Search

Your search keyword '"Andreea Georgescu"' showing total 15 results
Start Over Author "Andreea Georgescu"
15 results on '"Andreea Georgescu"'

1. IMPROVING THE SPEED OF 9-10-YEAR-OLD CHILDREN THROUGH A PROGRAM OF GAMES AND RELAYS SPECIFIC TO HANDBALL, APPLIED IN THE PHYSICAL EDUCATION LESSON OVER A 12-WEEK PERIOD.

Author

FLORIN, CAZAN, ABDELMONEIM, IBRAHIM ALIAA, GEORGE, MUȘAT, DIANA, GIDU, ANDREEA, GEORGESCU, and ADRIAN, GEORGESCU

Subjects
PHYSICAL education, STRATEGIC planning, EXPERIMENTAL groups, HANDBALL, CONTROL groups
Abstract

Aim. Play is necessary in education, it satisfies children's need for movement. Students accumulate useful information by playing, which forms them as adaptable people in society. By applying a program of handball-specific games and relays in physical education lessons over a period of 12 weeks, we aim to improve the speed of 9-10-year-old children. Methods. Experimental strategy implementation period was 12 weeks during March and June 2024. The research was conducted on 3rd grade students. The students were divided into two groups, an experimental group consisting of 20 students aged 9.08±1.66 and a control group consisting of 20 students aged 9.12±1.87. Both groups underwent a pre-test and a post-test. The tests were carried out under similar conditions, and there were 3 tests: 1. Shuttle test on 5 x 5 m - travel speed 2. Adams test - speed of exécution 3. Jump Rope Test - repetition speed The experimental group included in the period between the 2 tests, a program of games and relays selected by us, which also contained elements specific to the game of handball. This program was applied during the physical education class, in the fourth grade, twice a week, for 15 minutes each. Study took place in the Sports Hall of Secondary School 18 "Jean Bart" from Constanta, with the size of 30m/15m. Results. After applying the program designed by us, we managed to significantly improve the results of the 3 tests applied to the subjects in the experimental group compared to the control group, statistically proving that the program was effective because the difference is significant p<0.05 in two of the tests, and in the third difference was significant at p<0.0005. Conclusions. The hypothesis of the research was confirmed, by applying a program of games and relays specifically from the game of handball in physical education lessons for a period of 12 weeks, it is possible to improve speed in children aged 9-10 years. [ABSTRACT FROM AUTHOR]

Published
2024

4. Study on the Duration of High Jump Approach in Junior Female Athletes

Author

Aura Balaban, Gloria Rață, Andreea Georgescu, and Bogdan Constantin Rață

Subjects
General Medicine
Abstract

The study aims to highlight the running time and the number of steps in the high jump at the National Athletics Championships in the category of Juniors I and II, held in the "Lia Manoliu" Athletics Hall in Bucharest, in the 2020 indoor competitive season. To the research six girls, 16-17 years old, participated. In the two competitions, the running time on the elk and the number of steps were recorded for each trial. The analysis of the data revealed differences in terms of the time to complete the moose, but also a constancy in the 6 jumpers. Differences were found between the number of steps taken by the elk and the maximum performance achieved by each jumper, a good Pearson correlation value calculated for the number of steps and the mean time recorded for running the elk (withvalues of .998 and .986), which confirms the research hypothesis.

