activation predicting real-world: Topics by Science.gov

Sample records for activation predicting real-world

Multitasking capacities in persons diagnosed with schizophrenia: a preliminary examination of their neurocognitive underpinnings and ability to predict real world functioning.

PubMed

Laloyaux, Julien; Van der Linden, Martial; Levaux, Marie-Noëlle; Mourad, Haitham; Pirri, Anthony; Bertrand, Hervé; Domken, Marc-André; Adam, Stéphane; Larøi, Frank

2014-07-30

Difficulties in everyday life activities are core features of persons diagnosed with schizophrenia and in particular during multitasking activities. However, at present, patients׳ multitasking capacities have not been adequately examined in the literature due to the absence of suitable assessment strategies. We thus recently developed a computerized real-life activity task designed to take into account the complex and multitasking nature of certain everyday life activities where participants are required to prepare a room for a meeting. Twenty-one individuals diagnosed with schizophrenia and 20 matched healthy controls completed the computerized task. Patients were also evaluated with a cognitive battery, measures of symptomatology and real world functioning. To examine the ecological validity, 14 other patients were recruited and were given the computerized version and a real version of the meeting preparation task. Results showed that performance on the computerized task was significantly correlated with executive functioning, pointing to the major implication of these cognitive processes in multitasking situations. Performance on the computerized task also significantly predicted up to 50% of real world functioning. Moreover, the computerized task demonstrated good ecological validity. These findings suggest the importance of evaluating multitasking capacities in patients diagnosed with schizophrenia in order to predict real world functioning. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
The Neurodynamics of Affect in the Laboratory Predicts Persistence of Real-World Emotional Responses.

PubMed

Heller, Aaron S; Fox, Andrew S; Wing, Erik K; McQuisition, Kaitlyn M; Vack, Nathan J; Davidson, Richard J

2015-07-22

Failure to sustain positive affect over time is a hallmark of depression and other psychopathologies, but the mechanisms supporting the ability to sustain positive emotional responses are poorly understood. Here, we investigated the neural correlates associated with the persistence of positive affect in the real world by conducting two experiments in humans: an fMRI task of reward responses and an experience-sampling task measuring emotional responses to a reward obtained in the field. The magnitude of DLPFC engagement to rewards administered in the laboratory predicted reactivity of real-world positive emotion following a reward administered in the field. Sustained ventral striatum engagement in the laboratory positively predicted the duration of real-world positive emotional responses. These results suggest that common pathways are associated with the unfolding of neural processes over seconds and with the dynamics of emotions experienced over minutes. Examining such dynamics may facilitate a better understanding of the brain-behavior associations underlying emotion. Significance statement: How real-world emotion, experienced over seconds, minutes, and hours, is instantiated in the brain over the course of milliseconds and seconds is unknown. We combined a novel, real-world experience-sampling task with fMRI to examine how individual differences in real-world emotion, experienced over minutes and hours, is subserved by affective neurodynamics of brain activity over the course of seconds. When winning money in the real world, individuals sustaining positive emotion the longest were those with the most prolonged ventral striatal activity. These results suggest that common pathways are associated with the unfolding of neural processes over seconds and with the dynamics of emotions experienced over minutes. Examining such dynamics may facilitate a better understanding of the brain-behavior associations underlying emotion. Copyright © 2015 the authors 0270-6474/15/3510503-07$15.00/0.
Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications.

PubMed

Minor, Bryan; Doppa, Janardhan Rao; Cook, Diane J

2017-12-01

Recent progress in Internet of Things (IoT) platforms has allowed us to collect large amounts of sensing data. However, there are significant challenges in converting this large-scale sensing data into decisions for real-world applications. Motivated by applications like health monitoring and intervention and home automation we consider a novel problem called Activity Prediction , where the goal is to predict future activity occurrence times from sensor data. In this paper, we make three main contributions. First, we formulate and solve the activity prediction problem in the framework of imitation learning and reduce it to a simple regression learning problem. This approach allows us to leverage powerful regression learners that can reason about the relational structure of the problem with negligible computational overhead. Second, we present several metrics to evaluate activity predictors in the context of real-world applications. Third, we evaluate our approach using real sensor data collected from 24 smart home testbeds. We also embed the learned predictor into a mobile-device-based activity prompter and evaluate the app for 9 participants living in smart homes. Our results indicate that our activity predictor performs better than the baseline methods, and offers a simple approach for predicting activities from sensor data.
The value of surrogate endpoints for predicting real-world survival across five cancer types.

PubMed

Shafrin, Jason; Brookmeyer, Ron; Peneva, Desi; Park, Jinhee; Zhang, Jie; Figlin, Robert A; Lakdawalla, Darius N

2016-01-01

It is unclear how well different outcome measures in randomized controlled trials (RCTs) perform in predicting real-world cancer survival. We assess the ability of RCT overall survival (OS) and surrogate endpoints - progression-free survival (PFS) and time to progression (TTP) - to predict real-world OS across five cancers. We identified 20 treatments and 31 indications for breast, colorectal, lung, ovarian, and pancreatic cancer that had a phase III RCT reporting median OS and median PFS or TTP. Median real-world OS was determined using a Kaplan-Meier estimator applied to patients in the Surveillance and Epidemiology End Results (SEER)-Medicare database (1991-2010). Performance of RCT OS and PFS/TTP in predicting real-world OS was measured using t-tests, median absolute prediction error, and R(2) from linear regressions. Among 72,600 SEER-Medicare patients similar to RCT participants, median survival was 5.9 months for trial surrogates, 14.1 months for trial OS, and 13.4 months for real-world OS. For this sample, regression models using clinical trial OS and trial surrogates as independent variables predicted real-world OS significantly better than models using surrogates alone (P = 0.026). Among all real-world patients using sample treatments (N = 309,182), however, adding trial OS did not improve predictive power over predictions based on surrogates alone (P = 0.194). Results were qualitatively similar using median absolute prediction error and R(2) metrics. Among the five tumor types investigated, trial OS and surrogates were each independently valuable in predicting real-world OS outcomes for patients similar to trial participants. In broader real-world populations, however, trial OS added little incremental value over surrogates alone.
Neural activity during natural viewing of Sesame Street statistically predicts test scores in early childhood.

PubMed

Cantlon, Jessica F; Li, Rosa

2013-01-01

It is not currently possible to measure the real-world thought process that a child has while observing an actual school lesson. However, if it could be done, children's neural processes would presumably be predictive of what they know. Such neural measures would shed new light on children's real-world thought. Toward that goal, this study examines neural processes that are evoked naturalistically, during educational television viewing. Children and adults all watched the same Sesame Street video during functional magnetic resonance imaging (fMRI). Whole-brain intersubject correlations between the neural timeseries from each child and a group of adults were used to derive maps of "neural maturity" for children. Neural maturity in the intraparietal sulcus (IPS), a region with a known role in basic numerical cognition, predicted children's formal mathematics abilities. In contrast, neural maturity in Broca's area correlated with children's verbal abilities, consistent with prior language research. Our data show that children's neural responses while watching complex real-world stimuli predict their cognitive abilities in a content-specific manner. This more ecologically natural paradigm, combined with the novel measure of "neural maturity," provides a new method for studying real-world mathematics development in the brain.
Modeling Interdependent and Periodic Real-World Action Sequences

PubMed Central

Kurashima, Takeshi; Althoff, Tim; Leskovec, Jure

2018-01-01

Mobile health applications, including those that track activities such as exercise, sleep, and diet, are becoming widely used. Accurately predicting human actions in the real world is essential for targeted recommendations that could improve our health and for personalization of these applications. However, making such predictions is extremely difficult due to the complexities of human behavior, which consists of a large number of potential actions that vary over time, depend on each other, and are periodic. Previous work has not jointly modeled these dynamics and has largely focused on item consumption patterns instead of broader types of behaviors such as eating, commuting or exercising. In this work, we develop a novel statistical model, called TIPAS, for Time-varying, Interdependent, and Periodic Action Sequences. Our approach is based on personalized, multivariate temporal point processes that model time-varying action propensities through a mixture of Gaussian intensities. Our model captures short-term and long-term periodic interdependencies between actions through Hawkes process-based self-excitations. We evaluate our approach on two activity logging datasets comprising 12 million real-world actions (e.g., eating, sleep, and exercise) taken by 20 thousand users over 17 months. We demonstrate that our approach allows us to make successful predictions of future user actions and their timing. Specifically, TIPAS improves predictions of actions, and their timing, over existing methods across multiple datasets by up to 156%, and up to 37%, respectively. Performance improvements are particularly large for relatively rare and periodic actions such as walking and biking, improving over baselines by up to 256%. This demonstrates that explicit modeling of dependencies and periodicities in real-world behavior enables successful predictions of future actions, with implications for modeling human behavior, app personalization, and targeting of health interventions. PMID:29780977
Forecasting Occurrences of Activities.

PubMed

Minor, Bryan; Cook, Diane J

2017-07-01

While activity recognition has been shown to be valuable for pervasive computing applications, less work has focused on techniques for forecasting the future occurrence of activities. We present an activity forecasting method to predict the time that will elapse until a target activity occurs. This method generates an activity forecast using a regression tree classifier and offers an advantage over sequence prediction methods in that it can predict expected time until an activity occurs. We evaluate this algorithm on real-world smart home datasets and provide evidence that our proposed approach is most effective at predicting activity timings.
Prediction and Characterization of High-Activity Events in Social Media Triggered by Real-World News.

PubMed

Kalyanam, Janani; Quezada, Mauricio; Poblete, Barbara; Lanckriet, Gert

2016-01-01

On-line social networks publish information on a high volume of real-world events almost instantly, becoming a primary source for breaking news. Some of these real-world events can end up having a very strong impact on on-line social networks. The effect of such events can be analyzed from several perspectives, one of them being the intensity and characteristics of the collective activity that it produces in the social platform. We research 5,234 real-world news events encompassing 43 million messages discussed on the Twitter microblogging service for approximately 1 year. We show empirically that exogenous news events naturally create collective patterns of bursty behavior in combination with long periods of inactivity in the network. This type of behavior agrees with other patterns previously observed in other types of natural collective phenomena, as well as in individual human communications. In addition, we propose a methodology to classify news events according to the different levels of intensity in activity that they produce. In particular, we analyze the most highly active events and observe a consistent and strikingly different collective reaction from users when they are exposed to such events. This reaction is independent of an event's reach and scope. We further observe that extremely high-activity events have characteristics that are quite distinguishable at the beginning stages of their outbreak. This allows us to predict with high precision, the top 8% of events that will have the most impact in the social network by just using the first 5% of the information of an event's lifetime evolution. This strongly implies that high-activity events are naturally prioritized collectively by the social network, engaging users early on, way before they are brought to the mainstream audience.
In-use activity, fuel use, and emissions of heavy-duty diesel roll-off refuse trucks.

PubMed

Sandhu, Gurdas S; Frey, H Christopher; Bartelt-Hunt, Shannon; Jones, Elizabeth

2015-03-01

The objectives of this study were to quantify real-world activity, fuel use, and emissions for heavy duty diesel roll-off refuse trucks; evaluate the contribution of duty cycles and emissions controls to variability in cycle average fuel use and emission rates; quantify the effect of vehicle weight on fuel use and emission rates; and compare empirical cycle average emission rates with the U.S. Environmental Protection Agency's MOVES emission factor model predictions. Measurements were made at 1 Hz on six trucks of model years 2005 to 2012, using onboard systems. The trucks traveled 870 miles, had an average speed of 16 mph, and collected 165 tons of trash. The average fuel economy was 4.4 mpg, which is approximately twice previously reported values for residential trash collection trucks. On average, 50% of time is spent idling and about 58% of emissions occur in urban areas. Newer trucks with selective catalytic reduction and diesel particulate filter had NOx and PM cycle average emission rates that were 80% lower and 95% lower, respectively, compared to older trucks without. On average, the combined can and trash weight was about 55% of chassis weight. The marginal effect of vehicle weight on fuel use and emissions is highest at low loads and decreases as load increases. Among 36 cycle average rates (6 trucks×6 cycles), MOVES-predicted values and estimates based on real-world data have similar relative trends. MOVES-predicted CO2 emissions are similar to those of the real world, while NOx and PM emissions are, on average, 43% lower and 300% higher, respectively. The real-world data presented here can be used to estimate benefits of replacing old trucks with new trucks. Further, the data can be used to improve emission inventories and model predictions. In-use measurements of the real-world activity, fuel use, and emissions of heavy-duty diesel roll-off refuse trucks can be used to improve the accuracy of predictive models, such as MOVES, and emissions inventories. Further, the activity data from this study can be used to generate more representative duty cycles for more accurate chassis dynamometer testing. Comparisons of old and new model year diesel trucks are useful in analyzing the effect of fleet turnover. The analysis of effect of haul weight on fuel use can be used by fleet managers to optimize operations to reduce fuel cost.
Family Science Talk in Museums: Predicting Children's Engagement From Variations in Talk and Activity.

PubMed

Callanan, Maureen A; Castañeda, Claudia L; Luce, Megan R; Martin, Jennifer L

2017-09-01

Children's developing reasoning skills are better understood within the context of their social and cultural lives. As part of a research-museum partnership, this article reports a study exploring science-relevant conversations of 82 families, with children between 3 and 11 years, while visiting a children's museum exhibit about mammoth bones, and in a focused one-on-one exploration of a "mystery object." Parents' use of a variety of types of science talk predicted children's conceptual engagement in the exhibit, but interestingly, different types of parent talk predicted children's engagement depending on the order of the two activities. The findings illustrate the importance of studying children's thinking in real-world contexts and inform creation of effective real-world science experiences for children and families. © 2017 The Authors. Child Development © 2017 Society for Research in Child Development, Inc.
Student Use of Physics to Make Sense of Incomplete but Functional VPython Programs in a Lab Setting

NASA Astrophysics Data System (ADS)

Weatherford, Shawn A.

2011-12-01

Computational activities in Matter & Interactions, an introductory calculus-based physics course, have the instructional goal of providing students with the experience of applying the same set of a small number of fundamental principles to model a wide range of physical systems. However there are significant instructional challenges for students to build computer programs under limited time constraints, especially for students who are unfamiliar with programming languages and concepts. Prior attempts at designing effective computational activities were successful at having students ultimately build working VPython programs under the tutelage of experienced teaching assistants in a studio lab setting. A pilot study revealed that students who completed these computational activities had significant difficultly repeating the exact same tasks and further, had difficulty predicting the animation that would be produced by the example program after interpreting the program code. This study explores the interpretation and prediction tasks as part of an instructional sequence where students are asked to read and comprehend a functional, but incomplete program. Rather than asking students to begin their computational tasks with modifying program code, we explicitly ask students to interpret an existing program that is missing key lines of code. The missing lines of code correspond to the algebraic form of fundamental physics principles or the calculation of forces which would exist between analogous physical objects in the natural world. Students are then asked to draw a prediction of what they would see in the simulation produced by the VPython program and ultimately run the program to evaluate the students' prediction. This study specifically looks at how the participants use physics while interpreting the program code and creating a whiteboard prediction. This study also examines how students evaluate their understanding of the program and modification goals at the beginning of the modification task. While working in groups over the course of a semester, study participants were recorded while they completed three activities using these incomplete programs. Analysis of the video data showed that study participants had little difficulty interpreting physics quantities, generating a prediction, or determining how to modify the incomplete program. Participants did not base their prediction solely from the information from the incomplete program. When participants tried to predict the motion of the objects in the simulation, many turned to their knowledge of how the system would evolve if it represented an analogous real-world physical system. For example, participants attributed the real-world behavior of springs to helix objects even though the program did not include calculations for the spring to exert a force when stretched. Participants rarely interpreted lines of code in the computational loop during the first computational activity, but this changed during latter computational activities with most participants using their physics knowledge to interpret the computational loop. Computational activities in the Matter & Interactions curriculum were revised in light of these findings to include an instructional sequence of tasks to build a comprehension of the example program. The modified activities also ask students to create an additional whiteboard prediction for the time-evolution of the real-world phenomena which the example program will eventually model. This thesis shows how comprehension tasks identified by Palinscar and Brown (1984) as effective in improving reading comprehension are also effective in helping students apply their physics knowledge to interpret a computer program which attempts to model a real-world phenomena and identify errors in their understanding of the use, or omission, of fundamental physics principles in a computational model.
Nucleus accumbens response to gains in reputation for the self relative to gains for others predicts social media use.

PubMed

Meshi, Dar; Morawetz, Carmen; Heekeren, Hauke R

2013-01-01

Our reputation is important to us; we've experienced natural selection to care about our reputation. Recently, the neural processing of gains in reputation (positive social feedback concerning one's character) has been shown to occur in the human ventral striatum. It is still unclear, however, how individual differences in the processing of gains in reputation may lead to individual differences in real-world behavior. For example, in the real-world, one way that people currently maintain their reputation is by using social media websites, like Facebook. Furthermore, Facebook use consists of a social comparison component, where users observe others' behavior and can compare it to their own. Therefore, we hypothesized a relationship between the way the brain processes specifically self-relevant gains in reputation and one's degree of Facebook use. We recorded functional neuroimaging data while participants received gains in reputation, observed the gains in reputation of another person, or received monetary reward. We demonstrate that across participants, when responding to gains in reputation for the self, relative to observing gains for others, reward-related activity in the left nucleus accumbens predicts Facebook use. However, nucleus accumbens activity in response to monetary reward did not predict Facebook use. Finally, a control step-wise regression analysis showed that Facebook use primarily explains our results in the nucleus accumbens. Overall, our results demonstrate how individual sensitivity of the nucleus accumbens to the receipt of self-relevant social information leads to differences in real-world behavior.
Performance on a computerized shopping task significantly predicts real world functioning in persons diagnosed with bipolar disorder.

PubMed

Laloyaux, Julien; Pellegrini, Nadia; Mourad, Haitham; Bertrand, Hervé; Domken, Marc-André; Van der Linden, Martial; Larøi, Frank

2013-12-15

Persons diagnosed with bipolar disorder often suffer from cognitive impairments. However, little is known concerning how these cognitive deficits impact their real world functioning. We developed a computerized real-life activity task, where participants are required to shop for a list of grocery store items. Twenty one individuals diagnosed with bipolar disorder and 21 matched healthy controls were administered the computerized shopping task. Moreover, the patient group was assessed with a battery of cognitive tests and clinical scales. Performance on the shopping task significantly differentiated patients and healthy controls for two variables: Total time to complete the shopping task and Mean time spent to consult the shopping list. Moreover, in the patient group, performance on these variables from the shopping task correlated significantly with cognitive functioning (i.e. processing speed, verbal episodic memory, planning, cognitive flexibility, and inhibition) and with clinical variables including duration of illness and real world functioning. Finally, variables from the shopping task were found to significantly explain 41% of real world functioning of patients diagnosed with bipolar disorder. These findings suggest that the shopping task provides a good indication of real world functioning and cognitive functioning of persons diagnosed with bipolar disorder. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Predicting Reading and Mathematics from Neural Activity for Feedback Learning

ERIC Educational Resources Information Center

Peters, Sabine; Van der Meulen, Mara; Zanolie, Kiki; Crone, Eveline A.

2017-01-01

Although many studies use feedback learning paradigms to study the process of learning in laboratory settings, little is known about their relevance for real-world learning settings such as school. In a large developmental sample (N = 228, 8-25 years), we investigated whether performance and neural activity during a feedback learning task…
Training for Environmental Impact Assessment (E.I.A.).

ERIC Educational Resources Information Center

Vougias, S.

1988-01-01

Deals with the methodology and practices for Environmental Impact Assessment (EIA). Describes the EIA process, prediction process, alternative assessment methods, training needs, major activities, training provision and material, main deficiencies and the precautions, and real world training examples. (Author/YP)
Julius Edgar Lilienfeld Prize Lecture: The Higgs Boson, String Theory, and the Real World

NASA Astrophysics Data System (ADS)

Kane, Gordon

2012-03-01

In this talk I'll describe how string theory is exciting because it can address most, perhaps all, of the questions we hope to understand about our world: why quarks and leptons make up our world, what forces form our world, cosmology, parity violation, and much more. I'll explain why string theory is testable in basically the same ways as the rest of physics, and why much of what is written about that is misleading. String theory is already or soon being tested in several ways, including correctly predicting the recently observed Higgs boson properties and mass, and predictions for dark matter, LHC physics, cosmological history, and more, from work in the increasingly active subfield ``string phenomenology.''
On Predictability of System Anomalies in Real World

DTIC Science & Technology

2011-08-01

distributed system SETI @home [44]. Different from the above work, this work focuses on quantifying the predictability of real-world system anomalies. V...J.-M. Vincent, and D. Anderson, “Mining for statistical models of availability in large-scale distributed systems: An empirical study of seti @home,” in Proc. of MASCOTS, sept. 2009.
Nucleus accumbens response to gains in reputation for the self relative to gains for others predicts social media use

PubMed Central

Meshi, Dar; Morawetz, Carmen; Heekeren, Hauke R.

2013-01-01

Our reputation is important to us; we've experienced natural selection to care about our reputation. Recently, the neural processing of gains in reputation (positive social feedback concerning one's character) has been shown to occur in the human ventral striatum. It is still unclear, however, how individual differences in the processing of gains in reputation may lead to individual differences in real-world behavior. For example, in the real-world, one way that people currently maintain their reputation is by using social media websites, like Facebook. Furthermore, Facebook use consists of a social comparison component, where users observe others' behavior and can compare it to their own. Therefore, we hypothesized a relationship between the way the brain processes specifically self-relevant gains in reputation and one's degree of Facebook use. We recorded functional neuroimaging data while participants received gains in reputation, observed the gains in reputation of another person, or received monetary reward. We demonstrate that across participants, when responding to gains in reputation for the self, relative to observing gains for others, reward-related activity in the left nucleus accumbens predicts Facebook use. However, nucleus accumbens activity in response to monetary reward did not predict Facebook use. Finally, a control step-wise regression analysis showed that Facebook use primarily explains our results in the nucleus accumbens. Overall, our results demonstrate how individual sensitivity of the nucleus accumbens to the receipt of self-relevant social information leads to differences in real-world behavior. PMID:24009567
Cyberspace and Real-World Behavioral Relationships: Towards the Applications of Interest Search Queries to Identify Individuals At-Risk for Suicide

DTIC Science & Technology

2012-06-14

weight fat loss effects diet standard nutrition lose nfpa protein Topic 214: menu restaurant engineering restaurants jones wings seat wild buffalo...Selection ................................................................................... 30 3.5 Raw Data File Format...text mining to descriptions of biological activity and the target of the biological activity (i.e., gene, protein , cell, or microorganism) to predict
Predicting Online Harassment Victimization among a Juvenile Population

ERIC Educational Resources Information Center

Bossler, Adam M.; Holt, Thomas J.; May, David C.

2012-01-01

Online harassment can consist of threatening, worrisome, emotionally hurtful, or sexual messages delivered via an electronic medium that can lead victims to feel fear or distress much like real-world harassment and stalking. This activity is especially prevalent among middle and high school populations who frequently use technology as a means to…

Description and Prediction of Age-Related Change in Everyday Task Performance.

ERIC Educational Resources Information Center

Marsiske, Michael; Willis, Sherry L.

Traditionally, assessment of the cognitive competencies of older adults has focused on abstract laboratory tests, which have often seemed quite unlike the demands of tasks encountered in everyday activities. Consequently, external validity of these laboratory tasks has been questioned, and their utility for assessing real-world competence has been…
Understanding Human Original Actions Directed at Real-World Goals: The Role of the Lateral Prefrontal Cortex

PubMed Central

Sitnikova, Tatiana; Rosen, Bruce R.; Lord, Louis-David; West, W. Caroline

2014-01-01

Adaptive, original actions, which can succeed in multiple contextual situations, require understanding of what is relevant to a goal. Recognizing what is relevant may also help in predicting kinematics of observed, original actions. During action observation, comparisons between sensory input and expected action kinematics have been argued critical to accurate goal inference. Experimental studies with laboratory tasks, both in humans and nonhuman primates, demonstrated that the lateral prefrontal cortex (LPFC) can learn, hierarchically organize, and use goal-relevant information. To determine whether this LPFC capacity is generalizable to real-world cognition, we recorded functional magnetic resonance imaging (fMRI) data in the human brain during comprehension of original and usual object-directed actions embedded in video-depictions of real-life behaviors. We hypothesized that LPFC will contribute to forming goal-relevant representations necessary for kinematic predictions of original actions. Additionally, resting-state fMRI was employed to examine functional connectivity between the brain regions delineated in the video fMRI experiment. According to behavioral data, original videos could be understood by identifying elements relevant to real-life goals at different levels of abstraction. Patterns of enhanced activity in four regions in the left LPFC, evoked by original, relative to usual, video scenes, were consistent with previous neuroimaging findings on representing abstract and concrete stimuli dimensions relevant to laboratory goals. In the anterior left LPFC, the activity increased selectively when representations of broad classes of objects and actions, which could achieve the perceived overall behavioral goal, were likely to bias kinematic predictions of original actions. In contrast, in the more posterior regions, the activity increased even when concrete properties of the target object were more likely to bias the kinematic prediction. Functional connectivity was observed between contiguous regions along the rostro-caudal LPFC axis, but not between the regions that were not immediately adjacent. These findings generalize the representational hierarchy account of LPFC function to diverse core principles that can govern both production and comprehension of flexible real-life behavior. PMID:25224997
Dorsomedial prefrontal cortex mediates rapid evaluations predicting the outcome of romantic interactions

PubMed Central

Cooper, Jeffrey C.; Dunne, Simon; Furey, Teresa; O’Doherty, John P.

2012-01-01

Humans frequently make real-world decisions based on rapid evaluations of minimal information – for example, should we talk to an attractive stranger at a party? Little is known, however, about how the brain makes rapid evaluations with real and immediate social consequences. To address this question, we scanned participants with FMRI while they viewed photos of individuals that they subsequently met at real-life “speed-dating” events. Neural activity in two areas of dorsomedial prefrontal cortex, paracingulate cortex and rostromedial prefrontal cortex (RMPFC), was predictive of whether each individual would be ultimately pursued for a romantic relationship or rejected. Activity in these areas was attributable to two distinct components of romantic evaluation: either consensus judgments about physical beauty (paracingulate cortex) or individualized preferences based on a partner’s perceived personality (RMPFC). These data identify novel computational roles for these regions of the dorsomedial prefrontal cortex in even very rapid social evaluations. Even a first glance, then, can accurately predict romantic desire, but that glance involves a mix of physical and psychological judgments that depend on specific regions of dorsomedial prefrontal cortex. PMID:23136406
Predictive factors of functional capacity and real-world functioning in patients with schizophrenia.

PubMed

Menendez-Miranda, I; Garcia-Portilla, M P; Garcia-Alvarez, L; Arrojo, M; Sanchez, P; Sarramea, F; Gomar, J; Bobes-Bascaran, M T; Sierra, P; Saiz, P A; Bobes, J

2015-07-01

This study was performed to identify the predictive factors of functional capacity assessed by the Spanish University of California Performance Skills Assessment (Sp-UPSA) and real-world functioning assessed by the Spanish Personal and Social Performance scale (PSP) in outpatients with schizophrenia. Naturalistic, 6-month follow-up, multicentre, validation study. Here, we report data on 139 patients with schizophrenia at their baseline visit. Positive and Negative Syndrome Scale (PANSS), Clinical Global Impression-Severity (CGI-S), Sp-UPSA and PSP. Pearson's correlation coefficient (r) was used to determine the relationships between variables, and multivariable stepwise linear regression analyses to identify predictive variables of Sp-UPSA and PSP total scores. Functional capacity: scores on the PSP and PANSS-GP entered first and second at P<0.0001 and accounted for 21% of variance (R(2)=0.208, model df=2, F=15.724, P<0.0001). Real-world functioning: scores on the CGI-S (B=-5.406), PANSS-N (B=-0.657) and Sp-UPSA (B=0.230) entered first, second and third, and accounted for 51% of variance (model df=3, F=37.741, P<0.0001). In patients with schizophrenia, functional capacity and real-world functioning are two related but different constructs. Each one predicts the other along with other factors; general psychopathology for functional capacity, and severity of the illness and negative symptoms for real-world functioning. These findings have important clinical implications: (1) both types of functioning should be assessed in patients with schizophrenia and (2) strategies for improving them should be different. Copyright © 2015 Elsevier Masson SAS. All rights reserved.
Predicting reading and mathematics from neural activity for feedback learning.

PubMed

Peters, Sabine; Van der Meulen, Mara; Zanolie, Kiki; Crone, Eveline A

2017-01-01

Although many studies use feedback learning paradigms to study the process of learning in laboratory settings, little is known about their relevance for real-world learning settings such as school. In a large developmental sample (N = 228, 8-25 years), we investigated whether performance and neural activity during a feedback learning task predicted reading and mathematics performance 2 years later. The results indicated that feedback learning performance predicted both reading and mathematics performance. Activity during feedback learning in left superior dorsolateral prefrontal cortex (DLPFC) predicted reading performance, whereas activity in presupplementary motor area/anterior cingulate cortex (pre-SMA/ACC) predicted mathematical performance. Moreover, left superior DLPFC and pre-SMA/ACC activity predicted unique variance in reading and mathematics ability over behavioral testing of feedback learning performance alone. These results provide valuable insights into the relationship between laboratory-based learning tasks and learning in school settings, and the value of neural assessments for prediction of school performance over behavioral testing alone. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
The Real World Significance of Performance Prediction

ERIC Educational Resources Information Center

Pardos, Zachary A.; Wang, Qing Yang; Trivedi, Shubhendu

2012-01-01

In recent years, the educational data mining and user modeling communities have been aggressively introducing models for predicting student performance on external measures such as standardized tests as well as within-tutor performance. While these models have brought statistically reliable improvement to performance prediction, the real world…
Predictive factors for structural remission using abatacept: results from the ABROAD study.

PubMed

Murakami, Kosaku; Sekiguchi, Masahiro; Hirata, Shintaro; Fujii, Takao; Matsui, Kiyoshi; Morita, Satoshi; Ohmura, Koichiro; Kawahito, Yutaka; Nishimoto, Norihiro; Mimori, Tsuneyo; Sano, Hajime

2018-05-29

To investigate the effect of abatacept (ABA) on preventing joint destruction in biological disease-modifying anti-rheumatic drug (bDMARD)-naïve rheumatoid arthritis (RA) patients in real-world clinical practice. RA patients were collected from the ABROAD (ABatacept Research Outcomes as a First-line Biological Agent in the Real WorlD) study cohort. They had moderate or high disease activity and were treated with ABA as a first-line bDMARD. Radiographic change between baseline and 1 year after ABA treatment was assessed with the van der Heijde's modified total Sharp score (mTSS). Predictive factors for structural remission (St-REM), defined as ΔmTSS ≤0.5/year, were determined. Among 118 patients, 81 (67.5%) achieved St-REM. Disease duration <3 years (odds ratio (OR) = 3.152, p = 0.007) and slower radiographic progression (shown as "baseline mTSS/year <3", OR = 3.727, p = 0.004) were independently significant baseline predictive factors for St-REM irrespective of age and sex. St-REM prevalence increased significantly if clinical remission based on the Simplified Disease Activity Index was achieved at least once until 24 weeks after ABA treatment. Shorter disease duration, smaller radiographic progression at baseline, and rapid clinical response were predictive factors for sustained St-REM after ABA therapy in bDMARD-naïve RA patients.
Cognitive tests predict real-world errors: the relationship between drug name confusion rates in laboratory-based memory and perception tests and corresponding error rates in large pharmacy chains

PubMed Central

Schroeder, Scott R; Salomon, Meghan M; Galanter, William L; Schiff, Gordon D; Vaida, Allen J; Gaunt, Michael J; Bryson, Michelle L; Rash, Christine; Falck, Suzanne; Lambert, Bruce L

2017-01-01

Background Drug name confusion is a common type of medication error and a persistent threat to patient safety. In the USA, roughly one per thousand prescriptions results in the wrong drug being filled, and most of these errors involve drug names that look or sound alike. Prior to approval, drug names undergo a variety of tests to assess their potential for confusability, but none of these preapproval tests has been shown to predict real-world error rates. Objectives We conducted a study to assess the association between error rates in laboratory-based tests of drug name memory and perception and real-world drug name confusion error rates. Methods Eighty participants, comprising doctors, nurses, pharmacists, technicians and lay people, completed a battery of laboratory tests assessing visual perception, auditory perception and short-term memory of look-alike and sound-alike drug name pairs (eg, hydroxyzine/hydralazine). Results Laboratory test error rates (and other metrics) significantly predicted real-world error rates obtained from a large, outpatient pharmacy chain, with the best-fitting model accounting for 37% of the variance in real-world error rates. Cross-validation analyses confirmed these results, showing that the laboratory tests also predicted errors from a second pharmacy chain, with 45% of the variance being explained by the laboratory test data. Conclusions Across two distinct pharmacy chains, there is a strong and significant association between drug name confusion error rates observed in the real world and those observed in laboratory-based tests of memory and perception. Regulators and drug companies seeking a validated preapproval method for identifying confusing drug names ought to consider using these simple tests. By using a standard battery of memory and perception tests, it should be possible to reduce the number of confusing look-alike and sound-alike drug name pairs that reach the market, which will help protect patients from potentially harmful medication errors. PMID:27193033
Oh, Deer!: Predator and Prey Relationships--Students Make Natural Connections through the Integration of Mathematics and Science

ERIC Educational Resources Information Center

Reeder, Stacy; Moseley, Christine

2006-01-01

This article describes an activity that integrates both mathematics and science while inviting students to make connections between the two and learn significant concepts in a meaningful way. Students work within the real-world context of wildlife population scenarios to make predictions, test their hypotheses, and determine and construct graphs…
Interreality: A New Paradigm for E-health.

PubMed

Riva, Giuseppe

2009-01-01

"Interreality" is a personalized immersive e-therapy whose main novelty is a hybrid, closed-loop empowering experience bridging physical and virtual worlds. The main feature of interreality is a twofold link between the virtual and the real world: (a) behavior in the physical world influences the experience in the virtual one; (b) behavior in the virtual world influences the experience in the real one. This is achieved through: (1) 3D Shared Virtual Worlds: role-playing experiences in which one or more users interact with one another within a 3D world; (2) Bio and Activity Sensors (From the Real to the Virtual World): They are used to track the emotional/health/activity status of the user and to influence his/her experience in the virtual world (aspect, activity and access); (3) Mobile Internet Appliances (From the Virtual to the Real One): In interreality, the social and individual user activity in the virtual world has a direct link with the users' life through a mobile phone/digital assistant. The different technologies that are involved in the interreality vision and its clinical rationale are addressed and discussed.
Predicting the evolution of complex networks via similarity dynamics

NASA Astrophysics Data System (ADS)

Wu, Tao; Chen, Leiting; Zhong, Linfeng; Xian, Xingping

2017-01-01

Almost all real-world networks are subject to constant evolution, and plenty of them have been investigated empirically to uncover the underlying evolution mechanism. However, the evolution prediction of dynamic networks still remains a challenging problem. The crux of this matter is to estimate the future network links of dynamic networks. This paper studies the evolution prediction of dynamic networks with link prediction paradigm. To estimate the likelihood of the existence of links more accurate, an effective and robust similarity index is presented by exploiting network structure adaptively. Moreover, most of the existing link prediction methods do not make a clear distinction between future links and missing links. In order to predict the future links, the networks are regarded as dynamic systems in this paper, and a similarity updating method, spatial-temporal position drift model, is developed to simulate the evolutionary dynamics of node similarity. Then the updated similarities are used as input information for the future links' likelihood estimation. Extensive experiments on real-world networks suggest that the proposed similarity index performs better than baseline methods and the position drift model performs well for evolution prediction in real-world evolving networks.
Multi-Perspective Indexing of Diverse Spatial Characteristics of an Outdoor Field toward Redesigning of Real-World Learning

ERIC Educational Resources Information Center

Okada, Masaya; Tada, Masahiro

2014-01-01

Real-world learning is important because it encourages learners to obtain knowledge through various experiences. To design effective real-world learning, it is necessary to analyze the diverse learning activities that occur in real-world learning and to develop effective strategies for learning support. By inventing the technologies of multimodal…
Self Efficacy in Depression: Bridging the Gap Between Competence and Real World Functioning.

PubMed

Milanovic, Melissa; Ayukawa, Emma; Usyatynsky, Aleksandra; Holshausen, Katherine; Bowie, Christopher R

2018-05-01

We investigated the discrepancy between competence and real-world performance in major depressive disorder (MDD) for adaptive and interpersonal behaviors, determining whether self-efficacy significantly predicts this discrepancy, after considering depressive symptoms. Forty-two participants (Mage = 37.64, 66.67% female) with MDD were recruited from mental health clinics. Competence, self-efficacy, and real-world functioning were evaluated in adaptive and interpersonal domains; depressive symptoms were assessed with the Beck Depression Inventory II. Hierarchical regression analysis identified predictors of functional disability and the discrepancy between competence and real-world functioning. Self-efficacy significantly predicted functioning in the adaptive and interpersonal domains over and above depressive symptoms. Interpersonal self-efficacy accounted for significant variance in the discrepancy between interpersonal competence and functioning beyond symptoms. Using a multilevel, multidimensional approach, we provide the first data regarding relationships among competence, functioning, and self-efficacy in MDD. Self-efficacy plays an important role in deployment of functional skills in everyday life for individuals with MDD.
Predictive Validity of Delay Discounting Behavior in Adolescence: A Longitudinal Twin Study

PubMed Central

Isen, Joshua D.; Sparks, Jordan C.; Iacono, William G.

2014-01-01

A standard assumption in the delay discounting literature is that individuals who exhibit steeper discounting of hypothetical rewards also experience greater difficulty deferring gratification to real-world rewards. There is ample cross-sectional evidence that delay discounting paradigms reflect a variety of maladaptive psychosocial outcomes, including substance use pathology. We sought to determine whether a computerized assessment of hypothetical delay discounting (HDD) taps into behavioral impulsivity in a community sample of adolescent twins (N = 675). Using a longitudinal design, we hypothesized that greater HDD at age 14–15 predicts real-world impulsive choices and risk for substance use disorders in late adolescence. We also examined the genetic and environmental structure of HDD performance. Individual differences in HDD behavior showed moderate heritability, and were prospectively associated with real-world temporal discounting at age 17–18. Contrary to expectations, HDD was not consistently related to substance use or trait impulsivity. Although a significant association between HDD behavior and past substance use emerged in males, this effect was mediated by cognitive ability. In both sexes, HDD failed to predict a comprehensive index of substance use problems and behavioral disinhibition in late adolescence. In sum, we present some of the first evidence that HDD performance is heritable and predictive of real-world temporal discounting of rewards. Nevertheless, HDD might not serve as a valid marker of substance use disorder risk in younger adolescents, particularly females. PMID:24999868
Altered striatal activation predicting real-world positive affect in adolescent major depressive disorder.

PubMed

Forbes, Erika E; Hariri, Ahmad R; Martin, Samantha L; Silk, Jennifer S; Moyles, Donna L; Fisher, Patrick M; Brown, Sarah M; Ryan, Neal D; Birmaher, Boris; Axelson, David A; Dahl, Ronald E

2009-01-01

Alterations in reward-related brain function and phenomenological aspects of positive affect are increasingly examined in the development of major depressive disorder. The authors tested differences in reward-related brain function in healthy and depressed adolescents, and the authors examined direct links between reward-related brain function and positive mood that occurred in real-world contexts. Fifteen adolescents with major depressive disorder and 28 adolescents with no history of psychiatric disorder, ages 8-17 years, completed a functional magnetic resonance imaging guessing task involving monetary reward. Participants also reported their subjective positive affect in natural environments during a 4-day cell-phone-based ecological momentary assessment. Adolescents with major depressive disorder exhibited less striatal response than healthy comparison adolescents during reward anticipation and reward outcome, but more response in dorsolateral and medial prefrontal cortex. Diminished activation in a caudate region associated with this depression group difference was correlated with lower subjective positive affect in natural environments, particularly within the depressed group. Results support models of altered reward processing and related positive affect in young people with major depressive disorder and indicate that depressed adolescents' brain response to monetary reward is related to their affective experience in natural environments. Additionally, these results suggest that reward-processing paradigms capture brain function relevant to real-world positive affect.
Real-time emissions from construction equipment compared with model predictions.

PubMed

Heidari, Bardia; Marr, Linsey C

2015-02-01

The construction industry is a large source of greenhouse gases and other air pollutants. Measuring and monitoring real-time emissions will provide practitioners with information to assess environmental impacts and improve the sustainability of construction. We employed a portable emission measurement system (PEMS) for real-time measurement of carbon dioxide (CO), nitrogen oxides (NOx), hydrocarbon, and carbon monoxide (CO) emissions from construction equipment to derive emission rates (mass of pollutant emitted per unit time) and emission factors (mass of pollutant emitted per unit volume of fuel consumed) under real-world operating conditions. Measurements were compared with emissions predicted by methodologies used in three models: NONROAD2008, OFFROAD2011, and a modal statistical model. Measured emission rates agreed with model predictions for some pieces of equipment but were up to 100 times lower for others. Much of the difference was driven by lower fuel consumption rates than predicted. Emission factors during idling and hauling were significantly different from each other and from those of other moving activities, such as digging and dumping. It appears that operating conditions introduce considerable variability in emission factors. Results of this research will aid researchers and practitioners in improving current emission estimation techniques, frameworks, and databases.
Motion sensors in mathematics teaching: learning tools for understanding general math concepts?

NASA Astrophysics Data System (ADS)

Urban-Woldron, Hildegard

2015-05-01

Incorporating technology tools into the mathematics classroom adds a new dimension to the teaching of mathematics concepts and establishes a whole new approach to mathematics learning. In particular, gathering data in a hands-on and real-time method helps classrooms coming alive. The focus of this paper is on bringing forward important mathematics concepts such as functions and rate of change with the motion detector. Findings from the author's studies suggest that the motion detector can be introduced from a very early age and used to enliven classes at any level. Using real-world data to present the main functions invites an experimental approach to mathematics and encourages students to engage actively in their learning. By emphasizing learning experiences with computer-based motion detectors and aiming to involve students in mathematical representations of real-world phenomena, six learning activities, which were developed in previous research studies, will be presented. Students use motion sensors to collect physical data that are graphed in real time and then manipulate and analyse them. Because data are presented in an immediately understandable graphical form, students are allowed to take an active role in their learning by constructing mathematical knowledge from observation of the physical world. By utilizing a predict-observe-explain format, students learn about slope, determining slope and distance vs. time graphs through motion-filled activities. Furthermore, exploring the meaning of slope, viewed as the rate of change, students acquire competencies for reading, understanding and interpreting kinematics graphs involving a multitude of mathematical representations. Consequently, the students are empowered to efficiently move among tabular, graphical and symbolic representation to analyse patterns and discover the relationships between different representations of motion. In fact, there is a need for further research to explore how mathematics teachers can integrate motion sensors into their classrooms.
Evidence for complex contagion models of social contagion from observational data

PubMed Central

Sprague, Daniel A.

2017-01-01

Social influence can lead to behavioural ‘fads’ that are briefly popular and quickly die out. Various models have been proposed for these phenomena, but empirical evidence of their accuracy as real-world predictive tools has so far been absent. Here we find that a ‘complex contagion’ model accurately describes the spread of behaviours driven by online sharing. We found that standard, ‘simple’, contagion often fails to capture both the rapid spread and the long tails of popularity seen in real fads, where our complex contagion model succeeds. Complex contagion also has predictive power: it successfully predicted the peak time and duration of the ALS Icebucket Challenge. The fast spread and longer duration of fads driven by complex contagion has important implications for activities such as publicity campaigns and charity drives. PMID:28686719
Cognitive tests predict real-world errors: the relationship between drug name confusion rates in laboratory-based memory and perception tests and corresponding error rates in large pharmacy chains.

PubMed

Schroeder, Scott R; Salomon, Meghan M; Galanter, William L; Schiff, Gordon D; Vaida, Allen J; Gaunt, Michael J; Bryson, Michelle L; Rash, Christine; Falck, Suzanne; Lambert, Bruce L

2017-05-01

Drug name confusion is a common type of medication error and a persistent threat to patient safety. In the USA, roughly one per thousand prescriptions results in the wrong drug being filled, and most of these errors involve drug names that look or sound alike. Prior to approval, drug names undergo a variety of tests to assess their potential for confusability, but none of these preapproval tests has been shown to predict real-world error rates. We conducted a study to assess the association between error rates in laboratory-based tests of drug name memory and perception and real-world drug name confusion error rates. Eighty participants, comprising doctors, nurses, pharmacists, technicians and lay people, completed a battery of laboratory tests assessing visual perception, auditory perception and short-term memory of look-alike and sound-alike drug name pairs (eg, hydroxyzine/hydralazine). Laboratory test error rates (and other metrics) significantly predicted real-world error rates obtained from a large, outpatient pharmacy chain, with the best-fitting model accounting for 37% of the variance in real-world error rates. Cross-validation analyses confirmed these results, showing that the laboratory tests also predicted errors from a second pharmacy chain, with 45% of the variance being explained by the laboratory test data. Across two distinct pharmacy chains, there is a strong and significant association between drug name confusion error rates observed in the real world and those observed in laboratory-based tests of memory and perception. Regulators and drug companies seeking a validated preapproval method for identifying confusing drug names ought to consider using these simple tests. By using a standard battery of memory and perception tests, it should be possible to reduce the number of confusing look-alike and sound-alike drug name pairs that reach the market, which will help protect patients from potentially harmful medication errors. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.
Mining and Modeling Real-World Networks: Patterns, Anomalies, and Tools

ERIC Educational Resources Information Center

Akoglu, Leman

2012-01-01

Large real-world graph (a.k.a network, relational) data are omnipresent, in online media, businesses, science, and the government. Analysis of these massive graphs is crucial, in order to extract descriptive and predictive knowledge with many commercial, medical, and environmental applications. In addition to its general structure, knowing what…

Evaluating the real-world predictive validity of the Body Image Quality of Life Inventory using Ecological Momentary Assessment.

PubMed

Heron, Kristin E; Mason, Tyler B; Sutton, Tiphanie G; Myers, Taryn A

2015-09-01

Perceptions of physical appearance, or body image, can affect psychosocial functioning and quality of life (QOL). The present study evaluated the real-world predictive validity of the Body Image Quality of Life Inventory (BIQLI) using Ecological Momentary Assessment (EMA). College women reporting subclinical disordered eating/body dissatisfaction (N=131) completed the BIQLI and related measures. For one week they then completed five daily EMA surveys of mood, social interactions, stress, and eating behaviors on palmtop computers. Results showed better body image QOL was associated with less negative affect, less overwhelming emotions, more positive affect, more pleasant social interactions, and higher self-efficacy for handling stress. Lower body image QOL was marginally related to less overeating and lower loss of control over eating in daily life. To our knowledge, this is the first study to support the real-world predictive validity of the BIQLI by identifying social, affective, and behavioral correlates in everyday life using EMA. Copyright © 2015 Elsevier Ltd. All rights reserved.
A Comprehensive Review of Low-Speed Rear Impact Volunteer Studies and a Comparison to Real-World Outcomes.

PubMed

Cormier, Joseph; Gwin, Lisa; Reinhart, Lars; Wood, Rawson; Bain, Charles

2018-02-27

This study combined all prior research involving human volunteers in low-speed rear-end impacts and performed a comparative analysis of real-world crashes using the National Automotive Sampling System - Crashworthiness Data System. The aim of this study was to assess the rates of neck pain between volunteer and real-world collisions as well as the likelihood of an injury beyond symptoms as a function of impact severity and occupant characteristics in real-world collisions. A total of 51 human volunteer studies were identified that produced a dataset of 1984 volunteer impacts along with a separate dataset of 515,601 weighted occupants in real-world rear impacts. Operating-characteristic curves were created to assess the utility of the volunteer dataset in making predictions regarding the overall population. Change in speed or delta-V was used to model the likelihood of reporting symptoms in both real-world and volunteer exposures and more severe injuries using real-world data. Logistic regression models were created for the volunteer data and survey techniques were used to analyze the weighted sampling scheme with the National Automotive Sampling System database. Symptom reporting rates were not different between males and females and were nearly identical between laboratory and real-world exposures. The minimal risk of injury predicted by real-world exposure is consistent with the statistical power of the large number of volunteer studies without any injury beyond the reporting of neck pain. This study shows that volunteer studies do not under-report symptoms and are sufficient in number to conclude that the risk of injury beyond neck strain under similar conditions is essentially zero. The real-world injury analyses demonstrate that rear impacts do not produce meaningful risks of cervical injury at impacts of similar and greater severity to those of the volunteer research. Future work concerning the mechanism of whiplash-related trauma should focus on impacts of severity greater than those in the current literature. 3This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0.
GetReal in mathematical modelling: a review of studies predicting drug effectiveness in the real world.

PubMed

Panayidou, Klea; Gsteiger, Sandro; Egger, Matthias; Kilcher, Gablu; Carreras, Máximo; Efthimiou, Orestis; Debray, Thomas P A; Trelle, Sven; Hummel, Noemi

2016-09-01

The performance of a drug in a clinical trial setting often does not reflect its effect in daily clinical practice. In this third of three reviews, we examine the applications that have been used in the literature to predict real-world effectiveness from randomized controlled trial efficacy data. We searched MEDLINE, EMBASE from inception to March 2014, the Cochrane Methodology Register, and websites of key journals and organisations and reference lists. We extracted data on the type of model and predictions, data sources, validation and sensitivity analyses, disease area and software. We identified 12 articles in which four approaches were used: multi-state models, discrete event simulation models, physiology-based models and survival and generalized linear models. Studies predicted outcomes over longer time periods in different patient populations, including patients with lower levels of adherence or persistence to treatment or examined doses not tested in trials. Eight studies included individual patient data. Seven examined cardiovascular and metabolic diseases and three neurological conditions. Most studies included sensitivity analyses, but external validation was performed in only three studies. We conclude that mathematical modelling to predict real-world effectiveness of drug interventions is not widely used at present and not well validated. © 2016 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd. © 2016 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.
Development of Predictive Energy Management Strategies for Hybrid Electric Vehicles

NASA Astrophysics Data System (ADS)

Baker, David

Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into the impact of real-world prediction error on FE improvements, and whether near-term technologies can be utilized to improve FE. This study seeks to research the effect of prediction error on FE. First, a speed prediction method is developed, and trained with real-world driving data gathered only from the subject vehicle (a local data collection method). This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a high-fidelity model of the FE of a Toyota Prius. A tradeoff analysis between prediction duration and prediction fidelity was completed to determine what duration of prediction resulted in the largest FE improvement. Results demonstrate that 60-90 second predictions resulted in the highest FE improvement over the baseline, achieving up to a 4.8% FE increase. A second speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication was developed to understand if incorporating near-term technologies could be utilized to further improve prediction fidelity. This prediction method produced lower variation in speed prediction error, and was able to realize a larger FE improvement over the local prediction method for longer prediction durations, achieving up to 6% FE improvement. This study concludes that speed prediction and prediction-informed optimal vehicle energy management can produce FE improvements with real-world prediction error and drive cycle variability, as up to 85% of the FE benefit of perfect speed prediction was achieved with the proposed prediction methods.
Working memory is not fixed-capacity: More active storage capacity for real-world objects than for simple stimuli

PubMed Central

Brady, Timothy F.; Störmer, Viola S.; Alvarez, George A.

2016-01-01

Visual working memory is the cognitive system that holds visual information active to make it resistant to interference from new perceptual input. Information about simple stimuli—colors and orientations—is encoded into working memory rapidly: In under 100 ms, working memory ‟fills up,” revealing a stark capacity limit. However, for real-world objects, the same behavioral limits do not hold: With increasing encoding time, people store more real-world objects and do so with more detail. This boost in performance for real-world objects is generally assumed to reflect the use of a separate episodic long-term memory system, rather than working memory. Here we show that this behavioral increase in capacity with real-world objects is not solely due to the use of separate episodic long-term memory systems. In particular, we show that this increase is a result of active storage in working memory, as shown by directly measuring neural activity during the delay period of a working memory task using EEG. These data challenge fixed-capacity working memory models and demonstrate that working memory and its capacity limitations are dependent upon our existing knowledge. PMID:27325767
Working memory is not fixed-capacity: More active storage capacity for real-world objects than for simple stimuli.

PubMed

Brady, Timothy F; Störmer, Viola S; Alvarez, George A

2016-07-05

Visual working memory is the cognitive system that holds visual information active to make it resistant to interference from new perceptual input. Information about simple stimuli-colors and orientations-is encoded into working memory rapidly: In under 100 ms, working memory ‟fills up," revealing a stark capacity limit. However, for real-world objects, the same behavioral limits do not hold: With increasing encoding time, people store more real-world objects and do so with more detail. This boost in performance for real-world objects is generally assumed to reflect the use of a separate episodic long-term memory system, rather than working memory. Here we show that this behavioral increase in capacity with real-world objects is not solely due to the use of separate episodic long-term memory systems. In particular, we show that this increase is a result of active storage in working memory, as shown by directly measuring neural activity during the delay period of a working memory task using EEG. These data challenge fixed-capacity working memory models and demonstrate that working memory and its capacity limitations are dependent upon our existing knowledge.
Interreality in practice: bridging virtual and real worlds in the treatment of posttraumatic stress disorders.

PubMed

Riva, Giuseppe; Raspelli, Simona; Algeri, Davide; Pallavicini, Federica; Gorini, Alessandra; Wiederhold, Brenda K; Gaggioli, Andrea

2010-02-01

The use of new technologies, particularly virtual reality, is not new in the treatment of posttraumatic stress disorders (PTSD): VR is used to facilitate the activation of the traumatic event during exposure therapy. However, during the therapy, VR is a new and distinct realm, separate from the emotions and behaviors experienced by the patient in the real world: the behavior of the patient in VR has no direct effects on the real-life experience; the emotions and problems experienced by the patient in the real world are not directly addressed in the VR exposure. In this article, we suggest that the use of a new technological paradigm, Interreality, may improve the clinical outcome of PTSD. The main feature of Interreality is a twofold link between the virtual and real worlds: (a) behavior in the physical world influences the experience in the virtual one; (b) behavior in the virtual world influences the experience in the real one. This is achieved through 3D shared virtual worlds; biosensors and activity sensors (from the real to the virtual world); and personal digital assistants and/or mobile phones (from the virtual world to the real one). We describe different technologies that are involved in the Interreality vision and its clinical rationale. To illustrate the concept of Interreality in practice, a clinical scenario is also presented and discussed: Rosa, a 55-year-old nurse, involved in a major car accident.
Confronting Analytical Dilemmas for Understanding Complex Human Interactions in Design-Based Research from a Cultural-Historical Activity Theory (CHAT) Framework

ERIC Educational Resources Information Center

Yamagata-Lynch, Lisa C.

2007-01-01

Understanding human activity in real-world situations often involves complicated data collection, analysis, and presentation methods. This article discusses how Cultural-Historical Activity Theory (CHAT) can inform design-based research practices that focus on understanding activity in real-world situations. I provide a sample data set with…
Venture Evaluation and Review Technique (VERT). Users’/Analysts’ Manual

DTIC Science & Technology

1979-10-01

real world. Additionally, activity pro- cessing times could be entered as a normal, uniform or triangular distribution. Activity times can also be...work or tasks, or if the unit activities are such abstractions of the real world that the estimation of the time , cost and performance parameters for...utilized in that con- straining capacity. 7444 The network being processed has passed all the previous error checks. It currently has a real time
Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks

PubMed Central

Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S.

2017-01-01

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a=(u,v) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages. PMID:28771201
Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

PubMed

Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S

2017-08-03

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.
Brain-to-Brain Synchrony Tracks Real-World Dynamic Group Interactions in the Classroom.

PubMed

Dikker, Suzanne; Wan, Lu; Davidesco, Ido; Kaggen, Lisa; Oostrik, Matthias; McClintock, James; Rowland, Jess; Michalareas, Georgios; Van Bavel, Jay J; Ding, Mingzhou; Poeppel, David

2017-05-08

The human brain has evolved for group living [1]. Yet we know so little about how it supports dynamic group interactions that the study of real-world social exchanges has been dubbed the "dark matter of social neuroscience" [2]. Recently, various studies have begun to approach this question by comparing brain responses of multiple individuals during a variety of (semi-naturalistic) tasks [3-15]. These experiments reveal how stimulus properties [13], individual differences [14], and contextual factors [15] may underpin similarities and differences in neural activity across people. However, most studies to date suffer from various limitations: they often lack direct face-to-face interaction between participants, are typically limited to dyads, do not investigate social dynamics across time, and, crucially, they rarely study social behavior under naturalistic circumstances. Here we extend such experimentation drastically, beyond dyads and beyond laboratory walls, to identify neural markers of group engagement during dynamic real-world group interactions. We used portable electroencephalogram (EEG) to simultaneously record brain activity from a class of 12 high school students over the course of a semester (11 classes) during regular classroom activities (Figures 1A-1C; Supplemental Experimental Procedures, section S1). A novel analysis technique to assess group-based neural coherence demonstrates that the extent to which brain activity is synchronized across students predicts both student class engagement and social dynamics. This suggests that brain-to-brain synchrony is a possible neural marker for dynamic social interactions, likely driven by shared attention mechanisms. This study validates a promising new method to investigate the neuroscience of group interactions in ecologically natural settings. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.
A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis

NASA Astrophysics Data System (ADS)

Wang, Jun; Wang, Yang; Zeng, Hui

2016-01-01

A key issue to address in synthesizing spatial data with variable-support in spatial analysis and modeling is the change-of-support problem. We present an approach for solving the change-of-support and variable-support data fusion problems. This approach is based on geostatistical inverse modeling that explicitly accounts for differences in spatial support. The inverse model is applied here to produce both the best predictions of a target support and prediction uncertainties, based on one or more measurements, while honoring measurements. Spatial data covering large geographic areas often exhibit spatial nonstationarity and can lead to computational challenge due to the large data size. We developed a local-window geostatistical inverse modeling approach to accommodate these issues of spatial nonstationarity and alleviate computational burden. We conducted experiments using synthetic and real-world raster data. Synthetic data were generated and aggregated to multiple supports and downscaled back to the original support to analyze the accuracy of spatial predictions and the correctness of prediction uncertainties. Similar experiments were conducted for real-world raster data. Real-world data with variable-support were statistically fused to produce single-support predictions and associated uncertainties. The modeling results demonstrate that geostatistical inverse modeling can produce accurate predictions and associated prediction uncertainties. It is shown that the local-window geostatistical inverse modeling approach suggested offers a practical way to solve the well-known change-of-support problem and variable-support data fusion problem in spatial analysis and modeling.
Robust human body model injury prediction in simulated side impact crashes.

PubMed

Golman, Adam J; Danelson, Kerry A; Stitzel, Joel D

2016-01-01

This study developed a parametric methodology to robustly predict occupant injuries sustained in real-world crashes using a finite element (FE) human body model (HBM). One hundred and twenty near-side impact motor vehicle crashes were simulated over a range of parameters using a Toyota RAV4 (bullet vehicle), Ford Taurus (struck vehicle) FE models and a validated human body model (HBM) Total HUman Model for Safety (THUMS). Three bullet vehicle crash parameters (speed, location and angle) and two occupant parameters (seat position and age) were varied using a Latin hypercube design of Experiments. Four injury metrics (head injury criterion, half deflection, thoracic trauma index and pelvic force) were used to calculate injury risk. Rib fracture prediction and lung strain metrics were also analysed. As hypothesized, bullet speed had the greatest effect on each injury measure. Injury risk was reduced when bullet location was further from the B-pillar or when the bullet angle was more oblique. Age had strong correlation to rib fractures frequency and lung strain severity. The injuries from a real-world crash were predicted using two different methods by (1) subsampling the injury predictors from the 12 best crush profile matching simulations and (2) using regression models. Both injury prediction methods successfully predicted the case occupant's low risk for pelvic injury, high risk for thoracic injury, rib fractures and high lung strains with tight confidence intervals. This parametric methodology was successfully used to explore crash parameter interactions and to robustly predict real-world injuries.
Evaluation of a Pharmacokinetic-Pharmacodynamic Model for Hypouricemic Effects of Febuxostat Using Datasets Obtained from Real-world Patients.

