Search

Implementing an integrated sequence of concrete-representational-abstract depictions of mathematics concepts (CRA-I) can improve the mathematics achievement of students with disabilities, and explicit instructional strategies involving problem-solving heuristics and student verbalizations can help facilitate students' conceptual understanding of mathematics. Combining CRA-I and explicit instructional strategies may increase students' conceptual understanding and ability to express mathematical reasoning through writing. This study included three ninth-grade students with disabilities, and employed a multiple-probe design across-participants to investigate a functional relation between an explicit instructional strategy within a CRA-I framework and high school students' with disabilities proficiency in solving rate of change problems. Results showed that all three students improved their mathematics scores (combined Tau- U effect size = 0.77, p <.001) and maintained improvements during a 1- to 7-week post-instruction phase. Implications for research and practice related to mathematics instruction and intervention specifically for students with learning disabilities are discussed. [ABSTRACT FROM AUTHOR]

The coming of mathematicians to the United States fleeing the spread of Nazism presented a serious problem to the American mathematical community. The persistence of the Depression had endangered the promising growth of mathematics in the United States. Leading mathematicians were concerned about the career prospects of their students. They (and others) feared that placing large numbers of refugees would exacerbate already present nationalistic and anti Semitic sentiments. The paper surveys a sequence of events in which the leading mathematicians reacted to the foreign-born and to the spread of Nazism, culminating in the decisions by the American Mathematical Society to found the journal Mathematical reviews and to form a War Preparedness Committee in September 1939. The most obvious consequence of the migration was an enlarged role for applied mathematics. [ABSTRACT FROM AUTHOR]

The article presents information related to conference and proceedings in Mathematics Research. The 14th Benelux Meeting on Systems and Control will be held from March 29, 1995 to March 31, 1995 at Houthalen, Belgium. The Copper Mountain Conference on Multigrid Methods will be held from March 28, 1995 to March April 2, 1995 at Copper Mountain, Colorado. The Conference on Numerical Methods for Fluid Dynamics will be held from April 3, 1995 to April 6, 1995 at University of Oxford, England. The Conference on Iterative Methods will be held from April 10, 1995 to April 14, 1995 at Toulouse, France.

Opinion. Criticizes an article written by Sharon Griffin and Robbie Case in the 1997, zvolume II, numbger I edition of the `Issues in Education' journal, which focused on the impact of cognitive science on primary school students' mathematics curriculum in the United States. Examination of the capacities of student development; In-depth look at teacher development; Relationship between cognitive science and education.

Many studies have been made on the demand function for meat, often using two functional forms. The first is the linear formulation in which the quantity demanded is assumed to be a linear function of the explanatory variables. The second is the logarithmic formulation in which all the variables are transformed into the log form. Since there is no a priori rationale for the choice of one or the other form, choice between the two is often difficult. In addition, the use of one form or the other might have implications that are either highly restrictive or inconsistent with theoretical considerations or actual experience. The purpose of this paper is to adopt a more general functional form for the demand for meat and use the maximum likelihood method to estimate parameters in the general form. The linear and log formulations will also be examined. In the next section, the general functional form and the estimation approach will be introduced. Empirical results will be presented in the third section. In the last section, a brief summary and conclusion will be given.

Path dependent securities depend on current and past values of underlying state variables. Consequently, the usual backward evaluation technique is difficult to apply since state variable values existing earlier in real time are unknown. This paper develops a series of propositions which makes possible the pricing of a certain class of both American and European versions of these path dependent securities. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical model that selects locations for Internal Revenue Service Posts-of-Duty. The system is microcomputer-based and uses menus and graphically displayed zip code maps of IRS districts for interactive inputs and solution outputs. The mathematical model used for this problem is the uncapacitated, fixed charge, location-allocation model which minimizes travel and facility costs, given a specified level of activity. A greedy-interchange heuristic is used to obtain "good" solutions to the problem and is coupled with a lagrangian-relaxation technique providing a measure of how far from optimality the current solution can be. An example with Florida data illustrates the use of the model. [ABSTRACT FROM AUTHOR]

Focuses on the establishment of a mathematical community by Eliakim Hastings Moore in the U.S. Organization of an international congress of mathematicians; Delegation of students in Europe to study the advances in mathematics; Biography of Moore.

Doubt has recently been cast on the applicability of mathematical models in general and Lanchester-type equations in particular to real combat. A comparatively small amount of effort has been spent in the past. in testing theory against data, and so the general validity of such modeling largely remains to be demonstrated. In this paper, combat data on the U.S. Civil War is examined to determine the extent to which it can be explained by simple mathematical relations, and some support, as well as problems, for such modeling is developed. A model for probability of winning is developed, depending solely On the ability of each force to continue to fight after sustaining specified fractional losses. Distributions of parameters describing battles, force ratios, and loss ratios are derived. Limited applicability of Lanchester-type equal ions is indicated. It is concluded that although these hundred-year-old battles may have limited application to future combat, the possibilities of deriving improved combat models by analyzing real life data are essentially unlimited, and that fact differs sufficiently from theory to make it unlikely that pure conjecture will hit upon the proper form for a combat model structure. [ABSTRACT FROM AUTHOR]

The author reflects on the quality of gifted programming for gifted children in the U.S. The approaches of the program to curriculum focuses primarily on mathematics and language arts and grouping arrangement without real differentiation of curriculum. There was little parental involvement in the program and they have lack of communication about the program.

