Land Use Cover Mapping of Water Melon and Cereals in Southern Italy by Costanza Fiorentino, Domenico Ventrella, Luisa Giglio, Enza Di Giacomo, Raffaele Lopez

“…The new high-resolution images from the satellites as IKONOS, SPOT5, Quickbird2 give us the opportunity to map ground features, which were not detectable in the past, by using medium resolution remote sensed data (LANDSAT). …”
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A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud by Zeyu Wang, Xiaofang Chen, Yiwei Wu, Linke Jiang, Shiming Lin, Gang Qiu

“…This study aims to improve fraud detection accuracy using machine learning techniques. …”
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Sensitive neoantigen discovery by real-time mutanome-guided immunopeptidomics by Ilja E. Shapiro, Florian Huber, Justine Michaux, Michal Bassani-Sternberg

“…Implementation in clinical settings, however, must achieve global depth, capturing the entirety of the immunopeptidome, maintain high target sensitivity, and cater to scarce sample inputs and short turnaround time. …”
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Paradoxical relationship between speed and accuracy in olfactory figure-background segregation. by Lior Lebovich, Michael Yunerman, Viviana Scaiewicz, Yonatan Loewenstein, Dan Rokni

“…In natural settings, many stimuli impinge on our sensory organs simultaneously. …”
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Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo. by Ronald E Crump, Ching-I Huang, Edward S Knock, Simon E F Spencer, Paul E Brown, Erick Mwamba Miaka, Chansy Shampa, Matt J Keeling, Kat S Rock

“…It was found that the model needed to capture improvements in passive detection to match observed trends in the data within former Bandundu and Bas Congo provinces indicating these regions have substantially reduced time to detection. …”
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A comparison of CXR-CAD software to radiologists in identifying COVID-19 in individuals evaluated for Sars CoV-2 infection in Malawi and Zambia. by Sam Linsen, Aurélie Kamoun, Andrews Gunda, Tamara Mwenifumbo, Chancy Chavula, Lindiwe Nchimunya, Yucheng Tsai, Namwaka Mulenga, Godfrey Kadewele, Eunice Nahache Kajombo, Veronica Sunkutu, Jane Shawa, Rigveda Kadam, Matthew Arentz

“…In high burden TB countries, the use of highly portable CXR and computer aided detection software has been adopted more broadly to improve the screening and triage of individuals for TB, but there is little evidence in these settings regarding COVID-19 CAD performance. …”
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Two new Loci for body-weight regulation identified in a joint analysis of genome-wide association studies for early-onset extreme obesity in French and german study groups. by André Scherag, Christian Dina, Anke Hinney, Vincent Vatin, Susann Scherag, Carla I G Vogel, Timo D Müller, Harald Grallert, H-Erich Wichmann, Beverley Balkau, Barbara Heude, Marjo-Riitta Jarvelin, Anna-Liisa Hartikainen, Claire Levy-Marchal, Jacques Weill, Jérôme Delplanque, Antje Körner, Wieland Kiess, Peter Kovacs, Nigel W Rayner, Inga Prokopenko, Mark I McCarthy, Helmut Schäfer, Ivonne Jarick, Heiner Boeing, Eva Fisher, Thomas Reinehr, Joachim Heinrich, Peter Rzehak, Dietrich Berdel, Michael Borte, Heike Biebermann, Heiko Krude, Dieter Rosskopf, Christian Rimmbach, Winfried Rief, Tobias Fromme, Martin Klingenspor, Annette Schürmann, Nadja Schulz, Markus M Nöthen, Thomas W Mühleisen, Raimund Erbel, Karl-Heinz Jöckel, Susanne Moebus, Tanja Boes, Thomas Illig, Philippe Froguel, Johannes Hebebrand, David Meyre

“…In summary, we have completed a meta-analysis of two GWAS which both focus on extremely obese children and adolescents and replicated our findings in a large followed-up data set. We observed that genetic variants in or near FTO, MC4R, TMEM18, SDCCAG8, and TNKS/MSRA were robustly associated with early-onset obesity. …”
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Quantifying the Impacts of Grain Plantation Decline on Domestic Grain Supply in China During the Past Two Decades by Yizhu Liu, Jing Zhu, Tingting He, Hang Liu

“…To address these gaps, this study introduces trend-change detection and factor-contribution analysis, uses long-term grain sown area data to identify regions with significant grain retreat, and quantifies the relative influence of population shifts, crop yield improvements, and sown area changes on food supply. …”
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Improvement of ultrasonic inspection of wheelset axles of railway rolling stock by A. N. Kireev, V. A. Vitrenko

“…However, the application of the graphs method amplitude-distance-diameter leads to an increased amount of noises during the process of ultrasonic inspection at the beginning of the inspected range of flaw-detection. The paper presents a mathematical apparatus and on its basis a standardless method for the setting of time function is developed to adjust the sensitivity of the ultrasonic flaw detector during the inspection of wheelset axes of railway rolling stock, allowing to exclude disadvantages of application of standard samples of the enterprise during the control and the diagram method the amplitude-distance-diameter, and accordingly, increase the reliability of the results of ultrasonic testing. …”
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Lipopolysaccharide-binding protein (LBP): a prognostic biomarker for gastric cancer linked to immune infiltration by Hao Lv, Mei Lv, Xuyang Guo, Xiaoman Zhu, Yue Chao, Dandan Li