PubMed

Hirai, Toshinori; Itoh, Toshimasa; Kimura, Toshimi; Echizen, Hirotoshi

2018-06-06

Febuxostat is an active xanthine oxidase (XO) inhibitor that is widely used in the hyperuricemia treatment. We aimed to evaluate the predictive performance of a pharmacokinetic-pharmacodynamic (PK-PD) model for hypouricemic effects of febuxostat. Previously, we have formulated a PK--PD model for predicting hypouricemic effects of febuxostat as a function of baseline serum urate levels, body weight, renal function, and drug dose using datasets reported in preapproval studies (Hirai T et al., Biol Pharm Bull 2016; 39: 1013-21). Using an updated model with sensitivity analysis, we examined the predictive performance of the PK-PD model using datasets obtained from the medical records of patients who received febuxostat from March 2011 to December 2015 at Tokyo Women's Medical University Hospital. Multivariate regression analysis was performed to explore clinical variables to improve the predictive performance of the model. A total of 1,199 serum urate data were retrieved from 168 patients (age: 60.5 ±17.7 years, 71.4% males) who received febuxostat as hyperuricemia treatment. There was a significant correlation (r=0.68, p<0.01) between serum urate levels observed and those predicted by the modified PK-PD model. A multivariate regression analysis revealed that the predictive performance of the model may be improved further by considering comorbidities, such as diabetes mellitus, estimated glomerular filtration rate (eGFR), and co-administration of loop diuretics (r = 0.77, p<0.01). The PK-PD model may be useful for predicting individualized maintenance doses of febuxostat in real-world patients. This article is protected by copyright. All rights reserved.
Ecological validity of the five digit test and the oral trails test.

PubMed

Paiva, Gabrielle Chequer de Castro; Fialho, Mariana Braga; Costa, Danielle de Souza; Paula, Jonas Jardim de

2016-01-01

Tests evaluating the attentional-executive system are widely used in clinical practice. However, proximity of an objective cognitive test with real-world situations (ecological validity) is not frequently investigated. The present study evaluate the association between measures of the Five Digit Test (FDT) and the Oral Trails Test (OTT) with self-reported cognitive failures in everyday life as measured by the Cognitive Failures Questionnaire (CFQ). Brazilian adults from 18-to-65 years old voluntarily performed the FDT and OTT tests and reported the frequency of cognitive failures in their everyday life through the CFQ. After controlling for the age effect, the measures of controlled attentional processes were associated with cognitive failures, yet the cognitive flexibility of both FDT and OTT accounted for by the majority of variance in most aspects of the CFQ factors. The FDT and the OTT measures were predictive of real-world problems such as cognitive failures in everyday activities/situations.
Predictive sufficiency and the use of stored internal state

NASA Technical Reports Server (NTRS)

Musliner, David J.; Durfee, Edmund H.; Shin, Kang G.

1994-01-01

In all embedded computing systems, some delay exists between sensing and acting. By choosing an action based on sensed data, a system is essentially predicting that there will be no significant changes in the world during this delay. However, the dynamic and uncertain nature of the real world can make these predictions incorrect, and thus, a system may execute inappropriate actions. Making systems more reactive by decreasing the gap between sensing and action leaves less time for predictions to err, but still provides no principled assurance that they will be correct. Using the concept of predictive sufficiency described in this paper, a system can prove that its predictions are valid, and that it will never execute inappropriate actions. In the context of our CIRCA system, we also show how predictive sufficiency allows a system to guarantee worst-case response times to changes in its environment. Using predictive sufficiency, CIRCA is able to build real-time reactive control plans which provide a sound basis for performance guarantees that are unavailable with other reactive systems.
Advancing Drug Safety Through Prospective Pharmacovigilance.

PubMed

Pitts, Peter J; Le Louet, Hervé

2018-01-01

Much has changed in a relatively short period of time. There is a raging debate over the level of evidence expected to first introduce a treatment to patients based on smaller, more adaptive data sets. Some argue for less data followed by postapproval follow-up, others for more adaptive clinical trial designs and end-point modification driven by patient-focused drug development and use of real-world evidence. The transition in both the review and postmarketing regulatory framework is happening in front of our eyes in real time. To improve the ability of patients to receive high-quality, safe, effective, and timely care, better information via pharmacovigilance must be a priority as the world's many regulatory systems build the capacity to harness electronic health information to improve health, care quality, and safety. Globally, the widely variable ability of nations to build reliable regulatory systems (from precise review to robust pharmacovigilance) is a dangerous source of health care inequality. Developing validated tools and techniques for "predictive pharmacovigilance" will assist all health systems in better understanding the risks and benefits of the medicines they regulate by understanding what should be happening once a new medicine moves from risk-benefit regulatory efficacy to real-world risk-effectiveness. This will be of particular utility for smaller regulatory agencies with fewer resources. By comparing preapproval predictive pharmacovigilance data, developing regulatory authorities will be able to better understand the potential gap between what was predicted and what was actually measured (via more traditional pharmacovigilance methodologies). Predictive pharmacovigilance recognizes the value of understanding the imperfect reporting of real-world clinical use and that the absence of reporting is, in itself, an important postmarketing signal.
Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function.

PubMed

Subramaniam, Karuna; Hooker, Christine I; Biagianti, Bruno; Fisher, Melissa; Nagarajan, Srikantan; Vinogradov, Sophia

2015-01-01

Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC) participants, reward anticipation is associated with activity in frontal-striatal networks. By contrast, schizophrenia (SZ) participants show hypoactivation within these frontal-striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life.
Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function

PubMed Central

Subramaniam, Karuna; Hooker, Christine I.; Biagianti, Bruno; Fisher, Melissa; Nagarajan, Srikantan; Vinogradov, Sophia

2015-01-01

Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC) participants, reward anticipation is associated with activity in frontal–striatal networks. By contrast, schizophrenia (SZ) participants show hypoactivation within these frontal–striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life. PMID:26413478

Future Evolution of Virtual Worlds as Communication Environments

NASA Astrophysics Data System (ADS)

Prisco, Giulio

Extensive experience creating locations and activities inside virtual worlds provides the basis for contemplating their future. Users of virtual worlds are diverse in their goals for these online environments; for example, immersionists want them to be alternative realities disconnected from real life, whereas augmentationists want them to be communication media supporting real-life activities. As the technology improves, the diversity of virtual worlds will increase along with their significance. Many will incorporate more advanced virtual reality, or serve as major media for long-distance collaboration, or become the venues for futurist social movements. Key issues are how people can create their own virtual worlds, travel across worlds, and experience a variety of multimedia immersive environments. This chapter concludes by noting the view among some computer scientists that future technologies will permit uploading human personalities to artificial intelligence avatars, thereby enhancing human beings and rendering the virtual worlds entirely real.
Virtual Games and Real-World Communities: Environments That Constrain and Enable Physical Activity in Games for Health

ERIC Educational Resources Information Center

Stewart, Mary K.; Hagood, Danielle; Ching, Cynthia Carter

2017-01-01

This article examines two communities of youth who play an online game that integrates physical activity into virtual game play. Participating youth from two research sites--an urban middle school and a suburban junior high school--wore FitBits that tracked their physical activity and then integrated their real-world energy into game-world…
Predicting pedestrian flow: a methodology and a proof of concept based on real-life data.

PubMed

Davidich, Maria; Köster, Gerta

2013-01-01

Building a reliable predictive model of pedestrian motion is very challenging: Ideally, such models should be based on observations made in both controlled experiments and in real-world environments. De facto, models are rarely based on real-world observations due to the lack of available data; instead, they are largely based on intuition and, at best, literature values and laboratory experiments. Such an approach is insufficient for reliable simulations of complex real-life scenarios: For instance, our analysis of pedestrian motion under natural conditions at a major German railway station reveals that the values for free-flow velocities and the flow-density relationship differ significantly from widely used literature values. It is thus necessary to calibrate and validate the model against relevant real-life data to make it capable of reproducing and predicting real-life scenarios. In this work we aim at constructing such realistic pedestrian stream simulation. Based on the analysis of real-life data, we present a methodology that identifies key parameters and interdependencies that enable us to properly calibrate the model. The success of the approach is demonstrated for a benchmark model, a cellular automaton. We show that the proposed approach significantly improves the reliability of the simulation and hence the potential prediction accuracy. The simulation is validated by comparing the local density evolution of the measured data to that of the simulated data. We find that for our model the most sensitive parameters are: the source-target distribution of the pedestrian trajectories, the schedule of pedestrian appearances in the scenario and the mean free-flow velocity. Our results emphasize the need for real-life data extraction and analysis to enable predictive simulations.
The Role of Graphlets in Viral Processes on Networks

NASA Astrophysics Data System (ADS)

Khorshidi, Samira; Al Hasan, Mohammad; Mohler, George; Short, Martin B.

2018-05-01

Predicting the evolution of viral processes on networks is an important problem with applications arising in biology, the social sciences, and the study of the Internet. In existing works, mean-field analysis based upon degree distribution is used for the prediction of viral spreading across networks of different types. However, it has been shown that degree distribution alone fails to predict the behavior of viruses on some real-world networks and recent attempts have been made to use assortativity to address this shortcoming. In this paper, we show that adding assortativity does not fully explain the variance in the spread of viruses for a number of real-world networks. We propose using the graphlet frequency distribution in combination with assortativity to explain variations in the evolution of viral processes across networks with identical degree distribution. Using a data-driven approach by coupling predictive modeling with viral process simulation on real-world networks, we show that simple regression models based on graphlet frequency distribution can explain over 95% of the variance in virality on networks with the same degree distribution but different network topologies. Our results not only highlight the importance of graphlets but also identify a small collection of graphlets which may have the highest influence over the viral processes on a network.
Accounting for the Variation of Driver Aggression in the Simulation of Conventional and Advanced Vehicles (Presentation)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Neubauer, J.; Wood, E.

2013-05-01

This presentation discusses a method of accounting for realistic levels of driver aggression to higher-level vehicle studies, including the impact of variation in real-world driving characteristics (acceleration and speed) on vehicle energy consumption and different powertrains (e.g., conventionally powered vehicles versus electrified drive vehicles [xEVs]). Aggression variation between drivers can increase fuel consumption by more than 50% or decrease it by more than 20% from average. The normalized fuel consumption deviation from average as a function of population percentile was found to be largely insensitive to powertrain. However, the traits of ideal driving behavior are a function of powertrain. Inmore » conventional vehicles, kinetic losses dominate rolling resistance and aerodynamic losses. In xEVs with regenerative braking, rolling resistance and aerodynamic losses dominate. The relation of fuel consumption predicted from real-world drive data to that predicted by the industry-standard HWFET, UDDS, LA92, and US06 drive cycles was not consistent across powertrains, and varied broadly from the mean, median, and mode of real-world driving. A drive cycle synthesized by NREL's DRIVE tool accurately and consistently reproduces average real-world for multiple powertrains within 1%, and can be used to calculate the fuel consumption effects of varying levels of driver aggression.« less
Achieving simplified disease activity index remission in patients with active rheumatoid arthritis is associated with subsequent good functional and structural outcomes in a real-world clinical setting under a treat-to-target strategy.

PubMed

Hirano, Fumio; Yokoyama, Waka; Yamazaki, Hayato; Amano, Koichi; Kawakami, Atsushi; Hayashi, Taichi; Tamura, Naoto; Yasuda, Shinsuke; Dobashi, Hiroaki; Fujii, Takao; Ito, Satoshi; Kaneko, Yuko; Matsui, Toshihiro; Okuda, Yasuaki; Saito, Kazuyoshi; Suzuki, Fumihito; Yoshimi, Ryusuke; Sakai, Ryoko; Koike, Ryuji; Kohsaka, Hitoshi; Miyasaka, Nobuyuki; Harigai, Masayoshi

2017-09-01

To verify predictive validity of simplified disease activity index (SDAI) remission for subsequent functional and structural outcomes in real-world clinical settings under a treat-to-target strategy (T2T). In this multicenter, prospective cohort study, T2T was implemented in rheumatoid arthritis (RA) patients with moderate-to-high disease activity. SDAI or clinical disease activity index (CDAI) was assessed every 12 weeks, and treatment was adjusted to achieve clinical remission or low disease activity (LDA). Multivariate logistic regression models were used to examine the associations of SDAI remission (≤3.3) at week 24 with the health assessment questionnaire-disability index (HAQ-DI) ≤ 0.5 or with the delta van der Heijde-modified total Sharp score (ΔvdH-mTSS)
Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis.

PubMed

Kim, Young Bin; Lee, Sang Hyeok; Kang, Shin Jin; Choi, Myung Jin; Lee, Jung; Kim, Chang Hun

2015-01-01

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it.
Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis

PubMed Central

Kim, Young Bin; Lee, Sang Hyeok; Kang, Shin Jin; Choi, Myung Jin; Lee, Jung; Kim, Chang Hun

2015-01-01

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it. PMID:26241496
Real-World Literacy Activity in Pre-School

ERIC Educational Resources Information Center

Anderson, Jim; Purcell-Gates, Victoria; Lenters, Kimberly; McTavish, Marianne

2012-01-01

In this article, we share real-world literacy activities that we designed and implemented in two early literacy classes for preschoolers from two inner-city neighbourhoods that were part of an intergenerational family literacy program, Literacy for Life (LFL). The program was informed by research that shows that young children in high literate…
Increasing Student Engagement and Enthusiasm: A Projectile Motion Crime Scene

ERIC Educational Resources Information Center

Bonner, David

2010-01-01

Connecting physics concepts with real-world events allows students to establish a strong conceptual foundation. When such events are particularly interesting to students, it can greatly impact their engagement and enthusiasm in an activity. Activities that involve studying real-world events of high interest can provide students a long-lasting…
Prediction of human errors by maladaptive changes in event-related brain networks.

PubMed

Eichele, Tom; Debener, Stefan; Calhoun, Vince D; Specht, Karsten; Engel, Andreas K; Hugdahl, Kenneth; von Cramon, D Yves; Ullsperger, Markus

2008-04-22

Humans engaged in monotonous tasks are susceptible to occasional errors that may lead to serious consequences, but little is known about brain activity patterns preceding errors. Using functional MRI and applying independent component analysis followed by deconvolution of hemodynamic responses, we studied error preceding brain activity on a trial-by-trial basis. We found a set of brain regions in which the temporal evolution of activation predicted performance errors. These maladaptive brain activity changes started to evolve approximately 30 sec before the error. In particular, a coincident decrease of deactivation in default mode regions of the brain, together with a decline of activation in regions associated with maintaining task effort, raised the probability of future errors. Our findings provide insights into the brain network dynamics preceding human performance errors and suggest that monitoring of the identified precursor states may help in avoiding human errors in critical real-world situations.
Prediction of human errors by maladaptive changes in event-related brain networks

PubMed Central

Eichele, Tom; Debener, Stefan; Calhoun, Vince D.; Specht, Karsten; Engel, Andreas K.; Hugdahl, Kenneth; von Cramon, D. Yves; Ullsperger, Markus

2008-01-01

Humans engaged in monotonous tasks are susceptible to occasional errors that may lead to serious consequences, but little is known about brain activity patterns preceding errors. Using functional MRI and applying independent component analysis followed by deconvolution of hemodynamic responses, we studied error preceding brain activity on a trial-by-trial basis. We found a set of brain regions in which the temporal evolution of activation predicted performance errors. These maladaptive brain activity changes started to evolve ≈30 sec before the error. In particular, a coincident decrease of deactivation in default mode regions of the brain, together with a decline of activation in regions associated with maintaining task effort, raised the probability of future errors. Our findings provide insights into the brain network dynamics preceding human performance errors and suggest that monitoring of the identified precursor states may help in avoiding human errors in critical real-world situations. PMID:18427123
A common neural code for similar conscious experiences in different individuals

PubMed Central

Naci, Lorina; Cusack, Rhodri; Anello, Mimma; Owen, Adrian M.

2014-01-01

The interpretation of human consciousness from brain activity, without recourse to speech or action, is one of the most provoking and challenging frontiers of modern neuroscience. We asked whether there is a common neural code that underpins similar conscious experiences, which could be used to decode these experiences in the absence of behavior. To this end, we used richly evocative stimulation (an engaging movie) portraying real-world events to elicit a similar conscious experience in different people. Common neural correlates of conscious experience were quantified and related to measurable, quantitative and qualitative, executive components of the movie through two additional behavioral investigations. The movie’s executive demands drove synchronized brain activity across healthy participants’ frontal and parietal cortices in regions known to support executive function. Moreover, the timing of activity in these regions was predicted by participants’ highly similar qualitative experience of the movie’s moment-to-moment executive demands, suggesting that synchronization of activity across participants underpinned their similar experience. Thus we demonstrate, for the first time to our knowledge, that a neural index based on executive function reliably predicted every healthy individual’s similar conscious experience in response to real-world events unfolding over time. This approach provided strong evidence for the conscious experience of a brain-injured patient, who had remained entirely behaviorally nonresponsive for 16 y. The patient’s executive engagement and moment-to-moment perception of the movie content were highly similar to that of every healthy participant. These findings shed light on the common basis of human consciousness and enable the interpretation of conscious experience in the absence of behavior. PMID:25225384
Emissions During and Real-world Frequency of Heavy-duty Diesel Particulate Filter Regeneration.

PubMed

Ruehl, Chris; Smith, Jeremy D; Ma, Yilin; Shields, Jennifer Erin; Burnitzki, Mark; Sobieralski, Wayne; Ianni, Robert; Chernich, Donald J; Chang, M-C Oliver; Collins, John Francis; Yoon, Seungju; Quiros, David; Hu, Shaohua; Dwyer, Harry

2018-05-15

Recent tightening of particulate matter (PM) emission standards for heavy-duty engines has spurred the widespread adoption of diesel particulate filters (DPFs), which need to be regenerated periodically to remove trapped PM. The total impact of DPFs therefore depends not only on their filtering efficiency during normal operation, but also on the emissions during and the frequency of regeneration events. We performed active (parked and driving) and passive regenerations on two heavy-duty diesel vehicles (HDDVs), and report the chemical composition of emissions during these events, as well as the efficiency with which trapped PM is converted to gas-phase products. We also collected activity data from 85 HDDVs to determine how often regeneration occurs during real-world operation. PM emitted during regeneration ranged from 0.2 to 16.3 g, and the average time and distance between real-world active regenerations was 28.0 h and 599 miles. These results indicate that regeneration of real-world DPFs does not substantially offset the reduction of PM by DPFs during normal operation. The broad ranges of regeneration frequency per truck (3-100 h and 23-4078 miles) underscore the challenges in designing engines and associated aftertreatments that reduce emissions for all real-world duty cycles.
Predicting Persuasion-Induced Behavior Change from the Brain

PubMed Central

Falk, Emily B.; Berkman, Elliot T.; Mann, Traci; Harrison, Brittany; Lieberman, Matthew D.

2011-01-01

Although persuasive messages often alter people’s self-reported attitudes and intentions to perform behaviors, these self-reports do not necessarily predict behavior change. We demonstrate that neural responses to persuasive messages can predict variability in behavior change in the subsequent week. Specifically, an a priori region of interest (ROI) in medial prefrontal cortex (MPFC) was reliably associated with behavior change (r = 0.49, p < 0.05). Additionally, an iterative cross-validation approach using activity in this MPFC ROI predicted an average 23% of the variance in behavior change beyond the variance predicted by self-reported attitudes and intentions. Thus, neural signals can predict behavioral changes that are not predicted from self-reported attitudes and intentions alone. Additionally, this is the first functional magnetic resonance imaging study to demonstrate that a neural signal can predict complex real world behavior days in advance. PMID:20573889
EEG-based decoding of error-related brain activity in a real-world driving task

NASA Astrophysics Data System (ADS)

Zhang, H.; Chavarriaga, R.; Khaliliardali, Z.; Gheorghe, L.; Iturrate, I.; Millán, J. d. R.

2015-12-01

Objectives. Recent studies have started to explore the implementation of brain-computer interfaces (BCI) as part of driving assistant systems. The current study presents an EEG-based BCI that decodes error-related brain activity. Such information can be used, e.g., to predict driver’s intended turning direction before reaching road intersections. Approach. We executed experiments in a car simulator (N = 22) and a real car (N = 8). While subject was driving, a directional cue was shown before reaching an intersection, and we classified the presence or not of an error-related potentials from EEG to infer whether the cued direction coincided with the subject’s intention. In this protocol, the directional cue can correspond to an estimation of the driving direction provided by a driving assistance system. We analyzed ERPs elicited during normal driving and evaluated the classification performance in both offline and online tests. Results. An average classification accuracy of 0.698 ± 0.065 was obtained in offline experiments in the car simulator, while tests in the real car yielded a performance of 0.682 ± 0.059. The results were significantly higher than chance level for all cases. Online experiments led to equivalent performances in both simulated and real car driving experiments. These results support the feasibility of decoding these signals to help estimating whether the driver’s intention coincides with the advice provided by the driving assistant in a real car. Significance. The study demonstrates a BCI system in real-world driving, extending the work from previous simulated studies. As far as we know, this is the first online study in real car decoding driver’s error-related brain activity. Given the encouraging results, the paradigm could be further improved by using more sophisticated machine learning approaches and possibly be combined with applications in intelligent vehicles.
Is Behavior in a Commons Dilemma Game Related to Real World Behavior

DTIC Science & Technology

1975-11-03

validity. Do variables or parameters affecting game behavior have similar effects upon real world behavior ? The present research has uncovered...Post experimental comments of subjects suggest that behavior in repeated plays would be greatly affected by the outcomes of earlier trials...one’s own decision and to what extent one’s own decision may lead to rationalizations that might affect one’s predictions (Dawes and McTavish, in
Alice in the Real World

ERIC Educational Resources Information Center

Parker, Tom

2012-01-01

As a fifth-grade mathematics teacher, the author tries to create authentic problem-solving activities that connect to the world in which his students live. He discovered a natural connection to his students' real world at a computer camp. A friend introduced him to Alice, a computer application developed at Carnegie Mellon, under the leadership of…
Physics-based enzyme design: predicting binding affinity and catalytic activity.

PubMed

Sirin, Sarah; Pearlman, David A; Sherman, Woody

2014-12-01

Computational enzyme design is an emerging field that has yielded promising success stories, but where numerous challenges remain. Accurate methods to rapidly evaluate possible enzyme design variants could provide significant value when combined with experimental efforts by reducing the number of variants needed to be synthesized and speeding the time to reach the desired endpoint of the design. To that end, extending our computational methods to model the fundamental physical-chemical principles that regulate activity in a protocol that is automated and accessible to a broad population of enzyme design researchers is essential. Here, we apply a physics-based implicit solvent MM-GBSA scoring approach to enzyme design and benchmark the computational predictions against experimentally determined activities. Specifically, we evaluate the ability of MM-GBSA to predict changes in affinity for a steroid binder protein, catalytic turnover for a Kemp eliminase, and catalytic activity for α-Gliadin peptidase variants. Using the enzyme design framework developed here, we accurately rank the most experimentally active enzyme variants, suggesting that this approach could provide enrichment of active variants in real-world enzyme design applications. © 2014 Wiley Periodicals, Inc.
Cognitive and neural plasticity in older adults’ prospective memory following training with the Virtual Week computer game

PubMed Central

Rose, Nathan S.; Rendell, Peter G.; Hering, Alexandra; Kliegel, Matthias; Bidelman, Gavin M.; Craik, Fergus I. M.

2015-01-01

Prospective memory (PM) – the ability to remember and successfully execute our intentions and planned activities – is critical for functional independence and declines with age, yet few studies have attempted to train PM in older adults. We developed a PM training program using the Virtual Week computer game. Trained participants played the game in 12, 1-h sessions over 1 month. Measures of neuropsychological functions, lab-based PM, event-related potentials (ERPs) during performance on a lab-based PM task, instrumental activities of daily living, and real-world PM were assessed before and after training. Performance was compared to both no-contact and active (music training) control groups. PM on the Virtual Week game dramatically improved following training relative to controls, suggesting PM plasticity is preserved in older adults. Relative to control participants, training did not produce reliable transfer to laboratory-based tasks, but was associated with a reduction of an ERP component (sustained negativity over occipito-parietal cortex) associated with processing PM cues, indicative of more automatic PM retrieval. Most importantly, training produced far transfer to real-world outcomes including improvements in performance on real-world PM and activities of daily living. Real-world gains were not observed in either control group. Our findings demonstrate that short-term training with the Virtual Week game produces cognitive and neural plasticity that may result in real-world benefits to supporting functional independence in older adulthood. PMID:26578936

Potential predictability and forecast skill in ensemble climate forecast: a skill-persistence rule

NASA Astrophysics Data System (ADS)

Jin, Yishuai; Rong, Xinyao; Liu, Zhengyu

2017-12-01

This study investigates the factors relationship between the forecast skills for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill for sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further proved using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but could be distorted by sampling errors and non-AR1 processes. This study suggests that the so called "perfect skill" is model dependent and cannot serve as an accurate estimate of the true upper limit of real world prediction skill, unless the model can capture at least the persistence property of the observation.
Project Real World: Economic Living Skills for High School Students. Module IV, Entrepreneurship and the World of Work.

ERIC Educational Resources Information Center

Federal/Provincial Consumer Education and Plain Language Task Force (Canada).

Project Real World, a self-contained, activity-based Canadian consumer science program, provides students with systematic instruction in economic living skills. It gives students in grades 10-12 an orientation to the economic realities and opportunities in society. The program helps students understand the marketplace; manage resources; apply…
Words matter: Implementing the electronically activated recorder in schizotypy.

PubMed

Minor, Kyle S; Davis, Beshaun J; Marggraf, Matthew P; Luther, Lauren; Robbins, Megan L

2018-03-01

In schizophrenia-spectrum populations, analyzing the words people use has offered promise for unlocking information about affective states and social behaviors. The electronically activated recorder (EAR) is an application-based program that is combined with widely used smartphone technology to capture a person's real-world interactions via audio recordings. It improves on the ecological validity of current methodologies by providing objective and naturalistic samples of behavior. This study is the first to implement the EAR in people endorsing elevated traits of schizophrenia-spectrum personality disorders (i.e., schizotypy), and we expected the EAR to (a) differentiate high and low schizotypy groups on affective disturbances and social engagement and (b) show that high schizotypy status moderates links between affect and social behavior using a multimethod approach. Lexical analysis of EAR recordings revealed greater negative affect and decreased social engagement in those high in schizotypy. When assessing specific traits, EAR and ecological momentary assessment (EMA) converged to show that positive schizotypy predicted negative affect. Finally, high schizotypy status moderated links between negative affect and social engagement when the EAR was combined with EMA. Adherence did not influence results, as both groups wore the EAR more than 90% of their waking hours. Findings supported using the EAR to assess real-world expressions of personality and functioning in schizotypy. Evidence also showed that the EAR can be used alongside EMA to provide a mixed-method, real-world assessment that is high in ecological validity and offers a window into the daily lives of those with elevated traits of schizophrenia-spectrum personality disorders. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
A Model of the Superior Colliculus Predicts Fixation Locations during Scene Viewing and Visual Search.

PubMed

Adeli, Hossein; Vitu, Françoise; Zelinsky, Gregory J

2017-02-08

Modern computational models of attention predict fixations using saliency maps and target maps, which prioritize locations for fixation based on feature contrast and target goals, respectively. But whereas many such models are biologically plausible, none have looked to the oculomotor system for design constraints or parameter specification. Conversely, although most models of saccade programming are tightly coupled to underlying neurophysiology, none have been tested using real-world stimuli and tasks. We combined the strengths of these two approaches in MASC, a model of attention in the superior colliculus (SC) that captures known neurophysiological constraints on saccade programming. We show that MASC predicted the fixation locations of humans freely viewing naturalistic scenes and performing exemplar and categorical search tasks, a breadth achieved by no other existing model. Moreover, it did this as well or better than its more specialized state-of-the-art competitors. MASC's predictive success stems from its inclusion of high-level but core principles of SC organization: an over-representation of foveal information, size-invariant population codes, cascaded population averaging over distorted visual and motor maps, and competition between motor point images for saccade programming, all of which cause further modulation of priority (attention) after projection of saliency and target maps to the SC. Only by incorporating these organizing brain principles into our models can we fully understand the transformation of complex visual information into the saccade programs underlying movements of overt attention. With MASC, a theoretical footing now exists to generate and test computationally explicit predictions of behavioral and neural responses in visually complex real-world contexts. SIGNIFICANCE STATEMENT The superior colliculus (SC) performs a visual-to-motor transformation vital to overt attention, but existing SC models cannot predict saccades to visually complex real-world stimuli. We introduce a brain-inspired SC model that outperforms state-of-the-art image-based competitors in predicting the sequences of fixations made by humans performing a range of everyday tasks (scene viewing and exemplar and categorical search), making clear the value of looking to the brain for model design. This work is significant in that it will drive new research by making computationally explicit predictions of SC neural population activity in response to naturalistic stimuli and tasks. It will also serve as a blueprint for the construction of other brain-inspired models, helping to usher in the next generation of truly intelligent autonomous systems. Copyright © 2017 the authors 0270-6474/17/371453-15$15.00/0.
Problem-Based Learning and Earth System Science - The ESSEA High School Earth System Science Online Course

NASA Astrophysics Data System (ADS)

Myers, R.; Botti, J.

2002-12-01

The high school Earth system science course is web based and designed to meet the professional development needs of science teachers in grades 9-12. Three themes predominate this course: Earth system science (ESS) content, collaborative investigations, and problem-based learning (PBL) methodology. PBL uses real-world contexts for in-depth investigations of a subject matter. Participants predict the potential impacts of the selected event on Earth's spheres and the subsequent feedback and potential interactions that might result. PBL activities start with an ill-structured problem that serves as a springboard to team engagement. These PBL scenarios contain real-world situations. Teams of learners conduct an Earth system science analysis of the event and make recommendations or offer solutions regarding the problem. The course design provides an electronic forum for conversations, debate, development, and application of ideas. Samples of threaded discussions built around ESS thinking in science and PBL pedagogy will be presented.
Problem-Based Learning and Earth System Science - The ESSEA High School Earth System Science Online Course

NASA Astrophysics Data System (ADS)

Myers, R. J.; Botti, J. A.

2001-12-01

The high school Earth system science course is web based and designed to meet the professional development needs of science teachers in grades 9-12. Three themes predominate this course: Earth system science (ESS) content, collaborative investigations, and problem-based learning (PBL) methodology. PBL uses real-world contexts for in-depth investigations of a subject matter. Participants predict the potential impacts of the selected event on Earth's spheres and the subsequent feedback and potential interactions that might result. PBL activities start with an ill-structured problem that serves as a springboard to team engagement. These PBL scenarios contain real-world situations. Teams of learners conduct an Earth system science analysis of the event and make recommendations or offer solutions regarding the problem. The course design provides an electronic forum for conversations, debate, development, and application of ideas. Samples of threaded discussions built around ESS thinking in science and PBL pedagogy will be presented.
Homophyly/kinship hypothesis: Natural communities, and predicting in networks

NASA Astrophysics Data System (ADS)

Li, Angsheng; Li, Jiankou; Pan, Yicheng

2015-02-01

It has been a longstanding challenge to understand natural communities in real world networks. We proposed a community finding algorithm based on fitness of networks, two algorithms for prediction, accurate prediction and confirmation of keywords for papers in the citation network Arxiv HEP-TH (high energy physics theory), and the measures of internal centrality, external de-centrality, internal and external slopes to characterize the structures of communities. We implemented our algorithms on 2 citation and 5 cooperation graphs. Our experiments explored and validated a homophyly/kinship principle of real world networks. The homophyly/kinship principle includes: (1) homophyly is the natural selection in real world networks, similar to Darwin's kinship selection in nature, (2) real world networks consist of natural communities generated by the natural selection of homophyly, (3) most individuals in a natural community share a short list of common attributes, (4) natural communities have an internal centrality (or internal heterogeneity) that a natural community has a few nodes dominating most of the individuals in the community, (5) natural communities have an external de-centrality (or external homogeneity) that external links of a natural community homogeneously distributed in different communities, and (6) natural communities of a given network have typical structures determined by the internal slopes, and have typical patterns of outgoing links determined by external slopes, etc. Our homophyly/kinship principle perfectly matches Darwin's observation that animals from ants to people form social groups in which most individuals work for the common good, and that kinship could encourage altruistic behavior. Our homophyly/kinship principle is the network version of Darwinian theory, and builds a bridge between Darwinian evolution and network science.
Separate neural mechanisms underlie choices and strategic preferences in risky decision making.

PubMed

Venkatraman, Vinod; Payne, John W; Bettman, James R; Luce, Mary Frances; Huettel, Scott A

2009-05-28

Adaptive decision making in real-world contexts often relies on strategic simplifications of decision problems. Yet, the neural mechanisms that shape these strategies and their implementation remain largely unknown. Using an economic decision-making task, we dissociate brain regions that predict specific choices from those predicting an individual's preferred strategy. Choices that maximized gains or minimized losses were predicted by functional magnetic resonance imaging activation in ventromedial prefrontal cortex or anterior insula, respectively. However, choices that followed a simplifying strategy (i.e., attending to overall probability of winning) were associated with activation in parietal and lateral prefrontal cortices. Dorsomedial prefrontal cortex, through differential functional connectivity with parietal and insular cortex, predicted individual variability in strategic preferences. Finally, we demonstrate that robust decision strategies follow from neural sensitivity to rewards. We conclude that decision making reflects more than compensatory interaction of choice-related regions; in addition, specific brain systems potentiate choices depending on strategies, traits, and context.
Separate neural mechanisms underlie choices and strategic preferences in risky decision making

PubMed Central

Venkatraman, Vinod; Payne, John W.; Bettman, James R.; Luce, Mary Frances; Huettel, Scott A.

2011-01-01

Adaptive decision making in real-world contexts often relies on strategic simplifications of decision problems. Yet, the neural mechanisms that shape these strategies and their implementation remain largely unknown. Using a novel economic decision-making task, we dissociate brain regions that predict specific choices from those predicting an individual’s preferred strategy. Choices that maximized gains or minimized losses were predicted by fMRI activation in ventromedial prefrontal cortex or anterior insula, respectively. However, choices that followed a simplifying strategy (i.e., attending to overall probability of winning) were associated with activation in parietal and lateral prefrontal cortices. Dorsomedial prefrontal cortex, through differential functional connectivity with parietal and insular cortex, predicted individual variability in strategic preferences. Finally, we demonstrate that robust decision strategies follow from neural sensitivity to rewards. We conclude that decision making reflects more than compensatory interaction of choice-related regions; in addition, specific brain systems potentiate choices depending upon strategies, traits, and context. PMID:19477159
United Space Alliance LLC Parachute Refurbishment Facility Model

NASA Technical Reports Server (NTRS)

Esser, Valerie; Pessaro, Martha; Young, Angela

2007-01-01

The Parachute Refurbishment Facility Model was created to reflect the flow of hardware through the facility using anticipated start and delivery times from a project level IV schedule. Distributions for task times were built using historical build data for SFOC work and new data generated for CLV/ARES task times. The model currently processes 633 line items from 14 SFOC builds for flight readiness, 16 SFOC builds returning from flight for defoul, wash, and dry operations, 12 builds for CLV manufacturing operations, and 1 ARES 1X build. Modeling the planned workflow through the PRF is providing a reliable way to predict the capability of the facility as well as the manpower resource need. Creating a real world process allows for real world problems to be identified and potential workarounds to be implemented in a safe, simulated world before taking it to the next step, implementation in the real world.
Football league win prediction based on online and league table data

NASA Astrophysics Data System (ADS)

Par, Prateek; Gupt, Ankit Kumar; Singh, Samarth; Khare, Neelu; Bhattachrya, Sweta

2017-11-01

As we are proceeding towards an internet driven world, the impact of internet is increasing in our day to lives. This not only gives impact on the virtual world but also leave a mark in the real world. The social media sites contains huge amount of information, the only thing is to collect the relevant data and analyse the data to form a real world prediction and it can do far more than that. In this paper we study the relationship between the twitter data and the normal data analysis to predict the winning team in the NFL (National Football League).The prediction is based on the data collected on the on-going league which includes performance of each player and their previous statistics. Alongside with the data available online we are combining the twitter data which we extracted by the tweets pertaining to specific teams and games in the NFL season and use them alongside statistical game data to build predictive models for future or the outcome of the game i.e. which team will lose or win depending upon the statistical data available. Specifically the tweets within the 24 hours of match will be considered and the main focus of twitter data will be upon the last hours of tweets i.e. pre-match twitter data and post-match twitter data. We are experimenting on the data and using twitter data we are trying to increase the performance of the existing predictive models that uses only the game stats to predict the future.
Neural mechanisms tracking popularity in real-world social networks.

PubMed

Zerubavel, Noam; Bearman, Peter S; Weber, Jochen; Ochsner, Kevin N

2015-12-08

Differences in popularity are a key aspect of status in virtually all human groups and shape social interactions within them. Little is known, however, about how we track and neurally represent others' popularity. We addressed this question in two real-world social networks using sociometric methods to quantify popularity. Each group member (perceiver) viewed faces of every other group member (target) while whole-brain functional MRI data were collected. Independent functional localizer tasks were used to identify brain systems supporting affective valuation (ventromedial prefrontal cortex, ventral striatum, amygdala) and social cognition (dorsomedial prefrontal cortex, precuneus, temporoparietal junction), respectively. During the face-viewing task, activity in both types of neural systems tracked targets' sociometric popularity, even when controlling for potential confounds. The target popularity-social cognition system relationship was mediated by valuation system activity, suggesting that observing popular individuals elicits value signals that facilitate understanding their mental states. The target popularity-valuation system relationship was strongest for popular perceivers, suggesting enhanced sensitivity to differences among other group members' popularity. Popular group members also demonstrated greater interpersonal sensitivity by more accurately predicting how their own personalities were perceived by other individuals in the social network. These data offer insights into the mechanisms by which status guides social behavior.
Allocating effort and anticipating pleasure in schizophrenia: Relationship with real world functioning.

PubMed

Serper, M; Payne, E; Dill, C; Portillo, C; Taliercio, J

2017-10-01

Poor motivation to engage in goal-oriented behavior has been recognized as a hallmark feature of schizophrenia spectrum disorders (SZ). Low drive in SZ may be related to anticipating rewards as well as to poor working memory. However, few studies to date have examined beliefs about self-efficacy and satisfaction for future rewards (anticipatory pleasure). Additionally, few studies to date have examined how these deficits may impact SZ patients' real world functioning. The present study examined SZ patients' (n=57) anticipatory pleasure, working memory, self-efficacy and real world functioning in relation to their negative symptom severity. Results revealed that SZ patients' negative symptom severity was related to decisions in effort allocation and reward probability, working memory deficits, self-efficacy and anticipatory pleasure for future reward. Effort allocation deficits also predicted patients' daily functioning skills. SZ patients with high levels of negative symptoms are not merely effort averse, but have more difficulty effectively allocating effort and anticipating pleasure engaging in effortful activities. It may be the case that continuously failing to achieve reinforcement from engagement and participation may lead SZ patients to form certain negative beliefs about their abilities which contributes to amotivation and cognitive deficits. Lastly, our findings provide further support for a link between SZ patients functional daily living skills their effort allocation. Copyright © 2017 Elsevier Masson SAS. All rights reserved.
Forecasting seasonal outbreaks of influenza.

PubMed

Shaman, Jeffrey; Karspeck, Alicia

2012-12-11

Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003-2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza.
Forecasting seasonal outbreaks of influenza

PubMed Central

Shaman, Jeffrey; Karspeck, Alicia

2012-01-01

Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003–2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza. PMID:23184969
Using electronic medical records analysis to investigate the effectiveness of lifestyle programs in real-world primary care is challenging: a case study in diabetes mellitus.

PubMed

Linmans, Joris J; Viechtbauer, Wolfgang; Koppenaal, Tjarco; Spigt, Mark; Knottnerus, J André

2012-07-01

The increasing prevalence of diabetes suggests a gap between real world and controlled trial effectiveness of lifestyle interventions, but real-world investigations are rare. Electronic medical registration facilitates research on real-world effectiveness, although such investigations may require specific methodology and statistics. We investigated the effects of real-world primary care for patients with type 2 diabetes mellitus (T2DM). We used medical records of patients (n=2,549) with T2DM from 10 primary health care centers. A mixed-effects regression model for repeated measurements was used to evaluate the changes in weight and Hemoglobin A1c (HbA1c) over time. There was no statistically significant change in weight (+0.07 kg, P=0.832) and HbA1c (+0.03%, P=0.657) during the observation period of 972 days. Most patients maintained their physical activity level (70%), and 54 % had an insufficient activity level. The variability in the course of weight and HbA1c was because of differences between patients and not between health care providers. Despite effective lifestyle interventions in controlled trial settings, we found that real-world primary care is only able to stabilize weight and HbA1c in patients with T2DM over time. Medical registration can be used to monitor the actual effectiveness of interventions in primary care. Copyright © 2012 Elsevier Inc. All rights reserved.
Turning Virtual Reality into Reality: A Checklist to Ensure Virtual Reality Studies of Eating Behavior and Physical Activity Parallel the Real World

PubMed Central

Tal, Aner; Wansink, Brian

2011-01-01

Virtual reality (VR) provides a potentially powerful tool for researchers seeking to investigate eating and physical activity. Some unique conditions are necessary to ensure that the psychological processes that influence real eating behavior also influence behavior in VR environments. Accounting for these conditions is critical if VR-assisted research is to accurately reflect real-world situations. The current work discusses key considerations VR researchers must take into account to ensure similar psychological functioning in virtual and actual reality and does so by focusing on the process of spontaneous mental simulation. Spontaneous mental simulation is prevalent under real-world conditions but may be absent under VR conditions, potentially leading to differences in judgment and behavior between virtual and actual reality. For simulation to occur, the virtual environment must be perceived as being available for action. A useful chart is supplied as a reference to help researchers to investigate eating and physical activity more effectively. PMID:21527088
Turning virtual reality into reality: a checklist to ensure virtual reality studies of eating behavior and physical activity parallel the real world.

PubMed

Tal, Aner; Wansink, Brian

2011-03-01

Virtual reality (VR) provides a potentially powerful tool for researchers seeking to investigate eating and physical activity. Some unique conditions are necessary to ensure that the psychological processes that influence real eating behavior also influence behavior in VR environments. Accounting for these conditions is critical if VR-assisted research is to accurately reflect real-world situations. The current work discusses key considerations VR researchers must take into account to ensure similar psychological functioning in virtual and actual reality and does so by focusing on the process of spontaneous mental simulation. Spontaneous mental simulation is prevalent under real-world conditions but may be absent under VR conditions, potentially leading to differences in judgment and behavior between virtual and actual reality. For simulation to occur, the virtual environment must be perceived as being available for action. A useful chart is supplied as a reference to help researchers to investigate eating and physical activity more effectively. © 2011 Diabetes Technology Society.
Investigating the Effect of Advanced Automatic Transmissions of Fuel Consumption Using Vehicle Testing and Modeling (SAE 2016-01-1142)

EPA Science Inventory

EPA used the validated ALPHA model to predict the effectiveness improvement of real-world transmissions over a baseline four-speed transmission and to predict further improvements possible from future eight-speed transmissions.
Is “morphodynamic equilibrium” an oxymoron?

USGS Publications Warehouse

Zhou, Zeng; Coco, Giovanni; Townend, Ian; Olabarrieta, Maitane; van der Wegen, Mick; Gong, Zheng; D'Alpaos, Andrea; Gao, Shu; Jaffe, Bruce E.; Gelfenbaum, Guy R.; He, Qing; Wang, Yaping; Lanzoni, Stefano; Wang, Zhengbing; Winterwerp, Han; Zhang, Changkuan

2017-01-01

Morphodynamic equilibrium is a widely adopted yet elusive concept in the field of geomorphology of coasts, rivers and estuaries. Based on the Exner equation, an expression of mass conservation of sediment, we distinguish three types of equilibrium defined as static and dynamic, of which two different types exist. Other expressions such as statistical and quasi-equilibrium which do not strictly satisfy the Exner conditions are also acknowledged for their practical use. The choice of a temporal scale is imperative to analyse the type of equilibrium. We discuss the difference between morphodynamic equilibrium in the “real world” (nature) and the “virtual world” (model). Modelling studies rely on simplifications of the real world and lead to understanding of process interactions. A variety of factors affect the use of virtual-world predictions in the real world (e.g., variability in environmental drivers and variability in the setting) so that the concept of morphodynamic equilibrium should be mathematically unequivocal in the virtual world and interpreted over the appropriate spatial and temporal scale in the real world. We draw examples from estuarine settings which are subject to various governing factors which broadly include hydrodynamics, sedimentology and landscape setting. Following the traditional “tide-wave-river” ternary diagram, we summarize studies to date that explore the “virtual world”, discuss the type of equilibrium reached and how it relates to the real world.

Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand

PubMed Central

Lauer, Stephen A.; Sakrejda, Krzysztof; Iamsirithaworn, Sopon; Hinjoy, Soawapak; Suangtho, Paphanij; Suthachana, Suthanun; Clapham, Hannah E.; Salje, Henrik; Cummings, Derek A. T.; Lessler, Justin

2016-01-01

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making. PMID:27304062
Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand.

PubMed

Reich, Nicholas G; Lauer, Stephen A; Sakrejda, Krzysztof; Iamsirithaworn, Sopon; Hinjoy, Soawapak; Suangtho, Paphanij; Suthachana, Suthanun; Clapham, Hannah E; Salje, Henrik; Cummings, Derek A T; Lessler, Justin

2016-06-01

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making.
Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches

PubMed Central

Memarian, Negar; Torre, Jared B.; Haltom, Kate E.; Stanton, Annette L.

2017-01-01

Abstract Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. PMID:28992270
Real Time On-line Space Research Laboratory Environment Monitoring with Off-line Trend and Prediction Analysis

NASA Technical Reports Server (NTRS)

Jules, Kenol; Lin, Paul P.

2006-01-01

One of the responsibilities of the NASA Glenn Principal Investigator Microgravity Services is to support NASA sponsored investigators in the area of reduced-gravity acceleration data analysis, interpretation and the monitoring of the reduced-gravity environment on-board various carriers. With the International Space Station currently operational, a significant amount of acceleration data is being down-linked and processed on ground for both the space station onboard environment characterization (and verification) and scientific experiments. Therefore, to help principal investigator teams monitor the acceleration level on-board the International Space Station to avoid undesirable impact on their experiment, when possible, the NASA Glenn Principal Investigator Microgravity Services developed an artificial intelligence monitoring system, which detects in near real time any change in the environment susceptible to affect onboard experiments. The main objective of the monitoring system is to help research teams identify the vibratory disturbances that are active at any instant of time onboard the International Space Station that might impact the environment in which their experiment is being conducted. The monitoring system allows any space research scientist, at any location and at any time, to see the current acceleration level on-board the Space Station via the World Wide Web. From the NASA Glenn s Exploration Systems Division web site, research scientists can see in near real time the active disturbances, such as pumps, fans, compressor, crew exercise, re-boost, extra-vehicular activity, etc., and decide whether or not to continue operating or stopping (or making note of such activity for later correlation with science results) their experiments based on the g-level associated with that specific event. A dynamic graphical display accessible via the World Wide Web shows the status of all the vibratory disturbance activities with their degree of confidence as well as their g-level contribution to the environment. The system can detect both known and unknown vibratory disturbance activities. It can also perform trend analysis and prediction by analyzing past data over many Increments of the space station for selected disturbance activities. This feature can be used to monitor the health of onboard mechanical systems to detect and prevent potential system failure as well as for use by research scientists during their science results analysis. Examples of both real time on-line vibratory disturbance detection and off-line trend analysis are presented in this paper. Several soft computing techniques such as Kohonen s Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.
Fiduciary and Legal Considerations for Student-Managed Investment Funds

ERIC Educational Resources Information Center

Gradisher, Suzanne; Kahl, Douglas R.; Clinebell, John M.; Stevens, Jerry L.

2016-01-01

Student-managed investment funds are popular forms of experiential learning in business schools and finance departments. The investment management experience is a real world activity and the structure of the fund may also introduce real world fiduciary and legal responsibilities for students, faculty, and administrators. The authors review how the…
Virtual healthcare delivery: defined, modeled, and predictive barriers to implementation identified.

PubMed

Harrop, V M

2001-01-01

Provider organizations lack: 1. a definition of "virtual" healthcare delivery relative to the products, services, and processes offered by dot.coms, web-compact disk healthcare content providers, telemedicine, and telecommunications companies, and 2. a model for integrating real and virtual healthcare delivery. This paper defines virtual healthcare delivery as asynchronous, outsourced, and anonymous, then proposes a 2x2 Real-Virtual Healthcare Delivery model focused on real and virtual patients and real and virtual provider organizations. Using this model, provider organizations can systematically deconstruct healthcare delivery in the real world and reconstruct appropriate pieces in the virtual world. Observed barriers to virtual healthcare delivery are: resistance to telecommunication integrated delivery networks and outsourcing; confusion over virtual infrastructure requirements for telemedicine and full-service web portals, and the impact of integrated delivery networks and outsourcing on extant cultural norms and revenue generating practices. To remain competitive provider organizations must integrate real and virtual healthcare delivery.
Virtual healthcare delivery: defined, modeled, and predictive barriers to implementation identified.

PubMed Central

Harrop, V. M.

2001-01-01

Provider organizations lack: 1. a definition of "virtual" healthcare delivery relative to the products, services, and processes offered by dot.coms, web-compact disk healthcare content providers, telemedicine, and telecommunications companies, and 2. a model for integrating real and virtual healthcare delivery. This paper defines virtual healthcare delivery as asynchronous, outsourced, and anonymous, then proposes a 2x2 Real-Virtual Healthcare Delivery model focused on real and virtual patients and real and virtual provider organizations. Using this model, provider organizations can systematically deconstruct healthcare delivery in the real world and reconstruct appropriate pieces in the virtual world. Observed barriers to virtual healthcare delivery are: resistance to telecommunication integrated delivery networks and outsourcing; confusion over virtual infrastructure requirements for telemedicine and full-service web portals, and the impact of integrated delivery networks and outsourcing on extant cultural norms and revenue generating practices. To remain competitive provider organizations must integrate real and virtual healthcare delivery. PMID:11825189
A balance of activity in brain control and reward systems predicts self-regulatory outcomes

PubMed Central

Chen, Pin-Hao A.; Huckins, Jeremy F.; Hofmann, Wilhelm; Kelley, William M.; Heatherton, Todd F.

2017-01-01

Abstract Previous neuroimaging work has shown that increased reward-related activity following exposure to food cues is predictive of self-control failure. The balance model suggests that self-regulation failures result from an imbalance in reward and executive control mechanisms. However, an open question is whether the relative balance of activity in brain systems associated with executive control (vs reward) supports self-regulatory outcomes when people encounter tempting cues in daily life. Sixty-nine chronic dieters, a population known for frequent lapses in self-control, completed a food cue-reactivity task during an fMRI scanning session, followed by a weeklong sampling of daily eating behaviors via ecological momentary assessment. We related participants’ food cue activity in brain systems associated with executive control and reward to real-world eating patterns. Specifically, a balance score representing the amount of activity in brain regions associated with self-regulatory control, relative to automatic reward-related activity, predicted dieters’ control over their eating behavior during the following week. This balance measure may reflect individual self-control capacity and be useful for examining self-regulation success in other domains and populations. PMID:28158874
A balance of activity in brain control and reward systems predicts self-regulatory outcomes.

PubMed

Lopez, Richard B; Chen, Pin-Hao A; Huckins, Jeremy F; Hofmann, Wilhelm; Kelley, William M; Heatherton, Todd F

2017-05-01

Previous neuroimaging work has shown that increased reward-related activity following exposure to food cues is predictive of self-control failure. The balance model suggests that self-regulation failures result from an imbalance in reward and executive control mechanisms. However, an open question is whether the relative balance of activity in brain systems associated with executive control (vs reward) supports self-regulatory outcomes when people encounter tempting cues in daily life. Sixty-nine chronic dieters, a population known for frequent lapses in self-control, completed a food cue-reactivity task during an fMRI scanning session, followed by a weeklong sampling of daily eating behaviors via ecological momentary assessment. We related participants' food cue activity in brain systems associated with executive control and reward to real-world eating patterns. Specifically, a balance score representing the amount of activity in brain regions associated with self-regulatory control, relative to automatic reward-related activity, predicted dieters' control over their eating behavior during the following week. This balance measure may reflect individual self-control capacity and be useful for examining self-regulation success in other domains and populations. © The Author (2017). Published by Oxford University Press.
Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.
Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: A multicenter case crossover study

PubMed Central

Kanai, Masashi; Okamoto, Kazuya; Yamamoto, Yosuke; Yoshioka, Akira; Hiramoto, Shuji; Nozaki, Akira; Nishikawa, Yoshitaka; Yamaguchi, Daisuke; Tomono, Teruko; Nakatsui, Masahiko; Baba, Mika; Morita, Tatsuya; Matsumoto, Shigemi; Kuroda, Tomohiro; Okuno, Yasushi; Muto, Manabu

2017-01-01

Background We aimed to develop an adaptable prognosis prediction model that could be applied at any time point during the treatment course for patients with cancer receiving chemotherapy, by applying time-series real-world big data. Methods Between April 2004 and September 2014, 4,997 patients with cancer who had received systemic chemotherapy were registered in a prospective cohort database at the Kyoto University Hospital. Of these, 2,693 patients with a death record were eligible for inclusion and divided into training (n = 1,341) and test (n = 1,352) cohorts. In total, 3,471,521 laboratory data at 115,738 time points, representing 40 laboratory items [e.g., white blood cell counts and albumin (Alb) levels] that were monitored for 1 year before the death event were applied for constructing prognosis prediction models. All possible prediction models comprising three different items from 40 laboratory items (40C3 = 9,880) were generated in the training cohort, and the model selection was performed in the test cohort. The fitness of the selected models was externally validated in the validation cohort from three independent settings. Results A prognosis prediction model utilizing Alb, lactate dehydrogenase, and neutrophils was selected based on a strong ability to predict death events within 1–6 months and a set of six prediction models corresponding to 1,2, 3, 4, 5, and 6 months was developed. The area under the curve (AUC) ranged from 0.852 for the 1 month model to 0.713 for the 6 month model. External validation supported the performance of these models. Conclusion By applying time-series real-world big data, we successfully developed a set of six adaptable prognosis prediction models for patients with cancer receiving chemotherapy. PMID:28837592
Unified underpinning of human mobility in the real world and cyberspace

NASA Astrophysics Data System (ADS)

Zhao, Yi-Ming; Zeng, An; Yan, Xiao-Yong; Wang, Wen-Xu; Lai, Ying-Cheng

2016-05-01

Human movements in the real world and in cyberspace affect not only dynamical processes such as epidemic spreading and information diffusion but also social and economical activities such as urban planning and personalized recommendation in online shopping. Despite recent efforts in characterizing and modeling human behaviors in both the real and cyber worlds, the fundamental dynamics underlying human mobility have not been well understood. We develop a minimal, memory-based random walk model in limited space for reproducing, with a single parameter, the key statistical behaviors characterizing human movements in both cases. The model is validated using relatively big data from mobile phone and online commerce, suggesting memory-based random walk dynamics as the unified underpinning for human mobility, regardless of whether it occurs in the real world or in cyberspace.
Investigating the Effect of Advanced Automatic Transmissions ...

EPA Pesticide Factsheets

EPA used the validated ALPHA model to predict the effectiveness improvement of real-world transmissions over a baseline four-speed transmission and to predict further improvements possible from future eight-speed transmissions. In preparation for the midterm evaluation (MTE) of the 2017-2025 light-duty GHG emissions rule.
Tracking dynamic team activity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tambe, M.

1996-12-31

AI researchers are striving to build complex multi-agent worlds with intended applications ranging from the RoboCup robotic soccer tournaments, to interactive virtual theatre, to large-scale real-world battlefield simulations. Agent tracking - monitoring other agent`s actions and inferring their higher-level goals and intentions - is a central requirement in such worlds. While previous work has mostly focused on tracking individual agents, this paper goes beyond by focusing on agent teams. Team tracking poses the challenge of tracking a team`s joint goals and plans. Dynamic, real-time environments add to the challenge, as ambiguities have to be resolved in real-time. The central hypothesismore » underlying the present work is that an explicit team-oriented perspective enables effective team tracking. This hypothesis is instantiated using the model tracing technology employed in tracking individual agents. Thus, to track team activities, team models are put to service. Team models are a concrete application of the joint intentions framework and enable an agent to track team activities, regardless of the agent`s being a collaborative participant or a non-participant in the team. To facilitate real-time ambiguity resolution with team models: (i) aspects of tracking are cast as constraint satisfaction problems to exploit constraint propagation techniques; and (ii) a cost minimality criterion is applied to constrain tracking search. Empirical results from two separate tasks in real-world, dynamic environments one collaborative and one competitive - are provided.« less
A New Approach to Teaching Biomechanics Through Active, Adaptive, and Experiential Learning.

PubMed

Singh, Anita

2017-07-01

Demand of biomedical engineers continues to rise to meet the needs of healthcare industry. Current training of bioengineers follows the traditional and dominant model of theory-focused curricula. However, the unmet needs of the healthcare industry warrant newer skill sets in these engineers. Translational training strategies such as solving real world problems through active, adaptive, and experiential learning hold promise. In this paper, we report our findings of adding a real-world 4-week problem-based learning unit into a biomechanics capstone course for engineering students. Surveys assessed student perceptions of the activity and learning experience. While students, across three cohorts, felt challenged to solve a real-world problem identified during the simulation lab visit, they felt more confident in utilizing knowledge learned in the biomechanics course and self-directed research. Instructor evaluations indicated that the active and experiential learning approach fostered their technical knowledge and life-long learning skills while exposing them to the components of adaptive learning and innovation.
Popularity and Novelty Dynamics in Evolving Networks.

PubMed

Abbas, Khushnood; Shang, Mingsheng; Abbasi, Alireza; Luo, Xin; Xu, Jian Jun; Zhang, Yu-Xia

2018-04-20

Network science plays a big role in the representation of real-world phenomena such as user-item bipartite networks presented in e-commerce or social media platforms. It provides researchers with tools and techniques to solve complex real-world problems. Identifying and predicting future popularity and importance of items in e-commerce or social media platform is a challenging task. Some items gain popularity repeatedly over time while some become popular and novel only once. This work aims to identify the key-factors: popularity and novelty. To do so, we consider two types of novelty predictions: items appearing in the popular ranking list for the first time; and items which were not in the popular list in the past time window, but might have been popular before the recent past time window. In order to identify the popular items, a careful consideration of macro-level analysis is needed. In this work we propose a model, which exploits item level information over a span of time to rank the importance of the item. We considered ageing or decay effect along with the recent link-gain of the items. We test our proposed model on four various real-world datasets using four information retrieval based metrics.
Enhanced Positive Emotional Reactivity Undermines Empathy in Behavioral Variant Frontotemporal Dementia.

PubMed

Hua, Alice Y; Sible, Isabel J; Perry, David C; Rankin, Katherine P; Kramer, Joel H; Miller, Bruce L; Rosen, Howard J; Sturm, Virginia E

2018-01-01

Behavioral variant frontotemporal dementia (bvFTD) is a neurodegenerative disease characterized by profound changes in emotions and empathy. Although most patients with bvFTD become less sensitive to negative emotional cues, some patients become more sensitive to positive emotional stimuli. We investigated whether dysregulated positive emotions in bvFTD undermine empathy by making it difficult for patients to share (emotional empathy), recognize (cognitive empathy), and respond (real-world empathy) to emotions in others. Fifty-one participants (26 patients with bvFTD and 25 healthy controls) viewed photographs of neutral, positive, negative, and self-conscious emotional faces and then identified the emotions displayed in the photographs. We used facial electromyography to measure automatic, sub-visible activity in two facial muscles during the task: Zygomaticus major ( ZM ), which is active during positive emotional reactions (i.e., smiling), and Corrugator supercilii ( CS ), which is active during negative emotional reactions (i.e., frowning). Participants rated their baseline positive and negative emotional experience before the task, and informants rated participants' real-world empathic behavior on the Interpersonal Reactivity Index. The majority of participants also underwent structural magnetic resonance imaging. A mixed effects model found a significant diagnosis X trial interaction: patients with bvFTD showed greater ZM reactivity to neutral, negative (disgust and surprise), self-conscious (proud), and positive (happy) faces than healthy controls. There was no main effect of diagnosis or diagnosis X trial interaction on CS reactivity. Compared to healthy controls, patients with bvFTD had impaired emotion recognition. Multiple regression analyses revealed that greater ZM reactivity predicted worse negative emotion recognition and worse real-world empathy. At baseline, positive emotional experience was higher in bvFTD than healthy controls and also predicted worse negative emotion recognition. Voxel-based morphometry analyses found that smaller volume in the thalamus, midcingulate cortex, posterior insula, anterior temporal pole, amygdala, precentral gyrus, and inferior frontal gyrus-structures that support emotion generation, interoception, and emotion regulation-was associated with greater ZM reactivity in bvFTD. These findings suggest that dysregulated positive emotional reactivity may relate to reduced empathy in bvFTD by making patients less likely to tune their reactions to the social context and to share, recognize, and respond to others' feelings and needs.
Real-time Fatigue and Free-Living Physical Activity in Hematopoietic Stem Cell Transplantation Cancer Survivors and Healthy Controls: A Preliminary Examination of the Temporal, Dynamic Relationship.