Charlotte Angas Scott (1858–1932) was an internationally renowned geometer, the first British woman to earn a doctorate in mathematics, and the chair of the Bryn Mawr mathematics department for forty years. There she helped shape the burgeoning mathematics community in the United States. Scott often motivated her research as providing a "geometric treatment" of results that had previously been derived algebraically. The adjective "geometric" likely entailed many things for Scott, from her careful illustration of diagrams to her choice of references and citations. This article will focus on Scott's striking and consistent use of geometric to describe a reality of dynamic points, lines, planes, and spaces that could be manipulated analogously to physical objects. By providing geometric interpretations of algebraic derivations, Scott committed to an early-nineteenth-century aesthetic vision of a "whole" analytical geometry that she adapted to modern research areas. [ABSTRACT FROM AUTHOR]

This qualitative research study investigates dialogic discourse (i.e., give-and-take communication where students actively construct meaning) aimed at supporting mathematical meaning making in linguistically diverse elementary classrooms. For this study, linguistically diverse classrooms refer to classrooms where Spanish is the home language of many of the students--both dual-language (Spanish and English) and English-only elementary classrooms that include English learners (ELs). Multiple classrooms in two schools in the western and eastern United States were observed. Data sources include field notes, audio and video recordings, transcriptions of dialogue, and translations of dialogue (as appropriate). Qualitative methods including constant comparative methods, thematic coding, and discourse analysis techniques are used to examine verbal moves, exchanges, sequences, and episodes of discourse from mathematics lessons. A first-grade dual-language classroom is highlighted to illustrate methods and findings. Analysis demonstrates that, similar to monolingual classrooms, specific verbal moves (e.g., exploratory talk, accountable talk, and generative assessment) may shift discourse toward dialogic. Other supporting moves and practices (e.g., think time, visuals, use of primary language, and code-switching) are identified. Models of teaching [ABSTRACT FROM AUTHOR]

In this article, we begin by taking stock of broad trends related to gender and mathematics, focusing primarily on patterns within the United States and considering how these patterns may vary by social class and race. This article is not a traditional, empirical piece but instead pulls together evidence from quantitative and qualitative studies to argue that gender remains an issue worthy of our attention within mathematics education. Given that much recent work on gender and mathematics has been situated outside of traditional mathematics education frames, we consider how ongoing work in psychology and gender studies can contribute to our understanding, and we use this work in conjunction with interview data from a study of women's experiences in a mathematics Ph.D. program. Ultimately, we argue that mathematics education researchers bring unique expertise to the table and have a particular role to play in building upon findings in other fields to further the work on gender and mathematics. [ABSTRACT FROM AUTHOR]

Data from 403 third graders were analyzed to determine relative and combined efficacy of group-administered Curriculum-Based Measures (CBMs) and Teacher Rankings of student reading and math performance taken early in the school year to predict end-of-year achievement scores. Teacher Rankings added to the power of CBMs to predict reading (R 2 change =.18) and math (R 2 change =.22). Combined CBMs and Teacher Rankings predicted at-risk status in reading (82%) and math (86%), based on logistic regression, and yielded strong area under the curve (AUC) statistics, defining risk status.88 (reading) and.82 (math). Surprisingly, Teacher Rankings yielded higher correlations with end-of-year scores than CBMs. Findings support using rankings as a simple, efficient strategy to add to the predictive power of CBMs readily available within a response to intervention (RTI) context and depicts a methodology school personnel can use to determine the relative/combined predictive power of CBMs and rankings. Of note, predictions based on Teacher Rankings vary across end-of-year performance levels. [ABSTRACT FROM AUTHOR]

Early math success in the PreK classroom is recognized as a strong predictor of later academic success. However, many early childhood teachers report they lack the content knowledge, confidence and beliefs to deliver high quality math experiences in their classrooms. Professional development opportunities to gain and expand these skills are often difficult for early childhood educators to access. The purpose of this article is to examine the professional development opportunities available to early childhood educators in the United States and to present a model of professional development that is accessible and effective. This article provides a description of the importance of math in the early years and reveals the need for early math professional development opportunities for early childhood teachers. A portrait of the current state of professional development opportunities for early childhood teachers in the United States is presented along with a presentation of a successful online early math professional development program that was developed to meet the need of early childhood educators. The article ends with implications for current professional development opportunities and ideas for future directions. [ABSTRACT FROM AUTHOR]

Developmental mathematics, which is designed to prepare students for college-level mathematics courses, can be a barrier to students' success. In the United States, the majority of students placed into developmental mathematics courses fail to complete the developmental sequence. Alternative mathematics pathways offer some benefits when integrated with "just-intime support" or expedited instruction on specific prerequisite concepts needed solely for the current lesson. This study compares two statistics courses taught at a public community college: a complete course taught in one semester and a two-semester version with just-in-time develop - mental content integrated into the course. The study found that students placed into the onesemester statistics course accumulated significantly more credits after one and two years of college. These students also completed an associate's degree within two years at a significantly higher rate than students placed into the two-semester statistics course. The study also found students deemed non-proficient by the college's placement exam, who also had a strong high school average were significantly more likely to earn a grade of A- or higher or a grade of B- or higher compared to students originally deemed proficient. [ABSTRACT FROM AUTHOR]

