What Mobile Learning and Working Remotely Can Learn from Each Other

ERIC Educational Resources Information Center

Depryck, Koen

2014-01-01

To a large extent, developments in the workplace and in (especially formal) education still take place independently from each other, regardless of a strong (market driven) demand to bring both closer to each other. The divide is especially visible when looking at developments towards e-working (telecommuting, …) on the one hand and developments…

Burnout and engagement at work as a function of demands and control.

PubMed

Demerouti, E; Bakker, A B; de Jonge, J; Janssen, P P; Schaufeli, W B

2001-08-01

The present study was designed to test the demand-control model using indicators of both health impairment and active learning or motivation. A total of 381 insurance company employees participated in the study. Discriminant analysis was used to examine the relationship between job demands and job control on one hand and health impairment and active learning on the other. The amount of demands and control could be predicted on the basis of employees' perceived health impairment (exhaustion and health complaints) and active learning (engagement and commitment). Each of the four combinations of demand and control differentially affected the perception of strain or active learning. Job demands were the most clearly related to health impairment, whereas job control was the most clearly associated with active learning. These findings partly contradict the demand-control model, especially with respect to the validity of the interaction between demand and control. Job demands and job control seem to initiate two essentially independent processes, and this occurrence is consistent with the recently proposed job demands-resources model.

One-way coupling of an integrated assessment model and a water resources model: evaluation and implications of future changes over the US Midwest

NASA Astrophysics Data System (ADS)

Voisin, N.; Liu, L.; Hejazi, M.; Tesfa, T.; Li, H.; Huang, M.; Liu, Y.; Leung, L. R.

2013-11-01

An integrated model is being developed to advance our understanding of the interactions between human activities, terrestrial system and water cycle, and to evaluate how system interactions will be affected by a changing climate at the regional scale. As a first step towards that goal, a global integrated assessment model, which includes a water-demand model driven by socioeconomics at regional and global scales, is coupled in a one-way fashion with a land surface hydrology-routing-water resources management model. To reconcile the scale differences between the models, a spatial and temporal disaggregation approach is developed to downscale the annual regional water demand simulations into a daily time step and subbasin representation. The model demonstrates reasonable ability to represent the historical flow regulation and water supply over the US Midwest (Missouri, Upper Mississippi, and Ohio river basins). Implications for future flow regulation, water supply, and supply deficit are investigated using climate change projections with the B1 and A2 emission scenarios, which affect both natural flow and water demand. Although natural flow is projected to increase under climate change in both the B1 and A2 scenarios, there is larger uncertainty in the changes of the regulated flow. Over the Ohio and Upper Mississippi river basins, changes in flow regulation are driven by the change in natural flow due to the limited storage capacity. However, both changes in flow and demand have effects on the Missouri River Basin summer regulated flow. Changes in demand are driven by socioeconomic factors, energy and food demands, global markets and prices with rainfed crop demand handled directly by the land surface modeling component. Even though most of the changes in supply deficit (unmet demand) and the actual supply (met demand) are driven primarily by the change in natural flow over the entire region, the integrated framework shows that supply deficit over the Missouri River Basin sees an increasing sensitivity to changes in demand in future periods. It further shows that the supply deficit is six times as sensitive as the actual supply to changes in flow and demand. A spatial analysis of the supply deficit demonstrates vulnerabilities of urban areas located along mainstream with limited storage.

Demand-driven care and hospital choice. Dutch health policy toward demand-driven care: results from a survey into hospital choice.

PubMed

Lako, Christiaan J; Rosenau, Pauline

2009-03-01

In the Netherlands, current policy opinion emphasizes demand-driven health care. Central to this model is the view, advocated by some Dutch health policy makers, that patients should be encouraged to be aware of and make use of health quality and health outcomes information in making personal health care provider choices. The success of the new health care system in the Netherlands is premised on this being the case. After a literature review and description of the new Dutch health care system, the adequacy of this demand-driven health policy is tested. The data from a July 2005, self-administered questionnaire survey of 409 patients (response rate of 94%) as to how they choose a hospital are presented. Results indicate that most patients did not choose by actively employing available quality and outcome information. They were, rather, referred by their general practitioner. Hospital choice is highly related to the importance a patient attaches to his or her physician's opinion about a hospital. Some patients indicated that their hospital choice was affected by the reputation of the hospital, by the distance they lived from the hospital, etc. but physician's advice was, by far, the most important factor. Policy consequences are important; the assumptions underlying the demand-driven model of patient health provider choice are inadequate to explain the pattern of observed responses. An alternative, more adequate model is required, one that takes into account the patient's confidence in physician referral and advice.

Learning and strain among newcomers: a three-wave study on the effects of job demands and job control.

PubMed

Taris, Toon W; Feij, Jan A

2004-11-01

The present 3-wave longitudinal study was an examination of job-related learning and strain as a function of job demand and job control. The participants were 311 newcomers to their jobs. On the basis of R. A. Karasek and T. Theorell's (1990) demand-control model, the authors predicted that high demand and high job control would lead to high levels of learning; low demand and low job control should lead to low levels of learning; high demand and low job control should lead to high levels of strain; and low demand and high job control should lead to low levels of strain. The relation between strain and learning was also examined. The authors tested the hypotheses using ANCOVA and structural equation modeling. The results revealed that high levels of strain have an adverse effect on learning; the reverse effect was not confirmed. It appears that Karasek and Theorell's model is very relevant when examining work socialization processes.

How Dynamics of Learning Are Linked to Innovation Support Services: Insights from a Smallholder Commercialization Project in Kenya

ERIC Educational Resources Information Center

Kilelu, Catherine W.; Klerkx, Laurens; Leeuwis, Cees

2014-01-01

Purpose: The important role of learning is noted in the literature on demand-driven approaches to supporting agricultural innovation. Most of this literature has focused on macrolevel structural perspectives on the organization of pluralistic innovation support systems. This has provided little insight at the micro-level on the dynamics of demand…

The Origin of Mathematics and Number Sense in the Cerebellum: with Implications for Finger Counting and Dyscalculia.

PubMed

Vandervert, Larry

2017-01-01

Mathematicians and scientists have struggled to adequately describe the ultimate foundations of mathematics. Nobel laureates Albert Einstein and Eugene Wigner were perplexed by this issue, with Wigner concluding that the workability of mathematics in the real world is a mystery we cannot explain. In response to this classic enigma, the major purpose of this article is to provide a theoretical model of the ultimate origin of mathematics and "number sense" (as defined by S. Dehaene) that is proposed to involve the learning of inverse dynamics models through the collaboration of the cerebellum and the cerebral cortex (but prominently cerebellum-driven). This model is based upon (1) the modern definition of mathematics as the "science of patterns," (2) cerebellar sequence (pattern) detection, and (3) findings that the manipulation of numbers is automated in the cerebellum. This cerebro-cerebellar approach does not necessarily conflict with mathematics or number sense models that focus on brain functions associated with especially the intraparietal sulcus region of the cerebral cortex. A direct corollary purpose of this article is to offer a cerebellar inner speech explanation for difficulty in developing "number sense" in developmental dyscalculia. It is argued that during infancy the cerebellum learns (1) a first tier of internal models for a primitive physics that constitutes the foundations of visual-spatial working memory, and (2) a second (and more abstract) tier of internal models based on (1) that learns "number" and relationships among dimensions across the primitive physics of the first tier. Within this context it is further argued that difficulty in the early development of the second tier of abstraction (and "number sense") is based on the more demanding attentional requirements imposed on cerebellar inner speech executive control during the learning of cerebellar inverse dynamics models. Finally, it is argued that finger counting improves (does not originate) "number sense" by extending focus of attention in executive control of silent cerebellar inner speech. It is suggested that (1) the origin of mathematics has historically been an enigma only because it is learned below the level of conscious awareness in cerebellar internal models, (2) understandings of the development of "number sense" and developmental dyscalculia can be advanced by first understanding the ultimate foundations of number and mathematics do not simply originate in the cerebral cortex, but rather in cerebro-cerebellar collaboration (predominately driven by the cerebellum). It is concluded that difficulty with "number sense" results from the extended demands on executive control in learning inverse dynamics models associated with cerebellar inner speech related to the second tier of abstraction (numbers) of the infant's primitive physics.

Data-driven modeling, control and tools for cyber-physical energy systems

NASA Astrophysics Data System (ADS)

Behl, Madhur

Energy systems are experiencing a gradual but substantial change in moving away from being non-interactive and manually-controlled systems to utilizing tight integration of both cyber (computation, communications, and control) and physical representations guided by first principles based models, at all scales and levels. Furthermore, peak power reduction programs like demand response (DR) are becoming increasingly important as the volatility on the grid continues to increase due to regulation, integration of renewables and extreme weather conditions. In order to shield themselves from the risk of price volatility, end-user electricity consumers must monitor electricity prices and be flexible in the ways they choose to use electricity. This requires the use of control-oriented predictive models of an energy system's dynamics and energy consumption. Such models are needed for understanding and improving the overall energy efficiency and operating costs. However, learning dynamical models using grey/white box approaches is very cost and time prohibitive since it often requires significant financial investments in retrofitting the system with several sensors and hiring domain experts for building the model. We present the use of data-driven methods for making model capture easy and efficient for cyber-physical energy systems. We develop Model-IQ, a methodology for analysis of uncertainty propagation for building inverse modeling and controls. Given a grey-box model structure and real input data from a temporary set of sensors, Model-IQ evaluates the effect of the uncertainty propagation from sensor data to model accuracy and to closed-loop control performance. We also developed a statistical method to quantify the bias in the sensor measurement and to determine near optimal sensor placement and density for accurate data collection for model training and control. Using a real building test-bed, we show how performing an uncertainty analysis can reveal trends about inverse model accuracy and control performance, which can be used to make informed decisions about sensor requirements and data accuracy. We also present DR-Advisor, a data-driven demand response recommender system for the building's facilities manager which provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. We develop a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based demand response methods for a large DoE commercial reference building and leads to a significant amount of load curtailment (of 380kW) and over $45,000 in savings which is 37.9% of the summer energy bill for the building. The performance of DR-Advisor is also evaluated for 8 buildings on Penn's campus; where it achieves 92.8% to 98.9% prediction accuracy. We also compare DR-Advisor with other data driven methods and rank 2nd on ASHRAE's benchmarking data-set for energy prediction.

Data-driven behavioural modelling of residential water consumption to inform water demand management strategies

NASA Astrophysics Data System (ADS)

Giuliani, Matteo; Cominola, Andrea; Alshaf, Ahmad; Castelletti, Andrea; Anda, Martin

2016-04-01

The continuous expansion of urban areas worldwide is expected to highly increase residential water demand over the next few years, ultimately challenging the distribution and supply of drinking water. Several studies have recently demonstrated that actions focused only on the water supply side of the problem (e.g., augmenting existing water supply infrastructure) will likely fail to meet future demands, thus calling for the concurrent deployment of effective water demand management strategies (WDMS) to pursue water savings and conservation. However, to be effective WDMS do require a substantial understanding of water consumers' behaviors and consumption patterns at different spatial and temporal resolutions. Retrieving information on users' behaviors, as well as their explanatory and/or causal factors, is key to spot potential areas for targeting water saving efforts and to design user-tailored WDMS, such as education campaigns and personalized recommendations. In this work, we contribute a data-driven approach to identify household water users' consumption behavioural profiles and model their water use habits. State-of-the-art clustering methods are coupled with big data machine learning techniques with the aim of extracting dominant behaviors from a set of water consumption data collected at the household scale. This allows identifying heterogeneous groups of consumers from the studied sample and characterizing them with respect to several consumption features. Our approach is validated onto a real-world household water consumption dataset associated with a variety of demographic and psychographic user data and household attributes, collected in nine towns of the Pilbara and Kimberley Regions of Western Australia. Results show the effectiveness of the proposed method in capturing the influence of candidate determinants on residential water consumption profiles and in attaining sufficiently accurate predictions of users' consumption behaviors, ultimately providing valuable information to water utilities and managers.

Rule-Based Categorization Deficits in Focal Basal Ganglia Lesion and Parkinson’s Disease Patients

PubMed Central

Ell, Shawn W.; Weinstein, Andrea; Ivry, Richard B.

2010-01-01

Patients with basal ganglia (BG) pathology are consistently found to be impaired on rule-based category learning tasks in which learning is thought to depend upon the use of an explicit, hypothesis-guided strategy. The factors that influence this impairment remain unclear. Moreover, it remains unknown if the impairments observed in patients with degenerative disorders such as Parkinson's disease (PD) are also observed in those with focal BG lesions. In the present study, we tested patients with either focal BG lesions or PD on two categorization tasks that varied in terms of their demands on selective attention and working memory. Individuals with focal BG lesions were impaired on the task in which working-memory demand was high and performed similarly to healthy controls on the task in which selective-attention demand was high. In contrast, individuals with PD were impaired on both tasks, and accuracy rates did not differ between on- and off-medication states for a subset of patients who were also tested after abstaining from dopaminergic medication. Quantitative, model-based analyses attributed the performance deficit for both groups in the task with high working-memory demand to the utilization of suboptimal strategies, whereas the PD-specific impairment on the task with high selective-attention demand was driven by the inconsistent use of an optimal strategy. These data suggest that the demands on selective attention and working memory affect the presence of impairment in patients with focal BG lesions and the nature of the impairment in patients with PD. PMID:20600196

Understanding well-being and learning of Nigerian nurses: a job demand control support model approach.

PubMed

van Doorn, Yvonne; van Ruysseveldt, Joris; van Dam, Karen; Mistiaen, Wilhelm; Nikolova, Irina

2016-10-01

This study investigated whether Nigerian nurses' emotional exhaustion and active learning were predicted by job demands, control and social support. Limited research has been conducted concerning nurses' work stress in developing countries, such as Nigeria. Accordingly, it is not clear whether work interventions for improving nurses' well-being in these countries can be based on work stress models that are developed in Western countries, such as the job demand control support model, as well as on empirical findings of job demand control support research. Nurses from Nurses Across the Borders Nigeria were invited to complete an online questionnaire containing validated scales; 210 questionnaires were fully completed and analysed. Multiple regression analysis was used to test the hypotheses. Emotional exhaustion was higher for nurses who experienced high demands and low supervisor support. Active learning occurred when nurses worked under conditions of high control and high supervisor support. The findings suggest that the job demand control support model is applicable in a Nigerian nursing situation; the model indicates which occupational stressors contribute to poor well-being in Nigerian nurses and which work characteristics may boost nurses' active learning. Job (re)design interventions can enhance nurses' well-being and learning by guarding nurses' job demands, and stimulating job control and supervisor support. © 2016 John Wiley & Sons Ltd.

Educational Leadership for E-Learning in the Healthcare Workplace

ERIC Educational Resources Information Center

Fahlman, Dorothy

2012-01-01

Effective educational leadership can make a difference in the resolution of complex issues that impact today's demand-driven educational marketplace. The ongoing professional and skill development needs of human health resources may be best managed through distributed strategic leadership blended with servant leadership. Together these two…

The application of domain-driven design in NMS

NASA Astrophysics Data System (ADS)

Zhang, Jinsong; Chen, Yan; Qin, Shengjun

2011-12-01

In the traditional design approach of data-model-driven, system analysis and design phases are often separated which makes the demand information can not be expressed explicitly. The method is also easy to lead developer to the process-oriented programming, making codes between the modules or between hierarchies disordered. So it is hard to meet requirement of system scalability. The paper proposes a software hiberarchy based on rich domain model according to domain-driven design named FHRDM, then the Webwork + Spring + Hibernate (WSH) framework is determined. Domain-driven design aims to construct a domain model which not only meets the demand of the field where the software exists but also meets the need of software development. In this way, problems in Navigational Maritime System (NMS) development like big system business volumes, difficulty of requirement elicitation, high development costs and long development cycle can be resolved successfully.

The NTeQ ISD Model: A Tech-Driven Model for Digital Natives (DNs)

ERIC Educational Resources Information Center

Williams, C.; Anekwe, J. U.

2017-01-01

Integrating Technology for enquiry (NTeQ) instructional development model (ISD), is believed to be a technology-driven model. The authors x-rayed the ten-step model to reaffirm the ICT knowledge demand of the learner and the educator; hence computer-based activities at various stages of the model are core elements. The model also is conscious of…

Retail Consulting Class: Experiential Learning Platform to Develop Future Retail Talents

ERIC Educational Resources Information Center

Oh, Hyunjoo; Polidan, Mary

2018-01-01

The retail industry is undergoing a significant transformation. Factors such as technological advancement and evolving consumer demands have forced companies to rethink their traditional approaches to retail. Retailers have since embraced data-driven strategies with real-time implementation to stay relevant in this complex, ever-changing industry.…

Towards a Job Demands-Resources Health Model: Empirical Testing with Generalizable Indicators of Job Demands, Job Resources, and Comprehensive Health Outcomes.

PubMed

Brauchli, Rebecca; Jenny, Gregor J; Füllemann, Désirée; Bauer, Georg F

2015-01-01

Studies using the Job Demands-Resources (JD-R) model commonly have a heterogeneous focus concerning the variables they investigate-selective job demands and resources as well as burnout and work engagement. The present study applies the rationale of the JD-R model to expand the relevant outcomes of job demands and job resources by linking the JD-R model to the logic of a generic health development framework predicting more broadly positive and negative health. The resulting JD-R health model was operationalized and tested with a generalizable set of job characteristics and positive and negative health outcomes among a heterogeneous sample of 2,159 employees. Applying a theory-driven and a data-driven approach, measures which were generally relevant for all employees were selected. Results from structural equation modeling indicated that the model fitted the data. Multiple group analyses indicated invariance across six organizations, gender, job positions, and three times of measurement. Initial evidence was found for the validity of an expanded JD-R health model. Thereby this study contributes to the current research on job characteristics and health by combining the core idea of the JD-R model with the broader concepts of salutogenic and pathogenic health development processes as well as both positive and negative health outcomes.

Limitations of demand- and pressure-driven modeling for large deficient networks

NASA Astrophysics Data System (ADS)

Braun, Mathias; Piller, Olivier; Deuerlein, Jochen; Mortazavi, Iraj

2017-10-01

The calculation of hydraulic state variables for a network is an important task in managing the distribution of potable water. Over the years the mathematical modeling process has been improved by numerous researchers for utilization in new computer applications and the more realistic modeling of water distribution networks. But, in spite of these continuous advances, there are still a number of physical phenomena that may not be tackled correctly by current models. This paper will take a closer look at the two modeling paradigms given by demand- and pressure-driven modeling. The basic equations are introduced and parallels are drawn with the optimization formulations from electrical engineering. These formulations guarantee the existence and uniqueness of the solution. One of the central questions of the French and German research project ResiWater is the investigation of the network resilience in the case of extreme events or disasters. Under such extraordinary conditions where models are pushed beyond their limits, we talk about deficient network models. Examples of deficient networks are given by highly regulated flow, leakage or pipe bursts and cases where pressure falls below the vapor pressure of water. These examples will be presented and analyzed on the solvability and physical correctness of the solution with respect to demand- and pressure-driven models.

High performance cellular level agent-based simulation with FLAME for the GPU.

PubMed

Richmond, Paul; Walker, Dawn; Coakley, Simon; Romano, Daniela

2010-05-01

Driven by the availability of experimental data and ability to simulate a biological scale which is of immediate interest, the cellular scale is fast emerging as an ideal candidate for middle-out modelling. As with 'bottom-up' simulation approaches, cellular level simulations demand a high degree of computational power, which in large-scale simulations can only be achieved through parallel computing. The flexible large-scale agent modelling environment (FLAME) is a template driven framework for agent-based modelling (ABM) on parallel architectures ideally suited to the simulation of cellular systems. It is available for both high performance computing clusters (www.flame.ac.uk) and GPU hardware (www.flamegpu.com) and uses a formal specification technique that acts as a universal modelling format. This not only creates an abstraction from the underlying hardware architectures, but avoids the steep learning curve associated with programming them. In benchmarking tests and simulations of advanced cellular systems, FLAME GPU has reported massive improvement in performance over more traditional ABM frameworks. This allows the time spent in the development and testing stages of modelling to be drastically reduced and creates the possibility of real-time visualisation for simple visual face-validation.

Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments

ERIC Educational Resources Information Center

Kinnebrew, John S.; Segedy, James R.; Biswas, Gautam

2017-01-01

Research in computer-based learning environments has long recognized the vital role of adaptivity in promoting effective, individualized learning among students. Adaptive scaffolding capabilities are particularly important in open-ended learning environments, which provide students with opportunities for solving authentic and complex problems, and…

Befriending the Two-Headed Monster: Personal, Social and Emotional Development in Schools in Challenging Times

ERIC Educational Resources Information Center

Harris, Belinda

2008-01-01

Schools in the UK and beyond continue to experience the damaging effects of "top down," "one size fits all" "outcome-based" educational reforms. Educators struggle to meet the dual demands of a punishing performativity- and accountability-driven regime alongside the personal, social, emotional and learning needs of…

Chantey Castings: A Hands-On Simulation to Teach Constraint Management and Demand-Driven Supply Chain Approaches

ERIC Educational Resources Information Center

Grandzol, Christian J.; Grandzol, John R.

2018-01-01

Supply chain design and constraint management are widely-adopted techniques in industry, necessitating that operations and supply chain educators teach these topics in ways that enhance student learning and retention, optimize resource utilization (especially time), and maximize student interest. The Chantey Castings Simulation provides a platform…

State Education Data Systems That Increase Learning and Improve Accountability. Policy Issues. Number 16

ERIC Educational Resources Information Center

Palaich, Robert M.; Griffin Good, Dixie; van der Ploeg, Arie

2004-01-01

Driven by growing accountability pressures, states and districts have invested in a variety of computerized systems for data storage, analysis, and reporting. As accountability policies demand access to more transparent and accurate data about every aspect of the education process, developing linkages among historically disparate systems is…

Active machine learning-driven experimentation to determine compound effects on protein patterns.

PubMed

Naik, Armaghan W; Kangas, Joshua D; Sullivan, Devin P; Murphy, Robert F

2016-02-03

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance.

Machine Learning

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

Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networksmore » and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.« less

Can an Opportunity to Learn at Work Reduce Stress?: A Revisitation of the Job Demand-Control Model

ERIC Educational Resources Information Center

Panari, Chiara; Guglielmi, Dina; Simbula, Silvia; Depolo, Marco

2010-01-01

Purpose: This paper aims to extend the stress-buffering hypothesis of the demand-control model. In addition to the control variable, it seeks to analyse the role of an opportunity for learning and development (L&D) in the workplace as a moderator variable between increased demands and need for recovery. Design/methodology/approach: A…

Designing Cognitively Diagnostic Assessment for Algebraic Content Knowledge and Thinking Skills

ERIC Educational Resources Information Center

Zhang, Zhidong

2018-01-01

This study explored a diagnostic assessment method that emphasized the cognitive process of algebra learning. The study utilized a design and a theory-driven model to examine the content knowledge. Using the theory driven model, the thinking skills of algebra learning was also examined. A Bayesian network model was applied to represent the theory…

Application-Driven Educational Game to Assist Young Children in Learning English Vocabulary

ERIC Educational Resources Information Center

Chen, Zhi-Hong; Lee, Shu-Yu

2018-01-01

This paper describes the development of an educational game, named My-Pet-Shop, to enhance young children's learning of English vocabulary. The educational game is underpinned by an application-driven model, which consists of three components: application scenario, subject learning, and learning regulation. An empirical study is further conducted…

Photosynthetic capacity regulation is uncoupled from nutrient limitation

NASA Astrophysics Data System (ADS)

Smith, N. G.; Keenan, T. F.; Prentice, I. C.; Wang, H.

2017-12-01

Ecosystem and Earth system models need information on leaf-level photosynthetic capacity, but to date typically rely on empirical estimates and an assumed dependence on nitrogen supply. Recent evidence suggests that leaf nitrogen is actively controlled though plant responses to photosynthetic demand. Here, we propose and test a theory of demand-driven coordination of photosynthetic processes, and use it to assess the relative roles of nutrient supply and photosynthetic demand. The theory captured 63% of observed variability in a global dataset of Rubisco carboxylation capacity (Vcmax; 3,939 values at 219 sites), suggesting that environmentally regulated biophysical costs and light availability are the first-order drivers of photosynthetic capacity. Leaf nitrogen, on the other hand, was a weak secondary driver of Vcmax, explaining less than 6% of additional observed variability. We conclude that leaf nutrient allocation is primarily driven by demand. Our theory offers a simple, robust strategy for dynamically predicting leaf-level photosynthetic capacity in global models.

Educational Modelling Language: Modelling Reusable, Interoperable, Rich and Personalised Units of Learning

ERIC Educational Resources Information Center

Koper, Rob; Manderveld, Jocelyn

2004-01-01

Nowadays there is a huge demand for flexible, independent learning without the constraints of time and place. Various trends in the field of education and training are the bases for the development of new technologies for education. This article describes the development of a learning technology specification, which supports these new demands for…

Towards a Job Demands-Resources Health Model: Empirical Testing with Generalizable Indicators of Job Demands, Job Resources, and Comprehensive Health Outcomes

PubMed Central

Brauchli, Rebecca; Jenny, Gregor J.; Füllemann, Désirée; Bauer, Georg F.

2015-01-01

Studies using the Job Demands-Resources (JD-R) model commonly have a heterogeneous focus concerning the variables they investigate—selective job demands and resources as well as burnout and work engagement. The present study applies the rationale of the JD-R model to expand the relevant outcomes of job demands and job resources by linking the JD-R model to the logic of a generic health development framework predicting more broadly positive and negative health. The resulting JD-R health model was operationalized and tested with a generalizable set of job characteristics and positive and negative health outcomes among a heterogeneous sample of 2,159 employees. Applying a theory-driven and a data-driven approach, measures which were generally relevant for all employees were selected. Results from structural equation modeling indicated that the model fitted the data. Multiple group analyses indicated invariance across six organizations, gender, job positions, and three times of measurement. Initial evidence was found for the validity of an expanded JD-R health model. Thereby this study contributes to the current research on job characteristics and health by combining the core idea of the JD-R model with the broader concepts of salutogenic and pathogenic health development processes as well as both positive and negative health outcomes. PMID:26557718

Model-Driven Design: Systematically Building Integrated Blended Learning Experiences

ERIC Educational Resources Information Center

Laster, Stephen

2010-01-01

Developing and delivering curricula that are integrated and that use blended learning techniques requires a highly orchestrated design. While institutions have demonstrated the ability to design complex curricula on an ad-hoc basis, these projects are generally successful at a great human and capital cost. Model-driven design provides a…

Flexible explicit but rigid implicit learning in a visuomotor adaptation task

PubMed Central

Bond, Krista M.

2015-01-01

There is mounting evidence for the idea that performance in a visuomotor rotation task can be supported by both implicit and explicit forms of learning. The implicit component of learning has been well characterized in previous experiments and is thought to arise from the adaptation of an internal model driven by sensorimotor prediction errors. However, the role of explicit learning is less clear, and previous investigations aimed at characterizing the explicit component have relied on indirect measures such as dual-task manipulations, posttests, and descriptive computational models. To address this problem, we developed a new method for directly assaying explicit learning by having participants verbally report their intended aiming direction on each trial. While our previous research employing this method has demonstrated the possibility of measuring explicit learning over the course of training, it was only tested over a limited scope of manipulations common to visuomotor rotation tasks. In the present study, we sought to better characterize explicit and implicit learning over a wider range of task conditions. We tested how explicit and implicit learning change as a function of the specific visual landmarks used to probe explicit learning, the number of training targets, and the size of the rotation. We found that explicit learning was remarkably flexible, responding appropriately to task demands. In contrast, implicit learning was strikingly rigid, with each task condition producing a similar degree of implicit learning. These results suggest that explicit learning is a fundamental component of motor learning and has been overlooked or conflated in previous visuomotor tasks. PMID:25855690

Foresee: A user-centric home energy management system for energy efficiency and demand response

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

Jin, Xin; Baker, Kyri A.; Christensen, Dane T.

This paper presents foresee, a user-centric home energy management system that can help optimize how a home operates to concurrently meet users' needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee is built on a multiobjective model predictive control framework, wherein the objectives consist of energy cost, thermal comfort, user convenience, and carbon emission. Foresee learns user preferences on different objectives and acts on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage. In this work, machine-learning algorithms were used to derive data-driven appliancemore » models and usage patterns to predict the home's future energy consumption. This approach enables highly accurate predictions of comfort needs, energy costs, environmental impacts, and grid service availability. Simulation studies were performed on field data from a residential building stock data set collected in the Pacific Northwest. Results indicated that foresee generated up to 7.6% whole-home energy savings without requiring substantial behavioral changes. When responding to demand response events, foresee was able to provide load forecasts upon receipt of event notifications and delivered the committed demand response services with 10% or fewer errors. Foresee fully utilized the potential of the battery storage and controllable building loads and delivered up to 7.0-kW load reduction and 13.5-kW load increase. As a result, these benefits are provided while maintaining the occupants' thermal comfort or convenience in using their appliances.« less

Foresee: A user-centric home energy management system for energy efficiency and demand response

DOE PAGES

Jin, Xin; Baker, Kyri A.; Christensen, Dane T.; ...

2017-08-23

This paper presents foresee, a user-centric home energy management system that can help optimize how a home operates to concurrently meet users' needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee is built on a multiobjective model predictive control framework, wherein the objectives consist of energy cost, thermal comfort, user convenience, and carbon emission. Foresee learns user preferences on different objectives and acts on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage. In this work, machine-learning algorithms were used to derive data-driven appliancemore » models and usage patterns to predict the home's future energy consumption. This approach enables highly accurate predictions of comfort needs, energy costs, environmental impacts, and grid service availability. Simulation studies were performed on field data from a residential building stock data set collected in the Pacific Northwest. Results indicated that foresee generated up to 7.6% whole-home energy savings without requiring substantial behavioral changes. When responding to demand response events, foresee was able to provide load forecasts upon receipt of event notifications and delivered the committed demand response services with 10% or fewer errors. Foresee fully utilized the potential of the battery storage and controllable building loads and delivered up to 7.0-kW load reduction and 13.5-kW load increase. As a result, these benefits are provided while maintaining the occupants' thermal comfort or convenience in using their appliances.« less

At the Altar of Educational Efficiency: Performativity and the Role of the Teacher

ERIC Educational Resources Information Center

Hennessy, Jennifer; McNamara, Patricia Mannix

2013-01-01

This paper critiques the impact of neo-liberalism on postprimary education, and in particular on the teaching of English. The paper explores the implications of performativity and exam-driven schooling on the teaching and learning of poetry. The authors argue that meeting the demands of an education system dominated by technicism and…

Data-Driven School Improvement: Linking Data and Learning. Technology, Education--Connections (TEC) Series

ERIC Educational Resources Information Center

Mandinach, Ellen B., Ed.; Honey, Margaret, Ed.

2008-01-01

With federal and local demands for increased accountability, educators at all levels are now expected to acquire the necessary skills and knowledge to be effective data users and decision makers. This book brings together stakeholders representing a variety of perspectives to explore how educators actually use data and technology tools to achieve…

Examining Learning Styles and Perceived Benefits of Analogical Problem Construction on SQL Knowledge Acquisition

ERIC Educational Resources Information Center

Mills, Robert J.; Dupin-Bryant, Pamela A.; Johnson, John D.; Beaulieu, Tanya Y.

2015-01-01

The demand for Information Systems (IS) graduates with expertise in Structured Query Language (SQL) and database management is vast and projected to increase as "big data" becomes ubiquitous. To prepare students to solve complex problems in a data-driven world, educators must explore instructional strategies to help link prior knowledge…

Reducing cognitive load in the chemistry laboratory by using technology-driven guided inquiry experiments

NASA Astrophysics Data System (ADS)

Hubacz, Frank, Jr.

The chemistry laboratory is an integral component of the learning experience for students enrolled in college-level general chemistry courses. Science education research has shown that guided inquiry investigations provide students with an optimum learning environment within the laboratory. These investigations reflect the basic tenets of constructivism by engaging students in a learning environment that allows them to experience what they learn and to then construct, in their own minds, a meaningful understanding of the ideas and concepts investigated. However, educational research also indicates that the physical plant of the laboratory environment combined with the procedural requirements of the investigation itself often produces a great demand upon a student's working memory. This demand, which is often superfluous to the chemical concept under investigation, creates a sensory overload or extraneous cognitive load within the working memory and becomes a significant obstacle to student learning. Extraneous cognitive load inhibits necessary schema formation within the learner's working memory thereby impeding the transfer of ideas to the learner's long-term memory. Cognitive Load Theory suggests that instructional material developed to reduce extraneous cognitive load leads to an improved learning environment for the student which better allows for schema formation. This study first compared the cognitive load demand, as measured by mental effort, experienced by 33 participants enrolled in a first-year general chemistry course in which the treatment group, using technology based investigations, and the non-treatment group, using traditional labware, investigated identical chemical concepts on five different exercises. Mental effort was measured via a mental effort survey, a statistical comparison of individual survey results to a procedural step count, and an analysis of fourteen post-treatment interviews. Next, a statistical analysis of achievement was completed by comparing lab grade averages, final exam averages, and final course grade averages between the two groups. Participant mental effort survey results showed significant positive effects of technology in reducing cognitive load for two laboratory investigations. One investigation revealed a significant difference in achievement measured by lab grade average comparisons. Although results of this study are inconclusive as to the usefulness of technology-driven investigations to affect learning, recommendations for further study are discussed.

Geoscience Meets Social Science: A Flexible Data Driven Approach for Developing High Resolution Population Datasets at Global Scale

NASA Astrophysics Data System (ADS)

Rose, A.; McKee, J.; Weber, E.; Bhaduri, B. L.

2017-12-01

Leveraging decades of expertise in population modeling, and in response to growing demand for higher resolution population data, Oak Ridge National Laboratory is now generating LandScan HD at global scale. LandScan HD is conceived as a 90m resolution population distribution where modeling is tailored to the unique geography and data conditions of individual countries or regions by combining social, cultural, physiographic, and other information with novel geocomputation methods. Similarities among these areas are exploited in order to leverage existing training data and machine learning algorithms to rapidly scale development. Drawing on ORNL's unique set of capabilities, LandScan HD adapts highly mature population modeling methods developed for LandScan Global and LandScan USA, settlement mapping research and production in high-performance computing (HPC) environments, land use and neighborhood mapping through image segmentation, and facility-specific population density models. Adopting a flexible methodology to accommodate different geographic areas, LandScan HD accounts for the availability, completeness, and level of detail of relevant ancillary data. Beyond core population and mapped settlement inputs, these factors determine the model complexity for an area, requiring that for any given area, a data-driven model could support either a simple top-down approach, a more detailed bottom-up approach, or a hybrid approach.

Pedagogy and Japanese Culture in a Distance Learning Environment

ERIC Educational Resources Information Center

Anderson, Bodi O.

2012-01-01

Current theoretical models of distance learning are driven by two impetuses: a technical CMC element, and a pedagogical foundation rooted strongly in the Western world, and driven by social constructivism. By and large these models have been exported throughout the world as-is. However, previous research has hinted at potential problems with these…

Active machine learning-driven experimentation to determine compound effects on protein patterns

PubMed Central

Naik, Armaghan W; Kangas, Joshua D; Sullivan, Devin P; Murphy, Robert F

2016-01-01

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance. DOI: http://dx.doi.org/10.7554/eLife.10047.001 PMID:26840049

Beyond Performativity: A Pragmatic Model of Teacher Professional Learning

ERIC Educational Resources Information Center

Lloyd, Margaret; Davis, James P.

2018-01-01

The intent and content of teacher professional learning has changed in recent times to meet the demands of performativity. In this article, we offer and demonstrate a pragmatic way to map teacher professional learning that both meets current demands and secures a place for teacher-led catalytic learning. To achieve this, we position identified…

Enrichment Clusters: A Practical Plan for Real-World, Student-Driven Learning.

ERIC Educational Resources Information Center

Renzulli, Joseph S.; Gentry, Marcia; Reis, Sally M.

This guidebook provides a rationale and guidelines for implementing a student-driven learning approach using enrichment clusters. Enrichment clusters allow students who share a common interest to meet each week to produce a product, performance, or targeted service based on that common interest. Chapter 1 discusses different models of learning.…

How Leaders Can Support Teachers with Data-Driven Decision Making: A Framework for Understanding Capacity Building

ERIC Educational Resources Information Center

Marsh, Julie A.; Farrell, Caitlin C.

2015-01-01

As accountability systems have increased demands for evidence of student learning, the use of data in education has become more prevalent in many countries. Although school and administrative leaders are recognizing the need to provide support to teachers on how to interpret and respond to data, there is little theoretically sound research on…

Technical Communication--The Need and the Demand of Global World

ERIC Educational Resources Information Center

Patel, Dipika S.

2013-01-01

The present world is known as Hi-tech world as it is driven by technology. It is the vehicle to get access with this modernized world. However, due to continuous changes taking place in the field of technology, people keep looking for new developments for improving the quality of teaching and learning methodologies. In the fast developing 21st…

Advanced Networks in Dental Rich Online MEDiA (ANDROMEDA)

NASA Astrophysics Data System (ADS)

Elson, Bruce; Reynolds, Patricia; Amini, Ardavan; Burke, Ezra; Chapman, Craig

There is growing demand for dental education and training not only in terms of knowledge but also skills. This demand is driven by continuing professional development requirements in the more developed economies, personnel shortages and skills differences across the European Union (EU) accession states and more generally in the developing world. There is an excellent opportunity for the EU to meet this demand by developing an innovative online flexible learning platform (FLP). Current clinical online systems are restricted to the delivery of general, knowledge-based training with no easy method of personalization or delivery of skill-based training. The PHANTOM project, headed by Kings College London is developing haptic-based virtual reality training systems for clinical dental training. ANDROMEDA seeks to build on this and establish a Flexible Learning Platform that can integrate the haptic and sensor based training with rich media knowledge transfer, whilst using sophisticated technologies such as including service-orientated architecture (SOA), Semantic Web technologies, knowledge-based engineering, business intelligence (BI) and virtual worlds for personalization.

Development of a Pilot Learning Module on Water Energy Nexus Using a Data-Analytic and Hypothesis-Driven Approach

NASA Astrophysics Data System (ADS)

Eldardiry, H. A.; Unruh, H. G., Sr.; Habib, E. H.; Tidwell, V. C.

