COVID-19 infection prevention practices among a sample of food handlers of food and drink establishments in Ethiopia. by Atsedemariam Andualem, Belachew Tegegne, Sewunet Ademe, Tarikuwa Natnael, Gete Berihun, Masresha Abebe, Yeshiwork Alemnew, Alemebante Mulu, Yordanos Mezemir, Abayneh Melaku, Taffere Addis, Emaway Belay, Zebader Walle, Lake Kumlachew, Abraham Teym, Metadel Adane

“…The study participants were selected using a simple random sampling technique. Data were collected by trained data collectors using a pretested structured questionnaire and an on-the-spot observational checklist. …”
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Differences in the isotopic composition of individual grains and aggregated seed samples affect interpretation of ancient plant cultivation practices by Nathaniel James, Nathaniel James, Christine Winter-Schuh, Christine Winter-Schuh, J. Mark Kenoyer, Jade D'Alpoim Guedes, Cheryl A. Makarewicz, Cheryl A. Makarewicz, Cheryl A. Makarewicz

“…Aggregate samples of a random selection of Trench 42 barley demonstrated remarkable inter-sample homogeneity, with a < 0.5‰ difference in δ13C and δ15N values, demonstrating aggregate samples capture well a representative isotopic average of a single depositional context. …”
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Comparison of sample preparation methods for higher heating values in various sugarcane varieties using near-infrared spectroscopy by Kantisa Phoomwarin, Khwantri Saengprachatanarug, Jetsada Posom, Seree Wongpichet, Kittipong Laloon, Arthit Phuphaphud

“…However, this traditional method is time-consuming because it requires pre-drying of samples and is limited by small sample sizes, potentially leading to inaccuracies and the need for repeated measurements, thereby increasing the overall cost. …”
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Association between metabolic risk, oxidative stress and psychological parameters amongst sample of nurses working in Doha, Qatar by Kirti S. Prabhu, Ummu Habeeba, Mohammad Usman Asad Rahman, Maria Salvador Perez, Salam AlMoghrabi Ramirez, Bilal Uddin Khan, Shilpa Kuttikrishnan, John Paul Ben Tabar Silang, Noora Ahmed Kh A. Al Kaabi, Abdul Quaiyoom Khan, Afsheen Raza, Mohamed Izham Mohamed Ibrahim, Shahab Uddin

“…This study compared day and rotating shift nurses' metabolic, oxidative, and psychological profiles. Methods: A random sample of 212 eligible employees from a tertiary care hospital at Hamad Medical Corporation in Doha, Qatar, were assessed for anthropometric and laboratory parameters related to metabolic risk, oxidative stress and psychological impact. …”
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Identification of aging-related biomarkers for intervertebral disc degeneration in whole blood samples based on bioinformatics and machine learning by Zi-hang Li, Shi-pian Li, Shi-pian Li, Ya-hao Li, Yu-cheng Wang, Zhen-yu Tang, Kai-yang Xu, Xiao-rong Li, Zhen Tan, Jiao-yi Pan, Jin-tao Liu, Hong Jiang, Zhi-jia Ma, Yu-xiang Dai, Yu-xiang Dai, Yu-xiang Dai, Peng-fei Yu

“…IntroductionAging is characterized by gradual structural and functional changes in the body over time, with intervertebral disc degeneration (IVDD) representing a key manifestation of spinal aging and a major contributor to low back pain (LBP).MethodsThis study utilized bioinformatics and machine learning approaches to identify aging-related biomarkers associated with IVDD in whole blood samples. By analyzing GEO datasets alongside aging-related databases such as GeneCards, HAGR, and AgeAnno, we identified 15 aging-related differentially expressed genes (AIDEGs). …”
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Genetic Stability and Inbreeding in a Synthetic Maize Variety Based on a Finite Model by Juan Enrique Rodríguez-Pérez, Jaime Sahagún-Castellanos, Aureliano Peña-Lomelí, Clemente Villanueva-Verduzco, Denise Arellano-Suarez

“…This research aimed to evaluate the effect of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi></mrow></semantics></math></inline-formula> on the inbreeding coefficient (IC) of the SV (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>F</mi><mi>S</mi><mi>y</mi><msub><mrow><mi>n</mi></mrow><mrow><mi>L</mi></mrow></msub></mrow></semantics></math></inline-formula>) and on the stability of its genetic constitution. An SV formed by randomly mating <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">l</mi></mrow></semantics></math></inline-formula> unrelated lines whose inbreeding coefficient is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>F</mi></mrow></semantics></math></inline-formula> was considered, and a random sample was taken from the genotypic array of the progeny produced by selfing a parental line <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>A</mi></mrow><mrow><mn>1</mn></mrow></msub><msub><mrow><mi>A</mi></mrow><mrow><mn>2</mn></mrow></msub><mo> </mo><mo>(</mo><mi>G</mi><mi>A</mi><mo>)</mo></mrow></semantics></math></inline-formula> This sample was visualized as a set of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>g</mi></mrow></semantics></math></inline-formula> groups of four plants whose genotypes are all four of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>A</mi></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>e</mi></mrow></semantics></math></inline-formula> represented the number of plants that failed to form a group. …”
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Sparsity covariance: a source of uncertainty when estimating correlation functions with a discrete sample of observations in the sky by Pierre Fleury

