Comparative Effect of Breast Milk and Infant Formulae on Neonatal Gut Microbiome within Katsina Metropolis by Usman, A., Abdulkadir, B., Lawal, B., Isah, M. B.

“…This study aimed to compare the effects of breast milk and infant formulae on the gut microbiota of newborns in Katsina metropolis. Faecal samples were obtained from 46 neonates (33 exclusively breastfed, 10 formula-fed, and 3 mix-fed) and analyzed using a culture-dependent method. …”
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Assessing data and sample complexity in unmanned aerial vehicle imagery for agricultural pattern classification by Linara Arslanova, Sören Hese, Marcel Fölsch, Friedemann Scheibler, Christiane Schmullius

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Deep reinforcement learning-empowered anti-jamming strategy aided by sample information entropy by LI Gang, WU Qi, WANG Xiang, LUO Hao, LI Lianghong, JING Xiaorong, CHEN Qianbin

“…The information entropy prediction network was utilized for fine-grained selection of training samples of the anti-jamming strategy network to improve the quality of training samples, thereby enhancing the ultimate online decision-making capability and generalization performance of the anti-jamming strategy. …”
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Evaluation of Traditional and Data-Driven Algorithms for Energy Disaggregation Under Sampling and Filtering Conditions by Carlos Rodriguez-Navarro, Francisco Portillo, Isabel Robalo, Alfredo Alcayde

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Comparison of metagenomic analysis of fecal and gastrointestinal tract samples for identifying beneficial gut microorganisms by Ji-Seon Ahn, Eui-Jeong Han, Hea-Jong Chung, Hea-Jong Chung, Hea-Jong Chung

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Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep Learning by Fudong Ren, Koichi Isobe

“…By experimenting with CNN input window sizes ranging from 3 × 3 to 27 × 27 pixels and employing diverse sampling techniques, we demonstrate that larger windows enhance the model’s predictive accuracy by capturing a wider range of environmental interactions critical for effective landslide modeling. …”
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Influence of abrasive materials on the quality of analytical surfaces during preparation of samples for spectral analysis by M. V. Lagoyskaya

“…For the experiment, three standard samples of steel composition were selected with certified values of the mass fraction of aluminum and calcium in different ranges. …”
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Determination of Chlorogenic Acid, Caffeine and Antioxidant Capacities in Turkish-Style Coffee Bean Samples by Haydar Özpınar, Johanna Rebekka Morche, Harshadrai M. Rawel

“…The aim of this study was to investigate the total chlorogenic acids (CQAs), caffeine and antioxidant capacities of coffees commonly consumed in Turkey. For this purpose, three green and three roasted coffee samples of the <i>Coffea arabica</i>, <i>Coffea canephora</i> and <i>Coffea liberica</i> varieties were selected for this study and characterized. …”
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SBNNR: Small-Size Bat-Optimized KNN Regression by Rasool Seyghaly, Jordi Garcia, Xavi Masip-Bruin, Jovana Kuljanin

“…For feature selection, instance selection, and hyperparameter tuning, we use the bat optimization algorithm (BA). …”
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Influence of Sampling Rate on Wearable IMU Orientation Estimation Accuracy for Human Movement Analysis by Bingfei Fan, Luobin Zhang, Shibo Cai, Mingyu Du, Tao Liu, Qingguo Li, Peter Shull

“…The accelerometer sampling rate is less important than the gyroscope sampling rate. …”
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Forest Change Monitoring Based on Block Instance Sampling and Homomorphic Hypothesis Margin Evaluation by Wei Feng, Fan Bu, Puxia Wu, Gabriel Dauphin, Yinghui Quan, Mengdao Xing

“…While remote sensing image classification offers substantial advantages over ground surveys for monitoring changes in forests, it encounters several challenges. Firstly, training samples in classification algorithms are typically selected through pixel-based random sampling or manual regional sampling. …”
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The complex world of eutectic solvents: Guidelines for a correct characterization and use in sample preparation by Chiara Dal Bosco, Massimo Giuseppe De Cesaris, Lorenzo Antonelli, Nina Felli, Alessandra Gentili

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Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization by Haifei Xia, Haiyan Zhou, Mingao Zhang, Qingyi Zhang, Chenlong Fan, Yutu Yang, Shuang Xi, Ying Liu

