Activities in the Mathematics Classroom that Promote Mathematical Fluency

Mathematics, in and of itself, is a language— reading notations, writing solutions, and communicating explanations. The importance of developing mathematical fluency is frequently overshadowed by an emphasis on implementation of memorized formulas in mathematics classrooms. The National Council of Teachers of Mathematics (NCTM) has recognized the relevance of using mathematics as a language as early as 1989 and promotes learning to communicate mathematically as a major goal for students. Hufferd-Ackles, Fuson, and Sherin (2004) recognize the importance of a math-talk community in the classrooms to encourage students’ understanding of mathematics. This self-study focuses on the advantages of writing, reading, and speaking mathematics in students’ learning. It is conducted the study in two Algebra II classes at a rural high school in Central Illinois. Different activities, students’ work, and analyzed personal reflective journals are content analyzed to draw conclusions on the ways these instructional activities promote mathematical fluency and mathematical understanding

Sensitivity of Air Pollution-Induced Premature Mortality to Precursor Emissions under the Influence of Climate Change

The relative contributions of PM2.5 and ozone precursor emissions to air pollution-related premature mortality modulated by climate change are estimated for the U.S. using sensitivities of air pollutants to precursor emissions and health outcomes for 2001 and 2050. Result suggests that states with high emission rates and significant premature mortality increases induced by PM2.5 will substantially benefit in the future from SO2, anthropogenic NOX and NH3 emissions reductions while states with premature mortality increases induced by O3 will benefit mainly from anthropogenic NOX emissions reduction. Much of the increase in premature mortality expected from climate change-induced pollutant increases can be offset by targeting a specific precursor emission in most states based on the modeling approach followed here

Adaptation and contextualization of deep neural network models

The ability of Deep Neural Networks (DNNs) to provide very high accuracy in classification and recognition problems makes them the major tool for developments in such problems. It is, however, known that DNNs are currently used in a ‘black box’ manner, lacking transparency and interpretability of their decision-making process. Moreover, DNNs should use prior information on data classes, or object categories, so as to provide efficient classification of new data, or objects, without forgetting their previous knowledge. In this paper, we propose a novel class of systems that are able to adapt and contextualize the structure of trained DNNs, providing ways for handling the above-mentioned problems. A hierarchical and distributed system memory is generated and used for this purpose. The main memory is composed of the trained DNN architecture for classification/prediction, i.e., its structure and weights, as well as of an extracted - equivalent – Clustered Representation Set (CRS) generated by the DNN during training at its final - before the output – hidden layer. The latter includes centroids - ‘points of attraction’ - which link the extracted representation to a specific area in the existing system memory. Drift detection, occurring, for example, in personalized data analysis, can be accomplished by comparing the distances of new data from the centroids, taking into account the intra-cluster distances. Moreover, using the generated CRS, the system is able to contextualize its decision-making process, when new data become available. A new public medical database on Parkinson’s disease is used as testbed to illustrate the capabilities of the proposed architecture

Sensitivity of Air Pollution-Induced Premature Mortality to Precursor Emissions Under the Influence of Climate Change

The relative contributions of PM2.5 and ozone precursor emissions to air pollution-related premature mortality modulated by climate change are estimated for the U.S. using sensitivities of air pollutants to precursor emissions and health outcomes for 2001 and 2050. Result suggests that states with high emission rates and significant premature mortality increases induced by PM2.5 will substantially benefit in the future from SO2, anthropogenic NOX and NH3 emissions reductions while states with premature mortality increases induced by O3 will benefit mainly from anthropogenic NOX emissions reduction. Much of the increase in premature mortality expected from climate changeinduced pollutant increases can be offset by targeting a specific precursor emission in most states based on the modeling approach followed here

Quantification of the impact of climate uncertainty on regional air quality

Uncertainties in calculated impacts of climate forecasts on future regional air quality are investigated using downscaled MM5 meteorological fields from the NASA GISS and MIT IGSM global models and the CMAQ model in 2050 in the continental US. Differences between three future scenarios: high-extreme, low-extreme and base case, are used for quantifying effects of climate uncertainty on regional air quality. GISS, with the IPCC A1B scenario, is used for the base case simulations. IGSM results, in the form of probabilistic distributions, are used to perturb the base case climate to provide the high- and low-extreme scenarios. Impacts of the extreme climate scenarios on concentrations of summertime fourth-highest daily maximum 8-h average ozone are predicted to be up to 10 ppbV (about one-seventh of the current US ozone standard of 75 ppbV) in urban areas of the Northeast, Midwest and Texas due to impacts of meteorological changes, especially temperature and humidity, on the photochemistry of tropospheric ozone formation and increases in biogenic VOC emissions, though the differences in average peak ozone concentrations are about 1–2 ppbV on a regional basis. Differences between the extreme and base scenarios in annualized PM2.5 levels are very location dependent and predicted to range between −1.0 and +1.5 μg m−3. Future annualized PM2.5 is less sensitive to the extreme climate scenarios than summertime peak ozone since precipitation scavenging is only slightly affected by the extreme climate scenarios examined. Relative abundances of biogenic VOC and anthropogenic NOx lead to the areas that are most responsive to climate change. Overall, planned controls for decreasing regional ozone and PM2.5 levels will continue to be effective in the future under the extreme climate scenarios. However, the impact of climate uncertainties may be substantial in some urban areas and should be included in assessing future regional air quality and emission control requirements.United States. Environmental Protection Agency (Science To Achieve Results (STAR) grant No. RD83096001)United States. Environmental Protection Agency (Science To Achieve Results (STAR) grant No. RD82897602)United States. Environmental Protection Agency (Science To Achieve Results (STAR) grant No. RD83107601)East Tennessee State Universit