Published
2022
Full Text
View/download PDF

5. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Author

Estee Y. Cramer, Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Katie H. House, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Ayush Khandelwal, Khoa Le, Anja Mühlemann, Jarad Niemi, Apurv Shah, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Youyang Gu, Sansiddh Jain, Nayana Bannur, Ayush Deva, Mihir Kulkarni, Srujana Merugu, Alpan Raval, Siddhant Shingi, Avtansh Tiwari, Jerome White, Neil F. Abernethy, Spencer Woody, Maytal Dahan, Spencer Fox, Kelly Gaither, Michael Lachmann, Lauren Ancel Meyers, James G. Scott, Mauricio Tec, Ajitesh Srivastava, Glover E. George, Jeffrey C. Cegan, Ian D. Dettwiller, William P. England, Matthew W. Farthing, Robert H. Hunter, Brandon Lafferty, Igor Linkov, Michael L. Mayo, Matthew D. Parno, Michael A. Rowland, Benjamin D. Trump, Yanli Zhang-James, Samuel Chen, Stephen V. Faraone, Jonathan Hess, Christopher P. Morley, Asif Salekin, Dongliang Wang, Sabrina M. Corsetti, Thomas M. Baer, Marisa C. Eisenberg, Karl Falb, Yitao Huang, Emily T. Martin, Ella McCauley, Robert L. Myers, Tom Schwarz, Daniel Sheldon, Graham Casey Gibson, Rose Yu, Liyao Gao, Yian Ma, Dongxia Wu, Xifeng Yan, Xiaoyong Jin, Yu-Xiang Wang, YangQuan Chen, Lihong Guo, Yanting Zhao, Quanquan Gu, Jinghui Chen, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Hannah Biegel, Joceline Lega, Steve McConnell, V. P. Nagraj, Stephanie L. Guertin, Christopher Hulme-Lowe, Stephen D. Turner, Yunfeng Shi, Xuegang Ban, Robert Walraven, Qi-Jun Hong, Stanley Kong, Axel van de Walle, James A. Turtle, Michal Ben-Nun, Steven Riley, Pete Riley, Ugur Koyluoglu, David DesRoches, Pedro Forli, Bruce Hamory, Christina Kyriakides, Helen Leis, John Milliken, Michael Moloney, James Morgan, Ninad Nirgudkar, Gokce Ozcan, Noah Piwonka, Matt Ravi, Chris Schrader, Elizabeth Shakhnovich, Daniel Siegel, Ryan Spatz, Chris Stiefeling, Barrie Wilkinson, Alexander Wong, Sean Cavany, Guido España, Sean Moore, Rachel Oidtman, Alex Perkins, David Kraus, Andrea Kraus, Zhifeng Gao, Jiang Bian, Wei Cao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Alessandro Vespignani, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana Pastore y Piontti, Xinyue Xiong, Andrew Zheng, Jackie Baek, Vivek Farias, Andreea Georgescu, Retsef Levi, Deeksha Sinha, Joshua Wilde, Georgia Perakis, Mohammed Amine Bennouna, David Nze-Ndong, Divya Singhvi, Ioannis Spantidakis, Leann Thayaparan, Asterios Tsiourvas, Arnab Sarker, Ali Jadbabaie, Devavrat Shah, Nicolas Della Penna, Leo A. Celi, Saketh Sundar, Russ Wolfinger, Dave Osthus, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dean Karlen, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Elizabeth C. Lee, Juan Dent, Kyra H. Grantz, Alison L. Hill, Joshua Kaminsky, Kathryn Kaminsky, Lindsay T. Keegan, Stephen A. Lauer, Joseph C. Lemaitre, Justin Lessler, Hannah R. Meredith, Javier Perez-Saez, Sam Shah, Claire P. Smith, Shaun A. Truelove, Josh Wills, Maximilian Marshall, Lauren Gardner, Kristen Nixon, John C. Burant, Lily Wang, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Yueying Wang, Shan Yu, Robert C. Reiner, Ryan Barber, Emmanuela Gakidou, Simon I. Hay, Steve Lim, Chris Murray, David Pigott, Heidi L. Gurung, Prasith Baccam, Steven A. Stage, Bradley T. Suchoski, B. Aditya Prakash, Bijaya Adhikari, Jiaming Cui, Alexander Rodríguez, Anika Tabassum, Jiajia Xie, Pinar Keskinocak, John Asplund, Arden Baxter, Buse Eylul Oruc, Nicoleta Serban, Sercan O. Arik, Mike Dusenberry, Arkady Epshteyn, Elli Kanal, Long T. Le, Chun-Liang Li, Tomas Pfister, Dario Sava, Rajarishi Sinha, Thomas Tsai, Nate Yoder, Jinsung Yoon, Leyou Zhang, Sam Abbott, Nikos I. Bosse, Sebastian Funk, Joel Hellewell, Sophie R. Meakin, Katharine Sherratt, Mingyuan Zhou, Rahi Kalantari, Teresa K. Yamana, Sen Pei, Jeffrey Shaman, Michael L. Li, Dimitris Bertsimas, Omar Skali Lami, Saksham Soni, Hamza Tazi Bouardi, Turgay Ayer, Madeline Adee, Jagpreet Chhatwal, Ozden O. Dalgic, Mary A. Ladd, Benjamin P. Linas, Peter Mueller, Jade Xiao, Yuanjia Wang, Qinxia Wang, Shanghong Xie, Donglin Zeng, Alden Green, Jacob Bien, Logan Brooks, Addison J. Hu, Maria Jahja, Daniel McDonald, Balasubramanian Narasimhan, Collin Politsch, Samyak Rajanala, Aaron Rumack, Noah Simon, Ryan