2016-12-01

Recent community initiatives have identified key foundational knowledge gaps that need to be addressed before transformative solutions can be made in the area of Food, Energy and Water (FEW) nexus. In addition, knowledge gaps also exist in the area of FEW education and needs to be addressed before we can make an impact on building the next generation FEW workforces. This study reports on the development of a pilot learning-module that focuses on two elements of the FEW nexus, Energy and Water. The module follows an active-learning approach to develop a set of student-centered learning activities using FEW datasets situated in real-world settings in the contiguous US. The module is based on data-driven learning exercises that incorporate different geospatial layers and manipulate datasets at a watershed scale representing the eight-digit Hydrologic Unit Code (HUC8). Examples of such datasets include water usage by different demand sectors (available from the US Geological Survey, USGS), and power plants stratified by energy source, cooling technology, and plant capacity (available from the US Energy Information Administration, EIA). The module is structured in three sections: (1) introduction to the water and energy systems, (2) quantifying stresses on water system at a catchment scale, and (3) scenario-based analysis on the interdependencies in the water-energy systems. Following a data-analytic framework, the module guides students to make different assumptions about water use growth rates and see how these new water demands will impinge on freshwater supplies. The module engages students in analysis that examines how thermoelectric water use would depend on assumptions about future demand for electricity, power plant fuel source, cooling type, and carbon sequestration. Students vary the input parameters, observe and assess the effect on water use, and address gaps via non-potable water resources (e.g., municipal wastewater). The module is implemented using a web-based platform where datasets, lesson contents, and student learning activities are presented within a geo-spatial context. The presentation will share insight on how the dynamics of FEW systems can be taught using meaningful educational experiences that promote students' understanding of FEW systems and their complex inter-connections.

Threat driven modeling framework using petri nets for e-learning system.

PubMed

Khamparia, Aditya; Pandey, Babita

2016-01-01

Vulnerabilities at various levels are main cause of security risks in e-learning system. This paper presents a modified threat driven modeling framework, to identify the threats after risk assessment which requires mitigation and how to mitigate those threats. To model those threat mitigations aspects oriented stochastic petri nets are used. This paper included security metrics based on vulnerabilities present in e-learning system. The Common Vulnerability Scoring System designed to provide a normalized method for rating vulnerabilities which will be used as basis in metric definitions and calculations. A case study has been also proposed which shows the need and feasibility of using aspect oriented stochastic petri net models for threat modeling which improves reliability, consistency and robustness of the e-learning system.

Data-driven methods towards learning the highly nonlinear inverse kinematics of tendon-driven surgical manipulators.

PubMed

Xu, Wenjun; Chen, Jie; Lau, Henry Y K; Ren, Hongliang

2017-09-01

Accurate motion control of flexible surgical manipulators is crucial in tissue manipulation tasks. The tendon-driven serpentine manipulator (TSM) is one of the most widely adopted flexible mechanisms in minimally invasive surgery because of its enhanced maneuverability in torturous environments. TSM, however, exhibits high nonlinearities and conventional analytical kinematics model is insufficient to achieve high accuracy. To account for the system nonlinearities, we applied a data driven approach to encode the system inverse kinematics. Three regression methods: extreme learning machine (ELM), Gaussian mixture regression (GMR) and K-nearest neighbors regression (KNNR) were implemented to learn a nonlinear mapping from the robot 3D position states to the control inputs. The performance of the three algorithms was evaluated both in simulation and physical trajectory tracking experiments. KNNR performed the best in the tracking experiments, with the lowest RMSE of 2.1275 mm. The proposed inverse kinematics learning methods provide an alternative and efficient way to accurately model the tendon driven flexible manipulator. Copyright © 2016 John Wiley & Sons, Ltd.

Time to Loosen the Apron Strings: Cohort-based Evaluation of a Learner-driven Remediation Model at One Medical School.

PubMed

Bierer, S Beth; Dannefer, Elaine F; Tetzlaff, John E

2015-09-01

Remediation in the era of competency-based assessment demands a model that empowers students to improve performance. To examine a remediation model where students, rather than faculty, develop remedial plans to improve performance. Private medical school, 177 medical students. A promotion committee uses student-generated portfolios and faculty referrals to identify struggling students, and has them develop formal remediation plans with personal reflections, improvement strategies, and performance evidence. Students submit reports to document progress until formally released from remediation by the promotion committee. Participants included 177 students from six classes (2009-2014). Twenty-six were placed in remediation, with more referrals occurring during Years 1 or 2 (n = 20, 76 %). Unprofessional behavior represented the most common reason for referral in Years 3-5. Remedial students did not differ from classmates (n = 151) on baseline characteristics (Age, Gender, US citizenship, MCAT) or willingness to recommend their medical school to future students (p < 0.05). Two remedial students did not graduate and three did not pass USLME licensure exams on first attempt. Most remedial students (92 %) generated appropriate plans to address performance deficits. Students can successfully design remedial interventions. This learner-driven remediation model promotes greater autonomy and reinforces self-regulated learning.

The active learning hypothesis of the job-demand-control model: an experimental examination.

PubMed

Häusser, Jan Alexander; Schulz-Hardt, Stefan; Mojzisch, Andreas

2014-01-01

The active learning hypothesis of the job-demand-control model [Karasek, R. A. 1979. "Job Demands, Job Decision Latitude, and Mental Strain: Implications for Job Redesign." Administration Science Quarterly 24: 285-307] proposes positive effects of high job demands and high job control on performance. We conducted a 2 (demands: high vs. low) × 2 (control: high vs. low) experimental office workplace simulation to examine this hypothesis. Since performance during a work simulation is confounded by the boundaries of the demands and control manipulations (e.g. time limits), we used a post-test, in which participants continued working at their task, but without any manipulation of demands and control. This post-test allowed for examining active learning (transfer) effects in an unconfounded fashion. Our results revealed that high demands had a positive effect on quantitative performance, without affecting task accuracy. In contrast, high control resulted in a speed-accuracy tradeoff, that is participants in the high control conditions worked slower but with greater accuracy than participants in the low control conditions.

Tallying the Costs of Post-Secondary Education: The Challenge of Managing Student Debt and Loan Repayment in Canada. Challenges in Canadian Post-Secondary Education

ERIC Educational Resources Information Center

Canadian Council on Learning, 2010

2010-01-01

As the global marketplace becomes increasingly competitive and knowledge-driven the potential social and economic benefits of education have increased. As a result, the past few decades have witnessed an unprecedented expansion in the demand for post-secondary education (PSE) worldwide. The Canadian Council on Learning monograph series,…

Learning Our Way into Communication: The Making of the Communication and Information Strategy "with" the National Agricultural Advisory Services Programme in Uganda

ERIC Educational Resources Information Center

Ramirez, Ricardo

2005-01-01

This paper reports on the making of the Communication and Information Strategy with the National Agricultural Advisory Services Programme (NAADS) in Uganda. The NAADS is a new organization in government responsible for the implementation of a demand-driven agricultural extension approach. The new extension approach calls for fundamental changes in…

(Un)certainty in climate change impacts on global energy consumption

NASA Astrophysics Data System (ADS)

van Ruijven, B. J.; De Cian, E.; Sue Wing, I.

2017-12-01

Climate change is expected to have an influence on the energy sector, especially on energy demand. For many locations, this change in energy demand is a balance between increase of demand for space cooling and a decrease of space heating demand. We perform a large-scale uncertainty analysis to characterize climate change risk on energy consumption as driven by climate and socioeconomic uncertainty. We combine a dynamic econometric model1 with multiple realizations of temperature projections from all 21 CMIP5 models (from the NASA Earth Exchange Global Daily Downscaled Projections2) under moderate (RCP4.5) and vigorous (RCP8.5) warming. Global spatial population projections for five SSPs are combined with GDP projections to construct scenarios for future energy demand driven by socioeconomic change. Between the climate models, we find a median global increase in climate-related energy demand of around 24% by 2050 under RCP8.5 with an interquartile range of 18-38%. Most climate models agree on increases in energy demand of more than 25% or 50% in tropical regions, the Southern USA and Southern China (see Figure). With respect to socioeconomic scenarios, we find wide variations between the SSPs for the number of people in low-income countries who are exposed to increases in energy demand. Figure attached: Number of models that agree on total climate-related energy consumption to increase or decrease by more than 0, 10, 25 or 50% by 2050 under RCP8.5 and SSP5 as result of the CMIP5 ensemble of temperature projections. References1. De Cian, E. & Sue Wing, I. Global Energy Demand in a Warming Climate. (FEEM, 2016). 2. Thrasher, B., Maurer, E. P., McKellar, C. & Duffy, P. B. Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol Earth Syst Sci 16, 3309-3314 (2012).

Supervised dictionary learning for inferring concurrent brain networks.

PubMed

Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

2015-10-01

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

A Model for the Transfer of Perceptual-Motor Skill Learning in Human Behaviors

ERIC Educational Resources Information Center

Rosalie, Simon M.; Muller, Sean

2012-01-01

This paper presents a preliminary model that outlines the mechanisms underlying the transfer of perceptual-motor skill learning in sport and everyday tasks. Perceptual-motor behavior is motivated by performance demands and evolves over time to increase the probability of success through adaptation. Performance demands at the time of an event…

Planning, Enactment, and Reflection in Inquiry-Based Learning: Validating the McGill Strategic Demands of Inquiry Questionnaire

ERIC Educational Resources Information Center

Shore, Bruce M.; Chichekian, Tanya; Syer, Cassidy A.; Aulls, Mark W.; Frederiksen, Carl H.

2012-01-01

Tools are needed to track the elements of students' successful engagement in inquiry. The "McGill Strategic Demands of Inquiry Questionnaire" (MSDIQ) is a 79-item, criterion-referenced, learner-focused questionnaire anchored in Schon's model and related models of self-regulated learning. The MSDIQ addresses three phases of inquiry…

Investigating Users' Requirements

PubMed Central

Walker, Deborah S.; Lee, Wen-Yu; Skov, Neil M.; Berger, Carl F.; Athley, Brian D.

2002-01-01

Objective: User data and information about anatomy education were used to guide development of a learning environment that is efficient and effective. The research question focused on how to design instructional software suitable for the educational goals of different groups of users of the Visible Human data set. The ultimate goal of the study was to provide options for students and teachers to use different anatomy learning modules corresponding to key topics, for course work and professional training. Design: The research used both qualitative and quantitative methods. It was driven by the belief that good instructional design must address learning context information and pedagogic content information. The data collection emphasized measurement of users' perspectives, experience, and demands in anatomy learning. Measurement: Users' requirements elicited from 12 focus groups were combined and rated by 11 researchers. Collective data were sorted and analyzed by use of multidimensional scaling and cluster analysis. Results: A set of functions and features in high demand across all groups of users was suggested by the results. However, several subgroups of users shared distinct demands. The design of the learning modules will encompass both unified core components and user-specific applications. The design templates will allow sufficient flexibility for dynamic insertion of different learning applications for different users. Conclusion: This study describes how users' requirements, associated with users' learning experiences, were systematically collected and analyzed and then transformed into guidelines informing the iterative design of multiple learning modules. Information about learning challenges and processes was gathered to define essential anatomy teaching strategies. A prototype instrument to design and polish the Visible Human user interface system is currently being developed using ideas and feedback from users. PMID:12087112

Socioeconomic Forecasting : [Technical Summary

DOT National Transportation Integrated Search

2012-01-01

Because the traffic forecasts produced by the Indiana : Statewide Travel Demand Model (ISTDM) are driven by : the demographic and socioeconomic inputs to the model, : particular attention must be given to obtaining the most : accurate demographic and...

Potential environmental benefits from woodfuel transitions in Haiti: Geospatial scenarios to 2027

NASA Astrophysics Data System (ADS)

Ghilardi, Adrian; Tarter, Andrew; Bailis, Robert

2018-03-01

Woodfuels constitute nearly 80% of Haiti’s primary energy supply. Forests are severely degraded and the nation has long been considered an archetypal case of woodfuel-driven deforestation. However, there is little empirical evidence that woodfuel demand directly contributes to deforestation, but may contribute to degradation. We use MoFuSS (Modeling Fuelwood Sustainability Scenarios), a dynamic landscape model, to assess whether current woodfuel demand is as impactful as it is often depicted by simulating changes in land cover that would result if current demand continues unabated. We also simulate several near-term interventions focused on woodfuel demand reduction to analyze the land cover impacts of different energy trajectories. We find that current demand may contribute to moderate levels of degradation, but it is not as severe as is typically portrayed. Under a business-as-usual scenario, the simulated regenerative capacity of woody biomass is insufficient to meet Haiti’s increasing demand for wood energy and, as a result, between 2017 and 2027 stocks of above-ground (woody) biomass could decline by 4% ± 1%. This is an annual loss of 302 ± 29 kton of wood and would emit 555 ± 54 kton CO2 yr-1. Aggressive interventions to reduce woodfuel demand could slow or even reverse woodfuel-driven degradation, allowing woody biomass to recover in some regions. We discuss the policy implications and propose steps to reduce uncertainty and validate the model.

Bayesian modeling of flexible cognitive control

PubMed Central

Jiang, Jiefeng; Heller, Katherine; Egner, Tobias

2014-01-01

“Cognitive control” describes endogenous guidance of behavior in situations where routine stimulus-response associations are suboptimal for achieving a desired goal. The computational and neural mechanisms underlying this capacity remain poorly understood. We examine recent advances stemming from the application of a Bayesian learner perspective that provides optimal prediction for control processes. In reviewing the application of Bayesian models to cognitive control, we note that an important limitation in current models is a lack of a plausible mechanism for the flexible adjustment of control over conflict levels changing at varying temporal scales. We then show that flexible cognitive control can be achieved by a Bayesian model with a volatility-driven learning mechanism that modulates dynamically the relative dependence on recent and remote experiences in its prediction of future control demand. We conclude that the emergent Bayesian perspective on computational mechanisms of cognitive control holds considerable promise, especially if future studies can identify neural substrates of the variables encoded by these models, and determine the nature (Bayesian or otherwise) of their neural implementation. PMID:24929218

Influencing Work-Related Learning: The Role of Job Characteristics and Self-Directed Learning Orientation in Part-Time Vocational Education

ERIC Educational Resources Information Center

Gijbels, David; Raemdonck, Isabel; Vervecken, Dries

2010-01-01

Based on the Demand-Control-Support (DCS) model, the present paper aims to investigate the influence of job characteristics such as job demands, job control, social support at work and self-directed learning orientation on the work-related learning behaviour of workers. The present study was conducted in a centre for part-time vocational education…

Letter from America: UK and US state-funded dental provision.

PubMed

Currie, R B; Pretty, I A; Tickle, M; Maupomé, G

2012-12-01

Current UK and US economic conditions have re-focussed attention on the need to deliver dental care with limited finance and resources. This raises hard questions determining which services will be offered and what they should achieve to satisfy public demands and needs. We consider impending dental health reforms in the US and UK within the context of contemporary experiences to identify issues and delivery goals for the two nations. The paper provides a brief history and background of the development of social dental care models in the UK and US, highlighting some differences in state-funded delivery of dental care. SHIFTING DEMAND: From the 1950s, demand for dental treatment has increased and acquired a more complex composition growing from predominantly surgical and restorative treatment to encompass preventive care and cosmetic services. PRIORITISING CARE ACCORDING TO NEED: Despite improvements in general health and technology, inequalities in access and utilisation of dental care are still experienced, primarily by groups with low socio-economic status. DELIVERY: BALANCING RESOURCES, DEMAND AND NEED: In developing and delivering reform agendas, much can be learned from previous policy interventions. Pressures of cost, coverage, and capacity, besides demand versus need must be carefully considered and balanced to deliver quality service and value for users and taxpayers. Ethical and moral consideration should be given to making services needs-driven to address high treatment requirements rather than the high care demands of the worried well. This challenge brings the additional political pressure of convincing many of the voters (and subsequent complainers) that their demands may be less important than the needs of others.

Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality

NASA Astrophysics Data System (ADS)

Bradford, S. A.; Liang, J.; Li, W.; Murata, T.; Simunek, J.

2017-12-01

Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models, which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with physically-based models, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. In this presentation we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport (the HYDRUS-1D overland flow module). A large number of numerical simulations were carried out to develop a database containing information about the impact of various input parameters (weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices) on runoff water quantity and quality outputs. This database was used to train data-driven models. Three different methods (Neural Networks, Support Vector Machines, and Recurrence Neural Networks) were explored to prepare input- output functional relations. Results demonstrate the ability and limitations of machine learning and deep learning models to predict runoff water quantity and quality.

Acting to gain information

NASA Technical Reports Server (NTRS)

Rosenchein, Stanley J.; Burns, J. Brian; Chapman, David; Kaelbling, Leslie P.; Kahn, Philip; Nishihara, H. Keith; Turk, Matthew

1993-01-01

This report is concerned with agents that act to gain information. In previous work, we developed agent models combining qualitative modeling with real-time control. That work, however, focused primarily on actions that affect physical states of the environment. The current study extends that work by explicitly considering problems of active information-gathering and by exploring specialized aspects of information-gathering in computational perception, learning, and language. In our theoretical investigations, we analyzed agents into their perceptual and action components and identified these with elements of a state-machine model of control. The mathematical properties of each was developed in isolation and interactions were then studied. We considered the complexity dimension and the uncertainty dimension and related these to intelligent-agent design issues. We also explored active information gathering in visual processing. Working within the active vision paradigm, we developed a concept of 'minimal meaningful measurements' suitable for demand-driven vision. We then developed and tested an architecture for ongoing recognition and interpretation of visual information. In the area of information gathering through learning, we explored techniques for coping with combinatorial complexity. We also explored information gathering through explicit linguistic action by considering the nature of conversational rules, coordination, and situated communication behavior.

From Learning Object to Learning Cell: A Resource Organization Model for Ubiquitous Learning

ERIC Educational Resources Information Center

Yu, Shengquan; Yang, Xianmin; Cheng, Gang

2013-01-01

The key to implementing ubiquitous learning is the construction and organization of learning resources. While current research on ubiquitous learning has primarily focused on concept models, supportive environments and small-scale empirical research, exploring ways to organize learning resources to make them available anywhere on-demand is also…

Using articulation and inscription as catalysts for reflection: Design principles for reflective inquiry

NASA Astrophysics Data System (ADS)

Loh, Ben Tun-Bin

2003-07-01

The demand for students to engage in complex student-driven and information-rich inquiry investigations poses challenges to existing learning environments. Students are not familiar with this style of work, and lack the skills, tools, and expectations it demands, often forging blindly forward in the investigation. If students are to be successful, they need to learn to be reflective inquirers, periodically stepping back from an investigation to evaluate their work. The fundamental goal of my dissertation is to understand how to design learning environments to promote and support reflective inquiry. I have three basic research questions: how to define this mode of work, how to help students learn it, and understanding how it facilitates reflection when enacted in a classroom. I take an exploratory approach in which, through iterative cycles of design, development, and reflection, I develop principles of design for reflective inquiry, instantiate those principles in the design of a software environment, and test that software in the context of classroom work. My work contributes to the understanding of reflective inquiry in three ways: First, I define a task model that describes the kinds of operations (cognitive tasks) that students should engage in as reflective inquirers. These operations are defined in terms of two basic tasks: articulation and inscription, which serve as catalysts for externalizing student thinking as objects of and triggers for reflection. Second, I instantiate the task model in the design of software tools (the Progress Portfolio). And, through proof of concept pilot studies, I examine how the task model and tools helped students with their investigative classroom work. Finally, I take a step back from these implementations and articulate general design principles for reflective inquiry with the goal of informing the design of other reflective inquiry learning environments. There are three design principles: (1) Provide a designated work space for reflection activities to focus student attention on reflection. (2) Help students create and use artifacts that represent their work and their thinking as a means to create referents for reflection. (3) Support and take advantage of social processes that help students reflect on their own work.

Stochastic Online Learning in Dynamic Networks under Unknown Models

DTIC Science & Technology

2016-08-02

Repeated Game with Incomplete Information, IEEE International Conference on Acoustics, Speech, and Signal Processing. 20-MAR-16, Shanghai, China...in a game theoretic framework for the application of multi-seller dynamic pricing with unknown demand models. We formulated the problem as an...infinitely repeated game with incomplete information and developed a dynamic pricing strategy referred to as Competitive and Cooperative Demand Learning

An Experience Sampling Study of Learning, Affect, and the Demands Control Support Model

ERIC Educational Resources Information Center

Daniels, Kevin; Boocock, Grahame; Glover, Jane; Hartley, Ruth; Holland, Julie

2009-01-01

The demands control support model (R. A. Karasek & T. Theorell, 1990) indicates that job control and social support enable workers to engage in problem solving. In turn, problem solving is thought to influence learning and well-being (e.g., anxious affect, activated pleasant affect). Two samples (N = 78, N = 106) provided data up to 4 times per…

Evaluating data distribution and drift vulnerabilities of machine learning algorithms in secure and adversarial environments

NASA Astrophysics Data System (ADS)

Nelson, Kevin; Corbin, George; Blowers, Misty

2014-05-01

Machine learning is continuing to gain popularity due to its ability to solve problems that are difficult to model using conventional computer programming logic. Much of the current and past work has focused on algorithm development, data processing, and optimization. Lately, a subset of research has emerged which explores issues related to security. This research is gaining traction as systems employing these methods are being applied to both secure and adversarial environments. One of machine learning's biggest benefits, its data-driven versus logic-driven approach, is also a weakness if the data on which the models rely are corrupted. Adversaries could maliciously influence systems which address drift and data distribution changes using re-training and online learning. Our work is focused on exploring the resilience of various machine learning algorithms to these data-driven attacks. In this paper, we present our initial findings using Monte Carlo simulations, and statistical analysis, to explore the maximal achievable shift to a classification model, as well as the required amount of control over the data.

The dynamic model on the impact of biodiesel blend mandate (B5) on Malaysian palm oil domestic demand: A preliminary finding

NASA Astrophysics Data System (ADS)

Abidin, Norhaslinda Zainal; Applanaidu, Shri-Dewi; Sapiri, Hasimah

2014-12-01

Over the last ten years, world biofuels production has increased dramatically. The biodiesel demand is driven by the increases in fossil fuel prices, government policy mandates, income from gross domestic product and population growth. In the European Union, biofuel consumption is mostly driven by blending mandates in both France and Germany. In the case of Malaysia, biodiesel has started to be exported since 2006. The B5 of 5% blend of palm oil based biodiesel into diesel in all government vehicles was implemented in February 2009 and it is expected to be implemented nationwide in the nearest time. How will the blend mandate will project growth in the domestic demand of palm oil in Malaysia? To analyze this issue, a system dynamics model was constructed to evaluate the impact of blend mandate implementation on the palm oil domestic demand influence. The base run of simulation analysis indicates that the trend of domestic demand will increase until 2030 in parallel with the implementation of 5 percent of biodiesel mandate. Finally, this study depicts that system dynamics is a useful tool to gain insight and to experiment with the impact of changes in blend mandate implementation on the future growth of Malaysian palm oil domestic demand sector.

Parasitoidism, not sociality, is associated with the evolution of elaborate mushroom bodies in the brains of hymenopteran insects.

PubMed

Farris, Sarah M; Schulmeister, Susanne

2011-03-22

The social brain hypothesis posits that the cognitive demands of social behaviour have driven evolutionary expansions in brain size in some vertebrate lineages. In insects, higher brain centres called mushroom bodies are enlarged and morphologically elaborate (having doubled, invaginated and subcompartmentalized calyces that receive visual input) in social species such as the ants, bees and wasps of the aculeate Hymenoptera, suggesting that the social brain hypothesis may also apply to invertebrate animals. In a quantitative and qualitative survey of mushroom body morphology across the Hymenoptera, we demonstrate that large, elaborate mushroom bodies arose concurrent with the acquisition of a parasitoid mode of life at the base of the Euhymenopteran (Orussioidea + Apocrita) lineage, approximately 90 Myr before the evolution of sociality in the Aculeata. Thus, sociality could not have driven mushroom body elaboration in the Hymenoptera. Rather, we propose that the cognitive demands of host-finding behaviour in parasitoids, particularly the capacity for associative and spatial learning, drove the acquisition of this evolutionarily novel mushroom body architecture. These neurobehavioural modifications may have served as pre-adaptations for central place foraging, a spatial learning-intensive behaviour that is widespread across the Aculeata and may have contributed to the multiple acquisitions of sociality in this taxon.

Neural priming in human frontal cortex: multiple forms of learning reduce demands on the prefrontal executive system.

PubMed

Race, Elizabeth A; Shanker, Shanti; Wagner, Anthony D

2009-09-01

Past experience is hypothesized to reduce computational demands in PFC by providing bottom-up predictive information that informs subsequent stimulus-action mapping. The present fMRI study measured cortical activity reductions ("neural priming"/"repetition suppression") during repeated stimulus classification to investigate the mechanisms through which learning from the past decreases demands on the prefrontal executive system. Manipulation of learning at three levels of representation-stimulus, decision, and response-revealed dissociable neural priming effects in distinct frontotemporal regions, supporting a multiprocess model of neural priming. Critically, three distinct patterns of neural priming were identified in lateral frontal cortex, indicating that frontal computational demands are reduced by three forms of learning: (a) cortical tuning of stimulus-specific representations, (b) retrieval of learned stimulus-decision mappings, and (c) retrieval of learned stimulus-response mappings. The topographic distribution of these neural priming effects suggests a rostrocaudal organization of executive function in lateral frontal cortex.

Data-Driven H∞ Control for Nonlinear Distributed Parameter Systems.

PubMed

Luo, Biao; Huang, Tingwen; Wu, Huai-Ning; Yang, Xiong

2015-11-01

The data-driven H∞ control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H∞ control policy from real system data rather than the mathematical model. First, Karhunen-Loève decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H∞ control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.

Elucidating the electron transport in semiconductors via Monte Carlo simulations: an inquiry-driven learning path for engineering undergraduates

NASA Astrophysics Data System (ADS)

Persano Adorno, Dominique; Pizzolato, Nicola; Fazio, Claudio

2015-09-01

Within the context of higher education for science or engineering undergraduates, we present an inquiry-driven learning path aimed at developing a more meaningful conceptual understanding of the electron dynamics in semiconductors in the presence of applied electric fields. The electron transport in a nondegenerate n-type indium phosphide bulk semiconductor is modelled using a multivalley Monte Carlo approach. The main characteristics of the electron dynamics are explored under different values of the driving electric field, lattice temperature and impurity density. Simulation results are presented by following a question-driven path of exploration, starting from the validation of the model and moving up to reasoned inquiries about the observed characteristics of electron dynamics. Our inquiry-driven learning path, based on numerical simulations, represents a viable example of how to integrate a traditional lecture-based teaching approach with effective learning strategies, providing science or engineering undergraduates with practical opportunities to enhance their comprehension of the physics governing the electron dynamics in semiconductors. Finally, we present a general discussion about the advantages and disadvantages of using an inquiry-based teaching approach within a learning environment based on semiconductor simulations.

Sensitivity to value-driven attention is predicted by how we learn from value.

PubMed

Jahfari, Sara; Theeuwes, Jan

2017-04-01

Reward learning is known to influence the automatic capture of attention. This study examined how the rate of learning, after high- or low-value reward outcomes, can influence future transfers into value-driven attentional capture. Participants performed an instrumental learning task that was directly followed by an attentional capture task. A hierarchical Bayesian reinforcement model was used to infer individual differences in learning from high or low reward. Results showed a strong relationship between high-reward learning rates (or the weight that is put on learning after a high reward) and the magnitude of attentional capture with high-reward colors. Individual differences in learning from high or low rewards were further related to performance differences when high- or low-value distractors were present. These findings provide novel insight into the development of value-driven attentional capture by showing how information updating after desired or undesired outcomes can influence future deployments of automatic attention.

Drivers of Change in Managed Water Resources: Modeling the Impacts of Climate and Socioeconomic Changes Using the US Midwest as a Case Study

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

Voisin, Nathalie; Leung, Lai-Yung R.; Hejazi, Mohamad I.

A global integrated assessment model including a water-demand model driven by socio-economics, is coupled in a one-way fashion with a land surface hydrology – routing – water resources management model. The integrated modeling framework is applied to the U.S. Upper Midwest (Missouri, Upper Mississippi, and Ohio) to advance understanding of the regional impacts of climate and socio-economic changes on integrated water resources. Implications for future flow regulation, water supply, and supply deficit are investigated using climate change projections with the B1 and A2 emission scenarios, which affect both natural flow and water demand. Changes in water demand are driven bymore » socio-economic factors, energy and food demands, global markets and prices. The framework identifies the multiple spatial scales of interactions between the drivers of changes (natural flow and water demand) and the managed water resources (regulated flow, supply and supply deficit). The contribution of the different drivers of change are quantified regionally, and also evaluated locally, using covariances. The integrated framework shows that water supply deficit is more predictable over the Missouri than the other regions in the Midwest. The predictability of the supply deficit mostly comes from long term changes in water demand although changes in runoff has a greater contribution, comparable to the contribution of changes in demand, over shorter time periods. The integrated framework also shows that spatially, water demand drives local supply deficit. Using elasticity, the sensitivity of supply deficit to drivers of change is established. The supply deficit is found to be more sensitive to changes in runoff than to changes in demand regionally. It contrasts with the covariance analysis that shows that water demand is the dominant driver of supply deficit over the analysed periods. The elasticity indicates the level of mitigation needed to control the demand in order to reduce the vulnerability of the integrated system in future periods. The elasticity analyses also emphasize the need to address uncertainty with respect to changes in natural flow in integrated assessment.« less

A data-driven emulation framework for representing water-food nexus in a changing cold region

NASA Astrophysics Data System (ADS)

Nazemi, A.; Zandmoghaddam, S.; Hatami, S.

2017-12-01

Water resource systems are under increasing pressure globally. Growing population along with competition between water demands and emerging effects of climate change have caused enormous vulnerabilities in water resource management across many regions. Diagnosing such vulnerabilities and provision of effective adaptation strategies requires the availability of simulation tools that can adequately represent the interactions between competing water demands for limiting water resources and inform decision makers about the critical vulnerability thresholds under a range of potential natural and anthropogenic conditions. Despite a significant progress in integrated modeling of water resource systems, regional models are often unable to fully represent the contemplating dynamics within the key elements of water resource systems locally. Here we propose a data-driven approach to emulate a complex regional water resource system model developed for Oldman River Basin in southern Alberta, Canada. The aim of the emulation is to provide a detailed understanding of the trade-offs and interaction at the Oldman Reservoir, which is the key to flood control and irrigated agriculture in this over-allocated semi-arid cold region. Different surrogate models are developed to represent the dynamic of irrigation demand and withdrawal as well as reservoir evaporation and release individually. The nan-falsified offline models are then integrated through the water balance equation at the reservoir location to provide a coupled model for representing the dynamic of reservoir operation and water allocation at the local scale. The performance of individual and integrated models are rigorously examined and sources of uncertainty are highlighted. To demonstrate the practical utility of such surrogate modeling approach, we use the integrated data-driven model for examining the trade-off in irrigation water supply, reservoir storage and release under a range of changing climate, upstream streamflow and local irrigation conditions.

Water Network Tool for Resilience v. 1.0

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

2015-12-09

WNTR is a python package designed to simulate and analyze resilience of water distribution networks. The software includes: - Pressure driven and demand driven hydraulic simulation - Water quality simulation to track concentration, trace, and water age - Conditional controls to simulate power outages - Models to simulate pipe breaks - A wide range of resilience metrics - Analysis and visualization tools

A Model Driven Framework to Address Challenges in a Mobile Learning Environment

ERIC Educational Resources Information Center

Khaddage, Ferial; Christensen, Rhonda; Lai, Wing; Knezek, Gerald; Norris, Cathie; Soloway, Elliot

2015-01-01

In this paper a review of the pedagogical, technological, policy and research challenges and concepts underlying mobile learning is presented, followed by a brief description of categories of implementations. A model Mobile learning framework and dynamic criteria for mobile learning implementations are proposed, along with a case study of one site…

Learning to Argue and Arguing to Learn: Argument-Driven Inquiry as a Way to Help Undergraduate Chemistry Students Learn How to Construct Arguments and Engage in Argumentation during a Laboratory Course

ERIC Educational Resources Information Center

Walker, Joi Phelps; Sampson, Victor

2013-01-01

This study examines whether students enrolled in a general chemistry I laboratory course developed the ability to participate in scientific argumentation over the course of a semester. The laboratory activities that the students participated in during the course were designed using the Argument-Driven Inquiry (ADI) an instructional model. This…

Predicting Chronic Climate-Driven Disturbances and Their Mitigation

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

McDowell, Nate G.; Michaletz, Sean T.; Bennett, Katrina E.

Society increasingly demands the stable provision of ecosystem resources to support our population. Resource risks from climate-driven disturbances--including drought, heat, insect outbreaks, and wildfire--are rising as a chronic state of disequilibrium results from increasing temperatures and a greater frequency of extreme events. This confluence of increased demand and risk may soon reach critical thresholds. We explain here why extreme chronic disequilibrium of ecosystem function is likely to increase dramatically across the globe, creating no-analog conditions that challenge adaptation. We also present novel mechanistic theory that combines models for disturbance mortality and metabolic scaling to link size-dependent plant mortality to changesmore » in ecosystem stocks and fluxes. Efforts must anticipate and model chronic ecosystem disequilibrium to properly prepare for resilience planning.« less

Predicting Chronic Climate-Driven Disturbances and Their Mitigation

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

McDowell, Nate G.; Michaletz, Sean T.; Bennett, Katrina E.

Society increasingly demands the stable provision of ecosystem resources to support our population. Resource risks from climate-driven disturbances, including drought, heat, insect outbreaks, and wildfire, are growing as a chronic state of disequilibrium results from increasing temperatures and a greater frequency of extreme events. This confluence of increased demand and risk may soon reach critical thresholds. Here, we explain here why extreme chronic disequilibrium of ecosystem function is likely to increase dramatically across the globe, creating no-analog conditions that challenge adaptation. We also present novel mechanistic theory that combines models for disturbance mortality and metabolic scaling to link size-dependent plantmore » mortality to changes in ecosystem stocks and fluxes. Our efforts must anticipate and model chronic ecosystem disequilibrium to properly prepare for resilience planning.« less

Predicting Chronic Climate-Driven Disturbances and Their Mitigation

DOE PAGES

McDowell, Nate G.; Michaletz, Sean T.; Bennett, Katrina E.; ...

2017-11-13

Society increasingly demands the stable provision of ecosystem resources to support our population. Resource risks from climate-driven disturbances, including drought, heat, insect outbreaks, and wildfire, are growing as a chronic state of disequilibrium results from increasing temperatures and a greater frequency of extreme events. This confluence of increased demand and risk may soon reach critical thresholds. Here, we explain here why extreme chronic disequilibrium of ecosystem function is likely to increase dramatically across the globe, creating no-analog conditions that challenge adaptation. We also present novel mechanistic theory that combines models for disturbance mortality and metabolic scaling to link size-dependent plantmore » mortality to changes in ecosystem stocks and fluxes. Our efforts must anticipate and model chronic ecosystem disequilibrium to properly prepare for resilience planning.« less

Mining residential water and electricity demand data in Southern California to inform demand management strategies

NASA Astrophysics Data System (ADS)

Cominola, A.; Spang, E. S.; Giuliani, M.; Castelletti, A.; Loge, F. J.; Lund, J. R.

2016-12-01

Demand side management strategies are key to meet future water and energy demands in urban contexts, promote water and energy efficiency in the residential sector, provide customized services and communications to consumers, and reduce utilities' costs. Smart metering technologies allow gathering high temporal and spatial resolution water and energy consumption data and support the development of data-driven models of consumers' behavior. Modelling and predicting resource consumption behavior is essential to inform demand management. Yet, analyzing big, smart metered, databases requires proper data mining and modelling techniques, in order to extract useful information supporting decision makers to spot end uses towards which water and energy efficiency or conservation efforts should be prioritized. In this study, we consider the following research questions: (i) how is it possible to extract representative consumers' personalities out of big smart metered water and energy data? (ii) are residential water and energy consumption profiles interconnected? (iii) Can we design customized water and energy demand management strategies based on the knowledge of water- energy demand profiles and other user-specific psychographic information? To address the above research questions, we contribute a data-driven approach to identify and model routines in water and energy consumers' behavior. We propose a novel customer segmentation procedure based on data-mining techniques. Our procedure consists of three steps: (i) extraction of typical water-energy consumption profiles for each household, (ii) profiles clustering based on their similarity, and (iii) evaluation of the influence of candidate explanatory variables on the identified clusters. The approach is tested onto a dataset of smart metered water and energy consumption data from over 1000 households in South California. Our methodology allows identifying heterogeneous groups of consumers from the studied sample, as well as characterizing them with respect to consumption profiles features and socio- demographic information. Results show how such better understanding of the considered users' community allows spotting potentially interesting areas for water and energy demand management interventions.

The Development of a Peer Assisted Learning Model for the Clinical Education of Physiotherapy Students

ERIC Educational Resources Information Center

Sevenhuysen, Samantha L.; Nickson, Wendy; Farlie, Melanie K.; Raitman, Lyn; Keating, Jennifer L.; Molloy, Elizabeth; Skinner, Elizabeth; Maloney, Stephen; Haines, Terry P.

2013-01-01

Demand for clinical placements in physiotherapy education continues to outstrip supply. Peer assisted learning, in various formats, has been trialled to increase training capacity and facilitate student learning during clinical education. There are no documented examples of measurable or repeatable peer assisted learning models to aid clinicians…

The curse of planning: dissecting multiple reinforcement-learning systems by taxing the central executive.

PubMed

Otto, A Ross; Gershman, Samuel J; Markman, Arthur B; Daw, Nathaniel D

2013-05-01

A number of accounts of human and animal behavior posit the operation of parallel and competing valuation systems in the control of choice behavior. In these accounts, a flexible but computationally expensive model-based reinforcement-learning system has been contrasted with a less flexible but more efficient model-free reinforcement-learning system. The factors governing which system controls behavior-and under what circumstances-are still unclear. Following the hypothesis that model-based reinforcement learning requires cognitive resources, we demonstrated that having human decision makers perform a demanding secondary task engenders increased reliance on a model-free reinforcement-learning strategy. Further, we showed that, across trials, people negotiate the trade-off between the two systems dynamically as a function of concurrent executive-function demands, and people's choice latencies reflect the computational expenses of the strategy they employ. These results demonstrate that competition between multiple learning systems can be controlled on a trial-by-trial basis by modulating the availability of cognitive resources.

The Curse of Planning: Dissecting multiple reinforcement learning systems by taxing the central executive

PubMed Central

Otto, A. Ross; Gershman, Samuel J.; Markman, Arthur B.; Daw, Nathaniel D.

2013-01-01

A number of accounts of human and animal behavior posit the operation of parallel and competing valuation systems in the control of choice behavior. Along these lines, a flexible but computationally expensive model-based reinforcement learning system has been contrasted with a less flexible but more efficient model-free reinforcement learning system. The factors governing which system controls behavior—and under what circumstances—are still unclear. Based on the hypothesis that model-based reinforcement learning requires cognitive resources, we demonstrate that having human decision-makers perform a demanding secondary task engenders increased reliance on a model-free reinforcement learning strategy. Further, we show that across trials, people negotiate this tradeoff dynamically as a function of concurrent executive function demands and their choice latencies reflect the computational expenses of the strategy employed. These results demonstrate that competition between multiple learning systems can be controlled on a trial-by-trial basis by modulating the availability of cognitive resources. PMID:23558545

Neural Mechanisms for Adaptive Learned Avoidance of Mental Effort.

