Physical Sciences and Mathematics Commons^™

Learning About Modeling In Teacher Preparation Programs, Hyunyi Jung, Eryn Stehr, Jia He, Sharon L. Senk

Hyunyi Jung

This study explores opportunities that secondary mathematics teacher preparation programs provide to learn about modeling in algebra. Forty-eight course instructors and ten focus groups at five universities were interviewed to answer questions related to modeling. With the analysis of the interview transcripts and related course materials, we found few opportunities for PSTs to engage with the full modeling cycle. Examples of opportunities to learn about algebraic modeling and the participants’ perspectives on the opportunities can contribute to the study of modeling and algebra in teacher education.

Observation And Modeling Of Gravity Wave Propagation Through Reflection And Critical Layers Above Andes Lidar Observatory At Cerro Pachón, Chile, Bing Cao, Christopher J. Heale, Yafang Guo, Alan Z. Liu, Jonathan B. Snively

Jonathan B. Snively

A complex gravity wave event was observed from 04:30 to 08:10 UTC on 16 January 2015 by a narrow-band sodium lidar and an all-sky airglow imager located at Andes Lidar Observatory (ALO) in Cerro Pachón (30.25∘S, 70.73∘W), Chile. The gravity wave packet had a period of 18–35 min and a horizontal wavelength of about 40–50 km. Strong enhancements of the vertical wind perturbation, exceeding10 m s−1, were found at ∼90 km and ∼103 km, consistent with nearly evanescent wave behavior near a reflection layer. A reduction in vertical wavelength was found as the phase speed approached the background wind speed …

Numerical Modeling Of A Multiscale Gravity Wave Event And Its Airglow Signatures Over Mount Cook, New Zealand, During The Deepwave Campaign, C. J. Heale, K. Bossert, J. B. Snively, D. C. Fritts, P. -D. Pautet, M. J. Taylor

Jonathan B. Snively

A 2-D nonlinear compressible model is used to simulate a large-amplitude, multiscale mountain wave event over Mount Cook, NZ, observed as part of the Deep Propagating Gravity Wave Experiment (DEEPWAVE) campaign and to investigate its observable signatures in the hydroxyl (OH) layer. The campaign observed the presence of a �x = 200 km mountain wave as part of the 22nd research flight with amplitudes of >20 K in the upper stratosphere that decayed rapidly at airglow heights. Advanced Mesospheric Temperature Mapper (AMTM) showed the presence of small-scale (25–28 km) waves within the warm phase of the large mountain wave. The …

A Realistic Meteorological Assessment Of Perennial Biofuel Crop Deployment: A Southern Great Plains Perspective, Melissa Wagner, Meng Wang, Gonzalo Miguez-Macho, Jesse Miller, Andy Vanloocke, Justin E. Bagley, Carl J. Bernacchi, Matei Georgescu

Andy VanLoocke

Utility of perennial bioenergy crops (e.g., switchgrass and miscanthus) offers unique opportunities to transition toward a more sustainable energy pathway due to their reduced carbon footprint, averted competition with food crops, and ability to grow on abandoned and degraded farmlands. Studies that have examined biogeophysical impacts of these crops noted a positive feedback between near-surface cooling and enhanced evapotranspiration (ET), but also potential unintended consequences of soil moisture and groundwater depletion. To better understand hydrometeorological effects of perennial bioenergy crop expansion, this study conducted high-resolution (2-km grid spacing) simulations with a state-of-the-art atmospheric model (Weather Research and Forecasting system) dynamically …

The Costs Of Photorespiration To Food Production Now And In The Future, Berkley J. Walker, Andy Vanloocke, Carl J. Bernacchi, Donald R. Ort

Andy VanLoocke

Photorespiration is essential for C3 plants but operates at the massive expense of fixed carbon dioxide and energy. Photorespiration is initiated when the initial enzyme of photosynthesis, ribulose-1,5-bisphosphate carboxylase/ oxygenase (Rubisco), reacts with oxygen instead of carbon dioxide and produces a toxic compound that is then recycled by photorespiration. Photorespiration can be modeled at the canopy and regional scales to determine its cost under current and future atmospheres. A regional-scale model reveals that photorespiration currently decreases US soybean and wheat yields by 36% and 20%, respectively, and a 5% decrease in the losses due to photorespiration would be worth approximately …

