV Data Photogrammetry, AI and CV: qAicedrone-Rail by Innes Barbero-García, Diego Guerrero-Sevilla, David Sánchez-Jiménez, David Hernández-López

“…The approach integrates YOLOv8 for initial detection and segmentation, photogrammetry for 3D data extraction and computer vision techniques with a Multiview approach to enhance accuracy. …”
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Data-driven cultural background fusion for environmental art image classification: Technical support of the dual Kernel squeeze and excitation network. by Chenchen Liu, Haoyue Guo

“…This study aims to explore a data-driven cultural background fusion method to improve the accuracy of environmental art image classification. …”
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22 Advancing clinical trial reporting and AI integration: Optimizing protocol data extraction using LLMs and regulatory best practices by Ramya Sri Baluguri, Nicholas Anderson

“…Objectives/Goals: This study aimed to enhance clinical trial data management through large language model information retrieval and generation techniques within the clinical trial reporting workflow. …”
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Financial Performance of Moroccan Listed Companies: A Multidimensional Analysis of Internal, Macroeconomic, and Institutional Determinants Using Dynamic Panel Data by Asmae AMARHYOUZ, Jalal Azegagh

“…To validate the robustness of the outcomes (heteroskedasticity, autocorrelation, and endogeneity), several panel data estimation techniques were applied, including pooled OLS, fixed effects models, and the Generalized Method of Moments (GMM). …”
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Machine learning-based mapping of Acidobacteriota and Planctomycetota using 16 S rRNA gene metabarcoding data across soils in Russia by E. A. Ivanova, A. R. Suleymanov, D. A. Nikitin, M. V. Semenov, E. V. Abakumov

“…Studying its biogeographical structure and distribution is essential for understanding the rates and mechanisms of microbially mediated soil ecosystem services. This study aimed to investigate the spatial distribution patterns of Acidobacteriota and Planctomycetota across soils in Russia, summarizing data from 16S rRNA gene metabarcoding of topsoils. …”
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Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH by Pouya Aghelpour, Hadigheh Bahrami-Pichaghchi, Farzaneh Karimpour

“…Field measurements of evapotranspiration have always been associated with many difficulties that have led researchers to seek a way to remotely measure this component in horticultural and agricultural areas. This study aims to investigate an indirect approach for daily rice crop evapotranspiration (ETc) measurement by machine learning (ML) techniques and the least available climatic variables. …”
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A Deep Learning‐Based Data Assimilation Approach to Characterizing Coastal Aquifers Amid Non‐Linearity and Non‐Gaussianity Challenges by Chenglong Cao, Jiangjiang Zhang, Wei Gan, Tongchao Nan, Chunhui Lu

“…However, the inherent heterogeneity of coastal aquifers introduces significant uncertainties into SI predictions, potentially diminishing their effectiveness in management decisions. Data assimilation (DA) offers a solution by integrating various types of observational data with the model to characterize heterogeneous coastal aquifers. …”
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Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in Djibouti by Fathi Farah Fadoul, Abdoulaziz Ahmed Hassan, Ramazan Çağlar

“…This paper proposes a model that integrates the autoencoder with the decision tree model using six months of meteorological data, including solar radiation, wind speed, temperature, and other meteorological data. …”
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An Optimization Framework for Waste Treatment Center Site Selection Considering Nighttime Light Remote Sensing Data and Waste Production Fluctuations by Junbao Xia, Yanping Liu, Haozhong Yang, Guodong Zhu

“…In conclusion, by incorporating nighttime light remote sensing data along with advanced machine learning techniques, this study markedly improves forecasting accuracy for waste production while offering effective optimization strategies for site selection and recovery route planning—thereby establishing a robust data foundation aimed at refining urban solid waste management systems.…”
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Comparative evaluation of the effectiveness of video modeling, mobile app (Little Lovely Dentist), and animated video modeling technique in the management of dental anxiety in chil... by Manasa Chinthala, P. V. Ravindranath Reddy, Aruna Kumari Ganapathi, Vem Preethi, Kodali Srija

“…Dental treatments for children are focused on patient comfort and anxiety reduction in clinical settings employing behavior guidance strategies. Aim: To compare and evaluate the effectiveness of three different behavioral modification techniques (video modeling, mobile apps, and animated videos) in alleviating dental anxiety in children. …”
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Assessment of groundwater resources from geophysical and remote sensing data in a basement complex environment using fuzzy-topsis algorithm by Kola Abdul-Nafiu Adiat, Abdulgafar Opeyemi Kolawole, Igbagbo Adedotun Adeyemo, Ayokunle Adewale Akinlalu, Daniel Oluwafunmilade Afolabi

“…To overcome these challenges, this research aims to enhance the accuracy and reliability of groundwater potential assessments by incorporating crucial groundwater-related factors.The Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) method was adopted due to its ability to improve multi-criteria decision-making (MCDM) techniques for effective groundwater resource management. …”
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Automated diagnosis and classification of metacarpal and phalangeal fractures using a convolutional neural network: a retrospective data analysis study by Michael Axenhus, Anna Wallin, Jonas Havela, Sara Severin, Ablikim Karahan, Max Gordon, Martin Magnéli