PubMed

Mitsuto Nagase, Asako; Onoda, Keiichi; Clifford Foo, Jerome; Haji, Tomoki; Akaishi, Rei; Yamaguchi, Shuhei; Sakai, Katsuyuki; Morita, Kenji

2018-02-05

Humans tend to avoid mental effort. Previous studies have demonstrated this tendency using various demand-selection tasks; participants generally avoid options associated with higher cognitive demand. However, it remains unclear whether humans avoid mental effort adaptively in uncertain and non-stationary environments, and if so, what neural mechanisms underlie this learned avoidance and whether they remain the same irrespective of cognitive-demand types. We addressed these issues by developing novel demand-selection tasks where associations between choice options and cognitive-demand levels change over time, with two variations using mental arithmetic and spatial reasoning problems (29:4 and 18:2 males:females). Most participants showed avoidance, and their choices depended on the demand experienced on multiple preceding trials. We assumed that participants updated the expected cost of mental effort through experience, and fitted their choices by reinforcement learning models, comparing several possibilities. Model-based fMRI analyses revealed that activity in the dorsomedial and lateral frontal cortices was positively correlated with the trial-by-trial expected cost for the chosen option commonly across the different types of cognitive demand, and also revealed a trend of negative correlation in the ventromedial prefrontal cortex. We further identified correlates of cost-prediction-error at time of problem-presentation or answering the problem, the latter of which partially overlapped with or were proximal to the correlates of expected cost at time of choice-cue in the dorsomedial frontal cortex. These results suggest that humans adaptively learn to avoid mental effort, having neural mechanisms to represent expected cost and cost-prediction-error, and the same mechanisms operate for various types of cognitive demand. SIGNIFICANCE STATEMENT In daily life, humans encounter various cognitive demands, and tend to avoid high-demand options. However, it remains unclear whether humans avoid mental effort adaptively under dynamically changing environments, and if so, what are the underlying neural mechanisms and whether they operate irrespective of cognitive-demand types. To address these issues, we developed novel tasks, where participants could learn to avoid high-demand options under uncertain and non-stationary environments. Through model-based fMRI analyses, we found regions whose activity was correlated with the expected mental effort cost, or cost-prediction-error, regardless of demand-type, with overlap or adjacence in the dorsomedial frontal cortex. This finding contributes to clarifying the mechanisms for cognitive-demand avoidance, and provides empirical building blocks for the emerging computational theory of mental effort. Copyright © 2018 the authors.

Argument-Driven Inquiry as a Way to Help Undergraduate Students Write to Learn by Learning to Write in Chemistry

ERIC Educational Resources Information Center

Sampson, Victor; Walker, Joi Phelps

2012-01-01

This exploratory study examined how undergraduate students' ability to write in science changed over time as they completed a series of laboratory activities designed using a new instructional model called argument-driven inquiry. The study was conducted in a single section of an undergraduate general chemistry lab course offered at a large…

Do job demands and job control affect problem-solving?

PubMed

Bergman, Peter N; Ahlberg, Gunnel; Johansson, Gun; Stoetzer, Ulrich; Aborg, Carl; Hallsten, Lennart; Lundberg, Ingvar

2012-01-01

The Job Demand Control model presents combinations of working conditions that may facilitate learning, the active learning hypothesis, or have detrimental effects on health, the strain hypothesis. To test the active learning hypothesis, this study analysed the effects of job demands and job control on general problem-solving strategies. A population-based sample of 4,636 individuals (55% women, 45% men) with the same job characteristics measured at two times with a three year time lag was used. Main effects of demands, skill discretion, task authority and control, and the combined effects of demands and control were analysed in logistic regressions, on four outcomes representing general problem-solving strategies. Those reporting high on skill discretion, task authority and control, as well as those reporting high demand/high control and low demand/high control job characteristics were more likely to state using problem solving strategies. Results suggest that working conditions including high levels of control may affect how individuals cope with problems and that workplace characteristics may affect behaviour in the non-work domain.

Data-Driven Learning of Q-Matrix

ERIC Educational Resources Information Center

Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2012-01-01

The recent surge of interests in cognitive assessment has led to developments of novel statistical models for diagnostic classification. Central to many such models is the well-known "Q"-matrix, which specifies the item-attribute relationships. This article proposes a data-driven approach to identification of the "Q"-matrix and estimation of…

A Machine LearningFramework to Forecast Wave Conditions

NASA Astrophysics Data System (ADS)

Zhang, Y.; James, S. C.; O'Donncha, F.

2017-12-01

Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in computational expense. The low computational cost (and by association low computer-power requirement) means that the machine learning algorithms could be installed on a wave-energy converter as a form of "edge computing" where a device could forecast its own 48-hour energy production.

Global assessment of soil organic carbon stocks and spatial distribution of histosols: the Machine Learning approach

NASA Astrophysics Data System (ADS)

Hengl, Tomislav

2016-04-01

Preliminary results of predicting distribution of soil organic soils (Histosols) and soil organic carbon stock (in tonnes per ha) using global compilations of soil profiles (about 150,000 points) and covariates at 250 m spatial resolution (about 150 covariates; mainly MODIS seasonal land products, SRTM DEM derivatives, climatic images, lithological and land cover and landform maps) are presented. We focus on using a data-driven approach i.e. Machine Learning techniques that often require no knowledge about the distribution of the target variable or knowledge about the possible relationships. Other advantages of using machine learning are (DOI: 10.1371/journal.pone.0125814): All rules required to produce outputs are formalized. The whole procedure is documented (the statistical model and associated computer script), enabling reproducible research. Predicted surfaces can make use of various information sources and can be optimized relative to all available quantitative point and covariate data. There is more flexibility in terms of the spatial extent, resolution and support of requested maps. Automated mapping is also more cost-effective: once the system is operational, maintenance and production of updates are an order of magnitude faster and cheaper. Consequently, prediction maps can be updated and improved at shorter and shorter time intervals. Some disadvantages of automated soil mapping based on Machine Learning are: Models are data-driven and any serious blunders or artifacts in the input data can propagate to order-of-magnitude larger errors than in the case of expert-based systems. Fitting machine learning models is at the order of magnitude computationally more demanding. Computing effort can be even tens of thousands higher than if e.g. linear geostatistics is used. Many machine learning models are fairly complex often abstract and any interpretation of such models is not trivial and require special multidimensional / multivariable plotting and data mining tools. Results of model fitting using the R packages nnet, randomForest and the h2o software (machine learning functions) show that significant models can be fitted for soil classes, bulk density (R-square 0.76), soil organic carbon (R-square 0.62) and coarse fragments (R-square 0.59). Consequently, we were able to estimate soil organic carbon stock for majority of the land mask (excluding permanent ice) and detect patches of landscape containing mainly organic soils (peat and similar). Our results confirm that hotspots of soil organic carbon in Tropics are peatlands in Indonesia, north of Peru, west Amazon and Congo river basin. Majority of world soil organic carbon stock is likely in the Northern latitudes (tundra and taiga of the north). Distribution of histosols seems to be mainly controlled by climatic conditions (especially temperature regime and water vapor) and hydrologic position in the landscape. Predicted distributions of organic soils (probability of occurrence) and total soil organic carbon stock at resolutions of 1 km and 250 m are available via the SoilGrids.org project homepage.

Market Model of Financing Higher Education in Sub-Saharan Africa: Examples from Kenya.

ERIC Educational Resources Information Center

Oketch, Moses O.

2003-01-01

Examines some of the rationales for financial diversification and partial privatization of state universities in Kenya and the different manifestations of market-driven approaches to university education. Explores whether the market model can address increased demand while maintaining educational quality. (EV)

Community-driven demand creation for the use of routine viral load testing: a model to scale up routine viral load testing.

PubMed

Killingo, Bactrin M; Taro, Trisa B; Mosime, Wame N

2017-11-01

HIV treatment outcomes are dependent on the use of viral load measurement. Despite global and national guidelines recommending the use of routine viral load testing, these policies alone have not translated into widespread implementation or sufficiently increased access for people living with HIV (PLHIV). Civil society and communities of PLHIV recognize the need to close this gap and to enable the scale up of routine viral load testing. The International Treatment Preparedness Coalition (ITPC) developed an approach to community-led demand creation for the use of routine viral load testing. Using this Community Demand Creation Model, implementers follow a step-wise process to capacitate and empower communities to address their most pressing needs. This includes utlizing a specific toolkit that includes conducting a baseline assessment, developing a treatment education toolkit, organizing mobilization workshops for knowledge building, provision of small grants to support advocacy work and conducting benchmark evaluations. The Community Demand Creation Model to increase demand for routine viral load testing services by PLHIV has been delivered in diverse contexts including in the sub-Saharan African, Asian, Latin American and the Caribbean regions. Between December 2015 and December 2016, ITPC trained more than 240 PLHIV activists, and disbursed US$90,000 to network partners in support of their national advocacy work. The latter efforts informed a regional, community-driven campaign calling for domestic investment in the expeditious implementation of national viral load testing guidelines. HIV treatment education and community mobilization are critical components of demand creation for access to optimal HIV treatment, especially for the use of routine viral load testing. ITPC's Community Demand Creation Model offers a novel approach to achieving this goal. © 2017 The Authors. Journal of the International AIDS Society published by John Wiley & sons Ltd on behalf of the International AIDS Society.

Large Field Visualization with Demand-Driven Calculation

NASA Technical Reports Server (NTRS)

Moran, Patrick J.; Henze, Chris

1999-01-01

We present a system designed for the interactive definition and visualization of fields derived from large data sets: the Demand-Driven Visualizer (DDV). The system allows the user to write arbitrary expressions to define new fields, and then apply a variety of visualization techniques to the result. Expressions can include differential operators and numerous other built-in functions, ail of which are evaluated at specific field locations completely on demand. The payoff of following a demand-driven design philosophy throughout becomes particularly evident when working with large time-series data, where the costs of eager evaluation alternatives can be prohibitive.

Gradient boosting machine for modeling the energy consumption of commercial buildings

DOE PAGES

Touzani, Samir; Granderson, Jessica; Fernandes, Samuel

2017-11-26

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradientmore » boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.« less

Gradient boosting machine for modeling the energy consumption of commercial buildings

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

Touzani, Samir; Granderson, Jessica; Fernandes, Samuel

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradientmore » boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.« less

A Demand-Driven Approach for a Multi-Agent System in Supply Chain Management

NASA Astrophysics Data System (ADS)

Kovalchuk, Yevgeniya; Fasli, Maria

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit.

Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

PubMed

Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

2017-11-01

Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

Integrating cortico-limbic-basal ganglia architectures for learning model-based and model-free navigation strategies

PubMed Central

Khamassi, Mehdi; Humphries, Mark D.

2012-01-01

Behavior in spatial navigation is often organized into map-based (place-driven) vs. map-free (cue-driven) strategies; behavior in operant conditioning research is often organized into goal-directed vs. habitual strategies. Here we attempt to unify the two. We review one powerful theory for distinct forms of learning during instrumental conditioning, namely model-based (maintaining a representation of the world) and model-free (reacting to immediate stimuli) learning algorithms. We extend these lines of argument to propose an alternative taxonomy for spatial navigation, showing how various previously identified strategies can be distinguished as “model-based” or “model-free” depending on the usage of information and not on the type of information (e.g., cue vs. place). We argue that identifying “model-free” learning with dorsolateral striatum and “model-based” learning with dorsomedial striatum could reconcile numerous conflicting results in the spatial navigation literature. From this perspective, we further propose that the ventral striatum plays key roles in the model-building process. We propose that the core of the ventral striatum is positioned to learn the probability of action selection for every transition between states of the world. We further review suggestions that the ventral striatal core and shell are positioned to act as “critics” contributing to the computation of a reward prediction error for model-free and model-based systems, respectively. PMID:23205006

Configurable product design considering the transition of multi-hierarchical models

NASA Astrophysics Data System (ADS)

Ren, Bin; Qiu, Lemiao; Zhang, Shuyou; Tan, Jianrong; Cheng, Jin

2013-03-01

The current research of configurable product design mainly focuses on how to convert a predefined set of components into a valid set of product structures. With the scale and complexity of configurable products increasing, the interdependencies between customer demands and product structures grow up as well. The result is that existing product structures fails to satisfy the individual customer requirements and hence product variants are needed. This paper is aimed to build a bridge between customer demands and product structures in order to make demand-driven fast response design feasible. First of all, multi-hierarchical models of configurable product design are established with customer demand model, technical requirement model and product structure model. Then, the transition of multi-hierarchical models among customer demand model, technical requirement model and product structure model is solved with fuzzy analytic hierarchy process (FAHP) and the algorithm of multi-level matching. Finally, optimal structure according to the customer demands is obtained with the calculation of Euclidean distance and similarity of some cases. In practice, the configuration design of a clamping unit of injection molding machine successfully performs an optimal search strategy for the product variants with reasonable satisfaction to individual customer demands. The proposed method can automatically generate a configuration design with better alternatives for each product structures, and shorten the time of finding the configuration of a product.

From task characteristics to learning: A systematic review.

PubMed

Wielenga-Meijer, Etty G A; Taris, Toon W; Kompier, Michiel A J; Wigboldus, Daniël H J

2010-10-01

Although many theoretical approaches propose that job characteristics affect employee learning, the question is why and how job characteristics influence learning. The present study reviews the evidence on the relationships among learning antecedents (i.e., job characteristics: demands, variety, autonomy and feedback), learning processes (including motivational, meta-cognitive, cognitive and behavioral processes) and learning consequences. Building on an integrative heuristic model, we quantitatively reviewed 85 studies published between 1969 and 2005. Our analyses revealed strong evidence for a positive relation between job demands and autonomy on the one hand and motivational and meta-cognitive learning processes on the other. Furthermore, these learning processes were positively related to learning consequences. © 2010 The Authors. Scandinavian Journal of Psychology © 2010 The Scandinavian Psychological Associations.

An experience sampling study of learning, affect, and the demands control support model.

PubMed

Daniels, Kevin; Boocock, Grahame; Glover, Jane; Holland, Julie; Hartley, Ruth

2009-07-01

The demands control support model (R. A. Karasek & T. Theorell, 1990) indicates that job control and social support enable workers to engage in problem solving. In turn, problem solving is thought to influence learning and well-being (e.g., anxious affect, activated pleasant affect). Two samples (N = 78, N = 106) provided data up to 4 times per day for up to 5 working days. The extent to which job control was used for problem solving was assessed by measuring the extent to which participants changed aspects of their work activities to solve problems. The extent to which social support was used to solve problems was assessed by measuring the extent to which participants discussed problems to solve problems. Learning mediated the relationship between changing aspects of work activities to solve problems and activated pleasant affect. Learning also mediated the relationship between discussing problems to solve problems and activated pleasant affect. The findings indicated that how individuals use control and support to respond to problem-solving demands is associated with organizational and individual phenomena, such as learning and affective well-being.

Research on influence factor about the dynamic characteristic of armored vehicle hydraulic-driven fan system

NASA Astrophysics Data System (ADS)

Chao, Zhiqiang; Mao, Feiyue; Liu, Xiangbo; Li, Huaying; Han, Shousong

2017-01-01

In view of the large power of armored vehicle cooling system, the demand for high fan speed control and energy saving, this paper expounds the basic composition and principle of hydraulic-driven fan system and establishes the mathematical model of the system. Through the simulation analysis of different parameters, such as displacement of motor and working volume of fan system, the influences of performance parameters on the dynamic characteristic of hydraulic-driven fan system are obtained, which can provide theoretical guidance for system optimization design.

Development of a business plan for women's health services, using Malcolm Baldrige Performance Excellence Criteria.

PubMed

Caramanica, L; Maxwell, S; Curry, S

2000-06-01

A new process for business planning at Hartford Hospital was needed to achieve critical business results. This article describes the Hospital's use of the Malcolm Baldrige Performance Excellence Criteria as a way to standardize and improve business planning. Women's Health Services is one of Hartford Hospital's "centers for excellence" and one of the first to use these criteria to improve its service. Staff learned how to build their business plan upon a set of core values and concepts such as customer-driven quality, leadership that sets high expectations, continuous improvement and learning, valuing employees, faster response to market demands, management by fact, and a long-range view of the future.

Contextualizing Learning Scenarios According to Different Learning Management Systems

ERIC Educational Resources Information Center

Drira, R.; Laroussi, M.; Le Pallec, X.; Warin, B.

2012-01-01

In this paper, we first demonstrate that an instructional design process of Technology Enhanced Learning (TEL) systems based on a Model Driven Approach (MDA) addresses the limits of Learning Technology Standards (LTS), such as SCORM and IMS-LD. Although these standards ensure the interoperability of TEL systems across different Learning Management…

Watchable Wildlife and Demand-Driven General Education

ERIC Educational Resources Information Center

Alley, Richard B.

2013-01-01

The societal benefits of an educated citizenry may be lost if "customers" at tuition-driven universities demand less of what they pay for because they value a credential more than the education it represents. Insights from potential employers may help students see the value of education and demand their money's worth.

Flexible Programmes in Higher Professional Education: Expert Validation of a Flexible Educational Model

ERIC Educational Resources Information Center

Schellekens, Ad; Paas, Fred; Verbraeck, Alexander; van Merrienboer, Jeroen J. G.

2010-01-01

In a preceding case study, a process-focused demand-driven approach for organising flexible educational programmes in higher professional education (HPE) was developed. Operations management and instructional design contributed to designing a flexible educational model by means of discrete-event simulation. Educational experts validated the model…

Modeling complexity in engineered infrastructure system: Water distribution network as an example

NASA Astrophysics Data System (ADS)

Zeng, Fang; Li, Xiang; Li, Ke

2017-02-01

The complex topology and adaptive behavior of infrastructure systems are driven by both self-organization of the demand and rigid engineering solutions. Therefore, engineering complex systems requires a method balancing holism and reductionism. To model the growth of water distribution networks, a complex network model was developed following the combination of local optimization rules and engineering considerations. The demand node generation is dynamic and follows the scaling law of urban growth. The proposed model can generate a water distribution network (WDN) similar to reported real-world WDNs on some structural properties. Comparison with different modeling approaches indicates that a realistic demand node distribution and co-evolvement of demand node and network are important for the simulation of real complex networks. The simulation results indicate that the efficiency of water distribution networks is exponentially affected by the urban growth pattern. On the contrary, the improvement of efficiency by engineering optimization is limited and relatively insignificant. The redundancy and robustness, on another aspect, can be significantly improved through engineering methods.

Improving wave forecasting by integrating ensemble modelling and machine learning

NASA Astrophysics Data System (ADS)

O'Donncha, F.; Zhang, Y.; James, S. C.

2017-12-01

Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

Education and research in medical optronics in France

NASA Astrophysics Data System (ADS)

Demongeot, Jacques; Fleute, M.; Herve, T.; Lavallee, Stephane

2000-06-01

First we present here the main post-graduate courses proposed in France both for physicians and engineers in medical optronics. After we explain which medical domains are concerned by this teaching, essentially computer assisted surgery, telemedicine and functional exploration. Then we show the main research axes in these fields, in which new jobs have to be invented and new educational approaches have to be prepared in order to satisfy the demand coming both from hospitals (mainly referent hospitals) and from industry (essentially medical imaging and instrumentation companies). Finally we will conclude that medical optronics is an important step in an entire chain of acquisition and processing of medical data, capable to create the medical knowledge a surgeon or a physician needs for diagnosis or therapy purposes. Optimizing the teaching of medical optronics needs a complete integration from acquiring to modeling the medical reality. This tendency to give a holistic education in medical imaging and instrumentation is called `Model driven Acquisition' learning.

A model for the transfer of perceptual-motor skill learning in human behaviors.

PubMed

Rosalie, Simon M; Müller, Sean

2012-09-01

This paper presents a preliminary model that outlines the mechanisms underlying the transfer of perceptual-motor skill learning in sport and everyday tasks. Perceptual-motor behavior is motivated by performance demands and evolves over time to increase the probability of success through adaptation. Performance demands at the time of an event create a unique transfer domain that specifies a range of potentially successful actions. Transfer comprises anticipatory subconscious and conscious mechanisms. The model also outlines how transfer occurs across a continuum, which depends on the individual's expertise and contextual variables occurring at the incidence of transfer

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

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

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias

With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by firstmore » layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.« less

Enhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activities

NASA Astrophysics Data System (ADS)

Sanchez, Christopher A.; Ruddell, Benjamin L.; Schiesser, Roy; Merwade, Venkatesh

2016-03-01

Previous research has suggested that the use of more authentic learning activities can produce more robust and durable knowledge gains. This is consistent with calls within civil engineering education, specifically hydrology, that suggest that curricula should more often include professional perspective and data analysis skills to better develop the "T-shaped" knowledge profile of a professional hydrologist (i.e., professional breadth combined with technical depth). It was expected that the inclusion of a data-driven simulation lab exercise that was contextualized within a real-world situation and more consistent with the job duties of a professional in the field, would provide enhanced learning and appreciation of job duties beyond more conventional paper-and-pencil exercises in a lower-division undergraduate course. Results indicate that while students learned in both conditions, learning was enhanced for the data-driven simulation group in nearly every content area. This pattern of results suggests that the use of data-driven modeling and visualization activities can have a significant positive impact on instruction. This increase in learning likely facilitates the development of student perspective and conceptual mastery, enabling students to make better choices about their studies, while also better preparing them for work as a professional in the field.

Enhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activities

NASA Astrophysics Data System (ADS)

Sanchez, C. A.; Ruddell, B. L.; Schiesser, R.; Merwade, V.

2015-07-01

Previous research has suggested that the use of more authentic learning activities can produce more robust and durable knowledge gains. This is consistent with calls within civil engineering education, specifically hydrology, that suggest that curricula should more often include professional perspective and data analysis skills to better develop the "T-shaped" knowledge profile of a professional hydrologist (i.e., professional breadth combined with technical depth). It was expected that the inclusion of a data driven simulation lab exercise that was contextualized within a real-world situation and more consistent with the job duties of a professional in the field, would provide enhanced learning and appreciation of job duties beyond more conventional paper-and-pencil exercises in a lower division undergraduate course. Results indicate that while students learned in both conditions, learning was enhanced for the data-driven simulation group in nearly every content area. This pattern of results suggests that the use of data-driven modeling and visualization activities can have a significant positive impact on instruction. This increase in learning likely facilitates the development of student perspective and conceptual mastery, enabling students to make better choices about their studies, while also better preparing them for work as a professional in the field.

Bayesian network learning for natural hazard assessments

NASA Astrophysics Data System (ADS)

Vogel, Kristin

2016-04-01

Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables and incomplete observations. Further studies rise the challenge of relying on very small data sets. Since parameter estimates for complex models based on few observations are unreliable, it is necessary to focus on simplified, yet still meaningful models. A so called Markov Blanket approach is developed to identify the most relevant model components and to construct a simple Bayesian network based on those findings. Since the proceeding is completely data driven, it can easily be transferred to various applications in natural hazard domains. This study is funded by the Deutsche Forschungsgemeinschaft (DFG) within the research training programme GRK 2043/1 "NatRiskChange - Natural hazards and risks in a changing world" at Potsdam University.

Closing the Loop: Automated Data-Driven Cognitive Model Discoveries Lead to Improved Instruction and Learning Gains

ERIC Educational Resources Information Center

Liu, Ran; Koedinger, Kenneth R.

2017-01-01

As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it…

A Model for an Open and Flexible E-Training Platform To Encourage Companies' Learning Culture and Meet Employees' Learning Needs.

ERIC Educational Resources Information Center

Bagnasco, Andrea; Chirico, Marco; Parodi, Giancarlo; Scapolla, A. Marina

2003-01-01

Distance education is an answer to the demand for flexibility in training. The aim is to build a virtual learning community on the basis of a knowledge model that meets different learning needs. This article analyzes possible innovations in corporate training, and proposes a framework that integrates all information sources and offers practice…

State-of-the-Art Model Driven Game Development: A Survey of Technological Solutions for Game-Based Learning

ERIC Educational Resources Information Center

Tang, Stephen; Hanneghan, Martin

2011-01-01

Game-based learning harnesses the advantages of computer games technology to create a fun, motivating and interactive virtual learning environment that promotes problem-based experiential learning. Such an approach is advocated by many commentators to provide an enhanced learning experience than those based on traditional didactic methods.…

How Evolution May Work Through Curiosity-Driven Developmental Process.

PubMed

Oudeyer, Pierre-Yves; Smith, Linda B

2016-04-01

Infants' own activities create and actively select their learning experiences. Here we review recent models of embodied information seeking and curiosity-driven learning and show that these mechanisms have deep implications for development and evolution. We discuss how these mechanisms yield self-organized epigenesis with emergent ordered behavioral and cognitive developmental stages. We describe a robotic experiment that explored the hypothesis that progress in learning, in and for itself, generates intrinsic rewards: The robot learners probabilistically selected experiences according to their potential for reducing uncertainty. In these experiments, curiosity-driven learning led the robot learner to successively discover object affordances and vocal interaction with its peers. We explain how a learning curriculum adapted to the current constraints of the learning system automatically formed, constraining learning and shaping the developmental trajectory. The observed trajectories in the robot experiment share many properties with those in infant development, including a mixture of regularities and diversities in the developmental patterns. Finally, we argue that such emergent developmental structures can guide and constrain evolution, in particular with regard to the origins of language. Copyright © 2016 Cognitive Science Society, Inc.

Demand-driven energy requirement of world economy 2007: A multi-region input-output network simulation

NASA Astrophysics Data System (ADS)

Chen, Zhan-Ming; Chen, G. Q.

2013-07-01

This study presents a network simulation of the global embodied energy flows in 2007 based on a multi-region input-output model. The world economy is portrayed as a 6384-node network and the energy interactions between any two nodes are calculated and analyzed. According to the results, about 70% of the world's direct energy input is invested in resource, heavy manufacture, and transportation sectors which provide only 30% of the embodied energy to satisfy final demand. By contrast, non-transportation services sectors contribute to 24% of the world's demand-driven energy requirement with only 6% of the direct energy input. Commodity trade is shown to be an important alternative to fuel trade in redistributing energy, as international commodity flows embody 1.74E + 20 J of energy in magnitude up to 89% of the traded fuels. China is the largest embodied energy exporter with a net export of 3.26E + 19 J, in contrast to the United States as the largest importer with a net import of 2.50E + 19 J. The recent economic fluctuations following the financial crisis accelerate the relative expansions of energy requirement by developing countries, as a consequence China will take over the place of the United States as the world's top demand-driven energy consumer in 2022 and India will become the third largest in 2015.

Autonomous Soil Assessment System: A Data-Driven Approach to Planetary Mobility Hazard Detection

NASA Astrophysics Data System (ADS)

Raimalwala, K.; Faragalli, M.; Reid, E.

2018-04-01

The Autonomous Soil Assessment System predicts mobility hazards for rovers. Its development and performance are presented, with focus on its data-driven models, machine learning algorithms, and real-time sensor data fusion for predictive analytics.

The Influence of Job Characteristics and Self-Directed Learning Orientation on Workplace Learning

ERIC Educational Resources Information Center

Raemdonck, Isabel; Gijbels, David; van Groen, Willemijn

2014-01-01

Given the increasing importance of learning at work, we set out to examine the factors which influence workplace learning behaviour. The study investigated the influence of the job characteristics from Karasek's Job Demand Control Support model and the personal characteristic self-directed learning orientation on workplace learning. A total…

Evidence of market-driven size-selective fishing and the mediating effects of biological and institutional factors.

PubMed

Reddy, Sheila M W; Wentz, Allison; Aburto-Oropeza, Octavio; Maxey, Martin; Nagavarapu, Sriniketh; Leslie, Heather M

2013-06-01

Market demand is often ignored or assumed to lead uniformly to the decline of resources. Yet little is known about how market demand influences natural resources in particular contexts, or the mediating effects of biological or institutional factors. Here, we investigate this problem by examining the Pacific red snapper (Lutjanus peru) fishery around La Paz, Mexico, where medium or "plate-sized" fish are sold to restaurants at a premium price. If higher demand for plate-sized fish increases the relative abundance of the smallest (recruit size class) and largest (most fecund) fish, this may be a market mechanism to increase stocks and fishermen's revenues. We tested this hypothesis by estimating the effect of prices on the distribution of catch across size classes using daily records of prices and catch. We linked predictions from this economic choice model to a staged-based model of the fishery to estimate the effects on the stock and revenues from harvest. We found that the supply of plate-sized fish increased by 6%, while the supply of large fish decreased by 4% as a result of a 13% price premium for plate-sized fish. This market-driven size selection increased revenues (14%) but decreased total fish biomass (-3%). However, when market-driven size selection was combined with limited institutional constraints, both fish biomass (28%) and fishermen's revenue (22%) increased. These results show that the direction and magnitude of the effects of market demand on biological populations and human behavior can depend on both biological attributes and institutional constraints. Fisheries management may capitalize on these conditional effects by implementing size-based regulations when economic and institutional incentives will enhance compliance, as in the case we describe here, or by creating compliance enhancing conditions for existing regulations.

MICRO-U 70.1: Training Model of an Instructional Institution, Users Manual.

ERIC Educational Resources Information Center

Springer, Colby H.

MICRO-U is a student demand driven deterministic model. Student enrollment, by degree program, is used to develop an Instructional Work Load Matrix. Linear equations using Weekly Student Contact Hours (WSCH), Full Time Equivalent (FTE) students, FTE faculty, and number of disciplines determine library, central administration, and physical plant…

Real-Time Global Nonlinear Aerodynamic Modeling for Learn-To-Fly

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2016-01-01

Flight testing and modeling techniques were developed to accurately identify global nonlinear aerodynamic models for aircraft in real time. The techniques were developed and demonstrated during flight testing of a remotely-piloted subscale propeller-driven fixed-wing aircraft using flight test maneuvers designed to simulate a Learn-To-Fly scenario. Prediction testing was used to evaluate the quality of the global models identified in real time. The real-time global nonlinear aerodynamic modeling algorithm will be integrated and further tested with learning adaptive control and guidance for NASA Learn-To-Fly concept flight demonstrations.

Blending Student Technology Experiences in Formal and Informal Learning

ERIC Educational Resources Information Center

Lai, K.-W.; Khaddage, F.; Knezek, Gerald

2013-01-01

In this article, we discuss the importance of recognizing students' technology-enhanced informal learning experiences and develop pedagogies to connect students' formal and informal learning experiences, in order to meet the demands of the knowledge society. The Mobile-Blended Collaborative Learning model is proposed as a framework to…

Interpersonal Congruence, Transactive Memory, and Feedback Processes: An Integrative Model of Group Learning

ERIC Educational Resources Information Center

London, Manuel; Polzer, Jeffrey T.; Omoregie, Heather

2005-01-01

This article presents a multilevel model of group learning that focuses on antecedents and consequences of interpersonal congruence, transactive memory, and feedback processes. The model holds that members' self-verification motives and situational conditions (e.g., member diversity and task demands) give rise to identity negotiation behaviors…

eFSM--a novel online neural-fuzzy semantic memory model.

PubMed

Tung, Whye Loon; Quek, Chai

2010-01-01

Fuzzy rule-based systems (FRBSs) have been successfully applied to many areas. However, traditional fuzzy systems are often manually crafted, and their rule bases that represent the acquired knowledge are static and cannot be trained to improve the modeling performance. This subsequently leads to intensive research on the autonomous construction and tuning of a fuzzy system directly from the observed training data to address the knowledge acquisition bottleneck, resulting in well-established hybrids such as neural-fuzzy systems (NFSs) and genetic fuzzy systems (GFSs). However, the complex and dynamic nature of real-world problems demands that fuzzy rule-based systems and models be able to adapt their parameters and ultimately evolve their rule bases to address the nonstationary (time-varying) characteristics of their operating environments. Recently, considerable research efforts have been directed to the study of evolving Tagaki-Sugeno (T-S)-type NFSs based on the concept of incremental learning. In contrast, there are very few incremental learning Mamdani-type NFSs reported in the literature. Hence, this paper presents the evolving neural-fuzzy semantic memory (eFSM) model, a neural-fuzzy Mamdani architecture with a data-driven progressively adaptive structure (i.e., rule base) based on incremental learning. Issues related to the incremental learning of the eFSM rule base are carefully investigated, and a novel parameter learning approach is proposed for the tuning of the fuzzy set parameters in eFSM. The proposed eFSM model elicits highly interpretable semantic knowledge in the form of Mamdani-type if-then fuzzy rules from low-level numeric training data. These Mamdani fuzzy rules define the computing structure of eFSM and are incrementally learned with the arrival of each training data sample. New rules are constructed from the emergence of novel training data and obsolete fuzzy rules that no longer describe the recently observed data trends are pruned. This enables eFSM to maintain a current and compact set of Mamdani-type if-then fuzzy rules that collectively generalizes and describes the salient associative mappings between the inputs and outputs of the underlying process being modeled. The learning and modeling performances of the proposed eFSM are evaluated using several benchmark applications and the results are encouraging.

Knowledge acquisition and interface design for learning on demand systems

NASA Technical Reports Server (NTRS)

Nelson, Wayne A.

1993-01-01

The rapid changes in our world precipitated by technology have created new problems and new challenges for education and training. A knowledge 'explosion' is occurring as our society moves toward a service oriented economy that relies on information as the major resource. Complex computer systems are beginning to dominate the workplace, causing alarming growth and change in many fields. The rapidly changing nature of the workplace, especially in fields related to information technology, requires that our knowledge be updated constantly. This characteristic of modern society poses seemingly unsolvable instructional problems involving coverage and obsolescence. The sheer amount of information to be learned is rapidly increasing, while at the same time some information becomes obsolete in light of new information. Education, therefore, must become a lifelong process that features learning of new material and skills as needed in relation to the job to be done. Because of the problems cited above, the current model of learning in advance may no longer be feasible in our high-technology world. In many cases, learning in advance is impossible because there are simply too many things to learn. In addition, learning in advance can be time consuming, and often results in decontextualized knowledge that does not readily transfer to the work environment. The large and growing discrepancy between the amount of potentially relevant knowledge available and the amount a person can know and remember makes learning on demand an important alternative to current instructional practices. Learning on demand takes place whenever an individual must learn something new in order to perform a task or make a decision. Learning on demand is a promising approach for addressing the problems of coverage and obsolescence because learning is contextualized and integrated into the task environment rather than being relegated to a separate phase that precedes work. Learning on demand allows learners to see for themselves the usefulness of new knowledge for actual problem situations, thereby increasing the motivation for learning new information. Finally, learning on demand makes new information relevant to the task at hand, leading to more informed decision making, better quality products, and improved performance.

Data Mining for Understanding and Impriving Decision-Making Affecting Ground Delay Programs

NASA Technical Reports Server (NTRS)

Kulkarni, Deepak; Wang, Yao Xun; Sridhar, Banavar

2013-01-01

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions. The work reported in this paper is focused on ground delay programs. Data mining algorithms have the potential to develop associations between weather patterns and the corresponding ground delay program responses. If successful, they can be used to improve and standardize TFM decision resulting in better predictability of traffic flows on days with reliable weather forecasts. The approach here seeks to develop a set of data mining and machine learning models and apply them to historical archives of weather observations and forecasts and TFM initiatives to determine the extent to which the theory can predict and explain the observed traffic flow behaviors.

Competency--and Process-Driven e-Learning--A Model-Based Approach

ERIC Educational Resources Information Center

Leyking, Katrina; Chikova, Pavlina; Loos, Peter

2007-01-01

As a matter of fact e-Learning still has not really caught on for corporate training purposes. Investigations on the reasons reveal that e-Learning modules like WBTs often miss any relevance for the tasks to be accomplished in the day-to-day workplace settings. The very learning needs both from an organizational and individual perspective are…

Blurring the Boundaries? Supporting Students and Staff within an Online Learning Environment

ERIC Educational Resources Information Center

Quinsee, Susannah; Hurst, Judith

2005-01-01

The inclusion of online learning technologies into the higher education (HE) curriculum is frequently associated with the design and development of new models of learning. One could argue that e-learning even demands a reconfiguration of traditional methods of learning and teaching. One of the key elements of this transformational process is…

Using the Flipped Classroom to Enhance EFL Learning

ERIC Educational Resources Information Center

Chen Hsieh, Jun Scott; Wu, Wen-Chi Vivian; Marek, Michael W.

2017-01-01

Instruction in English is a priority around the globe, but instructional methodologies have not always kept pace with the changing needs of students. To explore the benefits of the flipped classroom model for learners of English as a Foreign Language, the researchers used flipped learning and Wen's Output-driven/Input-enabled model to design a…

A Platform Independent Game Technology Model for Model Driven Serious Games Development

ERIC Educational Resources Information Center

Tang, Stephen; Hanneghan, Martin; Carter, Christopher

2013-01-01

Game-based learning (GBL) combines pedagogy and interactive entertainment to create a virtual learning environment in an effort to motivate and regain the interest of a new generation of "digital native" learners. However, this approach is impeded by the limited availability of suitable "serious" games and high-level design…

Synthesis of traveler choice research: improving modeling accuracy for better transportation decisionmaking.

DOT National Transportation Integrated Search

2013-08-01

"Over the last 50 years, advances in the fields of travel behavior research and travel demand forecasting have been : immense, driven by the increasing costs of infrastructure and spatial limitations in areas of high population density : together wit...

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

NASA Astrophysics Data System (ADS)

Forkel, Matthias; Dorigo, Wouter; Lasslop, Gitta; Teubner, Irene; Chuvieco, Emilio; Thonicke, Kirsten

2017-12-01

Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model-data integration approaches can guide the future development of global process-oriented vegetation-fire models.

Limited angle CT reconstruction by simultaneous spatial and Radon domain regularization based on TV and data-driven tight frame

NASA Astrophysics Data System (ADS)

Zhang, Wenkun; Zhang, Hanming; Wang, Linyuan; Cai, Ailong; Li, Lei; Yan, Bin

2018-02-01

Limited angle computed tomography (CT) reconstruction is widely performed in medical diagnosis and industrial testing because of the size of objects, engine/armor inspection requirements, and limited scan flexibility. Limited angle reconstruction necessitates usage of optimization-based methods that utilize additional sparse priors. However, most of conventional methods solely exploit sparsity priors of spatial domains. When CT projection suffers from serious data deficiency or various noises, obtaining reconstruction images that meet the requirement of quality becomes difficult and challenging. To solve this problem, this paper developed an adaptive reconstruction method for limited angle CT problem. The proposed method simultaneously uses spatial and Radon domain regularization model based on total variation (TV) and data-driven tight frame. Data-driven tight frame being derived from wavelet transformation aims at exploiting sparsity priors of sinogram in Radon domain. Unlike existing works that utilize pre-constructed sparse transformation, the framelets of the data-driven regularization model can be adaptively learned from the latest projection data in the process of iterative reconstruction to provide optimal sparse approximations for given sinogram. At the same time, an effective alternating direction method is designed to solve the simultaneous spatial and Radon domain regularization model. The experiments for both simulation and real data demonstrate that the proposed algorithm shows better performance in artifacts depression and details preservation than the algorithms solely using regularization model of spatial domain. Quantitative evaluations for the results also indicate that the proposed algorithm applying learning strategy performs better than the dual domains algorithms without learning regularization model

Adults as Learners. Increasing Participation and Facilitating Learning.