Evaluating And Predicting Agricultural Management Effects Under Tile Drainage Using Modified Apsim, Robert W. Malone, N. Huth, P. S. Carberry, Liwang Ma, Thomas C. Kaspar, Douglas L. Karlen, T. Meade, Ramesh S. Kanwar, Philip Heilman

Douglas L Karlen

An accurate and management sensitive simulation model for tile-drained Midwestern soils is needed to optimize the use of agricultural management practices (e.g., winter cover crops) to reduce nitrate leaching without adversely affecting corn yield. Our objectives were to enhance the Agricultural Production Systems Simulator (APSIM) for tile drainage, test the modified model for several management scenarios, and then predict nitrate leaching with and without winter wheat cover crop. Twelve years of data (1990–2001) from northeast Iowa were used for model testing. Management scenarios included continuous corn and corn–soybean rotations with single or split N applications. For 38 of 44 observations, …

Forecasting Climate Change Impacts On The Distribution Of Wetland Habitat In The Midwestern United States, Heath Garris, Randall Mitchell, Lauchlan Fraser, Linda Barrett

Randall J. Mitchell

Shifting precipitation patterns brought on by climate change threaten to alter the future distribution of wetlands. We developed a set of models to understand the role climate plays in determining wetland formation on a landscape scale and to forecast changes in wetland distribution for the Midwestern United States. These models combined 35 climate variables with 21 geographic and anthropogenic factors thought to encapsulate other major drivers of wetland distribution for the Midwest. All models successfully recreated a majority of the variation in current wetland area within the Midwest, and showed that wetland area was significantly associated with climate, even when …

Forecasting Climate Change Impacts On The Distribution Of Wetland Habitat In The Midwestern United States, Heath Garris, Randall Mitchell, Lauchlan Fraser, Linda Barrett

Linda R. Barrett

Shifting precipitation patterns brought on by climate change threaten to alter the future distribution of wetlands. We developed a set of models to understand the role climate plays in determining wetland formation on a landscape scale and to forecast changes in wetland distribution for the Midwestern United States. These models combined 35 climate variables with 21 geographic and anthropogenic factors thought to encapsulate other major drivers of wetland distribution for the Midwest. All models successfully recreated a majority of the variation in current wetland area within the Midwest, and showed that wetland area was significantly associated with climate, even when …

Mathematical Classification Of Tight Junction Protein Images, Katherine Ogawa, Caitlin Troyer, Robert Doss, Farzan Aminian, Eduardo Balreira, Jonathan King

Eduardo Cabral Balreira

We present the rationale for the development of mathematical features used for classification of images stained for selected tight junction proteins. The project examined localization of zonula occludens-1, claudin-1 and F-actin in a model epithelium, Madin-Darby canine kidney II cells. Cytochalasin D exposure was used to perturb junctional localization by actin cytoskeleton disruption. Mathematical features were extracted from images to reliably reveal characteristic information of the pattern of protein localization. Features, such as neighborhood standard deviation, gradient of pixel intensity measurement and conditional probability, provided meaningful information to classify complex image sets. The newly developed mathematical features were used as …

Data-Driven Modeling And Analysis Of Household Travel Mode Choice, Nagesh Shukla, Jun Ma, Rohan Wickramasuriya Denagamage, Nam N. Huynh

Nagesh Shukla

One of the important problems studied in the area of travel behavior analysis is travel mode choice which is one of the four crucial steps in transportation demand estimation for urban planning. State of the art models in travel demand modelling can be classified as trip based; tour based; and activity based. In trip based approach, each individual trips is modelled as independent and isolated trips i.e. no connections between different trips. In tour based approach, trips that start and end from the same location (home, work, etc) and trips within a tour are dependent on each other. In past …

Dependency-Topic-Affects-Sentiment-Lda Model For Sentiment Analysis, Shunshun Yin, Jun Han, Yu Huang, Kuldeep Kumar

Kuldeep Kumar

Sentiment analysis tends to use automated approaches to mine the sentiment information expressed in text, such as reviews, blogs and forum discussions. As most traditional approaches for sentiment analysis are based on supervised learning models and need many labeled corpora as their training data which are not always easily obtained, various unsupervised models based on Latent Dirichlet Allocation (LDA) have been proposed for sentiment classification. In this paper, we propose a novel probabilistic modeling framework based on LDA, called Dependency-Topic-Affects-Sentiment-LDA (DTAS) model, which drops the ”bag of words” assumption and assumes that the topics of sentences in a document form …