J. Tibshirani, Rob Tibshirani, Valerie Ventura, Larry Wasserman, Eamon B. O’Dea, John M. Drake, Robert Pagano, Quoc T. Tran, Lam Si Tung Ho, Huong Huynh, Jo W. Walker, Rachel B. Slayton, Michael A. Johansson, Matthew Biggerstaff, Nicholas G. Reich, Cramer, Estee Y [0000-0003-1373-3177], Ray, Evan L [0000-0003-4035-0243], Lopez, Velma K [0000-0003-2926-4010], Bracher, Johannes [0000-0002-3777-1410], Gneiting, Tilmann [0000-0001-9397-3271], Niemi, Jarad [0000-0002-5079-158X], White, Jerome [0000-0003-4148-8834], Woody, Spencer [0000-0002-2882-3450], Fox, Spencer [0000-0003-1969-3778], Gaither, Kelly [0000-0002-4272-175X], Meyers, Lauren Ancel [0000-0002-5828-8874], Tec, Mauricio [0000-0002-1853-5842], George, Glover E [0000-0003-4779-8702], Cegan, Jeffrey C [0000-0002-3065-3403], Hunter, Robert H [0000-0002-2382-7938], Lafferty, Brandon [0000-0002-2618-3787], Mayo, Michael L [0000-0001-9014-1859], Rowland, Michael A [0000-0002-6759-8225], Chen, Samuel [0000-0002-1070-9801], Salekin, Asif [0000-0002-0807-8967], Corsetti, Sabrina M [0000-0003-2216-2492], Falb, Karl [0000-0002-3465-3988], Huang, Yitao [0000-0001-7846-2174], Sheldon, Daniel [0000-0002-4257-2432], Guo, Lihong [0000-0003-4804-4005], Gu, Quanquan [0000-0001-9830-793X], Xu, Pan [0000-0002-2559-8622], Lega, Joceline [0000-0003-2064-229X], McConnell, Steve [0000-0002-0294-3737], Turner, Stephen D [0000-0001-9140-9028], Shi, Yunfeng [0000-0003-1700-6049], Walraven, Robert [0000-0002-5755-4325], van de Walle, Axel [0000-0002-3415-1494], Turtle, James A [0000-0003-0735-7769], Ben-Nun, Michal [0000-0002-9164-0008], Riley, Steven [0000-0001-7904-4804], Koyluoglu, Ugur [0000-0002-6286-351X], Cavany, Sean [0000-0002-2559-797X], España, Guido [0000-0002-9915-8056], Moore, Sean [0000-0001-9062-6100], Oidtman, Rachel [0000-0003-1773-9533], Perkins, Alex [0000-0002-7518-4014], Kraus, David [0000-0003-4376-3932], Cao, Wei [0000-0001-5640-0917], Lavista Ferres, Juan [0000-0002-9654-3178], Vespignani, Alessandro [0000-0003-3419-4205], Sinha, Deeksha [0000-0002-9788-728X], Perakis, Georgia [0000-0002-0888-9030], Bennouna, Mohammed Amine [0000-0002-9123-8588], Spantidakis, Ioannis [0000-0002-5149-6320], Tsiourvas, Asterios [0000-0002-2979-6300], Sarker, Arnab [0000-0003-1680-9421], Jadbabaie, Ali [0000-0003-1122-3069], Shah, Devavrat [0000-0003-0737-3259], Celi, Leo A [0000-0001-6712-6626], Osthus, Dave [0000-0002-4681-091X], Fairchild, Geoffrey [0000-0001-5500-8120], Mullany, Luke C [0000-0003-4668-9803], Rainwater-Lovett, Kaitlin [0000-0002-8707-7339], Lee, Elizabeth C [0000-0002-4156-9637], Dent, Juan [0000-0003-3154-0731], Hill, Alison L [0000-0002-6583-3623], Keegan, Lindsay T [0000-0002-8526-3007], Lemaitre, Joseph C [0000-0002-2677-6574], Truelove, Shaun A [0000-0003-0538-0607], Wills, Josh [0000-0001-7285-9349], Gao, Lei [0000-0002-4707-0933], Gu, Zhiling [0000-0002-8052-7608], Yu, Shan [0000-0002-0271-5726], Hay, Simon I [0000-0002-0611-7272], Murray, Chris [0000-0002-4930-9450], Stage, Steven A [0000-0001-5361-6464], Prakash, B Aditya [0000-0002-3252-455X], Rodríguez, Alexander [0000-0002-4313-9913], Xie, Jiajia [0000-0001-6530-2489], Keskinocak, Pinar [0000-0003-2686-546X], Baxter, Arden [0000-0002-6345-2229], Oruc, Buse Eylul [0000-0003-2431-3864], Sinha, Rajarishi [0000-0001-9157-674X], Yoder, Nate [0000-0003-4153-4722], Zhang, Leyou [0000-0002-2454-0082], Funk, Sebastian [0000-0002-2842-3406], Meakin, Sophie R [0000-0002-6385-2652], Sherratt, Katharine [0000-0003-2049-3423], Yamana, Teresa K [0000-0001-8349-3151], Pei, Sen [0000-0002-7072-2995], Shaman, Jeffrey [0000-0002-7216-7809], Li, Michael L [0000-0002-2456-4834], Bertsimas, Dimitris [0000-0002-1985-1003], Skali Lami, Omar [0000-0002-8208-3035], Soni, Saksham [0000-0002-8898-5726], Tazi Bouardi, Hamza [0000-0002-7871-325X], Wang, Yuanjia [0000-0002-1510-3315], McDonald, Daniel [0000-0002-0443-4282], Politsch, Collin [0000-0003-3727-9167], Rajanala, Samyak [0000-0002-5791-3789], Rumack, Aaron [0000-0002-9181-1794], Tibshirani, Ryan J [0000-0002-2158-8304], Drake, John M [0000-0003-4646-1235], Ho, Lam Si Tung [0000-0002-0453-8444], Slayton, Rachel B [0000-0003-4699-8040], Johansson, Michael A [0000-0002-5090-7722], Biggerstaff, Matthew [0000-0001-5108-8311], Reich, Nicholas G [0000-0003-3503-9899], and Apollo - University of Cambridge Repository