“…A CNN architecture was based on ResNet and implemented using PyTorch, with the integration of data augmentation techniques. Results: The CNN model achieved a mean weighted AUC of 0.84 for hand fractures, with 86% sensitivity and 76% specificity. …”
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The use of the SPAIRE technique in cases of cemented hemiarthroplasty in the management of displaced intracapsular neck of femur fractures – A pair-matched control study versus a d... by Martin S. Davey, David O'Sullivan, Robert Woods, Paddy Kenny, Fintan Doyle, Adrian J. Cassar Gheiti

“…Introduction: The aim of this study was to analyze and compare clinical, radiological and mortality outcomes of patients who underwent cemented hip hemiarthroplasty for displaced neck of femur fractures using a SPAIRE technique when compared to a pair-matched control cohort who underwent the same procedure using the direct lateral approach. …”
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All‐inside single‐bundle and modified double‐bundle anterior cruciate ligament reconstruction techniques guarantee stability and similar clinical results at over 5 year follow‐up... by Lorenzo Moretti, Raffaele Garofalo, Giuseppe Danilo Cassano, Roberto Calbi, Francesco Fiore, Giuseppe Solarino

“…KL grade increased significantly since pre‐op. Conclusions Our data show significant improvements in all clinical outcome measures, along with excellent KT‐1000 arthrometer values and low clinical failure rates for both the SB all‐inside and modified DB techniques at a mean follow‐up of over 6 years. …”
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Scalable Hyperspectral Enhancement via Patch-Wise Sparse Residual Learning: Insights from Super-Resolved EnMAP Data by Parth Naik, Rupsa Chakraborty, Sam Thiele, Richard Gloaguen

“…A majority of hyperspectral super-resolution methods aim to enhance the spatial resolution of hyperspectral imaging data (HSI) by integrating high-resolution multispectral imaging data (MSI), leveraging rich spectral information for various geospatial applications. …”
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Integration of ground-based and remote sensing data with deep learning algorithms for mapping habitats in Natura 2000 protected oak forests by Lucia Čahojová, Ivan Jarolímek, Barbora Klímová, Michal Kollár, Michaela Michalková, Karol Mikula, Aneta A. Ožvat, Denisa Slabejová, Mária Šibíková

“…Landscape changes caused by climate change require new methods for forest research, analysis, mapping, and monitoring. This study aims to combine ground-based and remote sensing data utilising deep learning techniques to map protected forest habitats and communities within the Natura 2000 network. …”
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Measuring Health System Resilience During the COVID‐19 Pandemic Using Dynamic Indicators of Resilience Based on Sick‐Leave Data by Tom H. Oreel, Sophie Hadjisotiriou, Vítor V. Vasconcelos, Vincent A. W. J. Marchau, Etiënne A.J.A. Rouwette, Rick Quax, Vittorio Nespeca, Jannie Coenen, Hubert P. L. M. Korzilius, Heiman Wertheim, Marcel G. M. Olde Rikkert

“…DIORs were estimated using moving window techniques on the time series data of each healthcare sector, each safety region in the Netherlands, and all healthcare sectors and safety regions in the Netherlands combined. …”
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Integrating multi-omics techniques and in vitro experiments reveals that GLRX3 regulates the immune microenvironment and promotes hepatocellular carcinoma cell proliferation and in... by Yang Li, Yuan Chen, Yuan Chen, Yang Zhang, Yunsheng Fang, Yunsheng Fang, Ling Wu, Ying Zhao, Danqiong Wang, Xiaoyuan Qiao

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Development of a core data set for describing, measuring and reporting the learning curve in studies of novel invasive procedures: study protocol by Deborah D Stocken, Natalie S Blencowe, Jane Blazeby, Kerry Avery, Rhiannon Macefield, Daisy Elliott, Shelley Potter, Neil Corrigan, Christin Hoffmann, Jozel Ramirez, Matthew Kobetic

“…Although the learning curve associated with invasive procedures is a critical part of innovation, it is currently inconsistently defined, measured and reported. This study aims to develop a core data set that can be applied in all studies describing or measuring the learning curve in novel invasive procedures.Methods A core data set will be developed using methods adapted from the Core Outcome Measures in Effectiveness Trials initiative. …”
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Ensemble learning for microbiome-based caries diagnosis: multi-group modeling and biological interpretation from salivary and plaque metagenomic data by Fangqiao Wei, Zailong Wu, Guanghui Li, Xiangyu Sun, Xiangru Shi, Lei Tan, Tianxiang Ai, Long Qu, Shuguo Zheng

“…Next-generation sequencing techniques have been widely used, generating vast amounts of data which is underexplored. …”
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