Long-Term Monitoring of Coastal Water Quality Using Sentinel-2 Satellite Images and Google Earth Engine: The Case Study Izmir and Erdek Bays by O. Y. Altuntas, M. Saygi, I. Colkesen

“…Indices such as the Normalized Difference Chlorophyll Index (NDCI) and the Normalized Difference Aquatic Vegetation Index (NDAVI) were used for a time series analysis to evaluate water quality, showing high values in both study areas during 2020, particularly in summer, when the correlation was strongest. …”
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Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data by Bo Lu, Bo Lu, Qingyun Liu, Hao Liu, Tianxiang Long, Tianxiang Long

“…By applying the XGBoost algorithm and SHAP (SHapley Additive exPlanations), an explainable machine learning framework was established to evaluate the importance of various factors, explore the nonlinear relationships between variables and walking activity, and analyze the interaction effects among these variables.ResultsThe findings underscore the significant impact of several key factors, including the proportion of sports land, proximity to water bodies, and Normalized Difference Vegetation Index NDVI, alongside the notable influence of six distinct campus area types. …”
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Impacts of Spatial and Temporal Resolution on Remotely Sensed Corn and Soybean Emergence Detection by Feng Gao, Martha Anderson, Rasmus Houborg

“…Planet Fusion captured all crop emergences and outperformed other datasets, with a mean difference (MD) of <1 day, a mean absolute difference (MAD) of <5 days, and a root mean square error (RMSE) of <6 days compared to the ground-observed emergence dates. …”
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Pressure-Related Discrepancies in Landsat 8 Level 2 Collection 2 Surface Reflectance Products and Their Correction by Santosh Adhikari, Larry Leigh, Dinithi Siriwardana Pathiranage

“…This study investigates the root cause of these differences by analyzing the assumptions made in the Land Surface Reflectance Code (LaSRC), the atmospheric correction algorithm of Landsat 8, as currently implemented at United States Geological Survey Earth Resources Observation and Science (USGS EROS), and proposes a correction method. …”
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Comprehensive benchmarking of genome editing quantification methods for plant applications by Zheng Gong, Yan Zhang, Di Xia, Sohye Yoon, Peter Alexander Crisp, José Ramón Botella

“…We measured genome editing efficiency from 20 transiently expressed Cas9 targets using different techniques, including targeted amplicon sequencing (AmpSeq), PCR-restriction fragment length polymorphism (RFLP) assays, T7 endonuclease 1 (T7E1) assays, Sanger sequencing of amplicon products (deconvoluted and analyzed using three algorithms), PCR-capillary electrophoresis/InDel detection by amplicon analysis (PCR-CE/IDAA), and droplet digital PCR (ddPCR). …”
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Mutational landscape of SARS-CoV-2 genome in Turkey and impact of mutations on spike protein structure. by Ozden Hatirnaz Ng, Sezer Akyoney, Ilayda Sahin, Huseyin Okan Soykam, Gunseli Bayram Akcapinar, Ozkan Ozdemir, Derya Dilek Kancagi, Gozde Sir Karakus, Bulut Yurtsever, Ayse Sesin Kocagoz, Ercument Ovali, Ugur Ozbek

“…We compared these findings to USA, Europe, and Asia data as well. We have also evaluated the effects of spike protein variations, detected in a group of genome sequences of 13 patients who applied to our clinic, by using 3D modeling algorithms. …”
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Development of Recurrent Neural Networks for Thermal/Electrical Analysis of Non-Residential Buildings Based on Energy Consumptions Data by Elisa Belloni, Flavia Forconi, Gabriele Maria Lozito, Martina Palermo, Michele Quercio, Francesco Riganti Fulginei

“…Uncertainty analyses are carried out to evaluate the behavior effectiveness of the artificial neural networks (ANNs) in different weather conditions, and the root mean square error (RMSE) is calculated in terms of mean air temperatures. …”
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Predicting reticuloruminal pH and subacute ruminal acidosis of individual cows using machine learning and Fourier-transform infrared spectroscopy milk analysis by T. Touil, F. Huot, S. Claveau, A. Bunel, D. Warner, D.E. Santschi, R. Gervais, É.R. Paquet

“…A total of 36 different models were developed and evaluated for both rpH and SARA prediction. …”
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Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis by Liping Tang, Dong Pang, Chengbang Wang, Jiali Lin, Shaohua Chen, Jiangchun Wu, Junqi Cui

“…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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Gender and racial bias issues in a commercial "tone of voice" analysis system. by Nicole R Holliday, Paul E Reed

“…This study evaluates how Halo performs for American English speakers of different races and genders. …”
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Optimising test intervals for individuals with type 2 diabetes: A machine learning approach. by Sasja Maria Pedersen, Nicolai Damslund, Trine Kjær, Kim Rose Olsen