Objectives: Determine whether a specific numeracy skill cut-point(s) reflects an empirical threshold in the context of preventive health service utilization, and identify associations between numeracy and preventive health services utilization among middle-aged and older adults in the United States. Methods: A nationally representative sample (n = 2,989) of adults 45 years and older from the International Assessment of Adult Competencies (PIAAC) was analyzed. Binary logistic regression was used to examine the utilization of dental checkup, vision screening, influenza vaccination, and osteoporosis screening, using multiple numeracy level classifications. Results: A dichotomous classification of numeracy skill levels (low vs. moderate to high proficiency) was associated with dental checkup utilization, but vision screening, influenza vaccination, and osteoporosis screening. Conclusions: Middle-aged and older adults with sufficient numeracy skills are more likely to have had a dental check up in the past 12 months. Findings suggest that numeracy may be more relevant for long-term vs. short-term risk assessment in determining preventive health care service utilization. Clinical Implications: Two-level numeracy categories are recommended in preventive health contexts. Numeracy proficiency-sensitive risk communication by health care providers and education programs may enhance awareness of preventive health care and promote the utilization of specific preventive health service utilization among older adults. [ABSTRACT FROM AUTHOR]

Community colleges serve a vital role in the education of science, technology, engineering, and mathematics (STEM) majors in the United States, however, most research to date on STEM pipeline persistence from academia to the workforce has focused on four‐year colleges, which limits understanding of the potential of two‐year pathways in diversifying STEM participation. One chronic issue is the vertical alignment of community college STEM education with workforce demands and advancement to baccalaureate institutions. This research builds upon prior work by exploring the initial mathematics enrollment and performance of STEM majors, and how this relates to demographic and socioeconomic variables and the likelihood of degree change from STEM to non‐STEM disciplines, graduation and transfer rates, attrition from college, science performance, and credit production. This explanatory observational study employed multiple regression techniques to examine transcript data from four cohorts of community college STEM majors (n = 1,511) over 3 years of enrollment in an institution in the Northeast U.S. Results indicated that students who first enrolled in remedial mathematics courses experienced a higher likelihood of changing to non‐STEM majors, greater attrition, lower credit production, weaker science performance, and lower rates of graduation and transfer to four‐year colleges. Students who completed developmental mathematics courses failed algebra and trigonometry at a rate of 68%, indicating remediation coursework did not prepare most students for the mathematics required for science degrees. Students who qualified for advanced mathematics as their entry level course outperformed students who first took lower level classes. Demographic variables and socioeconomic status had limited predictive value for STEM‐related outcomes. Results suggest that community college STEM majors may benefit from reconceptualized developmental curricula focused on essential skills for success in advanced mathematics and science, as well as clarity on transferable coursework and structured pathways to reach the milestones required for STEM degrees and careers. [ABSTRACT FROM AUTHOR]

The purpose of this study was to identify trends in the representation of female authorship regarding the topic of the status of women in science, technology, engineering, and mathematics (STEM) in higher education in the United States from 2007 to 2018 in prominent interdisciplinary journals. We conducted a comprehensive search for articles and collected the genders of the first and senior authors from 647 citations. We assessed the number of male versus female authors, the percentages of female first authors and senior authors, and the percentage of female corresponding authors for each year. Additionally, we also analyzed the citations to determine the journals and publishers who produced the most literature in this area. Women constituted 59% overall authorship and 34% first authorship. The top publishers in this area include Sage Publications, Springer/Nature, and Elsevier. Women constituted 60% of the first authors in STEM literature on the status of women and 38% of senior authors. Although there was growth over time in first authorship in STEM literature written by women, they continue to remain a minority in senior authorship. We suggest it is women that are leading this discussion, highlighting the additional lift that women in STEM in higher education must make; researching and publishing on their own experiences as part of their self-advocacy. [ABSTRACT FROM AUTHOR]

Background: Numeracy is one's ability to use numbers in context and influence's decision making and perceptions of health risk. Worry about lung cancer is an indicator of perceived risk (PR) and is related to interest in cessation and cancer screening. Purpose: The analysis sought to explore underlying relationships among worry about lung cancer, smoking-related disease risk perceptions, and numeracy in a web-based panel. Methods: A Web-based survey, including a numeracy measure, was completed by 1,682 participants aged 18–65 recruited in 2014. Former and current smokers were asked about LC worry and current smokers were asked their PR of diseases associated with smoking. Results: Females (m = 25.64, 95% CI [24.67, 26.61]), respondents aged 45–65 (m = 26.15, 95% CI [24.89, 27.41]), those who worry "all the time" about LC (m = 27.62, 95% CI [25.66, 29.58]), and respondents perceiving a higher risk of LC compared to other smokers (m = 28.84, 95% CI [27.66, 30.01]) expressed significantly higher PR means. Higher household income (OR = 1.20, 95% CI [1.08, 1.34]), age (OR = 0.86, 95% CI [0.77, 0.98]), and comparative LC risk (OR = 2.52, 95% CI [2.01, 3.17]) were significantly associated with greater worry about LC. As PR increases by one unit, the probability of worrying increases by 4.6%. For numeracy scores, odds ratio showed that as scores increased by one unit, the probability of worrying decreased by 11.9%. Discussion: In this study, we have shown that PR has a positive association with LC worry and that numeracy has a negative association with LC worry. Translation to Health Education Practice: This information can be used to target subpopulations with low PR and numeracy about their risks for lung cancer and to develop tailored messages to educate these people. A AJHE Self-Study quiz is online for this article via the SHAPE America Online Institute (SAOI) [ABSTRACT FROM AUTHOR]