PubMed

Hacker, Eileen Danaher; Kim, Inah; Park, Chang; Peters, Tara

Fatigue and physical inactivity, critical problems facing cancer survivors, impact overall health and functioning. Our group designed a novel methodology to evaluate the temporal, dynamic patterns in real-world settings. Using real-time technology, the temporal, dynamic relationship between real-time fatigue and free-living is described and compared in cancer survivors who were treated with hematopoietic stem cell transplantation (n = 25) and age- and gender-matched healthy controls (n = 25). Subjects wore wrist actigraphs on their nondominant hand to assess free-living physical activity, measured in 1-minute epochs, over 7 days. Subjects entered real-time fatigue assessments directly into the subjective event marker of the actigraph 5 times per day. Running averages of mean 1-minute activity counts 30, 60, and 120 minutes before and after each real-time fatigue score were correlated with real-time fatigue using generalized estimating equations, RESULTS:: A strong inverse relationship exists between real-time fatigue and subsequent free-living physical activity. This inverse relationship suggests that increasing real-time fatigue limits subsequent physical activity (B range= -0.002 to -0.004; P < .001). No significant differences in the dynamic patterns of real-time fatigue and free-living physical activity were found between groups. To our knowledge, this is the first study to document the temporal and potentially causal relationship between real-time fatigue and free-living physical activity in real-world setting. These findings suggest that fatigue drives the subsequent physical activity and the relationship may not be bidirectional. Understanding the temporal, dynamic relationship may have important health implications for developing interventions to address fatigue in cancer survivors.
Real-World Neuroimaging Technologies

DTIC Science & Technology

2013-05-10

system enables long-term wear of up to 10 consecutive hours of operation time. The system’s wireless technologies, light weight (200g), and dry sensor ...biomarkers, body sensor networks , brain computer interactionbrain, computer interfaces, data acquisition, electroencephalography monitoring, translational...brain activity in real-world scenarios. INDEX TERMS Behavioral science, biomarkers, body sensor networks , brain computer interfaces, brain computer
Developing Management Student Cultural Fluency for the Real World: A Situated Cultural Learning Approach

ERIC Educational Resources Information Center

Zhu, Yunxia; Okimoto, Tyler G.; Roan, Amanda; Xu, Henry

2017-01-01

Purpose: To connect students with the real world of management practice, the purpose of this paper is to extend and operationalize the situated cultural learning approach (SiCuLA) through five learning processes occurring within communities of practice. These include integration of cultural contexts, authentic activities, reflections,…

Building I.S. Professionals through a Real-World Client Project in a Database Application Development Course

ERIC Educational Resources Information Center

Podeschi, R. J.

2016-01-01

Information systems curricula are increasingly using active learning methodologies to help students learn "through" technology rather than just "about" technology. While one way to achieve this is through the assignment of semester-long projects, previous research suggests that real-world projects provide more meaningful…
21st-Century Urban Renewal: Mathematical Understanding of Real-World Graphical Data Using Geospatial Technologies

ERIC Educational Resources Information Center

DeBay, Dennis J.

2017-01-01

The introduction of real-world, meaningful tasks in mathematics classrooms promises to create opportunities for enhancing students' learning through active engagement with mathematical ideas; however, researchers have given little consideration to the contexts in which urban high-school students live. The case study of three students reported in…
Increasing Student Engagement and Enthusiasm: A Projectile Motion Crime Scene

NASA Astrophysics Data System (ADS)

Bonner, David

2010-05-01

Connecting physics concepts with real-world events allows students to establish a strong conceptual foundation. When such events are particularly interesting to students, it can greatly impact their engagement and enthusiasm in an activity. Activities that involve studying real-world events of high interest can provide students a long-lasting understanding and positive memorable experiences, both of which heighten the learning experiences of those students. One such activity, described in depth in this paper, utilizes a murder mystery and crime scene investigation as an application of basic projectile motion.
Are fixations in static natural scenes a useful predictor of attention in the real world?

PubMed

Foulsham, Tom; Kingstone, Alan

2017-06-01

Research investigating scene perception normally involves laboratory experiments using static images. Much has been learned about how observers look at pictures of the real world and the attentional mechanisms underlying this behaviour. However, the use of static, isolated pictures as a proxy for studying everyday attention in real environments has led to the criticism that such experiments are artificial. We report a new study that tests the extent to which the real world can be reduced to simpler laboratory stimuli. We recorded the gaze of participants walking on a university campus with a mobile eye tracker, and then showed static frames from this walk to new participants, in either a random or sequential order. The aim was to compare the gaze of participants walking in the real environment with fixations on pictures of the same scene. The data show that picture order affects interobserver fixation consistency and changes looking patterns. Critically, while fixations on the static images overlapped significantly with the actual real-world eye movements, they did so no more than a model that assumed a general bias to the centre. Remarkably, a model that simply takes into account where the eyes are normally positioned in the head-independent of what is actually in the scene-does far better than any other model. These data reveal that viewing patterns to static scenes are a relatively poor proxy for predicting real world eye movement behaviour, while raising intriguing possibilities for how to best measure attention in everyday life. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Vigilance in the Laboratory Predicts Avoidance in the Real World: A Dimensional Analysis of Neural, Behavioral, and Ecological Momentary Data in Anxious Youth

PubMed Central

Silk, Jennifer S.; Ladouceur, Cecile D.; Ryan, Neal D.; Dahl, Ronald E.; Forbes, Erika E.; Siegle, Greg J.

2016-01-01

Vigilance and avoidance of threat are observed in anxious adults during laboratory tasks, and are posited to have real-world clinical relevance, but data are mixed in anxious youth. We propose that vigilance-avoidance patterns will become evident in anxious youth through a focus on individual differences and real-world strategic avoidance. Decreased functional connectivity between the amygdala and prefrontal cortex (PFC) could play a mechanistic role in this link. 78 clinically anxious youth completed a dot-probe task to assess vigilance to threat while undergoing fMRI. Real-world avoidance was assessed using Ecological Momentary Assessment (EMA) of self-reported suppression and distraction during negative life events. Vigilance towards threat was positively associated with EMA distraction and suppression. Functional connectivity between a right amygdala seed region and dorsomedial and right dorsolateral PFC regions was inversely related to EMA distraction. Dorsolateral PFC-amygdalar connectivity statistically mediated the relationship between attentional vigilance and real-world distraction. Findings suggest anxious youth showing attentional vigilance toward threat are more likely to use suppression and distraction to regulate negative emotions. Reduced PFC control over limbic reactivity is a possible neural substrate of this pattern. These findings lend ecological validity to laboratory vigilance assessments and suggest PFC-amygdalar connectivity is a neural mechanism bridging laboratory and naturalistic contexts. PMID:27010577
Patent Analysis for Supporting Merger and Acquisition (M&A) Prediction: A Data Mining Approach

NASA Astrophysics Data System (ADS)

Wei, Chih-Ping; Jiang, Yu-Syun; Yang, Chin-Sheng

M&A plays an increasingly important role in the contemporary business environment. Companies usually conduct M&A to pursue complementarity from other companies for preserving and/or extending their competitive advantages. For the given bidder company, a critical first step to the success of M&A activities is the appropriate selection of target companies. However, existing studies on M&A prediction incur several limitations, such as the exclusion of technological variables in M&A prediction models and the omission of the profile of the respective bidder company and its compatibility with candidate target companies. In response to these limitations, we propose an M&A prediction technique which not only encompasses technological variables derived from patent analysis as prediction indictors but also takes into account the profiles of both bidder and candidate target companies when building an M&A prediction model. We collect a set of real-world M&A cases to evaluate the proposed technique. The evaluation results are encouraging and will serve as a basis for future studies.
Is ``the Theory of Everything'' Merely the Ultimate Ensemble Theory?

NASA Astrophysics Data System (ADS)

Tegmark, Max

1998-11-01

We discuss some physical consequences of what might be called "the ultimate ensemble theory,", where not only worlds corresponding to say different sets of initial data or different physical constants are considered equally real, but also worlds ruled by altogether different equations. The only postulate in this theory is that all structures that exist mathematically exist also physically, by which we mean that in those complex enough to contain self-aware substructures (SASs), these SASs will subjectively perceive themselves as existing in a physically "real" world. We find that it is far from clear that this simple theory, which has no free parameters whatsoever, is observationally ruled out. The predictions of the theory take the form of probability distributions for the outcome of experiments, which makes it testable. In addition, it may be possible to rule it out by comparing its a priori predictions for the observable attributes of nature (the particle masses, the dimensionality of spacetime, etc.) with what is observed.
The way to uncover community structure with core and diversity

NASA Astrophysics Data System (ADS)

Chang, Y. F.; Han, S. K.; Wang, X. D.

2018-07-01

Communities are ubiquitous in nature and society. Individuals that share common properties often self-organize to form communities. Avoiding the shortages of computation complexity, pre-given information and unstable results in different run, in this paper, we propose a simple and efficient method to deepen our understanding of the emergence and diversity of communities in complex systems. By introducing the rational random selection, our method reveals the hidden deterministic and normal diverse community states of community structure. To demonstrate this method, we test it with real-world systems. The results show that our method could not only detect community structure with high sensitivity and reliability, but also provide instructional information about the hidden deterministic community world and the real normal diverse community world by giving out the core-community, the real-community, the tide and the diversity. Thizs is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in complex systems.
Kinetics in the real world: linking molecules, processes, and systems.

PubMed

Kohse-Höinghaus, Katharina; Troe, Jürgen; Grabow, Jens-Uwe; Olzmann, Matthias; Friedrichs, Gernot; Hungenberg, Klaus-Dieter

2018-04-25

Unravelling elementary steps, reaction pathways, and kinetic mechanisms is key to understanding the behaviour of many real-world chemical systems that span from the troposphere or even interstellar media to engines and process reactors. Recent work in chemical kinetics provides detailed information on the reactive changes occurring in chemical systems, often on the atomic or molecular scale. The optimisation of practical processes, for instance in combustion, catalysis, battery technology, polymerisation, and nanoparticle production, can profit from a sound knowledge of the underlying fundamental chemical kinetics. Reaction mechanisms can combine information gained from theory and experiments to enable the predictive simulation and optimisation of the crucial process variables and influences on the system's behaviour that may be exploited for both monitoring and control. Chemical kinetics, as one of the pillars of Physical Chemistry, thus contributes importantly to understanding and describing natural environments and technical processes and is becoming increasingly relevant for interactions in and with the real world.
The ‘unskilled and unaware’ effect is linear in a real-world setting

PubMed Central

Sawdon, Marina; Finn, Gabrielle

2014-01-01

Self-assessment ability in medical students and practising physicians is generally poor, yet essential for academic progress and professional development. The aim of this study was to determine undergraduate medical students' ability to self-assess their exam performance accurately in a real-world, high-stakes exam setting, something not previously investigated. Year 1 and Year 2 medical students (n = 74) participated in a self-assessment exercise. Students predicted their exam grade (%) on the anatomy practical exam. This exercise was completed online immediately after the exam. Students' predicted exam grades were correlated with their actual attained exam grades using a Pearson's correlation. Demographic data were analysed using an independent t-test. A negative correlation was found between students' overall predicted and attained exam grades (P < 0.0001). There was a significant difference between the students' predicted grades and actual grades in the bottom, 3rd and top (P < 0.0001), but not 2nd quartiles of participants. There was no relationship between the students' entry status into medical school and self-assessment ability (Year 1: P = 0.112; Year 2: P = 0.236) or between males and females (Year 1: P = 0.174). However, a relationship was determined for these variables in Year 2 (P = 0.022). The number of hours of additional self-directed learning undertaken did not influence students' self-assessment in both years. Our results demonstrate the ‘unskilled and unaware’ phenomenon in a real-world, high-stakes and practice-related setting. Students in all quartiles were unable to self-assess their exam performance, except for a group of mid-range students in the 2nd quartile. Poor performers were shown to overestimate their ability and, conversely, high achievers to underestimate their performance. We present evidence of a strong, significant linear relationship between medical students' ability to self-assess their performance in an anatomy practical exam, and their actual performance; in a real world setting. Despite the limited ability to self-assess reported in the literature, our results may inform approaches to revalidation, which currently frequently rely on an ability to self-assess. PMID:23781887
Assessing spoken word recognition in children who are deaf or hard of hearing: a translational approach.

PubMed

Kirk, Karen Iler; Prusick, Lindsay; French, Brian; Gotch, Chad; Eisenberg, Laurie S; Young, Nancy

2012-06-01

Under natural conditions, listeners use both auditory and visual speech cues to extract meaning from speech signals containing many sources of variability. However, traditional clinical tests of spoken word recognition routinely employ isolated words or sentences produced by a single talker in an auditory-only presentation format. The more central cognitive processes used during multimodal integration, perceptual normalization, and lexical discrimination that may contribute to individual variation in spoken word recognition performance are not assessed in conventional tests of this kind. In this article, we review our past and current research activities aimed at developing a series of new assessment tools designed to evaluate spoken word recognition in children who are deaf or hard of hearing. These measures are theoretically motivated by a current model of spoken word recognition and also incorporate "real-world" stimulus variability in the form of multiple talkers and presentation formats. The goal of this research is to enhance our ability to estimate real-world listening skills and to predict benefit from sensory aid use in children with varying degrees of hearing loss. American Academy of Audiology.
Evidence-based occupational hearing screening II: validation of a screening methodology using measures of functional hearing ability.

PubMed

Soli, Sigfrid D; Amano-Kusumoto, Akiko; Clavier, Odile; Wilbur, Jed; Casto, Kristen; Freed, Daniel; Laroche, Chantal; Vaillancourt, Véronique; Giguère, Christian; Dreschler, Wouter A; Rhebergen, Koenraad S

2018-05-01

Validate use of the Extended Speech Intelligibility Index (ESII) for prediction of speech intelligibility in non-stationary real-world noise environments. Define a means of using these predictions for objective occupational hearing screening for hearing-critical public safety and law enforcement jobs. Analyses of predicted and measured speech intelligibility in recordings of real-world noise environments were performed in two studies using speech recognition thresholds (SRTs) and intelligibility measures. ESII analyses of the recordings were used to predict intelligibility. Noise recordings were made in prison environments and at US Army facilities for training ground and airborne forces. Speech materials included full bandwidth sentences and bandpass filtered sentences that simulated radio transmissions. A total of 22 adults with normal hearing (NH) and 15 with mild-moderate hearing impairment (HI) participated in the two studies. Average intelligibility predictions for individual NH and HI subjects were accurate in both studies (r 2 ≥ 0.94). Pooled predictions were slightly less accurate (0.78 ≤ r 2 ≤ 0.92). An individual's SRT and audiogram can accurately predict the likelihood of effective speech communication in noise environments with known ESII characteristics, where essential hearing-critical tasks are performed. These predictions provide an objective means of occupational hearing screening.
Kids Are Consumers, Too! Real-World Reading and Language Arts.

ERIC Educational Resources Information Center

Fair, Jan; Melvin, Mary; Bantz, Carol; Vause, Kate

Designed to help youngsters with real-world learning, and with being a smart consumer, this book focuses on having students participate in decisions facing consumers every day. The book contends that this is the best way to help students think critically and solve problems. Activities in the book require students to make consumer decisions related…
Project Real World: Economic Living Skills for High School Students. Module I, The Canadian Marketplace and You.

ERIC Educational Resources Information Center

Federal/Provincial Consumer Education and Plain Language Task Force (Canada).

Project Real World, a self-contained, activity-based Canadian consumer science program, provides students with systematic instruction in economic living skills. It gives students in grades 10-12 an orientation to the economic realities and opportunities in society. The program helps students function effectively within the rapidly changing…
Project Real World: Economic Living Skills for High School Students. Module V, Citizen Participation in Canada's Market-Based Society.

ERIC Educational Resources Information Center

Federal/Provincial Consumer Education and Plain Language Task Force (Canada).

Project Real World, a self-contained, activity-based Canadian consumer science program, provides students with systematic instruction in economic living skills. It gives students in grades 10-12 an orientation to the economic realities and opportunities in society. The program helps students understand the marketplace; manage resources; apply…
Project Real World: Economic Living Skills for High School Students. Module II, Your Economic Decisions and You.

ERIC Educational Resources Information Center

Federal/Provincial Consumer Education and Plain Language Task Force (Canada).

Project Real World, a self-contained, activity-based Canadian consumer science program, provides students with systematic instruction in economic living skills. It gives students in grades 10-12 an orientation to the economic realities and opportunities in society. The program helps students function effectively within the rapidly changing…
Visualisation of upper limb activity using spirals: A new approach to the assessment of daily prosthesis usage.

PubMed

Chadwell, Alix; Kenney, Laurence; Granat, Malcolm; Thies, Sibylle; Head, John S; Galpin, Adam

2018-02-01

Current outcome measures used in upper limb myoelectric prosthesis studies include clinical tests of function and self-report questionnaires on real-world prosthesis use. Research in other cohorts has questioned both the validity of self-report as an activity assessment tool and the relationship between clinical functionality and real-world upper limb activity. Previously, 1 we reported the first results of monitoring upper limb prosthesis use. However, the data visualisation technique used was limited in scope. Methodology development. To introduce two new methods for the analysis and display of upper limb activity monitoring data and to demonstrate the potential value of the approach with example real-world data. Upper limb activity monitors, worn on each wrist, recorded data on two anatomically intact participants and two prosthesis users over 1 week. Participants also filled in a diary to record upper limb activity. Data visualisation was carried out using histograms, and Archimedean spirals to illustrate temporal patterns of upper limb activity. Anatomically intact participants' activity was largely bilateral in nature, interspersed with frequent bursts of unilateral activity of each arm. At times when the prosthesis was worn prosthesis users showed very little unilateral use of the prosthesis (≈20-40 min/week compared to ≈350 min/week unilateral activity on each arm for anatomically intact participants), with consistent bias towards the intact arm throughout. The Archimedean spiral plots illustrated participant-specific patterns of non-use in prosthesis users. The data visualisation techniques allow detailed and objective assessment of temporal patterns in the upper limb activity of prosthesis users. Clinical relevance Activity monitoring offers an objective method for the assessment of upper limb prosthesis users' (PUs) activity outside of the clinic. By plotting data using Archimedean spirals, it is possible to visualise, in detail, the temporal patterns of upper limb activity. Further work is needed to explore the relationship between traditional functional outcome measures and real-world prosthesis activity.
Neural mechanisms tracking popularity in real-world social networks

PubMed Central

Zerubavel, Noam; Bearman, Peter S.; Weber, Jochen; Ochsner, Kevin N.

2015-01-01

Differences in popularity are a key aspect of status in virtually all human groups and shape social interactions within them. Little is known, however, about how we track and neurally represent others’ popularity. We addressed this question in two real-world social networks using sociometric methods to quantify popularity. Each group member (perceiver) viewed faces of every other group member (target) while whole-brain functional MRI data were collected. Independent functional localizer tasks were used to identify brain systems supporting affective valuation (ventromedial prefrontal cortex, ventral striatum, amygdala) and social cognition (dorsomedial prefrontal cortex, precuneus, temporoparietal junction), respectively. During the face-viewing task, activity in both types of neural systems tracked targets’ sociometric popularity, even when controlling for potential confounds. The target popularity–social cognition system relationship was mediated by valuation system activity, suggesting that observing popular individuals elicits value signals that facilitate understanding their mental states. The target popularity–valuation system relationship was strongest for popular perceivers, suggesting enhanced sensitivity to differences among other group members’ popularity. Popular group members also demonstrated greater interpersonal sensitivity by more accurately predicting how their own personalities were perceived by other individuals in the social network. These data offer insights into the mechanisms by which status guides social behavior. PMID:26598684
Parallel-distributed mobile robot simulator

NASA Astrophysics Data System (ADS)

Okada, Hiroyuki; Sekiguchi, Minoru; Watanabe, Nobuo

1996-06-01

The aim of this project is to achieve an autonomous learning and growth function based on active interaction with the real world. It should also be able to autonomically acquire knowledge about the context in which jobs take place, and how the jobs are executed. This article describes a parallel distributed movable robot system simulator with an autonomous learning and growth function. The autonomous learning and growth function which we are proposing is characterized by its ability to learn and grow through interaction with the real world. When the movable robot interacts with the real world, the system compares the virtual environment simulation with the interaction result in the real world. The system then improves the virtual environment to match the real-world result more closely. This the system learns and grows. It is very important that such a simulation is time- realistic. The parallel distributed movable robot simulator was developed to simulate the space of a movable robot system with an autonomous learning and growth function. The simulator constructs a virtual space faithful to the real world and also integrates the interfaces between the user, the actual movable robot and the virtual movable robot. Using an ultrafast CG (computer graphics) system (FUJITSU AG series), time-realistic 3D CG is displayed.
Real-time Kp predictions from ACE real time solar wind

NASA Astrophysics Data System (ADS)

Detman, Thomas; Joselyn, Joann

1999-06-01

The Advanced Composition Explorer (ACE) spacecraft provides nearly continuous monitoring of solar wind plasma, magnetic fields, and energetic particles from the Sun-Earth L1 Lagrange point upstream of Earth in the solar wind. The Space Environment Center (SEC) in Boulder receives ACE telemetry from a group of international network of tracking stations. One-minute, and 1-hour averages of solar wind speed, density, temperature, and magnetic field components are posted on SEC's World Wide Web page within 3 to 5 minutes after they are measured. The ACE Real Time Solar Wind (RTSW) can be used to provide real-time warnings and short term forecasts of geomagnetic storms based on the (traditional) Kp index. Here, we use historical data to evaluate the performance of the first real-time Kp prediction algorithm to become operational.

A Dynamic Time Warping Approach to Real-Time Activity Recognition for Food Preparation

NASA Astrophysics Data System (ADS)

Pham, Cuong; Plötz, Thomas; Olivier, Patrick

We present a dynamic time warping based activity recognition system for the analysis of low-level food preparation activities. Accelerometers embedded into kitchen utensils provide continuous sensor data streams while people are using them for cooking. The recognition framework analyzes frames of contiguous sensor readings in real-time with low latency. It thereby adapts to the idiosyncrasies of utensil use by automatically maintaining a template database. We demonstrate the effectiveness of the classification approach by a number of real-world practical experiments on a publically available dataset. The adaptive system shows superior performance compared to a static recognizer. Furthermore, we demonstrate the generalization capabilities of the system by gradually reducing the amount of training samples. The system achieves excellent classification results even if only a small number of training samples is available, which is especially relevant for real-world scenarios.
Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches.

PubMed

Memarian, Negar; Torre, Jared B; Haltom, Kate E; Stanton, Annette L; Lieberman, Matthew D

2017-09-01

Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. © The Author (2017). Published by Oxford University Press.
Similarity-based Regularized Latent Feature Model for Link Prediction in Bipartite Networks.

PubMed

Wang, Wenjun; Chen, Xue; Jiao, Pengfei; Jin, Di

2017-12-05

Link prediction is an attractive research topic in the field of data mining and has significant applications in improving performance of recommendation system and exploring evolving mechanisms of the complex networks. A variety of complex systems in real world should be abstractly represented as bipartite networks, in which there are two types of nodes and no links connect nodes of the same type. In this paper, we propose a framework for link prediction in bipartite networks by combining the similarity based structure and the latent feature model from a new perspective. The framework is called Similarity Regularized Nonnegative Matrix Factorization (SRNMF), which explicitly takes the local characteristics into consideration and encodes the geometrical information of the networks by constructing a similarity based matrix. We also develop an iterative scheme to solve the objective function based on gradient descent. Extensive experiments on a variety of real world bipartite networks show that the proposed framework of link prediction has a more competitive, preferable and stable performance in comparison with the state-of-art methods.
Evidence-Based Occupational Hearing Screening I: Modeling the Effects of Real-World Noise Environments on the Likelihood of Effective Speech Communication.

PubMed

Soli, Sigfrid D; Giguère, Christian; Laroche, Chantal; Vaillancourt, Véronique; Dreschler, Wouter A; Rhebergen, Koenraad S; Harkins, Kevin; Ruckstuhl, Mark; Ramulu, Pradeep; Meyers, Lawrence S

The objectives of this study were to (1) identify essential hearing-critical job tasks for public safety and law enforcement personnel; (2) determine the locations and real-world noise environments where these tasks are performed; (3) characterize each noise environment in terms of its impact on the likelihood of effective speech communication, considering the effects of different levels of vocal effort, communication distances, and repetition; and (4) use this characterization to define an objective normative reference for evaluating the ability of individuals to perform essential hearing-critical job tasks in noisy real-world environments. Data from five occupational hearing studies performed over a 17-year period for various public safety agencies were analyzed. In each study, job task analyses by job content experts identified essential hearing-critical tasks and the real-world noise environments where these tasks are performed. These environments were visited, and calibrated recordings of each noise environment were made. The extended speech intelligibility index (ESII) was calculated for each 4-sec interval in each recording. These data, together with the estimated ESII value required for effective speech communication by individuals with normal hearing, allowed the likelihood of effective speech communication in each noise environment for different levels of vocal effort and communication distances to be determined. These likelihoods provide an objective norm-referenced and standardized means of characterizing the predicted impact of real-world noise on the ability to perform essential hearing-critical tasks. A total of 16 noise environments for law enforcement personnel and eight noise environments for corrections personnel were analyzed. Effective speech communication was essential to hearing-critical tasks performed in these environments. Average noise levels, ranged from approximately 70 to 87 dBA in law enforcement environments and 64 to 80 dBA in corrections environments. The likelihood of effective speech communication at communication distances of 0.5 and 1 m was often less than 0.50 for normal vocal effort. Likelihood values often increased to 0.80 or more when raised or loud vocal effort was used. Effective speech communication at and beyond 5 m was often unlikely, regardless of vocal effort. ESII modeling of nonstationary real-world noise environments may prove an objective means of characterizing their impact on the likelihood of effective speech communication. The normative reference provided by these measures predicts the extent to which hearing impairments that increase the ESII value required for effective speech communication also decrease the likelihood of effective speech communication. These predictions may provide an objective evidence-based link between the essential hearing-critical job task requirements of public safety and law enforcement personnel and ESII-based hearing assessment of individuals who seek to perform these jobs.
Uncovering Camouflage: Amygdala Activation Predicts Long-Term Memory of Induced Perceptual Insight

PubMed Central

Ludmer, Rachel; Dudai, Yadin; Rubin, Nava

2012-01-01

What brain mechanisms underlie learning of new knowledge from single events? We studied encoding in long-term memory of a unique type of one-shot experience, induced perceptual insight. While undergoing an fMRI brain scan, participants viewed degraded images of real-world pictures where the underlying objects were hard to recognize (‘camouflage’), followed by brief exposures to the original images (‘solution’), which led to induced insight (“Aha!”). A week later, participants’ memory was tested; a solution image was classified as ‘remembered’ if detailed perceptual knowledge was elicited from the camouflage image alone. During encoding, subsequently remembered images enjoyed higher activity in mid-level visual cortex and medial frontal cortex, but most pronouncedly in the amygdala, whose activity could be used to predict which solutions will remain in long-term memory. Our findings extend the known roles of amygdala in memory to include promoting of long-term memory of the sudden reorganization of internal representations. PMID:21382558
Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach.

PubMed

Bourke, Alan K; Klenk, Jochen; Schwickert, Lars; Aminian, Kamiar; Ihlen, Espen A F; Mellone, Sabato; Helbostad, Jorunn L; Chiari, Lorenzo; Becker, Clemens

2016-08-01

Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of distinguishing falls from normal activities. However less than 7% of fall-detection algorithm studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events and to develop fall detection algorithms to combat the problems associated with falls. We have extracted 12 different kinematic, temporal and kinetic related features from a data-set of 89 real-world falls and 368 activities of daily living. Using the extracted features we applied machine learning techniques and produced a selection of algorithms based on different feature combinations. The best algorithm employs 10 different features and produced a sensitivity of 0.88 and a specificity of 0.87 in classifying falls correctly. This algorithm can be used distinguish real-world falls from normal activities of daily living in a sensor consisting of a tri-axial accelerometer and tri-axial gyroscope located at L5.
The social perception of emotional abilities: expanding what we know about observer ratings of emotional intelligence.

PubMed

Elfenbein, Hillary Anger; Barsade, Sigal G; Eisenkraft, Noah

2015-02-01

We examine the social perception of emotional intelligence (EI) through the use of observer ratings. Individuals frequently judge others' emotional abilities in real-world settings, yet we know little about the properties of such ratings. This article examines the social perception of EI and expands the evidence to evaluate its reliability and cross-judge agreement, as well as its convergent, divergent, and predictive validity. Three studies use real-world colleagues as observers and data from 2,521 participants. Results indicate significant consensus across observers about targets' EI, moderate but significant self-observer agreement, and modest but relatively consistent discriminant validity across the components of EI. Observer ratings significantly predicted interdependent task performance, even after controlling for numerous factors. Notably, predictive validity was greater for observer-rated than for self-rated or ability-tested EI. We discuss the minimal associations of observer ratings with ability-tested EI, study limitations, future directions, and practical implications. PsycINFO Database Record (c) 2015 APA, all rights reserved.
Perceptual quality prediction on authentically distorted images using a bag of features approach

PubMed Central

Ghadiyaram, Deepti; Bovik, Alan C.

2017-01-01

Current top-performing blind perceptual image quality prediction models are generally trained on legacy databases of human quality opinion scores on synthetically distorted images. Therefore, they learn image features that effectively predict human visual quality judgments of inauthentic and usually isolated (single) distortions. However, real-world images usually contain complex composite mixtures of multiple distortions. We study the perceptually relevant natural scene statistics of such authentically distorted images in different color spaces and transform domains. We propose a “bag of feature maps” approach that avoids assumptions about the type of distortion(s) contained in an image and instead focuses on capturing consistencies—or departures therefrom—of the statistics of real-world images. Using a large database of authentically distorted images, human opinions of them, and bags of features computed on them, we train a regressor to conduct image quality prediction. We demonstrate the competence of the features toward improving automatic perceptual quality prediction by testing a learned algorithm using them on a benchmark legacy database as well as on a newly introduced distortion-realistic resource called the LIVE In the Wild Image Quality Challenge Database. We extensively evaluate the perceptual quality prediction model and algorithm and show that it is able to achieve good-quality prediction power that is better than other leading models. PMID:28129417
Exploring the Role of Future Perspective in Predicting Turkish University Students' Beliefs about Global Climate Change

ERIC Educational Resources Information Center

Ates, Deniz; Teksöz, Gaye; Ertepinar, Hamide

2017-01-01

Recent studies indicate that limited understanding about causes and its potential impacts of climate change and fault beliefs by people across different countries of the world including Turkey is a real challenge. Acceptance of climate change as a real threat, believing its existence, and knowing causes and consequences are very significant for…
Using simplifications of reality in the real world: Robust benefits of models for decision making

NASA Astrophysics Data System (ADS)

Hunt, R. J.

2008-12-01

Models are by definition simplifications of reality; the degree and nature of simplification, however, is debated. One view is "the world is 3D, heterogeneous, and transient, thus good models are too" - the more a model directly simulates the complexity of the real world the better it is considered to be. An alternative view is to only use simple models up front because real-world complexity can never be truly known. A third view is construct and calibrate as many models as predictions. A fourth is to build highly parameterized models and either look at an ensemble of results, or use mathematical regularization to identify an optimal most reasonable parameter set and fit. Although each view may have utility for a given decision-making process, there are common threads that perhaps run through all views. First, the model-construction process itself can help the decision-making process because it raises the discussion of opposing parties from one of contrasting professional opinions to discussion of reasonable types and ranges of model inputs and processes. Secondly, no matter what view is used to guide the model building, model predictions for the future might be expected to perform poorly in the future due to unanticipated future changes and stressors to the underlying system simulated. Although this does not reduce the obligation of the modeler to build representative tools for the system, it should serve to temper expectations of model performance. Finally, perhaps the most under-appreciated utility of models is for calculating the reduction in prediction uncertainty resulting from different data collection strategies - an attractive feature separate from the calculation and minimization of absolute prediction uncertainty itself. This type of model output facilitates focusing on efficient use of current and future monitoring resources - something valued by many decision-makers regardless of background, system managed, and societal context.
"Why Isn't There a Cure?" Emerging Empathy and Prosocial Behaviors among Middle Childhood Children Responding to Real-World Issue Lessons

ERIC Educational Resources Information Center

Bang, Hyeyoung

2013-01-01

The purpose of this study was to explore empathy and prosocial behaviors within real-world issues among Korean middle-childhood children living in Australia. Using a qualitative approach, seven students were engaged in six sessions of group or individual activities including five sessions of responding to video vignettes which demonstrated…
Reality Imagined: The Choice to Use a Real-World Case in a Simulation

ERIC Educational Resources Information Center

Langfield, Danielle

2016-01-01

The use of a real-world case in a classroom simulation--in contrast to invented or disguised cases--is not widely recognized as a "combination" of two common active-learning strategies in political science: teaching with a case study and conducting a simulation. I argue that using such a simulation therefore can provide the benefits of…
Project Real World: Economic Living Skills for High School Students. Module III, Resource Management Skills--What Money Can't Buy.

ERIC Educational Resources Information Center

Federal/Provincial Consumer Education and Plain Language Task Force (Canada).

Project Real World, a self-contained, activity-based Canadian consumer science program, provides students with systematic instruction in economic living skills. It gives students in grades 10-12 an orientation to the economic realities and opportunities in society. The program helps students function effectively within the rapidly changing…
Why Can't I Play Here? The Classroom: A World in Miniature. Instructional Activities Series.

ERIC Educational Resources Information Center

Witthuhn, Burton O.

Third in the elementary set of teacher-developed instructional activities for teaching geography, this activity investigates spatial allocation through discussion and observation of classroom arrangements. Classroom space allocated for the teacher's desk, aisles, study area, and trash cans illustrates real-world locational concepts of geography…
Echoes of the spoken past: how auditory cortex hears context during speech perception

PubMed Central

Skipper, Jeremy I.

2014-01-01

What do we hear when someone speaks and what does auditory cortex (AC) do with that sound? Given how meaningful speech is, it might be hypothesized that AC is most active when other people talk so that their productions get decoded. Here, neuroimaging meta-analyses show the opposite: AC is least active and sometimes deactivated when participants listened to meaningful speech compared to less meaningful sounds. Results are explained by an active hypothesis-and-test mechanism where speech production (SP) regions are neurally re-used to predict auditory objects associated with available context. By this model, more AC activity for less meaningful sounds occurs because predictions are less successful from context, requiring further hypotheses be tested. This also explains the large overlap of AC co-activity for less meaningful sounds with meta-analyses of SP. An experiment showed a similar pattern of results for non-verbal context. Specifically, words produced less activity in AC and SP regions when preceded by co-speech gestures that visually described those words compared to those words without gestures. Results collectively suggest that what we ‘hear’ during real-world speech perception may come more from the brain than our ears and that the function of AC is to confirm or deny internal predictions about the identity of sounds. PMID:25092665
Bridging STEM in a Real World Problem

ERIC Educational Resources Information Center

English, Lyn D.; Mousoulides, Nicholas G.

2015-01-01

Engineering-based modeling activities provide a rich source of meaningful situations that capitalize on and extend students' routine learning. By integrating such activities within existing curricula, students better appreciate how their school learning in mathematics and science applies to problems in the outside world. Furthermore, modeling…
Vigilance in the laboratory predicts avoidance in the real world: A dimensional analysis of neural, behavioral, and ecological momentary data in anxious youth.

PubMed

Price, Rebecca B; Allen, Kristy Benoit; Silk, Jennifer S; Ladouceur, Cecile D; Ryan, Neal D; Dahl, Ronald E; Forbes, Erika E; Siegle, Greg J

2016-06-01

Vigilance and avoidance of threat are observed in anxious adults during laboratory tasks, and are posited to have real-world clinical relevance, but data are mixed in anxious youth. We propose that vigilance-avoidance patterns will become evident in anxious youth through a focus on individual differences and real-world strategic avoidance. Decreased functional connectivity between the amygdala and prefrontal cortex (PFC) could play a mechanistic role in this link. 78 clinically anxious youth completed a dot-probe task to assess vigilance to threat while undergoing fMRI. Real-world avoidance was assessed using Ecological Momentary Assessment (EMA) of self-reported suppression and distraction during negative life events. Vigilance toward threat was positively associated with EMA distraction and suppression. Functional connectivity between a right amygdala seed region and dorsomedial and right dorsolateral PFC regions was inversely related to EMA distraction. Dorsolateral PFC-amygdalar connectivity statistically mediated the relationship between attentional vigilance and real-world distraction. Findings suggest anxious youth showing attentional vigilance toward threat are more likely to use suppression and distraction to regulate negative emotions. Reduced PFC control over limbic reactivity is a possible neural substrate of this pattern. These findings lend ecological validity to laboratory vigilance assessments and suggest PFC-amygdalar connectivity is a neural mechanism bridging laboratory and naturalistic contexts. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
Understanding Is Key: An Analysis of Factors Pertaining to Trust in a Real-World Automation System.

PubMed

Balfe, Nora; Sharples, Sarah; Wilson, John R

2018-06-01

This paper aims to explore the role of factors pertaining to trust in real-world automation systems through the application of observational methods in a case study from the railway sector. Trust in automation is widely acknowledged as an important mediator of automation use, but the majority of the research on automation trust is based on laboratory work. In contrast, this work explored trust in a real-world setting. Experienced rail operators in four signaling centers were observed for 90 min, and their activities were coded into five mutually exclusive categories. Their observed activities were analyzed in relation to their reported trust levels, collected via a questionnaire. The results showed clear differences in activity, even when circumstances on the workstations were very similar, and significant differences in some trust dimensions were found between groups exhibiting different levels of intervention and time not involved with signaling. Although the empirical, lab-based studies in the literature have consistently found that reliability and competence of the automation are the most important aspects of trust development, understanding of the automation emerged as the strongest dimension in this study. The implications are that development and maintenance of trust in real-world, safety-critical automation systems may be distinct from artificial laboratory automation. The findings have important implications for emerging automation concepts in diverse industries including highly automated vehicles and Internet of things.
Ontario multidetector computed tomographic coronary angiography study: field evaluation of diagnostic accuracy.

PubMed

Chow, Benjamin J W; Freeman, Michael R; Bowen, James M; Levin, Leslie; Hopkins, Robert B; Provost, Yves; Tarride, Jean-Eric; Dennie, Carole; Cohen, Eric A; Marcuzzi, Dan; Iwanochko, Robert; Moody, Alan R; Paul, Narinder; Parker, John D; O'Reilly, Daria J; Xie, Feng; Goeree, Ron

2011-06-13

Computed tomographic coronary angiography (CTCA) has gained clinical acceptance for the detection of obstructive coronary artery disease. Although single-center studies have demonstrated excellent accuracy, multicenter studies have yielded variable results. The true diagnostic accuracy of CTCA in the "real world" remains uncertain. We conducted a field evaluation comparing multidetector CTCA with invasive CA (ICA) to understand CTCA's diagnostic accuracy in a real-world setting. A multicenter cohort study of patients awaiting ICA was conducted between September 2006 and June 2009. All patients had either a low or an intermediate pretest probability for coronary artery disease and underwent CTCA and ICA within 10 days. The results of CTCA and ICA were interpreted visually by local expert observers who were blinded to all clinical data and imaging results. Using a patient-based analysis (diameter stenosis ≥50%) of 169 patients, the sensitivity, specificity, positive predictive value, and negative predictive value were 81.3% (95% confidence interval [CI], 71.0%-89.1%), 93.3% (95% CI, 85.9%-97.5%), 91.6% (95% CI, 82.5%-96.8%), and 84.7% (95% CI, 76.0%-91.2%), respectively; the area under receiver operating characteristic curve was 0.873. The diagnostic accuracy varied across centers (P < .001), with a sensitivity, specificity, positive predictive value, and negative predictive value ranging from 50.0% to 93.2%, 92.0% to 100%, 84.6% to 100%, and 42.9% to 94.7%, respectively. Compared with ICA, CTCA appears to have good accuracy; however, there was variability in diagnostic accuracy across centers. Factors affecting institutional variability need to be better understood before CTCA is universally adopted. Additional real-world evaluations are needed to fully understand the impact of CTCA on clinical care. clinicaltrials.gov Identifier: NCT00371891.
Adaptive rival penalized competitive learning and combined linear predictor model for financial forecast and investment.

PubMed

Cheung, Y M; Leung, W M; Xu, L

1997-01-01

We propose a prediction model called Rival Penalized Competitive Learning (RPCL) and Combined Linear Predictor method (CLP), which involves a set of local linear predictors such that a prediction is made by the combination of some activated predictors through a gating network (Xu et al., 1994). Furthermore, we present its improved variant named Adaptive RPCL-CLP that includes an adaptive learning mechanism as well as a data pre-and-post processing scheme. We compare them with some existing models by demonstrating their performance on two real-world financial time series--a China stock price and an exchange-rate series of US Dollar (USD) versus Deutschmark (DEM). Experiments have shown that Adaptive RPCL-CLP not only outperforms the other approaches with the smallest prediction error and training costs, but also brings in considerable high profits in the trading simulation of foreign exchange market.

Predicting dermal penetration for Expocast chemicals using in silico approaches – should dermal metabolism be considered?

EPA Science Inventory

There are thousands of consumer product chemicals to which humans may be exposed to via direct (e.g. product use) or indirect (e.g. contact with contaminated media) pathways. The US EPA has developed a research program known as ExpoCast to predict exposures to give real-world con...
Tracking the Spatiotemporal Neural Dynamics of Real-world Object Size and Animacy in the Human Brain.

PubMed

Khaligh-Razavi, Seyed-Mahdi; Cichy, Radoslaw Martin; Pantazis, Dimitrios; Oliva, Aude

2018-06-07

Animacy and real-world size are properties that describe any object and thus bring basic order into our perception of the visual world. Here, we investigated how the human brain processes real-world size and animacy. For this, we applied representational similarity to fMRI and MEG data to yield a view of brain activity with high spatial and temporal resolutions, respectively. Analysis of fMRI data revealed that a distributed and partly overlapping set of cortical regions extending from occipital to ventral and medial temporal cortex represented animacy and real-world size. Within this set, parahippocampal cortex stood out as the region representing animacy and size stronger than most other regions. Further analysis of the detailed representational format revealed differences among regions involved in processing animacy. Analysis of MEG data revealed overlapping temporal dynamics of animacy and real-world size processing starting at around 150 msec and provided the first neuromagnetic signature of real-world object size processing. Finally, to investigate the neural dynamics of size and animacy processing simultaneously in space and time, we combined MEG and fMRI with a novel extension of MEG-fMRI fusion by representational similarity. This analysis revealed partly overlapping and distributed spatiotemporal dynamics, with parahippocampal cortex singled out as a region that represented size and animacy persistently when other regions did not. Furthermore, the analysis highlighted the role of early visual cortex in representing real-world size. A control analysis revealed that the neural dynamics of processing animacy and size were distinct from the neural dynamics of processing low-level visual features. Together, our results provide a detailed spatiotemporal view of animacy and size processing in the human brain.
RoboCup-Rescue: an international cooperative research project of robotics and AI for the disaster mitigation problem

NASA Astrophysics Data System (ADS)

Tadokoro, Satoshi; Kitano, Hiroaki; Takahashi, Tomoichi; Noda, Itsuki; Matsubara, Hitoshi; Shinjoh, Atsushi; Koto, Tetsuo; Takeuchi, Ikuo; Takahashi, Hironao; Matsuno, Fumitoshi; Hatayama, Mitsunori; Nobe, Jun; Shimada, Susumu

2000-07-01

This paper introduces the RoboCup-Rescue Simulation Project, a contribution to the disaster mitigation, search and rescue problem. A comprehensive urban disaster simulator is constructed on distributed computers. Heterogeneous intelligent agents such as fire fighters, victims and volunteers conduct search and rescue activities in this virtual disaster world. A real world interface integrates various sensor systems and controllers of infrastructures in the real cities with the real world. Real-time simulation is synchronized with actual disasters, computing complex relationship between various damage factors and agent behaviors. A mission-critical man-machine interface provides portability and robustness of disaster mitigation centers, and augmented-reality interfaces for rescue in real disasters. It also provides a virtual- reality training function for the public. This diverse spectrum of RoboCup-Rescue contributes to the creation of the safer social system.
Effects of Integrating an Active Learning-Promoting Mechanism into Location-Based Real-World Learning Environments on Students' Learning Performances and Behaviors

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Chang, Shao-Chen; Chen, Pei-Ying; Chen, Xiang-Ya

2018-01-01

Engaging students in real-world learning contexts has been identified by educators as being an important way of helping them learn to apply what they have learned from textbooks to practical problems. The advancements in mobile and image-processing technologies have enabled students to access learning resources and receive learning guidance in…
Student-Centered Pedagogy and Real-World Research: Using Documents as Sources of Data in Teaching Social Science Skills and Methods

ERIC Educational Resources Information Center

Peyrefitte, Magali; Lazar, Gillian

2018-01-01

This teaching note describes the design and implementation of an activity in a 90-minute teaching session that was developed to introduce a diverse cohort of first-year criminology and sociology students to the use of documents as sources of data. This approach was contextualized in real-world research through scaffolded, student-centered tasks…
Applied use of cardiac and respiration measures: practical considerations and precautions.

PubMed

Wilson, G F

1992-11-01

Cardiac and respiratory measures can be successfully applied to "real world" environments and these measures have certain advantages over both performance and subjective measures that are typically used to monitor operator state and workload. However, because of large differences between laboratory and "real world" environments one must utilize caution in directly applying laboratory data and theories to the day-to-day world environment. While most workers are highly over-trained in their jobs, laboratory subjects are often under-trained in the cognitive tasks that are used to study cognitive activity. It is possible that a substantial portion of experimental effects reported in laboratory studies is due to learning effects. In addition, relatively small changes in cardiac and respiration measures are reported to experimental manipulations in the laboratory while a much larger range of changes are reported in "real world" environments. These differences highlight questions about laboratory/real world similarities and the need to develop a database of actual work environment data. A third area of concern is the relative lack of control over the experimental situation that is the case with most applied research. The possible confounding of changes due to cognitive and physical activity levels is a major concern and strategies for overcoming these problems are suggested. The potential for valuable contributions by cardiac and respiratory measures to applied research make overcoming these difficulties worthwhile.
Online and Social Media Data As an Imperfect Continuous Panel Survey

PubMed Central

2016-01-01

There is a large body of research on utilizing online activity as a survey of political opinion to predict real world election outcomes. There is considerably less work, however, on using this data to understand topic-specific interest and opinion amongst the general population and specific demographic subgroups, as currently measured by relatively expensive surveys. Here we investigate this possibility by studying a full census of all Twitter activity during the 2012 election cycle along with the comprehensive search history of a large panel of Internet users during the same period, highlighting the challenges in interpreting online and social media activity as the results of a survey. As noted in existing work, the online population is a non-representative sample of the offline world (e.g., the U.S. voting population). We extend this work to show how demographic skew and user participation is non-stationary and difficult to predict over time. In addition, the nature of user contributions varies substantially around important events. Furthermore, we note subtle problems in mapping what people are sharing or consuming online to specific sentiment or opinion measures around a particular topic. We provide a framework, built around considering this data as an imperfect continuous panel survey, for addressing these issues so that meaningful insight about public interest and opinion can be reliably extracted from online and social media data. PMID:26730933
Online and Social Media Data As an Imperfect Continuous Panel Survey.

PubMed

Diaz, Fernando; Gamon, Michael; Hofman, Jake M; Kıcıman, Emre; Rothschild, David

2016-01-01

There is a large body of research on utilizing online activity as a survey of political opinion to predict real world election outcomes. There is considerably less work, however, on using this data to understand topic-specific interest and opinion amongst the general population and specific demographic subgroups, as currently measured by relatively expensive surveys. Here we investigate this possibility by studying a full census of all Twitter activity during the 2012 election cycle along with the comprehensive search history of a large panel of Internet users during the same period, highlighting the challenges in interpreting online and social media activity as the results of a survey. As noted in existing work, the online population is a non-representative sample of the offline world (e.g., the U.S. voting population). We extend this work to show how demographic skew and user participation is non-stationary and difficult to predict over time. In addition, the nature of user contributions varies substantially around important events. Furthermore, we note subtle problems in mapping what people are sharing or consuming online to specific sentiment or opinion measures around a particular topic. We provide a framework, built around considering this data as an imperfect continuous panel survey, for addressing these issues so that meaningful insight about public interest and opinion can be reliably extracted from online and social media data.
Dynamic Scheduling for Veterans Health Administration Patients using Geospatial Dynamic Overbooking.

PubMed

Adams, Stephen; Scherer, William T; White, K Preston; Payne, Jason; Hernandez, Oved; Gerber, Mathew S; Whitehead, N Peter

2017-10-12

The Veterans Health Administration (VHA) is plagued by abnormally high no-show and cancellation rates that reduce the productivity and efficiency of its medical outpatient clinics. We address this issue by developing a dynamic scheduling system that utilizes mobile computing via geo-location data to estimate the likelihood of a patient arriving on time for a scheduled appointment. These likelihoods are used to update the clinic's schedule in real time. When a patient's arrival probability falls below a given threshold, the patient's appointment is canceled. This appointment is immediately reassigned to another patient drawn from a pool of patients who are actively seeking an appointment. The replacement patients are prioritized using their arrival probability. Real-world data were not available for this study, so synthetic patient data were generated to test the feasibility of the design. The method for predicting the arrival probability was verified on a real set of taxicab data. This study demonstrates that dynamic scheduling using geo-location data can reduce the number of unused appointments with minimal risk of double booking resulting from incorrect predictions. We acknowledge that there could be privacy concerns with regards to government possession of one's location and offer strategies for alleviating these concerns in our conclusion.
Interictal to Ictal Phase Transition in a Small-World Network

NASA Astrophysics Data System (ADS)

Nemzer, Louis; Cravens, Gary; Worth, Robert

Real-time detection and prediction of seizures in patients with epilepsy is essential for rapid intervention. Here, we perform a full Hodgkin-Huxley calculation using n 50 in silico neurons configured in a small-world network topology to generate simulated EEG signals. The connectivity matrix, constructed using a Watts-Strogatz algorithm, admits randomized or deterministic entries. We find that situations corresponding to interictal (non-seizure) and ictal (seizure) states are separated by a phase transition that can be influenced by congenital channelopathies, anticonvulsant drugs, and connectome plasticity. The interictal phase exhibits scale-free phenomena, as characterized by a power law form of the spectral power density, while the ictal state suffers from pathological synchronization. We compare the results with intracranial EEG data and show how these findings may be used to detect or even predict seizure onset. Along with the balance of excitatory and inhibitory factors, the network topology plays a large role in determining the overall characteristics of brain activity. We have developed a new platform for testing the conditions that contribute to the phase transition between non-seizure and seizure states.
Disorganization and real-world functioning in schizophrenia: Results from the multicenter study of the Italian Network for Research on Psychoses.

PubMed

Rocca, P; Galderisi, S; Rossi, A; Bertolino, A; Rucci, P; Gibertoni, D; Montemagni, C; Bellino, S; Aguglia, E; Amore, M; Bellomo, A; Biondi, M; Carpiniello, B; Cuomo, A; D'Ambrosio, E; dell'Osso, L; Girardi, P; Marchesi, C; Monteleone, P; Montemitro, C; Oldani, L; Pacitti, F; Roncone, R; Siracusano, A; Tenconi, E; Vita, A; Zeppegno, P; Steardo, L; Vignapiano, A; Maj, M

2018-06-10

A general consensus has not yet been reached regarding the role of disorganization symptoms in real-world functioning in schizophrenia. We used structural equations modeling (SEM) to analyze the direct and indirect associations between disorganization and real-world functioning assessed through the Specific Levels of Functioning Scale (SLOF) in 880 subjects with schizophrenia. We found that: 1) conceptual disorganization was directly and strongly connected with SLOF daily activities; difficulty in abstract thinking was associated with moderate strength to all SLOF domains, and poor attention was connected with SLOF work skills; 2) grandiosity was only related with poor work skills, and delusions were associated with poor functioning in all SLOF domains; interpersonal relationships were weakly indirectly influenced by hallucinatory behavior, delusions and unusual thought contents through the mediation of social cognition (SC); 3) among the negative symptoms, avolition had only direct links with SLOF work skills and SLOF activities; anhedonia had direct links with SLOF work skills and SLOF interpersonal and indirect link with SLOF work skills through functional capacity (FC); asociality with SLOF interpersonal; blunted affect had direct links with SLOF activities and indirect links with SLOF interpersonal relationships mediated by SC. Lastly, alogia had only indirect links mediated by SC, FC, and neurocognition (NC). Overall conceptual disorganization is the symptom that contributed more (both directly and indirectly) to the activities of community living in real-world. Thus, it should be considered as a treatment target in intervention programs for patients with schizophrenia. Copyright © 2018. Published by Elsevier B.V.
Quantifying Ocean Acidification and its Impacts to Coral Reef Ecosystems

NASA Astrophysics Data System (ADS)

Manzello, D.; Gledhill, D. K.; Enochs, I.; Andersson, A. J.

2013-05-01

Ocean Acidification (OA) describes the uptake of anthropogenic CO2 by the world's oceans and consequent decline in seawater pH and calcium carbonate saturation state. OA is of particular concern for coral reef ecosystems because it is expected to reduce the calcification rates of reef-building corals and other calcifiers, and may simultaneously increase the erosive abilities of key bioeroding taxa. Despite these concerns, we have little understanding of how OA will manifest in the real-world or, if, and how much of the world-wide trajectory of reef decline can be attributed to OA. With this in mind, we will present recommendations for monitoring OA of coral reef waters, as well as its ecosystem impacts over time. Different approaches and metrics, including their individual strengths and weaknesses, will be discussed. The ultimate goal of these efforts is to quantify the effects of OA on coral reef ecosystems in the real-world to robustly predict their structure and function in a high-CO2 world.
A Dynamic Speech Comprehension Test for Assessing Real-World Listening Ability.

PubMed

Best, Virginia; Keidser, Gitte; Freeston, Katrina; Buchholz, Jörg M

2016-07-01

Many listeners with hearing loss report particular difficulties with multitalker communication situations, but these difficulties are not well predicted using current clinical and laboratory assessment tools. The overall aim of this work is to create new speech tests that capture key aspects of multitalker communication situations and ultimately provide better predictions of real-world communication abilities and the effect of hearing aids. A test of ongoing speech comprehension introduced previously was extended to include naturalistic conversations between multiple talkers as targets, and a reverberant background environment containing competing conversations. In this article, we describe the development of this test and present a validation study. Thirty listeners with normal hearing participated in this study. Speech comprehension was measured for one-, two-, and three-talker passages at three different signal-to-noise ratios (SNRs), and working memory ability was measured using the reading span test. Analyses were conducted to examine passage equivalence, learning effects, and test-retest reliability, and to characterize the effects of number of talkers and SNR. Although we observed differences in difficulty across passages, it was possible to group the passages into four equivalent sets. Using this grouping, we achieved good test-retest reliability and observed no significant learning effects. Comprehension performance was sensitive to the SNR but did not decrease as the number of talkers increased. Individual performance showed associations with age and reading span score. This new dynamic speech comprehension test appears to be valid and suitable for experimental purposes. Further work will explore its utility as a tool for predicting real-world communication ability and hearing aid benefit. American Academy of Audiology.
Long-term bleeding risk prediction in 'real world' patients with atrial fibrillation: Comparison of the HAS-BLED and ABC-Bleeding risk scores. The Murcia Atrial Fibrillation Project.

PubMed

Esteve-Pastor, María Asunción; Rivera-Caravaca, José Miguel; Roldan, Vanessa; Vicente, Vicente; Valdés, Mariano; Marín, Francisco; Lip, Gregory Y H

2017-10-05

Risk scores in patients with atrial fibrillation (AF) based on clinical factors alone generally have only modest predictive value for predicting high risk patients that sustain events. Biomarkers might be an attractive prognostic tool to improve bleeding risk prediction. The new ABC-Bleeding score performed better than HAS-BLED score in a clinical trial cohort but has not been externally validated. The aim of this study was to analyze the predictive performance of the ABC-Bleeding score compared to HAS-BLED score in an independent "real-world" anticoagulated AF patients with long-term follow-up. We enrolled 1,120 patients stable on vitamin K antagonist treatment. The HAS-BLED and ABC-Bleeding scores were quantified. Predictive values were compared by c-indexes, IDI, NRI, as well as decision curve analysis (DCA). Median HAS-BLED score was 2 (IQR 2-3) and median ABC-Bleeding was 16.5 (IQR 14.3-18.6). After 6.5 years of follow-up, 207 (2.84 %/year) patients had major bleeding events, of which 65 (0.89 %/year) had intracranial haemorrhage (ICH) and 85 (1.17 %/year) had gastrointestinal bleeding events (GIB). The c-index of HAS-BLED was significantly higher than ABC-Bleeding for major bleeding (0.583 vs 0.518; p=0.025), GIB (0.596 vs 0.519; p=0.017) and for the composite of ICH-GIB (0.593 vs 0.527; p=0.030). NRI showed a significant negative reclassification for major bleeding and for the composite of ICH-GIB with the ABC-Bleeding score compared to HAS-BLED. Using DCAs, the use of HAS-BLED score gave an approximate net benefit of 4 % over the ABC-Bleeding score. In conclusion, in the first "real-world" validation of the ABC-Bleeding score, HAS-BLED performed significantly better than the ABC-Bleeding score in predicting major bleeding, GIB and the composite of GIB and ICH.
ENDOCRINE DISRUPTORS: LESSONS LEARNED

EPA Science Inventory

For more than ten years, major international efforts have been aimed at understanding the mechanism and extent of endocrine disruption in experimental models, wildlife, and people; its occurrence in the real world; and in developing tools for screening and prediction of risk. Mu...
Safety, Effectiveness, and Treatment Persistence of Golimumab in Elderly Patients with Rheumatoid Arthritis in Real-World Clinical Practice in Japan.

PubMed

Okazaki, Masateru; Kobayashi, Hisanori; Shimizu, Hirohito; Ishii, Yutaka; Yajima, Tsutomu; Kanbori, Masayoshi

2018-06-01

Golimumab has been proven as an effective treatment for rheumatoid arthritis in clinical trials. However, there is a scarcity of data regarding its use in elderly patients in a real-world setting. This study aims to evaluate the safety, effectiveness, and treatment persistence of golimumab in elderly Japanese patients (≥ 75 years) with rheumatoid arthritis. This study was a post hoc analysis of post-marketing surveillance data on 5137 Japanese patients with active rheumatoid arthritis who received golimumab for 24 weeks. The study population was divided into two age groups (younger: < 75 years and elderly: ≥ 75 years), and the safety, effectiveness, and treatment persistence of golimumab were assessed. Also, the reasons for discontinuing golimumab treatment were analyzed by multi-logistic regression. During golimumab treatment over 24 weeks, younger and elderly groups exhibited comparable improvement of disease activity as measured by EULAR response criteria with similar overall rates of adverse events. However, the survival curve of golimumab for elderly patients was significantly different from that for younger patients due largely to the discontinuation at 4 weeks. The most common reason for discontinuation in elderly patients was patient choice, while it was disease progression in younger patients. Analysis of elderly patients who discontinued treatment by their own decision identified EULAR good response as a factor associated with continuation of golimumab treatment whereas no predictive factor associated with discontinuation was identified. The safety and effectiveness of golimumab treatment in elderly Japanese patients aged 75 years or older were comparable to those in younger patients in real-world clinical practice. Analysis of the survival curves suggested that continuous use of golimumab might further improve clinical benefit of golimumab in elderly patients, underpinning the importance of effective communication between physicians and elderly patients based on the treat-to-target strategy. Janssen Pharmaceutical K.K. and Mitsubishi Tanabe Pharma Corporation.
Real-world operation conditions and on-road emissions of Beijing diesel buses measured by using portable emission measurement system and electric low-pressure impactor.