The role of climate and emission changes in future air quality over southern Canada and northern Mexico

International audiencePotential impacts of global climate and emissions changes on regional air quality over southern (western and eastern) Canada and northern Mexico are examined by comparing future summers' (i.e., 2049?2051) average regional O3 and PM2.5 concentrations with historic concentrations (i.e., 2000?2002 summers). Air quality modeling was conducted using CMAQ and meteorology downscaled from the GISS-GCM using MM5. Emissions for North America are found using US EPA, Mexican and Canadian inventories and projected emissions following CAIR and IPCC A1B emissions scenario. Higher temperatures for all sub-regions and regional changes in mixing height, insolation and precipitation are forecast in the 2049?2051 period. Future emissions are calculated to be lower over both Canadian sub-regions, but higher over northern Mexico. Global climate change, alone, is predicted to affect PM2.5 concentrations more than O3: M8hO3 concentrations are estimated to be slightly different in all examined sub-regions while PM2.5 concentrations are estimated to be higher over both Canadian sub-regions (8% over western and 3% over eastern) but 11% lower over northern Mexico. Climate change combined with the projected emissions lead to greater change in pollutant concentrations: M8hO3 concentrations are simulated to be 6% lower over western Canada and 8% lower over eastern Canada while PM2.5 concentrations are simulated to be 5% lower over western Canada and 11% lower over eastern Canada. Although future emissions over northern Mexico are projected higher, pollutant concentrations are simulated to be lower due to US emissions reductions. Global climate change combined with the projected emissions will decrease M8hO3 4% and PM2.5 17% over northern Mexico

Quantification of impact of climate uncertainty on regional air quality

International audienceImpacts of uncertain climate forecasts on future regional air quality are investigated using downscaled MM5 meteorological fields from the NASA GISS and MIT IGSM global climate models and the CMAQ model in 2050 in the continental US. Three future climate scenarios: high-extreme, low-extreme and base, are developed for regional air quality simulations. GISS, with the IPCC A1B scenario, is used for the base case. IGSM results, in the form of probabilistic distributions, are used to perturb the base case climate to provide 0.5th and 99.5th percentile climate scenarios. Impacts of the extreme climate scenarios on concentrations of summertime fourth-highest daily maximum 8-h average ozone are predicted to be up to 10 ppbv (about one-eighth of the current NAAQS of ozone) in some urban areas, though average differences in ozone concentrations are about 1?2 ppbv on a regional basis. Differences between the extreme and base scenarios in annualized PM2.5 levels are very location dependent and predicted to range between ?1.0 and +1.5 ?g m?3. Future annualized PM2.5 is less sensitive to the extreme climate scenarios than summertime peak ozone since precipitation scavenging is only slightly affected by the extreme climate scenarios examined. Relative abundances of biogenic VOC and anthropogenic NOx lead to the areas that are most responsive to climate change. Such areas may find that climate change can significantly offset air quality improvements from emissions reductions, particularly during the most severe episodes

Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture

The objective of this work is to detect Alzheimer’s disease using Magnetic Resonance Imaging. For this, we use a three-dimensional densenet-121 architecture. With the use of only freely available tools, we obtain good results: a deep neural network showing metrics of 87% accuracy, 87% sensitivity (micro-average), 88% specificity (micro-average), and 92% AUROC (micro-average) for the task of classifying five different classes (disease stages). The use of tools available for free means that this work can be replicated in developing countries.UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ingeniería::Centro de Investigaciones en Tecnologías de Información y Comunicación (CITIC)UCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ciencias de la Computación e Informátic

A roadmap towards healthcare information systems interoperability in Greece, Journal of Telecommunications and Information Technology, 2006, nr 2

The advantages of the introduction of information and communication technologies (ICT) in the complex healthcare sector are already well known and well stated in the past. It is common knowledge that in order to install any type of information system in healthcare, six main groups of issues have to be dealt with: organizational and cultural matters related to healthcare, technological gap between healthcare professionals and information science experts, legal requirements on the confidentiality of personal data, of patient related data and on data privacy, industrial and market position of healthcare informatics and interoperability complexity, lack of vision and leadership of the health care managers and health authorities and user acceptability and usability of the proposed information systems. In order to meet these issues stated above, a special focus group (Z3) performed an assessment of the situation of healthcare informatics in Greece and of the main key points that would lead to success. In that sense it is now common knowledge that Greece is lagging information and communication technology progress in healthcare because almost none of the above mentioned issues were dealt with. This assessment is the result of the interaction of more than 150 decision makers, medical informaticians, healthcare practitioners and other individual involved in healthcare. As a conclusion, this focus group resulted in 4 major propositions that will lead to healthcare informatics introduction with better success chances: focus on terminologies and standards, focus on interoperability and information systems sustainability, focus on clear goals and system metrics that can create a healthcare performance management cockpit, and focus on people and what they have to say, by creating a e-health forum. These conclusions were taken into consideration by the Greek government and are incorporated the IASYS project, the national healthcare informatics framework for the next ten years