ERIC Educational Resources Information Center

Cross, K. Patricia

The literature on adult learners is reviewed, and two models of adult learning are developed. Demographic, social, and technological trends that stimulate the increasing demand for learning opportunities are examined, and the views of those who see dangers in new pressures on adults to participate in organized learning activities are considered.…

"Anyone Can Make It, but There Can Only Be One Winner": Modelling Neoliberal Learning and Work on Reality Television

ERIC Educational Resources Information Center

Windle, Joel

2010-01-01

This article investigates how reality television talent-quest formats model the normative neoliberal worker and learner--roles which are increasingly drawn together. In the age of "life-long learning" and shifting employment demands, new models of the supple, adaptable and willing learner are increasingly important both to meeting…

Extreme learning machine for reduced order modeling of turbulent geophysical flows.

PubMed

San, Omer; Maulik, Romit

2018-04-01

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

Extreme learning machine for reduced order modeling of turbulent geophysical flows

NASA Astrophysics Data System (ADS)

San, Omer; Maulik, Romit

2018-04-01

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

Optimal pricing policies for services with consideration of facility maintenance costs

NASA Astrophysics Data System (ADS)

Yeh, Ruey Huei; Lin, Yi-Fang

2012-06-01

For survival and success, pricing is an essential issue for service firms. This article deals with the pricing strategies for services with substantial facility maintenance costs. For this purpose, a mathematical framework that incorporates service demand and facility deterioration is proposed to address the problem. The facility and customers constitute a service system driven by Poisson arrivals and exponential service times. A service demand with increasing price elasticity and a facility lifetime with strictly increasing failure rate are also adopted in modelling. By examining the bidirectional relationship between customer demand and facility deterioration in the profit model, the pricing policies of the service are investigated. Then analytical conditions of customer demand and facility lifetime are derived to achieve a unique optimal pricing policy. The comparative statics properties of the optimal policy are also explored. Finally, numerical examples are presented to illustrate the effects of parameter variations on the optimal pricing policy.

Future land-use related water demand in California

USGS Publications Warehouse

Wilson, Tamara; Sleeter, Benjamin M.; Cameron, D. Richard

2016-01-01

Water shortages in California are a growing concern amidst ongoing drought, earlier spring snowmelt, projected future climate warming, and currently mandated water use restrictions. Increases in population and land use in coming decades will place additional pressure on already limited available water supplies. We used a state-and-transition simulation model to project future changes in developed (municipal and industrial) and agricultural land use to estimate associated water use demand from 2012 to 2062. Under current efficiency rates, total water use was projected to increase 1.8 billion cubic meters(+4.1%) driven primarily by urbanization and shifts to more water intensive crops. Only if currently mandated 25% reductions in municipal water use are continuously implemented would water demand in 2062 balance to water use levels in 2012. This is the first modeling effort of its kind to examine regional land-use related water demand incorporating historical trends of both developed and agricultural land uses.

Lifelong learning of human actions with deep neural network self-organization.

PubMed

Parisi, German I; Tani, Jun; Weber, Cornelius; Wermter, Stefan

2017-12-01

Lifelong learning is fundamental in autonomous robotics for the acquisition and fine-tuning of knowledge through experience. However, conventional deep neural models for action recognition from videos do not account for lifelong learning but rather learn a batch of training data with a predefined number of action classes and samples. Thus, there is the need to develop learning systems with the ability to incrementally process available perceptual cues and to adapt their responses over time. We propose a self-organizing neural architecture for incrementally learning to classify human actions from video sequences. The architecture comprises growing self-organizing networks equipped with recurrent neurons for processing time-varying patterns. We use a set of hierarchically arranged recurrent networks for the unsupervised learning of action representations with increasingly large spatiotemporal receptive fields. Lifelong learning is achieved in terms of prediction-driven neural dynamics in which the growth and the adaptation of the recurrent networks are driven by their capability to reconstruct temporally ordered input sequences. Experimental results on a classification task using two action benchmark datasets show that our model is competitive with state-of-the-art methods for batch learning also when a significant number of sample labels are missing or corrupted during training sessions. Additional experiments show the ability of our model to adapt to non-stationary input avoiding catastrophic interference. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

ARL and Association 3.0: Ten Management Challenges

ERIC Educational Resources Information Center

Funk, Carla J.

2009-01-01

Association management in today's "association 3.0" environment presents some new challenges and new perspectives on old ones. This paper summarizes 10 such challenges including collaboration, diversity, innovation, transparency, financial stability, member benefits, knowledge-based decision-making, a demand-driven association model, pro-activity…

Short-stay rural and remote placements in dental education, an effective model for rural exposure: a review of eight-year experience in Western Australia.

PubMed

Kruger, Estie; Tennant, Marc

2010-08-01

The increase in demand for dental care over the next 10 years is expected to outstrip the supply of dental visits in Australia, resulting in an ongoing shortage of dental practitioners. As trends in medicine have shown, the greatest effect will be felt in rural and remote regions, where an undersupply of dentists already exists. It is clearly evident that it is important to provide strategies that will increase the recruitment and retention of practitioners in rural and remote areas. Previous research suggested an increased likelihood for health graduates to choose rural practice if they have a rural background, or were exposed to rural practice during their education. Short-stay (three to four weeks) placements for final-year dental students has been part of dental education in Western Australia for near on a decade. This paper reflects on the experiences gained from managing this placement program. Short-stay placements are a quality learning initiative but need a high level of planning and a clear vision to be effective. The key factors in ensuring sustainable, student centred learning is driven through a small core group of staff who have strong direct links with rural and remote communities, students and support providers. The integration of service, education and research goals have played a critical role in sustaining placements. The philosophy underpinning the rural placements needs to be clearly articulated and applied effectively in each step of their implementation and a highly focused customer-service driven implementation is required to make short-stay rural and remote placements effective.

Knowledge Transfer among Projects Using a Learn-Forget Model

ERIC Educational Resources Information Center

Tukel, Oya I.; Rom, Walter O.; Kremic, Tibor

2008-01-01

Purpose: The purpose of this paper is to analyze the impact of learning in a project-driven organization and demonstrate analytically how the learning, which takes place during the execution of successive projects, and the forgetting that takes place during the dormant time between the project executions, can impact performance and productivity in…

Validated Predictions of Metabolic Energy Consumption for Submaximal Effort Movement

PubMed Central

Tsianos, George A.; MacFadden, Lisa N.

2016-01-01

Physical performance emerges from complex interactions among many physiological systems that are largely driven by the metabolic energy demanded. Quantifying metabolic demand is an essential step for revealing the many mechanisms of physical performance decrement, but accurate predictive models do not exist. The goal of this study was to investigate if a recently developed model of muscle energetics and force could be extended to reproduce the kinematics, kinetics, and metabolic demand of submaximal effort movement. Upright dynamic knee extension against various levels of ergometer load was simulated. Task energetics were estimated by combining the model of muscle contraction with validated models of lower limb musculotendon paths and segment dynamics. A genetic algorithm was used to compute the muscle excitations that reproduced the movement with the lowest energetic cost, which was determined to be an appropriate criterion for this task. Model predictions of oxygen uptake rate (VO2) were well within experimental variability for the range over which the model parameters were confidently known. The model's accurate estimates of metabolic demand make it useful for assessing the likelihood and severity of physical performance decrement for a given task as well as investigating underlying physiologic mechanisms. PMID:27248429

Distance Learning for Information Professionals: A Practical, Reality-Driven Model for Postgraduate Education

ERIC Educational Resources Information Center

Gauld, Craig; Whatley, Patricia

2017-01-01

The expansion of distance learning and an understanding of the benefits it can offer to both the university and the individual has led to the growth of methodologies, pedagogies and models aimed at diversifying and maximising the student experience and increasing student numbers. This paper will address these issues in relation to the…

Variations on a Theme: As Needs Change, New Models of Critical Friends Groups Emerge

ERIC Educational Resources Information Center

Fahey, Kevin; Ippolito, Jacy

2015-01-01

The Critical Friends Group, a highly articulated model of professional learning, posits that, in order for teachers to learn together in ways that change their practice, the content and nature of their conversations must change (National School Reform Faculty, 2012). The content needs to change from externally driven agendas that address (in a…

Writing to Learn by Learning to Write during the School Science Laboratory: Helping Middle and High School Students Develop Argumentative Writing Skills as They Learn Core Ideas

ERIC Educational Resources Information Center

Sampson, Victor; Enderle, Patrick; Grooms, Jonathon; Witte, Shelbie

2013-01-01

This study examined how students' science-specific argumentative writing skills and understanding of core ideas changed over the course of a school year as they participated in a series of science laboratories designed using the Argument-Driven Inquiry (ADI) instructional model. The ADI model is a student-centered and writing-intensive approach to…

Analyzing the Quality of Students Interaction in a Distance Learning Object-Oriented Programming Discipline

ERIC Educational Resources Information Center

Carvalho, Elizabeth Simão

2015-01-01

Teaching object-oriented programming to students in an in-classroom environment demands well-thought didactic and pedagogical strategies in order to guarantee a good level of apprenticeship. To teach it on a completely distance learning environment (e-learning) imposes possibly other strategies, besides those that the e-learning model of Open…

Using Learning Study to Understand Preschoolers' Learning: Challenges and Possibilities

ERIC Educational Resources Information Center

Ljung-Djarf, Agneta; Olander, Mona Holmqvist

2013-01-01

This article reports a meta-analysis based on a multiple case study of the use of learning study (LS) to understand children's learning in Swedish preschool. The aim is to investigate whether and how the LS model can be developed, adjusted and used to meet contemporary demands placed upon preschool teachers for increased content focus and improved…

Functionally dissociable influences on learning rate in a dynamic environment

PubMed Central

McGuire, Joseph T.; Nassar, Matthew R.; Gold, Joshua I.; Kable, Joseph W.

2015-01-01

Summary Maintaining accurate beliefs in a changing environment requires dynamically adapting the rate at which one learns from new experiences. Beliefs should be stable in the face of noisy data, but malleable in periods of change or uncertainty. Here we used computational modeling, psychophysics and fMRI to show that adaptive learning is not a unitary phenomenon in the brain. Rather, it can be decomposed into three computationally and neuroanatomically distinct factors that were evident in human subjects performing a spatial-prediction task: (1) surprise-driven belief updating, related to BOLD activity in visual cortex; (2) uncertainty-driven belief updating, related to anterior prefrontal and parietal activity; and (3) reward-driven belief updating, a context-inappropriate behavioral tendency related to activity in ventral striatum. These distinct factors converged in a core system governing adaptive learning. This system, which included dorsomedial frontal cortex, responded to all three factors and predicted belief updating both across trials and across individuals. PMID:25459409

Person-Oriented Approaches to Profiling Learners in Technology-Rich Learning Environments for Ecological Learner Modeling

ERIC Educational Resources Information Center

Jang, Eunice Eunhee; Lajoie, Susanne P.; Wagner, Maryam; Xu, Zhenhua; Poitras, Eric; Naismith, Laura

2017-01-01

Technology-rich learning environments (TREs) provide opportunities for learners to engage in complex interactions involving a multitude of cognitive, metacognitive, and affective states. Understanding learners' distinct learning progressions in TREs demand inquiry approaches that employ well-conceived theoretical accounts of these multiple facets.…

Developing high-resolution urban scale heavy-duty truck emission inventory using the data-driven truck activity model output

NASA Astrophysics Data System (ADS)

Perugu, Harikishan; Wei, Heng; Yao, Zhuo

2017-04-01

Air quality modelers often rely on regional travel demand models to estimate the vehicle activity data for emission models, however, most of the current travel demand models can only output reliable person travel activity rather than goods/service specific travel activity. This paper presents the successful application of data-driven, Spatial Regression and output optimization Truck model (SPARE-Truck) to develop truck-related activity inputs for the mobile emission model, and eventually to produce truck specific gridded emissions. To validate the proposed methodology, the Cincinnati metropolitan area in United States was selected as a case study site. From the results, it is found that the truck miles traveled predicted using traditional methods tend to underestimate - overall 32% less than proposed model- truck miles traveled. The coefficient of determination values for different truck types range between 0.82 and 0.97, except the motor homes which showed least model fit with 0.51. Consequently, the emission inventories calculated from the traditional methods were also underestimated i.e. -37% for NOx, -35% for SO2, -43% for VOC, -43% for BC, -47% for OC and - 49% for PM2.5. Further, the proposed method also predicted within ∼7% of the national emission inventory for all pollutants. The bottom-up gridding methodology used in this paper could allocate the emissions to grid cell where more truck activity is expected, and it is verified against regional land-use data. Most importantly, using proposed method it is easy to segregate gridded emission inventory by truck type, which is of particular interest for decision makers, since currently there is no reliable method to test different truck-category specific travel-demand management strategies for air pollution control.

Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

PubMed Central

Schöner, Gregor; Gail, Alexander

2012-01-01

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations. PMID:23166483

Theta Coordinated Error-Driven Learning in the Hippocampus

PubMed Central

Ketz, Nicholas; Morkonda, Srinimisha G.; O'Reilly, Randall C.

2013-01-01

The learning mechanism in the hippocampus has almost universally been assumed to be Hebbian in nature, where individual neurons in an engram join together with synaptic weight increases to support facilitated recall of memories later. However, it is also widely known that Hebbian learning mechanisms impose significant capacity constraints, and are generally less computationally powerful than learning mechanisms that take advantage of error signals. We show that the differential phase relationships of hippocampal subfields within the overall theta rhythm enable a powerful form of error-driven learning, which results in significantly greater capacity, as shown in computer simulations. In one phase of the theta cycle, the bidirectional connectivity between CA1 and entorhinal cortex can be trained in an error-driven fashion to learn to effectively encode the cortical inputs in a compact and sparse form over CA1. In a subsequent portion of the theta cycle, the system attempts to recall an existing memory, via the pathway from entorhinal cortex to CA3 and CA1. Finally the full theta cycle completes when a strong target encoding representation of the current input is imposed onto the CA1 via direct projections from entorhinal cortex. The difference between this target encoding and the attempted recall of the same representation on CA1 constitutes an error signal that can drive the learning of CA3 to CA1 synapses. This CA3 to CA1 pathway is critical for enabling full reinstatement of recalled hippocampal memories out in cortex. Taken together, these new learning dynamics enable a much more robust, high-capacity model of hippocampal learning than was available previously under the classical Hebbian model. PMID:23762019

Supply-chain management: exceeding the customer's expectations.

PubMed

Ramsay, B

2000-10-01

Driven by increasing competition, manufacturers are desperate to cut costs and are looking for increased efficiency and customer service from their supply chains. E-commerce offers a new model of supply and demand, but many companies do not have the processes in place to support this new model. By implementing the techniques discussed here they can achieve substantial improvements in performance.

When Does Model-Based Control Pay Off?

PubMed Central

2016-01-01

Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to “model-free” and “model-based” strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand. PMID:27564094

When Does Model-Based Control Pay Off?

PubMed

Kool, Wouter; Cushman, Fiery A; Gershman, Samuel J

2016-08-01

Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to "model-free" and "model-based" strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand.

Introducing Risk Management Techniques Within Project Based Software Engineering Courses

NASA Astrophysics Data System (ADS)

Port, Daniel; Boehm, Barry

2002-03-01

In 1996, USC switched its core two-semester software engineering course from a hypothetical-project, homework-and-exam course based on the Bloom taxonomy of educational objectives (knowledge, comprehension, application, analysis, synthesis, and evaluation). The revised course is a real-client team-project course based on the CRESST model of learning objectives (content understanding, problem solving, collaboration, communication, and self-regulation). We used the CRESST cognitive demands analysis to determine the necessary student skills required for software risk management and the other major project activities, and have been refining the approach over the last 5 years of experience, including revised versions for one-semester undergraduate and graduate project course at Columbia. This paper summarizes our experiences in evolving the risk management aspects of the project course. These have helped us mature more general techniques such as risk-driven specifications, domain-specific simplifier and complicator lists, and the schedule as an independent variable (SAIV) process model. The largely positive results in terms of review of pass / fail rates, client evaluations, product adoption rates, and hiring manager feedback are summarized as well.

Problem Based Learning in Design and Technology Education Supported by Hypermedia-Based Environments

ERIC Educational Resources Information Center

Page, Tom; Lehtonen, Miika

2006-01-01

Audio-visual advances in virtual reality (VR) technology have given rise to innovative new ways to teach and learn. However, so far teaching and learning processes have been technologically driven as opposed to pedagogically led. This paper identifies the development of a pedagogical model and its application for teaching, studying and learning…

Veterans Health Administration Office of Nursing Services exploration of positive patient care synergies fueled by consumer demand: care coordination, advanced clinic access, and patient self-management.

PubMed

Wertenberger, Sydney; Yerardi, Ruth; Drake, Audrey C; Parlier, Renee

2006-01-01

The consumers who utilize the Veterans Health Administration healthcare system are older, and most are learning to live with chronic diseases. Their desires and needs have driven changes within the Veterans Health Administration. Through patient satisfaction initiatives and other feedback sources, consumers have made it clear that they do not want to wait for their care, they want a say in what care is provided to them, and they want to remain as independent as possible. Two interdisciplinary processes/models of healthcare are being implemented on the national level to address these issues: advanced clinic access and care coordination. These programs have a synergistic relationship and are integrated with patient self-management initiatives. Positive outcomes of these programs also meet the needs of our staff. As these new processes and programs are implemented nationwide, skills of both patients and nursing staff who provide their care need to be enhanced to meet the challenges of providing nursing care now and into the 21st century. Veterans Health Administration Office of Nursing Services Strategic Planning Work Group is defining and implementing processes/programs to ensure nurses have the knowledge, information, and skills to meet these patient care demands at all levels within the organization.

Skills Certificates Signal Competencies in a Demand-Driven Economy.

ERIC Educational Resources Information Center

WorkAmerica, 2000

2000-01-01

This issue focuses on the National Alliance of Business's work with employers to sort out how certificates can most effectively indicate workplace skills and requirements and confirm that certified individuals possess them. "Skills Certificates Signal Competencies in a Demand-Driven Economy" discusses the needs to which certificates respond; how…

Effective production planning for purchased part under long lead time and uncertain demand: MRP Vs demand-driven MRP

NASA Astrophysics Data System (ADS)

Shofa, M. J.; Moeis, A. O.; Restiana, N.

2018-04-01

MRP as a production planning system is appropriate for the deterministic environment. Unfortunately, most production systems such as customer demands are stochastic, so that MRP is inappropriate at the time. Demand-Driven MRP (DDMRP) is new approach for production planning system dealing with demand uncertainty. The objective of this paper is to compare the MRP and DDMRP for purchased part under long lead time and uncertain demand in terms of average inventory levels. The evaluation is conducted through a discrete event simulation with the long lead time and uncertain demand scenarios. The next step is evaluating the performance of DDMRP by comparing the inventory level of DDMRP with MRP. As result, DDMRP is more effective production planning than MRP in terms of average inventory levels.

Partners with Clinical Practice: Evaluating the Student and Staff Experiences of On-Line Continuing Professional Development for Qualified Nephrology Practitioners

ERIC Educational Resources Information Center

Hurst, Judith; Quinsee, Susannah

2005-01-01

The inclusion of online learning technologies into the higher education (HE) curriculum is frequently associated with the design and development of new models of learning. One could argue that e-learning even demands a reconfiguration of traditional methods of learning and teaching. However, this transformation in pedagogic methodology does not…

2012: A Brave New World

ERIC Educational Resources Information Center

Jeffreys, Andrea

2012-01-01

The Australian Government decision in response to the Bradley review to introduce a demand-driven funding model for undergraduate university places from 2012 was met with mixed reaction across the higher education sector. The removal of caps without subsequent fee deregulation is considered by some to be unsustainable. Opinions suggest that…

Supporting Inquiry in Science Classrooms with the Web

ERIC Educational Resources Information Center

Simons, Krista; Clark, Doug

2005-01-01

This paper focuses on Web-based science inquiry and five representative science learning environments. The discussion centers around features that sustain science inquiry, namely, data-driven investigation, modeling, collaboration, and scaffolding. From the perspective of these features five science learning environments are detailed: Whyville,…

A Technology Enhanced Learning Model for Quality Education

NASA Astrophysics Data System (ADS)

Sherly, Elizabeth; Uddin, Md. Meraj

Technology Enhanced Learning and Teaching (TELT) Model provides learning through collaborations and interactions with a framework for content development and collaborative knowledge sharing system as a supplementary for learning to improve the quality of education system. TELT deals with a unique pedagogy model for Technology Enhanced Learning System which includes course management system, digital library, multimedia enriched contents and video lectures, open content management system and collaboration and knowledge sharing systems. Open sources like Moodle and Wiki for content development, video on demand solution with a low cost mid range system, an exhaustive digital library are provided in a portal system. The paper depicts a case study of e-learning initiatives with TELT model at IIITM-K and how effectively implemented.

Reduction of peak energy demand based on smart appliances energy consumption adjustment

NASA Astrophysics Data System (ADS)

Powroźnik, P.; Szulim, R.

2017-08-01

In the paper the concept of elastic model of energy management for smart grid and micro smart grid is presented. For the proposed model a method for reducing peak demand in micro smart grid has been defined. The idea of peak demand reduction in elastic model of energy management is to introduce a balance between demand and supply of current power for the given Micro Smart Grid in the given moment. The results of the simulations studies were presented. They were carried out on real household data available on UCI Machine Learning Repository. The results may have practical application in the smart grid networks, where there is a need for smart appliances energy consumption adjustment. The article presents a proposal to implement the elastic model of energy management as the cloud computing solution. This approach of peak demand reduction might have application particularly in a large smart grid.

A New Approach for New Demands: The Promise of Learning-Oriented School Leadership

ERIC Educational Resources Information Center

Drago-Severson, Eleanor; Blum-DeStefano, Jessica

2013-01-01

In response to the complexity and mounting adaptive challenges of teaching, learning and leadership today, this article presents an overview of a new "learning-oriented model of school leadership," which is composed of four pillar practices--teaming, mentoring, collegial inquiry, and providing leadership roles--that support internal…

A Faculty Evaluation Model for Online Instructors: Mentoring and Evaluation in the Online Classroom

ERIC Educational Resources Information Center

Mandernach, B. Jean; Donnelli, Emily; Dailey, Amber; Schulte, Marthann

2005-01-01

The rapid growth of online learning has mandated the development of faculty evaluation models geared specifically toward the unique demands of the online classroom. With a foundation in the best practices of online learning, adapted to meet the dynamics of a growing online program, the Online Instructor Evaluation System created at Park University…

Improving Critical Thinking Skills Using Learning Model Logan Avenue Problem Solving (LAPS)-Heuristic

ERIC Educational Resources Information Center

Anggrianto, Desi; Churiyah, Madziatul; Arief, Mohammad

2016-01-01

This research was conducted in order to know the effect of Logan Avenue Problem Solving (LAPS)-Heuristic learning model towards critical thinking skills of students of class X Office Administration (APK) in SMK Negeri 1 Ngawi, East Java, Indonesia on material curve and equilibrium of demand and supply, subject Introduction to Economics and…

The Study on "Academic Game"-Oriented English Course Model for Postgraduates in Agricultural Universities

ERIC Educational Resources Information Center

Xia, Xinrong

2010-01-01

Based on the analysis of the questionnaire survey on learning motivation and learning needs of postgraduates and their demands and suggestions on English teaching, the paper makes a beneficial exploration on English course model for postgraduates in agricultural universities. Under the guidance of academic game theory, the "language skills+…

Evidence of market-driven size-selective fishing and the mediating effects of biological and institutional factors

PubMed Central

Reddy, Sheila M. W.; Wentz, Allison; Aburto-Oropeza, Octavio; Maxey, Martin; Nagavarapu, Sriniketh; Leslie, Heather M.

2014-01-01

Market demand is often ignored or assumed to lead uniformly to the decline of resources. Yet little is known about how market demand influences natural resources in particular contexts, or the mediating effects of biological or institutional factors. Here, we investigate this problem by examining the Pacific red snapper (Lutjanus peru) fishery around La Paz, Mexico, where medium or “plate-sized” fish are sold to restaurants at a premium price. If higher demand for plate-sized fish increases the relative abundance of the smallest (recruit size class) and largest (most fecund) fish, this may be a market mechanism to increase stocks and fishermen’s revenues. We tested this hypothesis by estimating the effect of prices on the distribution of catch across size classes using daily records of prices and catch. We linked predictions from this economic choice model to a staged-based model of the fishery to estimate the effects on the stock and revenues from harvest. We found that the supply of plate-sized fish increased by 6%, while the supply of large fish decreased by 4% as a result of a 13% price premium for plate-sized fish. This market-driven size selection increased revenues (14%) but decreased total fish biomass (−3%). However, when market-driven size selection was combined with limited institutional constraints, both fish biomass (28%) and fishermen’s revenue (22%) increased. These results show that the direction and magnitude of the effects of market demand on biological populations and human behavior can depend on both biological attributes and institutional constraints. Fisheries management may capitalize on these conditional effects by implementing size-based regulations when economic and institutional incentives will enhance compliance, as in the case we describe here, or by creating compliance enhancing conditions for existing regulations. PMID:23865225

Data-driven advice for applying machine learning to bioinformatics problems

PubMed Central

Olson, Randal S.; La Cava, William; Mustahsan, Zairah; Varik, Akshay; Moore, Jason H.

2017-01-01

As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems. PMID:29218881

Effects of stimulus salience on touchscreen serial reversal learning in a mouse model of fragile X syndrome

PubMed Central

Dickson, Price E.; Corkill, Beau; McKimm, Eric; Miller, Mellessa M.; Calton, Michele A.; Goldowitz, Daniel; Blaha, Charles D.; Mittleman, Guy

2013-01-01

Fragile X syndrome (FXS) is the most common inherited form of intellectual disability in males and the most common genetic cause of autism. Although executive dysfunction is consistently found in humans with FXS, evidence of executive dysfunction in Fmr1 KO mice, a mouse model of FXS, has been inconsistent. One possible explanation for this is that executive dysfunction in Fmr1 KO mice, similar to humans with FXS, is only evident when cognitive demands are high. Using touchscreen operant conditioning chambers, male Fmr1 KO mice and their male wildtype littermates were tested on the acquisition of a pairwise visual discrimination followed by four serial reversals of the response rule. We assessed reversal learning performance under two different conditions. In the first, the correct stimulus was salient and the incorrect stimulus was non-salient. In the second and more challenging condition, the incorrect stimulus was salient and the correct stimulus was non-salient; this increased cognitive load by introducing conflict between sensory-driven (i.e., bottom-up) and task-dependent (i.e., top-down) signals. Fmr1 KOs displayed two distinct impairments relative to wildtype littermates. First, Fmr1 KOs committed significantly more learning-type errors during the second reversal stage, but only under high cognitive load. Second, during the first reversal stage, Fmr1 KOs committed significantly more attempts to collect a reward during the timeout following an incorrect response. These findings indicate that Fmr1 KO mice display executive dysfunction that, in some cases, is only evident under high cognitive load. PMID:23747611

CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system

NASA Astrophysics Data System (ADS)

Zhang, Hongbo; Singh, Vijay P.; Wang, Bin; Yu, Yinghao

2016-09-01

Hydrological forecasting is complicated by flow regime alterations in a coupled socio-hydrologic system, encountering increasingly non-stationary, nonlinear and irregular changes, which make decision support difficult for future water resources management. Currently, many hybrid data-driven models, based on the decomposition-prediction-reconstruction principle, have been developed to improve the ability to make predictions of annual streamflow. However, there exist many problems that require further investigation, the chief among which is the direction of trend components decomposed from annual streamflow series and is always difficult to ascertain. In this paper, a hybrid data-driven model was proposed to capture this issue, which combined empirical mode decomposition (EMD), radial basis function neural networks (RBFNN), and external forces (EF) variable, also called the CEREF model. The hybrid model employed EMD for decomposition and RBFNN for intrinsic mode function (IMF) forecasting, and determined future trend component directions by regression with EF as basin water demand representing the social component in the socio-hydrologic system. The Wuding River basin was considered for the case study, and two standard statistical measures, root mean squared error (RMSE) and mean absolute error (MAE), were used to evaluate the performance of CEREF model and compare with other models: the autoregressive (AR), RBFNN and EMD-RBFNN. Results indicated that the CEREF model had lower RMSE and MAE statistics, 42.8% and 7.6%, respectively, than did other models, and provided a superior alternative for forecasting annual runoff in the Wuding River basin. Moreover, the CEREF model can enlarge the effective intervals of streamflow forecasting compared to the EMD-RBFNN model by introducing the water demand planned by the government department to improve long-term prediction accuracy. In addition, we considered the high-frequency component, a frequent subject of concern in EMD-based forecasting, and results showed that removing high-frequency component is an effective measure to improve forecasting precision and is suggested for use with the CEREF model for better performance. Finally, the study concluded that the CEREF model can be used to forecast non-stationary annual streamflow change as a co-evolution of hydrologic and social systems with better accuracy. Also, the modification about removing high-frequency can further improve the performance of the CEREF model. It should be noted that the CEREF model is beneficial for data-driven hydrologic forecasting in complex socio-hydrologic systems, and as a simple data-driven socio-hydrologic forecasting model, deserves more attention.

Analysis of the Pricing Process in Electricity Market using Multi-Agent Model

NASA Astrophysics Data System (ADS)

Shimomura, Takahiro; Saisho, Yuichi; Fujii, Yasumasa; Yamaji, Kenji

Many electric utilities world-wide have been forced to change their ways of doing business, from vertically integrated mechanisms to open market systems. We are facing urgent issues about how we design the structures of power market systems. In order to settle down these issues, many studies have been made with market models of various characteristics and regulations. The goal of modeling analysis is to enrich our understanding of fundamental process that may appear. However, there are many kinds of modeling methods. Each has drawback and advantage about validity and versatility. This paper presents two kinds of methods to construct multi-agent market models. One is based on game theory and another is based on reinforcement learning. By comparing the results of the two methods, they can advance in validity and help us figure out potential problems in electricity markets which have oligopolistic generators, demand fluctuation and inelastic demand. Moreover, this model based on reinforcement learning enables us to consider characteristics peculiar to electricity markets which have plant unit characteristics, seasonable and hourly demand fluctuation, real-time regulation market and operating reserve market. This model figures out importance of the share of peak-load-plants and the way of designing operating reserve market.

Putting people first: re-thinking the role of technology in augmentative and alternative communication intervention.

PubMed

Light, Janice; McNaughton, David

2013-12-01

Current technologies provide individuals with complex communication needs with a powerful array of communication, information, organization, and social networking options. However, there is the danger that the excitement over these new devices will result in a misplaced focus on the technology, to the neglect of what must be the central focus - the people with complex communication needs who require augmentative and alternative communication (AAC). In order to truly harness the power of technology, rehabilitation and educational professionals must ensure that AAC intervention is driven, not by the devices, but rather by the communication needs of the individual. Furthermore, those involved in AAC research and development activities must ensure that the design of AAC technologies is driven by an understanding of motor, sensory, cognitive, and linguistic processing, in order to minimize learning demands and maximize communication power for individuals with complex communication needs across the life span.

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

Learning at work: competence development or competence-stress.

PubMed

Paulsson, Katarina; Ivergård, Toni; Hunt, Brian

2005-03-01

Changes in work and the ways in which it is carried out bring a need for upgrading workplace knowledge, skills and competencies. In today's workplaces, and for a number of reasons, workloads are higher than ever and stress is a growing concern (Health Risk Soc. 2(2) (2000) 173; Educat. Psychol. Meas. 61(5) (2001) 866). Increased demand for learning brings a risk that this will be an additional stress factor and thus a risk to health. Our research study is based on the control-demand-support model of Karasek and Theorell (Health Work: Stress, Productivity and the Reconstruction of Working Life, Basic Books/Harper, New York, 1990). We have used this model for our own empirical research with the aim to evaluate the model in the modern workplace. Our research enables us to expand the model in the light of current workplace conditions-especially those relating to learning. We report empirical data from a questionnaire survey of working conditions in two different branches of industry. We are able to define differences between companies in terms of working conditions and competence development. We describe and discuss the effects these conditions have on workplace competence development. Our research results show that increased workers' control of the learning process makes competence development more stimulating, is likely to simplify the work and reduces (learning-related) stress. It is therefore important that learning at work allows employees to control their learning and also allows time for the process of learning and reflection.

Integrating FMEA in a Model-Driven Methodology

NASA Astrophysics Data System (ADS)

Scippacercola, Fabio; Pietrantuono, Roberto; Russo, Stefano; Esper, Alexandre; Silva, Nuno

2016-08-01

Failure Mode and Effects Analysis (FMEA) is a well known technique for evaluating the effects of potential failures of components of a system. FMEA demands for engineering methods and tools able to support the time- consuming tasks of the analyst. We propose to make FMEA part of the design of a critical system, by integration into a model-driven methodology. We show how to conduct the analysis of failure modes, propagation and effects from SysML design models, by means of custom diagrams, which we name FMEA Diagrams. They offer an additional view of the system, tailored to FMEA goals. The enriched model can then be exploited to automatically generate FMEA worksheet and to conduct qualitative and quantitative analyses. We present a case study from a real-world project.

Regional health workforce monitoring as governance innovation: a German model to coordinate sectoral demand, skill mix and mobility.

PubMed

Kuhlmann, E; Lauxen, O; Larsen, C

2016-11-28

As health workforce policy is gaining momentum, data sources and monitoring systems have significantly improved in the European Union and internationally. Yet data remain poorly connected to policy-making and implementation and often do not adequately support integrated approaches. This brings the importance of governance and the need for innovation into play. The present case study introduces a regional health workforce monitor in the German Federal State of Rhineland-Palatinate and seeks to explore the capacity of monitoring to innovate health workforce governance. The monitor applies an approach from the European Network on Regional Labour Market Monitoring to the health workforce. The novel aspect of this model is an integrated, procedural approach that promotes a 'learning system' of governance based on three interconnected pillars: mixed methods and bottom-up data collection, strong stakeholder involvement with complex communication tools and shared decision- and policy-making. Selected empirical examples illustrate the approach and the tools focusing on two aspects: the connection between sectoral, occupational and mobility data to analyse skill/qualification mixes and the supply-demand matches and the connection between monitoring and stakeholder-driven policy. Regional health workforce monitoring can promote effective governance in high-income countries like Germany with overall high density of health workers but maldistribution of staff and skills. The regional stakeholder networks are cost-effective and easily accessible and might therefore be appealing also to low- and middle-income countries.

Assessing Regional-Scale Impacts of Short Rotation Coppices on Ecosystem Services by Modeling Land-Use Decisions.

PubMed

Schulze, Jule; Frank, Karin; Priess, Joerg A; Meyer, Markus A

2016-01-01

Meeting the world's growing energy demand through bioenergy production involves extensive land-use change which could have severe environmental and social impacts. Second generation bioenergy feedstocks offer a possible solution to this problem. They have the potential to reduce land-use conflicts between food and bioenergy production as they can be grown on low quality land not suitable for food production. However, a comprehensive impact assessment that considers multiple ecosystem services (ESS) and biodiversity is needed to identify the environmentally best feedstock option, as trade-offs are inherent. In this study, we simulate the spatial distribution of short rotation coppices (SRCs) in the landscape of the Mulde watershed in Central Germany by modeling profit-maximizing farmers under different economic and policy-driven scenarios using a spatially explicit economic simulation model. This allows to derive general insights and a mechanistic understanding of regional-scale impacts on multiple ESS in the absence of large-scale implementation. The modeled distribution of SRCs, required to meet the regional demand of combined heat and power (CHP) plants for solid biomass, had little or no effect on the provided ESS. In the policy-driven scenario, placing SRCs on low or high quality soils to provide ecological focus areas, as required within the Common Agricultural Policy in the EU, had little effect on ESS. Only a substantial increase in the SRC production area, beyond the regional demand of CHP plants, had a relevant effect, namely a negative impact on food production as well as a positive impact on biodiversity and regulating ESS. Beneficial impacts occurred for single ESS. However, the number of sites with balanced ESS supply hardly increased due to larger shares of SRCs in the landscape. Regression analyses showed that the occurrence of sites with balanced ESS supply was more strongly driven by biophysical factors than by the SRC share in the landscape. This indicates that SRCs negligibly affect trade-offs between individual ESS. Coupling spatially explicit economic simulation models with environmental and ESS assessment models can contribute to a comprehensive impact assessment of bioenergy feedstocks that have not yet been planted.

Assessing Regional-Scale Impacts of Short Rotation Coppices on Ecosystem Services by Modeling Land-Use Decisions

PubMed Central

Schulze, Jule; Frank, Karin; Priess, Joerg A.; Meyer, Markus A.

2016-01-01

Meeting the world’s growing energy demand through bioenergy production involves extensive land-use change which could have severe environmental and social impacts. Second generation bioenergy feedstocks offer a possible solution to this problem. They have the potential to reduce land-use conflicts between food and bioenergy production as they can be grown on low quality land not suitable for food production. However, a comprehensive impact assessment that considers multiple ecosystem services (ESS) and biodiversity is needed to identify the environmentally best feedstock option, as trade-offs are inherent. In this study, we simulate the spatial distribution of short rotation coppices (SRCs) in the landscape of the Mulde watershed in Central Germany by modeling profit-maximizing farmers under different economic and policy-driven scenarios using a spatially explicit economic simulation model. This allows to derive general insights and a mechanistic understanding of regional-scale impacts on multiple ESS in the absence of large-scale implementation. The modeled distribution of SRCs, required to meet the regional demand of combined heat and power (CHP) plants for solid biomass, had little or no effect on the provided ESS. In the policy-driven scenario, placing SRCs on low or high quality soils to provide ecological focus areas, as required within the Common Agricultural Policy in the EU, had little effect on ESS. Only a substantial increase in the SRC production area, beyond the regional demand of CHP plants, had a relevant effect, namely a negative impact on food production as well as a positive impact on biodiversity and regulating ESS. Beneficial impacts occurred for single ESS. However, the number of sites with balanced ESS supply hardly increased due to larger shares of SRCs in the landscape. Regression analyses showed that the occurrence of sites with balanced ESS supply was more strongly driven by biophysical factors than by the SRC share in the landscape. This indicates that SRCs negligibly affect trade-offs between individual ESS. Coupling spatially explicit economic simulation models with environmental and ESS assessment models can contribute to a comprehensive impact assessment of bioenergy feedstocks that have not yet been planted. PMID:27082742

Computational intelligence in earth sciences and environmental applications: issues and challenges.

PubMed

Cherkassky, V; Krasnopolsky, V; Solomatine, D P; Valdes, J

2006-03-01

This paper introduces a generic theoretical framework for predictive learning, and relates it to data-driven and learning applications in earth and environmental sciences. The issues of data quality, selection of the error function, incorporation of the predictive learning methods into the existing modeling frameworks, expert knowledge, model uncertainty, and other application-domain specific problems are discussed. A brief overview of the papers in the Special Issue is provided, followed by discussion of open issues and directions for future research.

The Past, Present, and Future of Demand-Driven Acquisitions in Academic Libraries

ERIC Educational Resources Information Center

Goedeken, Edward A.; Lawson, Karen

2015-01-01

Demand-driven acquisitions (DDA) programs have become a well-established approach toward integrating user involvement in the process of building academic library collections. However, these programs are in a constant state of evolution. A recent iteration in this evolution of ebook availability is the advent of large ebook collections whose…

VAMPnets for deep learning of molecular kinetics.