PubMed

Liu, Zhihua; Ge, Yunshan; Johnson, Kent C; Shah, Asad Naeem; Tan, Jianwei; Wang, Chu; Yu, Linxiao

2011-03-15

On-road measurement is an effective method to investigate real-world emissions generated from vehicles and estimate the difference between engine certification cycles and real-world operating conditions. This study presents the results of on-road measurements collected from urban buses which propelled by diesel engine in Beijing city. Two widely used Euro III emission level buses and two Euro IV emission level buses were chosen to perform on-road emission measurements using portable emission measurement system (PEMS) for gaseous pollutant and Electric Low Pressure Impactor (ELPI) for particulate matter (PM) number emissions. The results indicate that considerable discrepancies of engine operating conditions between real-world driving cycles and engine certification cycles have been observed. Under real-world operating conditions, carbon monoxide (CO) and hydrocarbon (HC) emissions can easily meet their respective regulations limits, while brake specification nitrogen oxide (bsNO(x)) emissions present a significant deviation from its corresponding limit. Compared with standard limits, the real-world bsNO(x) emission of the two Euro III emission level buses approximately increased by 60% and 120% respectively, and bsNO(x) of two Euro IV buses nearly twice standard limits because Selective Catalytic Reduction (SCR) system not active under low exhaust temperature. Particle mass were estimated via particle size distribution with the assumption that particle density and diameter is liner. The results demonstrate that nanometer size particulate matter make significant contribution to total particle number but play a minor role to total particle mass. It is suggested that specific certified cycle should be developed to regulate bus engines emissions on the test bench or use PEMS to control the bus emissions under real-world operating conditions. Copyright © 2010 Elsevier B.V. All rights reserved.
Understanding Is Key: An Analysis of Factors Pertaining to Trust in a Real-World Automation System

PubMed Central

Balfe, Nora; Sharples, Sarah; Wilson, John R.

2018-01-01

Objective: This paper aims to explore the role of factors pertaining to trust in real-world automation systems through the application of observational methods in a case study from the railway sector. Background: Trust in automation is widely acknowledged as an important mediator of automation use, but the majority of the research on automation trust is based on laboratory work. In contrast, this work explored trust in a real-world setting. Method: Experienced rail operators in four signaling centers were observed for 90 min, and their activities were coded into five mutually exclusive categories. Their observed activities were analyzed in relation to their reported trust levels, collected via a questionnaire. Results: The results showed clear differences in activity, even when circumstances on the workstations were very similar, and significant differences in some trust dimensions were found between groups exhibiting different levels of intervention and time not involved with signaling. Conclusion: Although the empirical, lab-based studies in the literature have consistently found that reliability and competence of the automation are the most important aspects of trust development, understanding of the automation emerged as the strongest dimension in this study. The implications are that development and maintenance of trust in real-world, safety-critical automation systems may be distinct from artificial laboratory automation. Application: The findings have important implications for emerging automation concepts in diverse industries including highly automated vehicles and Internet of things. PMID:29613815
Ecological validity of the screening module and the Daily Living tests of the Neuropsychological Assessment Battery using the Mayo-Portland Adaptability Inventory-4 in postacute brain injury rehabilitation.

PubMed

Zgaljardic, Dennis J; Yancy, Sybil; Temple, Richard O; Watford, Monica F; Miller, Rebekah

2011-11-01

The assessment of ecological validity of neuropsychological measures is an area of growing interest, particularly in the postacute brain injury rehabilitation (PABIR) setting, as there is an increasing demand for clinicians to address functional and real-world outcomes. In the current study, we assessed the predictive value of the Screening module and the Daily Living tests of the Neuropsychological Assessment Battery (NAB) using clinician ratings from the Mayo-Portland Adaptability Inventory-4 (MPAI-4) in patients with moderate to severe traumatic brain injury. Forty-seven individuals were each administered the NAB Screening module (NAB-SM) and the NAB Daily Living (NAB-DL) tests following admission to a residential PABIR program. MPAI-4 ratings were also obtained at admission. Linear regression analysis was used to examine the association between these functional and neuropsychological assessment measures. We replicated prior work (Temple at al., 2009) and expanded evidence for the ecological validity of the NAB-SM. Furthermore, our findings support the ecological validity of the NAB-DL Bill Payment, Judgment, and Map Reading tests with regards to functional skills and real-world activities. The current study supports prior work from our lab assessing the predictive value of the NAB-SM, as well as provides evidence for the ecological validity for select NAB-DL tests in patients with moderate to severe traumatic brain injury admitted to a residential PABIR program.
Learning from picture books: Infants' use of naming information.

PubMed

Khu, Melanie; Graham, Susan A; Ganea, Patricia A

2014-01-01

The present study investigated whether naming would facilitate infants' transfer of information from picture books to the real world. Eighteen- and 21-month-olds learned a novel label for a novel object depicted in a picture book. Infants then saw a second picture book in which an adult demonstrated how to elicit the object's non-obvious property. Accompanying narration described the pictures using the object's newly learnt label. Infants were subsequently tested with the real-world object depicted in the book, as well as a different-color exemplar. Infants' performance on the test trials was compared with that of infants in a no label condition. When presented with the exact object depicted in the picture book, 21-month-olds were significantly more likely to attempt to elicit the object's non-obvious property than were 18-month-olds. Learning the object's label before learning about the object's hidden property did not improve 18-month-olds' performance. At 21-months, the number of infants in the label condition who attempted to elicit the real-world object's non-obvious property was greater than would be predicted by chance, but the number of infants in the no label condition was not. Neither age group nor label condition predicted test performance for the different-color exemplar. The findings are discussed in relation to infants' learning and transfer from picture books.

Learning from picture books: Infants’ use of naming information

PubMed Central

Khu, Melanie; Graham, Susan A.; Ganea, Patricia A.

2014-01-01

The present study investigated whether naming would facilitate infants’ transfer of information from picture books to the real world. Eighteen- and 21-month-olds learned a novel label for a novel object depicted in a picture book. Infants then saw a second picture book in which an adult demonstrated how to elicit the object’s non-obvious property. Accompanying narration described the pictures using the object’s newly learnt label. Infants were subsequently tested with the real-world object depicted in the book, as well as a different-color exemplar. Infants’ performance on the test trials was compared with that of infants in a no label condition. When presented with the exact object depicted in the picture book, 21-month-olds were significantly more likely to attempt to elicit the object’s non-obvious property than were 18-month-olds. Learning the object’s label before learning about the object’s hidden property did not improve 18-month-olds’ performance. At 21-months, the number of infants in the label condition who attempted to elicit the real-world object’s non-obvious property was greater than would be predicted by chance, but the number of infants in the no label condition was not. Neither age group nor label condition predicted test performance for the different-color exemplar. The findings are discussed in relation to infants’ learning and transfer from picture books. PMID:24611058
CURRENT CHALLENGES ON ENDOCRINE DISRUPTORS

EPA Science Inventory

For over ten years, major international efforts have been aimed at understanding the mechanism and extent of endocrine disruption in experimental models, wildlife, and people; the occurrence of this in the real world and in developing tools for screening and prediction of risk. ...
Self-generated strategic behavior in an ecological shopping task.

PubMed

Bottari, Carolina; Wai Shun, Priscilla Lam; Dorze, Guylaine Le; Gosselin, Nadia; Dawson, Deirdre

2014-01-01

OBJECTIVES. The use of cognitive strategies optimizes performance in complex everyday tasks such as shopping. This exploratory study examined the cognitive strategies people with traumatic brain injury (TBI) effectively use in an unstructured, real-world situation. METHOD. A behavioral analysis of the self-generated strategic behaviors of 5 people with severe TBI using videotaped sessions of an ecological shopping task (Instrumental Activities of Daily Living Profile) was performed. RESULTS. All participants used some form of cognitive strategy in an unstructured real-world shopping task, although the number, type, and degree of effectiveness of the strategies in leading to goal attainment varied. The most independent person used the largest number and a broader repertoire of self-generated strategies. CONCLUSION. These results provide initial evidence that occupational therapists should examine the use of self-generated cognitive strategies in real-world contexts as a potential means of guiding therapy aimed at improving independence in everyday activities for people with TBI. Copyright © 2014 by the American Occupational Therapy Association, Inc.
Task directed sensing

NASA Technical Reports Server (NTRS)

Firby, R. James

1990-01-01

High-level robot control research must confront the limitations imposed by real sensors if robots are to be controlled effectively in the real world. In particular, sensor limitations make it impossible to maintain a complete, detailed world model of the situation surrounding the robot. To address the problems involved in planning with the resulting incomplete and uncertain world models, traditional robot control architectures must be altered significantly. Task-directed sensing and control is suggested as a way of coping with world model limitations by focusing sensing and analysis resources on only those parts of the world relevant to the robot's active goals. The RAP adaptive execution system is used as an example of a control architecture designed to deploy sensing resources in this way to accomplish both action and knowledge goals.
Activity Recognition on Streaming Sensor Data.

PubMed

Krishnan, Narayanan C; Cook, Diane J

2014-02-01

Many real-world applications that focus on addressing needs of a human, require information about the activities being performed by the human in real-time. While advances in pervasive computing have lead to the development of wireless and non-intrusive sensors that can capture the necessary activity information, current activity recognition approaches have so far experimented on either a scripted or pre-segmented sequence of sensor events related to activities. In this paper we propose and evaluate a sliding window based approach to perform activity recognition in an on line or streaming fashion; recognizing activities as and when new sensor events are recorded. To account for the fact that different activities can be best characterized by different window lengths of sensor events, we incorporate the time decay and mutual information based weighting of sensor events within a window. Additional contextual information in the form of the previous activity and the activity of the previous window is also appended to the feature describing a sensor window. The experiments conducted to evaluate these techniques on real-world smart home datasets suggests that combining mutual information based weighting of sensor events and adding past contextual information into the feature leads to best performance for streaming activity recognition.
Electrophysiology-based detection of emergency braking intention in real-world driving.

PubMed

Haufe, Stefan; Kim, Jeong-Woo; Kim, Il-Hwa; Sonnleitner, Andreas; Schrauf, Michael; Curio, Gabriel; Blankertz, Benjamin

2014-10-01

The fact that all human action is preceded by brain processes partially observable through neuroimaging devices such as electroencephalography (EEG) is currently being explored in a number of applications. A recent study by Haufe et al (2011 J. Neural Eng. 8 056001) demonstrates the possibility of performing fast detection of forced emergency brakings during driving based on EEG and electromyography, and discusses the use of such neurotechnology for braking assistance systems. Since the study was conducted in a driving simulator, its significance regarding real-world applicability needs to be assessed. Here, we replicate that experimental paradigm in a real car on a non-public test track. Our results resemble those of the simulator study, both qualitatively (in terms of the neurophysiological phenomena observed and utilized) and quantitatively (in terms of the predictive improvement achievable using electrophysiology in addition to behavioral measures). Moreover, our findings are robust with respect to a temporary secondary auditory task mimicking verbal input from a fellow passenger. Our study serves as a real-world verification of the feasibility of electrophysiology-based detection of emergency braking intention as proposed in Haufe et al (2011 J. Neural Eng. 8 056001).
Electrophysiology-based detection of emergency braking intention in real-world driving

NASA Astrophysics Data System (ADS)

Haufe, Stefan; Kim, Jeong-Woo; Kim, Il-Hwa; Sonnleitner, Andreas; Schrauf, Michael; Curio, Gabriel; Blankertz, Benjamin

2014-10-01

Objective. The fact that all human action is preceded by brain processes partially observable through neuroimaging devices such as electroencephalography (EEG) is currently being explored in a number of applications. A recent study by Haufe et al (2011 J. Neural Eng. 8 056001) demonstrates the possibility of performing fast detection of forced emergency brakings during driving based on EEG and electromyography, and discusses the use of such neurotechnology for braking assistance systems. Since the study was conducted in a driving simulator, its significance regarding real-world applicability needs to be assessed. Approach. Here, we replicate that experimental paradigm in a real car on a non-public test track. Main results. Our results resemble those of the simulator study, both qualitatively (in terms of the neurophysiological phenomena observed and utilized) and quantitatively (in terms of the predictive improvement achievable using electrophysiology in addition to behavioral measures). Moreover, our findings are robust with respect to a temporary secondary auditory task mimicking verbal input from a fellow passenger. Significance. Our study serves as a real-world verification of the feasibility of electrophysiology-based detection of emergency braking intention as proposed in Haufe et al (2011 J. Neural Eng. 8 056001).
People detection in crowded scenes using active contour models

NASA Astrophysics Data System (ADS)

Sidla, Oliver

2009-01-01

The detection of pedestrians in real-world scenes is a daunting task, especially in crowded situations. Our experience over the last years has shown that active shape models (ASM) can contribute significantly to a robust pedestrian detection system. The paper starts with an overview of shape model approaches, it then explains our approach which builds on top of Eigenshape models which have been trained using real-world data. These models are placed over candidate regions and matched to image gradients using a scoring function which integrates i) point distribution, ii) local gradient orientations iii) local image gradient strengths. A matching and shape model update process is iteratively applied in order to fit the flexible models to the local image content. The weights of the scoring function have a significant impact on the ASM performance. We analyze different settings of scoring weights for gradient magnitude, relative orientation differences, distance between model and gradient in an experiment which uses real-world data. Although for only one pedestrian model in an image computation time is low, the number of necessary processing cycles which is needed to track many people in crowded scenes can become the bottleneck in a real-time application. We describe the measures which have been taken in order to improve the speed of the ASM implementation and make it real-time capable.
A study of navigation in virtual space

NASA Technical Reports Server (NTRS)

Darken, Rudy; Sibert, John L.; Shumaker, Randy

1994-01-01

In the physical world, man has developed efficient methods for navigation and orientation. These methods are dependent on the high-fidelity stimuli presented by the environment. When placed in a virtual world which cannot offer stimuli of the same quality due to computing constraints and immature technology, tasks requiring the maintenance of position and orientation knowledge become laborious. In this paper, we present a representative set of techniques based on principles of navigation derived from real world analogs including human and avian navigation behavior and cartography. A preliminary classification of virtual worlds is presented based on the size of the world, the density of objects in the world, and the level of activity taking place in the world. We also summarize an informal study we performed to determine how the tools influenced the subjects' navigation strategies and behavior. We conclude that principles extracted from real world navigation aids such as maps can be seen to apply in virtual environments.
Realistic Real World Contexts: Model Eliciting Activities

ERIC Educational Resources Information Center

Doruk, Bekir Kürsat

2016-01-01

Researchers have proposed a variety of methods to make a connection between real life and mathematics so that it can be learned in a practical way and enable people to utilise mathematics in their daily lives. Model-eliciting activities (MEAs) were developed to fulfil this need and are very capable of serving this purpose. The reason MEAs are so…
Cultural group selection is plausible, but the predictions of its hypotheses should be tested with real-world data.

PubMed

Turchin, Peter; Currie, Thomas E

2016-01-01

The evidence compiled in the target article demonstrates that the assumptions of cultural group selection (CGS) theory are often met, and it is therefore a useful framework for generating plausible hypotheses. However, more can be said about how we can test the predictions of CGS hypotheses against competing explanations using historical, archaeological, and anthropological data.
Lateral prefrontal cortex activity during cognitive control of emotion predicts response to social stress in schizophrenia

PubMed Central

Tully, Laura M.; Lincoln, Sarah Hope; Hooker, Christine I.

2014-01-01

LPFC dysfunction is a well-established neural impairment in schizophrenia and is associated with worse symptoms. However, how LPFC activation influences symptoms is unclear. Previous findings in healthy individuals demonstrate that lateral prefrontal cortex (LPFC) activation during cognitive control of emotional information predicts mood and behavior in response to interpersonal conflict, thus impairments in these processes may contribute to symptom exacerbation in schizophrenia. We investigated whether schizophrenia participants show LPFC deficits during cognitive control of emotional information, and whether these LPFC deficits prospectively predict changes in mood and symptoms following real-world interpersonal conflict. During fMRI, 23 individuals with schizophrenia or schizoaffective disorder and 24 healthy controls completed the Multi-Source Interference Task superimposed on neutral and negative pictures. Afterwards, schizophrenia participants completed a 21-day online daily-diary in which they rated the extent to which they experienced mood and schizophrenia-spectrum symptoms, as well as the occurrence and response to interpersonal conflict. Schizophrenia participants had lower dorsal LPFC activity (BA9) during cognitive control of task-irrelevant negative emotional information. Within schizophrenia participants, DLPFC activity during cognitive control of emotional information predicted changes in positive and negative mood on days following highly distressing interpersonal conflicts. Results have implications for understanding the specific role of LPFC in response to social stress in schizophrenia, and suggest that treatments targeting LPFC-mediated cognitive control of emotion could promote adaptive response to social stress in schizophrenia. PMID:25379415
Finding the Discipline: Assessing Student Activity in "Second Life"

ERIC Educational Resources Information Center

Grant, Scott; Clerehan, Rosemary

2011-01-01

For the second-language learner, the affordances of a virtual world have the potential to confer benefits conventionally aligned with real world experiences. However, little is known about the pedagogical benefits linked to the specific characteristics of the virtual world, let alone the issues arising for staff hoping to assess students'…
Mortality prediction system for heart failure with orthogonal relief and dynamic radius means.

PubMed

Wang, Zhe; Yao, Lijuan; Li, Dongdong; Ruan, Tong; Liu, Min; Gao, Ju

2018-07-01

This paper constructs a mortality prediction system based on a real-world dataset. This mortality prediction system aims to predict mortality in heart failure (HF) patients. Effective mortality prediction can improve resources allocation and clinical outcomes, avoiding inappropriate overtreatment of low-mortality patients and discharging of high-mortality patients. This system covers three mortality prediction targets: prediction of in-hospital mortality, prediction of 30-day mortality and prediction of 1-year mortality. HF data are collected from the Shanghai Shuguang hospital. 10,203 in-patients records are extracted from encounters occurring between March 2009 and April 2016. The records involve 4682 patients, including 539 death cases. A feature selection method called Orthogonal Relief (OR) algorithm is first used to reduce the dimensionality. Then, a classification algorithm named Dynamic Radius Means (DRM) is proposed to predict the mortality in HF patients. The comparative experimental results demonstrate that mortality prediction system achieves high performance in all targets by DRM. It is noteworthy that the performance of in-hospital mortality prediction achieves 87.3% in AUC (35.07% improvement). Moreover, the AUC of 30-day and 1-year mortality prediction reach to 88.45% and 84.84%, respectively. Especially, the system could keep itself effective and not deteriorate when the dimension of samples is sharply reduced. The proposed system with its own method DRM can predict mortality in HF patients and achieve high performance in all three mortality targets. Furthermore, effective feature selection strategy can boost the system. This system shows its importance in real-world applications, assisting clinicians in HF treatment by providing crucial decision information. Copyright © 2018 Elsevier B.V. All rights reserved.
Pharmacodynamic analysis of eribulin safety in breast cancer patients using real-world post-marketing surveillance data.

PubMed

Kawamura, Takahisa; Kasai, Hidefumi; Fermanelli, Valentina; Takahashi, Toshiaki; Sakata, Yukinori; Matsuoka, Toshiyuki; Ishii, Mika; Tanigawara, Yusuke

2018-06-22

Post-marketing surveillance is useful to collect safety data in real-world clinical settings. In this study, we firstly applied the post-marketing real-world data on a mechanistic model analysis for neutropenic profiles of eribulin in patients with recurrent or metastatic breast cancer (RBC/MBC). Demographic and safety data were collected using an active surveillance method from eribulin-treated RBC/MBC patients. Changes in neutrophil counts over time were analyzed using a mechanistic pharmacodynamic model. Pathophysiological factors that may affect the severity of neutropenia were investigated and neutropenic patterns were simulated for different treatment schedules. Clinical and laboratory data were collected from 401 patients (5199 neutrophil count measurements) who had not received granulocyte colony stimulating factor and were eligible for pharmacodynamic analysis. The estimated mean parameters were: mean transit time = 104.5 h, neutrophil proliferation rate constant = 0.0377 h -1 , neutrophil elimination rate constant = 0.0295 h -1 , and linear coefficient of drug effect = 0.0413 mL/ng. Low serum albumin levels and low baseline neutrophil counts were associated with severe neutropenia. The probability of grade ≥3 neutropenia was predicted to be 69%, 27%, and 27% for patients on standard, biweekly, and triweekly treatment scenarios, respectively, based on virtual simulations using the developed pharmacodynamic model. In conclusion, this is the first application of post-marketing surveillance data to a model-based safety analysis. This analysis of safety data reflecting authentic clinical settings will provide useful information on the safe use and potential risk factors of eribulin. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
A Fast Surrogate-facilitated Data-driven Bayesian Approach to Uncertainty Quantification of a Regional Groundwater Flow Model with Structural Error

NASA Astrophysics Data System (ADS)

Xu, T.; Valocchi, A. J.; Ye, M.; Liang, F.

2016-12-01

Due to simplification and/or misrepresentation of the real aquifer system, numerical groundwater flow and solute transport models are usually subject to model structural error. During model calibration, the hydrogeological parameters may be overly adjusted to compensate for unknown structural error. This may result in biased predictions when models are used to forecast aquifer response to new forcing. In this study, we extend a fully Bayesian method [Xu and Valocchi, 2015] to calibrate a real-world, regional groundwater flow model. The method uses a data-driven error model to describe model structural error and jointly infers model parameters and structural error. In this study, Bayesian inference is facilitated using high performance computing and fast surrogate models. The surrogate models are constructed using machine learning techniques to emulate the response simulated by the computationally expensive groundwater model. We demonstrate in the real-world case study that explicitly accounting for model structural error yields parameter posterior distributions that are substantially different from those derived by the classical Bayesian calibration that does not account for model structural error. In addition, the Bayesian with error model method gives significantly more accurate prediction along with reasonable credible intervals.
Clinical Use of CT Perfusion For Diagnosis and Prediction of Lesion Growth in Acute Ischemic Stroke

PubMed Central

Huisa, Branko N; Neil, William P; Schrader, Ronald; Maya, Marcel; Pereira, Benedict; Bruce, Nhu T; Lyden, Patrick D

2012-01-01

Background and Purpose CT perfusion (CTP) mapping in research centers correlates well with diffusion weighted imaging (DWI) lesions and may accurately differentiate the infarct core from ischemic penumbra. The value of CTP in real-world clinical practice has not been fully established. We investigated the yield of CTP– derived cerebral blood volume (CBV) and mean transient time (MTT) for the detection of cerebral ischemia and ischemic penumbra in a sample of acute ischemic stroke (AIS) patients. Methods We studied 165 patients with initial clinical symptoms suggestive of AIS. All patients had an initial non-contrast head CT, CT Perfusion (CTP), CT angiogram (CTA) and follow up brain MRI. The obtained perfusion images were used for image processing. CBV, MTT and DWI lesion volumes were visually estimated and manually traced. Statistical analysis was done using R-2.14.and SAS 9.1. Results All normal DWI sequences had normal CBV and MTT studies (N=89). Seventy-three patients had acute DWI lesions. CBV was abnormal in 23.3% and MTT was abnormal in 42.5% of these patients. There was a high specificity (91.8%)but poor sensitivity (40.0%) for MTT maps predicting positive DWI. Spearman correlation was significant between MTT and DWI lesions (ρ=0.66, p>0.0001) only for abnormal MTT and DWI lesions>0cc. CBV lesions did not correlate with final DWI. Conclusions In real-world use, acute imaging with CTP did not predict stroke or DWI lesions with sufficient accuracy. Our findings argue against the use of CTP for screening AIS patients until real-world implementations match the accuracy reported from specialized research centers. PMID:23253533
The perception of spatial layout in real and virtual worlds.

PubMed

Arthur, E J; Hancock, P A; Chrysler, S T

1997-01-01

As human-machine interfaces grow more immersive and graphically-oriented, virtual environment systems become more prominent as the medium for human-machine communication. Often, virtual environments (VE) are built to provide exact metrical representations of existing or proposed physical spaces. However, it is not known how individuals develop representational models of these spaces in which they are immersed and how those models may be distorted with respect to both the virtual and real-world equivalents. To evaluate the process of model development, the present experiment examined participant's ability to reproduce a complex spatial layout of objects having experienced them previously under different viewing conditions. The layout consisted of nine common objects arranged on a flat plane. These objects could be viewed in a free binocular virtual condition, a free binocular real-world condition, and in a static monocular view of the real world. The first two allowed active exploration of the environment while the latter condition allowed the participant only a passive opportunity to observe from a single viewpoint. Viewing conditions were a between-subject variable with 10 participants randomly assigned to each condition. Performance was assessed using mapping accuracy and triadic comparisons of relative inter-object distances. Mapping results showed a significant effect of viewing condition where, interestingly, the static monocular condition was superior to both the active virtual and real binocular conditions. Results for the triadic comparisons showed a significant interaction for gender by viewing condition in which males were more accurate than females. These results suggest that the situation model resulting from interaction with a virtual environment was indistinguishable from interaction with real objects at least within the constraints of the present procedure.
Predictors of smoking lapse in a human laboratory paradigm.

PubMed

Roche, Daniel J O; Bujarski, Spencer; Moallem, Nathasha R; Guzman, Iris; Shapiro, Jenessa R; Ray, Lara A

2014-07-01

During a smoking quit attempt, a single smoking lapse is highly predictive of future relapse. While several risk factors for a smoking lapse have been identified during clinical trials, a laboratory model of lapse was until recently unavailable and, therefore, it is unclear whether these characteristics also convey risk for lapse in a laboratory environment. The primary study goal was to examine whether real-world risk factors of lapse are also predictive of smoking behavior in a laboratory model of smoking lapse. After overnight abstinence, 77 smokers completed the McKee smoking lapse task, in which they were presented with the choice of smoking or delaying in exchange for monetary reinforcement. Primary outcome measures were the latency to initiate smoking behavior and the number of cigarettes smoked during the lapse. Several baseline measures of smoking behavior, mood, and individual traits were examined as predictive factors. Craving to relieve the discomfort of withdrawal, withdrawal severity, and tension level were negatively predictive of latency to smoke. In contrast, average number of cigarettes smoked per day, withdrawal severity, level of nicotine dependence, craving for the positive effects of smoking, and craving to relieve the discomfort of withdrawal were positively predictive of number of cigarettes smoked. The results suggest that real-world risk factors for smoking lapse are also predictive of smoking behavior in a laboratory model of lapse. Future studies using the McKee lapse task should account for between subject differences in the unique factors that independently predict each outcome measure.
Effects of age on a real-world What-Where-When memory task

PubMed Central

Mazurek, Adèle; Bhoopathy, Raja Meenakshi; Read, Jenny C. A.; Gallagher, Peter; Smulders, Tom V.

2015-01-01

Many cognitive abilities decline with aging, making it difficult to detect pathological changes against a background of natural changes in cognition. Most of the tests to assess cognitive decline are artificial tasks that have little resemblance to the problems faced by people in everyday life. This means both that people may have little practice doing such tasks (potentially contributing to the decline in performance) and that the tasks may not be good predictors of real-world cognitive problems. In this study, we test the performance of young people (18–25 years) and older people (60+-year-olds) on a novel, more ecologically valid test of episodic memory: the real-world What-Where-When (WWW) memory test. We also compare them on a battery of other cognitive tests, including working memory, psychomotor speed, executive function, and episodic memory. Older people show the expected age-related declines on the test battery. In the WWW memory task, older people were more likely to fail to remember any WWW combination than younger people were, although they did not significantly differ in their overall WWW score due to some older people performing as well as or better than most younger people. WWW memory performance was significantly predicted by other measures of episodic memory, such as the single-trial learning and long-term retention in the Rey Auditory Verbal Learning task and Combined Object Location Memory in the Object Relocation task. Self-reported memory complaints also predicted performance on the WWW task. These findings confirm that our real-world WWW memory task is a valid measure of episodic memory, with high ecological validity, which may be useful as a predictor of everyday memory abilities. The task will require a bit more development to improve its sensitivity to cognitive declines in aging and to potentially distinguish between mentally healthy older adults and those with early signs of cognitive pathologies. PMID:26042030

Neural correlates of naturalistic social cognition: brain-behavior relationships in healthy adults

PubMed Central

Rademacher, L.M.; Winkler, L.; Schultz, R.T.; Gründer, G.; Lammertz, S.E.

2016-01-01

Being able to infer the thoughts, feelings and intentions of those around us is indispensable in order to function in a social world. Despite growing interest in social cognition and its neural underpinnings, the factors that contribute to successful mental state attribution remain unclear. Current knowledge is limited because the most widely used tasks suffer from two main constraints: (i) They fail to capture individual variability due to ceiling effects and (ii) they use highly simplistic, often artificial stimuli inapt to mirror real-world socio-cognitive demands. In the present study, we address these problems by employing complex depictions of naturalistic social interactions that vary in both valence (positive vs negative) and ambiguity (high vs low). Thirty-eight healthy participants (20 female) made mental state judgments while brain responses were obtained using functional magnetic resonance imaging (fMRI). Accuracy varied based on valence and ambiguity conditions and women were more accurate than men with highly ambiguous social stimuli. Activity of the orbitofrontal cortex predicted performance in the high ambiguity condition. The results shed light on subtle differences in mentalizing abilities and associated neural activity. PMID:27496338
Teaching the Assessment of Normality Using Large Easily-Generated Real Data Sets

ERIC Educational Resources Information Center

Kulp, Christopher W.; Sprechini, Gene D.

2016-01-01

A classroom activity is presented, which can be used in teaching students statistics with an easily generated, large, real world data set. The activity consists of analyzing a video recording of an object. The colour data of the recorded object can then be used as a data set to explore variation in the data using graphs including histograms,…
The status of rotor noise technology: One man's opinion

NASA Technical Reports Server (NTRS)

White, R. P., Jr.

1978-01-01

The problem of establishing the state of the technology is approached by first identifying the various characteristics of rotor noise and then assessing the state of technology in understanding and predicting the most important of these rotor noise characteristics in a real-world environment.
Connecting Representations: Using Predict, Check, Explain

ERIC Educational Resources Information Center

Roy, George J.; Fueyo, Vivian; Vahey, Philip; Knudsen, Jennifer; Rafanan, Ken; Lara-Meloy, Teresa

2016-01-01

Although educators agree that making connections with the real world, as advocated by "Principles to Actions: Ensuring Mathematical Success for All" (NCTM 2014), is important, making such connections while addressing important mathematics is elusive. The authors have found that math content coupled with the instructional strategy of…
Population pharmacokinetics of tacrolimus in paediatric systemic lupus erythematosus based on real-world study.

PubMed

Wang, D-D; Lu, J-M; Li, Q; Li, Z-P

2018-05-15

Different population pharmacokinetics (PPK) models of tacrolimus have been established in various populations. However, the tacrolimus PPK model in paediatric systemic lupus erythematosus (PSLE) is still undefined. This study aimed to establish the tacrolimus PPK model in Chinese PSLE. A total of nineteen Chinese patients with PSLE from real-world study were characterized with nonlinear mixed-effects modelling (NONMEM). The impact of demographic features, biological characteristics, and concomitant medications was evaluated. Model validation was assessed by bootstrap and prediction-corrected visual predictive check (VPC). A one-compartment model with first-order absorption and elimination was determined to be the most suitable model in PSLE. The typical values of apparent oral clearance (CL/F) and the apparent volume of distribution (V/F) in the final model were 2.05 L/h and 309 L, respectively. Methylprednisolone and simvastatin were included as significant. The first validated tacrolimus PPK model in patients with PSLE is presented. © 2018 John Wiley & Sons Ltd.
Choosing face: The curse of self in profile image selection.

PubMed

White, David; Sutherland, Clare A M; Burton, Amy L

2017-01-01

People draw automatic social inferences from photos of unfamiliar faces and these first impressions are associated with important real-world outcomes. Here we examine the effect of selecting online profile images on first impressions. We model the process of profile image selection by asking participants to indicate the likelihood that images of their own face ("self-selection") and of an unfamiliar face ("other-selection") would be used as profile images on key social networking sites. Across two large Internet-based studies (n = 610), in line with predictions, image selections accentuated favorable social impressions and these impressions were aligned to the social context of the networking sites. However, contrary to predictions based on people's general expertise in self-presentation, other-selected images conferred more favorable impressions than self-selected images. We conclude that people make suboptimal choices when selecting their own profile pictures, such that self-perception places important limits on facial first impressions formed by others. These results underscore the dynamic nature of person perception in real-world contexts.
Virtual reality in the treatment of persecutory delusions: randomised controlled experimental study testing how to reduce delusional conviction.

PubMed

Freeman, Daniel; Bradley, Jonathan; Antley, Angus; Bourke, Emilie; DeWeever, Natalie; Evans, Nicole; Černis, Emma; Sheaves, Bryony; Waite, Felicity; Dunn, Graham; Slater, Mel; Clark, David M

2016-07-01

Persecutory delusions may be unfounded threat beliefs maintained by safety-seeking behaviours that prevent disconfirmatory evidence being successfully processed. Use of virtual reality could facilitate new learning. To test the hypothesis that enabling patients to test the threat predictions of persecutory delusions in virtual reality social environments with the dropping of safety-seeking behaviours (virtual reality cognitive therapy) would lead to greater delusion reduction than exposure alone (virtual reality exposure). Conviction in delusions and distress in a real-world situation were assessed in 30 patients with persecutory delusions. Patients were then randomised to virtual reality cognitive therapy or virtual reality exposure, both with 30 min in graded virtual reality social environments. Delusion conviction and real-world distress were then reassessed. In comparison with exposure, virtual reality cognitive therapy led to large reductions in delusional conviction (reduction 22.0%, P = 0.024, Cohen's d = 1.3) and real-world distress (reduction 19.6%, P = 0.020, Cohen's d = 0.8). Cognitive therapy using virtual reality could prove highly effective in treating delusions. © The Royal College of Psychiatrists 2016.
Virtual reality in the treatment of persecutory delusions: randomised controlled experimental study testing how to reduce delusional conviction

PubMed Central

Freeman, Daniel; Bradley, Jonathan; Antley, Angus; Bourke, Emilie; DeWeever, Natalie; Evans, Nicole; Černis, Emma; Sheaves, Bryony; Waite, Felicity; Dunn, Graham; Slater, Mel; Clark, David M.

2016-01-01

Background Persecutory delusions may be unfounded threat beliefs maintained by safety-seeking behaviours that prevent disconfirmatory evidence being successfully processed. Use of virtual reality could facilitate new learning. Aims To test the hypothesis that enabling patients to test the threat predictions of persecutory delusions in virtual reality social environments with the dropping of safety-seeking behaviours (virtual reality cognitive therapy) would lead to greater delusion reduction than exposure alone (virtual reality exposure). Method Conviction in delusions and distress in a real-world situation were assessed in 30 patients with persecutory delusions. Patients were then randomised to virtual reality cognitive therapy or virtual reality exposure, both with 30 min in graded virtual reality social environments. Delusion conviction and real-world distress were then reassessed. Results In comparison with exposure, virtual reality cognitive therapy led to large reductions in delusional conviction (reduction 22.0%, P = 0.024, Cohen's d = 1.3) and real-world distress (reduction 19.6%, P = 0.020, Cohen's d = 0.8). Conclusion Cognitive therapy using virtual reality could prove highly effective in treating delusions. PMID:27151071
Impact of Middle School Student Energy Monitoring Activities on Climate Change Beliefs and Intentions

ERIC Educational Resources Information Center

Christensen, Rhonda; Knezek, Gerald

2018-01-01

The Going Green! Middle Schoolers Out to Save the World project aims to direct middle school students' enthusiasm for hands-on activities toward interest in science and other STEM areas while guiding them to solve real-world problems. Students in this project are taught by their teachers to use energy monitoring equipment to audit standby power…
Generation of a Combined Dataset of Simulated Radar and Electro-Optical Imagery

DTIC Science & Technology

2005-10-05

directional reflectance distribution function (BRDF) predictions and the geometry of a line scanner. Using programs such as MODTRAN and FASCODE, images can be...DIRSIG tries to accurately model scenes through various approaches that model real- world occurrences. MODTRAN is an atmospheric radiative transfer code...used to predict path transmissions and radiances within the atmosphere (DIRSIG Manual, 2004). FASCODE is similar to MODTRAN , however it works as a
Towards an Online Seizure Advisory System-An Adaptive Seizure Prediction Framework Using Active Learning Heuristics.

PubMed

Karuppiah Ramachandran, Vignesh Raja; Alblas, Huibert J; Le, Duc V; Meratnia, Nirvana

2018-05-24

In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20 % of the labelled data and also improve the prediction accuracy even under the noisy condition.
Development of a wound healing index for patients with chronic wounds.

PubMed

Horn, Susan D; Fife, Caroline E; Smout, Randall J; Barrett, Ryan S; Thomson, Brett

2013-01-01

Randomized controlled trials in wound care generalize poorly because they exclude patients with significant comorbid conditions. Research using real-world wound care patients is hindered by lack of validated methods to stratify patients according to severity of underlying illnesses. We developed a comprehensive stratification system for patients with wounds that predicts healing likelihood. Complete medical record data on 50,967 wounds from the United States Wound Registry were assigned a clear outcome (healed, amputated, etc.). Factors known to be associated with healing were evaluated using logistic regression models. Significant variables (p < 0.05) were determined and subsequently tested on a holdout sample of data. A different model predicted healing for each wound type. Some variables predicted significantly in nearly all models: wound size, wound age, number of wounds, evidence of bioburden, tissue type exposed (Wagner grade or stage), being nonambulatory, and requiring hospitalization during the course of care. Variables significant in some models included renal failure, renal transplant, malnutrition, autoimmune disease, and cardiovascular disease. All models validated well when applied to the holdout sample. The "Wound Healing Index" can validly predict likelihood of wound healing among real-world patients and can facilitate comparative effectiveness research to identify patients needing advanced therapeutics. © 2013 by the Wound Healing Society.
Leveraging knowledge from physiological data: on-body heat stress risk prediction with sensor networks.

PubMed

Gaura, Elena; Kemp, John; Brusey, James

2013-12-01

The paper demonstrates that wearable sensor systems, coupled with real-time on-body processing and actuation, can enhance safety for wearers of heavy protective equipment who are subjected to harsh thermal environments by reducing risk of Uncompensable Heat Stress (UHS). The work focuses on Explosive Ordnance Disposal operatives and shows that predictions of UHS risk can be performed in real-time with sufficient accuracy for real-world use. Furthermore, it is shown that the required sensory input for such algorithms can be obtained with wearable, non-intrusive sensors. Two algorithms, one based on Bayesian nets and another on decision trees, are presented for determining the heat stress risk, considering the mean skin temperature prediction as a proxy. The algorithms are trained on empirical data and have accuracies of 92.1±2.9% and 94.4±2.1%, respectively when tested using leave-one-subject-out cross-validation. In applications such as Explosive Ordnance Disposal operative monitoring, such prediction algorithms can enable autonomous actuation of cooling systems and haptic alerts to minimize casualties.
Constraints and entropy in a model of network evolution

NASA Astrophysics Data System (ADS)

Tee, Philip; Wakeman, Ian; Parisis, George; Dawes, Jonathan; Kiss, István Z.

2017-11-01

Barabási-Albert's "Scale Free" model is the starting point for much of the accepted theory of the evolution of real world communication networks. Careful comparison of the theory with a wide range of real world networks, however, indicates that the model is in some cases, only a rough approximation to the dynamical evolution of real networks. In particular, the exponent γ of the power law distribution of degree is predicted by the model to be exactly 3, whereas in a number of real world networks it has values between 1.2 and 2.9. In addition, the degree distributions of real networks exhibit cut offs at high node degree, which indicates the existence of maximal node degrees for these networks. In this paper we propose a simple extension to the "Scale Free" model, which offers better agreement with the experimental data. This improvement is satisfying, but the model still does not explain why the attachment probabilities should favor high degree nodes, or indeed how constraints arrive in non-physical networks. Using recent advances in the analysis of the entropy of graphs at the node level we propose a first principles derivation for the "Scale Free" and "constraints" model from thermodynamic principles, and demonstrate that both preferential attachment and constraints could arise as a natural consequence of the second law of thermodynamics.
Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

PubMed Central

Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

2015-01-01

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes. PMID:26294903
Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes.

PubMed

Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

2015-01-01

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.
Gaming across Cultures: Experimenting with Alternate Pedagogies

ERIC Educational Resources Information Center

Pillay, Soma; James, Reynold

2013-01-01

Purpose: Higher education is influenced, to an increasing extent, by changing student demographics. This requires educators to design and deliver learning systems which will enhance students' learning experience with innovative, real world and engaging resources. The authors predict that transformations in the learning systems will increase as…
The neural components of empathy: predicting daily prosocial behavior.

PubMed

Morelli, Sylvia A; Rameson, Lian T; Lieberman, Matthew D

2014-01-01

Previous neuroimaging studies on empathy have not clearly identified neural systems that support the three components of empathy: affective congruence, perspective-taking, and prosocial motivation. These limitations stem from a focus on a single emotion per study, minimal variation in amount of social context provided, and lack of prosocial motivation assessment. In the current investigation, 32 participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing painful, anxious, and happy events that varied in valence and amount of social context provided. They also completed a 14-day experience sampling survey that assessed real-world helping behaviors. The results demonstrate that empathy for positive and negative emotions selectively activates regions associated with positive and negative affect, respectively. In addition, the mirror system was more active during empathy for context-independent events (pain), whereas the mentalizing system was more active during empathy for context-dependent events (anxiety, happiness). Finally, the septal area, previously linked to prosocial motivation, was the only region that was commonly activated across empathy for pain, anxiety, and happiness. Septal activity during each of these empathic experiences was predictive of daily helping. These findings suggest that empathy has multiple input pathways, produces affect-congruent activations, and results in septally mediated prosocial motivation.
The neural components of empathy: Predicting daily prosocial behavior

PubMed Central

Rameson, Lian T.; Lieberman, Matthew D.

2014-01-01

Previous neuroimaging studies on empathy have not clearly identified neural systems that support the three components of empathy: affective congruence, perspective-taking, and prosocial motivation. These limitations stem from a focus on a single emotion per study, minimal variation in amount of social context provided, and lack of prosocial motivation assessment. In the current investigation, 32 participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing painful, anxious, and happy events that varied in valence and amount of social context provided. They also completed a 14-day experience sampling survey that assessed real-world helping behaviors. The results demonstrate that empathy for positive and negative emotions selectively activates regions associated with positive and negative affect, respectively. In addition, the mirror system was more active during empathy for context-independent events (pain), whereas the mentalizing system was more active during empathy for context-dependent events (anxiety, happiness). Finally, the septal area, previously linked to prosocial motivation, was the only region that was commonly activated across empathy for pain, anxiety, and happiness. Septal activity during each of these empathic experiences was predictive of daily helping. These findings suggest that empathy has multiple input pathways, produces affect-congruent activations, and results in septally mediated prosocial motivation. PMID:22887480
The real world and lunar base activation scenarios

NASA Technical Reports Server (NTRS)

Schmitt, Harrison H.

1992-01-01

A lunar base or a network of lunar bases may have highly desirable support functions in a national or international program to explore and settle Mars. In addition, He-3 exported from the Moon could be the basis for providing much of the energy needs of humankind in the twenty-first century. Both technical and managerial issues must be addressed when considering the establishment of a lunar base that can serve the needs of human civilization in space. Many of the technical issues become evident in the consideration of hypothetical scenarios for the activation of a network of lunar bases. Specific and realistic assumptions must be made about the conduct of various types of activities in addition to the general assumptions given above. These activities include landings, crew consumables, power production, crew selection, risk management, habitation, science station placement, base planning, science, agriculture, resource evaluation, readaptation, plant activation and test, storage module landings, resource transport module landings, integrated operations, maintenance, Base 2 activation, and management. The development of scenarios for the activation of a lunar base or network of bases will require close attention to the 'real world' of space operations. That world is defined by the natural environment, available technology, realistic objectives, and common sense.

Cryogenic Selective Surface - How Cold Can We Go?

NASA Technical Reports Server (NTRS)

Youngquist, Robert; Nurge, Mark

2015-01-01

Selective surfaces have wavelength dependent emissivitya bsorption. These surfaces can be designed to reflect solar radiation, while maximizing infrared emittance, yielding a cooling effect even in sunlight. On earth cooling to -50 C below ambient has been achieved, but in space, outside of the atmosphere, theory using ideal materials has predicted a maximum cooling to 40 K! If this result holds up for real world materials and conditions, then superconducting systems and cryogenic storage can be achieved in space without active cooling. Such a result would enable long term cryogenic storage in deep space and the use of large scale superconducting systems for such applications as galactic cosmic radiation (GCR) shielding and large scale energy storage.
Effects of Gait Self-Efficacy and Lower-Extremity Physical Function on Dual-Task Performance in Older Adults

PubMed Central

Banducci, Sarah E.; Daugherty, Ana M.; Fanning, Jason; Awick, Elizabeth A.; Porter, Gwenndolyn C.; Burzynska, Agnieszka; Shen, Sa; Kramer, Arthur F.; McAuley, Edward

2017-01-01

Objectives. Despite evidence of self-efficacy and physical function's influences on functional limitations in older adults, few studies have examined relationships in the context of complex, real-world tasks. The present study tested the roles of self-efficacy and physical function in predicting older adults' street-crossing performance in single- and dual-task simulations. Methods. Lower-extremity physical function, gait self-efficacy, and street-crossing success ratio were assessed in 195 older adults (60–79 years old) at baseline of a randomized exercise trial. During the street-crossing task, participants walked on a self-propelled treadmill in a virtual reality environment. Participants crossed the street without distraction (single-task trials) and conversed on a cell phone (dual-task trials). Structural equation modeling was used to test hypothesized associations independent of demographic and clinical covariates. Results. Street-crossing performance was better on single-task trials when compared with dual-task trials. Direct effects of self-efficacy and physical function on success ratio were observed in dual-task trials only. The total effect of self-efficacy was significant in both conditions. The indirect path through physical function was evident in the dual-task condition only. Conclusion. Physical function can predict older adults' performance on high fidelity simulations of complex, real-world tasks. Perceptions of function (i.e., self-efficacy) may play an even greater role. The trial is registered with United States National Institutes of Health ClinicalTrials.gov (ID: NCT01472744; Fit & Active Seniors Trial). PMID:28255557
Cars and Kinetic Energy -- Some Simple Physics with Real-World Relevance

NASA Astrophysics Data System (ADS)

Parthasarathy, Raghuveer

2012-10-01

Understanding energy usage is crucial to understanding modern civilization, as well as many of the challenges it faces. Energy-related issues also offer real-world examples of important physical concepts, and as such have been the focus of several articles in The Physics Teacher in the past few decades (e.g., Refs. 1-5, noted further below). Here, I illustrate how a basic understanding of kinetic energy—a topic encountered early in any introductory physics course—enables significant insights into the nature of automobile transportation. Specifically, we can accurately predict how much power the average driver in the United States uses, and explain what determines this, without needing to consider any aspects of mechanical engineering or engine design.
Intraindividual variability in basic reaction time predicts middle-aged and older pilots' flight simulator performance.

PubMed

Kennedy, Quinn; Taylor, Joy; Heraldez, Daniel; Noda, Art; Lazzeroni, Laura C; Yesavage, Jerome

2013-07-01

Intraindividual variability (IIV) is negatively associated with cognitive test performance and is positively associated with age and some neurological disorders. We aimed to extend these findings to a real-world task, flight simulator performance. We hypothesized that IIV predicts poorer initial flight performance and increased rate of decline in performance among middle-aged and older pilots. Two-hundred and thirty-six pilots (40-69 years) completed annual assessments comprising a cognitive battery and two 75-min simulated flights in a flight simulator. Basic and complex IIV composite variables were created from measures of basic reaction time and shifting and divided attention tasks. Flight simulator performance was characterized by an overall summary score and scores on communication, emergencies, approach, and traffic avoidance components. Although basic IIV did not predict rate of decline in flight performance, it had a negative association with initial performance for most flight measures. After taking into account processing speed, basic IIV explained an additional 8%-12% of the negative age effect on initial flight performance. IIV plays an important role in real-world tasks and is another aspect of cognition that underlies age-related differences in cognitive performance.
Intraindividual Variability in Basic Reaction Time Predicts Middle-Aged and Older Pilots’ Flight Simulator Performance

PubMed Central

2013-01-01

Objectives. Intraindividual variability (IIV) is negatively associated with cognitive test performance and is positively associated with age and some neurological disorders. We aimed to extend these findings to a real-world task, flight simulator performance. We hypothesized that IIV predicts poorer initial flight performance and increased rate of decline in performance among middle-aged and older pilots. Method. Two-hundred and thirty-six pilots (40–69 years) completed annual assessments comprising a cognitive battery and two 75-min simulated flights in a flight simulator. Basic and complex IIV composite variables were created from measures of basic reaction time and shifting and divided attention tasks. Flight simulator performance was characterized by an overall summary score and scores on communication, emergencies, approach, and traffic avoidance components. Results. Although basic IIV did not predict rate of decline in flight performance, it had a negative association with initial performance for most flight measures. After taking into account processing speed, basic IIV explained an additional 8%–12% of the negative age effect on initial flight performance. Discussion. IIV plays an important role in real-world tasks and is another aspect of cognition that underlies age-related differences in cognitive performance. PMID:23052365
Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching.

PubMed

Edwards, Ann L; Dawson, Michael R; Hebert, Jacqueline S; Sherstan, Craig; Sutton, Richard S; Chan, K Ming; Pilarski, Patrick M

2016-10-01

Myoelectric prostheses currently used by amputees can be difficult to control. Machine learning, and in particular learned predictions about user intent, could help to reduce the time and cognitive load required by amputees while operating their prosthetic device. The goal of this study was to compare two switching-based methods of controlling a myoelectric arm: non-adaptive (or conventional) control and adaptive control (involving real-time prediction learning). Case series study. We compared non-adaptive and adaptive control in two different experiments. In the first, one amputee and one non-amputee subject controlled a robotic arm to perform a simple task; in the second, three able-bodied subjects controlled a robotic arm to perform a more complex task. For both tasks, we calculated the mean time and total number of switches between robotic arm functions over three trials. Adaptive control significantly decreased the number of switches and total switching time for both tasks compared with the conventional control method. Real-time prediction learning was successfully used to improve the control interface of a myoelectric robotic arm during uninterrupted use by an amputee subject and able-bodied subjects. Adaptive control using real-time prediction learning has the potential to help decrease both the time and the cognitive load required by amputees in real-world functional situations when using myoelectric prostheses. © The International Society for Prosthetics and Orthotics 2015.
Information-theoretic model selection for optimal prediction of stochastic dynamical systems from data

NASA Astrophysics Data System (ADS)

Darmon, David

2018-03-01

In the absence of mechanistic or phenomenological models of real-world systems, data-driven models become necessary. The discovery of various embedding theorems in the 1980s and 1990s motivated a powerful set of tools for analyzing deterministic dynamical systems via delay-coordinate embeddings of observations of their component states. However, in many branches of science, the condition of operational determinism is not satisfied, and stochastic models must be brought to bear. For such stochastic models, the tool set developed for delay-coordinate embedding is no longer appropriate, and a new toolkit must be developed. We present an information-theoretic criterion, the negative log-predictive likelihood, for selecting the embedding dimension for a predictively optimal data-driven model of a stochastic dynamical system. We develop a nonparametric estimator for the negative log-predictive likelihood and compare its performance to a recently proposed criterion based on active information storage. Finally, we show how the output of the model selection procedure can be used to compare candidate predictors for a stochastic system to an information-theoretic lower bound.
A Signal to Noise Paradox in Climate Predictions

NASA Astrophysics Data System (ADS)

Eade, R.; Scaife, A. A.; Smith, D.; Dunstone, N. J.; MacLachlan, C.; Hermanson, L.; Ruth, C.

2017-12-01

Recent advances in climate modelling have resulted in the achievement of skilful long-range prediction, particular that associated with the winter circulation over the north Atlantic (e.g. Scaife et al 2014, Stockdale et al 2015, Dunstone et al 2016) including impacts over Europe and North America, and further afield. However, while highly significant and potentially useful skill exists, the signal-to-noise ratio of the ensemble mean to total variability in these ensemble predictions is anomalously small (Scaife et al 2014) and the correlation between the ensemble mean and historical observations exceeds the proportion of predictable variance in the ensemble (Eade et al 2014). This means the real world is more predictable than our climate models. Here we discuss a series of hypothesis tests that have been carried out to assess issues with model mechanisms compared to the observed world, and present the latest findings in our attempt to determine the cause of the anomalously weak predicted signals in our seasonal-to-decadal hindcasts.
Heparin-induced thrombocytopenia: real-world issues.

PubMed

Linkins, Lori-Ann; Warkentin, Theodore E

2011-09-01

Heparin-induced thrombocytopenia (HIT) is a prothrombotic drug reaction caused by platelet-activating antibodies. HIT sera often activate platelets without needing heparin-such heparin-"independent" platelet activation can be associated with HIT beginning or worsening despite stopping heparin ("delayed-onset HIT"). We address important issues in HIT diagnosis and therapy, using a recent cohort of HIT patients to illustrate influences of heparin type; triggers for HIT investigation; serological features of heparin-independent platelet activation; and treatment. In our cohort of recent HIT cases ( N = 13), low-molecular-weight heparin (dalteparin) was a common causative agent ( N = 8, 62%); most patients were diagnosed after HIT-thrombosis had occurred; and danaparoid was the most frequently selected treatment. Heparin-independent platelet activation was common (7/13 [54%]) and predicted slower platelet count recovery (>1 week) among evaluable patients (5/5 vs 1/6; P = 0.015). In our experience with argatroban-treated patients, HIT-associated consumptive coagulopathy confounds anticoagulant monitoring. Our observations provide guidance on practical aspects of HIT diagnosis and management. Thieme Medical Publishers.
A RE-AIM evaluation of theory-based physical activity interventions.

PubMed

Antikainen, Iina; Ellis, Rebecca

2011-04-01

Although physical activity interventions have been shown to effectively modify behavior, little research has examined the potential of these interventions for adoption in real-world settings. The purpose of this literature review was to evaluate the external validity of 57 theory-based physical activity interventions using the RE-AIM framework. The physical activity interventions included were more likely to report on issues of internal, rather than external validity and on individual, rather than organizational components of the RE-AIM framework, making the translation of many interventions into practice difficult. Furthermore, most studies included motivated, healthy participants, thus reducing the generalizability of the interventions to real-world settings that provide services to more diverse populations. To determine if a given intervention is feasible and effective in translational research, more information should be reported about the factors that affect external validity.
Active Learning with Irrelevant Examples

NASA Technical Reports Server (NTRS)

Mazzoni, Dominic; Wagstaff, Kiri L.; Burl, Michael

2006-01-01

Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled items that are irrelevant to the user's classification goals. Queries about these points slow down learning because they provide no information about the problem of interest. We have observed that when irrelevant items are present, active learning can perform worse than random selection, requiring more time (queries) to achieve the same level of accuracy. Therefore, we propose a novel approach, Relevance Bias, in which the active learner combines its default selection heuristic with the output of a simultaneously trained relevance classifier to favor items that are likely to be both informative and relevant. In our experiments on a real-world problem and two benchmark datasets, the Relevance Bias approach significantly improved the learning rate of three different active learning approaches.
Reading Stories Activates Neural Representations of Visual and Motor Experiences

PubMed Central

Speer, Nicole K.; Reynolds, Jeremy R.; Swallow, Khena M.; Zacks, Jeffrey M.

2010-01-01

To understand and remember stories, readers integrate their knowledge of the world with information in the text. Here we present functional neuroimaging evidence that neural systems track changes in the situation described by a story. Different brain regions track different aspects of a story, such as a character’s physical location or current goals. Some of these regions mirror those involved when people perform, imagine, or observe similar real-world activities. These results support the view that readers understand a story by simulating the events in the story world and updating their simulation when features of that world change. PMID:19572969
Language-driven anticipatory eye movements in virtual reality.

PubMed

Eichert, Nicole; Peeters, David; Hagoort, Peter

2018-06-01

Predictive language processing is often studied by measuring eye movements as participants look at objects on a computer screen while they listen to spoken sentences. This variant of the visual-world paradigm has revealed that information encountered by a listener at a spoken verb can give rise to anticipatory eye movements to a target object, which is taken to indicate that people predict upcoming words. The ecological validity of such findings remains questionable, however, because these computer experiments used two-dimensional stimuli that were mere abstractions of real-world objects. Here we present a visual-world paradigm study in a three-dimensional (3-D) immersive virtual reality environment. Despite significant changes in the stimulus materials and the different mode of stimulus presentation, language-mediated anticipatory eye movements were still observed. These findings thus indicate that people do predict upcoming words during language comprehension in a more naturalistic setting where natural depth cues are preserved. Moreover, the results confirm the feasibility of using eyetracking in rich and multimodal 3-D virtual environments.
Real-World Impact of Neurocognitive Deficits in Acute and Early HIV Infection

PubMed Central

Doyle, Katie L.; Morgan, Erin E.; Morris, Sheldon; Smith, Davey M.; Little, Susan; Iudicello, Jennifer E.; Blackstone, Kaitlin; Moore, David J.; Grant, Igor; Letendre, Scott L.; Woods, Steven Paul

2013-01-01

The acute and early period of HIV-1 infection (AEH) is characterized by neuroinflammatory and immunopathogenic processes that can alter the integrity of neural systems and neurocognitive functions. However, the extent to which central nervous system changes in AEH confer increased risk of real-world functioning (RWF) problems is not known. In the present study, 34 individuals with AEH and 39 seronegative comparison participants completed standardized neuromedical, psychiatric, and neurocognitive research evaluations, alongside a comprehensive assessment of RWF that included cognitive symptoms in daily life, basic and instrumental activities of daily living, clinician-rated global functioning, and employment. Results showed that AEH was associated with a significantly increased risk of dependence in RWF, which was particularly elevated among AEH persons with global neurocognitive impairment (NCI). Among those with AEH, NCI (i.e., deficits in learning and information processing speed), mood disorders (i.e., Bipolar Disorder), and substance dependence (e.g., methamphetamine dependence) were all independently predictive of RWF dependence. Findings suggest that neurocognitively impaired individuals with AEH are at notably elevated risk of clinically significant challenges in normal daily functioning. Screening for neurocognitive, mood, and substance use disorders in AEH may facilitate identification of individuals at high risk of functional dependence who may benefit from psychological and medical strategies to manage their neuropsychiatric conditions. PMID:24277439
Schools and Curricula for the 21st Century: Predictions, Visions, and Anticipations.

ERIC Educational Resources Information Center

Zenger, Weldon; Zenger, Sharon K.

1999-01-01

In tomorrow's schools, technology will strongly determine how and what teachers will use for instruction. Literacy requirements will include accessing, thinking, and communication skills. Curriculum planners will grasp real-world requirements and will set guidelines for needed skills. Children will begin education in their cribs surrounded by…
What Do the Stats Tell Us? Engaging Elementary Children in Probabilistic Reasoning Based on Data Analysis

ERIC Educational Resources Information Center

Hourigan, Mairéad; Leavy, Aisling

2016-01-01

As part of Japanese Lesson study research focusing on "comparing and describing likelihoods", fifth grade elementary students used real-world data in decision-making. Sporting statistics facilitated opportunities for informal inference, where data were used to make and justify predictions.
An Evolutionary Machine Learning Framework for Big Data Sequence Mining

ERIC Educational Resources Information Center

Kamath, Uday Krishna

2014-01-01

Sequence classification is an important problem in many real-world applications. Unlike other machine learning data, there are no "explicit" features or signals in sequence data that can help traditional machine learning algorithms learn and predict from the data. Sequence data exhibits inter-relationships in the elements that are…
Sports Institute for Research/Change Agent Research--SIR/CAR.

ERIC Educational Resources Information Center

Moriarty, Dick; Duthie, James

1974-01-01

The decline of the independent, scholar-scientist closeted in a library and/or laboratory resulting from increased social stress on universities for "more scholar per dollar" and "more relevance for the real world" predicts an inevitable shift to action research. The shift in system from relatively independent basic researchers…
Relationship of Temporal Lobe Volumes to Neuropsychological Test Performance in Healthy Children

ERIC Educational Resources Information Center

Wells, Carolyn T.; Mahone, E. Mark; Matson, Melissa A.; Kates, Wendy R.; Hay, Trisha; Horska, Alena

2008-01-01

Ecological validity of neuropsychological assessment includes the ability of tests to predict real-world functioning and/or covary with brain structures. Studies have examined the relationship between adaptive skills and test performance, with less focus on the association between regional brain volumes and neurobehavioral function in healthy…
Opportunities and Challenges in Employing In Vitro-In Vivo Extrapolation (IVIVE) to the Tox21 Dataset

EPA Science Inventory

In vitro-in vivo extrapolation (IVIVE), or the process of using in vitro data to predict in vivo phenomena, provides key opportunities to bridge the disconnect between high-throughput screening data and real-world human exposures and potential health effects. Strategies utilizing...

Simulation in International Studies

ERIC Educational Resources Information Center

Boyer, Mark A.

