Inverse design of high-NA metalens for maskless lithography by Chung Haejun, Zhang Feng, Li Hao, Miller Owen D., Smith Henry I.

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Phytobezoar: A Brief Report with Surgical and Radiological Correlation by Sameer A. Hirji, Faith C. Robertson, Grace F. Chao, Bharti Khurana, Jonathan D. Gates

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Female sexual response among Flo app users in the United States by Frederick G. B. Goddard, Jasmin Mahabamunuge, Adam C. Cunningham, Liudmila Zhaunova, Lubna Pal

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Cefazolin Alone Versus Cefazolin With Tobramycin or Gentamicin as Intraoperative Antibiotic Prophylaxis for Total Joint Arthroplasty by Meera M. Dhodapkar, MD, MHS, Scott J. Halperin, MD, MHS, Zachary Radford, MD, Lee E. Rubin, MD, FAAOS, FAOA, Jonathan N. Grauer, MD, Mengnai Li, FAAOS, MD, PhD

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Time series data on typhoid fever incidence during outbreaks from 2000 to 2022 by Dae-Hyup Koh, Monica Duong, Nodar Kipshidze, Virginia E. Pitzer, Jong-Hoon Kim

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Effects of Deer Settling Stimulus and Deer Density on Regeneration in a Harvested Southern New England Forest by Kevin J. Barrett, Oswald J. Schmitz

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HIV, smoking, and the brain: a convergence of neurotoxicities by Benjamin L. Orlinick, Shelli F. Farhadian

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Synaptic vesicle characterization of iPSC-derived dopaminergic neurons provides insight into distinct secretory vesicle pools by Kenshiro Fujise, Jaya Mishra, Martin Shaun Rosenfeld, Nisha Mohd Rafiq

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Bismarcks Sozialversicherung und ihr Einfluss auf Deutschlands demografischen Wandel by Timothy W. Guinnane, Tobias A. Jopp, Jochen Streb

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Stepped collaborative care for trauma: giant leaps for health equity by Lisa M Kodadek, Rachel Rivero

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Book review: Burke, Peter. Ignorance: a global history by Elena Maceviciute

“…Ignorance: a global history. New Haven, London: Yale University Press, 2023.  xiv, 310 p. ISBN 978-0-300-26595-8…”
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Email outreach attracts the US policymakers’ attention to climate change but common advocacy techniques do not improve engagement by Riley N. Loria, Jessica Pugel, Matthew H. Goldberg, Deborah A. Halla, Rebecca Bascom, J. Taylor Scott, Max Crowley, Elizabeth C. Long

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Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals by Tsung-Ting Kuo, Rodney A. Gabriel, Jejo Koola, Robert T. Schooley, Lucila Ohno-Machado

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Wavelet Decomposition-Based AVOA-DELM Model for Prediction of Monthly Runoff Time Series and Its Applications by ZHANG Yajie

“…For the improvement in prediction accuracy of monthly runoff time series,a prediction model is proposed,which combines the wavelet decomposition (WD),African vultures optimization algorithm (AVOA),and deep extreme learning machine (DELM),and it is applied to the monthly runoff prediction of Yale Hydrological Station in Yunnan Province.Specifically,WD decomposes the time-series data of monthly runoff to obtain highly regular subsequence components,and AVOA is employed to optimize the number of neurons in the hidden layers of DELM;then,the WD-AVOA-DELM model is built to predict each subsequence component,and the prediction results are summated and reconstructed to produce the final prediction results of monthly runoff.Meanwhile,models based on the support vector machine (SVM) and BP neural networks are constructed for comparative analysis,including WD-AVOA-SVM,WD-AVOA-BP,AVOA-DELM,AVOA-SVM,and AVOA-BP models.The results reveal that the average absolute percentage error of the WD-AVOA-DELM model for the monthly runoff prediction of Yale Hydrological Station is 3.02%;the prediction error is far less than that of WD-STOA-SVM and WD-AVOA-BP models,and the prediction accuracy is more than one order of magnitude higher than that of AVOA-SVM,AVOA-SVM,and AVOA-BP models.The result indicates that the proposed model has good prediction performance.In this model,WD can scientifically reduce the complexity of runoff series and raise the prediction accuracy;AVOA can effectively optimize the key parameters of DELM and improve the performance of DELM networks.…”
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Does Preoperative Weight Change Predict Postoperative Weight Loss after Laparoscopic Roux-en-Y Gastric Bypass in the Short Term? by Dan Eisenberg, Andrew J. Duffy, Robert L. Bell

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FOREWORD by N. Wolterstorff

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The Dynamical History of HIP-41378 f—Oblique Exorings Masquerading as a Puffy Planet by Tiger Lu, Gongjie Li, Ben Cassese, D. N. C. Lin

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Insight, Cognitive Insight and Sociodemographic Features in Obsessive Compulsive Disorder Presenting with Reactive and Autogeneus Features by Katre ÇAMLI, M. Hakan TÜRKÇAPAR, A. Emre SARGIN

“…The sociodemographic characteristics of patients and the symptomatology were evaluated using psychiatric scales including SCID-I, Yale Brown Obsessive-Compulsive Scale (YBOCS), Yale Brown Obsessive-Compulsive Scale-Symptom Checklist (YBOCS-SC) and Beck Insight Scale. …”
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Late Endocrine Effects after Stem Cell Transplant in a Young Girl with Griscelli Syndrome by Shana R. Mencher, William V. Tamborlane, Anisha D. Patel

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Brown Assessment of Beliefs Scale: A Study of Reliability and Validity by Türkan ÖZCAN, Erkan KURU, Yasir ŞAFAK, M. Emrah KARADERE, K. Fatih YAVUZ, M. Hakan TURKÇAPAR

“…BABS has strongcorelation with Beck Cognitive Insight Scale (r=-0.84, p<0.001), Schedule for Assesing the Three Components of InsightScale (r=-0.85, p<0.001) and insight item of Yale-Brown Obsessive Compulsive Scale (r=0.67, p<0.001).Conclusion: The data attained from the study of reliability and validity of the scale shows that The Brown Assesment ofBeliefs Scale supports reliability and validity in Turkish population.…”
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