PubMed

Mardt, Andreas; Pasquali, Luca; Wu, Hao; Noé, Frank

2018-01-02

There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead to large modeling errors. Here we employ the variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states, thus combining the whole data processing pipeline in a single end-to-end framework. Our method performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models.

BRIDGE21--Exploring the Potential to Foster Intrinsic Student Motivation through a Team-Based, Technology-Mediated Learning Model

ERIC Educational Resources Information Center

Lawlor, John; Marshall, Kevin; Tangney, Brendan

2016-01-01

It is generally accepted that intrinsic student motivation is a critical requirement for effective learning but formal learning in school places a huge reliance on extrinsic motivation to focus the learner. This reliance on extrinsic motivation is driven by the pressure on formal schooling to "deliver to the test." The experience of the…

Frequency-specific hippocampal-prefrontal interactions during associative learning

PubMed Central

Brincat, Scott L.; Miller, Earl K.

2015-01-01

Much of our knowledge of the world depends on learning associations (e.g., face-name), for which the hippocampus (HPC) and prefrontal cortex (PFC) are critical. HPC-PFC interactions have rarely been studied in monkeys, whose cognitive/mnemonic abilities are akin to humans. Here, we show functional differences and frequency-specific interactions between HPC and PFC of monkeys learning object-pair associations, an animal model of human explicit memory. PFC spiking activity reflected learning in parallel with behavioral performance, while HPC neurons reflected feedback about whether trial-and-error guesses were correct or incorrect. Theta-band HPC-PFC synchrony was stronger after errors, was driven primarily by PFC to HPC directional influences, and decreased with learning. In contrast, alpha/beta-band synchrony was stronger after correct trials, was driven more by HPC, and increased with learning. Rapid object associative learning may occur in PFC, while HPC may guide neocortical plasticity by signaling success or failure via oscillatory synchrony in different frequency bands. PMID:25706471

Interactive lesion segmentation with shape priors from offline and online learning.

PubMed

Shepherd, Tony; Prince, Simon J D; Alexander, Daniel C

2012-09-01

In medical image segmentation, tumors and other lesions demand the highest levels of accuracy but still call for the highest levels of manual delineation. One factor holding back automatic segmentation is the exemption of pathological regions from shape modelling techniques that rely on high-level shape information not offered by lesions. This paper introduces two new statistical shape models (SSMs) that combine radial shape parameterization with machine learning techniques from the field of nonlinear time series analysis. We then develop two dynamic contour models (DCMs) using the new SSMs as shape priors for tumor and lesion segmentation. From training data, the SSMs learn the lower level shape information of boundary fluctuations, which we prove to be nevertheless highly discriminant. One of the new DCMs also uses online learning to refine the shape prior for the lesion of interest based on user interactions. Classification experiments reveal superior sensitivity and specificity of the new shape priors over those previously used to constrain DCMs. User trials with the new interactive algorithms show that the shape priors are directly responsible for improvements in accuracy and reductions in user demand.

What Every Worker Wants? Evidence about Employee Demand for Learning

ERIC Educational Resources Information Center

Findlay, Jeanette; Findlay, Patricia; Warhurst, Chris

2012-01-01

In order to boost learning, recent UK governments have invested in trade union-led workplace learning. Investing in the supply of learning is useful but ignores the demand for learning by workers, about which there is little research. This paper addresses this lacunae by analysing worker demand for learning, which workers want learning, what…

DNA Dispose, but Subjects Decide. Learning and the Extended Synthesis.

PubMed

Lindholm, Markus

Adaptation by means of natural selection depends on the ability of populations to maintain variation in heritable traits. According to the Modern Synthesis this variation is sustained by mutations and genetic drift. Epigenetics, evodevo, niche construction and cultural factors have more recently been shown to contribute to heritable variation, however, leading an increasing number of biologists to call for an extended view of speciation and evolution. An additional common feature across the animal kingdom is learning, defined as the ability to change behavior according to novel experiences or skills. Learning constitutes an additional source for phenotypic variation, and change in behavior may induce long lasting shifts in fitness, and hence favor evolutionary novelties. Based on published studies, I demonstrate how learning about food, mate choice and habitats has contributed substantially to speciation in the canonical story of Darwin's finches on the Galapagos Islands. Learning cannot be reduced to genetics, because it demands decisions, which requires a subject. Evolutionary novelties may hence emerge both from shifts in allelic frequencies and from shifts in learned, subject driven behavior. The existence of two principally different sources of variation also prevents the Modern Synthesis from self-referring explanations.

Evaluation of Hybrid Learning in a Construction Engineering Context: A Mixed-Method Approach

ERIC Educational Resources Information Center

Karabulut-Ilgu, Aliye; Jahren, Charles

2016-01-01

Engineering educators call for a widespread implementation of hybrid learning to respond to rapidly changing demands of the 21st century. In response to this call, a junior-level course in the Construction Engineering program entitled Construction Equipment and Heavy Construction Methods was converted into a hybrid learning model. The overarching…

A Dynamic Programming Approach to Identifying the Shortest Path in Virtual Learning Environments

ERIC Educational Resources Information Center

Fazlollahtabar, Hamed

2008-01-01

E-learning has been widely adopted as a promising solution by many organizations to offer learning-on-demand opportunities to individual employees (learners) in order to reduce training time and cost. While successful information systems models have received much attention among researchers, little research has been conducted to assess the success…

Demand for private health insurance: how important is the quality gap?

PubMed

Costa, Joan; García, Jaume

2003-07-01

Perceived quality of private and public health care, income and insurance premium are among the determinants of demand for private health insurance (PHI). In the context of a model in which individuals are expected utility maximizers, the non purchasing choice can result in consuming either public health care or private health care with full cost paid out-of-pocket. This paper empirically analyses the effect of the determinants of the demand for PHI on the probability of purchasing PHI by estimating a pseudo-structural model to deal with missing data and endogeneity issues. Our findings support the hypothesis that the demand for PHI is indeed driven by the quality gap between private and public health care. As expected, PHI is a normal good and a rise in the insurance premium reduces the probability of purchasing PHI albeit displaying price elasticities smaller than one in absolute value for different groups of individuals. Copyright 2002 John Wiley & Sons, Ltd.

Situations, Interaction, Process and Affordances: An Ecological Psychology Perspective.

ERIC Educational Resources Information Center

Young, Michael F.; DePalma, Andrew; Garrett, Steven

2002-01-01

From an ecological psychology perspective, a full analysis of any learning context must acknowledge the complex nonlinear dynamics that unfold as an intentionally-driven learner interacts with a technology-based purposefully designed learning environment. A full situation model would need to incorporate constraints from the environment and also…

Semantic Web-Driven LMS Architecture towards a Holistic Learning Process Model Focused on Personalization

ERIC Educational Resources Information Center

Kerkiri, Tania

2010-01-01

A comprehensive presentation is here made on the modular architecture of an e-learning platform with a distinctive emphasis on content personalization, combining advantages from semantic web technology, collaborative filtering and recommendation systems. Modules of this architecture handle information about both the domain-specific didactic…

Assessment of Programming Language Learning Based on Peer Code Review Model: Implementation and Experience Report

ERIC Educational Resources Information Center

Wang, Yanqing; Li, Hang; Feng, Yuqiang; Jiang, Yu; Liu, Ying

2012-01-01

The traditional assessment approach, in which one single written examination counts toward a student's total score, no longer meets new demands of programming language education. Based on a peer code review process model, we developed an online assessment system called "EduPCR" and used a novel approach to assess the learning of computer…

The Effects of Mathematical Modeling on Creative Production Ability and Self-Directed Learning Attitude

ERIC Educational Resources Information Center

Kim, Sun Hee; Kim, Soojin

2010-01-01

What should we do to educate the mathematically gifted and how should we do it? In this research, to satisfy diverse mathematical and cognitive demands of the gifted who have excellent learning ability and task tenacity in mathematics, we sought to apply mathematical modeling. One of the objectives of the gifted education in Korea is cultivating…

Model-Driven Development of Interactive Multimedia Applications with MML

NASA Astrophysics Data System (ADS)

Pleuss, Andreas; Hussmann, Heinrich

There is an increasing demand for high-quality interactive applications which combine complex application logic with a sophisticated user interface, making use of individual media objects like graphics, animations, 3D graphics, audio or video. Their development is still challenging as it requires the integration of software design, user interface design, and media design.

Community College Dual Enrollment Faculty Orientation: A Utilization-Focused Approach

ERIC Educational Resources Information Center

Charlier, Hara D.; Duggan, Molly H.

2010-01-01

The current climate of accountability demands that institutions engage in data-driven program evaluation. In order to promote quality dual enrollment (DE) programs, institutions must support the adjunct faculty teaching college courses in high schools. This study uses Patton's utilization-focused model (1997) to conduct a formative evaluation of a…

Managing Reward in Developing Economies: The Challenge for Multinational Corporations

ERIC Educational Resources Information Center

Opute, John

2010-01-01

Reward has been, and continues to be, subject to significant changes in developing economies; the industrial relations model prevalent being driven by the complex socio-economic and cultural paradigms and the increasing demands of globalisation. The issue of reward in developing economies is therefore central and dependent on numerous contextual…

Gaussian Processes for Data-Efficient Learning in Robotics and Control.

PubMed

Deisenroth, Marc Peter; Fox, Dieter; Rasmussen, Carl Edward

2015-02-01

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

Flexible Demand Management under Time-Varying Prices

NASA Astrophysics Data System (ADS)

Liang, Yong

In this dissertation, the problem of flexible demand management under time-varying prices is studied. This generic problem has many applications, which usually have multiple periods in which decisions on satisfying demand need to be made, and prices in these periods are time-varying. Examples of such applications include multi-period procurement problem, operating room scheduling, and user-end demand scheduling in the Smart Grid, where the last application is used as the main motivating story throughout the dissertation. The current grid is experiencing an upgrade with lots of new designs. What is of particular interest is the idea of passing time-varying prices that reflect electricity market conditions to end users as incentives for load shifting. One key component, consequently, is the demand management system at the user-end. The objective of the system is to find the optimal trade-off between cost saving and discomfort increment resulted from load shifting. In this dissertation, we approach this problem from the following aspects: (1) construct a generic model, solve for Pareto optimal solutions, and analyze the robust solution that optimizes the worst-case payoffs, (2) extend to a distribution-free model for multiple types of demand (appliances), for which an approximate dynamic programming (ADP) approach is developed, and (3) design other efficient algorithms for practical purposes of the flexible demand management system. We first construct a novel multi-objective flexible demand management model, in which there are a finite number of periods with time-varying prices, and demand arrives in each period. In each period, the decision maker chooses to either satisfy or defer outstanding demand to minimize costs and discomfort over a certain number of periods. We consider both the deterministic model, models with stochastic demand or prices, and when only partial information about the stochastic demand or prices is known. We first analyze the stochastic optimization problem when the objective is to minimize the expected total cost and discomfort, then since the decision maker is likely to be risk-averse, and she wants to protect herself from price spikes, we study the robust optimization problem to address the risk-aversion of the decision maker. We conduct numerical studies to evaluate the price of robustness. Next, we present a detailed model that manages multiple types of flexible demand in the absence of knowledge regarding the distributions of related stochastic processes. Specifically, we consider the case in which time-varying prices with general structures are offered to users, and an energy management system for each household makes optimal energy usage, storage, and trading decisions according to the preferences of users. Because of the uncertainties associated with electricity prices, local generation, and the arrival processes of demand, we formulate a stochastic dynamic programming model, and outline a novel and tractable ADP approach to overcome the curses of dimensionality. Then, we perform numerical studies, whose results demonstrate the effectiveness of the ADP approach. At last, we propose another approximation approach based on Q-learning. In addition, we also develop another decentralization-based heuristic. Both the Q-learning approach and the heuristic make necessary assumptions on the knowledge of information, and each of them has unique advantages. We conduct numerical studies on a testing problem. The simulation results show that both the Q-learning and the decentralization based heuristic approaches work well. Lastly, we conclude the paper with some discussions on future extension directions.

Model-Driven Engineering of Machine Executable Code

NASA Astrophysics Data System (ADS)

Eichberg, Michael; Monperrus, Martin; Kloppenburg, Sven; Mezini, Mira

Implementing static analyses of machine-level executable code is labor intensive and complex. We show how to leverage model-driven engineering to facilitate the design and implementation of programs doing static analyses. Further, we report on important lessons learned on the benefits and drawbacks while using the following technologies: using the Scala programming language as target of code generation, using XML-Schema to express a metamodel, and using XSLT to implement (a) transformations and (b) a lint like tool. Finally, we report on the use of Prolog for writing model transformations.

Auditing the Numeracy Demands of the Australian Curriculum

ERIC Educational Resources Information Center

Goos, Merrilyn; Dole, Shelley; Geiger, Vince

2012-01-01

Numeracy is a general capability to be developed in all learning areas of the Australian Curriculum. We evaluated the numeracy demands of the F-10 curriculum, using a model of numeracy that incorporates mathematical knowledge, dispositions, tools, contexts, and a critical orientation to the use of mathematics. Findings of the history curriculum…

Putting the psychology back into psychological models: mechanistic versus rational approaches.

PubMed

Sakamoto, Yasuaki; Jones, Mattr; Love, Bradley C

2008-09-01

Two basic approaches to explaining the nature of the mind are the rational and the mechanistic approaches. Rational analyses attempt to characterize the environment and the behavioral outcomes that humans seek to optimize, whereas mechanistic models attempt to simulate human behavior using processes and representations analogous to those used by humans. We compared these approaches with regard to their accounts of how humans learn the variability of categories. The mechanistic model departs in subtle ways from rational principles. In particular, the mechanistic model incrementally updates its estimates of category means and variances through error-driven learning, based on discrepancies between new category members and the current representation of each category. The model yields a prediction, which we verify, regarding the effects of order manipulations that the rational approach does not anticipate. Although both rational and mechanistic models can successfully postdict known findings, we suggest that psychological advances are driven primarily by consideration of process and representation and that rational accounts trail these breakthroughs.

A Learning Framework for Control-Oriented Modeling of Buildings

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

Rubio-Herrero, Javier; Chandan, Vikas; Siegel, Charles M.

Buildings consume a significant amount of energy worldwide. Several building optimization and control use cases require models of energy consumption which are control oriented, have high predictive capability, imposes minimal data pre-processing requirements, and have the ability to be adapted continuously to account for changing conditions as new data becomes available. Data driven modeling techniques, that have been investigated so far, while promising in the context of buildings, have been unable to simultaneously satisfy all the requirements mentioned above. In this context, deep learning techniques such as Recurrent Neural Networks (RNNs) hold promise, empowered by advanced computational capabilities and bigmore » data opportunities. In this paper, we propose a deep learning based methodology for the development of control oriented models for building energy management and test in on data from a real building. Results show that the proposed methodology outperforms other data driven modeling techniques significantly. We perform a detailed analysis of the proposed methodology along dimensions such as topology, sensitivity, and downsampling. Lastly, we conclude by envisioning a building analytics suite empowered by the proposed deep framework, that can drive several use cases related to building energy management.« less

Learning clinically useful information from images: Past, present and future.

PubMed

Rueckert, Daniel; Glocker, Ben; Kainz, Bernhard

2016-10-01

Over the last decade, research in medical imaging has made significant progress in addressing challenging tasks such as image registration and image segmentation. In particular, the use of model-based approaches has been key in numerous, successful advances in methodology. The advantage of model-based approaches is that they allow the incorporation of prior knowledge acting as a regularisation that favours plausible solutions over implausible ones. More recently, medical imaging has moved away from hand-crafted, and often explicitly designed models towards data-driven, implicit models that are constructed using machine learning techniques. This has led to major improvements in all stages of the medical imaging pipeline, from acquisition and reconstruction to analysis and interpretation. As more and more imaging data is becoming available, e.g., from large population studies, this trend is likely to continue and accelerate. At the same time new developments in machine learning, e.g., deep learning, as well as significant improvements in computing power, e.g., parallelisation on graphics hardware, offer new potential for data-driven, semantic and intelligent medical imaging. This article outlines the work of the BioMedIA group in this area and highlights some of the challenges and opportunities for future work. Copyright © 2016 Elsevier B.V. All rights reserved.

Instructed knowledge shapes feedback-driven aversive learning in striatum and orbitofrontal cortex, but not the amygdala

PubMed Central

Atlas, Lauren Y; Doll, Bradley B; Li, Jian; Daw, Nathaniel D; Phelps, Elizabeth A

2016-01-01

Socially-conveyed rules and instructions strongly shape expectations and emotions. Yet most neuroscientific studies of learning consider reinforcement history alone, irrespective of knowledge acquired through other means. We examined fear conditioning and reversal in humans to test whether instructed knowledge modulates the neural mechanisms of feedback-driven learning. One group was informed about contingencies and reversals. A second group learned only from reinforcement. We combined quantitative models with functional magnetic resonance imaging and found that instructions induced dissociations in the neural systems of aversive learning. Responses in striatum and orbitofrontal cortex updated with instructions and correlated with prefrontal responses to instructions. Amygdala responses were influenced by reinforcement similarly in both groups and did not update with instructions. Results extend work on instructed reward learning and reveal novel dissociations that have not been observed with punishments or rewards. Findings support theories of specialized threat-detection and may have implications for fear maintenance in anxiety. DOI: http://dx.doi.org/10.7554/eLife.15192.001 PMID:27171199

The corporate university: a model for sustaining an expert workforce in the human services.

PubMed

Gould, Karen E

2005-05-01

The human service industry has become a complex industry in which agencies must respond to the demands of the marketplace. To respond to these demands, agencies must develop and maintain their knowledge capital by offering an extensive array of learning opportunities related to their business goals. The corporate university, a contemporary educational model designed to maintain an expert workforce, allows agencies to meet this need effectively.

Organizational Change, Leadership, and the Transformation of Continuing Professional Development: Lessons Learned From the American College of Cardiology.

PubMed

Beliveau, Mary Ellen; Warnes, Carole A; Harrington, Robert A; Nishimura, Rick A; O'Gara, Patrick T; Sibley, Janice B; Oetgen, William J

2015-01-01

There is a need for a transformational change in clinical education. In postgraduate medical education we have traditionally had a faculty-centric model. That is, faculty knew what needed to be taught and who were the best teachers to teach it. They built the agenda, and worked with staff to follow Accreditation Council for Continuing Medical Education (ACCME) accreditation criteria and manage logistics. Changes in the health care marketplace now demand a learner-centric model-one that embraces needs assessments, identification of practice gaps relative to competency, development of learning objectives, contemporary adult learning theory, novel delivery systems, and measurable outcomes. This article provides a case study of one medical specialty society's efforts to respond to this demand. © 2015 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

US Food Security and Climate Change: Mid-Century Projections of Commodity Crop Production by the IMPACT Model

NASA Astrophysics Data System (ADS)

Takle, E. S.; Gustafson, D. I.; Beachy, R.; Nelson, G. C.; Mason-D'Croz, D.; Palazzo, A.

2013-12-01

Agreement is developing among agricultural scientists on the emerging inability of agriculture to meet growing global food demands. The lack of additional arable land and availability of freshwater have long been constraints on agriculture. Changes in trends of weather conditions that challenge physiological limits of crops, as projected by global climate models, are expected to exacerbate the global food challenge toward the middle of the 21st century. These climate- and constraint-driven crop production challenges are interconnected within a complex global economy, where diverse factors add to price volatility and food scarcity. We use the DSSAT crop modeling suite, together with mid-century projections of four AR4 global models, as input to the International Food Policy Research Institute IMPACT model to project the impact of climate change on food security through the year 2050 for internationally traded crops. IMPACT is an iterative model that responds to endogenous and exogenous drivers to dynamically solve for the world prices that ensure global supply equals global demand. The modeling methodology reconciles the limited spatial resolution of macro-level economic models that operate through equilibrium-driven relationships at a national level with detailed models of biophysical processes at high spatial resolution. The analysis presented here suggests that climate change in the first half of the 21st century does not represent a near-term threat to food security in the US due to the availability of adaptation strategies (e.g., loss of current growing regions is balanced by gain of new growing regions). However, as climate continues to trend away from 20th century norms current adaptation measures will not be sufficient to enable agriculture to meet growing food demand. Climate scenarios from higher-level carbon emissions exacerbate the food shortfall, although uncertainty in climate model projections (particularly precipitation) is a limitation to impact studies.

Local Implementation Effectiveness of a Multi-Tier System of Support in Elementary School Settings

ERIC Educational Resources Information Center

Houlton, Terry P.

2017-01-01

Ensuring all students learn at high levels is demanding. Multi-tier systems of supports (MTSS) has shown promise as a way to promote high levels of learning for all students while catching students who are struggling to learn. However, implementing MTSS models in school districts and schools has seen its challenges. The context of an individual…

Effects of globalisation on higher engineering education in Germany - current and future demands

NASA Astrophysics Data System (ADS)

Morace, Christophe; May, Dominik; Terkowsky, Claudius; Reynet, Olivier

2017-03-01

Germany is well known around the world for the strength of its economy, its industry and for the 'German model' for higher engineering education based on developing technological skills at a very high level. In this article, we firstly describe the former and present model of engineering education in Germany in a context of the globalisation of the world economy and of higher education, in order to understand how it covers the current demand for engineering resources. Secondly, we analyse the impact of globalisation from a technological perspective. To this end, we describe initiatives for innovation driven by the German federal government and engineering societies, and summarise the first impacts on engineering education and on social competence for engineers. Thirdly, we explore to what extent engineering education in Germany trains engineers in social and intercultural competency to comply with the future demands of the challenge of globalisation.

The Adoption of On-Demand Learning in Organizations in the United States

ERIC Educational Resources Information Center

Cui, Lianbin

2010-01-01

There is a lack of studies on the current status of the use of on-demand learning in organizations and factors that may accelerate or hold back the acceptance and implementation of on-demand learning in organizations. The purpose of this study is to contribute to a better understanding of the adoption of on-demand learning in organizations in the…

Lazy evaluation of FP programs: A data-flow approach

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

Wei, Y.H.; Gaudiot, J.L.

1988-12-31

This paper presents a lazy evaluation system for the list-based functional language, Backus` FP in data-driven environment. A superset language of FP, called DFP (Demand-driven FP), is introduced. FP eager programs are transformed into DFP lazy programs which contain the notions of demands. The data-driven execution of DFP programs has the same effects of lazy evaluation. DFP lazy programs have the property of always evaluating a sufficient and necessary result. The infinite sequence generator is used to demonstrate the eager-lazy program transformation and the execution of the lazy programs.

Over-harvesting driven by consumer demand leads to population decline: big-leaf mahogany in South America

Treesearch

James Grogan; Arthur G. Blundell; R. Matthew Landis; Ani Youatt; Raymond E. Gullison; Martha Martinez; Roberto Kometter; Marco Lentini; Richard E. Rice

2010-01-01

Consumer demand for the premier neotropical luxury timber, big-leaf mahogany (Swietenia macrophylla), has driven boom-and-bust logging cycles for centuries, depleting local and regional supplies from Mexico to Bolivia. We revise the standard historic range map for mahogany in South America and estimate the extent to which commercial stocks have been depleted using...

A Customizable Language Learning Support System Using Ontology-Driven Engine

ERIC Educational Resources Information Center

Wang, Jingyun; Mendori, Takahiko; Xiong, Juan

2013-01-01

This paper proposes a framework for web-based language learning support systems designed to provide customizable pedagogical procedures based on the analysis of characteristics of both learner and course. This framework employs a course-centered ontology and a teaching method ontology as the foundation for the student model, which includes learner…

Learning through Geography. Pathways in Geography Series, Title No. 7.

ERIC Educational Resources Information Center

Slater, Frances

This teacher's guide is to enable the teacher to promote thinking through the use of geography. The book lays out the rationale in learning theory for an issues-based, question-driven inquiry method and proceeds through a simple model of progression from identifying key questions to developing generalizations. Students study issues of geographic…

Towards a Theory-Based Design Framework for an Effective E-Learning Computer Programming Course

ERIC Educational Resources Information Center

McGowan, Ian S.

2016-01-01

Built on Dabbagh (2005), this paper presents a four component theory-based design framework for an e-learning session in introductory computer programming. The framework, driven by a body of exemplars component, emphasizes the transformative interaction between the knowledge building community (KBC) pedagogical model, a mixed instructional…

The "Learning Games Design Model": Immersion, Collaboration, and Outcomes-Driven Development

ERIC Educational Resources Information Center

Chamberlin, Barbara; Trespalacios, Jesús; Gallagher, Rachel

2012-01-01

Instructional designers in the Learning Games Lab at New Mexico State University have developed a specific approach for the creation of educational games, one that has been used successfully in over 20 instructional design projects and is extensible to other developers. Using this approach, game developers and content experts (a) work…

Community as Teacher Model: Health Profession Students Learn Cultural Safety from an Aboriginal Community

ERIC Educational Resources Information Center

Kline, Cathy C.; Godolphin, William J.; Chhina, Gagun S.; Towle, Angela

2013-01-01

Communication between health care professionals and Aboriginal patients is complicated by cultural differences and the enduring effects of colonization. Health care providers need better training to meet the needs of Aboriginal patients and communities. We describe the development and outcomes of a community-driven service-learning program in…

An Effective Assessment Model for Implementing Change and Improving Learning

ERIC Educational Resources Information Center

Mince, Rose; Ebersole, Tara

2008-01-01

Assessment at Community College of Baltimore County (CCBC) involves asking the right questions and using data to determine what changes should be implemented to enhance student learning. Guided by a 5-stage design, CCBC's assessment program is faculty-driven, risk-free, and externally validated. Curricular and pedagogical changes have resulted in…

Machine Learning Prediction of the Energy Gap of Graphene Nanoflakes Using Topological Autocorrelation Vectors.

PubMed

Fernandez, Michael; Abreu, Jose I; Shi, Hongqing; Barnard, Amanda S

2016-11-14

The possibility of band gap engineering in graphene opens countless new opportunities for application in nanoelectronics. In this work, the energy gaps of 622 computationally optimized graphene nanoflakes were mapped to topological autocorrelation vectors using machine learning techniques. Machine learning modeling revealed that the most relevant correlations appear at topological distances in the range of 1 to 42 with prediction accuracy higher than 80%. The data-driven model can statistically discriminate between graphene nanoflakes with different energy gaps on the basis of their molecular topology.

Goal orientation, perceived task outcome and task demands in mathematics tasks: effects on students' attitude in actual task settings.

PubMed

Seegers, Gerard; van Putten, Cornelis M; de Brabander, Cornelis J

2002-09-01

In earlier studies, it has been found that students' domain-specific cognitions and personal learning goals (goal orientation) influence task-specific appraisals of actual learning tasks. The relations between domain-specific and task-specific variables have been specified in the model of adaptive learning. In this study, additional influences, i.e., perceived task outcome on a former occasion and variations in task demands, were investigated. The purpose of this study was to identify personality and situational variables that mediate students' attitude when confronted with a mathematics task. Students worked on a mathematics task in two subsequent sessions. Effects of perceived task outcome at the first session on students' attitude at the second session were investigated. In addition, we investigated how differences in task demands influenced students' attitude. Variations in task demands were provoked by different conditions in task-instruction. In one condition, students were told that the result on the test would add to their mark on mathematics. This outcome orienting condition was contrasted with a task-orienting condition where students were told that the results on the test would not be used to give individual grades. Participants were sixth grade students (N = 345; aged 11-12 years) from 14 primary schools. Multivariate and univariate analyses of (co)variance were applied to the data. Independent variables were goal orientation, task demands, and perceived task outcome, with task-specific variables (estimated competence for the task, task attraction, task relevance, and willingness to invest effort) as the dependent variables. The results showed that previous perceived task outcome had a substantial impact on students' attitude. Additional but smaller effects were found for variation in task demands. Furthermore, effects of previous perceived task outcome and task demands were related to goal orientation. The resulting pattern confirmed that, in general, performance-oriented learning goals emphasised the negative impact of failure experiences, whereas task-oriented learning goals had a strengthening effect on how success experiences influenced students' attitude.

Learning to perceive and recognize a second language: the L2LP model revised.

PubMed

van Leussen, Jan-Willem; Escudero, Paola

2015-01-01

We present a test of a revised version of the Second Language Linguistic Perception (L2LP) model, a computational model of the acquisition of second language (L2) speech perception and recognition. The model draws on phonetic, phonological, and psycholinguistic constructs to explain a number of L2 learning scenarios. However, a recent computational implementation failed to validate a theoretical proposal for a learning scenario where the L2 has less phonemic categories than the native language (L1) along a given acoustic continuum. According to the L2LP, learners faced with this learning scenario must not only shift their old L1 phoneme boundaries but also reduce the number of categories employed in perception. Our proposed revision to L2LP successfully accounts for this updating in the number of perceptual categories as a process driven by the meaning of lexical items, rather than by the learners' awareness of the number and type of phonemes that are relevant in their new language, as the previous version of L2LP assumed. Results of our simulations show that meaning-driven learning correctly predicts the developmental path of L2 phoneme perception seen in empirical studies. Additionally, and to contribute to a long-standing debate in psycholinguistics, we test two versions of the model, with the stages of phonemic perception and lexical recognition being either sequential or interactive. Both versions succeed in learning to recognize minimal pairs in the new L2, but make diverging predictions on learners' resulting phonological representations. In sum, the proposed revision to the L2LP model contributes to our understanding of L2 acquisition, with implications for speech processing in general.

A question driven socio-hydrological modeling process

NASA Astrophysics Data System (ADS)

Garcia, M.; Portney, K.; Islam, S.

2016-01-01

Human and hydrological systems are coupled: human activity impacts the hydrological cycle and hydrological conditions can, but do not always, trigger changes in human systems. Traditional modeling approaches with no feedback between hydrological and human systems typically cannot offer insight into how different patterns of natural variability or human-induced changes may propagate through this coupled system. Modeling of coupled human-hydrological systems, also called socio-hydrological systems, recognizes the potential for humans to transform hydrological systems and for hydrological conditions to influence human behavior. However, this coupling introduces new challenges and existing literature does not offer clear guidance regarding model conceptualization. There are no universally accepted laws of human behavior as there are for the physical systems; furthermore, a shared understanding of important processes within the field is often used to develop hydrological models, but there is no such consensus on the relevant processes in socio-hydrological systems. Here we present a question driven process to address these challenges. Such an approach allows modeling structure, scope and detail to remain contingent on and adaptive to the question context. We demonstrate the utility of this process by revisiting a classic question in water resources engineering on reservoir operation rules: what is the impact of reservoir operation policy on the reliability of water supply for a growing city? Our example model couples hydrological and human systems by linking the rate of demand decreases to the past reliability to compare standard operating policy (SOP) with hedging policy (HP). The model shows that reservoir storage acts both as a buffer for variability and as a delay triggering oscillations around a sustainable level of demand. HP reduces the threshold for action thereby decreasing the delay and the oscillation effect. As a result, per capita demand decreases during periods of water stress are more frequent but less drastic and the additive effect of small adjustments decreases the tendency of the system to overshoot available supplies. This distinction between the two policies was not apparent using a traditional noncoupled model.

Children's Early Approaches to Learning and Academic Trajectories through Fifth Grade

ERIC Educational Resources Information Center

Li-Grining, Christine P.; Votruba-Drzal, Elizabeth; Maldonado-Carreno, Carolina; Haas, Kelly

2010-01-01

Children's early approaches to learning (ATL) enhance their adaptation to the demands they experience with the start of formal schooling. The current study uses individual growth modeling to investigate whether children's early ATL, which includes persistence, emotion regulation, and attentiveness, explain individual differences in their academic…

E-Learning--A Financial and Strategic Perspective

ERIC Educational Resources Information Center

Ruth, Stephen R.

2006-01-01

In this article, the author discusses three distinct challenges that demand solutions if traditional universities are to successfully confront the economic realities of distance learning: (1) Many traditional universities are not willing to draw useful lessons from the more advantageous financial and IT models of for-profit or other nontraditional…

Machine learning based cloud mask algorithm driven by radiative transfer modeling

NASA Astrophysics Data System (ADS)

Chen, N.; Li, W.; Tanikawa, T.; Hori, M.; Shimada, R.; Stamnes, K. H.

2017-12-01

Cloud detection is a critically important first step required to derive many satellite data products. Traditional threshold based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and have difficulty over snow/ice covered areas. With the advance of computational power and machine learning techniques, we have developed a new algorithm based on a neural network classifier driven by extensive radiative transfer modeling. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over mid-latitude snow covered areas. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors.

Learning and exploration in action-perception loops.

PubMed

Little, Daniel Y; Sommer, Friedrich T

2013-01-01

Discovering the structure underlying observed data is a recurring problem in machine learning with important applications in neuroscience. It is also a primary function of the brain. When data can be actively collected in the context of a closed action-perception loop, behavior becomes a critical determinant of learning efficiency. Psychologists studying exploration and curiosity in humans and animals have long argued that learning itself is a primary motivator of behavior. However, the theoretical basis of learning-driven behavior is not well understood. Previous computational studies of behavior have largely focused on the control problem of maximizing acquisition of rewards and have treated learning the structure of data as a secondary objective. Here, we study exploration in the absence of external reward feedback. Instead, we take the quality of an agent's learned internal model to be the primary objective. In a simple probabilistic framework, we derive a Bayesian estimate for the amount of information about the environment an agent can expect to receive by taking an action, a measure we term the predicted information gain (PIG). We develop exploration strategies that approximately maximize PIG. One strategy based on value-iteration consistently learns faster than previously developed reward-free exploration strategies across a diverse range of environments. Psychologists believe the evolutionary advantage of learning-driven exploration lies in the generalized utility of an accurate internal model. Consistent with this hypothesis, we demonstrate that agents which learn more efficiently during exploration are later better able to accomplish a range of goal-directed tasks. We will conclude by discussing how our work elucidates the explorative behaviors of animals and humans, its relationship to other computational models of behavior, and its potential application to experimental design, such as in closed-loop neurophysiology studies.

Some Results of Weak Anticipative Concept Applied in Simulation Based Decision Support in Enterprise

NASA Astrophysics Data System (ADS)

Kljajić, Miroljub; Kofjač, Davorin; Kljajić Borštnar, Mirjana; Škraba, Andrej

2010-11-01

The simulation models are used as for decision support and learning in enterprises and in schools. Tree cases of successful applications demonstrate usefulness of weak anticipative information. Job shop scheduling production with makespan criterion presents a real case customized flexible furniture production optimization. The genetic algorithm for job shop scheduling optimization is presented. Simulation based inventory control for products with stochastic lead time and demand describes inventory optimization for products with stochastic lead time and demand. Dynamic programming and fuzzy control algorithms reduce the total cost without producing stock-outs in most cases. Values of decision making information based on simulation were discussed too. All two cases will be discussed from optimization, modeling and learning point of view.

Smoke consequences of new wildfire regimes driven by climate change

Treesearch

Donald McKenzie; Uma Shankar; Robert E. Keane; E. Natasha Stavros; Warren E. Heilman; Douglas G. Fox; Allen C. Riebau

2014-01-01

Smoke from wildfires has adverse biological and social consequences, and various lines of evidence suggest that smoke from wildfires in the future may be more intense and widespread, demanding that methods be developed to address its effects on people, ecosystems, and the atmosphere. In this paper, we present the essential ingredients of a modeling system for...

University Satellite Campus Management Models

ERIC Educational Resources Information Center

Fraser, Doug; Stott, Ken

2015-01-01

Among the 60 or so university satellite campuses in Australia are many that are probably failing to meet the high expectations of their universities and the communities they were designed to serve. While in some cases this may be due to the demand driven system, it may also be attributable in part to the ways in which they are managed. The…

Functional language and data flow architectures

NASA Technical Reports Server (NTRS)

Ercegovac, M. D.; Patel, D. R.; Lang, T.

1983-01-01

This is a tutorial article about language and architecture approaches for highly concurrent computer systems based on the functional style of programming. The discussion concentrates on the basic aspects of functional languages, and sequencing models such as data-flow, demand-driven and reduction which are essential at the machine organization level. Several examples of highly concurrent machines are described.

Identifying water price and population criteria for meeting future urban water demand targets

NASA Astrophysics Data System (ADS)

Ashoori, Negin; Dzombak, David A.; Small, Mitchell J.

2017-12-01

Predictive models for urban water demand can help identify the set of factors that must be satisfied in order to meet future targets for water demand. Some of the explanatory variables used in such models, such as service area population and changing temperature and rainfall rates, are outside the immediate control of water planners and managers. Others, such as water pricing and the intensity of voluntary water conservation efforts, are subject to decisions and programs implemented by the water utility. In order to understand this relationship, a multiple regression model fit to 44 years of monthly demand data (1970-2014) for Los Angeles, California was applied to predict possible future demand through 2050 under alternative scenarios for the explanatory variables: population, price, voluntary conservation efforts, and temperature and precipitation outcomes predicted by four global climate models with two CO2 emission scenarios. Future residential water demand in Los Angeles is projected to be largely driven by price and population rather than climate change and conservation. A median projection for the year 2050 indicates that residential water demand in Los Angeles will increase by approximately 36 percent, to a level of 620 million m3 per year. The Monte Carlo simulations of the fitted model for water demand were then used to find the set of conditions in the future for which water demand is predicted to be above or below the Los Angeles Department of Water and Power 2035 goal to reduce residential water demand by 25%. Results indicate that increases in price can not ensure that the 2035 water demand target can be met when population increases. Los Angeles must rely on furthering their conservation initiatives and increasing their use of stormwater capture, recycled water, and expanding their groundwater storage. The forecasting approach developed in this study can be utilized by other cities to understand the future of water demand in water-stressed areas. Improving water demand forecasts will help planners understand and optimize future investments in water supply infrastructure and related programs.

Virtual sensor models for real-time applications

NASA Astrophysics Data System (ADS)

Hirsenkorn, Nils; Hanke, Timo; Rauch, Andreas; Dehlink, Bernhard; Rasshofer, Ralph; Biebl, Erwin

2016-09-01

Increased complexity and severity of future driver assistance systems demand extensive testing and validation. As supplement to road tests, driving simulations offer various benefits. For driver assistance functions the perception of the sensors is crucial. Therefore, sensors also have to be modeled. In this contribution, a statistical data-driven sensor-model, is described. The state-space based method is capable of modeling various types behavior. In this contribution, the modeling of the position estimation of an automotive radar system, including autocorrelations, is presented. For rendering real-time capability, an efficient implementation is presented.

Social Learning Networks: From Data Analytics to Active Sensing

DTIC Science & Technology

2017-10-13

time updating of user models that in turn dictate the learning path of each student . In particular, we have designed , implemented, and evaluated our...decision, unless so designated by other documentation. 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS (ES) U.S. Army Research Office P.O. Box...social network that exists between students , instructors, and modules of learning. Between 2015 and 2017, we completed a variety of data-driven

Learning-based Wind Estimation using Distant Soundings for Unguided Aerial Delivery

NASA Astrophysics Data System (ADS)

Plyler, M.; Cahoy, K.; Angermueller, K.; Chen, D.; Markuzon, N.