2011-01-01

Social scientists have long worked to replicate real-world phenomena in their research and teaching environments. Unlike our biophysical science colleagues, we are faced with an area of study that is not governed by the laws of physics and other more predictable relationships. As a result, social scientists, and international studies scholars more…
Air Force Laboratory’s 2005 Technology Milestones

DTIC Science & Technology

2006-01-01

Computational materials science methods can benefit the design and property prediction of complex real-world materials. With these models , scientists and...Warfighter Page Air High - Frequency Acoustic System...800) 203-6451 High - Frequency Acoustic System Payoff Scientists created the High - Frequency Acoustic Suppression Technology (HiFAST) airflow control
Differential Lung Toxicity of Biomass Smoke from Smoldering and Flaming Phases Following Acute Inhalation Exposure

EPA Science Inventory

We previously demonstrated that, on a mass basis, lung toxicity associated with particulate matter (PM) from flaming smoke aspirated into mouse lungs is greater than smoldering PM. This finding however has to be validated in inhalation studies to better predict real-world exposu...
Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

2015-01-01

The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud-computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high-performance computing environments, supporting multi-node systems required for near real-time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on-demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on-demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre-compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF-EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher-resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short-term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building activities in environmental monitoring and prediction across a growing number of regional hubs throughout the world. Capacity-building applications that extend numerical weather prediction to developing countries are intended to provide near real-time applications to benefit public health, safety, and economic interests, but may have a greater impact during disaster events by providing a source for local predictions of weather-related hazards, or impacts that local weather events may have during the recovery phase.
Aging and autism spectrum disorder: Evidence from the broad autism phenotype.

PubMed

Wallace, Gregory L; Budgett, Jessica; Charlton, Rebecca A

2016-12-01

This study investigated for the first time the broad autism phenotype (BAP) in the context of older adulthood and its associations with real-world executive function, social support, and both depression and anxiety symptomatology. Based on self-ratings of autistic traits, 66 older adults (60+ years old, range = 61-88) were split into BAP (n = 20) and control (n = 46) groups. Individuals in the BAP group, even after controlling for age, education level, sex, and health problems, exhibited more real-world executive function problems in multiple domains, reported lower levels of social support, and self-rated increased depression and anxiety symptomatology compared to the control group. Regression analysis revealed that level of social support was the strongest predictor of BAP traits across both groups, although real-world executive function problems and depression symptomatology were also significant predictors. Moreover, when predicting anxiety and depression symptomatology, BAP traits were the strongest predictors above and beyond the effects of demographic factors, real-world executive function problems, and social support levels. These findings suggest that the BAP in older adulthood imparts additional risks to areas of functioning that are known to be crucial to aging-related outcomes in the context of typical development. These results might in turn inform aging in autism spectrum disorder, which has been largely unexplored to date. Autism Res 2016, 9: 1294-1303. © 2016 International Society for Autism Research, Wiley Periodicals, Inc. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.
Creativity and sensory gating indexed by the P50: selective versus leaky sensory gating in divergent thinkers and creative achievers.

PubMed

Zabelina, Darya L; O'Leary, Daniel; Pornpattananangkul, Narun; Nusslock, Robin; Beeman, Mark

2015-03-01

Creativity has previously been linked with atypical attention, but it is not clear what aspects of attention, or what types of creativity are associated. Here we investigated specific neural markers of a very early form of attention, namely sensory gating, indexed by the P50 ERP, and how it relates to two measures of creativity: divergent thinking and real-world creative achievement. Data from 84 participants revealed that divergent thinking (assessed with the Torrance Test of Creative Thinking) was associated with selective sensory gating, whereas real-world creative achievement was associated with "leaky" sensory gating, both in zero-order correlations and when controlling for academic test scores in a regression. Thus both creativity measures related to sensory gating, but in opposite directions. Additionally, divergent thinking and real-world creative achievement did not interact in predicting P50 sensory gating, suggesting that these two creativity measures orthogonally relate to P50 sensory gating. Finally, the ERP effect was specific to the P50 - neither divergent thinking nor creative achievement were related to later components, such as the N100 and P200. Overall results suggest that leaky sensory gating may help people integrate ideas that are outside of focus of attention, leading to creativity in the real world; whereas divergent thinking, measured by divergent thinking tests which emphasize numerous responses within a limited time, may require selective sensory processing more than previously thought. Copyright © 2015 Elsevier Ltd. All rights reserved.
Understanding real-world implementation quality and "active ingredients" of PBIS.

PubMed

Molloy, Lauren E; Moore, Julia E; Trail, Jessica; Van Epps, John James; Hopfer, Suellen

2013-12-01

Programs delivered in the "real world" often look substantially different from what was originally intended by program developers. Depending on which components of a program are being trimmed or altered, such modifications may seriously undermine the effectiveness of a program. In the present study, these issues are explored within a widely used school-based, non-curricular intervention, Positive Behavioral Intervention and Supports. The present study takes advantage of a uniquely large dataset to gain a better understanding of the "real-world" implementation quality of PBIS and to take a first step toward identifying the components of PBIS that "matter most" for student outcomes. Data from 27,689 students and 166 public primary and secondary schools across seven states included school and student demographics, indices of PBIS implementation quality, and reports of problem behaviors for any student who received an office discipline referral during the 2007-2008 school year. Results of the present study identify three key components of PBIS that many schools are failing to implement properly, three program components that were most related to lower rates of problem behavior (i.e., three "active ingredients" of PBIS), and several school characteristics that help to account for differences across schools in the quality of PBIS implementation. Overall, findings highlight the importance of assessing implementation quality in "real-world" settings, and the need to continue improving understanding of how and why programs work. Findings are discussed in terms of their implications for policy.
Real-world exhaust temperature profiles of on-road heavy-duty diesel vehicles equipped with selective catalytic reduction.

PubMed

Boriboonsomsin, Kanok; Durbin, Thomas; Scora, George; Johnson, Kent; Sandez, Daniel; Vu, Alexander; Jiang, Yu; Burnette, Andrew; Yoon, Seungju; Collins, John; Dai, Zhen; Fulper, Carl; Kishan, Sandeep; Sabisch, Michael; Jackson, Doug

2018-09-01

On-road heavy-duty diesel vehicles are a major contributor of oxides of nitrogen (NO x ) emissions. In the US, many heavy-duty diesel vehicles employ selective catalytic reduction (SCR) technology to meet the 2010 emission standard for NO x . Typically, SCR needs to be at least 200°C before a significant level of NO x reduction is achieved. However, this SCR temperature requirement may not be met under some real-world operating conditions, such as during cold starts, long idling, or low speed/low engine load driving activities. The frequency of vehicle operation with low SCR temperature varies partly by the vehicle's vocational use. In this study, detailed vehicle and engine activity data were collected from 90 heavy-duty vehicles involved in a range of vocations, including line haul, drayage, construction, agricultural, food distribution, beverage distribution, refuse, public work, and utility repair. The data were used to create real-world SCR temperature and engine load profiles and identify the fraction of vehicle operating time that SCR may not be as effective for NO x control. It is found that the vehicles participated in this study operate with SCR temperature lower than 200°C for 11-70% of the time depending on their vocation type. This implies that real-world NO x control efficiency could deviate from the control efficiency observed during engine certification. Copyright © 2018 Elsevier B.V. All rights reserved.
Neural representations of contextual guidance in visual search of real-world scenes.

PubMed

Preston, Tim J; Guo, Fei; Das, Koel; Giesbrecht, Barry; Eckstein, Miguel P

2013-05-01

Exploiting scene context and object-object co-occurrence is critical in guiding eye movements and facilitating visual search, yet the mediating neural mechanisms are unknown. We used functional magnetic resonance imaging while observers searched for target objects in scenes and used multivariate pattern analyses (MVPA) to show that the lateral occipital complex (LOC) can predict the coarse spatial location of observers' expectations about the likely location of 213 different targets absent from the scenes. In addition, we found weaker but significant representations of context location in an area related to the orienting of attention (intraparietal sulcus, IPS) as well as a region related to scene processing (retrosplenial cortex, RSC). Importantly, the degree of agreement among 100 independent raters about the likely location to contain a target object in a scene correlated with LOC's ability to predict the contextual location while weaker but significant effects were found in IPS, RSC, the human motion area, and early visual areas (V1, V3v). When contextual information was made irrelevant to observers' behavioral task, the MVPA analysis of LOC and the other areas' activity ceased to predict the location of context. Thus, our findings suggest that the likely locations of targets in scenes are represented in various visual areas with LOC playing a key role in contextual guidance during visual search of objects in real scenes.
First steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage

PubMed Central

Clark, Ian A.; Niehaus, Katherine E.; Duff, Eugene P.; Di Simplicio, Martina C.; Clifford, Gari D.; Smith, Stephen M.; Mackay, Clare E.; Woolrich, Mark W.; Holmes, Emily A.

2014-01-01

After psychological trauma, why do some only some parts of the traumatic event return as intrusive memories while others do not? Intrusive memories are key to cognitive behavioural treatment for post-traumatic stress disorder, and an aetiological understanding is warranted. We present here analyses using multivariate pattern analysis (MVPA) and a machine learning classifier to investigate whether peri-traumatic brain activation was able to predict later intrusive memories (i.e. before they had happened). To provide a methodological basis for understanding the context of the current results, we first show how functional magnetic resonance imaging (fMRI) during an experimental analogue of trauma (a trauma film) via a prospective event-related design was able to capture an individual's later intrusive memories. Results showed widespread increases in brain activation at encoding when viewing a scene in the scanner that would later return as an intrusive memory in the real world. These fMRI results were replicated in a second study. While traditional mass univariate regression analysis highlighted an association between brain processing and symptomatology, this is not the same as prediction. Using MVPA and a machine learning classifier, it was possible to predict later intrusive memories across participants with 68% accuracy, and within a participant with 97% accuracy; i.e. the classifier could identify out of multiple scenes those that would later return as an intrusive memory. We also report here brain networks key in intrusive memory prediction. MVPA opens the possibility of decoding brain activity to reconstruct idiosyncratic cognitive events with relevance to understanding and predicting mental health symptoms. PMID:25151915
Celebrate Mathematical Curiosity

ERIC Educational Resources Information Center

Redford, Christine

2011-01-01

Children's mathematical questions are often based in real-world experiences, as they instinctively make connections to the world around them. In teaching math methods courses, this author recently started to emphasize the importance of fostering curiosity in, and activating the thinking of, the students. In this article, she describes how to tap…
Road Risk Modeling and Cloud-Aided Safety-Based Route Planning.

PubMed

Li, Zhaojian; Kolmanovsky, Ilya; Atkins, Ella; Lu, Jianbo; Filev, Dimitar P; Michelini, John

2016-11-01

This paper presents a safety-based route planner that exploits vehicle-to-cloud-to-vehicle (V2C2V) connectivity. Time and road risk index (RRI) are considered as metrics to be balanced based on user preference. To evaluate road segment risk, a road and accident database from the highway safety information system is mined with a hybrid neural network model to predict RRI. Real-time factors such as time of day, day of the week, and weather are included as correction factors to the static RRI prediction. With real-time RRI and expected travel time, route planning is formulated as a multiobjective network flow problem and further reduced to a mixed-integer programming problem. A V2C2V implementation of our safety-based route planning approach is proposed to facilitate access to real-time information and computing resources. A real-world case study, route planning through the city of Columbus, Ohio, is presented. Several scenarios illustrate how the "best" route can be adjusted to favor time versus safety metrics.
Multi-Step Time Series Forecasting with an Ensemble of Varied Length Mixture Models.

PubMed

Ouyang, Yicun; Yin, Hujun

2018-05-01

Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model. They generally perform poorly in practical applications. In this paper, as an extension of the self-organizing mixture autoregressive (AR) model, the varied length mixture (VLM) models are proposed to model and forecast time series over multi-steps. The key idea is to preserve the dependencies between the time points within the prediction horizon. Training data are segmented to various lengths corresponding to various forecasting horizons, and the VLM models are trained in a self-organizing fashion on these segments to capture these dependencies in its component AR models of various predicting horizons. The VLM models form a probabilistic mixture of these varied length models. A combination of short and long VLM models and an ensemble of them are proposed to further enhance the prediction performance. The effectiveness of the proposed methods and their marked improvements over the existing methods are demonstrated through a number of experiments on synthetic data, real-world FX rates and weather temperatures.
Executive Function and ADHD: A Comparison of Children's Performance during Neuropsychological Testing and Real-World Activities

ERIC Educational Resources Information Center

Lawrence, Vivienne; Houghton, Stephen; Douglas, Graham; Durkin, Kevin; Whiting, Ken; Tannock, Rosemary

2004-01-01

Objective: Current understanding of executive function deficits in Attention-Deficit/Hyperactivity Disorder (ADHD) is derived almost exclusively from neuropsychological testing conducted in laboratory settings. This study compared children's performance on both neuropsychological and real-life measures of executive function and processing speed.…
Ideas.

ERIC Educational Resources Information Center

Cook, Marcy

1989-01-01

Provided are four activities focusing on the application of mathematics to real-world situations: (1) Baby Weight; (2) High Temperature; (3) Skin Weight; and (4) Whale Weight. Each activity contains the objective, directions, extensions, and answers with worksheet. The activities required include the skills of making charts and graphs. (YP)
Topics in Complexity: Dynamical Patterns in the Cyberworld

NASA Astrophysics Data System (ADS)

Qi, Hong

Quantitative understanding of mechanism in complex systems is a common "difficult" problem across many fields such as physical, biological, social and economic sciences. Investigation on underlying dynamics of complex systems and building individual-based models have recently been fueled by big data resulted from advancing information technology. This thesis investigates complex systems in social science, focusing on civil unrests on streets and relevant activities online. Investigation consists of collecting data of unrests from open digital source, featuring dynamical patterns underlying, making predictions and constructing models. A simple law governing the progress of two-sided confrontations is proposed with data of activities at micro-level. Unraveling the connections between activity of organizing online and outburst of unrests on streets gives rise to a further meso-level pattern of human behavior, through which adversarial groups evolve online and hyper-escalate ahead of real-world uprisings. Based on the patterns found, noticeable improvement of prediction of civil unrests is achieved. Meanwhile, novel model created from combination of mobility dynamics in the cyberworld and a traditional contagion model can better capture the characteristics of modern civil unrests and other contagion-like phenomena than the original one.
Entropy-based link prediction in weighted networks

NASA Astrophysics Data System (ADS)

Xu, Zhongqi; Pu, Cunlai; Ramiz Sharafat, Rajput; Li, Lunbo; Yang, Jian

2017-01-01

Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks. In the previous work (Xu et al, 2016 \\cite{xu2016}), we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight, and propose a weighted prediction index based on the contributions of paths, namely Weighted Path Entropy (WPE), to improve the prediction accuracy in weighted networks. Empirical experiments on six weighted real-world networks show that WPE achieves higher prediction accuracy than three typical weighted indices.
The role of the dorsal anterior insula in sexual risk: Evidence from an erotic Go/NoGo task and real-world risk-taking.

PubMed

Xue, Feng; Droutman, Vita; Barkley-Levenson, Emily E; Smith, Benjamin J; Xue, Gui; Miller, Lynn C; Bechara, Antoine; Lu, Zhong-Lin; Read, Stephen J

2018-04-01

The insula plays an important role in response inhibition. Most relevant here, it has been proposed that the dorsal anterior insular cortex (dAIC) plays a central role in a salience network that is responsible for switching between the default mode network and the executive control network. However, the insula's role in sexually motivated response inhibition has not yet been studied. In this study, eighty-five 18- to 30-year-old sexually active men who have sex with men (MSM) performed an erotic Go/NoGo task while in an MRI scanner. Participants' real-world sexual risk-taking (frequency of condomless anal intercourse over the past 90 days) was then correlated with their neural activity during the task. We found greater activity in bilateral anterior insular cortex (both dorsal and ventral) on contrasts with stronger motivational information (attractive naked male pictures versus pictures of clothed, middle-aged females) and on contrasts requiring greater response inhibition (NoGo versus Go). We also found that activity in the right dAIC was negatively correlated with participants' real-world sexual risk-taking. Our results confirmed the involvement of the insular cortex in motivated response inhibition. Especially, the decreased right dAIC activity may reduce the likelihood that the executive control network will come online when individuals are faced with situations requiring inhibitory control and thus lead them to make more risky choices. © 2018 Wiley Periodicals, Inc.
Linking LEGO and Algebra

ERIC Educational Resources Information Center

Özgün-Koca, S. Asli; Edwards, Thomas G.; Chelst, Kenneth R.

2015-01-01

In mathematics, students should represent, model, and work with such real-world situations as those found in the physical world, the public policy realm, and society (CCSSI 2010). Additionally, students need to make decisions and be flexible enough to improve their decisions after analyzing realistic situations. The LEGO® Pets activity does just…
Student Curators: Becoming Lifelong Learners.

ERIC Educational Resources Information Center

Koetsch, Peg; And Others

1994-01-01

Fifth graders at a Virginia school are applying new knowledge about world cultures by constructing artifacts for an Egyptian legacy exhibit. Exhibitions are a key facet of Museums-in-Progress (MIP), a program that links problem-solving activities with the real world. Students learn to develop, install, and interpret an exhibition by touring local…

Meeting George Bush versus meeting Cinderella: the neural response when telling apart what is real from what is fictional in the context of our reality.

PubMed

Abraham, Anna; von Cramon, D Yves; Schubotz, Ricarda I

2008-06-01

A considerable part of our lives is spent engaging in the entertaining worlds of fiction that are accessible through media such as books and television. Little is known, however, about how we are able to readily understand that fictional events are distinct from those occurring within our real world. The present functional imaging study explored the brain correlates underlying such abilities by having participants make judgments about the possibility of different scenarios involving either real or fictional characters being true, given the reality of our world. The processing of real and fictional scenarios activated a common set of regions including medial-temporal lobe structures. When the scenarios involved real people, brain regions associated with episodic memory retrieval and self-referential thinking, the anterior prefrontal cortex and the precuneus/posterior cingulate, were more active. In contrast, areas along the left lateral inferior frontal gyrus, associated with semantic memory retrieval, were implicated for scenarios with fictional characters. This implies that there is a fine distinction in the manner in which conceptual information concerning real persons in contrast to fictional characters is represented. In general terms, the findings suggest that fiction relative to reality tends to be represented in more factual terms, whereas our representations of reality relative to fiction are colored by personal subjectivity. What modulates our understanding of the relative difference between reality and fiction seems to be whether such character-type information is coded in self-relevant terms or not.
Real-world exhaust temperature and engine load distributions of on-road heavy-duty diesel vehicles in various vocations.

PubMed

Boriboonsomsin, Kanok; Durbin, Thomas; Scora, George; Johnson, Kent; Sandez, Daniel; Vu, Alexander; Jiang, Yu; Burnette, Andrew; Yoon, Seungju; Collins, John; Dai, Zhen; Fulper, Carl; Kishan, Sandeep; Sabisch, Michael; Jackson, Doug

2018-06-01

Real-world vehicle and engine activity data were collected from 90 heavy-duty vehicles in California, United States, most of which have engine model year 2010 or newer and are equipped with selective catalytic reduction (SCR). The 90 vehicles represent 19 different groups defined by a combination of vocational use and geographic region. The data were collected using advanced data loggers that recorded vehicle speed, position (latitude and longitude), and more than 170 engine and aftertreatment parameters (including engine load and exhaust temperature) at the frequency of one Hz. This article presents plots of real-world exhaust temperature and engine load distributions for the 19 vehicle groups. In each plot, both frequency distribution and cumulative frequency distribution are shown. These distributions are generated using the aggregated data from all vehicle samples in each group.
Real-time position reconstruction with hippocampal place cells.

PubMed

Guger, Christoph; Gener, Thomas; Pennartz, Cyriel M A; Brotons-Mas, Jorge R; Edlinger, Günter; Bermúdez I Badia, S; Verschure, Paul; Schaffelhofer, Stefan; Sanchez-Vives, Maria V

2011-01-01

Brain-computer interfaces (BCI) are using the electroencephalogram, the electrocorticogram and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells (PCs) to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80 cm × 80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rat's trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rat's position in real-time. The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4% using 5-6 neurons. It must be noted here that the encoding step was done with data recorded before the real-time experiment and comparable accuracies between off-line (mean error of 15.9% for three rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that PCs were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral-neuronal feedback loops or for implementing neuroprosthetic control.
Real-Time Position Reconstruction with Hippocampal Place Cells

PubMed Central

Guger, Christoph; Gener, Thomas; Pennartz, Cyriel M. A.; Brotons-Mas, Jorge R.; Edlinger, Günter; Bermúdez i Badia, S.; Verschure, Paul; Schaffelhofer, Stefan; Sanchez-Vives, Maria V.

2011-01-01

Brain–computer interfaces (BCI) are using the electroencephalogram, the electrocorticogram and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells (PCs) to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80 cm × 80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rat's trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rat's position in real-time. The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4% using 5–6 neurons. It must be noted here that the encoding step was done with data recorded before the real-time experiment and comparable accuracies between off-line (mean error of 15.9% for three rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that PCs were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral–neuronal feedback loops or for implementing neuroprosthetic control. PMID:21808603
Volcanic Ash and SO2 Monitoring Using Suomi NPP Direct Broadcast OMPS Data

NASA Astrophysics Data System (ADS)

Seftor, C. J.; Krotkov, N. A.; McPeters, R. D.; Li, J. Y.; Brentzel, K. W.; Habib, S.; Hassinen, S.; Heinrichs, T. A.; Schneider, D. J.

2014-12-01

NASA's Suomi NPP Ozone Science Team, in conjunction with Goddard Space Flight Center's (GSFC's) Direct Readout Laboratory, developed the capability of processing, in real-time, direct readout (DR) data from the Ozone Mapping and Profiler Suite (OMPS) to perform SO2 and Aerosol Index (AI) retrievals. The ability to retrieve this information from real-time processing of DR data was originally developed for the Ozone Monitoring Instrument (OMI) onboard the Aura spacecraft and is used by Volcano Observatories and Volcanic Ash Advisory Centers (VAACs) charged with mapping ash clouds from volcanic eruptions and providing predictions/forecasts about where the ash will go. The resulting real-time SO2 and AI products help to mitigate the effects of eruptions such as the ones from Eyjafjallajokull in Iceland and Puyehue-Cordón Caulle in Chile, which cause massive disruptions to airline flight routes for weeks as airlines struggle to avoid ash clouds that could cause engine failure, deeply pitted windshields impossible to see through, and other catastrophic events. We will discuss the implementation of real-time processing of OMPS DR data by both the Geographic Information Network of Alaska (GINA) and the Finnish Meteorological Institute (FMI), which provide real-time coverage over some of the most congested airspace and over many of the most active volcanoes in the world, and show examples of OMPS DR processing results from recent volcanic eruptions.
Experimenting with ecosystem interaction networks in search of threshold potentials in real-world marine ecosystems.

PubMed

Thrush, Simon F; Hewitt, Judi E; Parkes, Samantha; Lohrer, Andrew M; Pilditch, Conrad; Woodin, Sarah A; Wethey, David S; Chiantore, Mariachiara; Asnaghi, Valentina; De Juan, Silvia; Kraan, Casper; Rodil, Ivan; Savage, Candida; Van Colen, Carl

2014-06-01

Thresholds profoundly affect our understanding and management of ecosystem dynamics, but we have yet to develop practical techniques to assess the risk that thresholds will be crossed. Combining ecological knowledge of critical system interdependencies with a large-scale experiment, we tested for breaks in the ecosystem interaction network to identify threshold potential in real-world ecosystem dynamics. Our experiment with the bivalves Macomona liliana and Austrovenus stutchburyi on marine sandflats in New Zealand demonstrated that reductions in incident sunlight changed the interaction network between sediment biogeochemical fluxes, productivity, and macrofauna. By demonstrating loss of positive feedbacks and changes in the architecture of the network, we provide mechanistic evidence that stressors lead to break points in dynamics, which theory predicts predispose a system to a critical transition.
Directionality of real world networks as predicted by path length in directed and undirected graphs

NASA Astrophysics Data System (ADS)

Rosen, Yonatan; Louzoun, Yoram

2014-05-01

Many real world networks either support ordered processes, or are actually representations of such processes. However, the same networks contain large strong connectivity components and long circles, which hide a possible inherent order, since each vertex can be reached from each vertex in a directed path. Thus, the presence of an inherent directionality in networks may be hidden. We here discuss a possible definition of such a directionality and propose a method to detect it. Several common algorithms, such as the betweenness centrality or the degree, measure various aspects of centrality in networks. However, they do not address directly the issue of inherent directionality. The goal of the algorithm discussed here is the detection of global directionality in directed networks. Such an algorithm is essential to detangle complex networks into ordered process. We show that indeed the vast majority of measured real world networks have a clear directionality. Moreover, this directionality can be used to classify vertices in these networks from sources to sinks. Such an algorithm can be highly useful in order to extract a meaning from large interaction networks assembled in many domains.
Potential predictability and forecast skill in ensemble climate forecast: the skill-persistence rule

NASA Astrophysics Data System (ADS)

Jin, Y.; Rong, X.; Liu, Z.

2017-12-01

This study investigates the factors that impact the forecast skill for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill of sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further examined using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but can be distorted by the sampling error and non-AR1 processes.
Dopamine and the Creative Mind: Individual Differences in Creativity Are Predicted by Interactions between Dopamine Genes DAT and COMT.

PubMed

Zabelina, Darya L; Colzato, Lorenza; Beeman, Mark; Hommel, Bernhard

2016-01-01

The dopaminergic (DA) system may be involved in creativity, however results of past studies are mixed. We attempted to clarify this putative relation by considering the mediofrontal and the nigrostriatal DA pathways, uniquely and in combination, and their contribution to two different measures of creativity--an abbreviated version of the Torrance Test of Creative Thinking, assessing divergent thinking, and a real-world creative achievement index. We found that creativity can be predicted from interactions between genetic polymorphisms related to frontal (COMT) and striatal (DAT) DA pathways. Importantly, the Torrance test and the real-world creative achievement index related to different genetic patterns, suggesting that these two measures tap into different aspects of creativity, and depend on distinct, but interacting, DA sub-systems. Specifically, we report that successful performance on the Torrance test is linked with dopaminergic polymorphisms associated with good cognitive flexibility and medium top-down control, or with weak cognitive flexibility and strong top-down control. The latter is particularly true for the originality factor of divergent thinking. High real-world creative achievement, on the other hand, as assessed by the Creative Achievement Questionnaire, is linked with dopaminergic polymorphisms associated with weak cognitive flexibility and weak top-down control. Taken altogether, our findings support the idea that human creativity relies on dopamine, and on the interaction between frontal and striatal dopaminergic pathways in particular. This interaction may help clarify some apparent inconsistencies in the prior literature, especially if the genes and/or creativity measures were analyzed separately.
Dopamine and the Creative Mind: Individual Differences in Creativity Are Predicted by Interactions between Dopamine Genes DAT and COMT

PubMed Central

Zabelina, Darya L.; Colzato, Lorenza; Beeman, Mark; Hommel, Bernhard

2016-01-01

The dopaminergic (DA) system may be involved in creativity, however results of past studies are mixed. We attempted to clarify this putative relation by considering the mediofrontal and the nigrostriatal DA pathways, uniquely and in combination, and their contribution to two different measures of creativity–an abbreviated version of the Torrance Test of Creative Thinking, assessing divergent thinking, and a real-world creative achievement index. We found that creativity can be predicted from interactions between genetic polymorphisms related to frontal (COMT) and striatal (DAT) DA pathways. Importantly, the Torrance test and the real-world creative achievement index related to different genetic patterns, suggesting that these two measures tap into different aspects of creativity, and depend on distinct, but interacting, DA sub-systems. Specifically, we report that successful performance on the Torrance test is linked with dopaminergic polymorphisms associated with good cognitive flexibility and medium top-down control, or with weak cognitive flexibility and strong top-down control. The latter is particularly true for the originality factor of divergent thinking. High real-world creative achievement, on the other hand, as assessed by the Creative Achievement Questionnaire, is linked with dopaminergic polymorphisms associated with weak cognitive flexibility and weak top-down control. Taken altogether, our findings support the idea that human creativity relies on dopamine, and on the interaction between frontal and striatal dopaminergic pathways in particular. This interaction may help clarify some apparent inconsistencies in the prior literature, especially if the genes and/or creativity measures were analyzed separately. PMID:26783754
Posture and activity recognition and energy expenditure prediction in a wearable platform.

PubMed

Sazonova, Nadezhda; Browning, Raymond; Melanson, Edward; Sazonov, Edward

2014-01-01

The use of wearable sensors coupled with the processing power of mobile phones may be an attractive way to provide real-time feedback about physical activity and energy expenditure (EE). Here we describe use of a shoe-based wearable sensor system (SmartShoe) with a mobile phone for real-time prediction and display of time spent in various postures/physical activities and the resulting EE. To deal with processing power and memory limitations of the phone, we introduce new algorithms that require substantially less computational power. The algorithms were validated using data from 15 subjects who performed up to 15 different activities of daily living during a four-hour stay in a room calorimeter. Use of Multinomial Logistic Discrimination (MLD) for posture and activity classification resulted in an accuracy comparable to that of Support Vector Machines (SVM) (90% vs. 95%-98%) while reducing the running time by a factor of 190 and reducing the memory requirement by a factor of 104. Per minute EE estimation using activity-specific models resulted in an accurate EE prediction (RMSE of 0.53 METs vs. RMSE of 0.69 METs using previously reported SVM-branched models). These results demonstrate successful implementation of real-time physical activity monitoring and EE prediction system on a wearable platform.
Efficient Probabilistic Diagnostics for Electrical Power Systems

NASA Technical Reports Server (NTRS)

Mengshoel, Ole J.; Chavira, Mark; Cascio, Keith; Poll, Scott; Darwiche, Adnan; Uckun, Serdar

2008-01-01

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.
Comparative and Predictive Multimedia Assessments Using Monte Carlo Uncertainty Analyses

NASA Astrophysics Data System (ADS)

Whelan, G.

2002-05-01

Multiple-pathway frameworks (sometimes referred to as multimedia models) provide a platform for combining medium-specific environmental models and databases, such that they can be utilized in a more holistic assessment of contaminant fate and transport in the environment. These frameworks provide a relatively seamless transfer of information from one model to the next and from databases to models. Within these frameworks, multiple models are linked, resulting in models that consume information from upstream models and produce information to be consumed by downstream models. The Framework for Risk Analysis in Multimedia Environmental Systems (FRAMES) is an example, which allows users to link their models to other models and databases. FRAMES is an icon-driven, site-layout platform that is an open-architecture, object-oriented system that interacts with environmental databases; helps the user construct a Conceptual Site Model that is real-world based; allows the user to choose the most appropriate models to solve simulation requirements; solves the standard risk paradigm of release transport and fate; and exposure/risk assessments to people and ecology; and presents graphical packages for analyzing results. FRAMES is specifically designed allow users to link their own models into a system, which contains models developed by others. This paper will present the use of FRAMES to evaluate potential human health exposures using real site data and realistic assumptions from sources, through the vadose and saturated zones, to exposure and risk assessment at three real-world sites, using the Multimedia Environmental Pollutant Assessment System (MEPAS), which is a multimedia model contained within FRAMES. These real-world examples use predictive and comparative approaches coupled with a Monte Carlo analysis. A predictive analysis is where models are calibrated to monitored site data, prior to the assessment, and a comparative analysis is where models are not calibrated but based solely on literature or judgement and is usually used to compare alternatives. In many cases, a combination is employed where the model is calibrated to a portion of the data (e.g., to determine hydrodynamics), then used to compare alternatives. Three subsurface-based multimedia examples are presented, increasing in complexity. The first presents the application of a predictive, deterministic assessment; the second presents a predictive and comparative, Monte Carlo analysis; and the third presents a comparative, multi-dimensional Monte Carlo analysis. Endpoints are typically presented in terms of concentration, hazard, risk, and dose, and because the vadose zone model typically represents a connection between a source and the aquifer, it does not generally represent the final medium in a multimedia risk assessment.
Microinverter Thermal Performance in the Real-World: Measurements and Modeling

PubMed Central

Hossain, Mohammad Akram; Xu, Yifan; Peshek, Timothy J.; Ji, Liang; Abramson, Alexis R.; French, Roger H.

2015-01-01

Real-world performance, durability and reliability of microinverters are critical concerns for microinverter-equipped photovoltaic systems. We conducted a data-driven study of the thermal performance of 24 new microinverters (Enphase M215) connected to 8 different brands of PV modules on dual-axis trackers at the Solar Durability and Lifetime Extension (SDLE) SunFarm at Case Western Reserve University, based on minute by minute power and thermal data from the microinverters and PV modules along with insolation and environmental data from July through October 2013. The analysis shows the strengths of the associations of microinverter temperature with ambient temperature, PV module temperature, irradiance and AC power of the PV systems. The importance of the covariates are rank ordered. A multiple regression model was developed and tested based on stable solar noon-time data, which gives both an overall function that predicts the temperature of microinverters under typical local conditions, and coefficients adjustments reecting refined prediction of the microinverter temperature connected to the 8 brands of PV modules in the study. The model allows for prediction of internal temperature for the Enphase M215 given similar climatic condition and can be expanded to predict microinverter temperature in fixed-rack and roof-top PV systems. This study is foundational in that similar models built on later stage data in the life of a device could reveal potential influencing factors in performance degradation. PMID:26147339
Feature maps driven no-reference image quality prediction of authentically distorted images

NASA Astrophysics Data System (ADS)

Ghadiyaram, Deepti; Bovik, Alan C.

2015-03-01

Current blind image quality prediction models rely on benchmark databases comprised of singly and synthetically distorted images, thereby learning image features that are only adequate to predict human perceived visual quality on such inauthentic distortions. However, real world images often contain complex mixtures of multiple distortions. Rather than a) discounting the effect of these mixtures of distortions on an image's perceptual quality and considering only the dominant distortion or b) using features that are only proven to be efficient for singly distorted images, we deeply study the natural scene statistics of authentically distorted images, in different color spaces and transform domains. We propose a feature-maps-driven statistical approach which avoids any latent assumptions about the type of distortion(s) contained in an image, and focuses instead on modeling the remarkable consistencies in the scene statistics of real world images in the absence of distortions. We design a deep belief network that takes model-based statistical image features derived from a very large database of authentically distorted images as input and discovers good feature representations by generalizing over different distortion types, mixtures, and severities, which are later used to learn a regressor for quality prediction. We demonstrate the remarkable competence of our features for improving automatic perceptual quality prediction on a benchmark database and on the newly designed LIVE Authentic Image Quality Challenge Database and show that our approach of combining robust statistical features and the deep belief network dramatically outperforms the state-of-the-art.
Predictive Scheduling for Electric Vehicles Considering Uncertainty of Load and User Behaviors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Bin; Huang, Rui; Wang, Yubo

2016-05-02

Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for EV charging problem. Previous researches did not fully solve this problem due to lack of real-world EV charging data and proper stochastic model to describe these behaviors. In this paper, we propose a new predictive EV scheduling algorithm (PESA) inspired by Model Predictive Control (MPC), which includes a dynamic load estimation module and a predictive optimizationmore » module. The user-related EV load and base load are dynamically estimated based on the historical data. At each time interval, the predictive optimization program will be computed for optimal schedules given the estimated parameters. Only the first element from the algorithm outputs will be implemented according to MPC paradigm. Current-multiplexing function in each Electric Vehicle Supply Equipment (EVSE) is considered and accordingly a virtual load is modeled to handle the uncertainties of future EV energy demands. This system is validated by the real-world EV charging data collected on UCLA campus and the experimental results indicate that our proposed model not only reduces load variation up to 40% but also maintains a high level of robustness. Finally, IEC 61850 standard is utilized to standardize the data models involved, which brings significance to more reliable and large-scale implementation.« less
Apparent height and body mass index influence perceived leadership ability in three-dimensional faces.

PubMed

Re, Daniel E; Dzhelyova, Milena; Holzleitner, Iris J; Tigue, Cara C; Feinberg, David R; Perrett, David I

2012-01-01

Facial appearance has a well-documented effect on perceived leadership ability. Face judgments of leadership ability predict political election outcomes across the world, and similar judgments of business CEOs predict company profits. Body height is also associated with leadership ability, with taller people attaining positions of leadership more than their shorter counterparts in both politics and in the corporate world. Previous studies have found some face characteristics that are associated with leadership judgments, however there have been no studies with three-dimensional faces. We assessed which facial characteristics drive leadership judgments in three-dimensional faces. We found a perceptual relationship between height and leadership ability. We also found that facial maturity correlated with leadership judgments, and that faces of people with an unhealthily high body mass index received lower leadership ratings. We conclude that face attributes associated with body size and maturity alter leadership perception, and may influence real-world democratic leadership selection.
The Potential of Virtual Reality to Assess Functional Communication in Aphasia

ERIC Educational Resources Information Center

Garcia, Linda J.; Rebolledo, Mercedes; Metthe, Lynn; Lefebvre, Renee

2007-01-01

Speech-language pathologists (SLPs) who work with adults with cognitive-linguistic impairments, including aphasia, have long needed an assessment tool that predicts ability to function in the real world. In this article, it is argued that virtual reality (VR)-supported approaches can address this need. Using models of disability such as the…
Real Time Big Data Analytics for Predicting Terrorist Incidents

ERIC Educational Resources Information Center

Toure, Ibrahim

2017-01-01

Terrorism is a complex and evolving phenomenon. In the past few decades, we have witnessed an increase in the number of terrorist incidents in the world. The security and stability of many countries is threatened by terrorist groups. Perpetrators now use sophisticated weapons and the attacks are more and more lethal. Currently, terrorist incidents…
Anticipation in Real-world Scenes: The Role of Visual Context and Visual Memory

ERIC Educational Resources Information Center

Coco, Moreno I.; Keller, Frank; Malcolm, George L.

2016-01-01

The human sentence processor is able to make rapid predictions about upcoming linguistic input. For example, upon hearing the verb eat, anticipatory eye-movements are launched toward edible objects in a visual scene (Altmann & Kamide, 1999). However, the cognitive mechanisms that underlie anticipation remain to be elucidated in ecologically…

ECPC’s weekly to seasonal global forecasts

Treesearch

John O. Roads; Shyh-Chin Chen; Francis M. Fujioka

2001-01-01

The Scripps Experimental Climate Prediction Center (ECPC) has been making experimental, near-real-time seasonal global forecasts since 26 September 1997 with the NCEP global spectral model used for the reanalysis. Images of these forecasts, at daily to seasonal timescales, are provided on the World Wide Web and digital forecast products are provided on the ECPC...
Web Analytics Reveal User Behavior: TTU Libraries' Experience with Google Analytics

ERIC Educational Resources Information Center

Barba, Ian; Cassidy, Ryan; De Leon, Esther; Williams, B. Justin

2013-01-01

Proper planning and assessment surveys of projects for academic library Web sites will not always be predictive of real world use, no matter how many responses they might receive. In this case, multiple-phase development, librarian focus groups, and patron surveys performed before implementation of such a project inaccurately overrated utility and…
Long-Term Memory Biases Auditory Spatial Attention

ERIC Educational Resources Information Center

Zimmermann, Jacqueline F.; Moscovitch, Morris; Alain, Claude

2017-01-01

Long-term memory (LTM) has been shown to bias attention to a previously learned visual target location. Here, we examined whether memory-predicted spatial location can facilitate the detection of a faint pure tone target embedded in real world audio clips (e.g., soundtrack of a restaurant). During an initial familiarization task, participants…
Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-WorldChemical Production

EPA Science Inventory

A set of coupled semantic data models, i.e., ontologies, are presented to advance a methodology towards automated inventory modeling of chemical manufacturing in life cycle assessment. The cradle-to-gate life cycle inventory for chemical manufacturing is a detailed collection of ...
Utility of Policy Capturing as an Approach to Graduate Admissions Decision Making.

ERIC Educational Resources Information Center

Schmidt, Frank L.; And Others

1978-01-01

The present study examined and evaluated the application of linear policy-capturing models to the real-world decision task of graduate admissions. Utility of the policy-capturing models was great enough to be of practical significance, and least-squares weights showed no predictive advantage over equal weights. (Author/CTM)
Strategies for Selecting Routes through Real-World Environments: Relative Topography, Initial Route Straightness, and Cardinal Direction

PubMed Central

Brunyé, Tad T.; Collier, Zachary A.; Cantelon, Julie; Holmes, Amanda; Wood, Matthew D.; Linkov, Igor; Taylor, Holly A.

2015-01-01

Previous research has demonstrated that route planners use several reliable strategies for selecting between alternate routes. Strategies include selecting straight rather than winding routes leaving an origin, selecting generally south- rather than north-going routes, and selecting routes that avoid traversal of complex topography. The contribution of this paper is characterizing the relative influence and potential interactions of these strategies. We also examine whether individual differences would predict any strategy reliance. Results showed evidence for independent and additive influences of all three strategies, with a strong influence of topography and initial segment straightness, and relatively weak influence of cardinal direction. Additively, routes were also disproportionately selected when they traversed relatively flat regions, had relatively straight initial segments, and went generally south rather than north. Two individual differences, extraversion and sense of direction, predicted the extent of some effects. Under real-world conditions navigators indeed consider a route’s initial straightness, cardinal direction, and topography, but these cues differ in relative influence and vary in their application across individuals. PMID:25992685
Individual differences in cognitive functioning predict effectiveness of a heads-up Lane Departure Warning for younger and older drivers

PubMed Central

Aksan, Nazan; Sager, Lauren; Hacker, Sarah; Lester, Benjamin; Dawson, Jeffrey; Rizzo, Matthew; Ebe, Kazutoshi; Foley, James

2016-01-01

The effectiveness of an idealized lane departure warning (LDW) was evaluated in an interactive fixed base driving simulator. Thirty-eight older (mean age = 77 years) and 40 younger drivers (mean age = 35 years) took four different drives/routes similar in road culture composition and hazards encountered with and without LDW. The four drives were administered over visits separated approximately by two weeks to examine changes in long-term effectiveness of LDW. Performance metrics were number of LDW activations and average correction time to each LDW. LDW reduced correction time to re-center the vehicle by 1.34 seconds on average (95% CI = 1.12–1.57 seconds) but did not reduce the number of times the drivers drifted enough in their lanes to activate the system (LDW activations). The magnitude of reductions in average correction RT was similar for older and younger drivers and did not change with repeated exposures across visits. The contribution of individual differences in basic visual and motor function, as well as cognitive function to safety gains from LDW was also examined. Cognitive speed of processing predicted lane keeping performance for older and younger drivers. Differences in memory, visuospatial construction, and executive function tended to predict performance differences among older but not younger drivers. Cognitive functioning did not predict changes in the magnitude of safety benefits from LDW over time. Implications are discussed with respect to real-world safety systems. PMID:27898370
Individual differences in cognitive functioning predict effectiveness of a heads-up lane departure warning for younger and older drivers.

PubMed

Aksan, Nazan; Sager, Lauren; Hacker, Sarah; Lester, Benjamin; Dawson, Jeffrey; Rizzo, Matthew; Ebe, Kazutoshi; Foley, James

2017-02-01

The effectiveness of an idealized lane departure warning (LDW) was evaluated in an interactive fixed base driving simulator. Thirty-eight older (mean age=77years) and 40 younger drivers (mean age=35years) took four different drives/routes similar in road culture composition and hazards encountered with and without LDW. The four drives were administered over visits separated approximately by two weeks to examine changes in long-term effectiveness of LDW. Performance metrics were number of LDW activations and average correction time to each LDW. LDW reduced correction time to re-center the vehicle by 1.34s on average (95% CI=1.12-1.57s) but did not reduce the number of times the drivers drifted enough in their lanes to activate the system (LDW activations). The magnitude of reductions in average correction RT was similar for older and younger drivers and did not change with repeated exposures across visits. The contribution of individual differences in basic visual and motor function, as well as cognitive function to safety gains from LDW was also examined. Cognitive speed of processing predicted lane keeping performance for older and younger drivers. Differences in memory, visuospatial construction, and executive function tended to predict performance differences among older but not younger drivers. Cognitive functioning did not predict changes in the magnitude of safety benefits from LDW over time. Implications are discussed with respect to real-world safety systems. Copyright © 2016 Elsevier Ltd. All rights reserved.
Learning in a u-Museum: Developing a Context-Aware Ubiquitous Learning Environment

ERIC Educational Resources Information Center

Chen, Chia-Chen; Huang, Tien-Chi

2012-01-01

Context-awareness techniques can support learners in learning without time or location constraints by using mobile devices and associated learning activities in a real learning environment. Enrichment of context-aware technologies has enabled students to learn in an environment that integrates learning resources from both the real world and the…
Toward link predictability of complex networks

PubMed Central

Lü, Linyuan; Pan, Liming; Zhou, Tao; Zhang, Yi-Cheng; Stanley, H. Eugene

2015-01-01

The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that (i) structural consistency is a good estimation of link predictability and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners. PMID:25659742
Memorable Audiovisual Narratives Synchronize Sensory and Supramodal Neural Responses

PubMed Central

2016-01-01

Abstract Our brains integrate information across sensory modalities to generate perceptual experiences and form memories. However, it is difficult to determine the conditions under which multisensory stimulation will benefit or hinder the retrieval of everyday experiences. We hypothesized that the determining factor is the reliability of information processing during stimulus presentation, which can be measured through intersubject correlation of stimulus-evoked activity. We therefore presented biographical auditory narratives and visual animations to 72 human subjects visually, auditorily, or combined, while neural activity was recorded using electroencephalography. Memory for the narrated information, contained in the auditory stream, was tested 3 weeks later. While the visual stimulus alone led to no meaningful retrieval, this related stimulus improved memory when it was combined with the story, even when it was temporally incongruent with the audio. Further, individuals with better subsequent memory elicited neural responses during encoding that were more correlated with their peers. Surprisingly, portions of this predictive synchronized activity were present regardless of the sensory modality of the stimulus. These data suggest that the strength of sensory and supramodal activity is predictive of memory performance after 3 weeks, and that neural synchrony may explain the mnemonic benefit of the functionally uninformative visual context observed for these real-world stimuli. PMID:27844062
[Discussion on solutions to ethical issues of clinical researches in a real world].

PubMed

Wang, Si-Cheng; Liu, Bao-Yan; Xiong, Ning-Ning; Xie, Qi; Zhang, Run-Shun; Zhou, Xue-Zhong; Qiao, Jie

2013-04-01

The paradigm of a real world study has become the frontiers of clinical researches, especially in the field of Chinese medicine, all over the world in recent years. In this paper, ethical issues which probably exist in real-world studies are raised and reviewed. Moreover, some preliminary solutions to these issues such as protecting subjects during the process of real-world studies and performing ethical review are raised based on recent years' practices to enhance the scientificity and ethical level of real-world studies.
Simulating wildfire spread behavior between two NASA Active Fire data timeframes

NASA Astrophysics Data System (ADS)

Adhikari, B.; Hodza, P.; Xu, C.; Minckley, T. A.

2017-12-01

Although NASA's Active Fire dataset is considered valuable in mapping the spatial distribution and extent of wildfires across the world, the data is only available at approximately 12-hour time intervals, creating uncertainties and risks associated with fire spread and behavior between the two Visible Infrared Imaging Radiometer Satellite (VIIRS) data collection timeframes. Our study seeks to close the information gap for the United States by using the latest Active Fire data collected for instance around 0130 hours as an ignition source and critical inputs to a wildfire model by uniquely incorporating forecasted and real-time weather conditions for predicting fire perimeter at the next 12 hour reporting time (i.e. around 1330 hours). The model ingests highly dynamic variables such as fuel moisture, temperature, relative humidity, wind among others, and prompts a Monte Carlo simulation exercise that uses a varying range of possible values for evaluating all possible wildfire behaviors. The Monte Carlo simulation implemented in this model provides a measure of the relative wildfire risk levels at various locations based on the number of times those sites are intersected by simulated fire perimeters. Model calibration is achieved using data at next reporting time (i.e. after 12 hours) to enhance the predictive quality at further time steps. While initial results indicate that the calibrated model can predict the overall geometry and direction of wildland fire spread, the model seems to over-predict the sizes of most fire perimeters possibly due to unaccounted fire suppression activities. Nonetheless, the results of this study show great promise in aiding wildland fire tracking, fighting and risk management.
Neural correlates of naturalistic social cognition: brain-behavior relationships in healthy adults.

PubMed

Deuse, L; Rademacher, L M; Winkler, L; Schultz, R T; Gründer, G; Lammertz, S E

2016-11-01

Being able to infer the thoughts, feelings and intentions of those around us is indispensable in order to function in a social world. Despite growing interest in social cognition and its neural underpinnings, the factors that contribute to successful mental state attribution remain unclear. Current knowledge is limited because the most widely used tasks suffer from two main constraints: (i) They fail to capture individual variability due to ceiling effects and (ii) they use highly simplistic, often artificial stimuli inapt to mirror real-world socio-cognitive demands. In the present study, we address these problems by employing complex depictions of naturalistic social interactions that vary in both valence (positive vs negative) and ambiguity (high vs low). Thirty-eight healthy participants (20 female) made mental state judgments while brain responses were obtained using functional magnetic resonance imaging (fMRI). Accuracy varied based on valence and ambiguity conditions and women were more accurate than men with highly ambiguous social stimuli. Activity of the orbitofrontal cortex predicted performance in the high ambiguity condition. The results shed light on subtle differences in mentalizing abilities and associated neural activity. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.
Identifying factors that predict the choice and success rate of radial artery catheterisation in contemporary real world cardiology practice: a sub-analysis of the PREVAIL study data.

PubMed

Pristipino, Christian; Roncella, Adriana; Trani, Carlo; Nazzaro, Marco S; Berni, Andrea; Di Sciascio, Germano; Sciahbasi, Alessandro; Musarò, Salvatore Donato; Mazzarotto, Pietro; Gioffrè, Gaetano; Speciale, Giulio

2010-06-01

To assess: the reasons behind an operator choosing to perform radial artery catheterisation (RAC) as against femoral arterial catheterisation, and to explore why RAC may fail in the real world. A pre-determined analysis of PREVAIL study database was performed. Relevant data were collected in a prospective, observational survey of 1,052 consecutive patients undergoing invasive cardiovascular procedures at nine Italian hospitals over a one month observation period. By multivariate analysis, the independent predictors of RAC choice were having the procedure performed: (1) at a high procedural volume centre; and (2) by an operator who performs a high volume of radial procedures; clinical variables played no statistically significant role. RAC failure was predicted independently by (1) a lower operator propensity to use RAC; and (2) the presence of obstructive peripheral artery disease. A 10-fold lower rate of RAC failure was observed among operators who perform RAC for > 85% of their personal caseload than among those who use RAC < 25% of the time (3.8% vs. 33.0%, respectively); by receiver operator characteristic (ROC) analysis, no threshold value for operator RAC volume predicted RAC failure. A routine RAC in all-comers is superior to a selective strategy in terms of feasibility and success rate.
Activating Event Knowledge

ERIC Educational Resources Information Center

Hare, Mary; Jones, Michael; Thomson, Caroline; Kelly, Sarah; McRae, Ken

2009-01-01

An increasing number of results in sentence and discourse processing demonstrate that comprehension relies on rich pragmatic knowledge about real-world events, and that incoming words incrementally activate such knowledge. If so, then even outside of any larger context, nouns should activate knowledge of the generalized events that they denote or…
An Expert System-based Context-Aware Ubiquitous Learning Approach for Conducting Science Learning Activities

ERIC Educational Resources Information Center

Wu, Po-Han; Hwang, Gwo-Jen; Tsai, Wen-Hung

2013-01-01

Context-aware ubiquitous learning has been recognized as being a promising approach that enables students to interact with real-world learning targets with supports from the digital world. Several researchers have indicated the importance of providing learning guidance or hints to individual students during the context-aware ubiquitous learning…
An Interactive Concept Map Approach to Supporting Mobile Learning Activities for Natural Science Courses

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Wu, Po-Han; Ke, Hui-Ru

2011-01-01

Mobile and wireless communication technologies not only enable anytime and anywhere learning, but also provide the opportunity to develop learning environments that combine real-world and digital-world resources. Nevertheless, researchers have indicated that, without effective tools for helping students organize their observations in the field,…
Anticipation in Real-World Scenes: The Role of Visual Context and Visual Memory.

PubMed

Coco, Moreno I; Keller, Frank; Malcolm, George L

2016-11-01

The human sentence processor is able to make rapid predictions about upcoming linguistic input. For example, upon hearing the verb eat, anticipatory eye-movements are launched toward edible objects in a visual scene (Altmann & Kamide, 1999). However, the cognitive mechanisms that underlie anticipation remain to be elucidated in ecologically valid contexts. Previous research has, in fact, mainly used clip-art scenes and object arrays, raising the possibility that anticipatory eye-movements are limited to displays containing a small number of objects in a visually impoverished context. In Experiment 1, we confirm that anticipation effects occur in real-world scenes and investigate the mechanisms that underlie such anticipation. In particular, we demonstrate that real-world scenes provide contextual information that anticipation can draw on: When the target object is not present in the scene, participants infer and fixate regions that are contextually appropriate (e.g., a table upon hearing eat). Experiment 2 investigates whether such contextual inference requires the co-presence of the scene, or whether memory representations can be utilized instead. The same real-world scenes as in Experiment 1 are presented to participants, but the scene disappears before the sentence is heard. We find that anticipation occurs even when the screen is blank, including when contextual inference is required. We conclude that anticipatory language processing is able to draw upon global scene representations (such as scene type) to make contextual inferences. These findings are compatible with theories assuming contextual guidance, but posit a challenge for theories assuming object-based visual indices. Copyright © 2015 Cognitive Science Society, Inc.
Ranking in evolving complex networks

NASA Astrophysics Data System (ADS)

Liao, Hao; Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng; Zhou, Ming-Yang

2017-05-01

Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Many popular ranking algorithms (such as Google's PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. At the same time, recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of network traffic, prediction of future links, and identification of significant nodes.

The efficacy and safety of pomalidomide in relapsed/refractory multiple myeloma in a "real-world" study: Polish Myeloma Group experience.

PubMed

Charlinski, Grzegorz; Grzasko, Norbert; Jurczyszyn, Artur; Janczarski, Mariusz; Szeremet, Agnieszka; Waszczuk-Gajda, Anna; Bernatowicz, Paweł; Swiderska, Alina; Guzicka-Kazimierczak, Renata; Lech-Maranda, Ewa; Szczepaniak, Andrzej; Wichary, Ryszard; Dmoszynska, Anna

2018-06-08

Patients with relapsed/refractory multiple myeloma (RRMM) have poor prognosis. Pomalidomide is an immunomodulatory compound that has demonstrated activity in MM patients with disease refractory to lanlidomide and bortezomib. Participants of clinical trials are highly selected populations; therefore, the aim of this study was to present observations from real practice that might provide important information for practitioners. We analyzed retrospectively 50 patients treated with pomalidomide in 12 Polish sites between 2014 and 2017. Median age was 63 years, median time since diagnosis 4.5 years and median number of prior regimens 4. The overall response rate was 39.1%. Median progression-free survival (PFS) and overall survival (OS) were 10.0 and 14.0 months, respectively. Previous treatment with immunomodulatory drugs, bortezomib or stem cell transplant had no impact on PFS and OS. Most frequent grade 3/4 treatment-emergent adverse events were hematologic (neutropenia 24.0%, thrombocytopenia 10.0%, anemia 8.0%). Most common grade 3/4 non-hematologic toxicities were respiratory tract infection (14.0%) and neuropathy (4.0%). This real-world data have confirmed that pomalidomide is an active drug in RRMM and support results of published clinical trials and other real-world studies. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Mind over motor mapping: Driver response to changing vehicle dynamics.

PubMed

Bruno, Jennifer L; Baker, Joseph M; Gundran, Andrew; Harbott, Lene K; Stuart, Zachary; Piccirilli, Aaron M; Hosseini, S M Hadi; Gerdes, J Christian; Reiss, Allan L

2018-06-08

Improvements in vehicle safety require understanding of the neural systems that support the complex, dynamic task of real-world driving. We used functional near infrared spectroscopy (fNIRS) and pupilometry to quantify cortical and physiological responses during a realistic, simulated driving task in which vehicle dynamics were manipulated. Our results elucidate compensatory changes in driver behavior in response to changes in vehicle handling. We also describe associated neural and physiological responses under different levels of mental workload. The increased cortical activation we observed during the late phase of the experiment may indicate motor learning in prefrontal-parietal networks. Finally, relationships among cortical activation, steering control, and individual personality traits suggest that individual brain states and traits may be useful in predicting a driver's response to changes in vehicle dynamics. Results such as these will be useful for informing the design of automated safety systems that facilitate safe and supportive driver-car communication. © 2018 Wiley Periodicals, Inc.
Validating agent based models through virtual worlds.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lakkaraju, Kiran; Whetzel, Jonathan H.; Lee, Jina

As the US continues its vigilance against distributed, embedded threats, understanding the political and social structure of these groups becomes paramount for predicting and dis- rupting their attacks. Agent-based models (ABMs) serve as a powerful tool to study these groups. While the popularity of social network tools (e.g., Facebook, Twitter) has provided extensive communication data, there is a lack of ne-grained behavioral data with which to inform and validate existing ABMs. Virtual worlds, in particular massively multiplayer online games (MMOG), where large numbers of people interact within a complex environ- ment for long periods of time provide an alternative sourcemore » of data. These environments provide a rich social environment where players engage in a variety of activities observed between real-world groups: collaborating and/or competing with other groups, conducting battles for scarce resources, and trading in a market economy. Strategies employed by player groups surprisingly re ect those seen in present-day con icts, where players use diplomacy or espionage as their means for accomplishing their goals. In this project, we propose to address the need for ne-grained behavioral data by acquiring and analyzing game data a commercial MMOG, referred to within this report as Game X. The goals of this research were: (1) devising toolsets for analyzing virtual world data to better inform the rules that govern a social ABM and (2) exploring how virtual worlds could serve as a source of data to validate ABMs established for analogous real-world phenomena. During this research, we studied certain patterns of group behavior to compliment social modeling e orts where a signi cant lack of detailed examples of observed phenomena exists. This report outlines our work examining group behaviors that underly what we have termed the Expression-To-Action (E2A) problem: determining the changes in social contact that lead individuals/groups to engage in a particular behavior. Results from our work indicate that virtual worlds have the potential for serving as a proxy in allocating and populating behaviors that would be used within further agent-based modeling studies.« less
On the potential for using immersive virtual environments to support laboratory experiment contextualisation

NASA Astrophysics Data System (ADS)

Machet, Tania; Lowe, David; Gütl, Christian

2012-12-01

This paper explores the hypothesis that embedding a laboratory activity into a virtual environment can provide a richer experimental context and hence improve the understanding of the relationship between a theoretical model and the real world, particularly in terms of the model's strengths and weaknesses. While an identified learning objective of laboratories is to support the understanding of the relationship between models and reality, the paper illustrates that this understanding is hindered by inherently limited experiments and that there is scope for improvement. Despite the contextualisation of learning activities having been shown to support learning objectives in many fields, there is traditionally little contextual information presented during laboratory experimentation. The paper argues that the enhancing laboratory activity with contextual information affords an opportunity to improve students' understanding of the relationship between the theoretical model and the experiment (which is effectively a proxy for the complex real world), thereby improving their understanding of the relationship between the model and reality. The authors propose that these improvements can be achieved by setting remote laboratories within context-rich virtual worlds.
Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models

USGS Publications Warehouse

Curtis, Gary P.; Lu, Dan; Ye, Ming

2015-01-01

While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. This study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict the reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. These reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Limitations of applying MLBMA to the synthetic study and future real-world modeling are discussed.
A motor unit-based model of muscle fatigue

PubMed Central

2017-01-01

Muscle fatigue is a temporary decline in the force and power capacity of skeletal muscle resulting from muscle activity. Because control of muscle is realized at the level of the motor unit (MU), it seems important to consider the physiological properties of motor units when attempting to understand and predict muscle fatigue. Therefore, we developed a phenomenological model of motor unit fatigue as a tractable means to predict muscle fatigue for a variety of tasks and to illustrate the individual contractile responses of MUs whose collective action determines the trajectory of changes in muscle force capacity during prolonged activity. An existing MU population model was used to simulate MU firing rates and isometric muscle forces and, to that model, we added fatigue-related changes in MU force, contraction time, and firing rate associated with sustained voluntary contractions. The model accurately estimated endurance times for sustained isometric contractions across a wide range of target levels. In addition, simulations were run for situations that have little experimental precedent to demonstrate the potential utility of the model to predict motor unit fatigue for more complicated, real-world applications. Moreover, the model provided insight into the complex orchestration of MU force contributions during fatigue, that would be unattainable with current experimental approaches. PMID:28574981
The effects of musical and linguistic components in recognition of real-world musical excerpts by cochlear implant recipients and normal-hearing adults.