“…Model performance is evaluated using ROC-AUC, and optimal intervals are determined based on a "time-to-next-positive-test" concept, with different durations associated with specific intervals.…”
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Causal estimation of the relationship between reproductive performance and the fecal bacteriome in cattle by Yutaka Taguchi, Haruki Yamano, Yudai Inabu, Hirokuni Miyamoto, Koki Hayasaki, Noriyuki Maeda, Yoshiro Kanmera, Seiji Yamasaki, Noboru Ota, Kenji Mukawa, Atsushi Kurotani, Shigeharu Moriya, Teruno Nakaguma, Chitose Ishii, Makiko Matsuura, Tetsuji Etoh, Yuji Shiotsuka, Ryoichi Fujino, Motoaki Udagawa, Satoshi Wada, Jun Kikuchi, Hiroshi Ohno, Hideyuki Takahashi

“…Artificial insemination (AI) was performed after 300 days of age, and the number of AI required for pregnancy (AI number) was evaluated. The relationship of the fecal bacteriome at 150 and 300 days of age and reproductive performance was visualized using statistical structural equation modelling between traits based on four types of machine-learning algorithms (linear discriminant analysis, association analysis, random forest, and XGBoost). …”
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Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review by Seng Hansun, Ahmadreza Argha, Ivan Bakhshayeshi, Arya Wicaksana, Hamid Alinejad-Rokny, Greg J Fox, Siaw-Teng Liaw, Branko G Celler, Guy B Marks

“…ObjectiveWe aimed to provide a comprehensive evaluation of AI-based algorithms for TB detection across various data modalities. …”
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Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach by Li X, Xiao Y, Yang M, Zhang X, Yuan Z, Zhang Z, Zhang H, Liu L, Zhao M

“…Functional enrichment analyses were conducted to explore the biological roles of these genes. LASSO and SVM-RFE algorithms identified shared diagnostic genes, which were evaluated using receiver operating characteristic (ROC) curves. …”
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Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study by Zhen Lu, Binhua Dong, Hongning Cai, Tian Tian, Junfeng Wang, Leiwen Fu, Bingyi Wang, Weijie Zhang, Shaomei Lin, Xunyuan Tuo, Juntao Wang, Tianjie Yang, Xinxin Huang, Zheng Zheng, Huifeng Xue, Shuxia Xu, Siyang Liu, Pengming Sun, Huachun Zou

“…We trained a supervised machine learning model and developed pathways to classify individuals before evaluating its diagnostic validity and usability on an external cohort. …”
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Layer-by-Layer Multifractal Scanning of Optically Anisotropic Architectonics of Blood Plasma Films: Fundamental and Applied Aspects by Alexander Ushenko, Natalia Pavlyukovich, Oksana Khukhlina, Olexander Pavlyukovich, Mykhaylo Gorsky, Iryna Soltys, Alexander Dubolazov, Yurii Ushenko, Olexander Salega, Ivan Mikirin, Jun Zheng, Zhebo Chen, Lin Bin

“…The practical aspect of this work is to evaluate the diagnostic potential of the Jones-matrix theziography method for identifying and differentiating changes in the birefringence of supramolecular networks in blood plasma facies caused by the long-term effects of COVID-19. …”
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ImmuProgML: machine learning-based dissection of cancer-immune dynamics during tumor progression to improve immunotherapy by Hanxiao Zhou, Lan Mei, Qianyi Lu, Yakun Zhang, Yue Sun, Caiyu Zhang, Han Jiang, Jiajun Zhou, Xia Li, Yunpeng Zhang, Shangwei Ning

“…By introducing the DNEX score, an integrative metric combining differential expression and network analysis, ImmuProgML evaluated gene immunomodulation activity during tumor progression and identified immunotherapy targets. …”
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Incorporating genotype information in a precise prediction model for platinum sensitivity in epithelial ovarian cancer by Nai-Yi Du, Yan Li, Hui Zheng, Ya-Kun Liu, Lu-Sha Liu, Jianbang Xie, Shan Kang

“…Coefficients of the synergistic effect for each SNP were determined, and an algorithm of the Drug Sensitivity Index (DSI) was built. …”
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Energy Efficient Heat Exchange Network for the Oil Vacuum Distillation Facility by Ved V.E., Ilchenko M.V., Myronov A.N.

“…The task is achieved by applying design algorithms of a pinch analysis. The most important result of the work is the proven possibility of reducing the external heat carriers’ energy by 1.87 MW and increasing the thermal energy recovery inside the system to 11.26 MW. …”
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Addressing data imbalance in collision risk prediction with active generative oversampling by Li Li, Xiaoliang Zhang

“…Specifically, our method’s results are above 0.92 on all evaluation indicators, with an average improvement of 23–28.3% compared to the worst-performing ENN method. …”
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