First performance evaluation of easyPET.3D, a high-resolution and cost-effective benchtop preclinical PET scanner by Fabiana M. Ribeiro, Pedro M. C. C. Encarnação, Ana L. M. Silva, Pedro M. M. Correia, Afonso X. Pinto, Regina G. Oliveira, José Sereno, Mariana Lapo Pais, Antero Abrunhosa, Ismael F. Castro, Ana C. Santos, João F. C. A. Veloso

“…The overall performance showed that easyPET.3D has potential for entry-level preclinical applications.…”
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River suspended-sand flux computation with uncertainty estimation using water samples and high-resolution ADCP measurements by J. Marggraf, G. Dramais, J. Le Coz, B. Calmel, B. Camenen, D. J. Topping, W. Santini, G. Pierrefeu, F. Lauters

“…This method that comes with a flexible, open-source code is the first to propose an applicable uncertainty estimation that could be adapted to other flux computation methods.…”
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Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches by Mehmet Yilmaz, Erkut Yalcin, Fatih Demir, Ahmet Munir Ozdemir, Muhammed Atar, Aysegul Gunes, Beyza Furtana Yalcin, Ertugrul Cambay

“…Unlike traditional manual inspections, which are time-consuming and error-prone, our approach leverages advanced segmentation techniques to ensure accurate, pixel-level classification of various crack types. …”
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IMERG-Like Precipitation Retrieval From Geo-Kompsat-2A Observations Using Conditional Generative Adversarial Networks by Kyung-Hoon Han, Jaehoon Jeong, Sungwook Hong

“…The results demonstrated a probability of detection of 0.607, a critical success index of 0.482, a root-mean-square error of 0.759 mm/h, and a correlation coefficient of 0.671. …”
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Unmanned Aerial Vehicle–Unmanned Ground Vehicle Centric Visual Semantic Simultaneous Localization and Mapping Framework with Remote Interaction for Dynamic Scenarios by Chang Liu, Yang Zhang, Liqun Ma, Yong Huang, Keyan Liu, Guangwei Wang

“…In dynamic scenarios, the localization accuracy attains an average absolute pose error (APE) of 0.1275 m. This outperforms state–of–the–art methods like Dynamic–VINS (0.211 m) and ORB–SLAM3 (0.148 m) on the EuRoC MAV Dataset. …”
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Minimum Audible Angle in 3rd-Order Ambisonics in Horizontal Plane for Different Ambisonic Decoders by Katarzyna Sochaczewska, Karolina Prawda, Paweł Małecki, Magdalena Piotrowska, Jerzy Wiciak

“…The findings of this study provide valuable insights for spatial audio applications based on ambisonic technology.…”
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Using machine learning to predict the rupture risk of multiple intracranial aneurysms by Junqiang Feng, Chunyi Wang, Yu Wang, He Liu, He Liu

“…However, its clinical applicability is currently limited by suboptimal performance for ruptured aneurysms, which is more suited for identifying MIAs after rupture rather than predicting future rupture risk, and the lack of external validation. …”
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Point-to-Interval Prediction Method for Key Soil Property Contents Utilizing Multi-Source Spectral Data by Shuyan Liu, Dongyan Huang, Lili Fu, Shengxian Wu, Yanlei Xu, Yibing Chen, Qinglai Zhao

“…Accurate point prediction and interval prediction of soil properties serve as crucial references for making informed decisions regarding fertilizer applications. Traditional soil testing methods often entail laborious and resource-intensive chemical analyses. …”
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Linear Active Disturbance Rejection Control-Based Voltage Controller for Buck and Boost DC/DC Converters in DC Distribution Grids by Asimenia Korompili, Oemer Ekin, Marija Stevic, Veit Hagenmeyer, Antonello Monti

“…This L-ADRC model is formulated according to the generalised ADRC concept, making the voltage controller applicable to converters of the non-minimum phase (NMP) class, like the boost DC/DC converters in voltage control mode, and suitable for matched and mismatched disturbances, opposite to the original ADRC, which exists mostly in literature of converter controllers. …”
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Predicting Oncological and Functional Outcomes by Nephrectomy Type for T1 Renal Tumors Using Machine Learning Models by Dongrul Shin, Maisy Song, Jungyo Suh, Cheryn Song

