Household latrine utilization and associated factors in semi-urban areas of northeastern Ethiopia. by Deres Asnake, Metadel Adane

“…Household family size from one to three persons (AOR: 3.99, 95% CI [1.20-6.24]), presence of primary or secondary school student in a house (AOR: 2.33, 95% CI [1.42-3.83]), number of years since latrine was constructed (≥ 2 years) (AOR: 1.82, 95% CI [1.12-2.95]) and a frequency of daily cleaning of the latrine (AOR: 2.19, 95% CI [1.12-4.28]) were factors significantly associated with latrine utilization.…”
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Prevalence and determinants of depression among women with urinary incontinence: a systematic review and meta-analysis worldwide by Sileshi Ayele Abebe, Fanuel Gashaw, Alebachew Tsegaye, Dessie Abebaw, Endeshaw Asaye Kindie, Addisu Minaye Dejen

“…Unable to read and write (OR: 1.34, 95% CI: [1.01, 1.78], I²= 0%), caesarean delivery (OR: 1.48, 95% CI: [1.30, 1.70], I² = 0%), obesity (OR: 1.37, 95% CI: [1.19, 1.58], I² = 0%), party greater than three (OR: 1.26, 95% CI: [0.98, 1.62], I²= 0%), comorbidity (OR: 1.32, 95% CI: [1.16, 1.49], I² = 85.3%), smoking (OR: 1.58, 95% CI: [0.99, 1.92], I2 = 0%) and single, divorced and widowed (OR: 1.67, 95% CI: [1.45, 1.92], I² = 0%) were the major factors associated with depression. …”
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Design a Robust DDoS Attack Detection and Mitigation Scheme in SDN-Edge-IoT by Leveraging Machine Learning by Habtamu Molla Belachew, Mulatu Yirga Beyene, Abinet Bizuayehu Desta, Behaylu Tadele Alemu, Salahadin Seid Musa, Alemu Jorgi Muhammed

“…We evaluated four popular classifiers (K-Nearest Neighbor (K-NN), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and FeedForward Neural Network (FFNN)) on benchmark datasets CICIDS2017 and Edge-IIoTset, conducting both binary and multi-class classifications. …”
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Generative learning of continuous data by tensor networks by Alex Meiburg, Jing Chen, Jacob Miller, Raphaëlle Tihon, Guillaume Rabusseau, Alejandro Perdomo-Ortiz

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Optimization of Base Location and Patrol Routes for Unmanned Aerial Vehicles in Border Intelligence, Surveillance, and Reconnaissance by Yao Liu, Zhong Liu, Jianmai Shi, Guohua Wu, Chao Chen

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The determinants of household poverty: the case of berehet woreda, amhara regional state, Ethiopia by Markew Mengiste Neway, Solomon Estifanos Massresha

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Speckle-Shifting Ghost Imaging by Tianyi Mao, Qian Chen, Weiji He, Yunhao Zou, Huidong Dai, Guohua Gu

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Factors affecting refractoriness or recurrence in diffuse large B-cell lymphoma: development and validation of a novel predictive nomogram by Yiwei Guo, Jie Lian, Yao Chen, Lina Quan, Xiuchen Guo, Jingbo Zhang, Zhiqiang Liu, Aichun Liu

“…The training cohort underwent univariable analysis to select variables for a binary logistic regression model. These variables were also prioritized using a random forest algorithm. …”
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Predicting chronic kidney disease progression using small pathology datasets and explainable machine learning models by Sandeep Reddy, Supriya Roy, Kay Weng Choy, Sourav Sharma, Karen M Dwyer, Chaitanya Manapragada, Zane Miller, Joy Cheon, Bahareh Nakisa

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Barriers to birth preparedness and complication readiness among pregnant women in rural Ethiopia: using a mixed study design, 2020 by Abdi Geda Gedefa, Alazar Ayalew Bekele, Keno Melkamu Kitila, Lemi Bacha Eba