Economics Of The Queensland Mud Crab Fishery, Tor Hundloe

Tor Hundloe

A series of analyses of catch-effort data from compulsory commercial logbooks and from the Department’s Long-Term Monitoring Programme (LTMP) were conducted after the Workshop. Although not part of the Project plan, these were initiated as a result of questions arising from the Workshop participants about the reliability of the data used in the simulation modelling. Exploration of the logbook data and results of the analyses suggest that biases in the data (from a variety of sources, but principally the widespread use of more than the permitted number of pots) may be giving an over-optimistic view of the status of the …

Responses Of Hydrological Processes And Water Quality To Land Use/Cover (Lulc) And Climate Change In A Coastal Watershed, Ruoyu Wang

Ruoyu Wang

Prescribed Fire Effects On Resource Selection By Cattle In Mesic Sagebrush Steppe. Part 1: Spring Grazing, Patrick Clark, Jaechoul Lee, Kyungduk Ko, Ryan Nielson, Douglas Johnson, David Ganskopp, Joe Chigbrow, Frederick Pierson, Stuart Hardegree

Kyungduk Ko

Prescribed fire is commonly applied world-wide as a tool for enhancing habitats and altering resource-selection patterns of grazing animals. A scientific basis for this practice has been established in some ecosystems but its efficacy has not been rigorously evaluated on mesic sagebrush steppe. Beginning in 2003, resource-selection patterns of beef cows were investigated using global positioning system (GPS) collars for 2 years before and for 5 years after a fall prescribed burn was applied to mesic sagebrush steppe in the Owyhee Mountains of southwestern Idaho, USA. Resource-selection functions (RSF) developed from these data indicated cattle selected for lightly to moderately …

A Gaussian-Rayleigh Mixture Modeling Approach For Through-The-Wall Radar Image Segmentation, Cher Hau Seng, Abdesselam Bouzerdoum, Moeness Amin, F Ahmad

Cher Hau Seng

In this paper, we propose a Gausssian-Rayleigh mixture modeling approach to segment indoor radar images in urban sensing applications. The performance of the proposed method is evaluated on real 2D polarimetric data. Experimental results show that the proposed method enhances image quality by distinguishing between target and clutter regions. The proposed method is also compared to an existing Neyman-Pearson (NP) target detector that has been recently devised for through-the-wall radar imaging. Performance evaluation of both methods shows that the proposed method outperforms the NP detector in enhancing the input images.

Synthesis, Modeling, And Characterization Of Conducting Polymers, Geoffrey M. Spinks, Lianbin Zhao, Weihua Li, Yanzhe Wu, Dezhi Zhou, Gordon G. Wallace

Gordon Wallace

This paper presents synthesis and characterization of polypyrrole based conducting polymers in terms of electronic and mechanical disciplines. Using the electrochemical polymerization approach, conducting polymer samples with different dimensions (length, width, and thickness) was fabricated. For each sample, both sinusoidal and step excitations were used to study its mechanical and electrical properties. An equivalent electric circuit based on constant phase element (CPE) is proposed to model such responses. Electrochemical impedance spectroscopy (EIS) method was used to identify the relationship between the dimensions of conducting polymers and model elements parameters.

Random Set Theory And Problems Of Modeling, Noel A. Cressie, G M. Laslett

Professor Noel Cressie

The three- or four-dimensional world in which we live is full of objects to be measured and summarized. Very often a parsimonious finite collection of measurements is enough for scientific investigation into an object’s genesis and evolution. There is a growing need, however, to describe and model objects through their form as well as their size. The purpose of this article is to show the potentials and limitations of a probabilistic and statistical approach. Collections of objects (the data) are assimilated to a random set (the model), whose parameters provide description and/or explanation.

