Academic literature on the topic 'Computer confidence'

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Journal articles on the topic "Computer confidence"

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Weber, James E., Steven R. Ash, and Paula S. Weber. "Side Effects of Incidental Computer Use: Increased Confidence." Psychological Reports 83, no. 1 (1998): 211–14. http://dx.doi.org/10.2466/pr0.1998.83.1.211.

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100 students were administered a pretest based on two of Chickering's 1990 seven vectors of educational development. 67 participated in one of two management classes which required use of computers. 33 acted as a control group with no in-class computer use. Analyses indicate that in addition to intended gains in learning, students also showed related developmental effects including increased confidence in using computers. Changes in scores on perceived Autonomy, controlling for prior computer use and outside exposure to computers, were not significant. Results show some effects from using comp
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Lazenby, Paul. "Confidence Intervals: A Computer Approach." Mathematical Gazette 70, no. 451 (1986): 23. http://dx.doi.org/10.2307/3615820.

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Garcia-Santillan, Arturo, Elena Moreno-Garcia, Milka E. Escalera-Chávez, Carlos A. Rojas-Kramer, and Felipe Pozos-Texon. "Structural Equation Model to Validate: Mathematics-Computer Interaction, Computer Confidence, Mathematics Commitment, Mathematics Motivation and Mathematics Confidence." International Journal of Research in Education and Science 2, no. 2 (2016): 518. http://dx.doi.org/10.21890/ijres.81576.

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Plumlee, Matthew. "Computer model calibration with confidence and consistency." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 81, no. 3 (2019): 519–45. http://dx.doi.org/10.1111/rssb.12314.

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Temple, Lori L., and Margaret Gavillet. "The Development of Computer Confidence in Seniors." Activities, Adaptation & Aging 14, no. 3 (1989): 63–76. http://dx.doi.org/10.1300/j016v14n03_06.

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Bean, Jonathan. "Modeling confidence." Interactions 25, no. 3 (2018): 25–27. http://dx.doi.org/10.1145/3194383.

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Nash, John B., and Pauline A. Moroz. "An Examination of the Factor Structures of the Computer Attitude Scale." Journal of Educational Computing Research 17, no. 4 (1997): 341–56. http://dx.doi.org/10.2190/ngdu-h73e-xmr3-tg5j.

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Previous research regarding the popular Computer Attitude Scale (CAS) has indicated that the computer confidence and computer anxiety subscales measure the same trait. This study, utilizing data yielded from 208 educators, obtained estimates of the reliability of the four subscale version of the forty item CAS; provided detailed information regarding the factor patterns of the CAS subscales; and provided evidence about the differential validity of the CAS among four groups with differing intensity of computer usage. Correlations and exploratory factor analysis were used to analyze the data. Th
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WEBER, JAMES E. "SIDE EFFECTS OF INCIDENTAL COMPUTER USE: INCREASED CONFIDENCE." Psychological Reports 83, no. 5 (1998): 211. http://dx.doi.org/10.2466/pr0.83.5.211-214.

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Dyck, Jennifer L., and Janan Al-Awar Smither. "Age Differences in Computer Anxiety: The Role of Computer Experience, Gender and Education." Journal of Educational Computing Research 10, no. 3 (1994): 239–48. http://dx.doi.org/10.2190/e79u-vcrc-el4e-hryv.

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Research in the area of computer anxiety has traditionally concentrated on the younger adult. In this study older adults (55 years and over) were compared to younger adults (30 years and under) on levels of computer anxiety and computer experience. Subjects completed a demographic and computer experience questionnaire, a computer anxiety scale, and a computer attitude scale. Findings indicated that older adults were less computer anxious (as measured by both scales), had more positive attitudes toward computers, and had more liking for computers than younger adults. Older adults also had less
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Levine, Tamar, and Smadar Donitsa-Schmidt. "Commitment to Learning: Effects of Computer Experience, Confidence and Attitudes." Journal of Educational Computing Research 16, no. 1 (1997): 83–105. http://dx.doi.org/10.2190/qq9m-4yg0-pxy2-hmmw.

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Based on attitude-behavior theory which suggests that beliefs about an object lead to an attitude toward it, and that attitudes are an important precursor of behavior, this study proposes a causal model relating measures of computer-experience (degree of computer use at home and in school), computer-related attitudes (dispositions concerning the computer as an important, interesting, educational, and stereotypical tool), computer-related confidence (degree of confidence when using a computer), and commitment to computer learning (difference between self-perceived current level of computer-appl
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Dissertations / Theses on the topic "Computer confidence"

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Burford, Bryan Christopher. "Contextual effects on computer users' confidence." Thesis, Northumbria University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410387.