PubMed

Gfeller, Kate; Jiang, Dingfeng; Oleson, Jacob J; Driscoll, Virginia; Olszewski, Carol; Knutson, John F; Turner, Christopher; Gantz, Bruce

2012-01-01

Cochlear implants (CI) are effective in transmitting salient features of speech, especially in quiet, but current CI technology is not well suited in transmission of key musical structures (e.g., melody, timbre). It is possible, however, that sung lyrics, which are commonly heard in real-world music may provide acoustical cues that support better music perception. The purpose of this study was to examine how accurately adults who use CIs (n = 87) and those with normal hearing (NH) (n = 17) are able to recognize real-world music excerpts based upon musical and linguistic (lyrics) cues. CI recipients were significantly less accurate than NH listeners on recognition of real-world music with or, in particular, without lyrics; however, CI recipients whose devices transmitted acoustic plus electric stimulation were more accurate than CI recipients reliant upon electric stimulation alone (particularly items without linguistic cues). Recognition by CI recipients improved as a function of linguistic cues. Participants were tested on melody recognition of complex melodies (pop, country, & classical styles). Results were analyzed as a function of: hearing status and history, device type (electric only or acoustic plus electric stimulation), musical style, linguistic and musical cues, speech perception scores, cognitive processing, music background, age, and in relation to self-report on listening acuity and enjoyment. Age at time of testing was negatively correlated with recognition performance. These results have practical implications regarding successful participation of CI users in music-based activities that include recognition and accurate perception of real-world songs (e.g., reminiscence, lyric analysis, & listening for enjoyment).
Using string invariants for prediction searching for optimal parameters

NASA Astrophysics Data System (ADS)

Bundzel, Marek; Kasanický, Tomáš; Pinčák, Richard

2016-02-01

We have developed a novel prediction method based on string invariants. The method does not require learning but a small set of parameters must be set to achieve optimal performance. We have implemented an evolutionary algorithm for the parametric optimization. We have tested the performance of the method on artificial and real world data and compared the performance to statistical methods and to a number of artificial intelligence methods. We have used data and the results of a prediction competition as a benchmark. The results show that the method performs well in single step prediction but the method's performance for multiple step prediction needs to be improved. The method works well for a wide range of parameters.
Application of satellite-based rainfall and medium range meteorological forecast in real-time flood forecasting in the Mahanadi River basin

NASA Astrophysics Data System (ADS)

Nanda, Trushnamayee; Beria, Harsh; Sahoo, Bhabagrahi; Chatterjee, Chandranath

2016-04-01

Increasing frequency of hydrologic extremes in a warming climate call for the development of reliable flood forecasting systems. The unavailability of meteorological parameters in real-time, especially in the developing parts of the world, makes it a challenging task to accurately predict flood, even at short lead times. The satellite-based Tropical Rainfall Measuring Mission (TRMM) provides an alternative to the real-time precipitation data scarcity. Moreover, rainfall forecasts by the numerical weather prediction models such as the medium term forecasts issued by the European Center for Medium range Weather Forecasts (ECMWF) are promising for multistep-ahead flow forecasts. We systematically evaluate these rainfall products over a large catchment in Eastern India (Mahanadi River basin). We found spatially coherent trends, with both the real-time TRMM rainfall and ECMWF rainfall forecast products overestimating low rainfall events and underestimating high rainfall events. However, no significant bias was found for the medium rainfall events. Another key finding was that these rainfall products captured the phase of the storms pretty well, but suffered from consistent under-prediction. The utility of the real-time TRMM and ECMWF forecast products are evaluated by rainfall-runoff modeling using different artificial neural network (ANN)-based models up to 3-days ahead. Keywords: TRMM; ECMWF; forecast; ANN; rainfall-runoff modeling
Behavior change

USDA-ARS?s Scientific Manuscript database

This brief entry presents the mediating-moderating variable model as a conceptual framework for understanding behavior change in regard to physical activity/exercise and adiposity. The ideas are applied to real world situations....
Classifying performance impairment in response to sleep loss using pattern recognition algorithms on single session testing

PubMed Central

St. Hilaire, Melissa A.; Sullivan, Jason P.; Anderson, Clare; Cohen, Daniel A.; Barger, Laura K.; Lockley, Steven W.; Klerman, Elizabeth B.

2012-01-01

There is currently no “gold standard” marker of cognitive performance impairment resulting from sleep loss. We utilized pattern recognition algorithms to determine which features of data collected under controlled laboratory conditions could most reliably identify cognitive performance impairment in response to sleep loss using data from only one testing session, such as would occur in the “real world” or field conditions. A training set for testing the pattern recognition algorithms was developed using objective Psychomotor Vigilance Task (PVT) and subjective Karolinska Sleepiness Scale (KSS) data collected from laboratory studies during which subjects were sleep deprived for 26 – 52 hours. The algorithm was then tested in data from both laboratory and field experiments. The pattern recognition algorithm was able to identify performance impairment with a single testing session in individuals studied under laboratory conditions using PVT, KSS, length of time awake and time of day information with sensitivity and specificity as high as 82%. When this algorithm was tested on data collected under real-world conditions from individuals whose data were not in the training set, accuracy of predictions for individuals categorized with low performance impairment were as high as 98%. Predictions for medium and severe performance impairment were less accurate. We conclude that pattern recognition algorithms may be a promising method for identifying performance impairment in individuals using only current information about the individual’s behavior. Single testing features (e.g., number of PVT lapses) with high correlation with performance impairment in the laboratory setting may not be the best indicators of performance impairment under real-world conditions. Pattern recognition algorithms should be further tested for their ability to be used in conjunction with other assessments of sleepiness in real-world conditions to quantify performance impairment in response to sleep loss. PMID:22959616
Smart Sampling and HPC-based Probabilistic Look-ahead Contingency Analysis Implementation and its Evaluation with Real-world Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yousu; Etingov, Pavel V.; Ren, Huiying

This paper describes a probabilistic look-ahead contingency analysis application that incorporates smart sampling and high-performance computing (HPC) techniques. Smart sampling techniques are implemented to effectively represent the structure and statistical characteristics of uncertainty introduced by different sources in the power system. They can significantly reduce the data set size required for multiple look-ahead contingency analyses, and therefore reduce the time required to compute them. High-performance-computing (HPC) techniques are used to further reduce computational time. These two techniques enable a predictive capability that forecasts the impact of various uncertainties on potential transmission limit violations. The developed package has been tested withmore » real world data from the Bonneville Power Administration. Case study results are presented to demonstrate the performance of the applications developed.« less
Real-World Application of Robust Design Optimization Assisted by Response Surface Approximation and Visual Data-Mining

NASA Astrophysics Data System (ADS)

Shimoyama, Koji; Jeong, Shinkyu; Obayashi, Shigeru

A new approach for multi-objective robust design optimization was proposed and applied to a real-world design problem with a large number of objective functions. The present approach is assisted by response surface approximation and visual data-mining, and resulted in two major gains regarding computational time and data interpretation. The Kriging model for response surface approximation can markedly reduce the computational time for predictions of robustness. In addition, the use of self-organizing maps as a data-mining technique allows visualization of complicated design information between optimality and robustness in a comprehensible two-dimensional form. Therefore, the extraction and interpretation of trade-off relations between optimality and robustness of design, and also the location of sweet spots in the design space, can be performed in a comprehensive manner.
Near-roadway monitoring of vehicle emissions as a function of mode of operation for light-duty vehicles.

PubMed

Wen, Dongqi; Zhai, Wenjuan; Xiang, Sheng; Hu, Zhice; Wei, Tongchuan; Noll, Kenneth E

2017-11-01

Determination of the effect of vehicle emissions on air quality near roadways is important because vehicles are a major source of air pollution. A near-roadway monitoring program was undertaken in Chicago between August 4 and October 30, 2014, to measure ultrafine particles, carbon dioxide, carbon monoxide, traffic volume and speed, and wind direction and speed. The objective of this study was to develop a method to relate short-term changes in traffic mode of operation to air quality near roadways using data averaged over 5-min intervals to provide a better understanding of the processes controlling air pollution concentrations near roadways. Three different types of data analysis are provided to demonstrate the type of results that can be obtained from a near-roadway sampling program based on 5-min measurements: (1) development of vehicle emission factors (EFs) for ultrafine particles as a function of vehicle mode of operation, (2) comparison of measured and modeled CO 2 concentrations, and (3) application of dispersion models to determine concentrations near roadways. EFs for ultrafine particles are developed that are a function of traffic volume and mode of operation (free flow and congestion) for light-duty vehicles (LDVs) under real-world conditions. Two air quality models-CALINE4 (California Line Source Dispersion Model, version 4) and AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model)-are used to predict the ultrafine particulate concentrations near roadways for comparison with measured concentrations. When using CALINE4 to predict air quality levels in the mixing cell, changes in surface roughness and stability class have no effect on the predicted concentrations. However, when using AERMOD to predict air quality in the mixing cell, changes in surface roughness have a significant impact on the predicted concentrations. The paper provides emission factors (EFs) that are a function of traffic volume and mode of operation (free flow and congestion) for LDVs under real-world conditions. The good agreement between monitoring and modeling results indicates that high-resolution, simultaneous measurements of air quality and meteorological and traffic conditions can be used to determine real-world, fleet-wide vehicle EFs as a function of vehicle mode of operation under actual driving conditions.
Characterizing and Discovering Spatiotemporal Social Contact Patterns for Healthcare.

PubMed

Yang, Bo; Pei, Hongbin; Chen, Hechang; Liu, Jiming; Xia, Shang

2017-08-01

During an epidemic, the spatial, temporal and demographic patterns of disease transmission are determined by multiple factors. In addition to the physiological properties of the pathogens and hosts, the social contact of the host population, which characterizes the reciprocal exposures of individuals to infection according to their demographic structure and various social activities, are also pivotal to understanding and predicting the prevalence of infectious diseases. How social contact is measured will affect the extent to which we can forecast the dynamics of infections in the real world. Most current work focuses on modeling the spatial patterns of static social contact. In this work, we use a novel perspective to address the problem of how to characterize and measure dynamic social contact during an epidemic. We propose an epidemic-model-based tensor deconvolution framework in which the spatiotemporal patterns of social contact are represented by the factors of the tensors. These factors can be discovered using a tensor deconvolution procedure with the integration of epidemic models based on rich types of data, mainly heterogeneous outbreak surveillance data, socio-demographic census data and physiological data from medical reports. Using reproduction models that include SIR/SIS/SEIR/SEIS models as case studies, the efficacy and applications of the proposed framework are theoretically analyzed, empirically validated and demonstrated through a set of rigorous experiments using both synthetic and real-world data.
A Context-Aware Knowledge Map to Support Ubiquitous Learning Activities for a u-Botanical Museum

ERIC Educational Resources Information Center

Wang, Shu-Lin; Chen, Chia-Chen; Zhang, Zhe George

2015-01-01

Recent developments in mobile and wireless communication technologies have played a vital role in building the u-learning environment that now combines both real-world and digital learning resources. However, learners still require assistance to control real objects and manage the abundance of available materials; otherwise, their mental workload…
Modeling Water Filtration

ERIC Educational Resources Information Center

Parks, Melissa

2014-01-01

Model-eliciting activities (MEAs) are not new to those in engineering or mathematics, but they were new to Melissa Parks. Model-eliciting activities are simulated real-world problems that integrate engineering, mathematical, and scientific thinking as students find solutions for specific scenarios. During this process, students generate solutions…
The Human HPLC Column

ERIC Educational Resources Information Center

Frantz, Kyle

2007-01-01

Initiatives in education reform emphasize inquiry-based active learning and real-world relevance to increase science literacy nationwide. Active teaching and learning approaches yield rapid intellectual development and may increase interest and motivation to learn science. Incorporating the topic of drug use with neuroscience, biology, psychology,…
"You Can't Go on the Other Side of the Fence": Preservice Teachers and Real-World Problems

ERIC Educational Resources Information Center

Simic-Muller, Ksenija; Fernandes, Anthony; Felton-Koestler, Mathew D.

2016-01-01

Our study investigates preservice teachers' perceptions of real-world problems; their beliefs about teaching real-world contexts, especially ones sociopolitical in nature; and their ability to pose meaningful real-world problems. In this paper we present cases of three preservice teachers who participated in interviews that probed their thinking…
Real-Time Simulation

NASA Technical Reports Server (NTRS)

1997-01-01

Coryphaeus Software, founded in 1989 by former NASA electronic engineer Steve Lakowske, creates real-time 3D software. Designer's Workbench, the company flagship product, is a modeling and simulation tool for the development of both static and dynamic 3D databases. Other products soon followed. Activation, specifically designed for game developers, allows developers to play and test the 3D games before they commit to a target platform. Game publishers can shorten development time and prove the "playability" of the title, maximizing their chances of introducing a smash hit. Another product, EasyT, lets users create massive, realistic representation of Earth terrains that can be viewed and traversed in real time. Finally, EasyScene software control the actions among interactive objects within a virtual world. Coryphaeus products are used on Silican Graphics workstation and supercomputers to simulate real-world performance in synthetic environments. Customers include aerospace, aviation, architectural and engineering firms, game developers, and the entertainment industry.

The Beginner's Guide to Interactive Virtual Field Trips

ERIC Educational Resources Information Center

Zanetis, Jan

2010-01-01

For students, field trips can be the best of both worlds: a welcome and exciting break from day-to-day classroom activities and a memorable, real-world experience that will solidify the curriculum in their minds. Unfortunately, the most desirable trips--those to far-away, enticing destinations--have long been inaccessible to all but a select few,…
Analysing the Suitability of Virtual Worlds for Direct Instruction and Individual Learning Activities

ERIC Educational Resources Information Center

Zarraonandia, Telmo; Francese, Rita; Passero, Ignazio; Diaz, Paloma; Tortora, Genoveffa

2014-01-01

Despite several researchers reporting evidence that 3D Virtual Worlds can be used to effectively support educational processes in recent years, the integration of this technology in real learning processes is not as commonplace as in other educational technologies. Instructional designers have to balance the cost associated with the development of…
Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory

PubMed Central

Tao, Qing

2017-01-01

Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM. PMID:29391864
Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory.

PubMed

Yang, Haimin; Pan, Zhisong; Tao, Qing

2017-01-01

Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.
Daclatasvir and asunaprevir combination therapy for patients with chronic hepatitis C virus genotype 1b infection in real world.

PubMed

Oh, Jae Young; Kim, Byung Seok; Lee, Chang Hyeong; Song, Jeong Eun; Lee, Heon Ju; Park, Jung Gil; Hwang, Jae Seok; Chung, Woo Jin; Jang, Byoung Kuk; Kweon, Young Oh; Tak, Won Young; Park, Soo Young; Jang, Se Young; Suh, Jeong Ill; Kwak, Sang Gyu

2018-05-25

Previous studies have reported a high rate of sustained virologic response (SVR) and a low rate of serious adverse events with the use of daclatasvir (DCV) and asunaprevir (ASV) combination therapy. We evaluated the efficacy and safety of DCV and ASV combination therapy for patients with chronic hepatitis C virus (HCV) genotype 1b infection in real world. We enrolled 278 patients (184 treatment-naïve patients) from five hospitals in Daegu and Gyeongsangbuk-do. We evaluated the rates of rapid virologic response (RVR), end-of-treatment response (ETR), and SVR at 12 weeks after completion of treatment (SVR12). Furthermore, we investigated the rate of adverse events and predictive factors of SVR12 failure. The mean age of patients was 59.5 ± 10.6 years, and 140 patients (50.2%) were men. Seventy-seven patients had cirrhosis. Baseline information regarding nonstructural protein 5A (NS5A) sequences was available in 268 patients. Six patients presented with pretreatment NS5A resistance-associated variants. The RVR and the ETR rates were 96.6% (258/267) and 95.2% (223/232), respectively. The overall SVR12 rate was 91.6% (197/215). Adverse events occurred in 17 patients (7.9%). Six patients discontinued treatment because of liver enzyme elevation (n = 4) and severe nausea (n = 2). Among these, four achieved SVR12. Other adverse events observed were fatigue, headache, diarrhea, dizziness, loss of appetite, skin rash, and dyspnea. Univariate analysis did not show significant predictive factors of SVR12 failure. DCV and ASV combination therapy showed high rates of RVR, ETR, and SVR12 in chronic HCV genotype 1b-infected patients in real world and was well tolerated without serious adverse events.
SuperIdentity: Fusion of Identity across Real and Cyber Domains

DOE Office of Scientific and Technical Information (OSTI.GOV)

Black, Sue; Creese, Sadie; Guest, Richard

Under both benign and malign circumstances, people now manage a spectrum of identities across both real-world and cyber domains. Our belief, however, is that all these instances ultimately track back for an individual to reflect a single 'SuperIdentity'. This paper outlines the assumptions underpinning the SuperIdentity Project, describing the innovative use of data fusion to incorporate novel real-world and cyber cues into a rich framework appropriate for modern identity. The proposed combinatorial model will support a robust identification or authentication decision, with confidence indexed both by the level of trust in data provenance, and the diagnosticity of the identity factorsmore » being used. Additionally, the exploration of correlations between factors may underpin the more intelligent use of identity information so that known information may be used to predict previously hidden information. With modern living supporting the 'distribution of identity' across real and cyber domains, and with criminal elements operating in increasingly sophisticated ways in the hinterland between the two, this approach is suggested as a way forwards, and is discussed in terms of its impact on privacy, security, and the detection of threat.« less
Modeling Heavy/Medium-Duty Fuel Consumption Based on Drive Cycle Properties

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Lijuan; Duran, Adam; Gonder, Jeffrey

This paper presents multiple methods for predicting heavy/medium-duty vehicle fuel consumption based on driving cycle information. A polynomial model, a black box artificial neural net model, a polynomial neural network model, and a multivariate adaptive regression splines (MARS) model were developed and verified using data collected from chassis testing performed on a parcel delivery diesel truck operating over the Heavy Heavy-Duty Diesel Truck (HHDDT), City Suburban Heavy Vehicle Cycle (CSHVC), New York Composite Cycle (NYCC), and hydraulic hybrid vehicle (HHV) drive cycles. Each model was trained using one of four drive cycles as a training cycle and the other threemore » as testing cycles. By comparing the training and testing results, a representative training cycle was chosen and used to further tune each method. HHDDT as the training cycle gave the best predictive results, because HHDDT contains a variety of drive characteristics, such as high speed, acceleration, idling, and deceleration. Among the four model approaches, MARS gave the best predictive performance, with an average absolute percent error of -1.84% over the four chassis dynamometer drive cycles. To further evaluate the accuracy of the predictive models, the approaches were first applied to real-world data. MARS outperformed the other three approaches, providing an average absolute percent error of -2.2% of four real-world road segments. The MARS model performance was then compared to HHDDT, CSHVC, NYCC, and HHV drive cycles with the performance from Future Automotive System Technology Simulator (FASTSim). The results indicated that the MARS method achieved a comparative predictive performance with FASTSim.« less
Learning Physics from the Real World by Direct Observation

NASA Astrophysics Data System (ADS)

Shaibani, Saami J.

2012-03-01

It is axiomatic that hands-on experience provides many learning opportunities, which lectures and textbooks cannot match. Moreover, experiments involving the real world are beneficial in helping students to gain a level of understanding that they might not otherwise achieve. One practical limitation with the real world is that simplifications and approximations are sometimes necessary to make the material accessible; however, these types of adjustments can be viewed with misgiving when they appear arbitrary and/or convenience-based. The present work describes a very familiar feature of everyday life, whose underlying physics is examined without modifications to mitigate difficulties from the lack of control in a non-laboratory environment. In the absence of any immediate formula to process results, students are encouraged to reach ab initio answers with guidance provided by a structured series of worksheets. Many of the latter can be completed as homework assignments prior to activity in the field. This approach promotes thinking and inquiry as valuable attributes instead of unquestioningly following a prescribed path.
Familiar real-world spatial cues provide memory benefits in older and younger adults.

PubMed

Robin, Jessica; Moscovitch, Morris

2017-05-01

Episodic memory, future thinking, and memory for scenes have all been proposed to rely on the hippocampus, and evidence suggests that these all decline in healthy aging. Despite this age-related memory decline, studies examining the effects of context reinstatement on episodic memory have demonstrated that reinstating elements of the encoding context of an event leads to better memory retrieval in both younger and older adults. The current study was designed to test whether more familiar, real-world contexts, such as locations that participants visited often, would improve the detail richness and vividness of memory for scenes, autobiographical events, and imagination of future events in young and older adults. The predicted age-related decline in internal details across all 3 conditions was accompanied by persistent effects of contextual familiarity, in which a more familiar spatial context led to increased detail and vividness of remembered scenes, autobiographical events, and, to some extent, imagined future events. This study demonstrates that autobiographical memory, imagination of the future, and scene memory are similarly affected by aging, and all benefit from being associated with more familiar (real-world) contexts, illustrating the stability of contextual reinstatement effects on memory throughout the life span. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Identifying important nodes by adaptive LeaderRank

NASA Astrophysics Data System (ADS)

Xu, Shuang; Wang, Pei

2017-03-01

Spreading process is a common phenomenon in complex networks. Identifying important nodes in complex networks is of great significance in real-world applications. Based on the spreading process on networks, a lot of measures have been proposed to evaluate the importance of nodes. However, most of the existing measures are appropriate to static networks, which are fragile to topological perturbations. Many real-world complex networks are dynamic rather than static, meaning that the nodes and edges of such networks may change with time, which challenge numerous existing centrality measures. Based on a new weighted mechanism and the newly proposed H-index and LeaderRank (LR), this paper introduces a variant of the LR measure, called adaptive LeaderRank (ALR), which is a new member of the LR-family. Simulations on six real-world networks reveal that the new measure can well balance between prediction accuracy and robustness. More interestingly, the new measure can better adapt to the adjustment or local perturbations of network topologies, as compared with the existing measures. By discussing the detailed properties of the measures from the LR-family, we illustrate that the ALR has its competitive advantages over the other measures. The proposed algorithm enriches the measures to understand complex networks, and may have potential applications in social networks and biological systems.
A generalized procedure for the prediction of multicomponent adsorption equilibria

DOE PAGES

Ladshaw, Austin; Yiacoumi, Sotira; Tsouris, Costas

2015-04-07

Prediction of multicomponent adsorption equilibria has been investigated for several decades. While there are theories available to predict the adsorption behavior of ideal mixtures, there are few purely predictive theories to account for nonidealities in real systems. Most models available for dealing with nonidealities contain interaction parameters that must be obtained through correlation with binary-mixture data. However, as the number of components in a system grows, the number of parameters needed to be obtained increases exponentially. Here, a generalized procedure is proposed, as an extension of the predictive real adsorbed solution theory, for determining the parameters of any activity model,more » for any number of components, without correlation. This procedure is then combined with the adsorbed solution theory to predict the adsorption behavior of mixtures. As this method can be applied to any isotherm model and any activity model, it is referred to as the generalized predictive adsorbed solution theory.« less
Creative Stories: A Storytelling Game Fostering Creativity

ERIC Educational Resources Information Center

Koukourikos, Antonis; Karampiperis, Pythagoras; Panagopoulos, George

2014-01-01

The process of identifying techniques for fostering creativity, and applying these theoretical constructs in real-world educational activities, is, by nature, multifaceted and not straightforward, pertaining to several fields such as cognitive theory and psychology. Furthermore, the quantification of the impact of different activities on…
Defining and Measuring Psychomotor Performance

ERIC Educational Resources Information Center

Autio, Ossi

2007-01-01

Psychomotor performance is fundamental to human existence. It is important in many real world activities and nowadays psychomotor tests are used in several fields of industry, army, and medical sciences in employee selection. This article tries to define psychomotor activity by introducing some psychomotor theories. Furthermore the…
Integrating ecology and social science using two examples of wandering wildlife and human activity

EPA Science Inventory

Many researchers have studied impacts of human activity on wildlife or human attitudes toward wildlife, but not both simultaneously. Understanding these interactions is critical to better understand the intricacies of real world conservation issues. The goal of my presentation ...
Association between volume and momentum of online searches and real-world collective unrest

NASA Astrophysics Data System (ADS)

Qi, Hong; Manrique, Pedro; Johnson, Daniela; Restrepo, Elvira; Johnson, Neil F.

A fundamental idea from physics is that macroscopic transitions can occur as a result of an escalation in the correlated activity of a many-body system's constituent particles. Here we apply this idea in an interdisciplinary setting, whereby the particles are individuals, their correlated activity involves online search activity surrounding the topics of social unrest, and the macroscopic phenomenon being measured are real-world protests. Our empirical study covers countries in Latin America during 2011-2014 using datasets assembled from multiple sources by subject matter experts. We find specifically that the volume and momentum of searches on Google Trends surrounding mass protest language, can detect - and may even pre-empt - the macroscopic on-street activity. Not only can this simple open-source solution prove an invaluable aid for monitoring civil order, our study serves to strengthen the increasing literature in the physics community aimed at understanding the collective dynamics of interacting populations of living objects across the life sciences.
Properties of four real world collaboration--competition networks

NASA Astrophysics Data System (ADS)

Fu, Chun-Hua; Xu, Xiu-Lian; He, Da-Ren

2009-03-01

Our research group has empirically investigated 9 real world collaboration networks and 25 real world cooperation-competition networks. Among the 34 real world systems, all the 9 real world collaboration networks and 6 real world cooperation-competition networks show the unimodal act-size distribution and the shifted power law distribution of degree and act-degree. We have proposed a collaboration network evolution model for an explanation of the rules [1]. The other 14 real world cooperation-competition networks show that the act-size distributions are not unimodal; instead, they take qualitatively the same shifted power law forms as the degree and act-degree distributions. The properties of four systems (the main land movie film network, Beijing restaurant network, 2004 Olympic network, and Tao-Bao notebook computer sale network) are reported in detail as examples. Via a numerical simulation, we show that the new rule can still be explained by the above-mentioned model. [1] H. Chang, B. B. Su, et al. Phsica A, 2007, 383: 687-702.
Links between real and virtual networks: a comparative study of online communities in Japan and Korea.

PubMed

Ishii, Kenichi; Ogasahara, Morihiro

2007-04-01

The present study explores how online communities affect real-world personal relations based on a cross-cultural survey conducted in Japan and Korea. Findings indicate that the gratifications of online communities moderate the effects of online communities on social participation. Online communities are categorized into a real-group-based community and a virtual-network-based community. The membership of real-group-based online community is positively correlated with social bonding gratification and negatively correlated with information- seeking gratification. Japanese users prefer more virtual-network-based online communities, while their Korean counterparts prefer real-group-based online communities. Korean users are more active in online communities and seek a higher level of socializing gratifications, such as social bonding and making new friends, when compared with their Japanese counterparts. These results indicate that in Korea, personal relations via the online community are closely associated with the real-world personal relations, but this is not the case in Japan. This study suggests that the effects of the Internet are culture-specific and that the online community can serve a different function in different cultural environments.
An Active, Reflective Learning Cycle for E-Commerce Classes: Learning about E-Commerce by Doing and Teaching

ERIC Educational Resources Information Center

Abrahams, Alan S.; Singh, Tirna

2010-01-01

Active, experiential learning is an important component in information systems education, ensuring that students gain an appreciation for both practical and theoretical information systems concepts. Typically, students in active, experiential classes engage in real world projects for commercial companies or not-for-profit organizations. In the…
[Realization of design regarding experimental research in the clinical real-world research].

PubMed

He, Q; Shi, J P

2018-04-10

Real world study (RWS), a further verification and supplement for explanatory randomized controlled trial to evaluate the effectiveness of intervention measures in real clinical environment, has increasingly become the focus in the field of research on medical and health care services. However, some people mistakenly equate real world study with observational research, and argue that intervention and randomization cannot be carried out in real world study. In fact, both observational and experimental design are the basic designs in real world study, while the latter usually refers to pragmatic randomized controlled trial and registry-based randomized controlled trial. Other nonrandomized controlled and adaptive designs can also be adopted in the RWS.
Study on Development of 1D-2D Coupled Real-time Urban Inundation Prediction model

NASA Astrophysics Data System (ADS)

Lee, Seungsoo

2017-04-01

In recent years, we are suffering abnormal weather condition due to climate change around the world. Therefore, countermeasures for flood defense are urgent task. In this research, study on development of 1D-2D coupled real-time urban inundation prediction model using predicted precipitation data based on remote sensing technology is conducted. 1 dimensional (1D) sewerage system analysis model which was introduced by Lee et al. (2015) is used to simulate inlet and overflow phenomena by interacting with surface flown as well as flows in conduits. 2 dimensional (2D) grid mesh refinement method is applied to depict road networks for effective calculation time. 2D surface model is coupled with 1D sewerage analysis model in order to consider bi-directional flow between both. Also parallel computing method, OpenMP, is applied to reduce calculation time. The model is estimated by applying to 25 August 2014 extreme rainfall event which caused severe inundation damages in Busan, Korea. Oncheoncheon basin is selected for study basin and observed radar data are assumed as predicted rainfall data. The model shows acceptable calculation speed with accuracy. Therefore it is expected that the model can be used for real-time urban inundation forecasting system to minimize damages.

Differential Medial Temporal Lobe and Parietal Cortical Contributions to Real-world Autobiographical Episodic and Autobiographical Semantic Memory.

PubMed

Brown, Thackery I; Rissman, Jesse; Chow, Tiffany E; Uncapher, Melina R; Wagner, Anthony D

2018-04-18

Autobiographical remembering can depend on two forms of memory: episodic (event) memory and autobiographical semantic memory (remembering personally relevant semantic knowledge, independent of recalling a specific experience). There is debate about the degree to which the neural signals that support episodic recollection relate to or build upon autobiographical semantic remembering. Pooling data from two fMRI studies of memory for real-world personal events, we investigated whether medial temporal lobe (MTL) and parietal subregions contribute to autobiographical episodic and semantic remembering. During scanning, participants made memory judgments about photograph sequences depicting past events from their life or from others' lives, and indicated whether memory was based on episodic or semantic knowledge. Results revealed several distinct functional patterns: activity in most MTL subregions was selectively associated with autobiographical episodic memory; the hippocampal tail, superior parietal lobule, and intraparietal sulcus were similarly engaged when memory was based on retrieval of an autobiographical episode or autobiographical semantic knowledge; and angular gyrus demonstrated a graded pattern, with activity declining from autobiographical recollection to autobiographical semantic remembering to correct rejections of novel events. Collectively, our data offer insights into MTL and parietal cortex functional organization, and elucidate circuitry that supports different forms of real-world autobiographical memory.
Data-driven coarse graining in action: Modeling and prediction of complex systems

NASA Astrophysics Data System (ADS)

Krumscheid, S.; Pradas, M.; Pavliotis, G. A.; Kalliadasis, S.

2015-10-01

In many physical, technological, social, and economic applications, one is commonly faced with the task of estimating statistical properties, such as mean first passage times of a temporal continuous process, from empirical data (experimental observations). Typically, however, an accurate and reliable estimation of such properties directly from the data alone is not possible as the time series is often too short, or the particular phenomenon of interest is only rarely observed. We propose here a theoretical-computational framework which provides us with a systematic and rational estimation of statistical quantities of a given temporal process, such as waiting times between subsequent bursts of activity in intermittent signals. Our framework is illustrated with applications from real-world data sets, ranging from marine biology to paleoclimatic data.
The Effects of Musical and Linguistic Components in Recognition of Real-World Musical Excerpts by Cochlear Implant Recipients and Normal-Hearing Adults

PubMed Central

Gfeller, Kate; Jiang, Dingfeng; Oleson, Jacob; Driscoll, Virginia; Olszewski, Carol; Knutson, John F.; Turner, Christopher; Gantz, Bruce

2011-01-01

Background Cochlear implants (CI) are effective in transmitting salient features of speech, especially in quiet, but current CI technology is not well suited in transmission of key musical structures (e.g., melody, timbre). It is possible, however, that sung lyrics, which are commonly heard in real-world music may provide acoustical cues that support better music perception. Objective The purpose of this study was to examine how accurately adults who use CIs (n=87) and those with normal hearing (NH) (n=17) are able to recognize real-world music excerpts based upon musical and linguistic (lyrics) cues. Results CI recipients were significantly less accurate than NH listeners on recognition of real-world music with or, in particular, without lyrics; however, CI recipients whose devices transmitted acoustic plus electric stimulation were more accurate than CI recipients reliant upon electric stimulation alone (particularly items without linguistic cues). Recognition by CI recipients improved as a function of linguistic cues. Methods Participants were tested on melody recognition of complex melodies (pop, country, classical styles). Results were analyzed as a function of: hearing status and history, device type (electric only or acoustic plus electric stimulation), musical style, linguistic and musical cues, speech perception scores, cognitive processing, music background, age, and in relation to self-report on listening acuity and enjoyment. Age at time of testing was negatively correlated with recognition performance. Conclusions These results have practical implications regarding successful participation of CI users in music-based activities that include recognition and accurate perception of real-world songs (e.g., reminiscence, lyric analysis, listening for enjoyment). PMID:22803258
Systematic review with meta-analysis: real-world effectiveness and safety of vedolizumab in patients with inflammatory bowel disease.

PubMed

Schreiber, Stefan; Dignass, Axel; Peyrin-Biroulet, Laurent; Hather, Greg; Demuth, Dirk; Mosli, Mahmoud; Curtis, Rebecca; Khalid, Javaria Mona; Loftus, Edward Vincent

2018-06-04

Selective patient recruitment can produce discrepancies between clinical trial results and real-world effectiveness. A systematic literature review and meta-analysis were conducted to assess vedolizumab real-world effectiveness and safety in patients with ulcerative colitis (UC) or Crohn's disease (CD). MEDLINE, MEDLINE In-Process, EMBASE, and Cochrane databases were searched for real-world studies of vedolizumab in adult patients with UC/CD reporting clinical response, remission, corticosteroid-free remission, UC/CD-related surgery or hospitalization, mucosal healing, or safety published from May 1, 2014-June 22, 2017. Response and remission rates were combined in random-effects meta-analyses. At treatment week 14, 32% of UC patients [95% confidence interval (CI) 27-39%] and 30% of CD patients (95% CI 25-34%) were in remission; and at month 12, 46% for UC (95% CI 37-56%) and 30% for CD (95% CI 20-42%). For UC, the rates of corticosteroid-free remission were 26% at week 14 (95% CI 20-34%) and 42% at month 12 (95% CI 31-53%); for CD they were 25% at week 14 (95%, CI 20-31%) and 31% at month 12 (95%, CI 20-45%). At month 12, 33-77% of UC and 6-63% of CD patients had mucosal healing. Nine percent of patients reported serious adverse events. Vedolizumab demonstrated real-world effectiveness in patients with moderate-to-severely active UC or CD, with approximately one-half and one-third of patients, respectively, in remission at treatment month 12. These findings are consistent with clinical trial data and support the long-term benefit-risk profile of vedolizumab.
An Analysis of Frame Semantics of Continuous Processes

DTIC Science & Technology

2016-08-10

in natural text involving a variety of continuous processes. Keywords: Frame Semantics; Qualitative Reasoning Introduction & Background Daily...We evaluate our mapping on science texts , but expect our approach to be domain general. Qualitative Process Theory In QP theory, changes within a...fragments from text could reason about real-world scenarios, predicting, for example, that our tub of water may overflow. However, the incremental
Meta-Analytic Estimates Predict the Effectiveness of Emotion Regulation Strategies in the "Real World": Reply to Augustine and Hemenover (2013)

ERIC Educational Resources Information Center

Miles, Eleanor; Sheeran, Paschal; Webb, Thomas L.

2013-01-01

Augustine and Hemenover (2013) were right to state that meta-analyses should be accurate and generalizable. However, we disagree that our meta-analysis of emotion regulation strategies (Webb, Miles, & Sheeran, 2012) fell short in these respects. Augustine and Hemenover's concerns appear to have accrued from misunderstandings of our inclusion…
Young Adolescents' Metacognition and Domain Knowledge as Predictors of Hypothesis-Development Performance in a Computer-Supported Context

ERIC Educational Resources Information Center

Kim, Hye Jeong; Pedersen, Susan

2010-01-01

Recently, the importance of ill-structured problem-solving in real-world contexts has become a focus of educational research. Particularly, the hypothesis-development process has been examined as one of the keys to developing a high-quality solution in a problem context. The authors of this study examined predictive relations between young…
Does How Students Serve Matter? What Characteristics of Service Programs Predict Students' Social Justice Attitudes?

ERIC Educational Resources Information Center

Littenberg-Tobias, Joshua

2014-01-01

Volunteering is often touted as a method to educate college students about social justice by providing students with an opportunity to apply classroom knowledge in a real-world setting. However, many critics have noted that service does not necessarily lead to social justice outcomes and that some forms of service may reinforce students'…
Eco-morphological Real-time Forecasting tool to predict hydrodynamic, sediment and nutrient dynamic in Coastal Louisiana

NASA Astrophysics Data System (ADS)

Messina, F.; Meselhe, E. A.; Buckman, L.; Twight, D.

2017-12-01

Louisiana coastal zone is one of the most productive and dynamic eco-geomorphic systems in the world. This unique natural environment has been alternated by human activities and natural processes such as sea level rise, subsidence, dredging of canals for oil and gas production, the Mississippi River levees which don't allow the natural river sediment. As a result of these alterations land loss, erosion and flood risk are becoming real issues for Louisiana. Costal authorities have been studying the benefits and effects of several restoration projects, e.g. freshwater and sediment diversions. The protection of communities, wildlife and of the unique environments is a high priority in this region. The Water Institute of the Gulf, together with Deltares, has developed a forecasting and information system for a pilot location in Coastal Louisiana, specifically for Barataria Bay and Breton Sound Basins in the Mississippi River Deltaic Plain. The system provides a 7-day forecast of water level, salinity, and temperature, under atmospheric and coastal forecasted conditions, such as freshwater riverine inflow, rainfall, evaporation, wind, and tide. The system also forecasts nutrient distribution (e.g., Chla and dissolved oxygen) and sediment transport. The Flood Early Warning System FEWS is used as a platform to import multivariate data from several sources, use them to monitor the pilot location and to provide boundary conditions to the model. A hindcast model is applied to compare the model results to the observed data, and to provide the initial condition to the forecast model. This system represents a unique tool which provides valuable information regarding the overall conditions of the basins. It offers the opportunity to adaptively manage existing and planned diversions to meet certain salinity and water level targets or thresholds while maximizing land-building goals. Moreover, water quality predictions provide valuable information on the current ecological conditions of the area. Real time observations and model predictions can be used as guidance to decision makers regarding the operation of control structures in response to forecasted weather or river flood events. Coastal communities can benefit from water level, salinity and water quality forecast to manage their activities.
Predicting missing links in complex networks based on common neighbors and distance

PubMed Central

Yang, Jinxuan; Zhang, Xiao-Dong

2016-01-01

The algorithms based on common neighbors metric to predict missing links in complex networks are very popular, but most of these algorithms do not account for missing links between nodes with no common neighbors. It is not accurate enough to reconstruct networks by using these methods in some cases especially when between nodes have less common neighbors. We proposed in this paper a new algorithm based on common neighbors and distance to improve accuracy of link prediction. Our proposed algorithm makes remarkable effect in predicting the missing links between nodes with no common neighbors and performs better than most existing currently used methods for a variety of real-world networks without increasing complexity. PMID:27905526
Connecting Technological Innovation in Artificial Intelligence to Real-world Medical Practice through Rigorous Clinical Validation: What Peer-reviewed Medical Journals Could Do

PubMed Central

2018-01-01

Artificial intelligence (AI) is projected to substantially influence clinical practice in the foreseeable future. However, despite the excitement around the technologies, it is yet rare to see examples of robust clinical validation of the technologies and, as a result, very few are currently in clinical use. A thorough, systematic validation of AI technologies using adequately designed clinical research studies before their integration into clinical practice is critical to ensure patient benefit and safety while avoiding any inadvertent harms. We would like to suggest several specific points regarding the role that peer-reviewed medical journals can play, in terms of study design, registration, and reporting, to help achieve proper and meaningful clinical validation of AI technologies designed to make medical diagnosis and prediction, focusing on the evaluation of diagnostic accuracy efficacy. Peer-reviewed medical journals can encourage investigators who wish to validate the performance of AI systems for medical diagnosis and prediction to pay closer attention to the factors listed in this article by emphasizing their importance. Thereby, peer-reviewed medical journals can ultimately facilitate translating the technological innovations into real-world practice while securing patient safety and benefit. PMID:29805337
Connecting Technological Innovation in Artificial Intelligence to Real-world Medical Practice through Rigorous Clinical Validation: What Peer-reviewed Medical Journals Could Do.

PubMed

Park, Seong Ho; Kressel, Herbert Y

2018-05-28

Artificial intelligence (AI) is projected to substantially influence clinical practice in the foreseeable future. However, despite the excitement around the technologies, it is yet rare to see examples of robust clinical validation of the technologies and, as a result, very few are currently in clinical use. A thorough, systematic validation of AI technologies using adequately designed clinical research studies before their integration into clinical practice is critical to ensure patient benefit and safety while avoiding any inadvertent harms. We would like to suggest several specific points regarding the role that peer-reviewed medical journals can play, in terms of study design, registration, and reporting, to help achieve proper and meaningful clinical validation of AI technologies designed to make medical diagnosis and prediction, focusing on the evaluation of diagnostic accuracy efficacy. Peer-reviewed medical journals can encourage investigators who wish to validate the performance of AI systems for medical diagnosis and prediction to pay closer attention to the factors listed in this article by emphasizing their importance. Thereby, peer-reviewed medical journals can ultimately facilitate translating the technological innovations into real-world practice while securing patient safety and benefit.
A data acquisition protocol for a reactive wireless sensor network monitoring application.

PubMed

Aderohunmu, Femi A; Brunelli, Davide; Deng, Jeremiah D; Purvis, Martin K

2015-04-30

Limiting energy consumption is one of the primary aims for most real-world deployments of wireless sensor networks. Unfortunately, attempts to optimize energy efficiency are often in conflict with the demand for network reactiveness to transmit urgent messages. In this article, we propose SWIFTNET: a reactive data acquisition scheme. It is built on the synergies arising from a combination of the data reduction methods and energy-efficient data compression schemes. Particularly, it combines compressed sensing, data prediction and adaptive sampling strategies. We show how this approach dramatically reduces the amount of unnecessary data transmission in the deployment for environmental monitoring and surveillance networks. SWIFTNET targets any monitoring applications that require high reactiveness with aggressive data collection and transmission. To test the performance of this method, we present a real-world testbed for a wildfire monitoring as a use-case. The results from our in-house deployment testbed of 15 nodes have proven to be favorable. On average, over 50% communication reduction when compared with a default adaptive prediction method is achieved without any loss in accuracy. In addition, SWIFTNET is able to guarantee reactiveness by adjusting the sampling interval from 5 min up to 15 s in our application domain.
A Data Acquisition Protocol for a Reactive Wireless Sensor Network Monitoring Application

PubMed Central

Aderohunmu, Femi A.; Brunelli, Davide; Deng, Jeremiah D.; Purvis, Martin K.

2015-01-01

Limiting energy consumption is one of the primary aims for most real-world deployments of wireless sensor networks. Unfortunately, attempts to optimize energy efficiency are often in conflict with the demand for network reactiveness to transmit urgent messages. In this article, we propose SWIFTNET: a reactive data acquisition scheme. It is built on the synergies arising from a combination of the data reduction methods and energy-efficient data compression schemes. Particularly, it combines compressed sensing, data prediction and adaptive sampling strategies. We show how this approach dramatically reduces the amount of unnecessary data transmission in the deployment for environmental monitoring and surveillance networks. SWIFTNET targets any monitoring applications that require high reactiveness with aggressive data collection and transmission. To test the performance of this method, we present a real-world testbed for a wildfire monitoring as a use-case. The results from our in-house deployment testbed of 15 nodes have proven to be favorable. On average, over 50% communication reduction when compared with a default adaptive prediction method is achieved without any loss in accuracy. In addition, SWIFTNET is able to guarantee reactiveness by adjusting the sampling interval from 5 min up to 15 s in our application domain. PMID:25942642
Episodic autobiographical memory is associated with variation in the size of hippocampal subregions.

PubMed

Palombo, Daniela J; Bacopulos, Agnes; Amaral, Robert S C; Olsen, Rosanna K; Todd, Rebecca M; Anderson, Adam K; Levine, Brian

2018-02-01

Striking individual differences exist in the human capacity to recollect past events, yet, little is known about the neural correlates of such individual differences. Studies investigating hippocampal volume in relation to individual differences in laboratory measures of episodic memory in young adults suggest that whole hippocampal volume is unrelated (or even negatively associated) with episodic memory. However, anatomical and functional specialization across hippocampal subregions suggests that individual differences in episodic memory may be linked to particular hippocampal subregions, as opposed to whole hippocampal volume. Given that the DG/CA 2/3 circuitry is thought to be especially critical for supporting episodic memory in humans, we predicted that the volume of this region would be associated with individual variability in episodic memory. This prediction was supported using high-resolution MRI of the hippocampal subfields and measures of real-world (autobiographical) episodic memory. In addition to the association with DG/CA 2/3 , we further observed a relationship between episodic autobiographical memory and subiculum volume, whereas no association was observed with CA 1 or with whole hippocampal volume. These findings provide insight into the possible neural substrates that mediate individual differences in real-world episodic remembering in humans. © 2017 Wiley Periodicals, Inc.
Implementing Peer Learning in Clinical Education: A Framework to Address Challenges In the "Real World".

PubMed

Tai, Joanna Hong Meng; Canny, Benedict J; Haines, Terry P; Molloy, Elizabeth K

2017-01-01

Phenomenon: Peer learning has many benefits and can assist students in gaining the educational skills required in future years when they become teachers themselves. Peer learning may be particularly useful in clinical learning environments, where students report feeling marginalized, overwhelmed, and unsupported. Educational interventions often fail in the workplace environment, as they are often conceived in the "ideal" rather than the complex, messy real world. This work sought to explore barriers and facilitators to implementing peer learning activities in a clinical curriculum. Previous peer learning research results and a matrix of empirically derived peer learning activities were presented to local clinical education experts to generate discussion around the realities of implementing such activities. Potential barriers and limitations of and strategies for implementing peer learning in clinical education were the focus of the individual interviews. Thematic analysis of the data identified three key considerations for real-world implementation of peer learning: culture, epistemic authority, and the primacy of patient-centered care. Strategies for peer learning implementation were also developed from themes within the data, focusing on developing a culture of safety in which peer learning could be undertaken, engaging both educators and students, and establishing expectations for the use of peer learning. Insights: This study identified considerations and strategies for the implementation of peer learning activities, which took into account both educator and student roles. Reported challenges were reflective of those identified within the literature. The resultant framework may aid others in anticipating implementation challenges. Further work is required to test the framework's application in other contexts and its effect on learner outcomes.
Child Development: An Active Learning Approach

ERIC Educational Resources Information Center

Levine, Laura E.; Munsch, Joyce

2010-01-01

Within each chapter of this innovative topical text, the authors engage students by demonstrating the wide range of real-world applications of psychological research connected to child development. In particular, the distinctive Active Learning features incorporated throughout the book foster a dynamic and personal learning process for students.…
Ubiquitous Computing Technologies in Education

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Wu, Ting-Ting; Chen, Yen-Jung

2007-01-01

The prosperous development of wireless communication and sensor technologies has attracted the attention of researchers from both computer and education fields. Various investigations have been made for applying the new technologies to education purposes, such that more active and adaptive learning activities can be conducted in the real world.…
A Robust Adaptive Autonomous Approach to Optimal Experimental Design

NASA Astrophysics Data System (ADS)

Gu, Hairong

Experimentation is the fundamental tool of scientific inquiries to understand the laws governing the nature and human behaviors. Many complex real-world experimental scenarios, particularly in quest of prediction accuracy, often encounter difficulties to conduct experiments using an existing experimental procedure for the following two reasons. First, the existing experimental procedures require a parametric model to serve as the proxy of the latent data structure or data-generating mechanism at the beginning of an experiment. However, for those experimental scenarios of concern, a sound model is often unavailable before an experiment. Second, those experimental scenarios usually contain a large number of design variables, which potentially leads to a lengthy and costly data collection cycle. Incompetently, the existing experimental procedures are unable to optimize large-scale experiments so as to minimize the experimental length and cost. Facing the two challenges in those experimental scenarios, the aim of the present study is to develop a new experimental procedure that allows an experiment to be conducted without the assumption of a parametric model while still achieving satisfactory prediction, and performs optimization of experimental designs to improve the efficiency of an experiment. The new experimental procedure developed in the present study is named robust adaptive autonomous system (RAAS). RAAS is a procedure for sequential experiments composed of multiple experimental trials, which performs function estimation, variable selection, reverse prediction and design optimization on each trial. Directly addressing the challenges in those experimental scenarios of concern, function estimation and variable selection are performed by data-driven modeling methods to generate a predictive model from data collected during the course of an experiment, thus exempting the requirement of a parametric model at the beginning of an experiment; design optimization is performed to select experimental designs on the fly of an experiment based on their usefulness so that fewest designs are needed to reach useful inferential conclusions. Technically, function estimation is realized by Bayesian P-splines, variable selection is realized by Bayesian spike-and-slab prior, reverse prediction is realized by grid-search and design optimization is realized by the concepts of active learning. The present study demonstrated that RAAS achieves statistical robustness by making accurate predictions without the assumption of a parametric model serving as the proxy of latent data structure while the existing procedures can draw poor statistical inferences if a misspecified model is assumed; RAAS also achieves inferential efficiency by taking fewer designs to acquire useful statistical inferences than non-optimal procedures. Thus, RAAS is expected to be a principled solution to real-world experimental scenarios pursuing robust prediction and efficient experimentation.
Do dimensional psychopathology measures relate to creative achievement or divergent thinking?

PubMed Central

Zabelina, Darya L.; Condon, David; Beeman, Mark

2014-01-01

Previous research provides disparate accounts of the putative association between creativity and psychopathology, including schizotypy, psychoticism, hypomania, bipolar disorder, ADHD, and autism spectrum disorders. To examine these association, healthy, non-clinical participants completed several psychopathology-spectrum measures, often postulated to associate with creativity: the Schizotypal Personality Questionnaire, the Psychoticism scale, the Personality Inventory for DSM-5, the Hypomanic Personality Scale, the Attention Deficit/Hyperactivity Disorder scale, the Beck Depression Inventory, and the Autism-Spectrum Quotient. The goal of Study 1 was to evaluate the factor structure of these dimensional psychopathology measures and, in particular, to evaluate the case for a strong general factor(s). None of the factor solutions between 1 and 10 factors provided a strong fit with the data based on the most commonly used metrics. The goal of Study 2 was to determine whether these psychopathology scales predict, independently, two measures of creativity: 1. a measure of participants' real-world creative achievements, and 2. divergent thinking, a laboratory measure of creative cognition. After controlling for academic achievement, psychoticism and hypomania reliably predicted real-world creative achievement and divergent thinking scored with the consensual assessment technique. None of the psychopathology-spectrum scales reliably predicted divergent thinking scored with the manual scoring method. Implications for the potential links between several putative creative processes and risk factors for psychopathology are discussed. PMID:25278919

Do dimensional psychopathology measures relate to creative achievement or divergent thinking?

PubMed

Zabelina, Darya L; Condon, David; Beeman, Mark

2014-01-01

Previous research provides disparate accounts of the putative association between creativity and psychopathology, including schizotypy, psychoticism, hypomania, bipolar disorder, ADHD, and autism spectrum disorders. To examine these association, healthy, non-clinical participants completed several psychopathology-spectrum measures, often postulated to associate with creativity: the Schizotypal Personality Questionnaire, the Psychoticism scale, the Personality Inventory for DSM-5, the Hypomanic Personality Scale, the Attention Deficit/Hyperactivity Disorder scale, the Beck Depression Inventory, and the Autism-Spectrum Quotient. The goal of Study 1 was to evaluate the factor structure of these dimensional psychopathology measures and, in particular, to evaluate the case for a strong general factor(s). None of the factor solutions between 1 and 10 factors provided a strong fit with the data based on the most commonly used metrics. The goal of Study 2 was to determine whether these psychopathology scales predict, independently, two measures of creativity: 1. a measure of participants' real-world creative achievements, and 2. divergent thinking, a laboratory measure of creative cognition. After controlling for academic achievement, psychoticism and hypomania reliably predicted real-world creative achievement and divergent thinking scored with the consensual assessment technique. None of the psychopathology-spectrum scales reliably predicted divergent thinking scored with the manual scoring method. Implications for the potential links between several putative creative processes and risk factors for psychopathology are discussed.
A model of two-way selection system for human behavior.

PubMed

Zhou, Bin; Qin, Shujia; Han, Xiao-Pu; He, Zhe; Xie, Jia-Rong; Wang, Bing-Hong

2014-01-01

Two-way selection is a common phenomenon in nature and society. It appears in the processes like choosing a mate between men and women, making contracts between job hunters and recruiters, and trading between buyers and sellers. In this paper, we propose a model of two-way selection system, and present its analytical solution for the expectation of successful matching total and the regular pattern that the matching rate trends toward an inverse proportion to either the ratio between the two sides or the ratio of the state total to the smaller group's people number. The proposed model is verified by empirical data of the matchmaking fairs. Results indicate that the model well predicts this typical real-world two-way selection behavior to the bounded error extent, thus it is helpful for understanding the dynamics mechanism of the real-world two-way selection system.
Is Ki67 prognostic for aggressive prostate cancer? A multicenter real-world study.

PubMed

Fantony, Joseph J; Howard, Lauren E; Csizmadi, Ilona; Armstrong, Andrew J; Lark, Amy L; Galet, Colette; Aronson, William J; Freedland, Stephen J

2018-06-15

To test if Ki67 expression is prognostic for biochemical recurrence (BCR) after radical prostatectomy (RP). Ki67 immunohistochemistry was performed on tissue microarrays constructed from specimens obtained from 464 men undergoing RP at the Durham and West LA Veterans Affairs Hospitals. Hazard ratios (HR) for Ki67 expression and time to BCR were estimated using Cox regression. Ki67 was associated with more recent surgery year (p < 0.001), positive margins (p = 0.001) and extracapsular extension (p < 0.001). In center-stratified analyses, the adjusted HR for Ki67 expression and BCR approached statistical significance for west LA (HR: 1.54; p = 0.06), but not Durham (HR: 1.10; p = 0.74). This multi-institutional 'real-world' study provides limited evidence for the prognostic role of Ki67 in predicting outcome after RP.
On-road heavy-duty diesel particulate matter emissions modeled using chassis dynamometer data.

PubMed

Kear, Tom; Niemeier, D A

2006-12-15

This study presents a model, derived from chassis dynamometer test data, for factors (operational correction factors, or OCFs) that correct (g/mi) heavy-duty diesel particle emission rates measured on standard test cycles for real-world conditions. Using a random effects mixed regression model with data from 531 tests of 34 heavy-duty vehicles from the Coordinating Research Council's E55/E59 research project, we specify a model with covariates that characterize high power transient driving, time spent idling, and average speed. Gram per mile particle emissions rates were negatively correlated with high power transient driving, average speed, and time idling. The new model is capable of predicting relative changes in g/mi on-road heavy-duty diesel particle emission rates for real-world driving conditions that are not reflected in the driving cycles used to test heavy-duty vehicles.
Idiosyncratic responding during movie-watching predicted by age differences in attentional control

PubMed Central

Campbell, Karen L.; Shafto, Meredith A.; Wright, Paul; Tsvetanov, Kamen A.; Geerligs, Linda; Cusack, Rhodri; Tyler, Lorraine K.; Brayne, Carol; Bullmore, Ed; Calder, Andrew; Cusack, Rhodri; Dalgleish, Tim; Duncan, John; Henson, Rik; Matthews, Fiona; Marslen-Wilson, William; Rowe, James; Shafto, Meredith; Campbell, Karen; Cheung, Teresa; Davis, Simon; Geerligs, Linda; Kievit, Rogier; McCarrey, Anna; Price, Darren; Taylor, Jason; Tsvetanov, Kamen; Williams, Nitin; Bates, Lauren; Emery, Tina; Erzinçlioglu, Sharon; Gadie, Andrew; Gerbase, Sofia; Georgieva, Stanimira; Hanley, Claire; Parkin, Beth; Troy, David; Allen, Jodie; Amery, Gillian; Amunts, Liana; Barcroft, Anne; Castle, Amanda; Dias, Cheryl; Dowrick, Jonathan; Fair, Melissa; Fisher, Hayley; Goulding, Anna; Grewal, Adarsh; Hale, Geoff; Hilton, Andrew; Johnson, Frances; Johnston, Patricia; Kavanagh-Williamson, Thea; Kwasniewska, Magdalena; McMinn, Alison; Norman, Kim; Penrose, Jessica; Roby, Fiona; Rowland, Diane; Sargeant, John; Squire, Maggie; Stevens, Beth; Stoddart, Aldabra; Stone, Cheryl; Thompson, Tracy; Yazlik, Ozlem; Dixon, Marie; Barnes, Dan; Hillman, Jaya; Mitchell, Joanne; Villis, Laura; Tyler, Lorraine K.

2015-01-01

Much is known about how age affects the brain during tightly controlled, though largely contrived, experiments, but do these effects extrapolate to everyday life? Naturalistic stimuli, such as movies, closely mimic the real world and provide a window onto the brain's ability to respond in a timely and measured fashion to complex, everyday events. Young adults respond to these stimuli in a highly synchronized fashion, but it remains to be seen how age affects neural responsiveness during naturalistic viewing. To this end, we scanned a large (N = 218), population-based sample from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) during movie-watching. Intersubject synchronization declined with age, such that older adults' response to the movie was more idiosyncratic. This decreased synchrony related to cognitive measures sensitive to attentional control. Our findings suggest that neural responsivity changes with age, which likely has important implications for real-world event comprehension and memory. PMID:26359527
Classification versus inference learning contrasted with real-world categories.

PubMed

Jones, Erin L; Ross, Brian H

2011-07-01

Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories--what each category is like--while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.
Diving into Real World Challenges

ERIC Educational Resources Information Center

Saldana, Matt; Rodden, Leslie

2012-01-01

In this article, the authors discuss how educators can engage students in real world learning using their academic knowledge and technical skills. They describe how school districts have discovered that the world of robotics can help students use technical skills to solve simulated problems found in the real world, while understanding the…
Controlling Contagion Processes in Activity Driven Networks

NASA Astrophysics Data System (ADS)

Liu, Suyu; Perra, Nicola; Karsai, Márton; Vespignani, Alessandro

2014-03-01

The vast majority of strategies aimed at controlling contagion processes on networks consider the connectivity pattern of the system either quenched or annealed. However, in the real world, many networks are highly dynamical and evolve, in time, concurrently with the contagion process. Here, we derive an analytical framework for the study of control strategies specifically devised for a class of time-varying networks, namely activity-driven networks. We develop a block variable mean-field approach that allows the derivation of the equations describing the coevolution of the contagion process and the network dynamic. We derive the critical immunization threshold and assess the effectiveness of three different control strategies. Finally, we validate the theoretical picture by simulating numerically the spreading process and control strategies in both synthetic networks and a large-scale, real-world, mobile telephone call data set.
Virtual Reality As a Training Tool to Treat Physical Inactivity in Children.

PubMed

Kiefer, Adam W; Pincus, David; Richardson, Michael J; Myer, Gregory D

2017-01-01

Lack of adequate physical activity in children is an epidemic that can result in obesity and other poor health outcomes across the lifespan. Physical activity interventions focused on motor skill competence continue to be developed, but some interventions, such as neuromuscular training (NMT), may be limited in how early they can be implemented due to dependence on the child's level of cognitive and perceptual-motor development. Early implementation of motor-rich activities that support motor skill development in children is critical for the development of healthy levels of physical activity that carry through into adulthood. Virtual reality (VR) training may be beneficial in this regard. VR training, when grounded in an information-based theory of perceptual-motor behavior that modifies the visual information in the virtual world, can promote early development of motor skills in youth akin to more natural, real-world development as opposed to strictly formalized training. This approach can be tailored to the individual child and training scenarios can increase in complexity as the child develops. Ultimately, training in VR may help serve as a precursor to "real-world" NMT, and once the child reaches the appropriate training age can also augment more complex NMT regimens performed outside of the virtual environment.
The neuroscience of investing: fMRI of the reward system.

