Theory of an Automatic Seepage Meter and Ramifications for Applications by Vitaly A. Zlotnik, D. Kip Solomon, David P. Genereux, Troy E. Gilmore, C. Eric Humphrey, Aaron R. Mittelstet, Anatoly V. Zlotnik

“…We quantify how the accuracy of parameter estimation depends on test duration and noise amplitude and propose how our analysis can be used to optimize field test protocols. On this basis, changing the ASM geometry by increasing the radius and decreasing tube insertion depth may enable ASM field test protocols that estimate interface flux and hydraulic conductivity faster while maintaining desired accuracy. …”
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Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects by Soheil Mohseni, Alan C. Brent

“…These integrated resource planning endeavours primarily aim to minimise total discounted system costs while adhering to a network of interconnected technical constraints, encompassing considerations of reliability, resilience, and the integration of renewable energy sources. …”
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Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data by Michele Croci, Manuele Ragazzi, Alessandro Grassi, Giorgio Impollonia, Stefano Amaducci

“…TR prediction was more challenging while RF showed promising results in LOGOCV (nRMSE = 22.1 %), all ML models were outperformed by the NullModel in the more realistic LOYOCV scenario. …”
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Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks by Ahmed Abd El Moaty Mohamed Gouda, Ehab K. I. Hamad, Aziza I. Hussein, M. Mourad Mabrook, A. A. Donkol

“…For 16-beams, the accuracy increased from 86.17% to 94.64 %, while for 8-beams, the accuracy increased from 90.24% to 97.11%. …”
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A Comparative Performance Evaluation of OFDM, GFDM, and OTFS in Impulsive Noise Channels by Mohsen Sheikh-Hosseini, Farhad Rahdari, Hazhir Ghasemnezhad, Somayeh Ahmadi, Murat Uysal

“…This method examines the impact of variations in the precoder order and explores the application of iterative algorithms for more optimal designing of the precoder. …”
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Uterine hydatidosis: casuistry is possible by A. L. Tikhomirov, V. V. Kazenashev, A. A. Dubinin, R. R. Sadikova, M. V. Maminova, J. S. Globa, A. V. Bukharov

“…Compared with common gynecological disease such as uterine fibroids, ovarian cyst and malignancies uterine hydatidosis may be identified only in 0.16 % cases.Aim: to present a clinical case of uterine hydatid cyst in order to optimize algorithms for differential diagnosis of primary pelvic echinococcosis and gynecological pathology, which is necessary for successfully conducted timely surgical treatment.Clinical case. …”
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Development of an ensemble prediction model for acute graft-versus-host disease in allogeneic transplantation based on machine learning by Lin Song, Xingwei Wu, Mengjia Xu, Ling Xue, Xun Yu, Zongqi Cheng, Chenrong Huang, Liyan Miao

“…Thus, the purpose of this study was to develop and optimize models by Cox regression and machine learning algorithms to predict the risk of aGVHD in which cyclosporin A exposure and common clinical factors were included as variables. …”
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ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments by Fei Gao, Yang Tian, Yongliang Wu, Yunxia Zhang

“…Furthermore, the ST-YOLOv8 model outperforms several state-of-the-art multi-scale ship detection algorithms on both datasets. In summary, the ST-YOLOv8 model, by integrating advanced neural network architectures and optimization techniques, significantly improves detection accuracy and reduces false detection rates. …”
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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

“…Quantitative evaluation via mean squared error (MSE), Wasserstein distance, total variation, peak signal-to-noise ratio (PSNR), and runtime demonstrates that Wasserstein–Kaczmarz attains an optimal balance of speed (0.53 s per inversion) and accuracy (MSE = <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.7</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>8</mn></mrow></msup></mrow></semantics></math></inline-formula>), while TRAIn achieves the highest fidelity (MSE = <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.5</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>8</mn></mrow></msup></mrow></semantics></math></inline-formula>) at a modest computational cost. …”
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A novel lightweight YOLOv8-PSS model for obstacle detection on the path of unmanned agricultural vehicles by Zhijian Chen, Yijun Fang, Jianjun Yin, Shiyu Lv, Farhan Sheikh Muhammad, Lu Liu