Subjects
model evaluation, Multidisciplinary, COVID-19, prediction, United States, Data Accuracy, 510 Mathematics, 360 Social problems & social services, weather, Humans, Public Health, ddc:510, ensemble forecast, Pandemics, Mathematics, Forecasting, Probability
Abstract

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Published
2022
Full Text
View/download PDF

6. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Author

Estee Y Cramer, Evan L Ray, Velma K Lopez, Johannes Bracher, Andrea Brennen, Alvaro J Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Katie H House, Yuxin Huang, Dasuni Jayawardena, Abdul H Kanji, Ayush Khandelwal, Khoa Le, Anja Mühlemann, Jarad Niemi, Apurv Shah, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W Zorn, Youyang Gu, Sansiddh Jain, Nayana Bannur, Ayush Deva, Mihir Kulkarni, Srujana Merugu, Alpan Raval, Siddhant Shingi, Avtansh Tiwari, Jerome White, Neil F Abernethy, Spencer Woody, Maytal Dahan, Spencer Fox, Kelly Gaither, Michael Lachmann, Lauren Ancel Meyers, James G Scott, Mauricio Tec, Ajitesh Srivastava, Glover E George, Jeffrey C Cegan, Ian D Dettwiller, William P England, Matthew W Farthing, Robert H Hunter, Brandon Lafferty, Igor Linkov, Michael L Mayo, Matthew D Parno, Michael A Rowland, Benjamin D Trump, Yanli Zhang-James, Samuel Chen, Stephen V Faraone, Jonathan Hess, Christopher P Morley, Asif Salekin, Dongliang Wang, Sabrina M Corsetti, Thomas M Baer, Marisa C Eisenberg, Karl Falb, Yitao Huang, Emily T Martin, Ella McCauley, Robert L Myers, Tom Schwarz, Daniel Sheldon, Graham Casey Gibson, Rose Yu, Liyao Gao, Yian Ma, Dongxia Wu, Xifeng Yan, Xiaoyong Jin, Yu-Xiang Wang, YangQuan Chen, Lihong Guo, Yanting Zhao, Quanquan Gu, Jinghui Chen, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Hannah Biegel, Joceline Lega, Steve McConnell, VP Nagraj, Stephanie L Guertin, Christopher Hulme-Lowe, Stephen D Turner, Yunfeng Shi, Xuegang Ban, Robert Walraven, Qi-Jun Hong, Stanley Kong, Axel van de Walle, James A Turtle, Michal Ben-Nun, Steven Riley, Pete Riley, Ugur Koyluoglu, David DesRoches, Pedro Forli, Bruce Hamory, Christina Kyriakides, Helen Leis, John Milliken, Michael Moloney, James Morgan, Ninad Nirgudkar, Gokce Ozcan, Noah Piwonka, Matt Ravi, Chris Schrader, Elizabeth Shakhnovich, Daniel Siegel, Ryan Spatz, Chris Stiefeling, Barrie Wilkinson, Alexander Wong, Sean Cavany, Guido España, Sean Moore, Rachel Oidtman, Alex Perkins, David Kraus, Andrea Kraus, Zhifeng Gao, Jiang Bian, Wei Cao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Alessandro Vespignani, Matteo Chinazzi, Jessica T Davis, Kunpeng Mu, Ana Pastore y Piontti, Xinyue Xiong, Andrew Zheng, Jackie Baek, Vivek Farias, Andreea Georgescu, Retsef Levi, Deeksha Sinha, Joshua Wilde, Georgia Perakis, Mohammed Amine Bennouna, David Nze-Ndong, Divya Singhvi, Ioannis Spantidakis, Leann Thayaparan, Asterios Tsiourvas, Arnab Sarker, Ali Jadbabaie, Devavrat Shah, Nicolas Della Penna, Leo