“…Further multi-institutional validation is needed to confirm its generalizability and applicability across diverse clinical settings.…”
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Neural network-based snow depth retrieval from AMSR-2 brightness temperatures using ICESat-2 measurement as ground truth by Sunny Sun-Mack, Yongxiang Hu, Xiaomei Lu, Yan Chen, Ali Omar

“…The approach enables daily, long-term monitoring of snow depth over Arctic sea ice, offering significant benefits for climate research and operational applications such as snowstorm and blizzard monitoring.…”
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Guiding Field Measurement of Pine Tree Crowns: A Geometric Shape Comparison Using Drone Imagery by A. Hosingholizade, Y. Erfanifard, S. K. Alavipanah, V. García Millan, S. Pirasteh, S. Pirasteh

“…Three analyses were performed to assess the results: correlation analysis (R²), relative root mean square error (RRMSE), and shape analysis, which included overestimation (OverID) and underestimation (UnderID) indices. …”
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FISNET: A Learnable Fusion-Based Iris Segmentation Network Improving Robustness Across NIR and VIS Modalities by Geetanjali Sharma, Gaurav Jaswal, Aditya Nigam, Raghavendra Ramachandra

“…Iris is one of the most distinctive biometric traits used for reliable identity verification in applications such as border control, secure access systems, and national ID programs. …”
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Rapid, Precise, and Clinically Relevant Quantification of Urinary Albumin and Creatinine Using a NanoDrop UV/Vis Spectrophotometer by Keith E. Dias, Karly C. Sourris, Jay C. Jha, Karin Jandeleit-Dahm, Bayden R. Wood

“…Partial Least Squares Regression (PLS-R) analysis for BSA yielded a Root Mean Square Error of Calibration (RMSEC) and Cross-Validation (RMSECV) values of 66.93 and 73.92 mg/L, respectively. …”
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Regularized Kaczmarz Solvers for Robust Inverse Laplace Transforms by Marta González-Lázaro, Eduardo Viciana, Víctor Valdivieso, Ignacio Fernández, Francisco Manuel Arrabal-Campos

“…Inverse Laplace transforms (ILTs) are fundamental to a wide range of scientific and engineering applications—from diffusion NMR spectroscopy to medical imaging—yet their numerical inversion remains severely ill-posed, particularly in the presence of noise or sparse data. …”
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A big data analysis algorithm for massive sensor medical images by Sarah A. Alzakari, Nuha Alruwais, Shaymaa Sorour, Shouki A. Ebad, Asma Abbas Hassan Elnour, Ahmed Sayed

“…Data arrival rate, resource consumption, propagation delay, transaction epoch, true positive rate, false alarm rate, and root mean square error (RMSE) are some metrics used to evaluate the proposed task.…”
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Lightweight and hybrid transformer-based solution for quick and reliable deepfake detection by Geeta Rani, Atharv Kothekar, Shawn George Philip, Vijaypal Singh Dhaka, Ester Zumpano, Ester Zumpano, Eugenio Vocaturo, Eugenio Vocaturo

“…Its encoder captures the contextual information from the input patches, and Gaussian Error Linear Unit resolves the vanishing gradient problem.ResultsThe Linformer component reduces the size of the attention matrix. …”
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Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

“…First, in order to reduce the dimensionality of raw malware activity data by 50%, while at the same timestamp preserving critical information, as evidenced by minimal reconstruction error, we apply an autoencoder-based feature learning technique. …”
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Optimizing photocatalytic dye degradation: A machine learning and metaheuristic approach for predicting methylene blue in contaminated water by Yunus Ahmed, Keya Rani Dutta, Sharmin Nahar Chowdhury Nepu, Meherunnesa Prima, Hamad AlMohamadi, Parul Akhtar

“…It reached a very high R² of 0.9998 on the training set and 0.9915 on the test set, coupled with low error metrics, showcasing its strong generalization capability. …”
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A Refined Model for Ablation Through Cavitation Bubbles with Ultrashort Pulse Lasers by Shwetabh Verma, Samuel Arba Mosquera

“…Taking a photodisruption model coefficient commonly used in the literature, the root mean square error between the two interaction models was minimized using the generalized reduced gradient fitting method to calculate the optimum scaling factors for the plasma model. …”
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