Long-Lead Prediction Of Pacific Ssts Via Bayesian Dynamic Modeling, L M. Berliner, Christopher K. Wikle, Noel A. Cressie

Professor Noel Cressie

Tropical Pacific sea surface temperatures (SSTs) and the accompanying El Nino-Southern Oscillation phenomenon are recognized as significant components of climate behavior. The atmospheric and oceanic processes involved display highly complicated variability over both space and time. Researchers have applied both physically derived modeling and statistical approaches to develop long-lead predictions of tropical Pacific SSTs. The comparative successes of these two approaches are a subject of substantial inquiry and some controversy. Presented in this article is a new procedure for long-lead forecasting of tropical Pacific SST fields that expresses qualitative aspects of scientific paradigms for SST dynamics in a statistical manner. …

Research Of Unsupervised Posture Modeling And Action Recognition Based On Spatial-Temporal Interesting Points, Chuan-Xu Wang, Yun Liu, Wanqing Li

Associate Professor Wanqing Li

Posture modeling is critical for action description and recognition,a posture modeling and action recognition method is proposed in this paper.Spatial Temporal Interesting Points (STIPs) are extracted from learning samples,in fact,one posture consists of a set of STIPs;a unsupervised clustering method is adopted to classify salient postures from these posture samples,then a GMM model is established for each clustering result;transitional probability among salient postures are calculated,and a Visible state Markov Model(VMM) is learnt to describe various actions.Bi-gram method is put forward for action recognition,Extensive experiments are conducted and the results prove its robustness and validity.

Modelling Awareness Of Agents Using Policies, Amir Talaei-Khoei, Pradeep Ray, Nandan Parameswaran, Ghassan Beydoun

Associate Professor Ghassan Beydoun

In addition to cooperation, research in disaster management exposes the need for policy awareness to recognize relevant information in enhancing cooperation. Intelligent software agents have previously been employed for problem solving in disaster situations but without incorporating how the agents can create or model awareness. This paper presents an awareness based modelling method, called MAAP, to maintain awareness of software agents of a given set of policies. The paper presents preliminary results indicating that the use of policies as a source of awareness, as facilitated by MAAP, is a potentially effective method to enhance cooperation.

Modeling Mobile Learning System Using Anfis, Ahmed Al-Hmouz, Jun Shen, Jun Yan, Rami Al-Hmouz

Dr Jun Yan

Personalisation is becoming more important in the area of mobile learning. Learner model is logically partitioned into smaller elements or classes in the form of learner profiles, which can represent the entire learning process. Machine learning techniques have the ability to detect patterns from complicated data and learn how to perform activities based on learner profiles. This paper focuses on a systematic approach in reasoning the learner contexts to deliver adaptive learning content. A fuzzy rule base model that has been proposed in related work is found insufficient in deciding all possible conditions. To tackle this problem, this paper adopts …

Modeling And Solving Semiring Constraint Satisfaction Problems By Transformation To Weighted Semiring Max-Sat, Louise Leenen, Anbulagan Anbulagan, Thomas Meyer, Aditya K. Ghose

Professor Aditya K. Ghose

We present a variant of the Weighted Maximum Satisfiability Problem(Weighted Max-SAT), which is a modeling of the Semiring Con- straint Satisfaction framework. We show how to encode a Semiring Con- straint Satisfaction Problem (SCSP) into an instance of a propositional Weighted Max-SAT, and call the encoding Weighted Semiring Max-SAT (WS-Max-SAT). The clauses in our encoding are highly structured and we exploit this feature to develop two algorithms for solving WS-Max- SAT: an incomplete algorithm based on the well-known GSAT algorithm for Max-SAT, and a branch-and-bound algorithm which is complete. Our preliminary experiments show that the translation of SCSP into WS- …

Contextual Effects In Modeling For Small Domain Estimation, Mohammad-Reza Namazi-Rad, David G. Steel

Professor David Steel

Many different Small Area Estimation (SAE) methods have been proposed to overcome the challenge of findingreliable estimates for small domains. Often, the required data for various research purposes are available at differentlevels of aggregation. Based on the available data, individual-level or aggregated-level models are used in SAE.However, parameter estimates obtained from individual and aggregated level analysis may be different, in practice.This may happen due to some substantial contextual or area-level effects in the covariates which may be misspecifiedin individual-level analysis. If small area models are going to be interpretable in practice, possible contextualeffects should be included. Ignoring these effects leads …

Modeling Mobile Learning System Using Anfis, Ahmed Al-Hmouz, Jun Shen, Jun Yan, Rami Al-Hmouz

Dr Jun Shen

Personalisation is becoming more important in the area of mobile learning. Learner model is logically partitioned into smaller elements or classes in the form of learner profiles, which can represent the entire learning process. Machine learning techniques have the ability to detect patterns from complicated data and learn how to perform activities based on learner profiles. This paper focuses on a systematic approach in reasoning the learner contexts to deliver adaptive learning content. A fuzzy rule base model that has been proposed in related work is found insufficient in deciding all possible conditions. To tackle this problem, this paper adopts …