“…When compared with other algorithms, such as Faster RCNN, SSD, YOLOv3-tiny, and YOLOv5, the improved model strikes an optimal balance between parameter count, computational efficiency, detection speed, and accuracy, yielding superior results. …”
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Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning by Hui Xu, Xingwang Peng, Ziyu Peng, Rui Wang, Rui Zhou, Lianguo Fu

“…Second, ML models were constructed based on the logistic regression (LR), decision tree (DT), support vector machine (SVM) and eXtreme Gradient Boosting (XGBoost) algorithms, after which random sampling and tenfold cross-validation were separately used to evaluate and compare these models and identify the optimal model. …”
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Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…In this study, we investigate the predictive efficacy of distinguishing between benign and malignant thyroid nodules by employing traditional machine learning algorithms and a deep transfer learning model, aiming to advance the diagnostic paradigm in this field. …”
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Self-Organizing Wireless Sensor Networks Solving the Coverage Problem: Game-Theoretic Learning Automata and Cellular Automata-Based Approaches by Franciszek Seredynski, Miroslaw Szaban, Jaroslaw Skaruz, Piotr Switalski, Michal Seredynski

“…In this paper, we focus on developing self-organizing algorithms aimed at solving, in a distributed way, the coverage problem in Wireless Sensor Networks (WSNs). …”
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Analysis of spatio-temporal fungal growth dynamics under different environmental conditions by Liselotte De Ligne, Guillermo Vidal-Diez de Ulzurrun, Jan M. Baetens, Jan Van den Bulcke, Joris Van Acker, Bernard De Baets

“…An RH of 65% (independent of temperature) for C. puteana and a temperature of 30 °C (independent of RH) for both C. puteana and R. solani therefore always resulted in limited fungal growth, while the optimal growing conditions were at 20 °C and 75% RH and at 25 °C and 80% RH for R. solani and at 20 °C and 75% RH for C. puteana. …”
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Optimising management against dynamic threats: A spatially explicit approach based on integer programming by José Salgado‐Rojas, Virgilio Hermoso, Eduardo Álvarez‐Miranda

“…Employing a Warm‐start algorithmic strategy ensures rapid generation of feasible solutions, enhancing the model's practical applicability and scalability. …”
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Assessing the deep learning based image quality enhancements for the BGO based GE omni legend PET/CT by Meysam Dadgar, Amaryllis Verstraete, Jens Maebe, Yves D’Asseler, Stefaan Vandenberghe

“…Conclusion This study conducted a thorough evaluation of deep learning algorithms in the GE Omni Legend PET/CT scanner, demonstrating that these methods enhance image quality, with notable improvements in CRC and CNR, thereby optimizing lesion detectability and offering opportunities to reduce image acquisition time.…”
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Severe COVID-19 in Third Trimester Pregnancy: Multidisciplinary Approach by Jerald Pelayo, Gabriella Pugliese, Grace Salacup, Eduardo Quintero, Adeeb Khalifeh, David Jaspan, Bhavna Sharma

“…This report highlights the disease progression of COVID-19 in a pregnant woman, the clinical challenges in the critical care aspect of patient management, and the proposed multidisciplinary strategies utilizing an algorithmic approach to optimize maternal and neonatal outcomes.…”
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Revolutionize 3D-Chip Design With Open3DFlow, an Open-Source AI-Enhanced Solution by Yifei Zhu, Zhenxuan Luan, Dawei Feng, Weiwei Chen, Lei Ren, Zhangxi Tan

“…<italic>Open3DFlow</italic>&#x2019;s open-source nature allows seamless integration of custom AI optimization algorithms. As a showcase, we leverage large language models (LLMs) to help the bonding pad placement. …”
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Programmed cell death signatures-driven microglial transformation in Alzheimer’s disease: single-cell transcriptomics and functional validation by Mi-Mi Li, Ying-Xia Yang, Ya-Li Huang, Shu-Juan Wu, Wan-Li Huang, Li-Chao Ye, Ying-Ying Xu

“…High PCDS correlated with upregulated pathways related to inflammation and immune response, while low PCDS associated with protective pathways. …”
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Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease by Daryll Philip, Rebecca Hodgkiss, Swarnima Kollampallath Radhakrishnan, Akshat Sinha, Animesh Acharjee

“…These models were then employed for cross-disease analysis, revealing that models trained on GC data successfully predicted IBD biomarkers, while CRC models predicted GC biomarkers with optimal performance scores. …”
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