“…In many practical cases (e.g. the weak-lensing cosmic shear), those correlation functions are estimated from a finite, discrete sample of measurements that are randomly distributed in the sky. …”
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The levels of English teachers at primary schools to employ materials and activities based on multiple intelligences theory (Malatya sample) by Nihat ŞAD, Sebahattin ARIBAŞ

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Detection of Coxiella burnetii in cows’, goats’, and ewes’ bulk milk samples using polymerase chain reaction (PCR) by Hayriye Yesim Can, Mehmet Elmalı, Alper Karagöz

“…A total of 150 BM samples (50 samples of each cows’, goats’, and ewes’ milk) collected from 15 randomly selected dairy farms in Hatay province were analyzed. …”
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The Level of Musculoskeletal Fitness of Male Pupils (9-11) Years in a Sample of Algerian Primary School Children by Mohamed KADDOUR, Ladjel GUERRACH, Mohammed MEHIDI, Abderahmane BENGUENAB

“… This study aimed to identify the level of musculoskeletal fitness of male students 9-11 For this purpose, the researchers used the descriptive method on a randomly selected sample of 750 pupils from the primary education stage in schools in the state of Oran. …”
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Beyond Individual Differences in Affective Symptomatology: The Distinct Contributions of Emotional Competence and Rumination in a Nationally Representative Sample by Ruth Castillo-Gualda, Juan Ramos-Cejudo

“…In comparison, rumination reactions, such as passively overthinking about inner experiences, will exacerbate the affective symptomatology. A sample of 1703 participants over the country, 50.43% female (18–75 years of age, mean = 45.48, SD = 14.73), closely aligned with the mean age of the target population in Spain, which is 44.1 years, were randomly selected to participate in this cross-sectional study. …”
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Everyday Memory Questionnaire—Revised (EMQ-R): Psychometric Validation of the European Portuguese Version in Non-Clinical Sample by Pedro F. S. Rodrigues, Ana Bártolo, Bruna Ribeiro, Ramón López-Higes, Susana Rubio-Valdehita, Ana Paula Caetano, Sara M. Fernandes

“…Given the uncertainty about the optimal measurement model, the sample was randomly divided into two independent subsamples. …”
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Outcomes of Sjögren's versus non-Sjögren's related dry eye in a longitudinal, tertiary clinic-based sample. by David Cui, Priya Mathews, Gavin Li, Shanna VanCourt, Esen Akpek

“…<h4>Results</h4>Two hundred and two patients (101 SS and 101 randomly selected non-SS), with a mean follow-up of 7.1 years were included. …”
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Antimicrobial resistance of Staphylococcus aureus strains isolated from cow raw milk samples from Albania and Serbia by Erinda Lika, Milovan Rosić, Sonila Cocoli, Nikola Puvača, Gorica Vuković, Tana Shtylla Kika, Vojislava Bursić

“…This research aimed to determine the prevalence rate of antimicrobial resistance (AMR) of Staphylococcus aureus isolated from raw cow milk samples in Albania and Serbia. A total of 100 raw milk samples, 50 from Albania and 50 from Serbia, have been collected from randomly selected cattle farms. …”
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Estimation of adaptability parameters of winter common wheat samples at different sowing dates in the forest-steppe of Western Siberia by A. S. Surnachev, K. K. Musinov

“…There has been presented a three-year estimation of winter wheat samples according to adaptability indicators, the trait ‘productivity’, ‘winter resistance’, ‘1000-grain weight’, and ‘number of productive stems per m2’.   …”
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Immediate impact of augmented reality visual training on choroidal thickness and accommodative function: A small sample study by Guo-rui Hu, Zhi-xing Xu, Chang-feng Pan, Zheng-yang Tao, Hang Chu, Zong-yue Lv, Ze-feng Kang, Hong-wei Deng

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Hybrid path planning algorithm for underactuated AUV based on RRT star and APF by Boyu Zhang, Yishan Su, Shanlin Sun, Wei Luo, Qing Huang

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Enhancing Digital Twin Fidelity Through Low-Discrepancy Sequence and Hilbert Curve-Driven Point Cloud Down-Sampling by Yuening Ma, Liang Guo, Min Li

“…Through four comprehensive experiments covering parametric surface fitting, mesh reconstruction from basic closed geometries, complex CAD models, and real-world laser scans, we demonstrate that LDS-Hilbert consistently outperforms established methods, including Simple Random Sampling (SRS), Farthest Point Sampling (FPS), and Voxel Grid Filtering (Voxel). …”
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How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms by Sophie G. Zaloumis, Megha Rajasekhar, Julie A. Simpson

“…Results For this mock malaria prediction study, the balanced error rate on a test dataset not used for model training (208 samples) was 50% for sPLSDA + SVMs and 50% for random forests on the smallest training dataset evaluated (20 samples) and 14% for sPLSDA + SVMs and 22% for random forests on the largest training dataset evaluated (835 samples). …”
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Comment on “Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling” by Jasper A. Vrugt

“…Furthermore, HM23 misrepresent BMA literature, articulate a poor understanding of Markov chain Monte Carlo methods and misuse the autocorrelation function for monitoring convergence of the sampled Markov chains. A proper implementation of the random walk Metropolis algorithm would have led HM23 to substantially different results and findings about their ensemble of water stage predictions. …”
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