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Kamra, Varun. "Mining discriminating patterns in data with confidence." Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10196147.

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<p> There are many pattern mining algorithms available for classifying data. The main drawback of most of the algorithms is that they always focus on mining frequent patterns in data that may not always be discriminative enough for classification. There could exist patterns that are not frequent, but are efficient discriminators. In such cases these algorithms might not perform well. This project proposes the MDP algorithm, which aims to search for patterns that are good at discriminating between classes rather than searching for frequent patterns. The MDP ensures that there is at least one mo
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Applebee, Andrelyn C., and n/a. "Attitudes toward computers in the 1990s: a look at gender, age and previous computer experience on computer anxiety, confidence, liking and indifference." University of Canberra. Education, 1994. http://erl.canberra.edu.au./public/adt-AUC20060206.123119.

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The purpose of this study was to investigate the relationship between computer attitudes held by tertiary students and the selected variables of gender, age and previous computer experience. It was hypothesized that no statistically significant differences would be found within the relationships tested. A questionnaire comprising the Computer Attitude Scale (CAS), demographic and other questions was administered to the population enrolled in an introductory computer unit at the University of Canberra, Australian Capital Territory in Semester 1, 1992. The results were subjected to t-test and on
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Saxon, John Trevor. "Using traceability in model-to-model transformation to quantify confidence based on previous history." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8047/.

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A widely used method when generating code for the purposes oftransitioning systems, security, the automotive industry and other mission critical scenarios is model-to-model transformation. Traceability is a mechanism for relating the source model elements and the destination elements. It is used to identity how the latter came from the former as well as indicating when and in what order. In these application domains, traceability is a very useful tool for debugging, testing and performance tuning of model transformations. Recent advances in big data technologies have made it possible to produc
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Tsang, Kong Chau. "Confidence measures for disparity estimates from energy neuron populations /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?ECED%202007%20TSANG.

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Nandeshwar, Ashutosh R. "Models for calculating confidence intervals for neural networks." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4600.

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Thesis (M.S.)--West Virginia University, 2006.<br>Title from document title page. Document formatted into pages; contains x, 65 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 62-65).
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Eklöf, Patrik. "Implementing Confidence-based Work Stealing Search in Gecode." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-154475.

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Constraint programming is a field whose goal is to solve extremely large problems with a set of restrictions that define the problem. One such example is generating CPU instructions from source code, because a compiler must choose the optimal instructions that best matches the source code, schedule them optimally to minimize the amount of time it takes to execute the instructions and possibly at the same time also minimize power consumption. The difficulty of the problem lies in that there is no single good way to approach the problem since all parameters are so dependent on each other. For ex
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Lavallée-Adam, Mathieu. "Protein-protein interaction confidence assessment and network clustering computational analysis." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=121237.

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Protein-protein interactions represent a crucial source of information for the understanding of the biological mechanisms of the cell. In order to be useful, high quality protein-protein interactions must be computationally extracted from the noisy datasets produced by high-throughput experiments such as affinity purification. Even when filtered protein-protein interaction datasets are obtained, the task of analyzing the network formed by these numerous interactions remains tremendous. Protein-protein interaction networks are large, intricate, and require computational approaches to provide me
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Covington, Valerie A. "Lower confidence interval bounds for coherent systems with cyclic components." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA242713.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 1990.<br>Thesis Advisor(s): Woods, W. Max. Second Reader: Whitaker, Lyn R. "September 1990." Description based on title screen viewed on December 17, 2009. DTIC Descriptor(s): Computer programs, intervals, confidence limits, accuracy, theses, Monte Carlo method, cycles, fortran, reliability, yield, standardization, statistical distributions, equations, confidence level, poisson density functions, failure, coherence, binomials, computerized simulation. Author(s) subject terms: Reliability, lower confidence limit, coheren
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Kevork, Ilias. "Confidence interval methods in discrete event computer simulation : theoretical properties and practical recommendations." Thesis, London School of Economics and Political Science (University of London), 1990. http://etheses.lse.ac.uk/1257/.

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Most of steady state simulation outputs are characterized by some degree of dependency between successive observations at different lags measured by the autocorrelation function. In such cases, classical statistical techniques based on independent, identical and normal random variables are not recommended in the construction of confidence intervals for steady state means. Such confidence intervals would cover the steady state mean with probability different from the nominal confidence level. For the last two decades, alternative confidence interval methods have been proposed for stationary sim
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Books on the topic "Computer confidence"

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Brittain, White Kathy, ed. Computer confidence: A challenge for today. South-Western Pub. Co., 1986.