With the increasing number of Saudi Arabian scholarship students abroad, there is a need to conduct educational research on these students. The purpose of this study was to examine the differences between male and female Saudi scholarship students' attitudes toward mathematics in the United States. Five scales of the Fennema-Sherman Mathematics Attitude Scales were used to measure the attitudes of forty-nine Saudi undergraduate students (25 females and 24 males). An independent sample t-test was used to analyze the data and to determine that there was not a statistically significant difference between male and female Saudi scholarship students' attitudes toward mathematics in the United States; however, Saudi female students showed more positive attitudes than males. [ABSTRACT FROM AUTHOR]

We aim to determine whether a game-theoretic model between an insurer and a healthcare practice yields a predictive equilibrium that incentivizes either player to deviate from a fee-for-service to capitation payment system. Using United States data from various primary care surveys, we find that non-extreme equilibria (i.e., shares of patients, or shares of patient visits, seen under a fee-for-service payment system) can be derived from a Stackelberg game if insurers award a non-linear bonus to practices based on performance. Overall, both insurers and practices can be incentivized to embrace capitation payments somewhat, but potentially at the expense of practice performance. [ABSTRACT FROM AUTHOR]

Using information theory, we measure innovation systemness as synergy among size‐classes, ZIP Codes, and technological classes (NACE‐codes) for 8.5 million American companies. The synergy at the national level is decomposed at the level of states, Core‐Based Statistical Areas (CBSA), and Combined Statistical Areas (CSA). We zoom in to the state of California and in more detail to Silicon Valley. Our results do not support the assumption of a national system of innovations in the U.S.A. Innovation systems appear to operate at the level of the states; the CBSA are too small, so that systemness spills across their borders. Decomposition of the sample in terms of high‐tech manufacturing (HTM), medium‐high‐tech manufacturing (MHTM), knowledge‐intensive services (KIS), and high‐tech services (HTKIS) does not change this pattern, but refines it. The East Coast—New Jersey, Boston, and New York—and California are the major players, with Texas a third one in the case of HTKIS. Chicago and industrial centers in the Midwest also contribute synergy. Within California, Los Angeles contributes synergy in the sectors of manufacturing, the San Francisco area in KIS. KIS in Silicon Valley and the Bay Area—a CSA composed of seven CBSA—spill over to other regions and even globally. [ABSTRACT FROM AUTHOR]

Objective: The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random forests methods have shown promise in speeding up this process, but they lag behind human classification accuracy by about 5%. We explore whether more recently available document classification algorithms can close this gap. Materials and methods: Using data gathered from a single surveillance site, we applied 8 supervised learning algorithms to predict whether children meet the case definition for ASD based solely on the words in their evaluations. We compared the algorithms’ performance across 10 random train-test splits of the data, using classification accuracy, F1 score, and number of positive calls to evaluate their potential use for surveillance. Results: Across the 10 train-test cycles, the random forest and support vector machine with Naive Bayes features (NB-SVM) each achieved slightly more than 87% mean accuracy. The NB-SVM produced significantly more false negatives than false positives (P = 0.027), but the random forest did not, making its prevalence estimates very close to the true prevalence in the data. The best-performing neural network performed similarly to the random forest on both measures. Discussion: The random forest performed as well as more recently available models like the NB-SVM and the neural network, and it also produced good prevalence estimates. NB-SVM may not be a good candidate for use in a fully-automated surveillance workflow due to increased false negatives. More sophisticated algorithms, like hierarchical convolutional neural networks, may not be feasible to train due to characteristics of the data. Current algorithms might perform better if the data are abstracted and processed differently and if they take into account information about the children in addition to their evaluations. Conclusion: Deep learning models performed similarly to traditional machine learning methods at predicting the clinician-assigned case status for CDC’s autism surveillance system. While deep learning methods had limited benefit in this task, they may have applications in other surveillance systems. [ABSTRACT FROM AUTHOR]

Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source of soil moisture spatial information across large areas (e.g., continents) is satellite-based microwave remote sensing. However, satellite soil moisture datasets have coarse spatial resolution (e.g., 25–50 km grids); and large areas from regional-to-global scales have spatial information gaps. We provide an alternative approach to predict soil moisture spatial patterns (and associated uncertainty) with higher spatial resolution across areas where no information is otherwise available. This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information across the conterminous United States on an annual basis (1991–2016). We derived 15 primary and secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and annual satellite soil moisture estimates were used to train the models. The explained variance for all models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations (improving from r2 = 0.1 to r2 = 0.46) and lower bias (improving from 0.062 to 0.057 m3/m3) with field soil moisture observations from the North American Soil Moisture Database (n = 668 locations with available data between 1991–2013; 0-5cm depth). We conclude that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture estimates and regions across the world. [ABSTRACT FROM AUTHOR]

Introduction: Reproducible research is increasingly gaining interest in the research community. Automating the production of research manuscript tables from statistical software can help increase the reproducibility of findings. Logistic regression is used in studying disease prevalence and associated factors in epidemiological studies and can be easily performed using widely available software including SAS, SUDAAN, Stata or R. However, output from these software must be processed further to make it readily presentable. There exists a number of procedures developed to organize regression output, though many of them suffer limitations of flexibility, complexity, lack of validation checks for input parameters, as well as inability to incorporate survey design. Methods: We developed a SAS macro, %svy_logistic_regression, for fitting simple and multiple logistic regression models. The macro also creates quality publication-ready tables using survey or non-survey data which aims to increase transparency of data analyses. It further significantly reduces turn-around time for conducting analysis and preparing output tables while also addressing the limitations of existing procedures. In addition, the macro allows for user-specific actions to handle missing data as well as use of replication-based variance estimation methods. Results: We demonstrate the use of the macro in the analysis of the 2013–2014 National Health and Nutrition Examination Survey (NHANES), a complex survey designed to assess the health and nutritional status of adults and children in the United States. The output presented here is directly from the macro and is consistent with how regression results are often presented in the epidemiological and biomedical literature, with unadjusted and adjusted model results presented side by side. Conclusions: The SAS code presented in this macro is comprehensive, easy to follow, manipulate and to extend to other areas of interest. It can also be incorporated quickly by the statistician for immediate use. It is an especially valuable tool for generating quality, easy to review tables which can be incorporated directly in a publication. [ABSTRACT FROM AUTHOR]

Not much is known about the fleet level total seabird bycatch from pelagic longlines of United States vessels in the western North Atlantic or other fleets of the Atlantic or other oceans. Onboard observers generally only record seabird bycatch during line hauling. Seabirds are predominantly caught during the line setting stage, and, due to predation or mechanical action, those caught prior to the haul can drop off the hook and be lost to the onboard observer. We developed a model to gauge the size of this bycatch loss problem and provide a first approximation of its impact on estimates of total fleet bycatch. We started with a traditional loss-free bycatch model, which assumes that birds recorded were the only birds captured, and integrated into it two crucial components of the bycatch process: capture origin (set or haul) and bycatch loss of set-captures. We extracted count data on seabird bycatch loss and bycatch mortality from the literature on other longline fisheries and used these data to simulate potential total seabird bycatch in the western North Atlantic. Simulations revealed the shortcomings of both the traditional bycatch model and the current haul-only observer protocol, each of which contributed to biologically significant underestimation of total bycatch and estimation uncertainty. Based on our results, we recommend a loss-corrected modeling approach to provide a more accurate estimate of seabird mortalities in pelagic longline fisheries. Where possible, fishery-specific seabird bycatch loss rates need to be ascertained via specific set and haul observing protocols. But, even where fishery-specific estimates for a region are not available, the methodology developed here is applicable to other pelagic longline fisheries to approximate fleet-level loss-corrected bycatch. [ABSTRACT FROM AUTHOR]

Estimation of influenza-like illness (ILI) using search trends activity was intended to supplement traditional surveillance systems, and was a motivation behind the development of Google Flu Trends (GFT). However, several studies have previously reported large errors in GFT estimates of ILI in the US. Following recent release of time-stamped surveillance data, which better reflects real-time operational scenarios, we reanalyzed GFT errors. Using three data sources—GFT: an archive of weekly ILI estimates from Google Flu Trends; ILIf: fully-observed ILI rates from ILINet; and, ILIp: ILI rates available in real-time based on partial reporting—five influenza seasons were analyzed and mean square errors (MSE) of GFT and ILIp as estimates of ILIf were computed. To correct GFT errors, a random forest regression model was built with ILI and GFT rates from the previous three weeks as predictors. An overall reduction in error of 44% was observed and the errors of the corrected GFT are lower than those of ILIp. An 80% reduction in error during 2012/13, when GFT had large errors, shows that extreme failures of GFT could have been avoided. Using autoregressive integrated moving average (ARIMA) models, one- to four-week ahead forecasts were generated with two separate data streams: ILIp alone, and with both ILIp and corrected GFT. At all forecast targets and seasons, and for all but two regions, inclusion of GFT lowered MSE. Results from two alternative error measures, mean absolute error and mean absolute proportional error, were largely consistent with results from MSE. Taken together these findings provide an error profile of GFT in the US, establish strong evidence for the adoption of search trends based 'nowcasts' in influenza forecast systems, and encourage reevaluation of the utility of this data source in diverse domains. [ABSTRACT FROM AUTHOR]