PubMed

Peterson, Richard L

2005-11-15

Functional magnetic resonance imaging (fMRI) has proven a useful tool for observing neural BOLD signal changes during complex cognitive and emotional tasks. Yet the meaning and applicability of the fMRI data being gathered is still largely unknown. The brain's reward system underlies the fundamental neural processes of goal evaluation, preference formation, positive motivation, and choice behavior. fMRI technology allows researchers to dynamically visualize reward system processes. Experimenters can then correlate reward system BOLD activations with experimental behavior from carefully controlled experiments. In the SPAN lab at Stanford University, directed by Brian Knutson Ph.D., researchers have been using financial tasks during fMRI scanning to correlate emotion, behavior, and cognition with the reward system's fundamental neural activations. One goal of the SPAN lab is the development of predictive models of behavior. In this paper we extrapolate our fMRI results toward understanding and predicting individual behavior in the uncertain and high-risk environment of the financial markets. The financial market price anomalies of "value versus glamour" and "momentum" may be real-world examples of reward system activation biasing collective behavior. On the individual level, the investor's bias of overconfidence may similarly be related to reward system activation. We attempt to understand selected "irrational" investor behaviors and anomalous financial market price patterns through correlations with findings from fMRI research of the reward system.
A class-based link prediction using Distance Dependent Chinese Restaurant Process

NASA Astrophysics Data System (ADS)

Andalib, Azam; Babamir, Seyed Morteza

2016-08-01

One of the important tasks in relational data analysis is link prediction which has been successfully applied on many applications such as bioinformatics, information retrieval, etc. The link prediction is defined as predicting the existence or absence of edges between nodes of a network. In this paper, we propose a novel method for link prediction based on Distance Dependent Chinese Restaurant Process (DDCRP) model which enables us to utilize the information of the topological structure of the network such as shortest path and connectivity of the nodes. We also propose a new Gibbs sampling algorithm for computing the posterior distribution of the hidden variables based on the training data. Experimental results on three real-world datasets show the superiority of the proposed method over other probabilistic models for link prediction problem.
Behavioral and neural correlates of loss aversion and risk avoidance in adolescents and adults.

PubMed

Barkley-Levenson, Emily E; Van Leijenhorst, Linda; Galván, Adriana

2013-01-01

Individuals are frequently faced with risky decisions involving the potential for both gain and loss. Exploring the role of both potential gains and potential losses in predicting risk taking is critical to understanding how adolescents and adults make the choice to engage in or avoid a real-life risk. This study aimed to examine the impact of potential losses as well as gains on adolescent decisions during risky choice in a laboratory task. Adolescent (n=18) and adult (n=16) participants underwent functional magnetic resonance imaging (fMRI) during a mixed gambles task, and completed questionnaires measuring real-world risk-taking behaviors. While potential loss had a significantly greater effect on choice than potential gain in both adolescents and adults and there were no behavioral group differences on the task, adolescents recruited significantly more frontostriatal circuitry than adults when choosing to reject a gamble. During risk-seeking behavior, adolescent activation in medial prefrontal cortex (mPFC) was negatively correlated with self-reported likelihood of risk taking. During risk-avoidant behavior, mPFC activation of in adults was negatively correlated with self-reported benefits of risk-taking. Taken together, these findings reflect different neural patterns during risk-taking and risk-avoidant behaviors in adolescents and adults. Copyright © 2012 Elsevier Ltd. All rights reserved.
Freedom Quilts: Mathematics on the Underground Railroad

ERIC Educational Resources Information Center

Neumann, Maureen D.

2005-01-01

A mathematics activity is presented which is a lesson frequently taught to upper elementary school students. It helps the students to see the connection of mathematics with a real-world activity, appreciate the mathematical knowledge required of quilt makers, reinforce their knowledge of the geometrical properties of different shapes and bring…
Geocaching Is Catching Students' Attention in the Classroom

ERIC Educational Resources Information Center

Lisenbee, Peggy; Hallman, Christine; Landry, Debbie

2015-01-01

Geocaching is an inquiry-based activity encouraging creativity, active learning, and real-world problem solving. As such, it is an educational opportunity for students in all grade levels. Educators benefit by observing students using higher-order thinking instead of rote learning offered by using traditional worksheets, tests, or quizzes. Also,…
Brain Activity on Navigation in Virtual Environments.

ERIC Educational Resources Information Center

Mikropoulos, Tassos A.

2001-01-01

Assessed the cognitive processing that takes place in virtual environments by measuring electrical brain activity using Fast Fourier Transform analysis. University students performed the same task in a real and a virtual environment, and eye movement measurements showed that all subjects were more attentive when navigating in the virtual world.…
Navigation of Time-Coded Data

ERIC Educational Resources Information Center

Fouse, Adam S.

2013-01-01

Advances in technology now make it possible to capture detailed multimodal data about real-world everyday activity. Researchers have taken advantage of these advances to address questions about activity in more systematic and precise ways. Along with exciting opportunities to record data in ways that were not possible before, there are also…
Model Eliciting Activities: Fostering 21st Century Learners

ERIC Educational Resources Information Center

Stohlmann, Micah

2013-01-01

Real world mathematical modeling activities can develop needed and valuable 21st century skills. The knowledge and skills to become adept at mathematical modeling need to develop over time and students in the elementary grades should have experiences with mathematical modeling. For this to occur elementary teachers need to have positive…
Transitioning from Expository Laboratory Experiments to Course-Based Undergraduate Research in General Chemistry

ERIC Educational Resources Information Center

Clark, Ted M.; Ricciardo, Rebecca; Weaver, Tyler

2016-01-01

General chemistry courses predominantly use expository experiments that shape student expectations of what a laboratory activity entails. Shifting within a semester to course-based undergraduate research activities that include greater decision-making, collaborative work, and "messy" real-world data necessitates a change in student…
CDC Grand Rounds: Modeling and Public Health Decision-Making.

PubMed

Fischer, Leah S; Santibanez, Scott; Hatchett, Richard J; Jernigan, Daniel B; Meyers, Lauren Ancel; Thorpe, Phoebe G; Meltzer, Martin I

2016-12-09

Mathematical models incorporate various data sources and advanced computational techniques to portray real-world disease transmission and translate the basic science of infectious diseases into decision-support tools for public health. Unlike standard epidemiologic methods that rely on complete data, modeling is needed when there are gaps in data. By combining diverse data sources, models can fill gaps when critical decisions must be made using incomplete or limited information. They can be used to assess the effect and feasibility of different scenarios and provide insight into the emergence, spread, and control of disease. During the past decade, models have been used to predict the likelihood and magnitude of infectious disease outbreaks, inform emergency response activities in real time (1), and develop plans and preparedness strategies for future events, the latter of which proved invaluable during outbreaks such as severe acute respiratory syndrome and pandemic influenza (2-6). Ideally, modeling is a multistep process that involves communication between modelers and decision-makers, allowing them to gain a mutual understanding of the problem to be addressed, the type of estimates that can be reliably generated, and the limitations of the data. As models become more detailed and relevant to real-time threats, the importance of modeling in public health decision-making continues to grow.
In Search of a Better Bean: A Simple Activity to Introduce Plant Biology

ERIC Educational Resources Information Center

Spaccarotella, Kim; James, Roxie

2014-01-01

Measuring plant stem growth over time is a simple activity commonly used to introduce concepts in growth and development in plant biology (Reid & Pu, 2007). This Quick Fix updates the activity and incorporates a real-world application: students consider possible effects of soil substrate and sunlight conditions on plant growth without needing…

Juan's Dilemma: A New Twist on the Old Lemon Battery

ERIC Educational Resources Information Center

Hunt, Vanessa; Sorey, Timothy; Balandova, Evguenia; Palmquist, Bruce

2010-01-01

When life hands you lemons, make a battery! In this article, the authors describe an activity they refer to as "Juan's Dilemma," an extension of the familiar lemon-battery activity (Goodisman 2001). Juan's Dilemma integrates oxidation and reduction chemistry with circuit theory in a fun, real-world exercise. The authors designed this activity for…
Pre-Service Teachers' Modelling Processes through Engagement with Model Eliciting Activities with a Technological Tool

ERIC Educational Resources Information Center

Daher, Wajeeh M.; Shahbari, Juhaina Awawdeh

2015-01-01

Engaging mathematics students with modelling activities helps them learn mathematics meaningfully. This engagement, in the case of model eliciting activities, helps the students elicit mathematical models by interpreting real-world situation in mathematical ways. This is especially true when the students utilize technology to build the models.…
Clock Buddies: An Accessible, Engaging Problem-Solving Activity with Rich Mathematical Content

ERIC Educational Resources Information Center

Borkovitz, Debra K.; Haferd, Thomas

2017-01-01

Clock Buddies is our favorite first-day-of-class activity. It starts as a nonthreatening icebreaker activity that helps students learn one another's names, but it soon asks students to find their own strategies for solving a real-world scheduling problem. Even highly math phobic students work with others and succeed. Students gain insight from…
Virtual Learning is the Real Thing

ERIC Educational Resources Information Center

Tekaat-Davey, Diana

2006-01-01

In this article, the author discusses how in California, high school students are learning about real business through a virtual world. Virtual enterprise programs are helping students learn about the real business world. Learning about the business world has become about as real as it can in California high schools. Enrollment in the programs…
The architecture of dynamic reservoir in the echo state network

NASA Astrophysics Data System (ADS)

Cui, Hongyan; Liu, Xiang; Li, Lixiang

2012-09-01

Echo state network (ESN) has recently attracted increasing interests because of its superior capability in modeling nonlinear dynamic systems. In the conventional echo state network model, its dynamic reservoir (DR) has a random and sparse topology, which is far from the real biological neural networks from both structural and functional perspectives. We hereby propose three novel types of echo state networks with new dynamic reservoir topologies based on complex network theory, i.e., with a small-world topology, a scale-free topology, and a mixture of small-world and scale-free topologies, respectively. We then analyze the relationship between the dynamic reservoir structure and its prediction capability. We utilize two commonly used time series to evaluate the prediction performance of the three proposed echo state networks and compare them to the conventional model. We also use independent and identically distributed time series to analyze the short-term memory and prediction precision of these echo state networks. Furthermore, we study the ratio of scale-free topology and the small-world topology in the mixed-topology network, and examine its influence on the performance of the echo state networks. Our simulation results show that the proposed echo state network models have better prediction capabilities, a wider spectral radius, but retain almost the same short-term memory capacity as compared to the conventional echo state network model. We also find that the smaller the ratio of the scale-free topology over the small-world topology, the better the memory capacities.
PAMPA--critical factors for better predictions of absorption.

PubMed

Avdeef, Alex; Bendels, Stefanie; Di, Li; Faller, Bernard; Kansy, Manfred; Sugano, Kiyohiko; Yamauchi, Yukinori

2007-11-01

PAMPA, log P(OCT), and Caco-2 are useful tools in drug discovery for the prediction of oral absorption, brain penetration and for the development of structure-permeability relationships. Each approach has its advantages and limitations. Selection criteria for methods are based on many different factors: predictability, throughput, cost and personal preferences (people factor). The PAMPA concerns raised by Galinis-Luciani et al. (Galinis-Luciani et al., 2007, J Pharm Sci, this issue) are answered by experienced PAMPA practitioners, inventors and developers from diverse research organizations. Guidelines on how to use PAMPA are discussed. PAMPA and PAMPA-BBB have much better predictivity for oral absorption and brain penetration than log P(OCT) for real-world drug discovery compounds. PAMPA and Caco-2 have similar predictivity for passive oral absorption. However, it is not advisable to use PAMPA to predict absorption involving transporter-mediated processes, such as active uptake or efflux. Measurement of PAMPA is much more rapid and cost effective than Caco-2 and log P(OCT). PAMPA assay conditions are critical in order to generate high quality and relevant data, including permeation time, assay pH, stirring, use of cosolvents and selection of detection techniques. The success of using PAMPA in drug discovery depends on careful data interpretation, use of optimal assay conditions, implementation and integration strategies, and education of users. Copyright 2007 Wiley-Liss, Inc.
Foreign Language Vocabulary Development through Activities in an Online 3D Environment

ERIC Educational Resources Information Center

Milton, James; Jonsen, Sunniva; Hirst, Steven; Lindenburn, Sharn

2012-01-01

On-line virtual 3D worlds offer the opportunity for users to interact in real time with native speakers of the language they are learning. In principle, this ought to be of great benefit to learners, and mimicking the opportunity for immersion that real-life travel to a foreign country offers. We have very little research to show whether this is…
Boosting flood warning schemes with fast emulator of detailed hydrodynamic models

NASA Astrophysics Data System (ADS)

Bellos, V.; Carbajal, J. P.; Leitao, J. P.

2017-12-01

Floods are among the most destructive catastrophic events and their frequency has incremented over the last decades. To reduce flood impact and risks, flood warning schemes are installed in flood prone areas. Frequently, these schemes are based on numerical models which quickly provide predictions of water levels and other relevant observables. However, the high complexity of flood wave propagation in the real world and the need of accurate predictions in urban environments or in floodplains hinders the use of detailed simulators. This sets the difficulty, we need fast predictions that meet the accuracy requirements. Most physics based detailed simulators although accurate, will not fulfill the speed demand. Even if High Performance Computing techniques are used (the magnitude of required simulation time is minutes/hours). As a consequence, most flood warning schemes are based in coarse ad-hoc approximations that cannot take advantage a detailed hydrodynamic simulation. In this work, we present a methodology for developing a flood warning scheme using an Gaussian Processes based emulator of a detailed hydrodynamic model. The methodology consists of two main stages: 1) offline stage to build the emulator; 2) online stage using the emulator to predict and generate warnings. The offline stage consists of the following steps: a) definition of the critical sites of the area under study, and the specification of the observables to predict at those sites, e.g. water depth, flow velocity, etc.; b) generation of a detailed simulation dataset to train the emulator; c) calibration of the required parameters (if measurements are available). The online stage is carried on using the emulator to predict the relevant observables quickly, and the detailed simulator is used in parallel to verify key predictions of the emulator. The speed gain given by the emulator allows also to quantify uncertainty in predictions using ensemble methods. The above methodology is applied in real world scenario.
Processing counterfactual and hypothetical conditionals: an fMRI investigation.

PubMed

Kulakova, Eugenia; Aichhorn, Markus; Schurz, Matthias; Kronbichler, Martin; Perner, Josef

2013-05-15

Counterfactual thinking is ubiquitous in everyday life and an important aspect of cognition and emotion. Although counterfactual thought has been argued to differ from processing factual or hypothetical information, imaging data which elucidate these differences on a neural level are still scarce. We investigated the neural correlates of processing counterfactual sentences under visual and aural presentation. We compared conditionals in subjunctive mood which explicitly contradicted previously presented facts (i.e. counterfactuals) to conditionals framed in indicative mood which did not contradict factual world knowledge and thus conveyed a hypothetical supposition. Our results show activation in right occipital cortex (cuneus) and right basal ganglia (caudate nucleus) during counterfactual sentence processing. Importantly the occipital activation is not only present under visual presentation but also with purely auditory stimulus presentation, precluding a visual processing artifact. Thus our results can be interpreted as reflecting the fact that counterfactual conditionals pragmatically imply the relevance of keeping in mind both factual and supposed information whereas the hypothetical conditionals imply that real world information is irrelevant for processing the conditional and can be omitted. The need to sustain representations of factual and suppositional events during counterfactual sentence processing requires increased mental imagery and integration efforts. Our findings are compatible with predictions based on mental model theory. Copyright © 2013 Elsevier Inc. All rights reserved.
Rule extraction from minimal neural networks for credit card screening.

PubMed

Setiono, Rudy; Baesens, Bart; Mues, Christophe

2011-08-01

While feedforward neural networks have been widely accepted as effective tools for solving classification problems, the issue of finding the best network architecture remains unresolved, particularly so in real-world problem settings. We address this issue in the context of credit card screening, where it is important to not only find a neural network with good predictive performance but also one that facilitates a clear explanation of how it produces its predictions. We show that minimal neural networks with as few as one hidden unit provide good predictive accuracy, while having the added advantage of making it easier to generate concise and comprehensible classification rules for the user. To further reduce model size, a novel approach is suggested in which network connections from the input units to this hidden unit are removed by a very straightaway pruning procedure. In terms of predictive accuracy, both the minimized neural networks and the rule sets generated from them are shown to compare favorably with other neural network based classifiers. The rules generated from the minimized neural networks are concise and thus easier to validate in a real-life setting.
The legal and ethical concerns that arise from using complex predictive analytics in health care.

PubMed

Cohen, I Glenn; Amarasingham, Ruben; Shah, Anand; Xie, Bin; Lo, Bernard

2014-07-01

Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this article we analyze the major challenges to implementing predictive analytics in health care settings and make broad recommendations for overcoming challenges raised in the four phases of the life cycle of a predictive analytics model: acquiring data to build the model, building and validating it, testing it in real-world settings, and disseminating and using it more broadly. For instance, we recommend that model developers implement governance structures that include patients and other stakeholders starting in the earliest phases of development. In addition, developers should be allowed to use already collected patient data without explicit consent, provided that they comply with federal regulations regarding research on human subjects and the privacy of health information. Project HOPE—The People-to-People Health Foundation, Inc.
Multi-Instance Metric Transfer Learning for Genome-Wide Protein Function Prediction.

PubMed

Xu, Yonghui; Min, Huaqing; Wu, Qingyao; Song, Hengjie; Ye, Bicui

2017-02-06

Multi-Instance (MI) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with multiple instances. Many studies in this literature attempted to find an appropriate Multi-Instance Learning (MIL) method for genome-wide protein function prediction under a usual assumption, the underlying distribution from testing data (target domain, i.e., TD) is the same as that from training data (source domain, i.e., SD). However, this assumption may be violated in real practice. To tackle this problem, in this paper, we propose a Multi-Instance Metric Transfer Learning (MIMTL) approach for genome-wide protein function prediction. In MIMTL, we first transfer the source domain distribution to the target domain distribution by utilizing the bag weights. Then, we construct a distance metric learning method with the reweighted bags. At last, we develop an alternative optimization scheme for MIMTL. Comprehensive experimental evidence on seven real-world organisms verifies the effectiveness and efficiency of the proposed MIMTL approach over several state-of-the-art methods.
Looking Under the Hood of Third-Party Punishment Reveals Design for Personal Benefit.

PubMed

Krasnow, Max M; Delton, Andrew W; Cosmides, Leda; Tooby, John

2016-03-01

Third-party intervention, such as when a crowd stops a mugger, is common. Yet it seems irrational because it has real costs but may provide no personal benefits. In a laboratory analogue, the third-party-punishment game, third parties ("punishers") will often spend real money to anonymously punish bad behavior directed at other people. A common explanation is that third-party punishment exists to maintain a cooperative society. We tested a different explanation: Third-party punishment results from a deterrence psychology for defending personal interests. Because humans evolved in small-scale, face-to-face social worlds, the mind infers that mistreatment of a third party predicts later mistreatment of oneself. We showed that when punishers do not have information about how they personally will be treated, they infer that mistreatment of other people predicts mistreatment of themselves, and these inferences predict punishment. But when information about personal mistreatment is available, it drives punishment. This suggests that humans' punitive psychology evolved to defend personal interests. © The Author(s) 2016.
On the Prediction of Flickr Image Popularity by Analyzing Heterogeneous Social Sensory Data

PubMed Central

Aloufi, Samah; Zhu, Shiai; El Saddik, Abdulmotaleb

2017-01-01

The increase in the popularity of social media has shattered the gap between the physical and virtual worlds. The content generated by people or social sensors on social media provides information about users and their living surroundings, which allows us to access a user’s preferences, opinions, and interactions. This provides an opportunity for us to understand human behavior and enhance the services provided for both the real and virtual worlds. In this paper, we will focus on the popularity prediction of social images on Flickr, a popular social photo-sharing site, and promote the research on utilizing social sensory data in the context of assisting people to improve their life on the Web. Social data are different from the data collected from physical sensors; in the fact that they exhibit special characteristics that pose new challenges. In addition to their huge quantity, social data are noisy, unstructured, and heterogeneous. Moreover, they involve human semantics and contextual data that require analysis and interpretation based on human behavior. Accordingly, we address the problem of popularity prediction for an image by exploiting three main factors that are important for making an image popular. In particular, we investigate the impact of the image’s visual content, where the semantic and sentiment information extracted from the image show an impact on its popularity, as well as the textual information associated with the image, which has a fundamental role in boosting the visibility of the image in the keyword search results. Additionally, we explore social context, such as an image owner’s popularity and how it positively influences the image popularity. With a comprehensive study on the effect of the three aspects, we further propose to jointly consider the heterogeneous social sensory data. Experimental results obtained from real-world data demonstrate that the three factors utilized complement each other in obtaining promising results in the prediction of image popularity on social photo-sharing site. PMID:28335498
On the Prediction of Flickr Image Popularity by Analyzing Heterogeneous Social Sensory Data.

PubMed

Aloufi, Samah; Zhu, Shiai; El Saddik, Abdulmotaleb

2017-03-19

The increase in the popularity of social media has shattered the gap between the physical and virtual worlds. The content generated by people or social sensors on social media provides information about users and their living surroundings, which allows us to access a user's preferences, opinions, and interactions. This provides an opportunity for us to understand human behavior and enhance the services provided for both the real and virtual worlds. In this paper, we will focus on the popularity prediction of social images on Flickr, a popular social photo-sharing site, and promote the research on utilizing social sensory data in the context of assisting people to improve their life on the Web. Social data are different from the data collected from physical sensors; in the fact that they exhibit special characteristics that pose new challenges. In addition to their huge quantity, social data are noisy, unstructured, and heterogeneous. Moreover, they involve human semantics and contextual data that require analysis and interpretation based on human behavior. Accordingly, we address the problem of popularity prediction for an image by exploiting three main factors that are important for making an image popular. In particular, we investigate the impact of the image's visual content, where the semantic and sentiment information extracted from the image show an impact on its popularity, as well as the textual information associated with the image, which has a fundamental role in boosting the visibility of the image in the keyword search results. Additionally, we explore social context, such as an image owner's popularity and how it positively influences the image popularity. With a comprehensive study on the effect of the three aspects, we further propose to jointly consider the heterogeneous social sensory data. Experimental results obtained from real-world data demonstrate that the three factors utilized complement each other in obtaining promising results in the prediction of image popularity on social photo-sharing site.
Studying real-world perceptual expertise

PubMed Central

Shen, Jianhong; Mack, Michael L.; Palmeri, Thomas J.

2014-01-01

Significant insights into visual cognition have come from studying real-world perceptual expertise. Many have previously reviewed empirical findings and theoretical developments from this work. Here we instead provide a brief perspective on approaches, considerations, and challenges to studying real-world perceptual expertise. We discuss factors like choosing to use real-world versus artificial object domains of expertise, selecting a target domain of real-world perceptual expertise, recruiting experts, evaluating their level of expertise, and experimentally testing experts in the lab and online. Throughout our perspective, we highlight expert birding (also called birdwatching) as an example, as it has been used as a target domain for over two decades in the perceptual expertise literature. PMID:25147533
A Case Study on Using Prediction Markets as a Rich Environment for Active Learning

ERIC Educational Resources Information Center

Buckley, Patrick; Garvey, John; McGrath, Fergal

2011-01-01

In this paper, prediction markets are presented as an innovative pedagogical tool which can be used to create a Rich Environment for Active Learning (REAL). Prediction markets are designed to make forecasts about specific future events by using a market mechanism to aggregate the information held by a large group of traders about that event into a…
"Chemistry Is in the News": Taxonomy of Authentic News Media-Based Learning Activities. Research Report

ERIC Educational Resources Information Center

Glaser, Rainer E.; Carson, Kathleen M.

2005-01-01

A brief history is given of approaches that aim at achieving a connectedness of the content of organic chemistry courses to real world issues. Recently, such approaches have relied more and more on online media resources, the tools of the Internet and the World Wide Web. We propose a six-level taxonomy of 'authentic news media-based learning…
Real-time prediction of hand trajectory by ensembles of cortical neurons in primates

NASA Astrophysics Data System (ADS)

Wessberg, Johan; Stambaugh, Christopher R.; Kralik, Jerald D.; Beck, Pamela D.; Laubach, Mark; Chapin, John K.; Kim, Jung; Biggs, S. James; Srinivasan, Mandayam A.; Nicolelis, Miguel A. L.

2000-11-01

Signals derived from the rat motor cortex can be used for controlling one-dimensional movements of a robot arm. It remains unknown, however, whether real-time processing of cortical signals can be employed to reproduce, in a robotic device, the kind of complex arm movements used by primates to reach objects in space. Here we recorded the simultaneous activity of large populations of neurons, distributed in the premotor, primary motor and posterior parietal cortical areas, as non-human primates performed two distinct motor tasks. Accurate real-time predictions of one- and three-dimensional arm movement trajectories were obtained by applying both linear and nonlinear algorithms to cortical neuronal ensemble activity recorded from each animal. In addition, cortically derived signals were successfully used for real-time control of robotic devices, both locally and through the Internet. These results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.
Resting-State fMRI Activity Predicts Unsupervised Learning and Memory in an Immersive Virtual Reality Environment

PubMed Central

Wong, Chi Wah; Olafsson, Valur; Plank, Markus; Snider, Joseph; Halgren, Eric; Poizner, Howard; Liu, Thomas T.

2014-01-01

In the real world, learning often proceeds in an unsupervised manner without explicit instructions or feedback. In this study, we employed an experimental paradigm in which subjects explored an immersive virtual reality environment on each of two days. On day 1, subjects implicitly learned the location of 39 objects in an unsupervised fashion. On day 2, the locations of some of the objects were changed, and object location recall performance was assessed and found to vary across subjects. As prior work had shown that functional magnetic resonance imaging (fMRI) measures of resting-state brain activity can predict various measures of brain performance across individuals, we examined whether resting-state fMRI measures could be used to predict object location recall performance. We found a significant correlation between performance and the variability of the resting-state fMRI signal in the basal ganglia, hippocampus, amygdala, thalamus, insula, and regions in the frontal and temporal lobes, regions important for spatial exploration, learning, memory, and decision making. In addition, performance was significantly correlated with resting-state fMRI connectivity between the left caudate and the right fusiform gyrus, lateral occipital complex, and superior temporal gyrus. Given the basal ganglia's role in exploration, these findings suggest that tighter integration of the brain systems responsible for exploration and visuospatial processing may be critical for learning in a complex environment. PMID:25286145

Implementing Machine Learning in the PCWG Tool

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clifton, Andrew; Ding, Yu; Stuart, Peter

The Power Curve Working Group (www.pcwg.org) is an ad-hoc industry-led group to investigate the performance of wind turbines in real-world conditions. As part of ongoing experience-sharing exercises, machine learning has been proposed as a possible way to predict turbine performance. This presentation provides some background information about machine learning and how it might be implemented in the PCWG exercises.
Cortical midline involvement in autobiographical memory

PubMed Central

Summerfield, Jennifer J.; Hassabis, Demis; Maguire, Eleanor A.

2009-01-01

Recollecting autobiographical memories of personal past experiences is an integral part of our everyday lives and relies on a distributed set of brain regions. Their occurrence externally in the real world (‘realness’) and their self-relevance (‘selfness’) are two defining features of these autobiographical events. Distinguishing between personally experienced events and those that happened to other individuals, and between events that really occurred and those that were mere figments of the imagination, is clearly advantageous, yet the respective neural correlates remain unclear. Here we experimentally manipulated and dissociated realness and selfness during fMRI using a novel paradigm where participants recalled self (autobiographical) and non-self (from a movie or television news clips) events that were either real or previously imagined. Distinct sub-regions within dorsal and ventral medial prefrontal cortex, retrosplenial cortex and along the parieto-occipital sulcus preferentially coded for events (real or imagined) involving the self. By contrast, recollection of autobiographical events that really happened in the external world activated different areas within ventromedial prefrontal cortex and posterior cingulate cortex. In addition, recall of externally experienced real events (self or non-self) was associated with increased activity in areas of dorsomedial prefrontal cortex and posterior cingulate cortex. Taken together our results permitted a functional deconstruction of anterior (medial prefrontal) and posterior (retrosplenial cortex, posterior cingulate cortex, precuneus) cortical midline regions widely associated with autobiographical memory but whose roles have hitherto been poorly understood. PMID:18973817
Fuzzy Comprehensive Evaluation Method Applied in the Real Estate Investment Risks Research

NASA Astrophysics Data System (ADS)

ML(Zhang Minli), Zhang; Wp(Yang Wenpo), Yang

Real estate investment is a high-risk and high returned of economic activity, the key of real estate analysis is the identification of their types of investment risk and the risk of different types of effective prevention. But, as the financial crisis sweeping the world, the real estate industry also faces enormous risks, how effective and correct evaluation of real estate investment risks becomes the multitudinous scholar concern[1]. In this paper, real estate investment risks were summarized and analyzed, and comparative analysis method is discussed and finally presented fuzzy comprehensive evaluation method, not only in theory has the advantages of science, in the application also has the reliability, for real estate investment risk assessment provides an effective means for investors in real estate investing guidance on risk factors and forecasts.
Video Game Telemetry as a Critical Tool in the Study of Complex Skill Learning

PubMed Central

Thompson, Joseph J.; Blair, Mark R.; Chen, Lihan; Henrey, Andrew J.

2013-01-01

Cognitive science has long shown interest in expertise, in part because prediction and control of expert development would have immense practical value. Most studies in this area investigate expertise by comparing experts with novices. The reliance on contrastive samples in studies of human expertise only yields deep insight into development where differences are important throughout skill acquisition. This reliance may be pernicious where the predictive importance of variables is not constant across levels of expertise. Before the development of sophisticated machine learning tools for data mining larger samples, and indeed, before such samples were available, it was difficult to test the implicit assumption of static variable importance in expertise development. To investigate if this reliance may have imposed critical restrictions on the understanding of complex skill development, we adopted an alternative method, the online acquisition of telemetry data from a common daily activity for many: video gaming. Using measures of cognitive-motor, attentional, and perceptual processing extracted from game data from 3360 Real-Time Strategy players at 7 different levels of expertise, we identified 12 variables relevant to expertise. We show that the static variable importance assumption is false - the predictive importance of these variables shifted as the levels of expertise increased - and, at least in our dataset, that a contrastive approach would have been misleading. The finding that variable importance is not static across levels of expertise suggests that large, diverse datasets of sustained cognitive-motor performance are crucial for an understanding of expertise in real-world contexts. We also identify plausible cognitive markers of expertise. PMID:24058656
Video game telemetry as a critical tool in the study of complex skill learning.

PubMed

Thompson, Joseph J; Blair, Mark R; Chen, Lihan; Henrey, Andrew J

2013-01-01

Cognitive science has long shown interest in expertise, in part because prediction and control of expert development would have immense practical value. Most studies in this area investigate expertise by comparing experts with novices. The reliance on contrastive samples in studies of human expertise only yields deep insight into development where differences are important throughout skill acquisition. This reliance may be pernicious where the predictive importance of variables is not constant across levels of expertise. Before the development of sophisticated machine learning tools for data mining larger samples, and indeed, before such samples were available, it was difficult to test the implicit assumption of static variable importance in expertise development. To investigate if this reliance may have imposed critical restrictions on the understanding of complex skill development, we adopted an alternative method, the online acquisition of telemetry data from a common daily activity for many: video gaming. Using measures of cognitive-motor, attentional, and perceptual processing extracted from game data from 3360 Real-Time Strategy players at 7 different levels of expertise, we identified 12 variables relevant to expertise. We show that the static variable importance assumption is false--the predictive importance of these variables shifted as the levels of expertise increased--and, at least in our dataset, that a contrastive approach would have been misleading. The finding that variable importance is not static across levels of expertise suggests that large, diverse datasets of sustained cognitive-motor performance are crucial for an understanding of expertise in real-world contexts. We also identify plausible cognitive markers of expertise.
Seasonal forecasting of lightning and thunderstorm activity in tropical and temperate regions of the world.

PubMed

Dowdy, Andrew J

2016-02-11

Thunderstorms are convective systems characterised by the occurrence of lightning. Lightning and thunderstorm activity has been increasingly studied in recent years in relation to the El Niño/Southern Oscillation (ENSO) and various other large-scale modes of atmospheric and oceanic variability. Large-scale modes of variability can sometimes be predictable several months in advance, suggesting potential for seasonal forecasting of lightning and thunderstorm activity in various regions throughout the world. To investigate this possibility, seasonal lightning activity in the world's tropical and temperate regions is examined here in relation to numerous different large-scale modes of variability. Of the seven modes of variability examined, ENSO has the strongest relationship with lightning activity during each individual season, with relatively little relationship for the other modes of variability. A measure of ENSO variability (the NINO3.4 index) is significantly correlated to local lightning activity at 53% of locations for one or more seasons throughout the year. Variations in atmospheric parameters commonly associated with thunderstorm activity are found to provide a plausible physical explanation for the variations in lightning activity associated with ENSO. It is demonstrated that there is potential for accurately predicting lightning and thunderstorm activity several months in advance in various regions throughout the world.
Real-World Treatment Patterns, Survival, and Prediction of CNS Progression in ALK-Positive Non-Small-Cell Lung Cancer Patients Treated with First-Line Crizotinib in Latin America Oncology Practices.

PubMed

Martín, Claudio; Cardona, Andrés F; Zatarain-Barrón, Zyanya Lucia; Ruiz-Patiño, Alejandro; Castillo, Omar; Oblitas, George; Corrales, Luis; Lupinacci, Lorena; Pérez, María Angelina; Rojas, Leonardo; González, Lisde; Chirinos, Luis; Ortíz, Carlos; Lema, Mauricio; Vargas, Carlos; Puparelli, Carmen; Carranza, Hernán; Otero, Jorge; Arrieta, Oscar

2018-01-01

This study describes the real-world characteristics, treatment sequencing, and outcomes among Hispanic patients with locally advanced/metastatic ALK-positive non-small-cell lung cancer (NSCLC) treated with crizotinib. A retrospective patient review was conducted for several centers in Latin America. Clinicians identified ALK-positive NSCLC patients who received crizotinib and reported their clinical characteristics, treatments, and survival. Overall survival and progression-free survival (PFS) were described. A Random Forest Tree (RFT) model was constructed to predict brain progression. A total of 73 patients were included; median age at diagnosis was 58 years, 60.3% were female, and 93.2% had adenocarcinoma. Eighty-nine percent of patients were never smokers/former smokers, 71.1% had ≥2 sites of metastasis, and 20.5% had brain metastases at diagnosis. The median PFS on first-line crizotinib was 7.07 months (95% CI 3.77-12.37) and the overall response rate was 52%. Of those who discontinued crizotinib, 55.9% progressed in the central nervous system (CNS). The RFT model reached a sensitivity of 100% and a specificity of 88% for prediction of CNS progression. The overall response rate and the PFS observed in Hispanic patients with ALK-positive NSCLC treated with first-line crizotinib were similar to those in previous reports. An RFT model is helpful in predicting CNS progression and can help clinicians tailor treatments in a resource-limited practice. © 2018 S. Karger AG, Basel.
Abbreviated neuropsychological assessment in schizophrenia

PubMed Central

Harvey, Philip D.; Keefe, Richard S. E.; Patterson, Thomas L.; Heaton, Robert K.; Bowie, Christopher R.

2008-01-01

The aim of this study was to identify the best subset of neuropsychological tests for prediction of several different aspects of functioning in a large (n = 236) sample of older people with schizophrenia. While the validity of abbreviated assessment methods has been examined before, there has never been a comparative study of the prediction of different elements of cognitive impairment, real-world outcomes, and performance-based measures of functional capacity. Scores on 10 different tests from a neuropsychological assessment battery were used to predict global neuropsychological (NP) performance (indexed with averaged scores or calculated general deficit scores), performance-based indices of everyday-living skills and social competence, and case-manager ratings of real-world functioning. Forward entry stepwise regression analyses were used to identify the best predictors for each of the outcomes measures. Then, the analyses were adjusted for estimated premorbid IQ, which reduced the magnitude, but not the structure, of the correlations. Substantial amounts (over 70%) of the variance in overall NP performance were accounted for by a limited number of NP tests. Considerable variance in measures of functional capacity was also accounted for by a limited number of tests. Different tests constituted the best predictor set for each outcome measure. A substantial proportion of the variance in several different NP and functional outcomes can be accounted for by a small number of NP tests that can be completed in a few minutes, although there is considerable unexplained variance. However, the abbreviated assessments that best predict different outcomes vary across outcomes. Future studies should determine whether responses to pharmacological and remediation treatments can be captured with brief assessments as well. PMID:18720182
Social Cognition as Reinforcement Learning: Feedback Modulates Emotion Inference.

PubMed

Zaki, Jamil; Kallman, Seth; Wimmer, G Elliott; Ochsner, Kevin; Shohamy, Daphna

2016-09-01

Neuroscientific studies of social cognition typically employ paradigms in which perceivers draw single-shot inferences about the internal states of strangers. Real-world social inference features much different parameters: People often encounter and learn about particular social targets (e.g., friends) over time and receive feedback about whether their inferences are correct or incorrect. Here, we examined this process and, more broadly, the intersection between social cognition and reinforcement learning. Perceivers were scanned using fMRI while repeatedly encountering three social targets who produced conflicting visual and verbal emotional cues. Perceivers guessed how targets felt and received feedback about whether they had guessed correctly. Visual cues reliably predicted one target's emotion, verbal cues predicted a second target's emotion, and neither reliably predicted the third target's emotion. Perceivers successfully used this information to update their judgments over time. Furthermore, trial-by-trial learning signals-estimated using two reinforcement learning models-tracked activity in ventral striatum and ventromedial pFC, structures associated with reinforcement learning, and regions associated with updating social impressions, including TPJ. These data suggest that learning about others' emotions, like other forms of feedback learning, relies on domain-general reinforcement mechanisms as well as domain-specific social information processing.
Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, C. W.; Hood, Raleigh R.; Long, Wen

The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the concentrations of chlorophyll, nitrate, and dissolved oxygen, and the likelihood of encountering several noxious species, including harmful algal blooms and water-borne pathogens, for the purpose of monitoring the Bay's ecosystem. While the physical and biogeochemical variables are forecast mechanistically using the Regional Ocean Modeling System configured for the Chesapeake Bay, the species predictions are generated using a novel mechanistic empirical approach, whereby real-time output from the coupled physical biogeochemical model drives multivariate empirical habitat modelsmore » of the target species. The predictions, in the form of digital images, are available via the World Wide Web to interested groups to guide recreational, management, and research activities. Though full validation of the integrated forecasts for all species is still a work in progress, we argue that the mechanistic–empirical approach can be used to generate a wide variety of short-term ecological forecasts, and that it can be applied in any marine system where sufficient data exist to develop empirical habitat models. This paper provides an overview of this system, its predictions, and the approach taken.« less
Dopamine cells respond to predicted events during classical conditioning: evidence for eligibility traces in the reward-learning network.

PubMed

Pan, Wei-Xing; Schmidt, Robert; Wickens, Jeffery R; Hyland, Brian I

2005-06-29

Behavioral conditioning of cue-reward pairing results in a shift of midbrain dopamine (DA) cell activity from responding to the reward to responding to the predictive cue. However, the precise time course and mechanism underlying this shift remain unclear. Here, we report a combined single-unit recording and temporal difference (TD) modeling approach to this question. The data from recordings in conscious rats showed that DA cells retain responses to predicted reward after responses to conditioned cues have developed, at least early in training. This contrasts with previous TD models that predict a gradual stepwise shift in latency with responses to rewards lost before responses develop to the conditioned cue. By exploring the TD parameter space, we demonstrate that the persistent reward responses of DA cells during conditioning are only accurately replicated by a TD model with long-lasting eligibility traces (nonzero values for the parameter lambda) and low learning rate (alpha). These physiological constraints for TD parameters suggest that eligibility traces and low per-trial rates of plastic modification may be essential features of neural circuits for reward learning in the brain. Such properties enable rapid but stable initiation of learning when the number of stimulus-reward pairings is limited, conferring significant adaptive advantages in real-world environments.
Teaching problem solving using non-routine tasks

NASA Astrophysics Data System (ADS)

Chong, Maureen Siew Fang; Shahrill, Masitah; Putri, Ratu Ilma Indra; Zulkardi

2018-04-01

Non-routine problems are related to real-life context and require some realistic considerations and real-world knowledge in order to resolve them. This study examines several activity tasks incorporated with non-routine problems through the use of an emerging mathematics framework, at two junior colleges in Brunei Darussalam. The three sampled teachers in this study assisted in selecting the topics and the lesson plan designs. They also recommended the development of the four activity tasks: incorporating the use of technology; simulation of a reality television show; designing real-life sized car park spaces for the school; and a classroom activity to design a real-life sized dustpan. Data collected from all four of the activity tasks were analyzed based on the students' group work. The findings revealed that the most effective activity task in teaching problem solving was to design a real-life sized car park. This was because the use of real data gave students the opportunity to explore, gather information and give or receive feedback on the effect of their reasons and proposed solutions. The second most effective activity task was incorporating the use of technology as it enhanced the students' understanding of the concepts learnt in the classroom. This was followed by the classroom activity that used real data as it allowed students to work and assess the results mathematically. The simulation of a television show was found to be the least effective since it was viewed as not sufficiently challenging to the students.
Brain-to-Brain Synchrony and Learning Outcomes Vary by Student-Teacher Dynamics: Evidence from a Real-world Classroom Electroencephalography Study.

PubMed

Bevilacqua, Dana; Davidesco, Ido; Wan, Lu; Oostrik, Matthias; Chaloner, Kim; Rowland, Jess; Ding, Mingzhou; Poeppel, David; Dikker, Suzanne

2018-04-30

How does the human brain support real-world learning? We used wireless electroencephalography to collect neurophysiological data from a group of 12 senior high school students and their teacher during regular biology lessons. Six scheduled classes over the course of the semester were organized such that class materials were presented using different teaching styles (videos and lectures), and students completed a multiple-choice quiz after each class to measure their retention of that lesson's content. Both students' brain-to-brain synchrony and their content retention were higher for videos than lectures across the six classes. Brain-to-brain synchrony between the teacher and students varied as a function of student engagement as well as teacher likeability: Students who reported greater social closeness to the teacher showed higher brain-to-brain synchrony with the teacher, but this was only the case for lectures, that is, when the teacher is an integral part of the content presentation. Furthermore, students' retention of the class content correlated with student-teacher closeness, but not with brain-to-brain synchrony. These findings expand on existing social neuroscience research by showing that social factors such as perceived closeness are reflected in brain-to-brain synchrony in real-world group settings and can predict cognitive outcomes such as students' academic performance.
Comparison of Battery Life Across Real-World Automotive Drive-Cycles (Presentation)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, K.; Earleywine, M.; Wood, E.

2011-11-01

Laboratories run around-the-clock aging tests to try to understand as quickly as possible how long new Li-ion battery designs will last under certain duty cycles. These tests may include factors such as duty cycles, climate, battery power profiles, and battery stress statistics. Such tests are generally accelerated and do not consider possible dwell time at high temperatures and states-of-charge. Battery life-predictive models provide guidance as to how long Li-ion batteries may last under real-world electric-drive vehicle applications. Worst-case aging scenarios are extracted from hundreds of real-world duty cycles developed from vehicle travel surveys. Vehicles examined included PHEV10 and PHEV40 EDVsmore » under fixed (28 degrees C), limited cooling (forced ambient temperature), and aggressive cooling (20 degrees C chilled liquid) scenarios using either nightly charging or opportunity charging. The results show that battery life expectancy is 7.8 - 13.2 years for the PHEV10 using a nightly charge in Phoenix, AZ (hot climate), and that the 'aggressive' cooling scenario can extend battery life by 1-3 years, while the 'limited' cooling scenario shortens battery life by 1-2 years. Frequent (opportunity) charging can reduce battery life by 1 year for the PHEV10, while frequent charging can extend battery life by one-half year.« less
Real-world evidence concerning clinical and economic outcomes of switching to insulin glargine 300 units/mL vs other basal insulins in patients with type 2 diabetes using basal insulin.

PubMed

Zhou, Fang Liz; Ye, Fen; Berhanu, Paulos; Gupta, Vineet E; Gupta, Rishab A; Sung, Jennifer; Westerbacka, Jukka; Bailey, Timothy S; Blonde, Lawrence

2018-05-01

This retrospective cohort study compared real-world clinical and healthcare-resource utilization (HCRU) data in patients with type 2 diabetes using basal insulin (BI) who switched to insulin glargine 300 units/mL (Gla-300) or another BI. Data from the Predictive Health Intelligence Environment database 12 months before (baseline) and 6 months after (follow-up) the switch date (index date, March 1, 2015 to May 31, 2016) included glycated haemoglobin A1c (HbA1c), hypoglycaemia, HCRU and associated costs. Baseline characteristics were balanced using propensity score matching. Change in HbA1c from baseline was similar in both matched cohorts (n = 1819 in each). Hypoglycaemia incidence and adjusted event rate were significantly lower with Gla-300. Patients switching to Gla-300 had a significantly lower incidence of HCRU related to hypoglycaemia. All-cause and diabetes-related hospitalization and emergency-department HCRU were also favourable for Gla-300. Lower HCRU translated to lower costs in patients using Gla-300. In this real-world study, switching to Gla-300 reduced the risk of hypoglycaemia in patients with type 2 diabetes when compared with those switching to another BI, resulting in less HCRU and potential savings of associated costs. © 2017 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.
Real-World Data on Prognostic Factors for Overall Survival in EGFR Mutation-Positive Advanced Non-Small Cell Lung Cancer Patients Treated with First-Line Gefitinib.

PubMed

Yao, Zong-Han; Liao, Wei-Yu; Ho, Chao-Chi; Chen, Kuan-Yu; Shih, Jin-Yuan; Chen, Jin-Shing; Lin, Zhong-Zhe; Lin, Chia-Chi; Chih-Hsin Yang, James; Yu, Chong-Jen

2017-09-01

This study aimed to identify independent prognostic factors for overall survival (OS) of patients with advanced non-small cell lung cancer (NSCLC) harboring an activating epidermal growth factor receptor (EGFR) mutation and receiving gefitinib as first-line treatment in real-world practice. We enrolled 226 patients from June 2011 to May 2013. During this period, gefitinib was the only EGFR-tyrosine kinase inhibitor reimbursed by the Bureau of National Health Insurance of Taiwan. The median progression-free survival and median OS were 11.9 months (95% confidence interval [CI]: 9.7-14.2) and 26.9 months (21.2-32.5), respectively. The Cox proportional hazards regression model revealed that postoperative recurrence, performance status (Eastern Cooperative Oncology Grade [ECOG] ≥2), smoking index (≥20 pack-years), liver metastasis at initial diagnosis, and chronic hepatitis C virus (HCV) infection were independent prognostic factors for OS (hazard ratio [95% CI] 0.3 [0.11-0.83], p = .02; 2.69 [1.60-4.51], p < .001; 1.92 [1.24-2.97], p = .003; 2.26 [1.34-3.82], p = .002; 3.38 [1.85-7.78], p < .001, respectively). However, brain metastasis (BM) at initial diagnosis or intracranial progression during gefitinib treatment had no impact on OS (1.266 [0.83-1.93], p = .275 and 0.75 [0.48-1.19], p = .211, respectively). HCV infection, performance status (ECOG ≥2), newly diagnosed advanced NSCLC without prior operation, and liver metastasis predicted poor OS in EGFR mutation-positive advanced NSCLC patients treated with first-line gefitinib; however, neither BM at initial diagnosis nor intracranial progression during gefitinib treatment had an impact on OS. The finding that chronic hepatitis C virus (HCV) infection might predict poor overall survival (OS) in epidermal growth factor receptor mutation-positive advanced non-small cell lung cancer (NSCLC) patients treated with first-line gefitinib may raise awareness of benefit from anti-HCV treatment in this patient population. Brain metastasis in the initial diagnosis or intracranial progression during gefitinib treatment is not a prognostic factor for OS. This study, which enrolled a real-world population of NSCLC patients, including sicker patients who were not eligible for a clinical trial, may have impact on guiding usual clinical practice. © AlphaMed Press 2017.
Symplectic geometry spectrum regression for prediction of noisy time series

NASA Astrophysics Data System (ADS)

Xie, Hong-Bo; Dokos, Socrates; Sivakumar, Bellie; Mengersen, Kerrie

2016-05-01

We present the symplectic geometry spectrum regression (SGSR) technique as well as a regularized method based on SGSR for prediction of nonlinear time series. The main tool of analysis is the symplectic geometry spectrum analysis, which decomposes a time series into the sum of a small number of independent and interpretable components. The key to successful regularization is to damp higher order symplectic geometry spectrum components. The effectiveness of SGSR and its superiority over local approximation using ordinary least squares are demonstrated through prediction of two noisy synthetic chaotic time series (Lorenz and Rössler series), and then tested for prediction of three real-world data sets (Mississippi River flow data and electromyographic and mechanomyographic signal recorded from human body).
Validation Of The Airspace Concept Evaluation System Using Real World Data

NASA Technical Reports Server (NTRS)

Zelinski, Shannon

2005-01-01

This paper discusses the process of performing a validation of the Airspace Concept Evaluation System (ACES) using real world historical flight operational data. ACES inputs are generated from select real world data and processed to create a realistic reproduction of a single day of operations within the National Airspace System (NAS). ACES outputs are then compared to real world operational metrics and delay statistics for the reproduced day. Preliminary results indicate that ACES produces delays and airport operational metrics similar to the real world with minor variations of delay by phase of flight. ACES is a nation-wide fast-time simulation tool developed at NASA Ames Research Center. ACES models and simulates the NAS using interacting agents representing center control, terminal flow management, airports, individual flights, and other NAS elements. These agents pass messages between one another similar to real world communications. This distributed agent based system is designed to emulate the highly unpredictable nature of the NAS, making it a suitable tool to evaluate current and envisioned airspace concepts. To ensure that ACES produces the most realistic results, the system must be validated. There is no way to validate future concepts scenarios using real world historical data, but current day scenario validations increase confidence in the validity of future scenario results. Each operational day has unique weather and traffic demand schedules. The more a simulation utilizes the unique characteristic of a specific day, the more realistic the results should be. ACES is able to simulate the full scale demand traffic necessary to perform a validation using real world data. Through direct comparison with the real world, models may continuee to be improved and unusual trends and biases may be filtered out of the system or used to normalize the results of future concept simulations.
Real-World Efficacy of Azelaic Acid 15% Gel for the Reduction of Inflammatory Lesions of Rosacea.

PubMed

Wirth, P J; Henderson Berg, M H; Sadick, N

2017-11-01

Approximately 16 million Americans have rosacea, an inflammatory cutaneous disorder with central facial erythema, papules, pustules, telangiectasia, flushing, and swelling being among the more commonly recognized features. Overexpression of cathelicidin peptide LL-37 has been implicated in the pathophysiology of rosacea. Azelaic acid has been found to inhibit the pathologic expression of cathelicidin, as well as the hyperactive protease activity that cleaves cathelicidin into LL-37. Given these findings, a small prospective, open-label, interventional trial was undertaken to assess the effects of azelaic acid 15% gel on inflammatory lesions of papulopustular rosacea in a real-world setting. Use of azelaic acid was associated with a significant reduction in inflammatory lesions, which persisted beyond the active treatment phase. Overall, azelaic acid 15% gel is an appropriate initial topical therapy for the treatment of moderate facial rosacea.
Meta-path based heterogeneous combat network link prediction

NASA Astrophysics Data System (ADS)

Li, Jichao; Ge, Bingfeng; Yang, Kewei; Chen, Yingwu; Tan, Yuejin

2017-09-01

The combat system-of-systems in high-tech informative warfare, composed of many interconnected combat systems of different types, can be regarded as a type of complex heterogeneous network. Link prediction for heterogeneous combat networks (HCNs) is of significant military value, as it facilitates reconfiguring combat networks to represent the complex real-world network topology as appropriate with observed information. This paper proposes a novel integrated methodology framework called HCNMP (HCN link prediction based on meta-path) to predict multiple types of links simultaneously for an HCN. More specifically, the concept of HCN meta-paths is introduced, through which the HCNMP can accumulate information by extracting different features of HCN links for all the six defined types. Next, an HCN link prediction model, based on meta-path features, is built to predict all types of links of the HCN simultaneously. Then, the solution algorithm for the HCN link prediction model is proposed, in which the prediction results are obtained by iteratively updating with the newly predicted results until the results in the HCN converge or reach a certain maximum iteration number. Finally, numerical experiments on the dataset of a real HCN are conducted to demonstrate the feasibility and effectiveness of the proposed HCNMP, in comparison with 30 baseline methods. The results show that the performance of the HCNMP is superior to those of the baseline methods.

Design of flood early warning system with wifi network based on smartphone

NASA Astrophysics Data System (ADS)

Supani, Ahyar; Andriani, Yuli; Taqwa, Ahmad

2017-11-01

Today, the development using internet of things enables activities surrounding us to be monitored, controlled, predicted and calculated remotely through connections to the internet network such as monitoring activities of long-distance flood warning with information technology. Applying an information technology in the field of flood early warning has been developed in the world, either connected to internet network or not. The internet network that has been done in this paper is the design of WiFi network to access data of rainfall, water level and flood status at any time with a smartphone coming from flood early warning system. The results obtained when test of data accessing with smartphone are in form of rainfall and water level graphs against time and flood status indicators consisting of 3 flood states: Standby 2, Standby 1 and Flood. It is concluded that data are from flood early warning system has been able to accessed and displayed on smartphone via WiFi network in any time and real time.
ADVANTG An Automated Variance Reduction Parameter Generator, Rev. 1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mosher, Scott W.; Johnson, Seth R.; Bevill, Aaron M.

2015-08-01

The primary objective of ADVANTG is to reduce both the user effort and the computational time required to obtain accurate and precise tally estimates across a broad range of challenging transport applications. ADVANTG has been applied to simulations of real-world radiation shielding, detection, and neutron activation problems. Examples of shielding applications include material damage and dose rate analyses of the Oak Ridge National Laboratory (ORNL) Spallation Neutron Source and High Flux Isotope Reactor (Risner and Blakeman 2013) and the ITER Tokamak (Ibrahim et al. 2011). ADVANTG has been applied to a suite of radiation detection, safeguards, and special nuclear materialmore » movement detection test problems (Shaver et al. 2011). ADVANTG has also been used in the prediction of activation rates within light water reactor facilities (Pantelias and Mosher 2013). In these projects, ADVANTG was demonstrated to significantly increase the tally figure of merit (FOM) relative to an analog MCNP simulation. The ADVANTG-generated parameters were also shown to be more effective than manually generated geometry splitting parameters.« less
Real World Data Driven Evolution of Volvo Cars’ Side Impact Protection Systems and their Effectiveness

PubMed Central

Jakobsson, Lotta; Lindman, Magdalena; Svanberg, Bo; Carlsson, Henrik

2010-01-01

This study analyses the outcome of the continuous improved occupant protection over the last two decades for front seat near side occupants in side impacts based on a real world driven working process. The effectiveness of four generations of improved side impact protection are calculated based on data from Volvo’s statistical accident database of Volvo Cars in Sweden. Generation I includes vehicles with a new structural and interior concept (SIPS). Generation II includes vehicles with structural improvements and a new chest airbag (SIPSbag). Generation III includes vehicles with further improved SIPS and SIPSbag as well as the new concept with a head protecting Inflatable Curtain (IC). Generation IV includes the most recent vehicles with further improvements of all the systems plus advanced sensors and seat belt pretensioner activation. Compared to baseline vehicles, vehicles of generation I reduce MAIS2+ injuries by 54%, generation II by 61% and generation III by 72%. For generation IV effectiveness figures cannot be calculated because of the lack of MAIS2+ injuries. A continuous improved performance is also seen when studying the AIS2+ pelvis, abdomen, chest and head injuries separately. By using the same real world driven working process, future improvements and possibly new passive as well as active safety systems, will be developed with the aim of further improved protection to near side occupants in side impacts. PMID:21050597
Spontaneous mentalizing during an interactive real world task: an fMRI study.

PubMed

Spiers, Hugo J; Maguire, Eleanor A

2006-01-01

There are moments in everyday life when we need to consider the thoughts and intentions of other individuals in order to act in a socially appropriate manner. Most of this mentalizing occurs spontaneously as we go about our business in the complexity of the real world. As such, studying the neural basis of spontaneous mentalizing has been virtually impossible. Here we devised a means to achieve this by employing a unique combination of functional magnetic resonance imaging (fMRI), a detailed and interactive virtual reality simulation of a bustling familiar city, and a retrospective verbal report protocol. We were able to provide insights into the content of spontaneous mentalizing events and identify the brain regions that underlie them. We found increased activity in a number of regions, namely the right posterior superior temporal sulcus, the medial prefrontal cortex and the right temporal pole associated with spontaneous mentalizing. Furthermore, we observed the right posterior superior temporal sulcus to be consistently active during several different subtypes of mentalizing events. By contrast, medial prefrontal cortex seemed to be particularly involved in thinking about agents that were visible in the environment. Our findings show that it is possible to investigate the neural basis of mentalizing in a manner closer to its true context, the real world, opening up intriguing possibilities for making comparisons with those who have mentalizing problems.
Limits of Risk Predictability in a Cascading Alternating Renewal Process Model.

PubMed

Lin, Xin; Moussawi, Alaa; Korniss, Gyorgy; Bakdash, Jonathan Z; Szymanski, Boleslaw K

2017-07-27

Most risk analysis models systematically underestimate the probability and impact of catastrophic events (e.g., economic crises, natural disasters, and terrorism) by not taking into account interconnectivity and interdependence of risks. To address this weakness, we propose the Cascading Alternating Renewal Process (CARP) to forecast interconnected global risks. However, assessments of the model's prediction precision are limited by lack of sufficient ground truth data. Here, we establish prediction precision as a function of input data size by using alternative long ground truth data generated by simulations of the CARP model with known parameters. We illustrate the approach on a model of fires in artificial cities assembled from basic city blocks with diverse housing. The results confirm that parameter recovery variance exhibits power law decay as a function of the length of available ground truth data. Using CARP, we also demonstrate estimation using a disparate dataset that also has dependencies: real-world prediction precision for the global risk model based on the World Economic Forum Global Risk Report. We conclude that the CARP model is an efficient method for predicting catastrophic cascading events with potential applications to emerging local and global interconnected risks.
Rethinking the Impact of Activity Design on a Mobile Learning Trail: The Missing Dimension of the Physical Affordances

ERIC Educational Resources Information Center

Tan, Esther; So, Hyo-Jeong

2015-01-01

This paper investigates the relationship between activity design and discourse on a mobile learning trail, considering the physical affordances of the real world platform in designing contextual learning experiences. We adopted a "context-oriented" and "process-oriented" pedagogical approach in designing the mobile learning…
Teaching Note: Intimacy Timelines as a Tool for Teaching Feminism

ERIC Educational Resources Information Center

Briggs, Lindsay

2017-01-01

This essay will describe one activity that the author uses in her human sexuality course to illustrate how patriarchal systems have affected the experiences of females and males across the sexual lifespan. Through this fairly simple and straightforward activity students are able to utilize common experiences and knowledge of real-world issues and…
From Rhetoric to Reality: Designing Activities to Foster Creativity

ERIC Educational Resources Information Center

Cropley, David H.

2014-01-01

As teachers strive to make sense of and implement knowledge of creativity that is available from the research community, school librarians are called upon to help turn rhetoric into reality. Developing the creativity habit is far more meaningful and effective if the classroom activity is representative of the real-world problem-solving process.…
Predicting adult weight change in the real world: a systematic review and meta-analysis accounting for compensatory changes in energy intake or expenditure.

PubMed

Dhurandhar, E J; Kaiser, K A; Dawson, J A; Alcorn, A S; Keating, K D; Allison, D B

2015-08-01

Public health and clinical interventions for obesity in free-living adults may be diminished by individual compensation for the intervention. Approaches to predict weight outcomes do not account for all mechanisms of compensation, so they are not well suited to predict outcomes in free-living adults. Our objective was to quantify the range of compensation in energy intake or expenditure observed in human randomized controlled trials (RCTs). We searched multiple databases (PubMed, CINAHL, SCOPUS, Cochrane, ProQuest, PsycInfo) up to 1 August 2012 for RCTs evaluating the effect of dietary and/or physical activity interventions on body weight/composition. subjects per treatment arm ≥5; ≥1 week intervention; a reported outcome of body weight/body composition; the intervention was either a prescribed amount of over- or underfeeding and/or supervised or monitored physical activity was prescribed; ≥80% compliance; and an objective method was used to verify compliance with the intervention (for example, observation and electronic monitoring). Data were independently extracted and analyzed by multiple reviewers with consensus reached by discussion. We compared observed weight change with predicted weight change using two models that predict weight change accounting only for metabolic compensation. Twenty-eight studies met inclusion criteria. Overfeeding studies indicate 96% less weight gain than expected if no compensation occurred. Dietary restriction and exercise studies may result in up to 12-44% and 55-64% less weight loss than expected, respectively, under an assumption of no behavioral compensation. Compensation is substantial even in high-compliance conditions, resulting in far less weight change than would be expected. The simple algorithm we report allows for more realistic predictions of intervention effects in free-living populations by accounting for the significant compensation that occurs.
Generalized event knowledge activation during online sentence comprehension

PubMed Central

Metusalem, Ross; Kutas, Marta; Urbach, Thomas P.; Hare, Mary; McRae, Ken; Elman, Jeffrey L.