A Celi, Saketh Sundar, Russ Wolfinger, Dave Osthus, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dean Karlen, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Elizabeth C Lee, Juan Dent, Kyra H Grantz, Alison L Hill, Joshua Kaminsky, Kathryn Kaminsky, Lindsay T Keegan, Stephen A Lauer, Joseph C Lemaitre, Justin Lessler, Hannah R Meredith, Javier Perez-Saez, Sam Shah, Claire P Smith, Shaun A Truelove, Josh Wills, Maximilian Marshall, Lauren Gardner, Kristen Nixon, John C. Burant, Lily Wang, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Yueying Wang, Shan Yu, Robert C Reiner, Ryan Barber, Emmanuela Gakidou, Simon I. Hay, Steve Lim, Chris J.L. Murray, David Pigott, Heidi L Gurung, Prasith Baccam, Steven A Stage, Bradley T Suchoski, B. Aditya Prakash, Bijaya Adhikari, Jiaming Cui, Alexander Rodríguez, Anika Tabassum, Jiajia Xie, Pinar Keskinocak, John Asplund, Arden Baxter, Buse Eylul Oruc, Nicoleta Serban, Sercan O Arik, Mike Dusenberry, Arkady Epshteyn, Elli Kanal, Long T Le, Chun-Liang Li, Tomas Pfister, Dario Sava, Rajarishi Sinha, Thomas Tsai, Nate Yoder, Jinsung Yoon, Leyou Zhang, Sam Abbott, Nikos I Bosse, Sebastian Funk, Joel Hellewell, Sophie R Meakin, Katharine Sherratt, Mingyuan Zhou, Rahi Kalantari, Teresa K Yamana, Sen Pei, Jeffrey Shaman, Michael L Li, Dimitris Bertsimas, Omar Skali Lami, Saksham Soni, Hamza Tazi Bouardi, Turgay Ayer, Madeline Adee, Jagpreet Chhatwal, Ozden O Dalgic, Mary A Ladd, Benjamin P Linas, Peter Mueller, Jade Xiao, Yuanjia Wang, Qinxia Wang, Shanghong Xie, Donglin Zeng, Alden Green, Jacob Bien, Logan Brooks, Addison J Hu, Maria Jahja, Daniel McDonald, Balasubramanian Narasimhan, Collin Politsch, Samyak Rajanala, Aaron Rumack, Noah Simon, Ryan J Tibshirani, Rob Tibshirani, Valerie Ventura, Larry Wasserman, Eamon B O’Dea, John M Drake, Robert Pagano, Quoc T Tran, Lam Si Tung Ho, Huong Huynh, Jo W Walker, Rachel B Slayton, Michael A Johansson, Matthew Biggerstaff, and Nicholas G Reich

Subjects
Geospatial analysis, Coronavirus disease 2019 (COVID-19), Computer science, business.industry, Probabilistic logic, Staffing, computer.software_genre, Scientific modelling, Health care, Econometrics, National level, business, computer, Independent research
Abstract

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

Published
2021
Full Text
View/download PDF

7. The Limits to Learning a Diffusion Model

Author

Tianyi Peng, Andrew Zheng, Vivek F. Farias, Joshua Wilde, Andreea Georgescu, Jackie Baek, Deeksha Sinha, and Retsef Levi

Subjects
FOS: Computer and information sciences, Structure (mathematical logic), Computer Science - Machine Learning, Computer science, media_common.quotation_subject, Estimator, Context (language use), Machine Learning (cs.LG), Methodology (stat.ME), symbols.namesake, Core (game theory), Bias of an estimator, symbols, Econometrics, Imitation, Fisher information, Epidemic model, Statistics - Methodology, media_common
Abstract