2016-12-01

Delivering unguided, parachuted payloads from aircraft requires accurate knowledge of the wind field inside an operational zone. Usually, a dropsonde released from the aircraft over the drop zone gives a more accurate wind estimate than a forecast. Mission objectives occasionally demand releasing the dropsonde away from the drop zone, but still require accuracy and precision. Barnes interpolation and many other assimilation methods do poorly when the forecast error is inconsistent in a forecast grid. A machine learning approach can better leverage non-linear relations between different weather patterns and thus provide a better wind estimate at the target drop zone when using data collected up to 100 km away. This study uses the 13 km resolution Rapid Refresh (RAP) dataset available through NOAA and subsamples to an area around Yuma, AZ and up to approximately 10km AMSL. RAP forecast grids are updated with simulated dropsondes taken from analysis (historical weather maps). We train models using different data mining and machine learning techniques, most notably boosted regression trees, that can accurately assimilate the distant dropsonde. The model takes a forecast grid and simulated remote dropsonde data as input and produces an estimate of the wind stick over the drop zone. Using ballistic winds as a defining metric, we show our data driven approach does better than Barnes interpolation under some conditions, most notably when the forecast error is different between the two locations, on test data previously unseen by the model. We study and evaluate the model's performance depending on the size, the time lag, the drop altitude, and the geographic location of the training set, and identify parameters most contributing to the accuracy of the wind estimation. This study demonstrates a new approach for assimilating remotely released dropsondes, based on boosted regression trees, and shows improvement in wind estimation over currently used methods.

The Community Water Model (CWATM) / Development of a community driven global water model

NASA Astrophysics Data System (ADS)

Burek, Peter; Satoh, Yusuke; Greve, Peter; Kahil, Taher; Wada, Yoshihide

2017-04-01

With a growing population and economic development, it is expected that water demands will increase significantly in the future, especially in developing regions. At the same time, climate change is expected to alter spatial patterns of hydrological cycle and will have global, regional and local impacts on water availability. Thus, it is important to assess water supply, water demand and environmental needs over time to identify the populations and locations that will be most affected by these changes linked to water scarcity, droughts and floods. The Community Water Model (CWATM) will be designed for this purpose in that it includes an accounting of how future water demands will evolve in response to socioeconomic change and how water availability will change in response to climate. CWATM represents one of the new key elements of IIASA's Water program. It has been developed to work flexibly at both global and regional level at different spatial resolutions. The model is open source and community-driven to promote our work amongst the wider water community worldwide and is flexible enough linking to further planned developments such as water quality and hydro-economic modules. CWATM will be a basis to develop a next-generation global hydro-economic modeling framework that represents the economic trade-offs among different water management options over a basin looking at water supply infrastructure and demand managements. The integrated modeling framework will consider water demand from agriculture, domestic, energy, industry and environment, investment needs to alleviate future water scarcity, and will provide a portfolio of economically optimal solutions for achieving future water management options under the Sustainable Development Goals (SDG) for example. In addition, it will be able to track the energy requirements associated with the water supply system e.g., pumping, desalination and interbasin transfer to realize the linkage with the water-energy economy. In a bigger framework of nexus - water, energy, food, ecosystem - CWATM will be coupled to the existing IIASA models including the Integrated Assessment Model MESSAGE and the global land and ecosystem model GLOBIOM in order to realize an improved assessments of water-energy-food-ecosystem nexus and associated feedback. Our vision for the short to medium term work is to introduce water quality (e.g., salinization in deltas and eutrophication associated with mega cities) into CWATM and to consider qualitative and quantitative measures of transboundary river and groundwater governance into an integrated modelling framework.

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

PubMed Central

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Objectives Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Methods Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Results Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. Conclusion The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports. PMID:28166263

A Model-Driven Approach to Teaching Concurrency

ERIC Educational Resources Information Center

Carro, Manuel; Herranz, Angel; Marino, Julio

2013-01-01

We present an undergraduate course on concurrent programming where formal models are used in different stages of the learning process. The main practical difference with other approaches lies in the fact that the ability to develop correct concurrent software relies on a systematic transformation of formal models of inter-process interaction (so…

Job Design for Mindful Work: The Boosting Effect of Psychosocial Safety Climate.

PubMed

Lawrie, Emily J; Tuckey, Michelle R; Dollard, Maureen F

2017-12-28

Despite a surge in workplace mindfulness research, virtually nothing is known about how organizations can cultivate everyday mindfulness at work. Using the extended job demands-resources model, we explored daily psychological demands and job control as potential antecedents of daily mindfulness, and the moderating effect of psychosocial safety climate (PSC, which relates to the value organizations place on psychological health at work). We also examined the relationship between mindfulness and learning to augment understanding of the benefits of everyday mindfulness at work. A sample of 57 employees, primarily working in education, health care, and finance, completed a diary for five days within a 2-week period, covering mindfulness, psychological demands, job control, and learning. PSC was measured in a baseline survey, with individual ratings combined with those of up to four colleagues to tap objective (shared) climate. Hierarchical linear modeling showed that daily psychological demands were negatively related to daily mindfulness, and daily job control was positively related to daily mindfulness especially as PSC increased. Additionally, daily mindfulness was positively associated with daily workplace learning. This study is one of the first to identify work-related antecedents to everyday mindfulness. The findings suggest that (a) to support everyday mindfulness at work, jobs must be designed with manageable demands and a variety of tasks that allow for creativity and skill discretion, and (b) the benefits of mindfulness interventions for employee psychological health and well-being may not be sustainable unless employees have influence over when and how they do their work, in the "right" climate. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Ontology-driven education: Teaching anatomy with intelligent 3D games on the web

NASA Astrophysics Data System (ADS)

Nilsen, Trond

Human anatomy is a challenging and intimidating subject whose understanding is essential to good medical practice, taught primarily using a combination of lectures and the dissection of human cadavers. Lectures are cheap and scalable, but do a poor job of teaching spatial understanding, whereas dissection lets students experience the body's interior first-hand, but is expensive, cannot be repeated, and is often imperfect. Educational games and online learning activities have the potential to supplement these teaching methods in a cheap and relatively effective way, but they are difficult for educators to customize for particular curricula and lack the tutoring support that human instructors provide. I present an approach to the creation of learning activities for anatomy called ontology-driven education, in which the Foundational Model of Anatomy, an ontological representation of knowledge about anatomy, is leveraged to generate educational content, model student knowledge, and support learning activities and games in a configurable web-based educational framework for anatomy.

The Moderating Role of Self-Regulated Learning in Job Characteristics and Attitudes towards Web-Based Continuing Learning in the Airlines Workplace

ERIC Educational Resources Information Center

Lin, Xiao-fan; Liang, Jyh-Chong; Tsai, Chin-Chung; Hu, Qintai

2018-01-01

With the increasing importance of adult and continuing education, the present study aimed to examine the factors that influence continuing web-based learning at work. Three questionnaires were utilised to investigate the association of the job characteristics from Karasek et al.'s (1998) job demand-control-support model and the self-regulated…

Demand Driven Acquisition of E-Books in a Small Online Academic Library: Growing Pains and Assessing Gains

ERIC Educational Resources Information Center

Longley, Dana H.

2016-01-01

How does a smaller, fully online academic library offer a wide and deep collection of academic level e-books to its distance learners in a sustainable and affordable way? The State University of New York (SUNY) Empire State College Online Library, with a staff of four, has used demand-driven e-book acquisitions since September 2013. Despite…

America's water: Agricultural water demands and the response of groundwater

NASA Astrophysics Data System (ADS)

Ho, M.; Parthasarathy, V.; Etienne, E.; Russo, T. A.; Devineni, N.; Lall, U.

2016-07-01

Agricultural, industrial, and urban water use in the conterminous United States (CONUS) is highly dependent on groundwater that is largely drawn from nonsurficial wells (>30 m). We use a Demand-Sensitive Drought Index to examine the impacts of agricultural water needs, driven by low precipitation, high agricultural water demand, or a combination of both, on the temporal variability of depth to groundwater across the CONUS. We characterize the relationship between changes in groundwater levels, agricultural water deficits relative to precipitation during the growing season, and winter precipitation. We find that declines in groundwater levels in the High Plains aquifer and around the Mississippi River Valley are driven by groundwater withdrawals used to supplement agricultural water demands. Reductions in agricultural water demands for crops do not, however, lead to immediate recovery of groundwater levels due to the demand for groundwater in other sectors in regions such as Utah, Maryland, and Texas.

Argument-Driven Inquiry as a Way to Help Students Learn How to Participate in Scientific Argumentation and Craft Written Arguments: An Exploratory Study

ERIC Educational Resources Information Center

Sampson, Victor; Grooms, Jonathon; Walker, Joi Phelps

2011-01-01

This exploratory study examines how a series of laboratory activities designed using a new instructional model, called Argument-Driven Inquiry (ADI), influences the ways students participate in scientific argumentation and the quality of the scientific arguments they craft as part of this process. The two outcomes of interest were assessed with a…

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

PubMed

Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

2013-01-01

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Precise-Spike-Driven Synaptic Plasticity: Learning Hetero-Association of Spatiotemporal Spike Patterns

PubMed Central

Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

2013-01-01

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe. PMID:24223789

Data-Driven Learning of Total and Local Energies in Elemental Boron

NASA Astrophysics Data System (ADS)

Deringer, Volker L.; Pickard, Chris J.; Csányi, Gábor

2018-04-01

The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms to systematically construct an interatomic potential for boron. Starting from ensembles of randomized atomic configurations, we use alternating single-point quantum-mechanical energy and force computations, Gaussian approximation potential (GAP) fitting, and GAP-driven RSS to iteratively generate a representation of the element's potential-energy surface. Beyond the total energies of the very different boron allotropes, our model readily provides atom-resolved, local energies and thus deepened insight into the frustrated β -rhombohedral boron structure. Our results open the door for the efficient and automated generation of GAPs, and other machine-learning-based interatomic potentials, and suggest their usefulness as a tool for materials discovery.

Data-Driven Learning of Total and Local Energies in Elemental Boron.

PubMed

Deringer, Volker L; Pickard, Chris J; Csányi, Gábor

2018-04-13

The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms to systematically construct an interatomic potential for boron. Starting from ensembles of randomized atomic configurations, we use alternating single-point quantum-mechanical energy and force computations, Gaussian approximation potential (GAP) fitting, and GAP-driven RSS to iteratively generate a representation of the element's potential-energy surface. Beyond the total energies of the very different boron allotropes, our model readily provides atom-resolved, local energies and thus deepened insight into the frustrated β-rhombohedral boron structure. Our results open the door for the efficient and automated generation of GAPs, and other machine-learning-based interatomic potentials, and suggest their usefulness as a tool for materials discovery.

Dopamine selectively remediates ‘model-based’ reward learning: a computational approach

PubMed Central

Sharp, Madeleine E.; Foerde, Karin; Daw, Nathaniel D.

2016-01-01

Patients with loss of dopamine due to Parkinson’s disease are impaired at learning from reward. However, it remains unknown precisely which aspect of learning is impaired. In particular, learning from reward, or reinforcement learning, can be driven by two distinct computational processes. One involves habitual stamping-in of stimulus-response associations, hypothesized to arise computationally from ‘model-free’ learning. The other, ‘model-based’ learning, involves learning a model of the world that is believed to support goal-directed behaviour. Much work has pointed to a role for dopamine in model-free learning. But recent work suggests model-based learning may also involve dopamine modulation, raising the possibility that model-based learning may contribute to the learning impairment in Parkinson’s disease. To directly test this, we used a two-step reward-learning task which dissociates model-free versus model-based learning. We evaluated learning in patients with Parkinson’s disease tested ON versus OFF their dopamine replacement medication and in healthy controls. Surprisingly, we found no effect of disease or medication on model-free learning. Instead, we found that patients tested OFF medication showed a marked impairment in model-based learning, and that this impairment was remediated by dopaminergic medication. Moreover, model-based learning was positively correlated with a separate measure of working memory performance, raising the possibility of common neural substrates. Our results suggest that some learning deficits in Parkinson’s disease may be related to an inability to pursue reward based on complete representations of the environment. PMID:26685155

NASA Tech Briefs, March 2005

NASA Technical Reports Server (NTRS)

2005-01-01

Topics covered include: Scheme for Entering Binary Data Into a Quantum Computer; Encryption for Remote Control via Internet or Intranet; Coupled Receiver/Decoders for Low-Rate Turbo Codes; Processing GPS Occultation Data To Characterize Atmosphere; Displacing Unpredictable Nulls in Antenna Radiation Patterns; Integrated Pointing and Signal Detector for Optical Receiver; Adaptive Thresholding and Parameter Estimation for PPM; Data-Driven Software Framework for Web-Based ISS Telescience; Software for Secondary-School Learning About Robotics; Fuzzy Logic Engine; Telephone-Directory Program; Simulating a Direction-Finder Search for an ELT; Formulating Precursors for Coating Metals and Ceramics; Making Macroscopic Assemblies of Aligned Carbon Nanotubes; Ball Bearings Equipped for In Situ Lubrication on Demand; Synthetic Bursae for Robots; Robot Forearm and Dexterous Hand; Making a Metal-Lined Composite-Overwrapped Pressure Vessel; Ex Vivo Growth of Bioengineered Ligaments and Other Tissues; Stroboscopic Goggles for Reduction of Motion Sickness; Articulating Support for Horizontal Resistive Exercise; Modified Penning-Malmberg Trap for Storing Antiprotons; Tumbleweed Rovers; Two-Photon Fluorescence Microscope for Microgravity Research; Biased Randomized Algorithm for Fast Model-Based Diagnosis; Fast Algorithms for Model-Based Diagnosis; Simulations of Evaporating Multicomponent Fuel Drops; Formation Flying of Tethered and Nontethered Spacecraft; and Two Methods for Efficient Solution of the Hitting- Set Problem.

By the Numbers.

ERIC Educational Resources Information Center

Cooper, Bruce S.; McGrath, Michael; Monahan, Brian D.; Steele, Joanne Laughlin

1999-01-01

Educators can learn from business people accounting models that can be applied to managerial accounting, integrated information systems, focused/activity-based costing, decentralized information, and mission-driven costing. A sidebar discusses measuring technology's impact. (MLF)

Instructional control of reinforcement learning: A behavioral and neurocomputational investigation

PubMed Central

Doll, Bradley B.; Jacobs, W. Jake; Sanfey, Alan G.; Frank, Michael J.

2011-01-01

Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S. (Ed.). 1989. Rule-governed behavior: cognition, contingencies, and instructional control. Plenum Press.). Here we examine the control of behavior through instructions in a reinforcement learning task known to depend on striatal dopaminergic function. Participants selected between probabilistically reinforced stimuli, and were (incorrectly) told that a specific stimulus had the highest (or lowest) reinforcement probability. Despite experience to the contrary, instructions drove choice behavior. We present neural network simulations that capture the interactions between instruction-driven and reinforcement-driven behavior via two potential neural circuits: one in which the striatum is inaccurately trained by instruction representations coming from prefrontal cortex/hippocampus (PFC/HC), and another in which the striatum learns the environmentally based reinforcement contingencies, but is “overridden” at decision output. Both models capture the core behavioral phenomena but, because they differ fundamentally on what is learned, make distinct predictions for subsequent behavioral and neuroimaging experiments. Finally, we attempt to distinguish between the proposed computational mechanisms governing instructed behavior by fitting a series of abstract “Q-learning” and Bayesian models to subject data. The best-fitting model supports one of the neural models, suggesting the existence of a “confirmation bias” in which the PFC/HC system trains the reinforcement system by amplifying outcomes that are consistent with instructions while diminishing inconsistent outcomes. PMID:19595993

A two-phase model of resource allocation in visual working memory.

PubMed

Ye, Chaoxiong; Hu, Zhonghua; Li, Hong; Ristaniemi, Tapani; Liu, Qiang; Liu, Taosheng

2017-10-01

Two broad theories of visual working memory (VWM) storage have emerged from current research, a discrete slot-based theory and a continuous resource theory. However, neither the discrete slot-based theory or continuous resource theory clearly stipulates how the mental commodity for VWM (discrete slot or continuous resource) is allocated. Allocation may be based on the number of items via stimulus-driven factors, or it may be based on task demands via voluntary control. Previous studies have obtained conflicting results regarding the automaticity versus controllability of such allocation. In the current study, we propose a two-phase allocation model, in which the mental commodity could be allocated only by stimulus-driven factors in the early consolidation phase. However, when there is sufficient time to complete the early phase, allocation can enter the late consolidation phase, where it can be flexibly and voluntarily controlled according to task demands. In an orientation recall task, we instructed participants to store either fewer items at high-precision or more items at low-precision. In 3 experiments, we systematically manipulated memory set size and exposure duration. We did not find an effect of task demands when the set size was high and exposure duration was short. However, when we either decreased the set size or increased the exposure duration, we found a trade-off between the number and precision of VWM representations. These results can be explained by a two-phase model, which can also account for previous conflicting findings in the literature. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Projecting Future Land Use Changes in West Africa Driven by Climate and Socioeconomic Factors: Uncertainties and Implications for Adaptation

NASA Astrophysics Data System (ADS)

Wang, G.; Ahmed, K. F.; You, L.

2015-12-01

Land use changes constitute an important regional climate change forcing in West Africa, a region of strong land-atmosphere coupling. At the same time, climate change can be an important driver for land use, although its importance relative to the impact of socio-economic factors may vary significant from region to region. This study compares the contributions of climate change and socioeconomic development to potential future changes of agricultural land use in West Africa and examines various sources of uncertainty using a land use projection model (LandPro) that accounts for the impact of socioeconomic drivers on the demand side and the impact of climate-induced crop yield changes on the supply side. Future crop yield changes were simulated by a process-based crop model driven with future climate projections from a regional climate model, and future changes of food demand is projected using a model for policy analysis of agricultural commodities and trade. The impact of human decision-making on land use was explicitly considered through multiple "what-if" scenarios to examine the range of uncertainties in projecting future land use. Without agricultural intensification, the climate-induced decrease of crop yield together with increase of food demand are found to cause a significant increase in agricultural land use at the expense of forest and grassland by the mid-century, and the resulting land use land cover changes are found to feed back to the regional climate in a way that exacerbates the negative impact of climate on crop yield. Analysis of results from multiple decision-making scenarios suggests that human adaptation characterized by science-informed decision making to minimize land use could be very effective in many parts of the region.

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

PubMed

Shim, Yoonsik; Philippides, Andrew; Staras, Kevin; Husbands, Phil

2016-10-01

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP

PubMed Central

Staras, Kevin

2016-01-01

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture. PMID:27760125

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

PubMed Central

Potjans, Wiebke; Diesmann, Markus; Morrison, Abigail

2011-01-01

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards. PMID:21589888

Tobacco-free economy: A SAM-based multiplier model to quantify the impact of changes in tobacco demand in Bangladesh.

PubMed

Husain, Muhammad Jami; Khondker, Bazlul Haque

2016-01-01

In Bangladesh, where tobacco use is pervasive, reducing tobacco use is economically beneficial. This paper uses the latest Bangladesh social accounting matrix (SAM) multiplier model to quantify the economy-wide impact of demand-driven changes in tobacco cultivation, bidi industries, and cigarette industries. First, we compute various income multiplier values (i.e. backward linkages) for all production activities in the economy to quantify the impact of changes in demand for the corresponding products on gross output for 86 activities, demand for 86 commodities, returns to four factors of production, and income for eight household groups. Next, we rank tobacco production activities by income multiplier values relative to other sectors. Finally, we present three hypothetical 'tobacco-free economy' scenarios by diverting demand from tobacco products into other sectors of the economy and quantifying the economy-wide impact. The simulation exercises with three different tobacco-free scenarios show that, compared to the baseline values, total sectoral output increases by 0.92%, 1.3%, and 0.75%. The corresponding increases in the total factor returns (i.e. GDP) are 1.57%, 1.75%, and 1.75%. Similarly, total household income increases by 1.40%, 1.58%, and 1.55%.

Motivation, Interest, and Attention: Re-Defining Learning in the Autism Spectrum?

ERIC Educational Resources Information Center

Lequia, Jenna

2011-01-01

In "The Passionate Mind: How People with Autism Learn", Wendy Lawson presents readers with various cognitive theories of autism spectrum disorders (ASD). In this book, Lawson makes reference to the medical and social models of disability, urging readers to consider disability from a social rather than a medical or deficit-driven perspective. Each…

Using Tracker as a Pedagogical Tool for Understanding Projectile Motion

ERIC Educational Resources Information Center

Wee, Loo Kang; Chew, Charles; Goh, Giam Hwee; Tan, Samuel; Lee, Tat Leong

2012-01-01

This article reports on the use of Tracker as a pedagogical tool in the effective learning and teaching of projectile motion in physics. When a computer model building learning process is supported and driven by video analysis data, this free Open Source Physics tool can provide opportunities for students to engage in active enquiry-based…

What Drives Teachers to Improve? The Role of Teacher Mindset in Professional Learning

ERIC Educational Resources Information Center

Gero, Greg Philip

2013-01-01

Teacher quality has received increasing focus over the past decade, yet, by some measures, teachers rarely improve after their first few years of teaching, and not all teachers seem driven to improve. Traditional models of professional learning have emphasized the processes that teachers take part in as a facilitator of their improvement. Research…

Mechanisms of object recognition: what we have learned from pigeons

PubMed Central

Soto, Fabian A.; Wasserman, Edward A.

2014-01-01

Behavioral studies of object recognition in pigeons have been conducted for 50 years, yielding a large body of data. Recent work has been directed toward synthesizing this evidence and understanding the visual, associative, and cognitive mechanisms that are involved. The outcome is that pigeons are likely to be the non-primate species for which the computational mechanisms of object recognition are best understood. Here, we review this research and suggest that a core set of mechanisms for object recognition might be present in all vertebrates, including pigeons and people, making pigeons an excellent candidate model to study the neural mechanisms of object recognition. Behavioral and computational evidence suggests that error-driven learning participates in object category learning by pigeons and people, and recent neuroscientific research suggests that the basal ganglia, which are homologous in these species, may implement error-driven learning of stimulus-response associations. Furthermore, learning of abstract category representations can be observed in pigeons and other vertebrates. Finally, there is evidence that feedforward visual processing, a central mechanism in models of object recognition in the primate ventral stream, plays a role in object recognition by pigeons. We also highlight differences between pigeons and people in object recognition abilities, and propose candidate adaptive specializations which may explain them, such as holistic face processing and rule-based category learning in primates. From a modern comparative perspective, such specializations are to be expected regardless of the model species under study. The fact that we have a good idea of which aspects of object recognition differ in people and pigeons should be seen as an advantage over other animal models. From this perspective, we suggest that there is much to learn about human object recognition from studying the “simple” brains of pigeons. PMID:25352784

Underworld - Bringing a Research Code to the Classroom

NASA Astrophysics Data System (ADS)

Moresi, L. N.; Mansour, J.; Giordani, J.; Farrington, R.; Kaluza, O.; Quenette, S.; Woodcock, R.; Squire, G.

2017-12-01

While there are many reasons to celebrate the passing of punch card programming and flickering green screens,the loss of the sense of wonder at the very existence of computers and the calculations they make possible shouldnot be numbered among them. Computers have become so familiar that students are often unaware that formal and careful design of algorithms andtheir implementations remains a valuable and important skill that has to be learned and practiced to achieveexpertise and genuine understanding. In teaching geodynamics and geophysics at undergraduate level, we aimed to be able to bring our researchtools into the classroom - even when those tools are advanced, parallel research codes that we typically deploy on hundredsor thousands of processors, and we wanted to teach not just the physical concepts that are modelled by these codes but asense of familiarity with computational modelling and the ability to discriminate a reliable model from a poor one. The underworld code (www.underworldcode.org) was developed for modelling plate-scale fluid mechanics and studyingproblems in lithosphere dynamics. Though specialised for this task, underworld has a straightforwardpython user interface that allows it to run within the environment of jupyter notebooks on a laptop (at modest resolution, of course).The python interface was developed for adaptability in addressing new research problems, but also lends itself to integration intoa python-driven learning environment. To manage the heavy demands of installing and running underworld in a teaching laboratory, we have developed a workflow in whichwe install docker containers in the cloud which support a number of students to run their own environment independently. We share ourexperience blending notebooks and static webpages into a single web environment, and we explain how we designed our graphics andanalysis tools to allow notebook "scripts" to be queued and run on a supercomputer.

Impact of warmer weather on electricity sector emissions due to building energy use

NASA Astrophysics Data System (ADS)

Meier, Paul; Holloway, Tracey; Patz, Jonathan; Harkey, Monica; Ahl, Doug; Abel, David; Schuetter, Scott; Hackel, Scott

2017-06-01

Most US energy consumption occurs in buildings, with cooling demands anticipated to increase net building electricity use under warmer conditions. The electricity generation units that respond to this demand are major contributors to sulfur dioxide (SO2) and nitrogen oxides (NOx), both of which have direct impacts on public health, and contribute to the formation of secondary pollutants including ozone and fine particulate matter. This study quantifies temperature-driven changes in power plant emissions due to increased use of building air conditioning. We compare an ambient temperature baseline for the Eastern US to a model-calculated mid-century scenario with summer-average temperature increases ranging from 1 C to 5 C across the domain. We find a 7% increase in summer electricity demand and a 32% increase in non-coincident peak demand. Power sector modeling, assuming only limited changes to current generation resources, calculated a 16% increase in emissions of NOx and an 18% increase in emissions of SO2. There is a high level of regional variance in the response of building energy use to climate, and the response of emissions to associated demand. The East North Central census region exhibited the greatest sensitivity of energy demand and associated emissions to climate.

Improved spatial accuracy of functional maps in the rat olfactory bulb using supervised machine learning approach.

PubMed

Murphy, Matthew C; Poplawsky, Alexander J; Vazquez, Alberto L; Chan, Kevin C; Kim, Seong-Gi; Fukuda, Mitsuhiro

2016-08-15

Functional MRI (fMRI) is a popular and important tool for noninvasive mapping of neural activity. As fMRI measures the hemodynamic response, the resulting activation maps do not perfectly reflect the underlying neural activity. The purpose of this work was to design a data-driven model to improve the spatial accuracy of fMRI maps in the rat olfactory bulb. This system is an ideal choice for this investigation since the bulb circuit is well characterized, allowing for an accurate definition of activity patterns in order to train the model. We generated models for both cerebral blood volume weighted (CBVw) and blood oxygen level dependent (BOLD) fMRI data. The results indicate that the spatial accuracy of the activation maps is either significantly improved or at worst not significantly different when using the learned models compared to a conventional general linear model approach, particularly for BOLD images and activity patterns involving deep layers of the bulb. Furthermore, the activation maps computed by CBVw and BOLD data show increased agreement when using the learned models, lending more confidence to their accuracy. The models presented here could have an immediate impact on studies of the olfactory bulb, but perhaps more importantly, demonstrate the potential for similar flexible, data-driven models to improve the quality of activation maps calculated using fMRI data. Copyright © 2016 Elsevier Inc. All rights reserved.

Distance Learning and Cloud Computing: "Just Another Buzzword or a Major E-Learning Breakthrough?"

ERIC Educational Resources Information Center

Romiszowski, Alexander J.

2012-01-01

"Cloud computing is a model for the enabling of ubiquitous, convenient, and on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and other services) that can be rapidly provisioned and released with minimal management effort or service provider interaction." This…

Do E-Learning Tools Make a Difference? Results from a Case Study

ERIC Educational Resources Information Center

Desplaces, David; Blair, Carrie A.; Salvaggio, Trent

2015-01-01

Even as academics continue to debate whether distance education techniques are successful, the market demands increased distance education programs and a growing number of corporations are using e-learning to train their employees. We propose and examine a model comparing outcomes in 3 different pedagogical classroom settings: traditional,…

A Proposed Blueprint Model towards the Evaluation of Educational System in Iran

ERIC Educational Resources Information Center

Mehrafsha, S. Jahangir

2011-01-01

The pursuit of quality gave rise to the concept of Iran Universities as learning organizations. Iran Universities must have the capacity to learn if they are to survive the demands and requirements of the emerging times. This includes liberating traditional methodologies that are anchored on positivism and seemingly dependent on technical…

Aligning Professional Skills and Active Learning Methods: An Application for Information and Communications Technology Engineering

ERIC Educational Resources Information Center

Llorens, Ariadna; Berbegal-Mirabent, Jasmina; Llinàs-Audet, Xavier

2017-01-01

Engineering education is facing new challenges to effectively provide the appropriate skills to future engineering professionals according to market demands. This study proposes a model based on active learning methods, which is expected to facilitate the acquisition of the professional skills most highly valued in the information and…

Real Options Valuation of e-Learning Projects

ERIC Educational Resources Information Center

Freitas, Angilberto; Brandao, Luiz

2010-01-01

New information and communication technologies have been gaining widespread use in Distance Education (DE) models. At the same time, the uncertainty in the market demand for this form of higher education is such that the valuation of "e-Learning" projects has become increasingly difficult and complex. This is due to the fact that…

The Impact of Uncertainty and Irreversibility on Investments in Online Learning

ERIC Educational Resources Information Center

Oslington, Paul

2004-01-01

Uncertainty and irreversibility are central to online learning projects, but have been neglected in the existing educational cost-benefit analysis literature. This paper builds some simple illustrative models of the impact of irreversibility and uncertainty, and shows how different types of cost and demand uncertainty can have substantial impacts…

Organising Workplace Learning: An Inter-Organisational Perspective

ERIC Educational Resources Information Center

Svensson, Lennart; Randle, Hanne; Bennich, Maria

2009-01-01

Purpose: The purpose of this paper is to argue that both the supply-based model and the demand-based form of vocational education and training (VET) have their limitations and propose a "third way" in which reflective learning in the workplace is a central ingredient. Design/methodology/approach: The data was collected from several…

Demand-driven biogas production by flexible feeding in full-scale - Process stability and flexibility potentials.

PubMed

Mauky, Eric; Weinrich, Sören; Jacobi, Hans-Fabian; Nägele, Hans-Joachim; Liebetrau, Jan; Nelles, Michael

2017-08-01

For future energy supply systems with high proportions from renewable energy sources, biogas plants are a promising option to supply demand-driven electricity to compensate the divergence between energy demand and energy supply by uncontrolled sources like wind and solar. Apart expanding gas storage capacity a demand-oriented feeding with the aim of flexible gas production can be an effective alternative. The presented study demonstrated a high degree of intraday flexibility (up to 50% compared to the average) and a potential for an electricity shutdown of up to 3 days (decreasing gas production by more than 60%) by flexible feeding in full-scale. Furthermore, the long-term process stability was not affected negatively due to the flexible feeding. The flexible feeding resulted in a variable rate of gas production and a dynamic progression of individual acids and the respective pH-value. In consequence, a demand-driven biogas production may enable significant savings in terms of the required gas storage volume (up to 65%) and permit far greater plant flexibility compared to constant gas production. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Development of Intelligent Computer-Assisted Instruction Systems to Facilitate Reading Skills of Learning-Disabled Children

DTIC Science & Technology

1993-12-01

Unclassified/Unlimited 13. ABSTRACT ~Maximum 2W0 worr*J The purpose of this thesis is to develop a high-level model to create seli"adapting software which...Department of Computer Science ABSTRACT The purpose of this thesis is to develop a high-level model to create self-adapting software which teaches learning...stimulating and demanding. The power of the system model described herein is that it can vary as needed by the individual student. The system will

A Cyber Enabled Collaborative Environment for Creating, Sharing and Using Data and Modeling Driven Curriculum Modules for Hydrology Education

NASA Astrophysics Data System (ADS)

Merwade, V.; Ruddell, B. L.; Fox, S.; Iverson, E. A. R.

2014-12-01

With the access to emerging datasets and computational tools, there is a need to bring these capabilities into hydrology classrooms. However, developing curriculum modules using data and models to augment classroom teaching is hindered by a steep technology learning curve, rapid technology turnover, and lack of an organized community cyberinfrastructure (CI) for the dissemination, publication, and sharing of the latest tools and curriculum material for hydrology and geoscience education. The objective of this project is to overcome some of these limitations by developing a cyber enabled collaborative environment for publishing, sharing and adoption of data and modeling driven curriculum modules in hydrology and geosciences classroom. The CI is based on Carleton College's Science Education Resource Center (SERC) Content Management System. Building on its existing community authoring capabilities the system is being extended to allow assembly of new teaching activities by drawing on a collection of interchangeable building blocks; each of which represents a step in the modeling process. Currently the system hosts more than 30 modules or steps, which can be combined to create multiple learning units. Two specific units: Unit Hydrograph and Rational Method, have been used in undergraduate hydrology class-rooms at Purdue University and Arizona State University. The structure of the CI and the lessons learned from its implementation, including preliminary results from student assessments of learning will be presented.

Is the demand for alcoholic beverages in developing countries sensitive to price? Evidence from China.

PubMed

Tian, Guoqiang; Liu, Feng

2011-06-01

Economic literature in developed countries suggests that demand for alcoholic beverages is sensitive to price, with an estimated price elasticity ranging from -0.38 for beer and -0.7 for liquor. However, few studies have been conducted in developing countries. We employ a large individual-level dataset in China to estimate the effects of price on alcohol demand. Using the data from China Health and Nutrition Survey for the years 1993, 1997, 2000, 2004 and 2006, we estimate two-part models of alcohol demand. Results show the price elasticity is virtually zero for beer and only -0.12 for liquor, which is far smaller than those derived from developed countries. Separate regressions by gender reveals the results are mainly driven by men. The central implication of this study is, while alcohol tax increases can raise government revenue, it alone is not an effective policy to reduce alcohol related problems in China.

Adopting best practices: "Agility" moves from software development to healthcare project management.

PubMed

Kitzmiller, Rebecca; Hunt, Eleanor; Sproat, Sara Breckenridge

2006-01-01

It is time for a change in mindset in how nurses operationalize system implementations and manage projects. Computers and systems have evolved over time from unwieldy mysterious machines of the past to ubiquitous computer use in every aspect of daily lives and work sites. Yet, disconcertingly, the process used to implement these systems has not evolved. Technology implementation does not need to be a struggle. It is time to adapt traditional plan-driven implementation methods to incorporate agile techniques. Agility is a concept borrowed from software development and is presented here because it encourages flexibility, adaptation, and continuous learning as part of the implementation process. Agility values communication and harnesses change to an advantage, which facilitates the natural evolution of an adaptable implementation process. Specific examples of agility in an implementation are described, and plan-driven implementation stages are adapted to incorporate relevant agile techniques. This comparison demonstrates how an agile approach enhances traditional implementation techniques to meet the demands of today's complex healthcare environments.

Interpretable Deep Models for ICU Outcome Prediction

PubMed Central

Che, Zhengping; Purushotham, Sanjay; Khemani, Robinder; Liu, Yan

2016-01-01

Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful data-driven characteristics and patterns ofdiseases. Recently, deep learning models have been employedfor many computational phenotyping and healthcare prediction tasks to achieve state-of-the-art performance. However, deep models lack interpretability which is crucial for wide adoption in medical research and clinical decision-making. In this paper, we introduce a simple yet powerful knowledge-distillation approach called interpretable mimic learning, which uses gradient boosting trees to learn interpretable models and at the same time achieves strong prediction performance as deep learning models. Experiment results on Pediatric ICU dataset for acute lung injury (ALI) show that our proposed method not only outperforms state-of-the-art approaches for morality and ventilator free days prediction tasks but can also provide interpretable models to clinicians. PMID:28269832

The Integrated Air Transportation System Evaluation Tool

NASA Technical Reports Server (NTRS)

Wingrove, Earl R., III; Hees, Jing; Villani, James A.; Yackovetsky, Robert E. (Technical Monitor)

2002-01-01

Throughout U.S. history, our nation has generally enjoyed exceptional economic growth, driven in part by transportation advancements. Looking forward 25 years, when the national highway and skyway systems are saturated, the nation faces new challenges in creating transportation-driven economic growth and wealth. To meet the national requirement for an improved air traffic management system, NASA developed the goal of tripling throughput over the next 20 years, in all weather conditions while maintaining safety. Analysis of the throughput goal has primarily focused on major airline operations, primarily through the hub and spoke system.However, many suggested concepts to increase throughput may operate outside the hub and spoke system. Examples of such concepts include the Small Aircraft Transportation System, civil tiltrotor, and improved rotorcraft. Proper assessment of the potential contribution of these technologies to the domestic air transportation system requires a modeling capability that includes the country's numerous smaller airports, acting as a fundamental component of the National Air space System, and the demand for such concepts and technologies. Under this task for NASA, the Logistics Management Institute developed higher fidelity demand models that capture the interdependence of short-haul air travel with other transportation modes and explicitly consider the costs of commercial air and other transport modes. To accomplish this work, we generated forecasts of the distribution of general aviation based aircraft and GA itinerant operations at each of nearly 3.000 airport based on changes in economic conditions and demographic trends. We also built modules that estimate the demand for travel by different modes, particularly auto, commercial air, and GA. We examined GA demand from two perspectives: top-down and bottom-up, described in detail.

Stopping Distances: An Excellent Example of Empirical Modelling.

ERIC Educational Resources Information Center

Lawson, D. A.; Tabor, J. H.

2001-01-01

Explores the derivation of empirical models for the stopping distance of a car being driven at a range of speeds. Indicates that the calculation of stopping distances makes an excellent example of empirical modeling because it is a situation that is readily understood and particularly relevant to many first-year undergraduates who are learning or…

Encouraging Evidence on a Sector-Focused Advancement Strategy: Two-Year Impacts from the WorkAdvance Demonstration

ERIC Educational Resources Information Center

Hendra, Richard; Greenberg, David H.; Hamilton, Gayle; Oppenheim, Ari; Pennington, Alexandra; Schaberg, Kelsey; Tessler, Betsy L.

2016-01-01

This report summarizes the two-year findings of a rigorous random assignment evaluation of the WorkAdvance model, a sectoral training, and advancement initiative. Launched in 2011, WorkAdvance goes beyond the previous generation of employment programs by introducing demand-driven skills training and a focus on jobs that have career pathways. The…

Encouraging Evidence on a Sector-Focused Advancement Strategy: Two-Year Impacts from the WorkAdvance Demonstration. Preview Summary

ERIC Educational Resources Information Center

Hendra, Richard; Greenberg, David H.; Hamilton, Gayle; Oppenheim, Ari; Pennington, Alexandra; Schaberg, Kelsey; Tessler, Betsy L.

2016-01-01

This report summarizes the two-year findings of a rigorous random assignment evaluation of the WorkAdvance model, a sectoral training and advancement initiative. Launched in 2011, WorkAdvance goes beyond the previous generation of employment programs by introducing demand-driven skills training and a focus on jobs that have career pathways. The…

An assessment of the 1996 Beef NRC: Metabolizable protein supply and demand and effectiveness of model performance prediction of beef females within extensive grazing systems

USDA-ARS?s Scientific Manuscript database

Interannual variation of forage quantity and quality driven by precipitation events influence beef livestock production systems within the Southern and Northern Plains and Pacific West which combined represents 60% (approximately 17.5 million) of total beef cows in the United States. The beef NRC is...

Flipped classroom model for learning evidence-based medicine.