Assessment data from the United States and international reports of student achievement indicate that upper elementary students are failing to meet basic levels of proficiency in fractions and writing, and that this is particularly prevalent with students with or at-risk for learning disabilities in mathematics. Proficiency with fractions has been identified as foundational for learning higher-level mathematics but remains one of the most difficult skills for students to learn. In addition, students' difficulty with fractions is exacerbated because of increased chances of comorbidity with language learning problems, particularly difficulties constructing arguments and communicating using writing. We describe FACT + R2C2, a language-based, metacognitive instructional intervention that was designed using the Self-Regulated Strategy Development model (SRSD) for teaching foundational concepts of fractions. The results from two studies in which the intervention was administered to upper elementary students who exhibit mathematics difficulties indicated selected increases in students' computational accuracy, quality of mathematical reasoning, number of rhetorical elements, and total words. With evidence of improved performance in these areas, FACT + R2C2 holds promise for helping these students become proficient self-regulated learners. [ABSTRACT FROM AUTHOR]

The Medical Monitoring Project (MMP) is an HIV surveillance system that provides national estimates of HIV-related behaviors and clinical outcomes. When first implemented, MMP excluded persons living with HIV not receiving HIV care. This analysis will describe new case-surveillance-based methods to identify and recruit persons living with HIV who are out of care and at elevated risk for mortality and ongoing HIV transmission. Stratified random samples of all persons living with HIV were selected from the National HIV Surveillance System in five public health jurisdictions from 2012–2014. Sampled persons were located and contacted through seven different data sources and five methods of contact to collect interviews and medical record abstractions. Data were weighted for non-response and case reporting delay. The modified sampling methodology yielded 1159 interviews (adjusted response rate, 44.5%) and matching medical record abstractions for 1087 (93.8%). Of persons with both interview and medical record data, 264 (24.3%) would not have been included using prior MMP methods. Significant predictors were identified for successful contact (e.g., retention in care, adjusted Odds Ratio [aOR] 5.02; 95% Confidence Interval [CI] 1.98–12.73), interview (e.g. moving out of jurisdiction, aOR 0.24; 95% CI: 0.12–0.46) and case reporting delay (e.g. rural residence, aOR 3.18; 95% CI: 2.09–4.85). Case-surveillance-based sampling resulted in a comparable response rate to existing MMP methods while providing information on an important new population. These methods have since been adopted by the nationally representative MMP surveillance system, offering a model for public health program, research and surveillance endeavors seeking inclusion of all persons living with HIV. [ABSTRACT FROM AUTHOR]

Downscaled climate projections need to be linked to downscaled projections of population and economic growth to fully develop implications for land, natural resources, and ecosystems for future scenarios. We develop an empirical spatiotemporal approach for jointly projecting population and income at the county scale in the United States that is consistent with neoclassical economic growth theory and overlapping labor markets and that accounts for labor migration and spatial spillovers. Downscaled projections generated for the five Shared Socioeconomic Pathways used to support global scenario analysis generally show growth focused around relatively few centers especially in the southeast and western regions, with some areas in the Midwest and northeast experiencing population declines. Results are consistent with economic growth theory and with historical trends in population change and convergence of per capita personal income across US counties. [ABSTRACT FROM AUTHOR]

The article presents information related to proceedings in the field of Mathematics research. The Third Siam Conference on Applications of Dynamical Systems, will be held from May 21, 1995 to May 24, 1995, at Snowbird, Utah. The First International Conference on Neural, Parallel, Scientific Computations will be held from May 28, 1995 to May 31, 1995, at Atlanta, Georgia. The symposium on Inertial Manifolds, Approximate Inertial Manifolds and Related Numerical Algorithms will be held from June 6, 1995 to June 10, 1995, at Xi'an, China.

As emerging and re-emerging infectious arboviruses like dengue, chikungunya, and Zika threaten new populations worldwide, officials scramble to assess local severity and transmissibility, with little to no epidemiological history to draw upon. Indirect estimates of risk from vector habitat suitability maps are prone to great uncertainty, while direct estimates from epidemiological data are only possible after cases accumulate and, given environmental constraints on arbovirus transmission, cannot be widely generalized beyond the focal region. Combining these complementary methods, we use disease importation and transmission data to improve the accuracy and precision of a priori ecological risk estimates. We demonstrate this approach by estimating the spatiotemporal risks of Zika virus transmission throughout Texas, a high-risk region in the southern United States. Our estimates are, on average, 80% lower than published ecological estimates—with only six of 254 Texas counties deemed capable of sustaining a Zika epidemic—and they are consistent with the number of autochthonous cases detected in 2017. Importantly our method provides a framework for model comparison, as our mechanistic understanding of arbovirus transmission continues to improve. Real-time updating of prior risk estimates as importations and outbreaks arise can thereby provide critical, early insight into local transmission risks as emerging arboviruses expand their global reach. [ABSTRACT FROM AUTHOR]