“…Data were entered into EpiData V.3.1 and analysed using SPSS V.22. Binary and multivariable logistic regression analysis was done. …”
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Dietary Diversity among Students of Khulna University, Bangladesh by Mymuna Islam Moon, Tanjirul Islam, Mortuja Mahmud Tohan, Bristi Rani Shah, Nushrath Jahan Juhi, Tayeeba Tabussum Anni, Abu Mohammad Mujahidul Islam, Kazi Moushumi Akter, Md. Hasan Howlader, Md. Ashfikur Rahman

“…The respondents were selected by a simple random sampling method and the data were gathered via a questionnaire survey. …”
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Performance Comparison of Genomic Best Linear Unbiased Prediction and Four Machine Learning Models for Estimating Genomic Breeding Values in Working Dogs by Joseph A. Thorsrud, Katy M. Evans, Kyle C. Quigley, Krishnamoorthy Srikanth, Heather J. Huson

“…The phenotypic data include three binary health traits (anodontia, distichiasis, oral papillomatosis) and one behavioral trait (distraction) in a population of guide dogs. …”
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Prevalence and associated factors of psychological distress among internally displaced people in Mekelle, Tigray, Ethiopia by Dawit Bekuretsion, Ashenafi Damte, Hagos Tsegabirhan

“…Factors significantly associated with psychological distress included age (41–50 years, AOR = 2.15; 95% CI 1.04, 4.49; ≥ 50 years, AOR = 2.77; 95% CI 1.08, 7.13), being female (AOR = 2.78; 95% CI 1.52, 5.09), exposure to three or more traumatic events (AOR = 3.19; 95% CI 1.58, 6.46), poor social support (AOR = 2.52; 95% CI 1.23, 5.18), and chronic medical illness (AOR = 2.55; 95% CI 1.22, 5.33). …”
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Knowledge, perceptions and practices associated with timing for breast cancer screening among female students at the University of Zimbabwe: A cross-sectional study by Peter Chikuvadze, Grace Nkhoma, Maxwell Mhlanga, Augustine Ndaimani

“…Methods: This analytical cross-sectional study employed random sampling to recruit participants at the University of Zimbabwe from March 4 to April 8. …”
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A 15-Gb/s Wireless ON-OFF Keying Link by Lars Ohlsson, Lars-Erik Wernersson

“…Serial data-rates from 2.5 to 20 Gb/s were studied for a <inline-formula> <tex-math notation="LaTeX">$2^{31}$ </tex-math></inline-formula>-1 bit random sequence. Error-free data transfer over a 0.3-m link was achieved at up to 10 Gb/s. …”
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Zero Watermarking Algorithm for Hyperspectral Remote Sensing Images Considering Spectral and Spatial Features by Bingbing Yang, Haowen Yan, Liming Zhang, Qingbo Yan, Zhaoyang Hou, Xiaolong Wang, Xinyu Xu

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Prevalence of sleep disturbances and factors associated among school going children in Uganda, a cross-sectional study by Baluku Reagan Innocent, Oriokot Lorraine, Elly Katabira, Martha Sajatovic, Abbo Catherine, Kaddumukasa Mark

“…Random cluster sampling method was used to select participants from the 19 parishes in Kawempe district. …”
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Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques by Athanasios Angeioplastis, John Aliprantis, Markos Konstantakis, Alkiviadis Tsimpiris

“…The results indicated that courses with strong correlations (+0.3 and above) significantly enhanced predictive accuracy, particularly in binary classification tasks. kNN and neural networks emerged as the most robust models, achieving F1 scores exceeding 0.8. …”
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Constructing a fall risk prediction model for hospitalized patients using machine learning by Cheng-Wei Kang, Zhao-Kui Yan, Jia-Liang Tian, Xiao-Bing Pu, Li-Xue Wu

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Light in, sound keys out: photoacoustic PUFs from stochastic nanocomposites by Taehyun Park, Junhyung Kim, Raksan Ko, Byullee Park, Hocheon Yoo

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