This paper provides the first sample complexity lower bounds for the estimation of simple diffusion models which seek to explain the diffusion of an epidemic in a network. The Susceptible-Infected-Recovered (SIR) model is a classic example, proposed nearly a century ago [2]. The SIR model remains a cornerstone for the forecasting of epidemics. The so-called Bass model [1] remains a basic building block in forecasting consumer adoption of new products and services. The durability of these models arises from the fact that they have shown an excellent fit to data, in numerous studies spanning both the epidemiology and marketing literatures. Somewhat paradoxically, using these same models as reliable forecasting tools presents a challenge. While we are ultimately motivated by the problem of forecasting a diffusion model, this paper asks a more basic question that is surprisingly unanswered: What are the limits to learning a diffusion model? We answer this question by characterizing sample complexity lower bounds for a class of stochastic diffusion models that encompass both the Bass model and the SIR model. We show that the time to collect a number of observations that exceeds these lower bounds is too large to allow for accurate forecasts early in the process. In the context of the Bass model our results imply that when adoption is driven by imitation, one cannot hope to predict the eventual number of adopting customers until one is at least two-thirds of the way to the time at which the rate of new adopters is at its peak. In a similar vein, our results imply that in the case of an SIR model, one cannot hope to predict the eventual number of infections until one is approximately two-thirds of the way to the time at which the infection rate has peaked. Our analysis is conceptually simple and relies on the Cramer-Rao bound. The core technical difficulty in our analysis rests in characterizing the Fisher information in the observations available due to the fact that they have a non-trivial correlation structure. Maximum likelihood estimation of diffusion models on product adoption datasets (for products on Amazon.com), and epidemic data (from the ongoing COVID-19 epidemic) illustrate precisely the behavior predicted by our theory. As a byproduct of our analysis, we see that the difficulty in learning a diffusion model stems solely from uncertainty in a single unknown 'effective population size' parameter. In particular, other parameters, including those related to the 'rate of imitation' (in the Bass model) or the 'reproduction number' (in the SIR model) are easy to learn. This suggests that estimators that rely on an (informative) bias in this population size parameter can in fact overcome the limitations presented by our analysis. Although not a primary contribution of the present work, we describe a heuristic procedure used to construct such a biased estimator that yielded one of the first US county-level forecasters available for COVID-19. The full paper is available at https://arxiv.org/abs/2006.06373.

Published
2020

8. Effect of Convective Dryness Combined with Osmotic Dehydration, Blanching, Microwave and Ultrasonic Treatment on Bioactive Compounds and Rehydration Capacity of Dried Plums

Author

Ionica Ionita, Antonia Andreea Georgescu, Daniela Avram, and Elena Corina Popescu

Subjects
Chromatography, Chemistry, Blanching, Materials Science (miscellaneous), Process Chemistry and Technology, General Engineering, General Chemistry, General Medicine, General Biochemistry, Genetics and Molecular Biology, Materials Chemistry, medicine, Dryness, Ultrasonic sensor, General Pharmacology, Toxicology and Pharmaceutics, medicine.symptom, Microwave, Osmotic dehydration
Abstract

Increasing interest in keeping bioactive compounds (anthocyanins, vitamin C) and dried fruit quality has motivated the researchers to investigate new combined drying technologies. The objective of this study was to evaluate the effects of convective drying combined with osmotic dehydration, bleaching, microwave treatment and ultrasonic treatment of bioactive compounds (anthocyanins, vitamin C), rehydration capacity and prune drying rate. Osmotic dehydration was achieved by maintaining plums for 1 hour in sucrose solution (300Brix). For microwave treatment, the plums were kept at 400 W for 80 s. For ultrasonic treatment, plums were immersed in distilled water and sonicated for 30 min at 40 kHz and 200 W. The blanching consists of immersing plums in hot water at 900C for 20 s and cooling them rapidly. Convective drying was carried out at 700C. Blanching led to the largest amount of evaporated water. The lowest reduction in anthocyanins, of 34.5%, was obtained for osmotic dehydrated plums. The prunes blanched before drying have had the highest rehydration capacity.

Published
2018
Full Text
View/download PDF

9. Identifying ancient beer brewing through starch analysis: A methodology

Author

Silu Tang, Madeleine H. Ota, Jiajing Wang, Li Liu, Mahpiya Vanderbilt, Vivienne V. Le, and Andreea Georgescu

Subjects
010506 paleontology, Archeology, 060102 archaeology, Starch, business.industry, food and beverages, 06 humanities and the arts, 01 natural sciences, Starch analysis, Archaeological evidence, Biotechnology, chemistry.chemical_compound, Geography, chemistry, Archaeological research, Plant species, Starch granule, Brewing, 0601 history and archaeology, Domestication, business, 0105 earth and related environmental sciences
Abstract

As documented in written and artistic records, the production and consumption of beer played a significant role in the social, political, and economic activities of many ancient societies. However, direct archaeological evidence of beer making has been relatively sporadic. To address this gap, we need a better understanding of the microbotanical residues produced by brewing. Thus, we conducted cereal-based fermentation experiments by following some brewing methods likely used in antiquity. Based on previous publications in food science, we investigated how beer-making processes affect the properties of starch granules by documenting the resulting changes that occur in the starch granules of 17 domesticated and wild plant species. This paper introduces a method for identifying starch residues from cereal-based beer, some mixed with other plant additives, which can be applied to future archaeological research in the Old World.