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1948-, Oswalt Beverly J., ed. Computer confidence: A challenge for today. 2nd ed. South-Western Pub. Co., 1991.

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Grey, Tim. Color Confidence. John Wiley & Sons, Ltd., 2006.

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Grey, Tim. Color Confidence. John Wiley & Sons, Ltd., 2007.

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C, Arangno Deborah, ed. Simulation validation: A confidence assessment methodology. IEEE Computer Society Press, 1993.

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Cofta, Piotr. Trust, complexity and control confidence in a convergent world. John Wiley, 2007.

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Dotson, Kelly J. Development of confidence limits by pivotal functions for estimating software reliability. National Aeronautics and Space Administration, Scientific and Technical Information Office, 1987.

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Color confidence: The digital photographer's guide to color management. Sybex, 2004.

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C, Weil Jennifer, and Liu Hui-Han ill, eds. William's gift. Enchanté Pub., 1994.

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Farrington, Liz. William's gift. 2nd ed. Enchanté, 1995.

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Book chapters on the topic "Computer confidence"

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Couëtoux, Adrien, Jean-Baptiste Hoock, Nataliya Sokolovska, Olivier Teytaud, and Nicolas Bonnard. "Continuous Upper Confidence Trees." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25566-3_32.

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Śliwa, Leszek Stanislaw. "The Confidence Intervals in Computer Go." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39384-1_51.

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Kim, Eunju, Wooju Kim, and Yillbyung Lee. "Classifier Fusion Using Local Confidence." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-48050-1_62.

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Cherubin, Giovanni, and Ilia Nouretdinov. "Hidden Markov Models with Confidence." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33395-3_10.

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Cheetham, William. "Case-Based Reasoning with Confidence." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44527-7_3.

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Papadopoulos, Harris, Kostas Proedrou, Volodya Vovk, and Alex Gammerman. "Inductive Confidence Machines for Regression." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36755-1_29.

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Nouretdinov, Ilia, Vladimir V’yugin, and Alex Gammerman. "Transductive Confidence Machine Is Universal." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39624-6_23.

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Poggi, Matteo, Filippo Aleotti, Fabio Tosi, Giulio Zaccaroni, and Stefano Mattoccia. "Self-adapting Confidence Estimation for Stereo." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58586-0_42.

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Pailai, Jaruwat, Warunya Wunnasri, Yusuke Hayashi, and Tsukasa Hirashima. "Correctness- and Confidence-Based Adaptive Feedback of Kit-Build Concept Map with Confidence Tagging." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93843-1_29.

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Venkatesh, J. N., R. Uday Kiran, P. Krishna Reddy, and Masaru Kitsuregawa. "Discovering Periodic-Frequent Patterns in Transactional Databases Using All-Confidence and Periodic-All-Confidence." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44403-1_4.

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Conference papers on the topic "Computer confidence"

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Kiktenko, A. A., M. N. Lunkovskiy, and K. A. Nikiforov. "Confidence complexity of computer algorithms." In 2014 2nd International Conference on Emission Electronics (ICEE). IEEE, 2014. http://dx.doi.org/10.1109/emission.2014.6893971.

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Breedt, Hugo, and Vreda Pieterse. "Student confidence in using computers." In Second Computer Science Education Research Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2421277.2421279.

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Bisafar, Farnaz Irannejad, and Andrea Grimes Parker. "Confidence & Control." In CSCW '16: Computer Supported Cooperative Work and Social Computing. ACM, 2016. http://dx.doi.org/10.1145/2818048.2820028.

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McCloskey, Scott. "Confidence weighting for sensor fingerprinting." In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2008. http://dx.doi.org/10.1109/cvprw.2008.4562986.

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Zou, Yang, Zhiding Yu, Xiaofeng Liu, B. V. K. Vijaya Kumar, and Jinsong Wang. "Confidence Regularized Self-Training." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00608.

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Ying, Kimberly Michelle, Fernando J. Rodríguez, Alexandra Lauren Dibble, et al. "Confidence, Connection, and Comfort." In SIGCSE '21: The 52nd ACM Technical Symposium on Computer Science Education. ACM, 2021. http://dx.doi.org/10.1145/3408877.3432548.

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Mogre, Advait, Robert McLaren, and James Keller. "Utilizing Context In Computer Vision By Confidence Modification." In 1988 Robotics Conferences, edited by David P. Casasent. SPIE, 1989. http://dx.doi.org/10.1117/12.960299.

