Robust Variable Selection via Bayesian LASSO-Composite Quantile Regression with Empirical Likelihood: A Hybrid Sampling Approach by Ruisi Nan, Jingwei Wang, Hanfang Li, Youxi Luo

“…By constructing a hybrid sampling mechanism that incorporates the Expectation–Maximization (EM) algorithm and Metropolis–Hastings (M-H) algorithm within the Gibbs sampling scheme, this approach effectively tackles variable selection in high-dimensional settings with outlier contamination. …”
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The Effect of Emotion Dysregulation, Affect, Boredom, and Social Context on State Nomophobia among University Students: An Experience Sampling Study (State Nomophobia) by Özge Enez, Özden Yalçınkaya Alkar

“…As far as is known, there has been no experience sampling study examining state nomophobia. Determining the factors that may cause nomophobia may provide scientific insight into the content of programs developed for the prevention and treatment of nomophobia. …”
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Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley India by Devraj Dhakal, Kanwarpreet Singh, Kennedy C. Onyelowe, Steven Alejandro Salazar Cazco, Abhishek Sharma, Nassir Alarifi, Fakhrul Islam, Randeep, Krishna Prakash Arunachalam, Youssef M. Youssef

“…Advanced machine learning and new sampling procedures may improve LSM accuracy and reliability in high-risk zones. …”
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Optimisation of a Cytogenetic Sample Preparation Procedure for <i>In Vivo</i> Metaphase Analysis of Mammalian Bone Marrow Cells by A. O. Verner, T. M. Ustinova, N. G. Vengerovich

“…The study was performed in randomly bred male mice weighing 18–20 g. The authors prepared cytogenetic samples of bone marrow cells according to the procedures set forth in the Guidelines for Conducting Preclinical Studies of Drugs (A.N. …”
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Fault surface construction method based on point cloud surface reconstruction by Lin Dong, Weiqi Song, Jianlin Hu, Chao Zeng, Wenjian Tang

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Anopheles sampling collections in the health districts of Korhogo (Côte d’Ivoire) and Diébougou (Burkina Faso) between 2016 and 2018 by Paul Taconet, Barnabas Zogo, Dieudonné Diloma Soma, Ludovic P. Ahoua Alou, Karine Mouline, Roch Kounbobr Dabiré, Alphonsine Amanan Koffi, Cédric Pennetier, Nicolas Moiroux

“…More than 3,000 collection sessions were carried out, achieving about 45,000 h of sampling efforts. Over 60,000 Anopheles were collected (mainly A. gambiae s.s., A. coluzzii, and A. funestus). …”
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Four Node Sampling: Is It an Effective Alternative to Sentinel Lymph Node Biopsy in Low-resource Settings for Breast Cancer Management by Sreehari Pothina, Manisha Sharma, Puneet Takkar, Parameswar Reddy Gadikota

“…However, in austere settings scenarios where the facility for SLNB is not available, four-node axillary sampling (FNAS) has been explored with variable outcomes. …”
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Evaluation of residential structures not covered by aerial photographs used to generate a sampling frame – Nueva Santa Rosa, Guatemala by Jeffrey M. Switchenko, Sharon L. Roy, Fredy Muñoz, Gerard Lopez, Jose G. Rivera, Victoria M. Cuéllar, Patricia Juliao, Beatriz López, Andrew Thornton, Jaymin C. Patel, Maricruz Alvarez, Lissette Reyes, Gordana Derado, Wences Arvelo, Kim A. Lindblade

“… # Methods High-resolution aerial photographs of Nueva Santa Rosa were overlaid with a grid, and roof images were geo-located within randomly sampled cells, dichotomized by population as very high-density (VHD) or non-VHD. …”
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Nanopore adaptive sampling accurately detects nucleotide variants and improves the characterization of large‐scale rearrangement for the diagnosis of cancer predisposition by Sandy Chevrier, Corentin Richard, Marie Mille, Denis Bertrand, Romain Boidot

“…Methods Here, 30 well‐characterized clinical germline samples were studied with adaptive sampling to enrich the full sequence of 152 cancer predisposition genes. …”
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The effect of combined non-pharmacological interventions on venous blood sampling pain in preterm infants: a clinical trial study by Fatemeh Yadollahzadeh, Zahra Akbarianrad, Seyedeh Roghayeh Jafarian-Amiri, Ali Zabihi, Parvin Aziznejadroshan, Hossein-Ali Nikbakht

“…This study was conducted with the aim of investigating the effect of combined non-pharmacological interventions on venous blood sampling pain in preterm infants. Methods In this clinical trial study, 88 preterm infants admitted to the Neonatal Intensive Care Unit of Rouhani Hospital was randomly assigned to four groups of sucrose (S), the combined group of sucrose and non-nutritive sucking (NS), the combined group of swaddle and sucrose (SS) and the combined group of sucrose, non-nutritive sucking and swaddling (NSS). …”
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Planos de amostragem no desenvolvimento inicial do cafeeiro sob irrigação Sampling plans in the initial development of the irrigated coffee by Augusto R. de Morais, Myriane S. Scalco, Alberto Colombo, Manoel A. de Faria, Carlos H. M. de Carvalho, Leandro C. Paiva