Published
2017
Full Text
View/download PDF

10. ANALYSIS OF THE LEVEL OF TECHNICAL PREPARATION OF CHILDREN AGED 9-10 YEARS LONG JUMP.

Author

ANDREEA, GEORGESCU, GLORIA, RAŢĂ, and AURA, BALABAN

Subjects
BROAD jump, SPORTS records, VIDEO recording, CHILDREN with cerebral palsy
Abstract

Browsing the theoretical information and studies related to training children and specifically the level of motor capacity, we decided to check the motor possibilities of a group of children with a protocol of observation drafted after viewing the videos recorded when performing the assessed event, namely the 50m sprint event. Therefore, in conducting the research, we considered it necessary to verify the following hypothesis:-for 9-10-year-old children, the use of a grid for assessing the technical execution along with the sports result allows the detection of actions which require careful training. The objective of the research is to carry out a study in which we used an assessment grid for standing long jump technical execution, but also the sports result recorded by the children. We have developed our own grid and the assessment based on it, was made by watching the recordings and video of each child, being conducted with the performed motor capacity assessment events. The research subjects are represented by 14 children aged between 9-10 years, participants in athletics competitions from September 2020 to February 2021. For the research we used as evaluation tools: - the assessment grid of the technical execution for standing long jump, based on the development of the recording event;-the standing long jump event with recording the distance by using meter. The results show us a relatively good level of mastery of the execution technique, the scores obtained based on the assessment grid being above the value given to the qualifier "good". [ABSTRACT FROM AUTHOR]

Published
2021

11. IMPROVING EYE-HAND COORDINATION FOR THIRD GRADERS.

Author

FLORIN, CAZAN, DANIEL, CÎRCIUMARU, ANDREEA, GEORGESCU, and ADRIAN, GEORGESCU

Subjects
EYE-hand coordination, EXPERIMENTAL groups, TEACHING aids, POINT set theory, CONTROL groups
Abstract

Aim. In our scientific endeavor, we started from the hypothesis that the development of coordination between the visual analyzer and the hand can be achieved in the third class by carrying out games, sticks and application courses with components of throwing, catching or using some teaching materials (basketball, target, cubes, circles etc). Methods. The experiment was conducted between November 24, 2018 - March 27, 2019 and comprised two stages: the first - initial testing of the students of the third class and, secondly - the final testing of the students participating in the experiment. The duration of the experiment was, therefore, about 4 months, and the location was the High School No.1, Mangalia (the school's gymnasium). The experiment was attended by 28 students of the third class (all the components of the same class - the third C), 14 girls and 14 boys. After the initial testing they were divided into two groups: experimental and control. Each of the two groups has 14 components (7 girls and 7 boys in each group). Results. At the test throwing at the target, the experimental group recorded 74.28 ± 8.05 points, while the control group 54.64 ± 7.71 points. At the test throw the handball through the suspended circle, the experimental group recorded 9.64 ± 0.49 points and the control group 7.78 ± 0.97 points. Conclusion. The subjects of the experimental group obtained significantly better results at the final test, compared to the initial one (p <0.01). At the level of the control group there were no significant differences between the two tests - initial and final - (p> 0.05).The experimental group obtained significantly better results than the control at the final test, in all the tests that were administered. At the "target throw" test, at the final test, the boys in the experimental group obtained a significantly higher score than the girls in the same group. [ABSTRACT FROM AUTHOR]

Published
2020

12. Reevaluation of spin-dependent WIMP-proton interactions as an explanation of the DAMA data

Author

Graciela B. Gelmini, Eugenio Del Nobile, Ji-Haeng Huh, and Andreea Georgescu

Subjects
Coupling, Physics, Exothermic reaction, High Energy Physics - Phenomenology, Particle physics, High Energy Physics - Phenomenology (hep-ph), WIMP, Proton, Astrophysics::Instrumentation and Methods for Astrophysics, FOS: Physical sciences, Astronomy and Astrophysics, Inelastic scattering, Spin-½
Abstract

We reexamine the interpretation of the annual modulation signal observed by the DAMA experiment as due to WIMPs with a spin-dependent coupling mostly to protons. We consider both axial-vector and pseudo-scalar couplings, and elastic as well as endothermic and exothermic inelastic scattering. We conclude that the DAMA signal is in strong tension with null results of other direct detection experiments, particularly PICASSO and KIMS., Comment: Section on halo-independent analysis added and other small changes in the SHM data analysis made