“…Abstract Background Gastric cancer, characterized by rising global incidence and mortality, faces significant challenges due to the lack of effective early detection methods, delaying timely interventions and underscoring the need for novel biomarkers. …”
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Integrated Bioinformatics and Experimental Validation Reveal Macrophage Polarization-Related Biomarkers for Osteoarthritis Diagnosis by He Q, Liu L, Hu X, Lin L, Song Z, Xia Y, Lin Q, Wei J, Li S

“…Least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to identify hub genes and construct a diagnostic model validated through internal datasets and multiple external bulk RNA-seq and single-cell RNA-seq data. Additionally, various analyses, including immune infiltration, gene set enrichment analysis, competing endogenous RNA (ceRNA) construction, and drug prediction, were conducted. …”
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Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning by Viacheslav M. Sadykov, Leon Ofman, Scott A. Boardsen, Yogesh, Parisa Mostafavi, Lan K. Jian, Kristopher Klein, Mihailo Martinović

“…Using 34 hybrid particle-in-cell simulations of kinetic protons and α -particles initialized using plasma parameters derived from solar wind (SW) observations, we prepare a data set of nearly 1600 VDFs representing stable/unstable cases and associated plasma and wave properties. …”
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Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids by Xin-Ru Wen, Jia-Wei Tang, Jie Chen, Hui-Min Chen, Muhammad Usman, Quan Yuan, Yu-Rong Tang, Yu-Dong Zhang, Hui-Jin Chen, Liang Wang

“…Vaginal fluid samples were classified as BV positive or BV negative using the BVBlue Test and clinical microscopy, followed by SERS spectral acquisition to construct the data set. Preliminary SERS spectral analysis revealed notable disparities in characteristic peak features. …”
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The Automatic Joint Teeth Segmentation in Panoramic Dental Images using Mask Recurrent Convolutional Neural Networks with Residual Feature Extraction: by Raghavendra H. Bhalerao, Abhijeet Ashok Salunke, Shristi Sharan, Kamlesh Kumar, Priyank Rathod, Prince Kumar, Manish Chaturvedi, Nandlal Bharwani, Krupa Shah, Dhruv Patel, Keval Patel, Vikas Warikoo, Manisha Abhijeet Salunke, Shashank Pandya

“…To evaluate the efficacy of our method, the UFBA-UESC and Tufts dental image dataset (2500 panoramic dental x-rays) were utilised. 252 x-rays were used in test set, rest of the x-rays were utilised as training(1800 images) and validation datasets(448images) in ratio of 8:2 of the modified MRCNN model. …”
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A Dual-Technology Approach: Handheld NIR Spectrometer and CNN for <i>Fritillaria</i> spp. Quality Control by Fengling Li, Wen Lei, Juan Li, Xiaoting Wang, Jingyu Su, Tangnuer Sahati, Xiahenazi Aierkenjiang, Ruyi Tian, Weihong Zhou, Jixiong Zhang, Jingjing Xia

“…., compared with partial least squares regression (PLSR), the CNN yielded the best performance, with the R² of the test set being 0.9897. Additionally, to improve the interpretability of the adulteration model, a CNN model was established using data whose dimensions had been reduced by PCA (PCA–CNN), which also achieved an R² of 0.9876. …”
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Advancing autonomous vehicle safety assessment: A novel methodology for moving from functional to concrete scenarios using kinetic 3D-LiDAR and SHAP by Minhee Kang, Saeyan Eom, Keeyeon Hwang

“…Using actual driving data, including 3D LiDAR Point Cloud Data (3D-LiDAR PCD), we extracted kinetic properties such as vehicle speed, acceleration, and detected critical accident triggered vehicle (ATV). …”
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Brain Age Estimation from MRI Images using 2D-CNN instead of 3D-CNN by Muazzez Buket Darıcı, Şüheda Yıldırım, Murat Gezer

“…The dataset is selected from the IXI (Information Extraction from Images) MRI data repository. The MAE evaluation metric is used for each model with different input set to evaluate performance. …”
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Insights from UKCTOCS for design, conduct and analyses of large randomised controlled trials by Usha Menon, Aleksandra Gentry-Maharaj, Matthew Burnell, Sophia Apostolidou, Andy Ryan, Jatinderpal K Kalsi, Naveena Singh, Lesley Fallowfield, Alistair J McGuire, Stuart Campbell, Steven J Skates, Anne Dawnay, Mahesh Parmar, Ian J Jacobs

“…We faced issues with regard to our initial trial design and the way we planned to analyse the data. We feel that our solutions are highly relevant, especially as there is a renewed focus on trials for early detection of cancer.…”
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Decremental response in patients with amyotrophic lateral sclerosis during repetitive nerve stimulation and its relationships with impaired homeostasis by Jinghong Zhang, Yang Li, Qiang Shi

“…In addition, the impact of impaired homeostasis on NMJ stability in patients with ALS remains unclear.MethodsA total of 421 patients with ALS were enrolled. Data on their clinical, biochemical and electrophysiological indicators were divided into a training set (collected from June 2019 to June 2022) and a test set (collected from July 2022 to June 2023). …”
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Overview of smart sensors based on machine learning by Wang Shuaida, Lin Guanying, Wang Nuansheng, Li Yang, Yin Meihua

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