Evaluating Participatory Modeling: Developing A Framework For Cross-Case Analysis. Socio-Economics And The Environment In Discussion (Seed), Natalie A. Jones, Pascal Perez, Thomas G. Measham, Gail J. Kelly, Patrick D'Aquino, Katherine Daniell, Anne Dray, Nils Ferrand

Professor Pascal Perez

Participatory modeling is increasingly recognised as an effective way to assist collective decision-making processes in the domain of natural resource management. This paper introduces a framework for evaluating projects that have adopted a participatory modeling approach. This framework – known as the ‘Protocol of Canberra’ – was developed through a collaboration between French and Australian researchers engaged in participatory modeling and evaluation research. The framework seeks to assess the extent to which different participatory modeling practices reinforce or divert from the theoretical assumptions they are built upon. The paper discusses the application of the framework in three case-studies, two from …

3d Geometric And Haptic Modeling Of Hand-Woven Textile Artifacts, Hooman Shidanshidi, Fazel Naghdy, Golshah Naghdy, Diana Wood Conroy

Associate Professor Golshah Naghdy

Haptic Modeling of textile has attracted significant interest over the last decade. In spite of extensive research, no generic system has been proposed. The previous work mainly assumes that textile has a 2D planar structure. They also require time-consuming objective measurement of textile propel1ies in mechanicaVphysjcal model constrUction. A novel approach for haptic modeling of textile is proposed to overcome the existing shortcomings. The method is generic, assumes a 3D structure textile artifact, and deploys computational intelligence to estimate textile mechanical and physical properties. The approach is designed primarily for display of textile artifacts in museums. The haptic model is …

Modeling Dynamic Controls On Ice Streams: A Bayesian Statistical Approach, L Mark Berliner, Kenneth Jezek, Noel Cressie, Yongku Kim, Calvin Lam, Cornelis Van Der Veen

Professor Noel Cressie

Our main goal is to exemplify the study of ice-stream dynamics via Bayesian statistical analysis incorporating physical, though imperfectly known, models using data that are both incomplete and noisy. The physical-statistical models we propose account for these uncertainties in a coherent, hierarchical manner. The initial modeling assumption estimates basal shear stress as equal to driving stress, but subsequently includes a random corrector process to account for model error. The resulting stochastic equation is incorporated into a simple model for surface velocities. Use of Bayes' theorem allows us to make inferences on all unknowns given basal elevation, surface elevation and surface …

Dynamical Random-Set Modeling Of Concentrated Precipitation In North America, Noel Cressie, Renato Assuncao, Scott H. Holan, Michael Levine, Orietta Nicolis, Jun Zhang, Jian Zou

Professor Noel Cressie

In order to study climate at scales where policy decisions can be made, regional climate models (RCMs) have been developed with much finer resolution (~50 km) than the ~500 km resolution of atmosphere-ocean general circulation models (AOGCMs). The North American Regional Climate Change Assessment Program (NARCCAP) is an international program that provides 50-km resolution climate output for the United States, Canada, and northern Mexico. In Phase I, there are six RCMs, from which we choose one to illustrate our methodology. The RCMs are updated every 3 hours and contain a number of variables, including temperature, precipitation, wind speed, wind direction, …

Accounting For Uncertainty In Ecological Analysis: The Strengths And Limitations Of Hierarchical Statistical Modeling, Noel Cressie, Catherine Calder, James Clark, Jay Ver Hoef, Christopher Wikle

Professor Noel Cressie

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple …

Joint Modeling Of Additive And Non-Additive Genetic Line Effects In Single Field Trials, H Oakey, A Verbyla, Brian Cullis, W. Pitchford, H. Kuchel

Professor Brian Cullis

A statistical approach is presented for selection of best performing lines for commercial release and best parents for future breeding programs from standard agronomic trials. The method involves the partitioning of the genetic effect of a line into additive and non-additive effects using pedigree based inter-line relationships, in a similar manner to that used in animal breeding. A difference is the ability to estimate non-additive effects. Line performance can be assessed by an overall genetic line effect with greater accuracy than when ignoring pedigree information and the additive effects are predicted breeding values. A generalized definition of heritability is developed …