“…In addition, this paper proposes a state characterization strategy of multi-scale feature fusion, which integrates global features, local features and historical action sequences of the defect image and provides reliable guidance for action selection. On the particleboard defect dataset with limited images, PPOBoardNet achieves a mean average precision (mAP) of 79.0%, representing a 5.3% performance improvement over the YOLO series of optimal detection models. …”
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Sampling method influences Salmonella detection and quantification in pre-harvest commercial broiler production by Emily E. Cason, Marco Reina, David Ayala-Velasteguí, Nikki W. Shariat

“…Bootsocks and bootsock-rollers were selected as the most reproducible sampling method. …”
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Determining the Number of Measurements and Bootstrap Samples Required to Estimate of Long-Term Noise Indicators by Bartłomiej STĘPIEŃ

“…The simulation experiments have shown that estimation of long-term noise indicators with uncertainty below ±1 dB(A) requires all-day noise measurements during three randomly selected days during the year in a dense urban development. …”
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Hydrogen Column Density Variability in a Sample of Local Compton-thin AGN II by A. Pizzetti, N. Torres-Albà, S. Marchesi, J. Buchner, I. Cox, X. Zhao, S. Neal, D. Sengupta, R. Silver, M. Ajello

“…Noticeably, there is no discernible difference between geometrical and intrinsic properties among the three variability classes, suggesting no intrinsic differences between the N _H,los -variable and nonvariable sources within the considered sample. …”
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Soil Mapping of Small Fields with Limited Number of Samples by Coupling EMI and NIR Spectroscopy by Leonardo Pace, Simone Priori, Monica Zanini, Valerio Cristofori

“…Laboratory analyses were performed on only five samples (two profiles + three samples from the augerings). …”
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Effects of different storage conditions of semen samples on the detection results of sperm DNA damage by Yuan-Hua Xu, Jin-Chun Lu, Shan-Shan Tang, Yan-Mei Ge, Yuan-Jiao Liang

“…Each sample was frozen and thawed three times repeatedly. …”
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Stressless sampling to improve diagnostics of catecholamine excess: new assay to determine metanephrines in saliva by Per M Thorsby, Sandra R Dahl, Tone Smetop, Tone Hæg Lindholm, Svetlana N Zykova

“…Total analytical variation was <15% and the minimum turnaround time was 3 h. Among the sample collection devices tested, highest recovery was achieved with SalivaBio (93–104%). …”
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Analysis of the Full Spitzer Microlensing Sample. I. Dark Remnant Candidates and Gaia Predictions by Krzysztof A. Rybicki, Yossi Shvartzvald, Jennifer C. Yee, Sebastiano Calchi Novati, Eran O. Ofek, Ian A. Bond, Charles Beichman, Geoff Bryden, Sean Carey, Calen Henderson, Wei Zhu, Michael M. Fausnaugh, Benjamin Wibking, The Spitzer Team, Andrzej Udalski, Radek Poleski, Przemek Mróz, Michał K. Szymański, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Jan Skowron, Krzysztof Ulaczyk, Patryk Iwanek, Marcin Wrona, The OGLE Collaboration, Yoon-Hyun Ryu, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Cheongho Han, Kyu-Ha Hwang, Youn Kil Jung, In-Gu Shin, Hongjing Yang, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Hyoun-Woo Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, The KMTNet Collaboration, Fumio Abe, Richard Barry, David P. Bennett, Aparna Bhattacharya, Akihiko Fukui, Ryusei Hamada, Shunya Hamada, Naoto Hamasaki, Yuki Hirao, Stela Ishitani Silva, Yoshitaka Itow, Rintaro Kirikawa, Naoki Koshimoto, Yutaka Matsubara, Shota Miyazaki, Yasushi Muraki, Tutumi Nagai, Kansuke Nunota, Greg Olmschenk, Clement Ranc, Nicholas J. Rattenbury, Yuki K. Satoh, Takahiro Sumi, Daisuke Suzuki, Paul. J. Tristram, Aikaterini Vandorou, Hibiki Yama, MOA collaboration, Łukasz Wyrzykowski, Kornel Howil, Katarzyna Kruszyńska

“…Utilizing Bayesian analysis and Galactic models, along with the Gaia Data Release 3 proper-motion data, four good candidates for dark remnants were identified: OGLE-2016-BLG-0293, OGLE-2018-BLG-0483, OGLE-2018-BLG-0662, and OGLE-2015-BLG-0149, with lens masses of ${3.0}_{-1.3}^{+1.8}\,{M}_{\odot }$ , ${4.7}_{-2.1}^{+3.2}\,{M}_{\odot }$ , ${3.15}_{-0.64}^{+0.66}\,{M}_{\odot }$ and ${1.40}_{-0.55}^{+0.75}\,{M}_{\odot }$ , respectively. …”
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