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Staehr, Lorraine, Mary Martin, and Graeme Byrne. "Computer Attitudes and Computing Career Perceptions of First Year Computing Students." In 2001 Informing Science Conference. Informing Science Institute, 2001. http://dx.doi.org/10.28945/2360.

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This paper reports on a longitudinal research study on the attitudes to computers, and the perceptions of a computing career, of students enrolled in an introductory computing course in the years 1995 to 1998. Previous programming experience had a positive effect on computer confidence, and ownership of a home computer had a positive effect on computer anxiety and computer confidence. There was a gender difference in computer attitudes and perception of a computing career, with females scoring significantly lower than males on all measures. The analysis showed decreased computer liking over th
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von Schmieden, Karen, Thomas Staubitz, Lena Mayer, and Christoph Meinel. "Skill Confidence Ratings in a MOOC: Examining the Link between Skill Confidence and Learner Development." In 11th International Conference on Computer Supported Education. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007655405330540.

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TaheriNejad, Nima, and Axel Jantsch. "Improved Machine Learning using Confidence." In 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). IEEE, 2019. http://dx.doi.org/10.1109/ccece.2019.8861962.

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Reports on the topic "Computer confidence"

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Melby, Jeffrey, Thomas Massey, Abigail Stehno, Norberto Nadal-Caraballo, Shubhra Misra, and Victor Gonzalez. Sabine Pass to Galveston Bay, TX Pre-construction, Engineering and Design (PED) : coastal storm surge and wave hazard assessment : report 1 – background and approach. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41820.

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The US Army Corps of Engineers, Galveston District, is executing the Sabine Pass to Galveston Bay Coastal Storm Risk Management (CSRM) project for Brazoria, Jefferson, and Orange Counties regions. The project is currently in the Pre-construction, Engineering, and Design phase. This report documents coastal storm water level and wave hazards for the Port Arthur CSRM structures. Coastal storm water level (SWL) and wave loading and overtopping are quantified using high-fidelity hydrodynamic modeling and stochastic simulations. The CSTORM coupled water level and wave modeling system simulated 195
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Stehno, Abigail, Jeffrey Melby, Shubhra Misra, Norberto Nadal-Caraballo, and Victor Gonzalez. Sabine Pass to Galveston Bay, TX Pre-construction, Engineering and Design (PED) : coastal storm surge and wave hazard assessment : report 2 – Port Arthur. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41901.

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The US Army Corps of Engineers, Galveston District, is executing the Sabine Pass to Galveston Bay Coastal Storm Risk Management (CSRM) project for Brazoria, Jefferson, and Orange Counties regions. The project is currently in the Pre-construction, Engineering, and Design phase. This report documents coastal storm water level and wave hazards for the Port Arthur CSRM structures. Coastal storm water level (SWL) and wave loading and overtopping are quantified using high-fidelity hydrodynamic modeling and stochastic simulations. The CSTORM coupled water level and wave modeling system simulated 195
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Stehno, Abigail, Jeffrey Melby, Shubhra Misra, Norberto Nadal-Caraballo, and Victor Gonzalez. Sabine Pass to Galveston Bay, TX Pre-construction, Engineering and Design (PED) : coastal storm surge and wave hazard assessment : report 4 – Freeport. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41903.

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The US Army Corps of Engineers, Galveston District, is executing the Sabine Pass to Galveston Bay Coastal Storm Risk Management (CSRM) project for Brazoria, Jefferson, and Orange Counties regions. The project is currently in the Pre-construction, Engineering, and Design phase. This report documents coastal storm water level (SWL) and wave hazards for the Freeport CSRM structures. Coastal SWL and wave loading and overtopping are quantified using high-fidelity hydrodynamic modeling and stochastic simulations. The CSTORM coupled water level and wave modeling system simulated 195 synthetic tropica
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Stehno, Abigail, Jeffrey Melby, Shubhra Misra, Norberto Nadal-Caraballo, and Victor Gonzalez. Sabine Pass to Galveston Bay, TX Pre-construction, Engineering and Design (PED) : coastal storm surge and wave hazard assessment : report 3 – Orange County. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41902.

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The US Army Corps of Engineers, Galveston District, is executing the Sabine Pass to Galveston Bay Coastal Storm Risk Management (CSRM) project for Brazoria, Jefferson, and Orange Counties regions. The project is currently in the Pre-construction, Engineering, and Design phase. This report documents coastal storm water level (SWL) and wave hazards for the Orange County CSRM structures. Coastal SWL and wave loading and overtopping are quantified using high-fidelity hydrodynamic modeling and stochastic simulations. The CSTORM coupled water level and wave modeling system simulated 195 synthetic tr
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