Writing is recognized as a critical skill in science, technology, engineering, and mathematical fields, as it provides opportunities for individuals to think critically about learned information, integrate knowledge, communicate understanding, and contribute new information to the field. Requirements for applying mathematical knowledge to writing require a seamless blend of content-specific knowledge, domain-specific vocabulary, and an understanding of written expression. Relatively, little is known about how mathematics writing is currently being assessed in K-12 classrooms to promote the acquisition and growth of students' writing. This exploratory survey research sought to evaluate how undergraduate students across various career majors, at a large university in the USA, perceive the writing quality of elementary mathematics writing samples. Research determined that respondents valued accuracy of the mathematics, logical explanation, effort of the response, as well as organization/mechanics of the writing. The extremes of mathematical writing had the most reliable scores, bringing attention to the need to develop quality assessments (e.g., rubrics) that address subjectivity of writing quality indicators and explicitly communicate expectations of mathematics writing. Implications for practice and future research are presented. [ABSTRACT FROM AUTHOR]

Objectives: Reviews of violence against children in disaster settings focus on armed conflict. Little is understood about natural disasters which has implications in planning humanitarian response. We examined the magnitude and direction of the association between exposure to natural disasters and physical, emotional, and sexual violence against children, and assessed the quality of the evidence. Methods: We searched 15 health and social science databases from first record until May 16, 2018. Publications describing all types of quantitative study design were eligible for inclusion. We presented study characteristics and quality in a narrative form and generated pooled estimates using a three-level random effects model. We evaluated Cochrane’s Q with p-values below 0.10 and radial plots to assess heterogeneity. Planned subgroup analyses explored differential results by violence form, study design, and analysis method. Results: 11 publications met inclusion criteria. The majority were cross-sectional studies examining physical or sexual violence in the United States. We found no evidence of a consistent association or directional influence between natural disasters and violence against children. Combined categorical violence outcomes had substantial heterogeneity [Q (df = 66) = 252.83, p < 0.001]. Subgroups without evidence of heterogeneity had confidence intervals that included a possible null effect. Our findings were mainly limited by inconsistencies in operational definitions of violence, a lack of representative sampling, and unclear establishment of temporal order between natural disaster exposure and violence outcomes. Conclusions: Based on the available evidence, we cannot confidently conclude that natural disasters increase the level or severity of violence against children above non-disaster settings, however heterogeneity and study quality hamper our ability to draw firm conclusions. More nuanced and rigorous research is needed to inform practice and policy as natural disasters increasingly affect human populations. [ABSTRACT FROM AUTHOR]

The maximum entropy model, a commonly used species distribution model (SDM) normally combines observations of the species occurrence with environmental information to predict the geographic distributions of animal or plant species. However, it only produces point estimates for the probability of species existence. To understand the uncertainty of the point estimates, we analytically derived the variance of the outputs of the maximum entropy model from the variance of the input. We applied the analytic method to obtain the standard deviation of dengue importation probability and Aedes aegypti suitability. Dengue occurrence data and Aedes aegypti mosquito abundance data, combined with demographic and environmental data, were applied to obtain point estimates and the corresponding variance. To address the issue of not having the true distributions for comparison, we compared and contrasted the performance of the analytical expression with the bootstrap method and Poisson point process model which proved of equivalence of maximum entropy model with the assumption of independent point locations. Both Dengue importation probability and Aedes aegypti mosquito suitability examples show that the methods generate comparatively the same results and the analytic method we introduced is dramatically faster than the bootstrap method and directly apply to maximum entropy model. [ABSTRACT FROM AUTHOR]

This study examined the extent to which preservice teachers (PSTs) develop their capacity to attend to children's strategies and interpret and respond on the basis of children's mathematical understanding in the context of two well-designed assignments: Inquiry into Student Thinking assignment and tutoring assignment. The two assignments were assigned after 6 and 10 weeks of instruction, respectively. The analysis revealed that PSTs attended to children's strategies and interpreted children's mathematical understanding but struggled with the component skill of responding to children's mathematical understanding in the two assignments. Although the nature of tasks selected differed across the two assignments, generally PSTs focused on tasks that would develop children's mathematical understanding. The findings have theoretical implications for a hypothesized trajectory of professional noticing of children's mathematical understanding and the design of mathematics methods courses. [ABSTRACT FROM AUTHOR]

We present a mathematical activity called graph talks as a new pedagogical routine to intertwine social justice issues and mathematics. Adapted from number talks, graph talks involve students analyzing, interpreting, and discussing real-life data represented in graphs. Graphs may be strategically selected to both highlight a relevant social justice issue while also reinforcing the mathematics content of the course. We report on experiences using graph talks in undergraduate mathematics content courses for future teachers in the USA, and provide examples of the undergraduates doing mathematics while analyzing the social justice context of the graphs. [ABSTRACT FROM AUTHOR]

Given US students' lack of international competitiveness in mathematics and science, many states have adopted the Common Core State Standards for Mathematics (CCSSM). To help ensure that the Standards for Mathematical Practice within the CCSSM and effective teaching practices are implemented appropriately, many teachers are emerging as teacher leaders with the intent of supporting teachers with this implementation. Unfortunately, not all of these teachers are necessarily prepared in the field of teacher leadership. Recognizing the importance of having well-prepared teacher leaders, this article describes the case of Ms. Hodges, as she transitioned from mathematics teacher to teacher leader. Data revealed that several factors affected Ms. Hodges' transition, some positively and others negatively. These results as well as implications for teacher leader development are provided. [ABSTRACT FROM AUTHOR]