2012-01-01

Recent research has demonstrated that knowledge of real-world eventsplays an important role inguiding online language comprehension. The present study addresses the scope of event knowledge activation during the course of comprehension, specifically investigating whether activation is limited to those knowledge elements that align with the local linguistic context.The present study addresses this issue by analyzing event-related brain potentials (ERPs) recorded as participants read brief scenariosdescribing typical real-world events. Experiment 1 demonstratesthat a contextually anomalous word elicits a reduced N400 if it is generally related to the described event, even when controlling for the degree of association of this word with individual words in the preceding context and with the expected continuation. Experiment 2 shows that this effect disappears when the discourse context is removed.These findings demonstrate that during the course of incremental comprehension, comprehenders activate general knowledge about the described event, even at points at which this knowledge would constitute an anomalous continuation of the linguistic stream. Generalized event knowledge activationcontributes to mental representations of described events, is immediately available to influence language processing, and likely drives linguistic expectancy generation. PMID:22711976
The Effects of Duration of Exposure to the REAPS Model in Developing Students' General Creativity and Creative Problem Solving in Science

ERIC Educational Resources Information Center

Alhusaini, Abdulnasser Alashaal F.

2016-01-01

The Real Engagement in Active Problem Solving (REAPS) model was developed in 2004 by C. June Maker and colleagues as an intervention for gifted students to develop creative problem solving ability through the use of real-world problems. The primary purpose of this study was to examine the effects of the REAPS model on developing students' general…
Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models

DOE PAGES

Lu, Dan; Ye, Ming; Curtis, Gary P.

2015-08-01

While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. Our study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict themore » reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. Moreover, these reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Finally, limitations of applying MLBMA to the synthetic study and future real-world modeling are discussed.« less
Agent-based model for the h-index - exact solution

NASA Astrophysics Data System (ADS)

Żogała-Siudem, Barbara; Siudem, Grzegorz; Cena, Anna; Gagolewski, Marek

2016-01-01

Hirsch's h-index is perhaps the most popular citation-based measure of scientific excellence. In 2013, Ionescu and Chopard proposed an agent-based model describing a process for generating publications and citations in an abstract scientific community [G. Ionescu, B. Chopard, Eur. Phys. J. B 86, 426 (2013)]. Within such a framework, one may simulate a scientist's activity, and - by extension - investigate the whole community of researchers. Even though the Ionescu and Chopard model predicts the h-index quite well, the authors provided a solution based solely on simulations. In this paper, we complete their results with exact, analytic formulas. What is more, by considering a simplified version of the Ionescu-Chopard model, we obtained a compact, easy to compute formula for the h-index. The derived approximate and exact solutions are investigated on a simulated and real-world data sets.
An Overview of the NASA Aviation Safety Program (AVSP) Systemwide Accident Prevention (SWAP) Human Performance Modeling (HPM) Element

NASA Technical Reports Server (NTRS)

Foyle, David C.; Goodman, Allen; Hooley, Becky L.

2003-01-01

An overview is provided of the Human Performance Modeling (HPM) element within the NASA Aviation Safety Program (AvSP). Two separate model development tracks for performance modeling of real-world aviation environments are described: the first focuses on the advancement of cognitive modeling tools for system design, while the second centers on a prescriptive engineering model of activity tracking for error detection and analysis. A progressive implementation strategy for both tracks is discussed in which increasingly more complex, safety-relevant applications are undertaken to extend the state-of-the-art, as well as to reveal potential human-system vulnerabilities in the aviation domain. Of particular interest is the ability to predict the precursors to error and to assess potential mitigation strategies associated with the operational use of future flight deck technologies.
An Overview of NASA SPoRT GOES-R JPSS Proving Ground Testbed Activities

NASA Technical Reports Server (NTRS)

Berndt, Emily; Stano, Geoffrey; Fuell, Kevin; Leroy, Anita; Mcgrath, Kevin; Molthan, Andrew; Schultz, Lori; Smith, Matthew; White, Kris; Schultz, Christopher;

2017-01-01

The Short-term Prediction Research and Transition (SPoRT) Center is funded by NASA's Earth Science Division and NOAA's JPSS and GOES-R Proving Grounds to transition satellite products and capabilities to the NWS to improve short term (0-48 hr) forecasts on a regional and local scale. SPoRT currently collaborates with 30+ NWS WFOs (at least one in each NWS region) and 5 National Centers/Testbeds. SPoRT matches user-identified forecast challenges to specific products, providing access to these data in AWIPS through new plug-in development, and generating applications-based training to use the products for their needs (R20). Upon transition, SPoRT collaborates with the user to assess the product impact in a real-world environment for feedback to product developers (O2R) and to benefit their peers.

New online ecology of adversarial aggregates: ISIS and beyond.

PubMed

Johnson, N F; Zheng, M; Vorobyeva, Y; Gabriel, A; Qi, H; Velasquez, N; Manrique, P; Johnson, D; Restrepo, E; Song, C; Wuchty, S

2016-06-17

Support for an extremist entity such as Islamic State (ISIS) somehow manages to survive globally online despite considerable external pressure and may ultimately inspire acts by individuals having no history of extremism, membership in a terrorist faction, or direct links to leadership. Examining longitudinal records of online activity, we uncovered an ecology evolving on a daily time scale that drives online support, and we provide a mathematical theory that describes it. The ecology features self-organized aggregates (ad hoc groups formed via linkage to a Facebook page or analog) that proliferate preceding the onset of recent real-world campaigns and adopt novel adaptive mechanisms to enhance their survival. One of the predictions is that development of large, potentially potent pro-ISIS aggregates can be thwarted by targeting smaller ones. Copyright © 2016, American Association for the Advancement of Science.
The semantic richness of abstract concepts

PubMed Central

Recchia, Gabriel; Jones, Michael N.

2012-01-01

We contrasted the predictive power of three measures of semantic richness—number of features (NFs), contextual dispersion (CD), and a novel measure of number of semantic neighbors (NSN)—for a large set of concrete and abstract concepts on lexical decision and naming tasks. NSN (but not NF) facilitated processing for abstract concepts, while NF (but not NSN) facilitated processing for the most concrete concepts, consistent with claims that linguistic information is more relevant for abstract concepts in early processing. Additionally, converging evidence from two datasets suggests that when NSN and CD are controlled for, the features that most facilitate processing are those associated with a concept's physical characteristics and real-world contexts. These results suggest that rich linguistic contexts (many semantic neighbors) facilitate early activation of abstract concepts, whereas concrete concepts benefit more from rich physical contexts (many associated objects and locations). PMID:23205008
Regional Level Influenza Study with Geo-Tagged Twitter Data.

PubMed

Wang, Feng; Wang, Haiyan; Xu, Kuai; Raymond, Ross; Chon, Jaime; Fuller, Shaun; Debruyn, Anton

2016-08-01

The rich data generated and read by millions of users on social media tells what is happening in the real world in a rapid and accurate fashion. In recent years many researchers have explored real-time streaming data from Twitter for a broad range of applications, including predicting stock markets and public health trend. In this paper we design, implement, and evaluate a prototype system to collect and analyze influenza statuses over different geographical locations with real-time tweet streams. We investigate the correlation between the Twitter flu counts and the official statistics from the Center for Disease Control and Prevention (CDC) and discover that real-time tweet streams capture the dynamics of influenza cases at both national and regional level and could potentially serve as an early warning system of influenza epidemics. Furthermore, we propose a dynamic mathematical model which can forecast Twitter flu counts with high accuracy.
Revealing how network structure affects accuracy of link prediction

NASA Astrophysics Data System (ADS)

Yang, Jin-Xuan; Zhang, Xiao-Dong

2017-08-01

Link prediction plays an important role in network reconstruction and network evolution. The network structure affects the accuracy of link prediction, which is an interesting problem. In this paper we use common neighbors and the Gini coefficient to reveal the relation between them, which can provide a good reference for the choice of a suitable link prediction algorithm according to the network structure. Moreover, the statistical analysis reveals correlation between the common neighbors index, Gini coefficient index and other indices to describe the network structure, such as Laplacian eigenvalues, clustering coefficient, degree heterogeneity, and assortativity of network. Furthermore, a new method to predict missing links is proposed. The experimental results show that the proposed algorithm yields better prediction accuracy and robustness to the network structure than existing currently used methods for a variety of real-world networks.
[Virtual reality in neurosurgery].

PubMed

Tronnier, V M; Staubert, A; Bonsanto, M M; Wirtz, C R; Kunze, S

2000-03-01

Virtual reality enables users to immerse themselves in a virtual three-dimensional world and to interact in this world. The simulation is different from the kind in computer games, in which the viewer is active but acts in a nonrealistic world, or on the TV screen, where we are passively driven in an active world. In virtual reality elements look realistic, they change their characteristics and have almost real-world unpredictability. Virtual reality is not only implemented in gambling dens and the entertainment industry but also in manufacturing processes (cars, furniture etc.), military applications and medicine. Especially the last two areas are strongly correlated, because telemedicine or telesurgery was originated for military reasons to operate on war victims from a secure distance or to perform surgery on astronauts in an orbiting space station. In medicine and especially neurosurgery virtual-reality methods are used for education, surgical planning and simulation on a virtual patient.

Ecological Interventionist Causal Models in Psychosis: Targeting Psychological Mechanisms in Daily Life

PubMed Central

Reininghaus, Ulrich; Depp, Colin A.; Myin-Germeys, Inez

2016-01-01

Integrated models of psychotic disorders have posited a number of putative psychological mechanisms that may contribute to the development of psychotic symptoms, but it is only recently that a modest amount of experience sampling research has provided evidence on their role in daily life, outside the research laboratory. A number of methodological challenges remain in evaluating specificity of potential causal links between a given psychological mechanism and psychosis outcomes in a systematic fashion, capitalizing on longitudinal data to investigate temporal ordering. In this article, we argue for testing ecological interventionist causal models that draw on real world and real-time delivered, ecological momentary interventions for generating evidence on several causal criteria (association, time order, and direction/sole plausibility) under real-world conditions, while maximizing generalizability to social contexts and experiences in heterogeneous populations. Specifically, this approach tests whether ecological momentary interventions can (1) modify a putative mechanism and (2) produce changes in the mechanism that lead to sustainable changes in intended psychosis outcomes in individuals’ daily lives. Future research using this approach will provide translational evidence on the active ingredients of mobile health and in-person interventions that promote sustained effectiveness of ecological momentary interventions and, thereby, contribute to ongoing efforts that seek to enhance effectiveness of psychological interventions under real-world conditions. PMID:26707864
Modeling Humans as Reinforcement Learners: How to Predict Human Behavior in Multi-Stage Games

NASA Technical Reports Server (NTRS)

Lee, Ritchie; Wolpert, David H.; Backhaus, Scott; Bent, Russell; Bono, James; Tracey, Brendan

2011-01-01

This paper introduces a novel framework for modeling interacting humans in a multi-stage game environment by combining concepts from game theory and reinforcement learning. The proposed model has the following desirable characteristics: (1) Bounded rational players, (2) strategic (i.e., players account for one anothers reward functions), and (3) is computationally feasible even on moderately large real-world systems. To do this we extend level-K reasoning to policy space to, for the first time, be able to handle multiple time steps. This allows us to decompose the problem into a series of smaller ones where we can apply standard reinforcement learning algorithms. We investigate these ideas in a cyber-battle scenario over a smart power grid and discuss the relationship between the behavior predicted by our model and what one might expect of real human defenders and attackers.
Selecting lineup foils in eyewitness identification experiments: experimental control and real-world simulation.

PubMed

Clark, S E; Tunnicliff, J L

2001-06-01

Experimental research on eyewitness identification follows a standard principle of experimental design. Perpetrator-present and perpetrator-absent lineups are constructed with the same foils, so that the two conditions are identical except for the presence or absence ofthe trueperpetrator ofthe crime. However, this aspect of the design simulates conditions that do not correspond to those of real criminal investigations. Specifically, these conditions can create perp-absent lineups in which the foils are selected based on their similarity to an unknown person--the real perpetrator. Analysis of the similarity relations predicts that when foils for perp-absent lineups are selected based on their match to the perpetrator the false identification rate will be lower than if the foils are selected based on their match to the innocent suspect. This prediction was confirmed in an experiment that compared these two perp-absent lineup conditions. These results suggest that false identification rates in previous experiments would have been higher if the foils had been selected based on their match to the innocent suspect, rather than the absent perpetrator.
Single session real-time fMRI neurofeedback has a lasting impact on cognitive behavioral therapy strategies.

PubMed

MacDuffie, Katherine E; MacInnes, Jeff; Dickerson, Kathryn C; Eddington, Kari M; Strauman, Timothy J; Adcock, R Alison

2018-01-01

To benefit from cognitive behavioral therapy (CBT), individuals must not only learn new skills but also strategically implement them outside of session. Here, we tested a novel technique for personalizing CBT skills and facilitating their generalization to daily life. We hypothesized that showing participants the impact of specific CBT strategies on their own brain function using real-time functional magnetic imaging (rt-fMRI) neurofeedback would increase their metacognitive awareness, help them identify effective strategies, and motivate real-world use. In a within-subjects design, participants who had completed a clinical trial of a standardized course of CBT created a personal repertoire of negative autobiographical stimuli and mood regulation strategies. From each participant's repertoire, a set of experimental and control strategies were identified; only experimental strategies were practiced in the scanner. During the rt-fMRI neurofeedback session, participants used negative stimuli and strategies from their repertoire to manipulate activation in the anterior cingulate cortex, a region implicated in emotional distress. The primary outcome measures were changes in participant ratings of strategy difficulty, efficacy, and frequency of use. As predicted, ratings for unscanned control strategies were stable across observations, whereas ratings for experimental strategies changed after neurofeedback. At follow-up one month after the session, efficacy and frequency ratings for scanned strategies were predicted by neurofeedback during the rt-fMRI session. These results suggest that rt-fMRI neurofeedback created a salient and durable learning experience for patients, extending beyond the scan session to guide and motivate CBT skill use weeks later. This metacognitive approach to neurofeedback offers a promising model for increasing clinical benefits from cognitive behavioral therapy by personalizing skills and facilitating generalization.
Self-assessment in schizophrenia: Accuracy of evaluation of cognition and everyday functioning.

PubMed

Gould, Felicia; McGuire, Laura Stone; Durand, Dante; Sabbag, Samir; Larrauri, Carlos; Patterson, Thomas L; Twamley, Elizabeth W; Harvey, Philip D

2015-09-01

Self-assessment deficits, often referred to as impaired insight or unawareness of illness, are well established in people with schizophrenia. There are multiple levels of awareness, including awareness of symptoms, functional deficits, cognitive impairments, and the ability to monitor cognitive and functional performance in an ongoing manner. The present study aimed to evaluate the comparative predictive value of each aspect of awareness on the levels of everyday functioning in people with schizophrenia. We examined multiple aspects of self-assessment of functioning in 214 people with schizophrenia. We also collected information on everyday functioning rated by high contact clinicians and examined the importance of self-assessment for the prediction of real-world functional outcomes. The relative impact of performance-based measures of cognition, functional capacity, and metacognitive performance on everyday functioning was also examined. Misestimation of ability emerged as the strongest predictor of real-world functioning and exceeded the influences of cognitive performance, functional capacity performance, and performance-based assessment of metacognitive monitoring. The relative contribution of the factors other than self-assessment varied according to which domain of everyday functioning was being examined, but, in all cases, accounted for less predictive variance. These results underscore the functional impact of misestimating one's current functioning and relative level of ability. These findings are consistent with the use of insight-focused treatments and compensatory strategies designed to increase self-awareness in multiple functional domains. (c) 2015 APA, all rights reserved).
Self Assessment in Schizophrenia: Accuracy of Evaluation of Cognition and Everyday Functioning

PubMed Central

Gould, Felicia; McGuire, Laura Stone; Durand, Dante; Sabbag, Samir; Larrauri, Carlos; Patterson, Thomas L.; Twamley, Elizabeth W.; Harvey, Philip D.

2015-01-01

Objective Self-assessment deficits, often referred to as impaired insight or unawareness of illness, are well established in people with schizophrenia. There are multiple levels of awareness, including awareness of symptoms, functional deficits, cognitive impairments, and the ability to monitor cognitive and functional performance in an ongoing manner. The present study aimed to evaluate the comparative predictive value of each aspect of awareness on the levels of everyday functioning in people with schizophrenia. Method We examined multiple aspects of self-assessment of functioning in 214 people with schizophrenia. We also collected information on everyday functioning rated by high contact clinicians and examined the importance of self-assessment for the prediction of real world functional outcomes. The relative impact of performance based measures of cognition, functional capacity, and metacognitive performance on everyday functioning was also examined. Results Misestimation of ability emerged as the strongest predictor of real world functioning and exceeded the influences of cognitive performance, functional capacity performance, and performance-based assessment of metacognitive monitoring. The relative contribution of the factors other than self-assessment varied according to which domain of everyday functioning was being examined, but in all cases, accounted for less predictive variance. Conclusions These results underscore the functional impact of misestimating one’s current functioning and relative level of ability. These findings are consistent with the use of insight-focused treatments and compensatory strategies designed to increase self-awareness in multiple functional domains. PMID:25643212
Snapshots of Applications in Mathematics: Thermal Systems and the Solar Oven.

ERIC Educational Resources Information Center

Callas, Dennis, Ed.; Hildreth, David J., Ed.; Bickford, Carl

1998-01-01

Showcases applications of mathematics designed to demonstrate to students how the topics under study are used in the real world or to solve problems. Presents an activity on thermal systems using spreadsheets or graphing calculators. (ASK)
Technology Education and the Elementary School.

ERIC Educational Resources Information Center

Thode, Terry

1996-01-01

In the technology education program at Hemingway School in Ketchum, Idaho, students are involved in hands-on activities that encourage the use of critical thinking skills, tools, and high-tech equipment to solve problems related to real world situations. (Author)
Project Spectrum: An Innovative Assessment Alternative.

ERIC Educational Resources Information Center

Krechevsky, Mara

1991-01-01

Project Spectrum attempts to reconceptualize the traditional linguistic and logical/mathematical bases of intelligence. Spectrum blurs the line between curriculum and assessment, embeds assessment in meaningful, real-world activities, uses "intelligence-fair" measures, emphasizes children's strengths, and recognizes the stylistic…
Higher Throughput Toxicokinetics to Allow Extrapolation (EPA-Japan Bilateral EDSP meeting)

EPA Science Inventory

As part of "Ongoing EDSP Directions & Activities" I will present CSS research on high throughput toxicokinetics, including in vitro data and models to allow rapid determination of the real world doses that may cause endocrine disruption.
Mathematics Fitness.

ERIC Educational Resources Information Center

Trezise, Kathleen A.

1998-01-01

Students receiving mathematics instruction relevant to life will understand that mathematics is part of the real world and not just another school subject. Presents an activity that integrates rational numbers with health-related science content as students collect data and engage in decision making. (ASK)
Day, night and all-weather security surveillance automation synergy from combining two powerful technologies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morellas, Vassilios; Johnson, Andrew; Johnston, Chris

2006-07-01

Thermal imaging is rightfully a real-world technology proven to bring confidence to daytime, night-time and all weather security surveillance. Automatic image processing intrusion detection algorithms are also a real world technology proven to bring confidence to system surveillance security solutions. Together, day, night and all weather video imagery sensors and automated intrusion detection software systems create the real power to protect early against crime, providing real-time global homeland protection, rather than simply being able to monitor and record activities for post event analysis. These solutions, whether providing automatic security system surveillance at airports (to automatically detect unauthorized aircraft takeoff andmore » landing activities) or at high risk private, public or government facilities (to automatically detect unauthorized people or vehicle intrusion activities) are on the move to provide end users the power to protect people, capital equipment and intellectual property against acts of vandalism and terrorism. As with any technology, infrared sensors and automatic image intrusion detection systems for global homeland security protection have clear technological strengths and limitations compared to other more common day and night vision technologies or more traditional manual man-in-the-loop intrusion detection security systems. This paper addresses these strength and limitation capabilities. False Alarm (FAR) and False Positive Rate (FPR) is an example of some of the key customer system acceptability metrics and Noise Equivalent Temperature Difference (NETD) and Minimum Resolvable Temperature are examples of some of the sensor level performance acceptability metrics. (authors)« less
Exploring Non-Traditional Learning Methods in Virtual and Real-World Environments

ERIC Educational Resources Information Center

Lukman, Rebeka; Krajnc, Majda

2012-01-01

This paper identifies the commonalities and differences within non-traditional learning methods regarding virtual and real-world environments. The non-traditional learning methods in real-world have been introduced within the following courses: Process Balances, Process Calculation, and Process Synthesis, and within the virtual environment through…
Application Exercises Improve Transfer of Statistical Knowledge in Real-World Situations

ERIC Educational Resources Information Center

Daniel, Frances; Braasch, Jason L. G.

2013-01-01

The present research investigated whether real-world application exercises promoted students' abilities to spontaneously transfer statistical knowledge and to recognize the use of statistics in real-world contexts. Over the course of a semester of psychological statistics, two classes completed multiple application exercises designed to mimic…
Reflections on "Real-World" Community Psychology

ERIC Educational Resources Information Center

Wolff, Tom; Swift, Carolyn

2008-01-01

Reflections on the history of real-world (applied) community psychologists trace their participation in the field's official guild, the Society for Community Research and Action (SCRA), beginning with the Swampscott Conference in 1965 through the current date. Four benchmarks are examined. The issues these real-world psychologists bring to the…
Learning from Dealing with Real World Problems

ERIC Educational Resources Information Center

Akcay, Hakan

2017-01-01

The purpose of this article is to provide an example of using real world issues as tools for science teaching and learning. Using real world issues provides students with experiences in learning in problem-based environments and encourages them to apply their content knowledge to solving current and local problems.
Learning to Predict Social Influence in Complex Networks

DTIC Science & Technology

2012-03-29

03/2010 – 17/03/2012 Abstract: First, we addressed the problem of analyzing information diffusion process in a social network using two kinds...algorithm which avoids the inner loop optimization during the search. We tested the performance using the structures of four real world networks, and...result of information diffusion that starts from the node. 2 We use “infected” and “activated” interchangeably. Efficient Discovery of Influential
Stability analysis and application of a mathematical cholera model.

PubMed

Liao, Shu; Wang, Jin

2011-07-01

In this paper, we conduct a dynamical analysis of the deterministic cholera model proposed in [9]. We study the stability of both the disease-free and endemic equilibria so as to explore the complex epidemic and endemic dynamics of the disease. We demonstrate a real-world application of this model by investigating the recent cholera outbreak in Zimbabwe. Meanwhile, we present numerical simulation results to verify the analytical predictions.
Dynamic phenomena and human activity in an artificial society

NASA Astrophysics Data System (ADS)

Grabowski, A.; Kruszewska, N.; Kosiński, R. A.

2008-12-01

We study dynamic phenomena in a large social network of nearly 3×104 individuals who interact in the large virtual world of a massive multiplayer online role playing game. On the basis of a database received from the online game server, we examine the structure of the friendship network and human dynamics. To investigate the relation between networks of acquaintances in virtual and real worlds, we carried out a survey among the players. We show that, even though the virtual network did not develop as a growing graph of an underlying network of social acquaintances in the real world, it influences it. Furthermore we find very interesting scaling laws concerning human dynamics. Our research shows how long people are interested in a single task and how much time they devote to it. Surprisingly, exponent values in both cases are close to -1 . We calculate the activity of individuals, i.e., the relative time daily devoted to interactions with others in the artificial society. Our research shows that the distribution of activity is not uniform and is highly correlated with the degree of the node, and that such human activity has a significant influence on dynamic phenomena, e.g., epidemic spreading and rumor propagation, in complex networks. We find that spreading is accelerated (an epidemic) or decelerated (a rumor) as a result of superspreaders’ various behavior.
Wearable activity monitors in oncology trials: Current use of an emerging technology.

PubMed

Gresham, Gillian; Schrack, Jennifer; Gresham, Louise M; Shinde, Arvind M; Hendifar, Andrew E; Tuli, Richard; Rimel, B J; Figlin, Robert; Meinert, Curtis L; Piantadosi, Steven

2018-01-01

Physical activity is an important outcome in oncology trials. Physical activity is commonly assessed using self-reported questionnaires, which are limited by recall and response biases. Recent advancements in wearable technology have provided oncologists with new opportunities to obtain real-time, objective physical activity data. The purpose of this review was to describe current uses of wearable activity monitors in oncology trials. We searched Pubmed, Embase, and the Cochrane Central Register of Controlled Trials for oncology trials involving wearable activity monitors published between 2005 and 2016. We extracted details on study design, types of activity monitors used, and purpose for their use. We summarized activity monitor metrics including step counts, sleep and sedentary time, and time spent in moderate-to-vigorous activity. We identified 41 trials of which 26 (63%) involved cancer survivors (post-treatment) and 15 trials (37%) involved patients with active cancer. Most trials (65%) involved breast cancer patients. Wearable activity monitors were commonly used in exercise (54%) or behavioral (29%) trials. Cancer survivors take between 4660 and 11,000 steps/day and those undergoing treatment take 2885 to 8300steps/day. Wearable activity monitors are increasingly being used to obtain objective measures of physical activity in oncology trials. There is potential for their use to expand to evaluate and predict clinical outcomes such as survival, quality of life, and treatment tolerance in future studies. Currently, there remains a lack of standardization in the types of monitors being used and how their data are being collected, analyzed, and interpreted. Recent advancements in wearable activity monitor technology have provided oncologists with new opportunities to monitor their patients' daily activity in real-world settings. The integration of wearable activity monitors into cancer care will help increase our understanding of the associations between physical activity and the prevention and management of the disease, in addition to other important cancer outcomes. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

OpenDA-WFLOW framework for improving hydrologic predictions using distributed hydrologic models

NASA Astrophysics Data System (ADS)

Weerts, Albrecht; Schellekens, Jaap; Kockx, Arno; Hummel, Stef

2017-04-01

Data assimilation (DA) holds considerable potential for improving hydrologic predictions (Liu et al., 2012) and increase the potential for early warning and/or smart water management. However, advances in hydrologic DA research have not yet been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. The objective of this work is to highlight the development of a generic linkage of the open source OpenDA package and the open source community hydrologic modeling framework Openstreams/WFLOW and its application in operational hydrological forecasting on various spatial scales. The coupling between OpenDA and Openstreams/wflow framework is based on the emerging standard Basic Model Interface (BMI) as advocated by CSDMS using cross-platform webservices (i.e. Apache Thrift) developed by Hut et al. (2016). The potential application of the OpenDA-WFLOW for operational hydrologic forecasting including its integration with Delft-FEWS (used by more than 40 operational forecast centers around the world (Werner et al., 2013)) is demonstrated by the presented case studies. We will also highlight the possibility to give real-time insight into the working of the DA methods applied for supporting the forecaster as mentioned as one of the burning issues by Liu et al., (2012).
Real-time video quality monitoring

NASA Astrophysics Data System (ADS)

Liu, Tao; Narvekar, Niranjan; Wang, Beibei; Ding, Ran; Zou, Dekun; Cash, Glenn; Bhagavathy, Sitaram; Bloom, Jeffrey

2011-12-01

The ITU-T Recommendation G.1070 is a standardized opinion model for video telephony applications that uses video bitrate, frame rate, and packet-loss rate to measure the video quality. However, this model was original designed as an offline quality planning tool. It cannot be directly used for quality monitoring since the above three input parameters are not readily available within a network or at the decoder. And there is a great room for the performance improvement of this quality metric. In this article, we present a real-time video quality monitoring solution based on this Recommendation. We first propose a scheme to efficiently estimate the three parameters from video bitstreams, so that it can be used as a real-time video quality monitoring tool. Furthermore, an enhanced algorithm based on the G.1070 model that provides more accurate quality prediction is proposed. Finally, to use this metric in real-world applications, we present an example emerging application of real-time quality measurement to the management of transmitted videos, especially those delivered to mobile devices.
Prediction in a visual language: real-time sentence processing in American Sign Language across development.

PubMed

Lieberman, Amy M; Borovsky, Arielle; Mayberry, Rachel I

2018-01-01

Prediction during sign language comprehension may enable signers to integrate linguistic and non-linguistic information within the visual modality. In two eyetracking experiments, we investigated American Sign language (ASL) semantic prediction in deaf adults and children (aged 4-8 years). Participants viewed ASL sentences in a visual world paradigm in which the sentence-initial verb was either neutral or constrained relative to the sentence-final target noun. Adults and children made anticipatory looks to the target picture before the onset of the target noun in the constrained condition only, showing evidence for semantic prediction. Crucially, signers alternated gaze between the stimulus sign and the target picture only when the sentential object could be predicted from the verb. Signers therefore engage in prediction by optimizing visual attention between divided linguistic and referential signals. These patterns suggest that prediction is a modality-independent process, and theoretical implications are discussed.
Global Systems Science: A New World View

NASA Technical Reports Server (NTRS)

Sneider, Cary; Golden, Richard; Barrett, Katharine

1999-01-01

Global systems science is a new field of study about the interactions between Earth's natural systems and human activities. The people who study global systems science draw on methods and theories of many different fields from chemistry and biology to economics and politics-in order to predict how today's actions are likely to affect the world of tomorrow - our world and our children's world.
Real-time Tsunami Inundation Prediction Using High Performance Computers

NASA Astrophysics Data System (ADS)

Oishi, Y.; Imamura, F.; Sugawara, D.

2014-12-01

Recently off-shore tsunami observation stations based on cabled ocean bottom pressure gauges are actively being deployed especially in Japan. These cabled systems are designed to provide real-time tsunami data before tsunamis reach coastlines for disaster mitigation purposes. To receive real benefits of these observations, real-time analysis techniques to make an effective use of these data are necessary. A representative study was made by Tsushima et al. (2009) that proposed a method to provide instant tsunami source prediction based on achieving tsunami waveform data. As time passes, the prediction is improved by using updated waveform data. After a tsunami source is predicted, tsunami waveforms are synthesized from pre-computed tsunami Green functions of linear long wave equations. Tsushima et al. (2014) updated the method by combining the tsunami waveform inversion with an instant inversion of coseismic crustal deformation and improved the prediction accuracy and speed in the early stages. For disaster mitigation purposes, real-time predictions of tsunami inundation are also important. In this study, we discuss the possibility of real-time tsunami inundation predictions, which require faster-than-real-time tsunami inundation simulation in addition to instant tsunami source analysis. Although the computational amount is large to solve non-linear shallow water equations for inundation predictions, it has become executable through the recent developments of high performance computing technologies. We conducted parallel computations of tsunami inundation and achieved 6.0 TFLOPS by using 19,000 CPU cores. We employed a leap-frog finite difference method with nested staggered grids of which resolution range from 405 m to 5 m. The resolution ratio of each nested domain was 1/3. Total number of grid points were 13 million, and the time step was 0.1 seconds. Tsunami sources of 2011 Tohoku-oki earthquake were tested. The inundation prediction up to 2 hours after the earthquake occurs took about 2 minutes, which would be sufficient for a practical tsunami inundation predictions. In the presentation, the computational performance of our faster-than-real-time tsunami inundation model will be shown, and preferable tsunami wave source analysis for an accurate inundation prediction will also be discussed.
EEG predictors of covert vigilant attention

NASA Astrophysics Data System (ADS)

Martel, Adrien; Dähne, Sven; Blankertz, Benjamin

2014-06-01

Objective. The present study addressed the question whether neurophysiological signals exhibit characteristic modulations preceding a miss in a covert vigilant attention task which mimics a natural environment in which critical stimuli may appear in the periphery of the visual field. Approach. Subjective, behavioural and encephalographic (EEG) data of 12 participants performing a modified Mackworth Clock task were obtained and analysed offline. The stimulus consisted of a pointer performing regular ticks in a clockwise sequence across 42 dots arranged in a circle. Participants were requested to covertly attend to the pointer and press a response button as quickly as possible in the event of a jump, a rare and random event. Main results. Significant increases in response latencies and decreases in the detection rates were found as a function of time-on-task, a characteristic effect of sustained attention tasks known as the vigilance decrement. Subjective sleepiness showed a significant increase over the duration of the experiment. Increased activity in the α-frequency range (8-14 Hz) was observed emerging and gradually accumulating 10 s before a missed target. Additionally, a significant gradual attenuation of the P3 event-related component was found to antecede misses by 5 s. Significance. The results corroborate recent findings that behavioural errors are presaged by specific neurophysiological activity and demonstrate that lapses of attention can be predicted in a covert setting up to 10 s in advance reinforcing the prospective use of brain-computer interface (BCI) technology for the detection of waning vigilance in real-world scenarios. Combining these findings with real-time single-trial analysis from BCI may pave the way for cognitive states monitoring systems able to determine the current, and predict the near-future development of the brain's attentional processes.
Hydrologic Predictions in the Anthropocene: A Research Framework Based on a Co-evolutionary Socio-hydrologic Perspective

NASA Astrophysics Data System (ADS)

Sivapalan, M.; Bloeschl, G.

2012-12-01

The world is facing a water management crisis, in the context of fast rising demand for water due to growth of human populations and changing lifestyles, and depletion of freshwater resources. In many parts of the world, poor distribution of freshwater in relation to demand is already the cause of serious water scarcity, exacerbated by climate change. Cumulatively, these result in increased human appropriation of water resources, significant modification of landscapes, and a strong human imprint on water cycle dynamics from local to global scales. Hydrologic predictions in such a fast changing environment face significant challenges. Traditional models for predictions treat the hydrologic system as a simple input-output system, and propagate variability of external inputs or disturbances through the various hydrologic subsystems, but assuming stationarity. However, in a fast changing world, none of the subsystems can be assumed to be stationary, but as co-evolving parts of a complex system. The role of humans takes on an important role, which can no longer be assumed to independent of the natural system. We need new socio-hydrologic frameworks to observe, monitor, understand and predict the co-evolution of coupled human-natural systems. In this talk, using examples from one or more real-world settings (from Australia and Europe) involving human interactions with hydrologic systems, we will present new theoretical frameworks that should be adopted to advance the emergent new sub-discipline of socio-hydrology. The proposed research agenda is organized under (i) process socio-hydrology, (ii) comparative socio-hydrology, and (iii) historical socio-hydrology.
Hippocampus activation related to 'real-time' processing of visuospatial change.

PubMed

Beudel, M; Leenders, K L; de Jong, B M

2016-12-01

The delay associated with cerebral processing time implies a lack of real-time representation of changes in the observed environment. To bridge this gap for motor actions in a dynamical environment, the brain uses predictions of the most plausible future reality based on previously provided information. To optimise these predictions, adjustments to actual experiences are necessary. This requires a perceptual memory buffer. In our study we gained more insight how the brain treats (real-time) information by comparing cerebral activations related to judging past-, present- and future locations of a moving ball, respectively. Eighteen healthy subjects made these estimations while fMRI data was obtained. All three conditions evoked bilateral dorsal-parietal and premotor activations, while judgment of the location of the ball at the moment of judgment showed increased bilateral posterior hippocampus activation relative to making both future and past judgments at the one-second time-sale. Since the condition of such 'real-time' judgments implied undistracted observation of the ball's actual movements, the associated hippocampal activation is consistent with the concept that the hippocampus participates in a top-down exerted sensory gating mechanism. In this way, it may play a role in novelty (saliency) detection. Copyright © 2016 Elsevier B.V. All rights reserved.
Making Math Real: Effective Qualities of Guest Speaker Presentations and the Impact of Speakers on Student Attitude and Achievement in the Algebra Classroom

ERIC Educational Resources Information Center

McKain, Danielle R.

2012-01-01

The term real world is often used in mathematics education, yet the definition of real-world problems and how to incorporate them in the classroom remains ambiguous. One way real-world connections can be made is through guest speakers. Guest speakers can offer different perspectives and share knowledge about various subject areas, yet the impact…
Scaling of Perceptual Errors Can Predict the Shape of Neural Tuning Curves

NASA Astrophysics Data System (ADS)

Shouval, Harel Z.; Agarwal, Animesh; Gavornik, Jeffrey P.

2013-04-01

Weber’s law, first characterized in the 19th century, states that errors estimating the magnitude of perceptual stimuli scale linearly with stimulus intensity. This linear relationship is found in most sensory modalities, generalizes to temporal interval estimation, and even applies to some abstract variables. Despite its generality and long experimental history, the neural basis of Weber’s law remains unknown. This work presents a simple theory explaining the conditions under which Weber’s law can result from neural variability and predicts that the tuning curves of neural populations which adhere to Weber’s law will have a log-power form with parameters that depend on spike-count statistics. The prevalence of Weber’s law suggests that it might be optimal in some sense. We examine this possibility, using variational calculus, and show that Weber’s law is optimal only when observed real-world variables exhibit power-law statistics with a specific exponent. Our theory explains how physiology gives rise to the behaviorally characterized Weber’s law and may represent a general governing principle relating perception to neural activity.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Jamieson, Kevin; Davis, IV, Warren L.

Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather than simply applying algorithms to benchmark datasets, as is the norm in (passive) machine learning research. To facilitate the development, testing and deployment of active learning for real applications, we have built an open-source software system for large-scale active learning research and experimentation. The system, called NEXT, provides a unique platform for realworld, reproducible active learning research. This paper details the challenges of building themore » system and demonstrates its capabilities with several experiments. The results show how experimentation can help expose strengths and weaknesses of active learning algorithms, in sometimes unexpected and enlightening ways.« less
Full Scenes Produce More Activation than Close-Up Scenes and Scene-Diagnostic Objects in Parahippocampal and Retrosplenial Cortex: An fMRI Study

ERIC Educational Resources Information Center

Henderson, John M.; Larson, Christine L.; Zhu, David C.

2008-01-01

We used fMRI to directly compare activation in two cortical regions previously identified as relevant to real-world scene processing: retrosplenial cortex and a region of posterior parahippocampal cortex functionally defined as the parahippocampal place area (PPA). We compared activation in these regions to full views of scenes from a global…
Leveraging Real-World Evidence in Disease-Management Decision-Making with a Total Cost of Care Estimator.

PubMed

Nguyen, Thanh-Nghia; Trocio, Jeffrey; Kowal, Stacey; Ferrufino, Cheryl P; Munakata, Julie; South, Dell

2016-12-01

Health management is becoming increasingly complex, given a range of care options and the need to balance costs and quality. The ability to measure and understand drivers of costs is critical for healthcare organizations to effectively manage their patient populations. Healthcare decision makers can leverage real-world evidence to explore the value of disease-management interventions in shifting total cost trends. To develop a real-world, evidence-based estimator that examines the impact of disease-management interventions on the total cost of care (TCoC) for a patient population with nonvalvular atrial fibrillation (NVAF). Data were collected from a patient-level real-world evidence data set that uses the IMS PharMetrics Health Plan Claims Database. Pharmacy and medical claims for patients meeting the inclusion or exclusion criteria were combined in longitudinal cohorts with a 180-day preindex and 360-day follow-up period. Descriptive statistics, such as mean and median patient costs and event rates, were derived from a real-world evidence analysis and were used to populate the base-case estimates within the TCoC estimator, an exploratory economic model that was designed to estimate the potential impact of several disease-management activities on the TCoC for a patient population with NVAF. Using Microsoft Excel, the estimator is designed to compare current direct costs of medical care to projected costs by varying assumptions on the impact of disease-management activities and applying the associated changes in cost trends to the affected populations. Disease-management levers are derived from literature-based concepts affecting costs along the NVAF disease continuum. The use of the estimator supports analyses across 4 US geographic regions, age, cost types, and care settings during 1 year. All patients included in the study were continuously enrolled in their health plan (within the IMS PharMetrics Health Plan Claims Database) between July 1, 2010, and June 30, 2012. Patients were included in the final analytic file and were indexed based on (1) the service date of the first claim within the selection window (December 28, 2010-July 11, 2011) with a diagnosis of NVAF, or (2) the service date of the second claim for an NVAF medication of interest during the same selection window. The model estimates the current trends in national benchmark data for a hypothetical health plan with 1 million covered lives. The annual total direct healthcare costs (allowable and patient out-of-pocket costs) of managing patients with NVAF in this hypothetical plan are estimated at $184,981,245 ($25,754 per patient, for 7183 patients). A potential 25% improvement from the base-case disease burden and disease management could translate into TCoC savings from reducing the excess costs related to hypertension (-5.3%) and supporting the use of an appropriate antithrombotic treatment that prevents ischemic stroke (-0.7%) and reduces bleeding events (-0.1%). The use of the TCoC estimator supports population health management by providing real-world evidence benchmark data on NVAF disease burden and by quantifying the potential value of disease-management activities in shifting cost trends.
Leveraging Real-World Evidence in Disease-Management Decision-Making with a Total Cost of Care Estimator

PubMed Central

Nguyen, Thanh-Nghia; Trocio, Jeffrey; Kowal, Stacey; Ferrufino, Cheryl P.; Munakata, Julie; South, Dell

2016-01-01

Background Health management is becoming increasingly complex, given a range of care options and the need to balance costs and quality. The ability to measure and understand drivers of costs is critical for healthcare organizations to effectively manage their patient populations. Healthcare decision makers can leverage real-world evidence to explore the value of disease-management interventions in shifting total cost trends. Objective To develop a real-world, evidence-based estimator that examines the impact of disease-management interventions on the total cost of care (TCoC) for a patient population with nonvalvular atrial fibrillation (NVAF). Methods Data were collected from a patient-level real-world evidence data set that uses the IMS PharMetrics Health Plan Claims Database. Pharmacy and medical claims for patients meeting the inclusion or exclusion criteria were combined in longitudinal cohorts with a 180-day preindex and 360-day follow-up period. Descriptive statistics, such as mean and median patient costs and event rates, were derived from a real-world evidence analysis and were used to populate the base-case estimates within the TCoC estimator, an exploratory economic model that was designed to estimate the potential impact of several disease-management activities on the TCoC for a patient population with NVAF. Using Microsoft Excel, the estimator is designed to compare current direct costs of medical care to projected costs by varying assumptions on the impact of disease-management activities and applying the associated changes in cost trends to the affected populations. Disease-management levers are derived from literature-based concepts affecting costs along the NVAF disease continuum. The use of the estimator supports analyses across 4 US geographic regions, age, cost types, and care settings during 1 year. Results All patients included in the study were continuously enrolled in their health plan (within the IMS PharMetrics Health Plan Claims Database) between July 1, 2010, and June 30, 2012. Patients were included in the final analytic file and were indexed based on (1) the service date of the first claim within the selection window (December 28, 2010-July 11, 2011) with a diagnosis of NVAF, or (2) the service date of the second claim for an NVAF medication of interest during the same selection window. The model estimates the current trends in national benchmark data for a hypothetical health plan with 1 million covered lives. The annual total direct healthcare costs (allowable and patient out-of-pocket costs) of managing patients with NVAF in this hypothetical plan are estimated at $184,981,245 ($25,754 per patient, for 7183 patients). A potential 25% improvement from the base-case disease burden and disease management could translate into TCoC savings from reducing the excess costs related to hypertension (−5.3%) and supporting the use of an appropriate antithrombotic treatment that prevents ischemic stroke (−0.7%) and reduces bleeding events (−0.1%). Conclusions The use of the TCoC estimator supports population health management by providing real-world evidence benchmark data on NVAF disease burden and by quantifying the potential value of disease-management activities in shifting cost trends. PMID:28465775
Real-world and trial-based cost-effectiveness analysis of bevacizumab in HER2-negative metastatic breast cancer patients: a study of the Southeast Netherlands Breast Cancer Consortium.

PubMed

van Kampen, R J W; Ramaekers, B L T; Lobbezoo, D J A; de Boer, M; Dercksen, M W; van den Berkmortel, F; Smilde, T J; van de Wouw, A J; Peters, F P J; van Riel, J M G; Peters, N A J B; Tjan-Heijnen, V C G; Joore, M A

2017-07-01

The aim of our analysis was to assess the real-world cost-effectiveness of bevacizumab in addition to taxane treatment versus taxane monotherapy for HER2-negative metastatic breast cancer compared with the cost-effectiveness based on the efficacy results from a trial. A state transition model was built to estimate costs, life years (LYs) and quality-adjusted life years (QALYs) for both treatments. Two scenarios were examined: a real-world scenario and a trial-based scenario in which transition probabilities were primarily based on a real-world cohort study and the E2100 trial, respectively. In both scenarios, costs and utility parameter estimates were extracted from the real-world cohort study. Moreover, the Dutch health care perspective was adopted. In both the real-world and trial scenarios, bevacizumab-taxane is more expensive (incremental costs of €56,213 and €52,750, respectively) and more effective (incremental QALYs of 0.362 and 0.189, respectively) than taxane monotherapy. In the real-world scenario, bevacizumab-taxane compared to taxane monotherapy led to an incremental cost-effectiveness ratio (ICER) of €155,261 per QALY gained. In the trial scenario, the ICER amounted to €278,711 per QALY gained. According to the Dutch informal threshold, bevacizumab in addition to taxane treatment was not considered cost-effective for HER2-negative metastatic breast cancer both in a real-world and in a trial scenario. Copyright © 2017 Elsevier Ltd. All rights reserved.
Tearing Plastic: A Laboratory Exercise on Fractals and Hyperbolic Geometry

ERIC Educational Resources Information Center

Taylor, Ron; Timberlake, Todd

2007-01-01

In this article we describe a hands-on activity for a liberal arts mathematics course that focuses on the beauty and unity of mathematics. The purpose of the activity is to tie together several topics in the context of a "real-world" situation. These topics include: fractals, non-Euclidean geometry, symmetry, and Platonic solids. This activity…
Problem-Based Educational Environments: A Case Study in e-Commerce and Business Planning

ERIC Educational Resources Information Center

Megalakaki, Olga; Sotiriou, Sofoklis; Savas, Stavros; Manoussakis, Yannis

2012-01-01

Introduction: The purpose of the present study was to explore the educational and cognitive aspects of an innovative approach to Internet use within an interdisciplinary, integrated framework for activities set up to enable students to acquire knowledge informally. These activities had the potential to provide real-world results through a model…
Active Learning in the Atmospheric Science Classroom and beyond through High-Altitude Ballooning

ERIC Educational Resources Information Center

Coleman, Jill S. M.; Mitchell, Melissa

2014-01-01

This article describes the implementation of high-altitude balloon (HAB) research into a variety of undergraduate atmospheric science classes as a means of increasing active student engagement in real-world, problem-solving events. Because high-altitude balloons are capable of reaching heights of 80,000-100,000 ft (24-30 km), they provide a…
Friedrich Froebel's Gifts: Connecting the Spiritual and Aesthetic to the Real World of Play and Learning

ERIC Educational Resources Information Center

Provenzo, Eugene F., Jr.

2009-01-01

Friedrich Froebel, the German educator and founder of the Kindergarten Movement, developed a series of play materials including geometric building blocks and pattern activity blocks designed to teach children about forms and relationships found in nature. Froebel's notions about using activity and play in preschool education complement many…
Geography and Field Work: An Exercise for the Elementary School.

ERIC Educational Resources Information Center

Green-Milberg, Patricia

1999-01-01

Maintains that geography field work is enjoyable for students and provides them with real-world experiences. Describes an activity where students visit a supermarket in order to gather data for a graph and a shopping center to investigate the store layout and shopping traffic. Provides pre-visit preparation guidelines and post-visit activities.…

Design, Development and Delivery of Active Learning Tools in Software Verification & Validation Education

ERIC Educational Resources Information Center

Acharya, Sushil; Manohar, Priyadarshan Anant; Wu, Peter; Maxim, Bruce; Hansen, Mary

2018-01-01

Active learning tools are critical in imparting real world experiences to the students within a classroom environment. This is important because graduates are expected to develop software that meets rigorous quality standards in functional and application domains with little to no training. However, there is a well-recognized need for the…
Using Academia-Industry Partnerships to Enhance Software Verification & Validation Education via Active Learning Tools

ERIC Educational Resources Information Center

Acharya, Sushil; Manohar, Priyadarshan; Wu, Peter; Schilling, Walter

2017-01-01

Imparting real world experiences in a software verification and validation (SV&V) course is often a challenge due to the lack of effective active learning tools. This pedagogical requirement is important because graduates are expected to develop software that meets rigorous quality standards in functional and application domains. Realizing the…
Making a Low Cost Candy Floss Kit Gets Students Excited about Learning Physics

ERIC Educational Resources Information Center

Amir, Nazir; Subramaniam, R.

2009-01-01

An activity to excite kinaesthetically inclined students about learning physics is described in this article. Using only commonly available materials, a low cost candy floss kit is fabricated by students. A number of physics concepts are embedded contextually in the activity so that students get to learn these concepts in a real world setting…
"Infotectives" on the "Infobahn": Designing Internet-Aided Projects for the Social Studies Classroom.

ERIC Educational Resources Information Center

Maskin, Melvin R.

1996-01-01

Describes three social studies projects tackled by students at the Bronx High School of Science: an extracredit activity on doing business in Manhattan, a citizen-participation project on fixing the United States, and a cooperative activity to identify problems common to Tokyo and New York City. Using the Internet to solve real-world problems…
Mathematics & Science in the Real World.

ERIC Educational Resources Information Center

Thorson, Annette, Ed.

2000-01-01

This issue of ENC Focus is organized around the theme of mathematics and science in the real world. It intends to provide teachers with practical resources and suggestions for science and mathematics education. Featured articles include: (1) "Real-World Learning: A Necessity for the Success of Current Reform Efforts" (Robert E. Yager); (2)…
Learning through Real-World Problem Solving: The Power of Integrative Teaching.

ERIC Educational Resources Information Center

Nagel, Nancy G.

This book is based on the idea that curriculum development projects focused on integrated or interdisciplinary teaching within the context of real-world problem solving creates dynamics and meaningful learning experiences for students. The real-world, problem-solving units presented in this book were created by four intern teachers, their mentor…
Science Spots AR: A Platform for Science Learning Games with Augmented Reality

ERIC Educational Resources Information Center

Laine, Teemu H.; Nygren, Eeva; Dirin, Amir; Suk, Hae-Jung

2016-01-01

Lack of motivation and of real-world relevance have been identified as reasons for low interest in science among children. Game-based learning and storytelling are prominent methods for generating intrinsic motivation in learning. Real-world relevance requires connecting abstract scientific concepts with the real world. This can be done by…
Avatars, Virtual Reality Technology, and the U.S. Military: Emerging Policy Issues

DTIC Science & Technology

2008-04-09

called “ Sentient Worldwide Simulation,” which will “mirror” real life and automatically follow real-world events in real time. Some virtual world...cities, with the final goal of creating a fully functioning virtual model of the entire world, which will be known as the Sentient Worldwide Simulation
Students Develop Real-World Web and Pervasive Computing Systems.

ERIC Educational Resources Information Center

Tappert, Charles C.

In the academic year 2001-2002, Pace University (New York) Computer Science and Information Systems (CSIS) students developed real-world Web and pervasive computing systems for actual customers. This paper describes the general use of team projects in CSIS at Pace University, the real-world projects from this academic year, the benefits of…
Curricular Orientations to Real-World Contexts in Mathematics

ERIC Educational Resources Information Center

Smith, Cathy; Morgan, Candia

2016-01-01

A common claim about mathematics education is that it should equip students to use mathematics in the "real world". In this paper, we examine how relationships between mathematics education and the real world are materialised in the curriculum across a sample of eleven jurisdictions. In particular, we address the orientation of the…
Here in the Real World: MTV Meets the Communication Classroom.

ERIC Educational Resources Information Center

Grubbs, Jim

A study investigated how a contemporary, popular media program such as "The Real World" (on MTV) can be used most effectively in the classroom to illustrate the basic concepts of interpersonal, group, and family communication. The 21 individual 22-minute episodes of the second season of "The Real World" (a combination of…
Molding Students into Better Decisionmakers and Managers: An Experiential Learning Exercise.

ERIC Educational Resources Information Center

Babbar, Sunil

1994-01-01

Examples of real-world customer service situations were observed by business students, who then submitted papers and discussed them in class. Their questionnaire responses indicated the value of developing understanding through such an experiential class activity. (SK)
INTEGRATING ECOLOGY AND SOCIOECONOMICS FOR WILDLIFE MANAGEMENT AND CONSERVATION

EPA Science Inventory

Many researchers have studied impacts of human activity on wildlife or human attitudes toward wildlife, but not both simultaneously. Understanding these interactions is critical to better understand the intricacies of real world conservation issues. The goal of my presentation ...
Dosing of Intravenous Tocilizumab in a Real-World Setting of Rheumatoid Arthritis: Analyses from the Corrona Registry.

PubMed

Pappas, Dimitrios A; John, Ani; Curtis, Jeffrey R; Reed, George W; Karki, Chitra; Magner, Robert; Kremer, Joel M; Shewade, Ashwini; Greenberg, Jeffrey D

2016-06-01

In the United States, the recommended starting dose of intravenous tocilizumab (TCZ) is 4 mg/kg every 4 weeks, with an increase to 8 mg/kg based on clinical response for patients with moderate to severe rheumatoid arthritis; however, data on how TCZ dose is escalated in real life are missing. The objective of this analysis was to describe patterns of early intravenous TCZ dose escalation in a real-world setting using data from the Corrona registry. All patients enrolled in the comparative effectiveness substudy (CERTAIN) nested within Corrona who initiated TCZ and completed 3- and 6-month study visits were eligible for inclusion. Patients who initiated TCZ 4 mg/kg were categorized into 1 of 2 groups: those who remained on TCZ 4 mg/kg at 3 months (Group 1) and those who escalated to TCZ 8 mg/kg by or at 3 months (Group 2). Changes in clinical disease activity measures were provided. Of the 213 patients who were eligible for analysis, 86 (40.4%) remained on their initial dose of TCZ 4 mg/kg (Group 1) and 110 (51.6%) were escalated to TCZ 8 mg/kg by or at 3 months (Group 2). Baseline demographic and clinical characteristics were similar between the 2 groups; except in Group 2, patients were older (58.3 vs. 54.0 years) and a lower proportion was female (75.5% vs. 89.4%) than in Group 1. Significant improvements in disease activity measures were observed at 3 and 6 months in both groups, with the majority of patients in both groups achieving moderate or good European League Against Rheumatism response. Real-world data demonstrated that physicians escalate TCZ dose at varying frequencies. The ability to administer TCZ in varying doses allows physicians to tailor TCZ therapy to disease activity. ClinicalTrials.gov identifier, NCT01625650.
The real-world navigator

NASA Technical Reports Server (NTRS)

Balabanovic, Marko; Becker, Craig; Morse, Sarah K.; Nourbakhsh, Illah R.

1994-01-01

The success of every mobile robot application hinges on the ability to navigate robustly in the real world. The problem of robust navigation is separable from the challenges faced by any particular robot application. We offer the Real-World Navigator as a solution architecture that includes a path planner, a map-based localizer, and a motion control loop that combines reactive avoidance modules with deliberate goal-based motion. Our architecture achieves a high degree of reliability by maintaining and reasoning about an explicit description of positional uncertainty. We provide two implementations of real-world robot systems that incorporate the Real-World Navigator. The Vagabond Project culminated in a robot that successfully navigated a portion of the Stanford University campus. The Scimmer project developed successful entries for the AIAA 1993 Robotics Competition, placing first in one of the two contests entered.
Exposure to acute stress enhances decision-making competence: Evidence for the role of DHEA.

PubMed

Shields, Grant S; Lam, Jovian C W; Trainor, Brian C; Yonelinas, Andrew P

2016-05-01

Exposure to acute stress can impact performance on numerous cognitive abilities, but little is known about how acute stress affects real-world decision-making ability. In the present study, we induced acute stress with a standard laboratory task involving uncontrollable socio-evaluative stress and subsequently assessed decision-making ability using the Adult Decision Making Competence index. In addition, we took baseline and post-test saliva samples from participants to examine associations between decision-making competence and adrenal hormones. Participants in the stress induction group showed enhanced decision-making competence, relative to controls. Further, although both cortisol and dehydroepiandrosterone (DHEA) reactivity predicted decision-making competence when considered in isolation, DHEA was a significantly better predictor than cortisol when both hormones were considered simultaneously. Thus, our results show that exposure to acute stress can have beneficial effects on the cognitive ability underpinning real-world decision-making and that this effect relates to DHEA reactivity more than cortisol. Copyright © 2016 Elsevier Ltd. All rights reserved.
Idiosyncratic responding during movie-watching predicted by age differences in attentional control.

PubMed

Campbell, Karen L; Shafto, Meredith A; Wright, Paul; Tsvetanov, Kamen A; Geerligs, Linda; Cusack, Rhodri; Tyler, Lorraine K

2015-11-01

Much is known about how age affects the brain during tightly controlled, though largely contrived, experiments, but do these effects extrapolate to everyday life? Naturalistic stimuli, such as movies, closely mimic the real world and provide a window onto the brain's ability to respond in a timely and measured fashion to complex, everyday events. Young adults respond to these stimuli in a highly synchronized fashion, but it remains to be seen how age affects neural responsiveness during naturalistic viewing. To this end, we scanned a large (N = 218), population-based sample from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) during movie-watching. Intersubject synchronization declined with age, such that older adults' response to the movie was more idiosyncratic. This decreased synchrony related to cognitive measures sensitive to attentional control. Our findings suggest that neural responsivity changes with age, which likely has important implications for real-world event comprehension and memory. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms

PubMed Central

Daniel, Reka; Geana, Andra; Gershman, Samuel J.; Leong, Yuan Chang; Radulescu, Angela; Wilson, Robert C.

2015-01-01

In recent years, ideas from the computational field of reinforcement learning have revolutionized the study of learning in the brain, famously providing new, precise theories of how dopamine affects learning in the basal ganglia. However, reinforcement learning algorithms are notorious for not scaling well to multidimensional environments, as is required for real-world learning. We hypothesized that the brain naturally reduces the dimensionality of real-world problems to only those dimensions that are relevant to predicting reward, and conducted an experiment to assess by what algorithms and with what neural mechanisms this “representation learning” process is realized in humans. Our results suggest that a bilateral attentional control network comprising the intraparietal sulcus, precuneus, and dorsolateral prefrontal cortex is involved in selecting what dimensions are relevant to the task at hand, effectively updating the task representation through trial and error. In this way, cortical attention mechanisms interact with learning in the basal ganglia to solve the “curse of dimensionality” in reinforcement learning. PMID:26019331
Situations in 140 Characters: Assessing Real-World Situations on Twitter

PubMed Central

Serfass, David G.; Sherman, Ryne A.

2015-01-01

Over 20 million Tweets were used to study the psychological characteristics of real-world situations over the course of two weeks. Models for automatically and accurately scoring individual Tweets on the DIAMONDS dimensions of situations were developed. Stable daily and weekly fluctuations in the situations that people experience were identified. Predicted temporal trends were found, providing validation for this new method of situation assessment. On weekdays, Duty peaks in the midmorning and declines steadily thereafter while Sociality peeks in the evening. Negativity is highest during the workweek and lowest on the weekends. pOsitivity shows the opposite pattern. Additionally, gender and locational differences in the situations shared on Twitter are explored. Females share both more emotionally charged (pOsitive and Negative) situations, while no differences were found in the amount of Duty experienced by males and females. Differences in the situations shared from Rural and Urban areas were not found. Future applications of assessing situations using social media are discussed. PMID:26566125
Reinforcement learning in multidimensional environments relies on attention mechanisms.

PubMed

Niv, Yael; Daniel, Reka; Geana, Andra; Gershman, Samuel J; Leong, Yuan Chang; Radulescu, Angela; Wilson, Robert C

2015-05-27

In recent years, ideas from the computational field of reinforcement learning have revolutionized the study of learning in the brain, famously providing new, precise theories of how dopamine affects learning in the basal ganglia. However, reinforcement learning algorithms are notorious for not scaling well to multidimensional environments, as is required for real-world learning. We hypothesized that the brain naturally reduces the dimensionality of real-world problems to only those dimensions that are relevant to predicting reward, and conducted an experiment to assess by what algorithms and with what neural mechanisms this "representation learning" process is realized in humans. Our results suggest that a bilateral attentional control network comprising the intraparietal sulcus, precuneus, and dorsolateral prefrontal cortex is involved in selecting what dimensions are relevant to the task at hand, effectively updating the task representation through trial and error. In this way, cortical attention mechanisms interact with learning in the basal ganglia to solve the "curse of dimensionality" in reinforcement learning. Copyright © 2015 the authors 0270-6474/15/358145-13$15.00/0.

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