PubMed

Rucker, Sydney Y; Ozdogan, Zulfukar; Al Achkar, Morhaf

2017-01-01

Journal club (JC), as a pedagogical strategy, has long been used in graduate medical education (GME). As evidence-based medicine (EBM) becomes a mainstay in GME, traditional models of JC present a number of insufficiencies and call for novel models of instruction. A flipped classroom model appears to be an ideal strategy to meet the demands to connect evidence to practice while creating engaged, culturally competent, and technologically literate physicians. In this article, we describe a novel model of flipped classroom in JC. We present the flow of learning activities during the online and face-to-face instruction, and then we highlight specific considerations for implementing a flipped classroom model. We show that implementing a flipped classroom model to teach EBM in a residency program not only is possible but also may constitute improved learning opportunity for residents. Follow-up work is needed to evaluate the effectiveness of this model on both learning and clinical practice.

Flipped classroom model for learning evidence-based medicine

PubMed Central

Rucker, Sydney Y; Ozdogan, Zulfukar; Al Achkar, Morhaf

2017-01-01

Journal club (JC), as a pedagogical strategy, has long been used in graduate medical education (GME). As evidence-based medicine (EBM) becomes a mainstay in GME, traditional models of JC present a number of insufficiencies and call for novel models of instruction. A flipped classroom model appears to be an ideal strategy to meet the demands to connect evidence to practice while creating engaged, culturally competent, and technologically literate physicians. In this article, we describe a novel model of flipped classroom in JC. We present the flow of learning activities during the online and face-to-face instruction, and then we highlight specific considerations for implementing a flipped classroom model. We show that implementing a flipped classroom model to teach EBM in a residency program not only is possible but also may constitute improved learning opportunity for residents. Follow-up work is needed to evaluate the effectiveness of this model on both learning and clinical practice. PMID:28919831

The evolution of meaning: spatio-temporal dynamics of visual object recognition.

PubMed

Clarke, Alex; Taylor, Kirsten I; Tyler, Lorraine K

2011-08-01

Research on the spatio-temporal dynamics of visual object recognition suggests a recurrent, interactive model whereby an initial feedforward sweep through the ventral stream to prefrontal cortex is followed by recurrent interactions. However, critical questions remain regarding the factors that mediate the degree of recurrent interactions necessary for meaningful object recognition. The novel prediction we test here is that recurrent interactivity is driven by increasing semantic integration demands as defined by the complexity of semantic information required by the task and driven by the stimuli. To test this prediction, we recorded magnetoencephalography data while participants named living and nonliving objects during two naming tasks. We found that the spatio-temporal dynamics of neural activity were modulated by the level of semantic integration required. Specifically, source reconstructed time courses and phase synchronization measures showed increased recurrent interactions as a function of semantic integration demands. These findings demonstrate that the cortical dynamics of object processing are modulated by the complexity of semantic information required from the visual input.

Which types of mental work demands may be associated with reduced risk of dementia?

PubMed

Then, Francisca S; Luck, Tobias; Heser, Kathrin; Ernst, Annette; Posselt, Tina; Wiese, Birgitt; Mamone, Silke; Brettschneider, Christian; König, Hans-Helmut; Weyerer, Siegfried; Werle, Jochen; Mösch, Edelgard; Bickel, Horst; Fuchs, Angela; Pentzek, Michael; Maier, Wolfgang; Scherer, Martin; Wagner, Michael; Riedel-Heller, Steffi G

2017-04-01

Previous studies have demonstrated that an overall high level of mental work demands decreased dementia risk. In our study, we investigated whether this effect is driven by specific mental work demands and whether it is exposure dependent. Patients aged 75+ years were recruited from general practitioners and participated in up to seven assessment waves (every 1.5 years) of the longitudinal AgeCoDe study. Analyses of the impact of specific mental work demands on dementia risk were carried out via multivariate regression modeling (n = 2315). We observed a significantly lower dementia risk in individuals with a higher level of "information processing" (HR, 0.888), "pattern detection" (HR, 0.878), "mathematics" (HR, 0.878), and "creativity" (HR, 0.878). Yet, exposure-dependent effects were only significant for "information processing" and "pattern detection." Our longitudinal observations suggest that dementia risk may be reduced by some but not all types of mental work demands. Copyright © 2016 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Instrumentation: Software-Driven Instrumentation: The New Wave.

ERIC Educational Resources Information Center

Salit, M. L.; Parsons, M. L.

1985-01-01

Software-driven instrumentation makes measurements that demand a computer as an integral part of either control, data acquisition, or data reduction. The structure of such instrumentation, hardware requirements, and software requirements are discussed. Examples of software-driven instrumentation (such as wavelength-modulated continuum source…

Multivariate temporal dictionary learning for EEG.

PubMed

Barthélemy, Q; Gouy-Pailler, C; Isaac, Y; Souloumiac, A; Larue, A; Mars, J I

2013-04-30

This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential. Copyright © 2013 Elsevier B.V. All rights reserved.

Bio-Inspired Neural Model for Learning Dynamic Models

NASA Technical Reports Server (NTRS)

Duong, Tuan; Duong, Vu; Suri, Ronald

2009-01-01

A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

The recovery model and complex health needs: what health psychology can learn from mental health and substance misuse service provision.

PubMed

Webb, Lucy

2012-07-01

This article reviews key arguments around evidence-based practice and outlines the methodological demands for effective adoption of recovery model principles. The recovery model is outlined and demonstrated as compatible with current needs in substance misuse service provision. However, the concepts of evidence-based practice and the recovery model are currently incompatible unless the current value system of evidence-based practice changes to accommodate the methodologies demanded by the recovery model. It is suggested that critical health psychology has an important role to play in widening the scope of evidence-based practice to better accommodate complex social health needs.

Model-based learning and the contribution of the orbitofrontal cortex to the model-free world

PubMed Central

McDannald, Michael A.; Takahashi, Yuji K.; Lopatina, Nina; Pietras, Brad W.; Jones, Josh L.; Schoenbaum, Geoffrey

2012-01-01

Learning is proposed to occur when there is a discrepancy between reward prediction and reward receipt. At least two separate systems are thought to exist: one in which predictions are proposed to be based on model-free or cached values; and another in which predictions are model-based. A basic neural circuit for model-free reinforcement learning has already been described. In the model-free circuit the ventral striatum (VS) is thought to supply a common-currency reward prediction to midbrain dopamine neurons that compute prediction errors and drive learning. In a model-based system, predictions can include more information about an expected reward, such as its sensory attributes or current, unique value. This detailed prediction allows for both behavioral flexibility and learning driven by changes in sensory features of rewards alone. Recent evidence from animal learning and human imaging suggests that, in addition to model-free information, the VS also signals model-based information. Further, there is evidence that the orbitofrontal cortex (OFC) signals model-based information. Here we review these data and suggest that the OFC provides model-based information to this traditional model-free circuitry and offer possibilities as to how this interaction might occur. PMID:22487030

Job characteristics and safety climate: the role of effort-reward and demand-control-support models.

PubMed

Phipps, Denham L; Malley, Christine; Ashcroft, Darren M

2012-07-01

While safety climate is widely recognized as a key influence on organizational safety, there remain questions about the nature of its antecedents. One potential influence on safety climate is job characteristics (that is, psychosocial features of the work environment). This study investigated the relationship between two job characteristics models--demand-control-support (Karasek & Theorell, 1990) and effort-reward imbalance (Siegrist, 1996)--and safety climate. A survey was conducted with a random sample of 860 British retail pharmacists, using the job contents questionnaire (JCQ), effort-reward imbalance indicator (ERI) and a measure of safety climate in pharmacies. Multivariate data analyses found that: (a) both models contributed to the prediction of safety climate ratings, with the demand-control-support model making the largest contribution; (b) there were some interactions between demand, control and support from the JCQ in the prediction of safety climate scores. The latter finding suggests the presence of "active learning" with respect to safety improvement in high demand, high control settings. The findings provide further insight into the ways in which job characteristics relate to safety, both individually and at an aggregated level.

The Development of Teaching and Learning in Bright-Field Microscopy Technique

ERIC Educational Resources Information Center

Iskandar, Yulita Hanum P.; Mahmud, Nurul Ethika; Wahab, Wan Nor Amilah Wan Abdul; Jamil, Noor Izani Noor; Basir, Nurlida

2013-01-01

E-learning should be pedagogically-driven rather than technologically-driven. The objectives of this study are to develop an interactive learning system in bright-field microscopy technique in order to support students' achievement of their intended learning outcomes. An interactive learning system on bright-field microscopy technique was…

Collective states in social systems with interacting learning agents

NASA Astrophysics Data System (ADS)

Semeshenko, Viktoriya; Gordon, Mirta B.; Nadal, Jean-Pierre

2008-08-01

We study the implications of social interactions and individual learning features on consumer demand in a simple market model. We consider a social system of interacting heterogeneous agents with learning abilities. Given a fixed price, agents repeatedly decide whether or not to buy a unit of a good, so as to maximize their expected utilities. This model is close to Random Field Ising Models, where the random field corresponds to the idiosyncratic willingness to pay. We show that the equilibrium reached depends on the nature of the information agents use to estimate their expected utilities. It may be different from the systems’ Nash equilibria.

Demand-driven water withdrawals by Chinese industry: a multi-regional input-output analysis

NASA Astrophysics Data System (ADS)

Zhang, Bo; Chen, Z. M.; Zeng, L.; Qiao, H.; Chen, B.

2016-03-01

With ever increasing water demands and the continuous intensification of water scarcity arising from China's industrialization, the country is struggling to harmonize its industrial development and water supply. This paper presents a systems analysis of water withdrawals by Chinese industry and investigates demand-driven industrial water uses embodied in final demand and interregional trade based on a multi-regional input-output model. In 2007, the Electric Power, Steam, and Hot Water Production and Supply sector ranks first in direct industrial water withdrawal (DWW), and Construction has the largest embodied industrial water use (EWU). Investment, consumption, and exports contribute to 34.6%, 33.3%, and 30.6% of the national total EWU, respectively. Specifically, 58.0%, 51.1%, 48.6%, 43.3%, and 37.5% of the regional EWUs respectively in Guangdong, Shanghai, Zhejiang, Jiangsu, and Fujian are attributed to international exports. The total interregional import/export of embodied water is equivalent to about 40% of the national total DWW, of which 55.5% is associated with the DWWs of Electric Power, Steam, and Hot Water Production and Supply. Jiangsu is the biggest interregional exporter and deficit receiver of embodied water, in contrast to Guangdong as the biggest interregional importer and surplus receiver. Without implementing effective water-saving measures and adjusting industrial structures, the regional imbalance between water availability and water demand tends to intensify considering the water impact of domestic trade of industrial products. Steps taken to improve water use efficiency in production, and to enhance embodied water saving in consumption are both of great significance for supporting China's water policies.

Perceived information and communication technology (ICT) demands on employee outcomes: the moderating effect of organizational ICT support.

PubMed

Day, Arla; Paquet, Stephanie; Scott, Natasha; Hambley, Laura

2012-10-01

Although many employees are using more information communication technology (ICT) as part of their jobs, few studies have examined the impact of ICT on their well-being, and there is a lack of validated measures designed to assess the ICT factors that may impact employee well-being. Therefore, we developed and validated a measure of ICT demands and supports. Using Exploratory Structural Equation Modeling, we found support for 8 ICT demands (i.e., availability, communication, ICT control, ICT hassles, employee monitoring, learning, response expectations, and workload) and two facets of ICT support (personal assistance and resources/upgrades support). Jointly, the ICT demands were associated with increased strain, stress, and burnout and were still associated with stress and strain after controlling for demographics, job variables, and job demands. The two types of ICT support were associated with lower stress, strain, and burnout. Resources/upgrades support moderated the relationship between learning expectations and most strain outcomes and between ICT hassles and strain. Personal assistance support moderated the relationship between ICT hassles and strain.

Dopamine selectively remediates 'model-based' reward learning: a computational approach.

PubMed

Sharp, Madeleine E; Foerde, Karin; Daw, Nathaniel D; Shohamy, Daphna

2016-02-01

Patients with loss of dopamine due to Parkinson's disease are impaired at learning from reward. However, it remains unknown precisely which aspect of learning is impaired. In particular, learning from reward, or reinforcement learning, can be driven by two distinct computational processes. One involves habitual stamping-in of stimulus-response associations, hypothesized to arise computationally from 'model-free' learning. The other, 'model-based' learning, involves learning a model of the world that is believed to support goal-directed behaviour. Much work has pointed to a role for dopamine in model-free learning. But recent work suggests model-based learning may also involve dopamine modulation, raising the possibility that model-based learning may contribute to the learning impairment in Parkinson's disease. To directly test this, we used a two-step reward-learning task which dissociates model-free versus model-based learning. We evaluated learning in patients with Parkinson's disease tested ON versus OFF their dopamine replacement medication and in healthy controls. Surprisingly, we found no effect of disease or medication on model-free learning. Instead, we found that patients tested OFF medication showed a marked impairment in model-based learning, and that this impairment was remediated by dopaminergic medication. Moreover, model-based learning was positively correlated with a separate measure of working memory performance, raising the possibility of common neural substrates. Our results suggest that some learning deficits in Parkinson's disease may be related to an inability to pursue reward based on complete representations of the environment. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Decentering Self in Leadership: Putting Community at the Center in Leadership Studies.

PubMed

Hartman, Eric

2016-06-01

Although students' personal passions typically determine the issue addressed by service-learning leadership initiatives, this chapter advocates for a community-centered alternative. This in-depth exploration of a leadership development course series models a community-need driven project and explores the benefits for both community and student learning. © 2016 Wiley Periodicals, Inc., A Wiley Company.

Scenario-based water resources planning for utilities in the Lake Victoria region

NASA Astrophysics Data System (ADS)

Mehta, Vishal K.; Aslam, Omar; Dale, Larry; Miller, Norman; Purkey, David R.

Urban areas in the Lake Victoria (LV) region are experiencing the highest growth rates in Africa. As efforts to meet increasing demand accelerate, integrated water resources management (IWRM) tools provide opportunities for utilities and other stakeholders to develop a planning framework comprehensive enough to include short term (e.g. landuse change), as well as longer term (e.g. climate change) scenarios. This paper presents IWRM models built using the Water Evaluation And Planning (WEAP) decision support system, for three towns in the LV region - Bukoba (Tanzania), Masaka (Uganda), and Kisii (Kenya). Each model was calibrated under current system performance based on site visits, utility reporting and interviews. Projected water supply, demand, revenues and costs were then evaluated against a combination of climate, demographic and infrastructure scenarios up to 2050. Our results show that water supply in all three towns is currently infrastructure limited; achieving existing design capacity could meet most projected demand until 2020s in Masaka beyond which new supply and conservation strategies would be needed. In Bukoba, reducing leakages would provide little performance improvement in the short-term, but doubling capacity would meet all demands until 2050. In Kisii, major infrastructure investment is urgently needed. In Masaka, streamflow simulations show that wetland sources could satisfy all demand until 2050, but at the cost of almost no water downstream of the intake. These models demonstrate the value of IWRM tools for developing water management plans that integrate hydroclimatology-driven supply to demand projections on a single platform.

Process-Driven Culture Learning in American KFL Classroom Settings

ERIC Educational Resources Information Center

Byon, Andrew Sangpil

2007-01-01

Teaching second language (L2) culture can be either content- or process-driven. The content-driven approach refers to explicit instruction of L2 cultural information. On the other hand, the process-driven approach focuses on students' active participation in cultural learning processes. In this approach, teachers are not only information…

Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

PubMed Central

Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

2015-01-01

In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

Course Management Systems as Tools for the Creation of Online Learning Environments: Evaluation from a Social Constructivist Perspective and Implications for Their Design

ERIC Educational Resources Information Center

Papastergiou, Marina

2006-01-01

The Internet and the Web offer academic institutions solutions for covering the massive demand for education and transition towards student-centered, social constructivist educational models, in accordance with the demands of the knowledge-based society. This article reports on an investigation aimed at presenting a synthesis of recent research on…

A Suggested Model for a Working Cyberschool.

ERIC Educational Resources Information Center

Javid, Mahnaz A.

2000-01-01

Suggests a model for a working cyberschool based on a case study of Kamiak Cyberschool (Washington), a technology-driven public high school. Topics include flexible hours; one-to-one interaction with teachers; a supportive school environment; use of computers, interactive media, and online resources; and self-paced, project-based learning.…

Self-Regulated Learning Substudy: Systems Thinking and Curriculum Innovation (STACI) Project.

ERIC Educational Resources Information Center

Mandinach, Ellen B.

The Systems Thinking and Curriculum Innovation (STACI) Project is a multi-year research effort intended to examine the cognitive demands and consequences of learning from a systems thinking approach to instruction and from using simulation-modeling software. The purpose of the study is to test the potentials and effects of integrating the systems…

The Kentucky Community and Technical College System Learn on Demand Model

ERIC Educational Resources Information Center

McCall, Michael B.

2013-01-01

When colleges turned to online learning, they opened the door for a number of students who might have only dreamed of pursuing a degree or credential. In 2006, the Kentucky Community and Technical College System (KCTCS) surveyed prospective adult students without a college degree and discovered that they were three times more likely to enroll in…

Freedom Schools Then and Now: A Transformative Approach to Learning

ERIC Educational Resources Information Center

Watson, Marcia

2014-01-01

The purpose of this paper is to provide a historical and conceptual link between Ella Baker's Freedom School model and Paulo Freire's demand for critical education and emancipatory learning. Ella Baker, situated in the daunting environment of the Civil Rights Movement, saw education as a tool for social mobility for Mississippi residents in 1964.…

Towards Automated Support for Small-Group Instruction: Using Data from an ITS to Automatically Group Students

ERIC Educational Resources Information Center

Mendiburo, Maria; Williams, Laura; Segedy, James; Hasselbring, Ted

2013-01-01

In this paper, the authors explore the use of learning analytics as a method for easing the cognitive demands on teachers implementing the HALF instructional model. Learning analytics has been defined as "the measurement, collection, analysis and reporting of data about learners and their contexts for the purposes of understanding and…

When Are Workload and Workplace Learning Opportunities Related in a Curvilinear Manner? The Moderating Role of Autonomy

ERIC Educational Resources Information Center

van Ruysseveldt, Joris; van Dijke, Marius

2011-01-01

Building on theoretical frameworks like the Job Demands Control model and Action Theory we tested whether the relationship between workload and employees' experiences of opportunities for workplace learning is of an inverted u-shaped nature and whether autonomy moderates this relationship. We predicted that--at moderate levels of…

Modelling and Optimising the Value of a Hybrid Solar-Wind System

NASA Astrophysics Data System (ADS)

Nair, Arjun; Murali, Kartik; Anbuudayasankar, S. P.; Arjunan, C. V.

2017-05-01

In this paper, a net present value (NPV) approach for a solar hybrid system has been presented. The system, in question aims at supporting an investor by assessing an investment in solar-wind hybrid system in a given area. The approach follow a combined process of modelling the system, with optimization of major investment-related variables to maximize the financial yield of the investment. The consideration of solar wind hybrid supply presents significant potential for cost reduction. The investment variables concern the location of solar wind plant, and its sizing. The system demand driven, meaning that its primary aim is to fully satisfy the energy demand of the customers. Therefore, the model is a practical tool in the hands of investor to assess and optimize in financial terms an investment aiming at covering real energy demand. Optimization is performed by taking various technical, logical constraints. The relation between the maximum power obtained between individual system and the hybrid system as a whole in par with the net present value of the system has been highlighted.

Specialized Motor-Driven dusp1 Expression in the Song Systems of Multiple Lineages of Vocal Learning Birds

PubMed Central

Horita, Haruhito; Kobayashi, Masahiko; Liu, Wan-chun; Oka, Kotaro; Jarvis, Erich D.; Wada, Kazuhiro

2012-01-01

Mechanisms for the evolution of convergent behavioral traits are largely unknown. Vocal learning is one such trait that evolved multiple times and is necessary in humans for the acquisition of spoken language. Among birds, vocal learning is evolved in songbirds, parrots, and hummingbirds. Each time similar forebrain song nuclei specialized for vocal learning and production have evolved. This finding led to the hypothesis that the behavioral and neuroanatomical convergences for vocal learning could be associated with molecular convergence. We previously found that the neural activity-induced gene dual specificity phosphatase 1 (dusp1) was up-regulated in non-vocal circuits, specifically in sensory-input neurons of the thalamus and telencephalon; however, dusp1 was not up-regulated in higher order sensory neurons or motor circuits. Here we show that song motor nuclei are an exception to this pattern. The song nuclei of species from all known vocal learning avian lineages showed motor-driven up-regulation of dusp1 expression induced by singing. There was no detectable motor-driven dusp1 expression throughout the rest of the forebrain after non-vocal motor performance. This pattern contrasts with expression of the commonly studied activity-induced gene egr1, which shows motor-driven expression in song nuclei induced by singing, but also motor-driven expression in adjacent brain regions after non-vocal motor behaviors. In the vocal non-learning avian species, we found no detectable vocalizing-driven dusp1 expression in the forebrain. These findings suggest that independent evolutions of neural systems for vocal learning were accompanied by selection for specialized motor-driven expression of the dusp1 gene in those circuits. This specialized expression of dusp1 could potentially lead to differential regulation of dusp1-modulated molecular cascades in vocal learning circuits. PMID:22876306

The Kinematic Learning Model using Video and Interfaces Analysis

NASA Astrophysics Data System (ADS)

Firdaus, T.; Setiawan, W.; Hamidah, I.

2017-09-01

An educator currently in demand to apply the learning to not be separated from the development of technology. Educators often experience difficulties when explaining kinematics material, this is because kinematics is one of the lessons that often relate the concept to real life. Kinematics is one of the courses of physics that explains the cause of motion of an object, Therefore it takes the thinking skills and analytical skills in understanding these symptoms. Technology is one that can bridge between conceptual relationship with real life. A framework of technology-based learning models has been developed using video and interfaces analysis on kinematics concept. By using this learning model, learners will be better able to understand the concept that is taught by the teacher. This learning model is able to improve the ability of creative thinking, analytical skills, and problem-solving skills on the concept of kinematics.

Democracy versus dictatorship in self-organized models of financial markets

NASA Astrophysics Data System (ADS)

D'Hulst, R.; Rodgers, G. J.

2000-06-01

Models to mimic the transmission of information in financial markets are introduced. As an attempt to generate the demand process, we distinguish between dictatorship associations, where groups of agents rely on one of them to make decision, and democratic associations, where each agent takes part in the group decision. In the dictatorship model, agents segregate into two distinct populations, while the democratic model is driven towards a critical state where groups of agents of all sizes exist. Hence, both models display a level of organization, but only the democratic model is self-organized. We show that the dictatorship model generates less-volatile markets than the democratic model.

The Mind-Body-Spirit Learning Model: Transformative Learning Connections to Holistic Perspectives: Seizing Control of Your Healthcare--The Relationship among Self-Agency, Transformative Learning, and Wellness

ERIC Educational Resources Information Center

King, Kathleen P.

2012-01-01

At a time when the medical field is dominated by the pressures of private insurance demands and government regulations, many people discover they need to be self advocates in order to battle illness and regain their health. Moreover, these issues are not constant, as many countries (like the USA) face changing demographics and continuing radical…

Models in search of a brain.

PubMed

Love, Bradley C; Gureckis, Todd M

2007-06-01

Mental localization efforts tend to stress the where more than the what. We argue that the proper targets for localization are well-specified cognitive models. We make this case by relating an existing cognitive model of category learning to a learning circuit involving the hippocampus, perirhinal, and prefrontal cortices. Results from groups varying in function along this circuit (e.g., infants, amnesics, and older adults) are successfully simulated by reducing the model's ability to form new clusters in response to surprising events, such as an error in supervised learning or an unfamiliar stimulus in unsupervised learning. Clusters in the model are akin to conjunctive codes that are rooted in an episodic experience (the surprising event) yet can develop to resemble abstract codes as they are updated by subsequent experiences. Thus, the model holds that the line separating episodic and semantic information can become blurred. Dissociations (categorization vs. recognition) are explained in terms of cluster recruitment demands.

Embedded performance validity testing in neuropsychological assessment: Potential clinical tools.

PubMed

Rickards, Tyler A; Cranston, Christopher C; Touradji, Pegah; Bechtold, Kathleen T

2018-01-01

The article aims to suggest clinically-useful tools in neuropsychological assessment for efficient use of embedded measures of performance validity. To accomplish this, we integrated available validity-related and statistical research from the literature, consensus statements, and survey-based data from practicing neuropsychologists. We provide recommendations for use of 1) Cutoffs for embedded performance validity tests including Reliable Digit Span, California Verbal Learning Test (Second Edition) Forced Choice Recognition, Rey-Osterrieth Complex Figure Test Combination Score, Wisconsin Card Sorting Test Failure to Maintain Set, and the Finger Tapping Test; 2) Selecting number of performance validity measures to administer in an assessment; and 3) Hypothetical clinical decision-making models for use of performance validity testing in a neuropsychological assessment collectively considering behavior, patient reporting, and data indicating invalid or noncredible performance. Performance validity testing helps inform the clinician about an individual's general approach to tasks: response to failure, task engagement and persistence, compliance with task demands. Data-driven clinical suggestions provide a resource to clinicians and to instigate conversation within the field to make more uniform, testable decisions to further the discussion, and guide future research in this area.

Dynamics of market structure driven by the degree of consumer’s rationality

NASA Astrophysics Data System (ADS)

Yanagita, Tatsuo; Onozaki, Tamotsu

2010-03-01

We study a simple model of market share dynamics with boundedly rational consumers and firms interacting with each other. As the number of consumers is large, we employ a statistical description to represent firms’ distribution of consumer share, which is characterized by a single parameter representing how rationally the mass of consumers pursue higher utility. As the boundedly rational firm does not know the shape of demand function it faces, it revises production and price so as to raise its profit with the aid of a simple reinforcement learning rule. Simulation results show that (1) three phases of market structure, i.e. the uniform share phase, the oligopolistic phase, and the monopolistic phase, appear depending upon how rational consumers are, and (2) in an oligopolistic phase, the market share distribution of firms follows Zipf’s law and the growth-rate distribution of firms follows Gibrat’s law, and (3) an oligopolistic phase is the best state of market in terms of consumers’ utility but brings the minimum profit to the firms because of severe competition based on the moderate rationality of consumers.

Learning maximum entropy models from finite-size data sets: A fast data-driven algorithm allows sampling from the posterior distribution.

PubMed

Ferrari, Ulisse

2016-08-01

Maximum entropy models provide the least constrained probability distributions that reproduce statistical properties of experimental datasets. In this work we characterize the learning dynamics that maximizes the log-likelihood in the case of large but finite datasets. We first show how the steepest descent dynamics is not optimal as it is slowed down by the inhomogeneous curvature of the model parameters' space. We then provide a way for rectifying this space which relies only on dataset properties and does not require large computational efforts. We conclude by solving the long-time limit of the parameters' dynamics including the randomness generated by the systematic use of Gibbs sampling. In this stochastic framework, rather than converging to a fixed point, the dynamics reaches a stationary distribution, which for the rectified dynamics reproduces the posterior distribution of the parameters. We sum up all these insights in a "rectified" data-driven algorithm that is fast and by sampling from the parameters' posterior avoids both under- and overfitting along all the directions of the parameters' space. Through the learning of pairwise Ising models from the recording of a large population of retina neurons, we show how our algorithm outperforms the steepest descent method.

Neural dynamics of object-based multifocal visual spatial attention and priming: Object cueing, useful-field-of-view, and crowding

PubMed Central

Foley, Nicholas C.; Grossberg, Stephen; Mingolla, Ennio

2015-01-01

How are spatial and object attention coordinated to achieve rapid object learning and recognition during eye movement search? How do prefrontal priming and parietal spatial mechanisms interact to determine the reaction time costs of intra-object attention shifts, inter-object attention shifts, and shifts between visible objects and covertly cued locations? What factors underlie individual differences in the timing and frequency of such attentional shifts? How do transient and sustained spatial attentional mechanisms work and interact? How can volition, mediated via the basal ganglia, influence the span of spatial attention? A neural model is developed of how spatial attention in the where cortical stream coordinates view-invariant object category learning in the what cortical stream under free viewing conditions. The model simulates psychological data about the dynamics of covert attention priming and switching requiring multifocal attention without eye movements. The model predicts how “attentional shrouds” are formed when surface representations in cortical area V4 resonate with spatial attention in posterior parietal cortex (PPC) and prefrontal cortex (PFC), while shrouds compete among themselves for dominance. Winning shrouds support invariant object category learning, and active surface-shroud resonances support conscious surface perception and recognition. Attentive competition between multiple objects and cues simulates reaction-time data from the two-object cueing paradigm. The relative strength of sustained surface-driven and fast-transient motion-driven spatial attention controls individual differences in reaction time for invalid cues. Competition between surface-driven attentional shrouds controls individual differences in detection rate of peripheral targets in useful-field-of-view tasks. The model proposes how the strength of competition can be mediated, though learning or momentary changes in volition, by the basal ganglia. A new explanation of crowding shows how the cortical magnification factor, among other variables, can cause multiple object surfaces to share a single surface-shroud resonance, thereby preventing recognition of the individual objects. PMID:22425615

Neural dynamics of object-based multifocal visual spatial attention and priming: object cueing, useful-field-of-view, and crowding.

PubMed

Foley, Nicholas C; Grossberg, Stephen; Mingolla, Ennio

2012-08-01

How are spatial and object attention coordinated to achieve rapid object learning and recognition during eye movement search? How do prefrontal priming and parietal spatial mechanisms interact to determine the reaction time costs of intra-object attention shifts, inter-object attention shifts, and shifts between visible objects and covertly cued locations? What factors underlie individual differences in the timing and frequency of such attentional shifts? How do transient and sustained spatial attentional mechanisms work and interact? How can volition, mediated via the basal ganglia, influence the span of spatial attention? A neural model is developed of how spatial attention in the where cortical stream coordinates view-invariant object category learning in the what cortical stream under free viewing conditions. The model simulates psychological data about the dynamics of covert attention priming and switching requiring multifocal attention without eye movements. The model predicts how "attentional shrouds" are formed when surface representations in cortical area V4 resonate with spatial attention in posterior parietal cortex (PPC) and prefrontal cortex (PFC), while shrouds compete among themselves for dominance. Winning shrouds support invariant object category learning, and active surface-shroud resonances support conscious surface perception and recognition. Attentive competition between multiple objects and cues simulates reaction-time data from the two-object cueing paradigm. The relative strength of sustained surface-driven and fast-transient motion-driven spatial attention controls individual differences in reaction time for invalid cues. Competition between surface-driven attentional shrouds controls individual differences in detection rate of peripheral targets in useful-field-of-view tasks. The model proposes how the strength of competition can be mediated, though learning or momentary changes in volition, by the basal ganglia. A new explanation of crowding shows how the cortical magnification factor, among other variables, can cause multiple object surfaces to share a single surface-shroud resonance, thereby preventing recognition of the individual objects. Copyright © 2012 Elsevier Inc. All rights reserved.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

PubMed Central

Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning. PMID:28680387

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

PubMed

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Network-driven design principles for neuromorphic systems.

PubMed

Partzsch, Johannes; Schüffny, Rene

2015-01-01

Synaptic connectivity is typically the most resource-demanding part of neuromorphic systems. Commonly, the architecture of these systems is chosen mainly on technical considerations. As a consequence, the potential for optimization arising from the inherent constraints of connectivity models is left unused. In this article, we develop an alternative, network-driven approach to neuromorphic architecture design. We describe methods to analyse performance of existing neuromorphic architectures in emulating certain connectivity models. Furthermore, we show step-by-step how to derive a neuromorphic architecture from a given connectivity model. For this, we introduce a generalized description for architectures with a synapse matrix, which takes into account shared use of circuit components for reducing total silicon area. Architectures designed with this approach are fitted to a connectivity model, essentially adapting to its connection density. They are guaranteeing faithful reproduction of the model on chip, while requiring less total silicon area. In total, our methods allow designers to implement more area-efficient neuromorphic systems and verify usability of the connectivity resources in these systems.

Network-driven design principles for neuromorphic systems

PubMed Central

Partzsch, Johannes; Schüffny, Rene

2015-01-01

Synaptic connectivity is typically the most resource-demanding part of neuromorphic systems. Commonly, the architecture of these systems is chosen mainly on technical considerations. As a consequence, the potential for optimization arising from the inherent constraints of connectivity models is left unused. In this article, we develop an alternative, network-driven approach to neuromorphic architecture design. We describe methods to analyse performance of existing neuromorphic architectures in emulating certain connectivity models. Furthermore, we show step-by-step how to derive a neuromorphic architecture from a given connectivity model. For this, we introduce a generalized description for architectures with a synapse matrix, which takes into account shared use of circuit components for reducing total silicon area. Architectures designed with this approach are fitted to a connectivity model, essentially adapting to its connection density. They are guaranteeing faithful reproduction of the model on chip, while requiring less total silicon area. In total, our methods allow designers to implement more area-efficient neuromorphic systems and verify usability of the connectivity resources in these systems. PMID:26539079

The prefrontal cortex and hybrid learning during iterative competitive games.

PubMed

Abe, Hiroshi; Seo, Hyojung; Lee, Daeyeol

2011-12-01

Behavioral changes driven by reinforcement and punishment are referred to as simple or model-free reinforcement learning. Animals can also change their behaviors by observing events that are neither appetitive nor aversive when these events provide new information about payoffs available from alternative actions. This is an example of model-based reinforcement learning and can be accomplished by incorporating hypothetical reward signals into the value functions for specific actions. Recent neuroimaging and single-neuron recording studies showed that the prefrontal cortex and the striatum are involved not only in reinforcement and punishment, but also in model-based reinforcement learning. We found evidence for both types of learning, and hence hybrid learning, in monkeys during simulated competitive games. In addition, in both the dorsolateral prefrontal cortex and orbitofrontal cortex, individual neurons heterogeneously encoded signals related to actual and hypothetical outcomes from specific actions, suggesting that both areas might contribute to hybrid learning. © 2011 New York Academy of Sciences.

Prepared stimuli enhance aversive learning without weakening the impact of verbal instructions

PubMed Central

2018-01-01

Fear-relevant stimuli such as snakes and spiders are thought to capture attention due to evolutionary significance. Classical conditioning experiments indicate that these stimuli accelerate learning, while instructed extinction experiments suggest they may be less responsive to instructions. We manipulated stimulus type during instructed aversive reversal learning and used quantitative modeling to simultaneously test both hypotheses. Skin conductance reversed immediately upon instruction in both groups. However, fear-relevant stimuli enhanced dynamic learning, as measured by higher learning rates in participants conditioned with images of snakes and spiders. Results are consistent with findings that dissociable neural pathways underlie feedback-driven and instructed aversive learning. PMID:29339561

High-resolution integration of water, energy, and climate models to assess electricity grid vulnerabilities to climate change

NASA Astrophysics Data System (ADS)

Meng, M.; Macknick, J.; Tidwell, V. C.; Zagona, E. A.; Magee, T. M.; Bennett, K.; Middleton, R. S.

2017-12-01

The U.S. electricity sector depends on large amounts of water for hydropower generation and cooling thermoelectric power plants. Variability in water quantity and temperature due to climate change could reduce the performance and reliability of individual power plants and of the electric grid as a system. While studies have modeled water usage in power systems planning, few have linked grid operations with physical water constraints or with climate-induced changes in water resources to capture the role of the energy-water nexus in power systems flexibility and adequacy. In addition, many hydrologic and hydropower models have a limited representation of power sector water demands and grid interaction opportunities of demand response and ancillary services. A multi-model framework was developed to integrate and harmonize electricity, water, and climate models, allowing for high-resolution simulation of the spatial, temporal, and physical dynamics of these interacting systems. The San Juan River basin in the Southwestern U.S., which contains thermoelectric power plants, hydropower facilities, and multiple non-energy water demands, was chosen as a case study. Downscaled data from three global climate models and predicted regional water demand changes were implemented in the simulations. The Variable Infiltration Capacity hydrologic model was used to project inflows, ambient air temperature, and humidity in the San Juan River Basin. Resulting river operations, water deliveries, water shortage sharing agreements, new water demands, and hydroelectricity generation at the basin-scale were estimated with RiverWare. The impacts of water availability and temperature on electric grid dispatch, curtailment, cooling water usage, and electricity generation cost were modeled in PLEXOS. Lack of water availability resulting from climate, new water demands, and shortage sharing agreements will require thermoelectric generators to drastically decrease power production, as much as 50% during intensifying drought scenarios, which can have broader electricity sector system implications. Results relevant to stakeholder and power provider interests highlight the vulnerabilities in grid operations driven by water shortage agreements and changes in the climate.

Proceedings of the Military Operations Research Society (MORS) Simulation Validation Workshop (SIMVAL II) Held at Alexandria, Virginia on 31 March-2 April 1992

DTIC Science & Technology

1992-04-02

learned . demanded by VV&A. * The CM should maintain a knowl- The cost of configuration manage- edgeable staff that can support ade- ment is increased by of...Carter note: a model before seeing that the results make even the vaguest sense, or learning what ...the argument for paying aspect of the model drives...that person the researcher can find model. Strong teanis have certain things in what are the variables of interest, what will common. They are made up

Toward Computational Cumulative Biology by Combining Models of Biological Datasets

PubMed Central

Faisal, Ali; Peltonen, Jaakko; Georgii, Elisabeth; Rung, Johan; Kaski, Samuel

2014-01-01

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations—for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database. PMID:25427176

Toward computational cumulative biology by combining models of biological datasets.

PubMed

Faisal, Ali; Peltonen, Jaakko; Georgii, Elisabeth; Rung, Johan; Kaski, Samuel

2014-01-01

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations-for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.

Data-driven agent-based modeling, with application to rooftop solar adoption

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

Zhang, Haifeng; Vorobeychik, Yevgeniy; Letchford, Joshua

Agent-based modeling is commonly used for studying complex system properties emergent from interactions among many agents. We present a novel data-driven agent-based modeling framework applied to forecasting individual and aggregate residential rooftop solar adoption in San Diego county. Our first step is to learn a model of individual agent behavior from combined data of individual adoption characteristics and property assessment. We then construct an agent-based simulation with the learned model embedded in artificial agents, and proceed to validate it using a holdout sequence of collective adoption decisions. We demonstrate that the resulting agent-based model successfully forecasts solar adoption trends andmore » provides a meaningful quantification of uncertainty about its predictions. We utilize our model to optimize two classes of policies aimed at spurring solar adoption: one that subsidizes the cost of adoption, and another that gives away free systems to low-income house- holds. We find that the optimal policies derived for the latter class are significantly more efficacious, whereas the policies similar to the current California Solar Initiative incentive scheme appear to have a limited impact on overall adoption trends.« less

Data-driven agent-based modeling, with application to rooftop solar adoption

DOE PAGES

Zhang, Haifeng; Vorobeychik, Yevgeniy; Letchford, Joshua; ...