The purpose of this study was to review the existing research on affect (beliefs, attitudes, and emotions) of elementary prospective teachers (EPTs) in university mathematics content courses. We use as our time period from publication in the United States of the Curriculum and Evaluation Standards for Schools Mathematics through 2016. A search of a combination of electronic databases and targeted international journals resulted in a total of 11 studies that looked at some aspect of affect with EPTs in all or some part of a university mathematics course over the 27‐year time period. Nine of the 11 studies occurred in the context of a course or courses categorized as involving an alternative pedagogy that was student‐centered. Overall we found that a student‐centered approach to instruction supported changes in EPTs' affect that align with pedagogical recommendations in reform documents such as the NCTM Standards. However, shifts were sometimes difficult to come by and encountered resistance from EPTs. Implications for course learning experiences are offered and conflicting results between studies suggest directions for future research. [ABSTRACT FROM AUTHOR]

Most of future population growth will take place in the world’s cities and towns. Yet, there is no well-established, consistent way to measure either urban land or people. Even census-based urban concepts and measures undergo frequent revision, impeding rigorous comparisons over time and place. This study presents a new spatial approach to derive consistent urban proxies for the US. It compares census-designated urban blocks with proxies for land-based classifications of built-up areas derived from time-series of the Global Human Settlement Layer (GHSL) for 1990–2010. This comparison provides a new way to understand urban structure and its changes: Most land that is more than 50% built-up, and people living on such land, are officially classified as urban. However, 30% of the census-designated urban population and land is located in less built-up areas that can be characterized as mainly suburban and peri-urban in nature. Such insights are important starting points for a new urban research program: creating globally and temporally consistent proxies to guide modelling of urban change. [ABSTRACT FROM AUTHOR]

Breweries across the country are investing in energy efficient and low-carbon brewing practices. Drawing insights from the sustainable consumption and ecological economics literature, this analysis evaluates whether consumers are willing to pay more for sustainable beer and what predicts the value of the premium. Based on a survey of beer consumers from across the U.S. that contained one of two willingness-to-pay exercises, we evaluate what respondent attributes are associated with a higher willingness-to-pay for sustainably brewed beer. We find that the majority of beer consumers are willing to pay more for sustainable beer. Consumers who are prepared to pay a premium tend to already pay more per unit of beer, are more aware of their purchasing behavior and the manner in which their consumption patterns may affect the environment, and pursue lifestyles based on professional advancement, helping the environment, and helping other causes. [ABSTRACT FROM AUTHOR]

This study’s objective was to describe long-term trends and patterns in first cigarette use (cigarette initiation) and daily cigarette use (daily initiation) among youth and young adults in the U.S. We used cross-sectional survey data from the National Survey on Drug Use and Health, 2002–2015, to estimate annual incidence of first cigarette use (N = 270,556) and first daily cigarette use (N = 373,464) for each year by age groups, race/ethnicity and gender, examining trends over time and the average annual change in initiation for each group. Several clear patterns emerged: 1) cigarette initiation and daily initiation significantly decreased over time among those aged 12–14 and 15–17 and these trends were consistent among nearly all racial/ethnic and gender subgroups; 2) among 18–21 year olds, cigarette initiation sharply increased through 2009, surpassing rates among 15–17 year olds, and sharply declined through 2015 while remaining higher than rates among the younger group, and this trend was consistent for almost all racial/ethnic subgroups; 3) daily initiation for those aged 18–21 significantly declined, and this was significant among most subgroups 4) there was no change in cigarette initiation and daily initiation for 22–25 year olds overall and most subgroups; 5) there was a significant increase in cigarette initiation for 22–25 year old Hispanics males and daily initiation for 22–25 year old males. This study provides a comprehensive look at trends in cigarette and daily initiation among U.S. youth and young adults. Despite notable declines in smoking initiation among youth and young adult populations over the last two decades, targeted prevention and policy efforts are needed for subgroups at higher risk, including young adults and Hispanic males. [ABSTRACT FROM AUTHOR]

Family food routines are important contexts through which children develop math knowledge and skills. The Food For Thought (FFT) program teaches Latino parents strategies to develop their kindergarten children's math abilities during family food routines such as grocery shopping and cooking. We examined whether attendance to FFT was related to children's math outcomes at post-test (right after program completion). Sixty-eight low-income Latino parents and their kindergarten children (M = 71 months) participated in the four-week program taking place in schools. Children's math skills (counting, numeral recognition and cardinal understanding) were assessed at pre- and post-test. The association between parents' attendance to FFT meetings and children′s math outcomes at post-test depended on children's initial levels of math skills (i.e., math skills at pre-test). Children with lower initial math skills whose parents attended more FFT meetings had more advanced math outcomes at post-test, than children with lower initial math skills whose parents attended fewer FFT meetings. The effect size of this interaction was moderate, d = 0.46. Limitations and future directions of this early math intervention targeting Latino families are discussed. [ABSTRACT FROM AUTHOR]