Cross-Cultural adaptation and validation of adolescent menstrual bleeding questionnaire in a Turkish sample by Melike Punduk Yilmaz, Ismail Yilmaz, Iclal Ilknur Ozdemir, Selma Dagci, Filiz Yarsilikal Guleroglu, Ali Cetin

“…For validity assessment, the sample was randomly split into two subsets for exploratory (n = 91) and confirmatory (n = 90) factor analyses, with convergent validity testing and known-groups comparison. …”
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Average high school teacher salaries vs student performance on standardized tests: A correlational analysis of northern New Jersey high schools by Sousa, J.

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Spraint density of the Eurasian otter (Lutra lutra) is an accurate indicator of its population status in urban areas by Jooseong Kim, Sungwon Hong

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A pay-it-forward approach to improve feedback rate of HPV-based self-sampling in cervical cancer screening among women in ethnic minority regions of China: a randomized controlled trial protocol by Xueyuan Zhou, Rantong Bao, Xin Guan, Wenzhuo Li, Chenxin Yang, Ruijin Zhu, Hui Li, Xiaohua Wang

Subjects: “…HPV-based self-sampling…”
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Maximiser la représentativité de groupes dans un échantillonnage spatial by Estelle Kah, Michel Pruvot

“…Using an example of spatial sample of communes in the sub-regions of a department we present in a bunch of combined processes, a calculated example on a spreadsheet, and formulas to weight the random sampling.…”
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SALMONELLA SEROTYPES ISOLATED AND IDENTIFIED FROM LOCALLY WHITE SOFT CHEESE by Khulood K. Nazal

“…Fourty locally white soft cheese random samples were collected from different markets of Baghdad Algadeda city in order to investigate the presence of Salmonellae Spp. in cheese which produced and consumed locally in Baghdad. …”
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Calibration Estimators with Different Types of Distance Measures Under Stratified Sampling in the Presence of Measurement Error by Sat Gupta, Pidugu Trisandhya

“…The calibration method is used in stratified random sampling in the presence of measurement error to achieve optimum strata weights for better precision. …”
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Adaptive sampling-based optimization of quantics tensor trains for noisy functions: Applications to quantum simulations by Kohtaroh Sakaue, Hiroshi Shinaoka, Rihito Sakurai

“…Tensor cross interpolation (TCI) is a powerful technique for learning a tensor train (TT) by adaptively sampling a target tensor based on an interpolation formula. …”
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Energy‐Efficient Hardware Implementation of Spiking‐Restricted Boltzmann Machines Using Pseudo‐Synaptic Sampling by Hyunwoo Kim, Suyeon Jang, Uicheol Shin, Masatoshi Ishii, Atsuya Okazaki, Megumi Ito, Akiyo Nomura, Kohji Hosokawa, Sungmin Lee, Matthew BrightSky, Sangbum Kim

“…Stochastic sampling is performed to reduce hardware energy consumption and prevent overfitting by reducing parameters, because not all data are required for learning. …”
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Performance of Synthetic Double Sampling Chart with Estimated Parameters Based on Expected Average Run Length by Huay Woon You

“…A synthetic double sampling (SDS) chart is commonly evaluated based on the assumption that process parameters (namely, mean and standard deviation) are known. …”
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An internet-delivered acceptance and commitment therapy program for anxious affect, depression, and wellbeing: A randomized, parallel, two-group, waitlist-controlled trial in a Middle Eastern sample of college students. by Zahir Vally, Harshil Shah, Sabina-Ioana Varga, Widad Hassan, Mariam Kashakesh, Wafa Albreiki, Mai Helmy

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THE ORDINAL LOGISTIC REGRESSION MODEL WITH SAMPLING WEIGHTS ON DATA FROM THE NATIONAL SOCIO-ECONOMIC SURVEY by Reni Amelia, Indahwati Indahwati, Erfiani Erfiani

“…The parameter estimation of this model uses the maximum likelihood estimation having assumption that each sample unit having an equal chance of being selected, or using simple random sampling (SRS) design. …”
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Computation of separate ratio and regression estimator under Neutrosophic stratified sampling: an application to climate data by Abhishek Singh, Hemant Kulkarni, Florentin Smarandache, Gajendra Vishwakarma

“…Also, numerically based on real-life and artificial data, we have shown the supremacy of the neutrosophic stratified sampling over neutrosophic simple random sampling along with the supremacy of our proposed neutrosophic separate stratified estimators over neutrosophic stratified unbiased estimator. …”
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Two-Fold Sampling-Based Super-Resolution Estimation of Low-Rank MIMO-OFDM Channels by Tianle Liu, Khawaja Fahad Masood, Jun Tong, Jiguang He, and Jiangtao Xi

“…Low-rank matrix completion (LRMC) based on random sampling is then used to further reduce the training overhead. …”
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Research on Single Photon Laser Communication Detection and Receiving Technology Based on Baseband Pulse Sampling by Yapei Sheng, Min Zhang, Jiao Dong, Ziyi Gao, Li Xu, Peng Lin, Keyan Dong, Xiaonan Yu

“…Simulation results show that the pulse sampling technique solves the transmission problem of non-return-to-zero pseudo-random sequence code in a single-photon detector, and its bit error rate is lower than 10E-4 under the condition that the non-return-to-zero code cannot allow good communication. …”
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Lot quality assurance sampling for coverage evaluation of a new vaccine: A pilot study by Rhythm Hora, Arindam Ray, Imkongtemsu Longchar, G.R. Rio, Rashmi Mehra, Seema Singh Koshal, Amrita Kumari, Syed F. Quadri, Amanjot Kaur, Arup Deb Roy

“…For the concurrent field monitoring, a sample of 30 children in the same age group was selected through random sampling. …”
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Influences of Sampling Design and Model Selection on Predictions of Chemical Compounds in Petroferric Formations in the Brazilian Amazon by Niriele Bruno Rodrigues, Theresa Rocco Barbosa, Helena Saraiva Koenow Pinheiro, Marcelo Mancini, Quentin D. Read, Joshua Blackstock, Edwin H. Winzeler, David Miller, Phillip R. Owens, Zamir Libohova

“…The dataset comprised 341 samples from the Geological Survey of Brazil (CPRM). …”
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Residual channel attention based sample adaptation few-shot learning for hyperspectral image classification by Yuefeng Zhao, Jingqi Sun, Nannan Hu, Chengmin Zai, Yanwei Han

“…Specifically, a Deep Residual Feature Channel Attention Mechanism (DRFCAM) is designed to obtain cross-domain dependencies by residual concatenation, and further the residual structure is stacked for mining depth discrimination information. Furthermore, a new Random-based Feature Recalibration Module (RFRM) is proposed to reassign the feature weights via random matrix, which fully explore feature weight relationships to guide the sample adaptation process. …”
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Optimum Block Size in Separate Block Bootstrap to Estimate the Variance of Sample Mean for Lattice Data by M. Mohammadzadeh

“…The statistical analysis of spatial data is usually done under Gaussian assumption for the underlying random field model. When this assumption is not satisfied, block bootstrap methods can be used to analyze spatial data. …”
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CONDITIONS and DEVELOPMENT of CASH CROP IN IRAQ BREAK – EVEN POINT as an APPROACH … COTTON as a SAMPLE by Salim Y. Al Niaamy

“…The questionnaire included the cotton farming process. A random sample consisting of 15% of the study population, consisting of 100 farmers from all over Nineveh, was included. …”
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