Published
2015

13. Extended Maximum Likelihood Halo-independent Analysis of Dark Matter Direct Detection Data

Author

Paolo Gondolo, Graciela B. Gelmini, Ji-Haeng Huh, and Andreea Georgescu

Subjects
Physics, Exothermic reaction, Particle physics, Variables, media_common.quotation_subject, Dark matter, FOS: Physical sciences, Astronomy and Astrophysics, Inelastic scattering, 7. Clean energy, High Energy Physics - Phenomenology, medicine.anatomical_structure, High Energy Physics - Phenomenology (hep-ph), WIMP, medicine, Neutron, Halo, Nucleus, media_common
Abstract

We extend and correct a recently proposed maximum-likelihood halo-independent method to analyze unbinned direct dark matter detection data. Instead of the recoil energy as independent variable we use the minimum speed a dark matter particle must have to impart a given recoil energy to a nucleus. This has the advantage of allowing us to apply the method to any type of target composition and interaction, e.g. with general momentum and velocity dependence, and with elastic or inelastic scattering. We prove the method and provide a rigorous statistical interpretation of the results. As first applications, we find that for dark matter particles with elastic spin-independent interactions and neutron to proton coupling ratio $f_n/f_p=-0.7$, the WIMP interpretation of the signal observed by CDMS-II-Si is compatible with the constraints imposed by all other experiments with null results. We also find a similar compatibility for exothermic inelastic spin-independent interactions with $f_n/f_p=-0.8$., 26 pages and 15 figures. Some additional explanations added to the text in v3 with respect to v2

Published
2015

14. Direct detection of light 'Ge-phobic' exothermic dark matter

Author

Andreea Georgescu, Graciela B. Gelmini, and Ji-Haeng Huh

Subjects
Exothermic reaction, Physics, Coupling, Cosmology and Nongalactic Astrophysics (astro-ph.CO), Proton, Scattering, Dark matter, chemistry.chemical_element, FOS: Physical sciences, Astronomy and Astrophysics, Nuclear physics, High Energy Physics - Phenomenology, Xenon, High Energy Physics - Phenomenology (hep-ph), chemistry, Neutron, Halo, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present comparisons of direct dark matter (DM) detection data for light WIMPs with exothermic scattering with nuclei (exoDM), both assuming the Standard Halo Model (SHM) and in a halo model - independent manner. Exothermic interactions favor light targets, thus reducing the importance of upper limits derived from xenon targets, the most restrictive of which is at present the LUX limit. In our SHM analysis the CDMS-II-Si and CoGeNT regions become allowed by these bounds, however the recent SuperCDMS limit rejects both regions for exoDM with isospin-conserving couplings. An isospin-violating coupling of the exoDM, in particular one with a neutron to proton coupling ratio of $-0.8$ (which we call "Ge-phobic"), maximally reduces the DM coupling to germanium and allows the CDMS-II-Si region to become compatible with all bounds. This is also clearly shown in our halo-independent analysis., Comment: 14 pages, 10 figures

Published
2014

15. Locally disrupted synchronization in Langevin molecular dynamics

Author

Emilien Joly, Samuel J. Denny, Arthur F. Voter, Grace Chen, Andreea Georgescu, and Danny Perez

Subjects
Physics, Stochastic Processes, Models, Statistical, Time Factors, Biophysics, Molecular Conformation, Normal Distribution, Temperature, Perturbation (astronomy), Molecular Dynamics Simulation, Thermostat, Synchronization, law.invention, Motion, Molecular dynamics, Classical mechanics, law, Phase space, Lattice (order), Strong coupling, Computer Simulation, Algorithms, Curse of dimensionality
Abstract

Stochastic thermostats commonly used in molecular dynamics trajectories are known, under certain conditions, to exhibit a synchronization effect whereby trajectories initialized at different points in phase space synchronize to a single master trajectory if they are subjected to the same sequence of random forces. We investigate the spatiotemporal robustness of this effect analytically and with molecular dynamics simulations in one and three dimensions in the strong coupling limit. We first investigate the response of the system to a time- and spacewise local perturbation and show that desynchronization behaves diffusively at long times for infinite systems. We then explore the behavior of temporally persistent but spatially local perturbations and observe strikingly different behaviors as a function of dimensionality: in one dimension, the desynchronization propagates through the whole lattice and grows with time, while in three dimensions, the desynchronization remains localized in the neighborhood of the perturbation.

Published
2012
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results