“…The planting was accomplished in January, 2001. A completely randomized block design was used with four replications in split-plot. …”
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Nonuniform Sampled-Data Control for Synchronization of Semi-Markovian Jump Stochastic Complex Dynamical Networks with Time-Varying Delays by N. Sakthivel, Yong-Ki Ma, M. Mounika Devi, G. Manopriya, V. Vijayakumar, Mooyul Huh

“…In this paper, the problem of exponential synchronization of semi-Markov jump stochastic complex dynamical networks using nonuniform sampled-data control with random delayed information exchanges among dynamical nodes are discussed. …”
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Comparison of glucose concentrations in samples frozen for varying durations and subjected to freeze-thaw cycles using 2 assay systems by Anay D. Ravelo, Megan Ruch, Isaac J. Salfer, Luciano S. Caixeta

“…Thus, the first objective was to investigate the stability of glucose in samples that have been frozen for an increased duration or frozen and thawed up to 4 times. …”
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Feature-Driven Density Prediction of Maraging Steel Additively Manufactured Samples Using Pyrometer Sensor and Supervised Machine Learning by Rajesh Kumar Balaraman, Shaista Hussain, John Kgee Ong, Qing Yang Tan, Nagarajan Raghavan

“…With these three sets of input features, the study investigates the effectiveness of four supervised machine learning (ML) regression models: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP), for predicting the density of printed samples. …”
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The Italian Version of the Robotics Learning Self-Efficacy Scale (RLSES-IT): Assessment of psychometric features in a sample of young students by Mirian Agus, Arianna Marras, Lucio Negrini

“…This study evaluated the psychometric characteristics in the Italian context of the Robotics Learning Self-Efficacy Scale (RLSES). A sample of 353 young students attending an educational robotics (ER) course in their primary or secondary schools completed the assessment battery, including the RLSES and a validated questionnaire inquiring about students' self-efficacy. …”
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The impact of self-marketing on getting good work opportunity an exploratory study of a sample of working-age youth in Iraq by Dr. Samaa Ali ALzubaidie *

“…       The research aims to find out how self-marketing can improve the chance of getting a suitable job opportunity among the age groups coming to the labor market, and the main question to reach the goal was how the applicant's academic achievement can improve the quality of work that the applicant can get and this enhances the employers' knowledge of the orientation of the youth group and their views about the quality of work and its good classification, which allows them to reduce the time and effort in attracting the right human resource for work, and in order to collect data the researcher used the questionnaire method that was distributed to a random sample of working age groups to test the hypotheses of which the main one was “Self-marketing has a significant effect on good work”, The data were analyzed using SPSS statistical software, revealing that individuals place greater emphasis on their academic achievements when anticipating good job opportunities, rather than on the skills and experience they possess. …”
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A new method of semi-supervised learning classification based on multi-mode augmentation in small labeled sample environment by Yuxuan Liu, Chenglin Wen

“…To this end, this paper proposes a semi-supervised image classification method based on multi-mode augmentation, which mitigates the effects of insufficient quality and limited scale of unlabeled data by simultaneously improving the sample completeness within and between classes. Specifically, the model’s prediction confidence and bias are used for uncertainty-based screening to improve pseudo-label quality, while retaining as many unlabeled samples as possible to fully exploit their potential information. …”
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Population Median Estimation Using Auxiliary Variables: A Simulation Study with Real Data Across Sample Sizes and Parameters by Umer Daraz, Fatimah A. Almulhim, Mohammed Ahmed Alomair, Abdullah Mohammed Alomair

“…This paper introduces an enhanced class of ratio estimators, which employ the transformation technique on an auxiliary variable under simple random sampling to estimate the population median. …”
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Addressing Out‐of‐Sample Issues in Multi‐Layer Convolutional Neural‐Network Parameterization of Mesoscale Eddies Applied Near Coastlines by Cheng Zhang, Pavel Perezhogin, Alistair Adcroft, Laure Zanna

“…Offline evaluations revealed that these padding strategies significantly reduce root mean squared error (RMSE) in coastal regions by limiting the dependence on random initialization of weights and restricting the range of out‐of‐sample predictions. …”
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Psychometric comparison of two short versions of the Perceived Stress Scale (PSS-4) in a representative sample of the German population by Bjarne Schmalbach, Mareike Ernst, Mareike Ernst, Elmar Brähler, Elmar Brähler, Katja Petrowski

“…In the present study, we compare the two in a sample representative of the German general population (n = 2,527). …”
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