2016-01-25

Agent-based modeling is commonly used for studying complex system properties emergent from interactions among many agents. We present a novel data-driven agent-based modeling framework applied to forecasting individual and aggregate residential rooftop solar adoption in San Diego county. Our first step is to learn a model of individual agent behavior from combined data of individual adoption characteristics and property assessment. We then construct an agent-based simulation with the learned model embedded in artificial agents, and proceed to validate it using a holdout sequence of collective adoption decisions. We demonstrate that the resulting agent-based model successfully forecasts solar adoption trends andmore » provides a meaningful quantification of uncertainty about its predictions. We utilize our model to optimize two classes of policies aimed at spurring solar adoption: one that subsidizes the cost of adoption, and another that gives away free systems to low-income house- holds. We find that the optimal policies derived for the latter class are significantly more efficacious, whereas the policies similar to the current California Solar Initiative incentive scheme appear to have a limited impact on overall adoption trends.« less

Machine Learning-based discovery of closures for reduced models of dynamical systems

NASA Astrophysics Data System (ADS)

Pan, Shaowu; Duraisamy, Karthik

2017-11-01

Despite the successful application of machine learning (ML) in fields such as image processing and speech recognition, only a few attempts has been made toward employing ML to represent the dynamics of complex physical systems. Previous attempts mostly focus on parameter calibration or data-driven augmentation of existing models. In this work we present a ML framework to discover closure terms in reduced models of dynamical systems and provide insights into potential problems associated with data-driven modeling. Based on exact closure models for linear system, we propose a general linear closure framework from viewpoint of optimization. The framework is based on trapezoidal approximation of convolution term. Hyperparameters that need to be determined include temporal length of memory effect, number of sampling points, and dimensions of hidden states. To circumvent the explicit specification of memory effect, a general framework inspired from neural networks is also proposed. We conduct both a priori and posteriori evaluations of the resulting model on a number of non-linear dynamical systems. This work was supported in part by AFOSR under the project ``LES Modeling of Non-local effects using Statistical Coarse-graining'' with Dr. Jean-Luc Cambier as the technical monitor.

Data-Driven Learning of Speech Acts Based on Corpora of DVD Subtitles

ERIC Educational Resources Information Center

Kitao, S. Kathleen; Kitao, Kenji

2013-01-01

Data-driven learning (DDL) is an inductive approach to language learning in which students study examples of authentic language and use them to find patterns of language use. This inductive approach to learning has the advantages of being learner-centered, encouraging hypothesis testing and learner autonomy, and helping develop learning skills.…

A review on machine learning principles for multi-view biological data integration.

PubMed

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Input Skewedness, Consistency, and Order of Frequent Verbs in Frequency-Driven Second Language Construction Learning: A Replication and Extension of Casenhiser and Goldberg (2005) to Adult Second Language Acquisition

ERIC Educational Resources Information Center

Nakamura, Daisuke

2012-01-01

Recent usage-based models of language acquisition research has found that three frequency manipulations; (1) skewed input (Casenhiser & Goldberg 2005), (2) input consistency (Childers & Tomasello 2001), and (3) order of frequent verbs (Goldberg, Casenhiser, & White 2007) facilitated construction learning in children. The present paper addresses…

Predictive Modeling in Adult Education

ERIC Educational Resources Information Center

Lindner, Charles L.

2011-01-01

The current economic crisis, a growing workforce, the increasing lifespan of workers, and demanding, complex jobs have made organizations highly selective in employee recruitment and retention. It is therefore important, to the adult educator, to develop models of learning that better prepare adult learners for the workplace. The purpose of…

Tobacco-free economy: A SAM-based multiplier model to quantify the impact of changes in tobacco demand in Bangladesh

PubMed Central

Husain, Muhammad Jami; Khondker, Bazlul Haque

2017-01-01

In Bangladesh, where tobacco use is pervasive, reducing tobacco use is economically beneficial. This paper uses the latest Bangladesh social accounting matrix (SAM) multiplier model to quantify the economy-wide impact of demand-driven changes in tobacco cultivation, bidi industries, and cigarette industries. First, we compute various income multiplier values (i.e. backward linkages) for all production activities in the economy to quantify the impact of changes in demand for the corresponding products on gross output for 86 activities, demand for 86 commodities, returns to four factors of production, and income for eight household groups. Next, we rank tobacco production activities by income multiplier values relative to other sectors. Finally, we present three hypothetical ‘tobacco-free economy’ scenarios by diverting demand from tobacco products into other sectors of the economy and quantifying the economy-wide impact. The simulation exercises with three different tobacco-free scenarios show that, compared to the baseline values, total sectoral output increases by 0.92%, 1.3%, and 0.75%. The corresponding increases in the total factor returns (i.e. GDP) are 1.57%, 1.75%, and 1.75%. Similarly, total household income increases by 1.40%, 1.58%, and 1.55%. PMID:28845091

Application of stakeholder-based and modelling approaches for supporting robust adaptation decision making under future climatic uncertainty and changing urban-agricultural water demand

NASA Astrophysics Data System (ADS)

Bhave, Ajay; Dessai, Suraje; Conway, Declan; Stainforth, David

2016-04-01

Deep uncertainty in future climate change and socio-economic conditions necessitates the use of assess-risk-of-policy approaches over predict-then-act approaches for adaptation decision making. Robust Decision Making (RDM) approaches embody this principle and help evaluate the ability of adaptation options to satisfy stakeholder preferences under wide-ranging future conditions. This study involves the simultaneous application of two RDM approaches; qualitative and quantitative, in the Cauvery River Basin in Karnataka (population ~23 million), India. The study aims to (a) determine robust water resources adaptation options for the 2030s and 2050s and (b) compare the usefulness of a qualitative stakeholder-driven approach with a quantitative modelling approach. For developing a large set of future scenarios a combination of climate narratives and socio-economic narratives was used. Using structured expert elicitation with a group of climate experts in the Indian Summer Monsoon, climatic narratives were developed. Socio-economic narratives were developed to reflect potential future urban and agricultural water demand. In the qualitative RDM approach, a stakeholder workshop helped elicit key vulnerabilities, water resources adaptation options and performance criteria for evaluating options. During a second workshop, stakeholders discussed and evaluated adaptation options against the performance criteria for a large number of scenarios of climatic and socio-economic change in the basin. In the quantitative RDM approach, a Water Evaluation And Planning (WEAP) model was forced by precipitation and evapotranspiration data, coherent with the climatic narratives, together with water demand data based on socio-economic narratives. We find that compared to business-as-usual conditions options addressing urban water demand satisfy performance criteria across scenarios and provide co-benefits like energy savings and reduction in groundwater depletion, while options reducing agricultural water demand significantly affect downstream water availability. Water demand options demonstrate potential to improve environmental flow conditions and satisfy legal water supply requirements for downstream riparian states. On the other hand, currently planned large scale infrastructural projects demonstrate reduced value in certain scenarios, illustrating the impacts of lock-in effects of large scale infrastructure. From a methodological perspective, we find that while the stakeholder-driven approach revealed robust options in a resource-light manner and helped initiate much needed interaction amongst stakeholders, the modelling approach provides complementary quantitative information. The study reveals robust adaptation options for this important basin and provides a strong methodological basis for carrying out future studies that support adaptation decision making.

The everyday meets the academic: How bilingual Latino/a third graders use sociocultural resources to learn in science and social studies

NASA Astrophysics Data System (ADS)

McIntosh Ciechanowski, Kathryn E.

Driven by questions surrounding the documented "fourth-grade slump" in student test scores and about the content learning of English language learners, this dissertation examines the science and social studies literacy practices of third grade bilingual Latino/as in an urban school. Using qualitative and quantitative methods, I examined three questions: (a) What content area demands are evident in instruction and in the assigned texts that children read? (b) What sociocultural knowledge do students draw on in the reading and writing of content area texts? How does it shape their reading and writing? and (c) What linguistic knowledge do students draw on in the reading and writing of content area texts? How does it shape their reading and writing? These questions are premised on three key tenets from the extant research literature. First, research has documented that middle grade students struggle to make sense of content texts, which could be caused by not only a scarcity of expository texts in early grades but also by discipline-specific demands in the content texts. Second, although all students may struggle to read specialized texts, students from non-mainstream backgrounds may struggle more because they do not possess the social and linguistic capital valued in mainstream schools. Third, sociocultural research has documented the importance of social and cultural funds of knowledge in classroom learning and knowledge construction. Guided by these tenets, I observed for six months in 2 classes and recorded field notes, interviewed participants, collected artifacts, and conducted pre- and post-unit assessments. Analytic methods included quantitative evaluation of assessments and constant comparative and discourse analyses. Findings indicate that the textbooks posed linguistic and conceptual demands and represented multiple discourses including the discourses of the natural and social sciences. To make sense of texts, students drew from various sociocultural resources such as popular culture, family, and children's literature. The teacher was more likely to take up these resources (although briefly) when they tightly aligned with instructional goals. Bilingual students faced great complexity as they drew upon linguistic resources to learn technical language and content in two languages and within multiple academic and everyday discourses.

Evaluating the utility of dynamical downscaling in agricultural impacts projections

PubMed Central

Glotter, Michael; Elliott, Joshua; McInerney, David; Best, Neil; Foster, Ian; Moyer, Elisabeth J.

2014-01-01

Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling—nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output—to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn downscaled by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically downscaling raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections. PMID:24872455

Training demands on clerk burnout: determining whether achievement goal motivation orientations matter.

PubMed

Lin, Chia-Der; Lin, Blossom Yen-Ju

2016-08-22

In the education field, learning experiences are considered learners' properties and are viewed as a key determinant in explaining learners' learning processes, especially for training novices such as clerks with varying levels of commitment to the medical profession. This study explored whether clerks' achievement goal motivation orientations might buffer the negative well-being to a certain extent, considering their training demands during clinical training. Ninety-four clerks at a tertiary medical center were longitudinally traced during their 2-year clerkship spanning from September 2013 to April 2015. Web-based, validated, structured, self-administered questionnaires were used to evaluate the clerks' properties of achievement goal motivation orientation and personal background at the beginning of the clerkship. Regular surveys were conducted to evaluate their perceptions of training demands and burnout at each specialty rotation. Overall, 2230 responses were analyzed, and linear mixed-effects models were used to examine the repeated measures of the clerks. The results revealed that higher perceived psychological and physical demands of training were related to higher perceived burnout during the 2-year clerkship. Although both the clerks' task and ego orientations were related to reduced burnout (direct effects), only task orientation was indicated to exert a buffering effect on their perception of physical demands on burnout in the 1st year of the clerkship. Considering the negative effects of training demands (psychological and physical), we observed a limited effect of the task achievement motivation orientation of medical students; therefore, additional studies might focus on strategies to facilitate medical students in clerkships in addressing both the psychological and physical demands inherent in training workplaces to improve their learning experience and well-being.

A Study on Design-Oriented Demands of VR via ZMET-QFD Model for Industrial Design Education and Students' Learning

ERIC Educational Resources Information Center

Liang, Yo-Wen; Lee, An-Sheng; Liu, Shuo-Fang

2016-01-01

The difficulty of Virtual Reality application in industrial design education and learning is VR engineers cannot comprehend what the important functions or elements are for students. In addition, a general-purpose VR usually confuses the students and provides neither good manipulation means nor useful toolkits. To solve these problems, the…

Unlearning How to Teach

ERIC Educational Resources Information Center

McWilliam, Erica

2008-01-01

The twenty-first century demands not only that we learn new forms of social engagement but also that we "unlearn" habits that have been useful in the past but may no longer be valuable to the future. Teachers have "un-learned" the role of "Sage-on-the-stage" as the dominent model of teaching, and the shift to "Guide-on-the-side" has served an…

A Person-in-Context Approach to Student Engagement in Science: Examining Learning Activities and Choice

ERIC Educational Resources Information Center

Schmidt, Jennifer A.; Rosenberg, Joshua M.; Beymer, Patrick N.

2018-01-01

Science education reform efforts in the Unites States call for a dramatic shift in the way students are expected to engage with scientific concepts, core ideas, and practices in the classroom. This new vision of science learning demands a more complex conceptual understanding of student engagement and research models that capture both the…

Where Academia Meets Management: A Model for the Effective Development of Quality Learning Materials Using New Technologies.

ERIC Educational Resources Information Center

Kenny, John

This article explores the perennial tension between the demands of management for quality projects that can be used to attract new markets and students and the traditional scholarly approach to learning. Universities are unique environments compared to industry settings. They are unique in that the responsibility for the quality of the teaching…

Distance learning in the public health workplace.

PubMed

Patel, M

2000-09-01

The Master of Applied Epidemiology (MAE) Program implemented in Canberra to produce public health practitioners with specified competencies in the control of communicable diseases. Twenty one of the 24 months of training is distance learning defined as, 'where the learner is physically remote from the training institution'. During this time the trainees are in supervised employment in Public Health centres across the country. Here they learn directly from first hand experiences in the work place. They return to Canberra for short, intensive periods of interactive sessions with their peers and supervisors. Lessons learnt from conducting this program are discussed in this article. They include: all trainees are not suited to this form of training; the quality of support from the field supervisors is highly variable and their role in modelling crucial to the trainees performance; demands on the academic staff is high; and the frequency of contact between trainee and academic supervisor varies considerably. To date this program has made major contributions by enhancing communicable disease surveillance and control but it demands intensive resources to sustain, quality training, and support. This model of distance learning can be adapted in the Pacific both for graduate degree courses and also for continuing education for all levels of health professionals.

[Teaching practices and learning strategies in health careers].

PubMed

Carrasco Z, Constanza; Pérez V, Cristhian; Torres A, Graciela; Fasce H, Eduardo

2016-09-01

Medical Education, according to the constructivist education paradigm, puts students as the protagonists of the teaching and learning process. It demands changes in the practice of teaching. However, it is unclear whether this new model is coherent with the teachers’ ways to cope with learning. To analyze the relationship between teaching practices and learning strategies among teachers of health careers in Chilean universities. The Teaching Practices Questionnaire and Learning Strategies Inventory of Schmeck were applied to 200 teachers aged 24 to 72 years (64% females). Teachers use different types of teaching practices. They commonly use deep and elaborative learning strategies. A multiple regression analysis showed that learning strategies had a 13% predictive value to identify student-centered teaching, but they failed to predict teacher-centered teaching. Teaching practices and learning strategies of teachers are related. Teachers frequently select constructivist model strategies, using different teaching practices in their work.

Lessons learned while integrating habitat, dispersal, disturbance, and life-history traits into species habitat models under climate change

Treesearch

Louis R. Iverson; Anantha M. Prasad; Stephen N. Matthews; Matthew P. Peters

2011-01-01

We present an approach to modeling potential climate-driven changes in habitat for tree and bird species in the eastern United States. First, we took an empirical-statistical modeling approach, using randomForest, with species abundance data from national inventories combined with soil, climate, and landscape variables, to build abundance-based habitat models for 134...

A Neural Circuit Model of Flexible Sensori-motor Mapping: Learning and Forgetting on Multiple Timescales

PubMed Central

Fusi, Stefano; Asaad, Wael F.; Miller, Earl K.; Wang, Xiao-Jing

2007-01-01

Summary Volitional behavior relies on the brain’s ability to remap sensory flow to motor programs whenever demanded by a changed behavioral context. To investigate the circuit basis of such flexible behavior, we have developed a biophysically-based decision-making network model of spiking neurons for arbitrary sensorimotor mapping. The model quantitatively reproduces behavioral and prefrontal single-cell data from an experiment in which monkeys learn visuo-motor associations that are reversed unpredictably from time to time. We show that when synaptic modifications occur on multiple timescales, the model behavior becomes flexible only when needed: slow components of learning usually dominate the decision process. However, if behavioral contexts change frequently enough, fast components of plasticity take over, and the behavior exhibits a quick forget-and-learn pattern. This model prediction is confirmed by monkey data. Therefore, our work reveals a scenario for conditional associative learning that is distinct from instant switching between sets of well established sensorimotor associations. PMID:17442251

A neural circuit model of flexible sensorimotor mapping: learning and forgetting on multiple timescales.

PubMed

Fusi, Stefano; Asaad, Wael F; Miller, Earl K; Wang, Xiao-Jing

2007-04-19

Volitional behavior relies on the brain's ability to remap sensory flow to motor programs whenever demanded by a changed behavioral context. To investigate the circuit basis of such flexible behavior, we have developed a biophysically based decision-making network model of spiking neurons for arbitrary sensorimotor mapping. The model quantitatively reproduces behavioral and prefrontal single-cell data from an experiment in which monkeys learn visuomotor associations that are reversed unpredictably from time to time. We show that when synaptic modifications occur on multiple timescales, the model behavior becomes flexible only when needed: slow components of learning usually dominate the decision process. However, if behavioral contexts change frequently enough, fast components of plasticity take over, and the behavior exhibits a quick forget-and-learn pattern. This model prediction is confirmed by monkey data. Therefore, our work reveals a scenario for conditional associative learning that is distinct from instant switching between sets of well-established sensorimotor associations.

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking.

PubMed

Zhou, Ping; Guo, Dongwei; Wang, Hong; Chai, Tianyou

2017-09-29

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVR (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. This indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

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

Zhou, Ping; Guo, Dongwei; Wang, Hong

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVRmore » (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. In conclusion, this indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.« less

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

DOE PAGES

Zhou, Ping; Guo, Dongwei; Wang, Hong; ...

2017-09-29

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVRmore » (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. In conclusion, this indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.« less

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

NASA Astrophysics Data System (ADS)

Deo, Ravinesh C.; Şahin, Mehmet

2015-02-01

The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed data-driven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.

Choosing the Most Effective Pattern Classification Model under Learning-Time Constraint.

PubMed

Saito, Priscila T M; Nakamura, Rodrigo Y M; Amorim, Willian P; Papa, João P; de Rezende, Pedro J; Falcão, Alexandre X

2015-01-01

Nowadays, large datasets are common and demand faster and more effective pattern analysis techniques. However, methodologies to compare classifiers usually do not take into account the learning-time constraints required by applications. This work presents a methodology to compare classifiers with respect to their ability to learn from classification errors on a large learning set, within a given time limit. Faster techniques may acquire more training samples, but only when they are more effective will they achieve higher performance on unseen testing sets. We demonstrate this result using several techniques, multiple datasets, and typical learning-time limits required by applications.

Perceived Social Relationships and Science Learning Outcomes for Taiwanese Eighth Graders: Structural Equation Modeling with a Complex Sampling Consideration

ERIC Educational Resources Information Center

Jen, Tsung-Hau; Lee, Che-Di; Chien, Chin-Lung; Hsu, Ying-Shao; Chen, Kuan-Ming

2013-01-01

Based on the Trends in International Mathematics and Science Study 2007 study and a follow-up national survey, data for 3,901 Taiwanese grade 8 students were analyzed using structural equation modeling to confirm a social-relation-based affection-driven model (SRAM). SRAM hypothesized relationships among students' perceived social relationships in…

Pedagogical Distance: Explaining Misalignment in Student-Driven Online Learning Activities Using Activity Theory

ERIC Educational Resources Information Center

Westberry, Nicola; Franken, Margaret

2015-01-01

This paper provides an Activity Theory analysis of two online student-driven interactive learning activities to interrogate assumptions that such groups can effectively learn in the absence of the teacher. Such an analysis conceptualises learning tasks as constructed objects that drive pedagogical activity. The analysis shows a disconnect between…

The impact of social context on learning and cognitive demands for interactive virtual human simulations

PubMed Central

Lyons, Rebecca; Johnson, Teresa R.; Khalil, Mohammed K.

2014-01-01

Interactive virtual human (IVH) simulations offer a novel method for training skills involving person-to-person interactions. This article examines the effectiveness of an IVH simulation for teaching medical students to assess rare cranial nerve abnormalities in both individual and small-group learning contexts. Individual (n = 26) and small-group (n = 30) interaction with the IVH system was manipulated to examine the influence on learning, learner engagement, perceived cognitive demands of the learning task, and instructional efficiency. Results suggested the IVH activity was an equally effective and engaging instructional tool in both learning structures, despite learners in the group learning contexts having to share hands-on access to the simulation interface. Participants in both conditions demonstrated a significant increase in declarative knowledge post-training. Operation of the IVH simulation technology imposed moderate cognitive demand but did not exceed the demands of the task content or appear to impede learning. PMID:24883241

Climate change is projected to have severe impacts on the frequency and intensity of peak electricity demand across the United States.

PubMed

Auffhammer, Maximilian; Baylis, Patrick; Hausman, Catherine H

2017-02-21

It has been suggested that climate change impacts on the electric sector will account for the majority of global economic damages by the end of the current century and beyond [Rose S, et al. (2014) Understanding the Social Cost of Carbon: A Technical Assessment ]. The empirical literature has shown significant increases in climate-driven impacts on overall consumption, yet has not focused on the cost implications of the increased intensity and frequency of extreme events driving peak demand, which is the highest load observed in a period. We use comprehensive, high-frequency data at the level of load balancing authorities to parameterize the relationship between average or peak electricity demand and temperature for a major economy. Using statistical models, we analyze multiyear data from 166 load balancing authorities in the United States. We couple the estimated temperature response functions for total daily consumption and daily peak load with 18 downscaled global climate models (GCMs) to simulate climate change-driven impacts on both outcomes. We show moderate and heterogeneous changes in consumption, with an average increase of 2.8% by end of century. The results of our peak load simulations, however, suggest significant increases in the intensity and frequency of peak events throughout the United States, assuming today's technology and electricity market fundamentals. As the electricity grid is built to endure maximum load, our findings have significant implications for the construction of costly peak generating capacity, suggesting additional peak capacity costs of up to 180 billion dollars by the end of the century under business-as-usual.

Transferring control demands across incidental learning tasks – stronger sequence usage in serial reaction task after shortcut option in letter string checking

PubMed Central

Gaschler, Robert; Marewski, Julian N.; Wenke, Dorit; Frensch, Peter A.

2014-01-01

After incidentally learning about a hidden regularity, participants can either continue to solve the task as instructed or, alternatively, apply a shortcut. Past research suggests that the amount of conflict implied by adopting a shortcut seems to bias the decision for vs. against continuing instruction-coherent task processing. We explored whether this decision might transfer from one incidental learning task to the next. Theories that conceptualize strategy change in incidental learning as a learning-plus-decision phenomenon suggest that high demands to adhere to instruction-coherent task processing in Task 1 will impede shortcut usage in Task 2, whereas low control demands will foster it. We sequentially applied two established incidental learning tasks differing in stimuli, responses and hidden regularity (the alphabet verification task followed by the serial reaction task, SRT). While some participants experienced a complete redundancy in the task material of the alphabet verification task (low demands to adhere to instructions), for others the redundancy was only partial. Thus, shortcut application would have led to errors (high demands to follow instructions). The low control demand condition showed the strongest usage of the fixed and repeating sequence of responses in the SRT. The transfer results are in line with the learning-plus-decision view of strategy change in incidental learning, rather than with resource theories of self-control. PMID:25506336

Land cover change or land-use intensification: simulating land system change with a global-scale land change model.

PubMed

van Asselen, Sanneke; Verburg, Peter H

2013-12-01

Land-use change is both a cause and consequence of many biophysical and socioeconomic changes. The CLUMondo model provides an innovative approach for global land-use change modeling to support integrated assessments. Demands for goods and services are, in the model, supplied by a variety of land systems that are characterized by their land cover mosaic, the agricultural management intensity, and livestock. Land system changes are simulated by the model, driven by regional demand for goods and influenced by local factors that either constrain or promote land system conversion. A characteristic of the new model is the endogenous simulation of intensification of agricultural management versus expansion of arable land, and urban versus rural settlements expansion based on land availability in the neighborhood of the location. Model results for the OECD Environmental Outlook scenario show that allocation of increased agricultural production by either management intensification or area expansion varies both among and within world regions, providing useful insight into the land sparing versus land sharing debate. The land system approach allows the inclusion of different types of demand for goods and services from the land system as a driving factor of land system change. Simulation results are compared to observed changes over the 1970-2000 period and projections of other global and regional land change models. © 2013 John Wiley & Sons Ltd.

A manifold learning approach to data-driven computational materials and processes

NASA Astrophysics Data System (ADS)

Ibañez, Ruben; Abisset-Chavanne, Emmanuelle; Aguado, Jose Vicente; Gonzalez, David; Cueto, Elias; Duval, Jean Louis; Chinesta, Francisco

2017-10-01

Standard simulation in classical mechanics is based on the use of two very different types of equations. The first one, of axiomatic character, is related to balance laws (momentum, mass, energy, …), whereas the second one consists of models that scientists have extracted from collected, natural or synthetic data. In this work we propose a new method, able to directly link data to computers in order to perform numerical simulations. These simulations will employ universal laws while minimizing the need of explicit, often phenomenological, models. They are based on manifold learning methodologies.

A Novel approach for predicting monthly water demand by combining singular spectrum analysis with neural networks

NASA Astrophysics Data System (ADS)

Zubaidi, Salah L.; Dooley, Jayne; Alkhaddar, Rafid M.; Abdellatif, Mawada; Al-Bugharbee, Hussein; Ortega-Martorell, Sandra

2018-06-01

Valid and dependable water demand prediction is a major element of the effective and sustainable expansion of municipal water infrastructures. This study provides a novel approach to quantifying water demand through the assessment of climatic factors, using a combination of a pretreatment signal technique, a hybrid particle swarm optimisation algorithm and an artificial neural network (PSO-ANN). The Singular Spectrum Analysis (SSA) technique was adopted to decompose and reconstruct water consumption in relation to six weather variables, to create a seasonal and stochastic time series. The results revealed that SSA is a powerful technique, capable of decomposing the original time series into many independent components including trend, oscillatory behaviours and noise. In addition, the PSO-ANN algorithm was shown to be a reliable prediction model, outperforming the hybrid Backtracking Search Algorithm BSA-ANN in terms of fitness function (RMSE). The findings of this study also support the view that water demand is driven by climatological variables.

Is the Demand for Alcoholic Beverages in Developing Countries Sensitive to Price? Evidence from China

PubMed Central

Tian, Guoqiang; Liu, Feng

2011-01-01

Economic literature in developed countries suggests that demand for alcoholic beverages is sensitive to price, with an estimated price elasticity ranging from −0.38 for beer and −0.7 for liquor. However, few studies have been conducted in developing countries. We employ a large individual-level dataset in China to estimate the effects of price on alcohol demand. Using the data from China Health and Nutrition Survey for the years 1993, 1997, 2000, 2004 and 2006, we estimate two-part models of alcohol demand. Results show the price elasticity is virtually zero for beer and only −0.12 for liquor, which is far smaller than those derived from developed countries. Separate regressions by gender reveals the results are mainly driven by men. The central implication of this study is, while alcohol tax increases can raise government revenue, it alone is not an effective policy to reduce alcohol related problems in China. PMID:21776220

Evaluating a Computational Model of Emotion

DTIC Science & Technology

2006-01-01

empathy and intrinsic motivation in a learning-by-teaching system. Simulation-driven approaches aim at simulating the cognitive process underlying...9, pp. 1-44, 1999. [9] K. Ryokai, C. Vaucelle, and J. Cassell, "Virtual Peers as Partners in Storytelling and Literacy Learning," Journal of...Australia, 2003. [28] M. Cavazza, F. Charles, and S. J. Mead, "Agents’ Interaction in Virtual Storytelling ," presented at Third Interna- tional Workshop on

How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes

PubMed Central

Friedel, Eva; Sebold, Miriam; Kuitunen-Paul, Sören; Nebe, Stephan; Veer, Ilya M.; Zimmermann, Ulrich S.; Schlagenhauf, Florian; Smolka, Michael N.; Rapp, Michael; Walter, Henrik; Heinz, Andreas

2017-01-01

Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities. PMID:28642696

How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes.

PubMed

Friedel, Eva; Sebold, Miriam; Kuitunen-Paul, Sören; Nebe, Stephan; Veer, Ilya M; Zimmermann, Ulrich S; Schlagenhauf, Florian; Smolka, Michael N; Rapp, Michael; Walter, Henrik; Heinz, Andreas

2017-01-01

Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities.

Max-margin multiattribute learning with low-rank constraint.

PubMed

Zhang, Qiang; Chen, Lin; Li, Baoxin

2014-07-01

Attribute learning has attracted a lot of interests in recent years for its advantage of being able to model high-level concepts with a compact set of midlevel attributes. Real-world objects often demand multiple attributes for effective modeling. Most existing methods learn attributes independently without explicitly considering their intrinsic relatedness. In this paper, we propose max margin multiattribute learning with low-rank constraint, which learns a set of attributes simultaneously, using only relative ranking of the attributes for the data. By learning all the attributes simultaneously through low-rank constraint, the proposed method is able to capture their intrinsic correlation for improved learning; by requiring only relative ranking, the method avoids restrictive binary labels of attributes that are often assumed by many existing techniques. The proposed method is evaluated on both synthetic data and real visual data including a challenging video data set. Experimental results demonstrate the effectiveness of the proposed method.

Modelling the formation of working memory with networks of integrate-and-fire neurons connected by plastic synapses.

PubMed

Del Giudice, Paolo; Fusi, Stefano; Mattia, Maurizio

2003-01-01

In this paper we review a series of works concerning models of spiking neurons interacting via spike-driven, plastic, Hebbian synapses, meant to implement stimulus driven, unsupervised formation of working memory (WM) states. Starting from a summary of the experimental evidence emerging from delayed matching to sample (DMS) experiments, we briefly review the attractor picture proposed to underlie WM states. We then describe a general framework for a theoretical approach to learning with synapses subject to realistic constraints and outline some general requirements to be met by a mechanism of Hebbian synaptic structuring. We argue that a stochastic selection of the synapses to be updated allows for optimal memory storage, even if the number of stable synaptic states is reduced to the extreme (bistable synapses). A description follows of models of spike-driven synapses that implement the stochastic selection by exploiting the high irregularity in the pre- and post-synaptic activity. Reasons are listed why dynamic learning, that is the process by which the synaptic structure develops under the only guidance of neural activities, driven in turn by stimuli, is hard to accomplish. We provide a 'feasibility proof' of dynamic formation of WM states in this context the beneficial role of short-term depression (STD) is illustrated. by showing how an initially unstructured network autonomously develops a synaptic structure supporting simultaneously stable spontaneous and WM states in this context the beneficial role of short-term depression (STD) is illustrated. After summarizing heuristic indications emerging from the study performed, we conclude by briefly discussing open problems and critical issues still to be clarified.

Using the Time-Driven Activity-Based Costing Model in the Eye Clinic at The Hospital for Sick Children: A Case Study and Lessons Learned.

PubMed

Gulati, Sanchita; During, David; Mainland, Jeff; Wong, Agnes M F

2018-01-01

One of the key challenges to healthcare organizations is the development of relevant and accurate cost information. In this paper, we used time-driven activity-based costing (TDABC) method to calculate the costs of treating individual patients with specific medical conditions over their full cycle of care. We discussed how TDABC provides a critical, systematic and data-driven approach to estimate costs accurately and dynamically, as well as its potential to enable structural and rational cost reduction to bring about a sustainable healthcare system. © 2018 Longwoods Publishing.

Multi-day activity scheduling reactions to planned activities and future events in a dynamic model of activity-travel behavior

NASA Astrophysics Data System (ADS)

Nijland, Linda; Arentze, Theo; Timmermans, Harry

2014-01-01

Modeling multi-day planning has received scarce attention in activity-based transport demand modeling so far. However, new dynamic activity-based approaches are being developed at the current moment. The frequency and inflexibility of planned activities and events in activity schedules of individuals indicate the importance of incorporating those pre-planned activities in the new generation of dynamic travel demand models. Elaborating and combining previous work on event-driven activity generation, the aim of this paper is to develop and illustrate an extension of a need-based model of activity generation that takes into account possible influences of pre-planned activities and events. This paper describes the theory and shows the results of simulations of the extension. The simulation was conducted for six different activities, and the parameter values used were consistent with an earlier estimation study. The results show that the model works well and that the influences of the parameters are consistent, logical, and have clear interpretations. These findings offer further evidence of face and construct validity to the suggested modeling approach.

Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.

PubMed

Mendoza, Leonardo Forero; Vellasco, Marley; Figueiredo, Karla

2014-12-01

This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies.

Serving two masters: quality teaching and learning versus economic rationalism.

PubMed

Kenny, A J; Kendall, S

2001-11-01

Nurse educators face the challenge of competing pressures. Programmes must be developed that more adequately prepare students to meet the demands of a changing and complex health care system. These programmes must reflect excellence in teaching and learning and this needs to be achieved within the constraints of economic rationalism. The design of a model based on principles of self directed learning assisted one university to deliver a high quality clinical skills programme. Copyright 2001 Harcourt Publishers Ltd.

Analysis of factors affecting satisfaction level on problem based learning approach using structural equation modeling

NASA Astrophysics Data System (ADS)

Hussain, Nur Farahin Mee; Zahid, Zalina

2014-12-01

Nowadays, in the job market demand, graduates are expected not only to have higher performance in academic but they must also be excellent in soft skill. Problem-Based Learning (PBL) has a number of distinct advantages as a learning method as it can deliver graduates that will be highly prized by industry. This study attempts to determine the satisfaction level of engineering students on the PBL Approach and to evaluate their determinant factors. The Structural Equation Modeling (SEM) was used to investigate how the factors of Good Teaching Scale, Clear Goals, Student Assessment and Levels of Workload affected the student satisfaction towards PBL approach.

Discriminatively learning for representing local image features with quadruplet model

NASA Astrophysics Data System (ADS)

Zhang, Da-long; Zhao, Lei; Xu, Duan-qing; Lu, Dong-ming

2017-11-01

Traditional hand-crafted features for representing local image patches are evolving into current data-driven and learning-based image feature, but learning a robust and discriminative descriptor which is capable of controlling various patch-level computer vision tasks is still an open problem. In this work, we propose a novel deep convolutional neural network (CNN) to learn local feature descriptors. We utilize the quadruplets with positive and negative training samples, together with a constraint to restrict the intra-class variance, to learn good discriminative CNN representations. Compared with previous works, our model reduces the overlap in feature space between corresponding and non-corresponding patch pairs, and mitigates margin varying problem caused by commonly used triplet loss. We demonstrate that our method achieves better embedding result than some latest works, like PN-Net and TN-TG, on benchmark dataset.

Creating a culture of patient-focused care through a learner-centered philosophy.

PubMed

Linscott, J; Spee, R; Flint, F; Fisher, A

1999-01-01

This paper will discuss the teaching-learning process used in the Patient-Focused Care Course at a major teaching hospital in Canada that is transforming nursing practice from a provider driven to a patient-focused approach. The experiential and reflective nature of the course offers opportunities for nurses to link theory with practice, to think critically and reflectively about their own values and beliefs and to translate that meaning into practice. The learning process reflects principles of adult learning based on Knowles andragogical model which differs from the traditional pedagogical model of teaching. The essence of andragogy is a constant unfolding process of discovery based on dialogue. Utilization of adult learning principles that support critical thinking and foster transformational change present an alternative to traditional ways of teaching and learning the art and science of nursing practice.

The New Learning Market.

ERIC Educational Resources Information Center

Mager, Caroline; Robinson, Peter; Fletcher, Mick; Stanton, Geoff; Perry, Adrian; Westwood, Andy

This document contains seven papers examining the implications of proposed major reforms to the United Kingdom's post-16 sector. "The New Learning Market--Overview" (Caroline Mager) argues for balancing market-driven and planning-driven approaches to planning post-16 education. "Education and Training as a Learning Market"…

New perspectives on adolescent motivated behavior: attention and conditioning

PubMed Central

Ernst, Monique; Daniele, Teresa; Frantz, Kyle

2011-01-01

Adolescence is a critical transition period, during which fundamental changes prepare the adolescent for becoming an adult. Heuristic models of the neurobiology of adolescent behavior have emerged, promoting the central role of reward and motivation, coupled with cognitive immaturities. Here, we bring focus to two basic sets of processes, attention and conditioning, which are essential for adaptive behavior. Using the dual-attention model developed by Corbetta and Shulman (2002), which identifies a stimulus-driven and a goal-driven attention network, we propose a balance that favors stimulus-driven attention over goal-driven attention in youth. Regarding conditioning, we hypothesize that stronger associations tend to be made between environmental cues and appetitive stimuli, and weaker associations with aversive stimuli, in youth relative to adults. An attention system geared to prioritize stimulus-driven attention, together with more powerful associative learning with appetitive incentives, contribute to shape patterns of adolescent motivated behavior. This proposed bias in attention and conditioning function could facilitate the impulsive, novelty-seeking and risk-taking behavior that is typical of many adolescents. PMID:21977221

Mechanisms and time course of vocal learning and consolidation in the adult songbird.

PubMed

Warren, Timothy L; Tumer, Evren C; Charlesworth, Jonathan D; Brainard, Michael S

2011-10-01

In songbirds, the basal ganglia outflow nucleus LMAN is a cortical analog that is required for several forms of song plasticity and learning. Moreover, in adults, inactivating LMAN can reverse the initial expression of learning driven via aversive reinforcement. In the present study, we investigated how LMAN contributes to both reinforcement-driven learning and a self-driven recovery process in adult Bengalese finches. We first drove changes in the fundamental frequency of targeted song syllables and compared the effects of inactivating LMAN with the effects of interfering with N-methyl-d-aspartate (NMDA) receptor-dependent transmission from LMAN to one of its principal targets, the song premotor nucleus RA. Inactivating LMAN and blocking NMDA receptors in RA caused indistinguishable reversions in the expression of learning, indicating that LMAN contributes to learning through NMDA receptor-mediated glutamatergic transmission to RA. We next assessed how LMAN's role evolves over time by maintaining learned changes to song while periodically inactivating LMAN. The expression of learning consolidated to become LMAN independent over multiple days, indicating that this form of consolidation is not completed over one night, as previously suggested, and instead may occur gradually during singing. Subsequent cessation of reinforcement was followed by a gradual self-driven recovery of original song structure, indicating that consolidation does not correspond with the lasting retention of changes to song. Finally, for self-driven recovery, as for reinforcement-driven learning, LMAN was required for the expression of initial, but not later, changes to song. Our results indicate that NMDA receptor-dependent transmission from LMAN to RA plays an essential role in the initial expression of two distinct forms of vocal learning and that this role gradually wanes over a multiday process of consolidation. The results support an emerging view that cortical-basal ganglia circuits can direct the initial expression of learning via top-down influences on primary motor circuitry.

Mechanisms and time course of vocal learning and consolidation in the adult songbird

PubMed Central

Tumer, Evren C.; Charlesworth, Jonathan D.; Brainard, Michael S.

2011-01-01

In songbirds, the basal ganglia outflow nucleus LMAN is a cortical analog that is required for several forms of song plasticity and learning. Moreover, in adults, inactivating LMAN can reverse the initial expression of learning driven via aversive reinforcement. In the present study, we investigated how LMAN contributes to both reinforcement-driven learning and a self-driven recovery process in adult Bengalese finches. We first drove changes in the fundamental frequency of targeted song syllables and compared the effects of inactivating LMAN with the effects of interfering with N-methyl-d-aspartate (NMDA) receptor-dependent transmission from LMAN to one of its principal targets, the song premotor nucleus RA. Inactivating LMAN and blocking NMDA receptors in RA caused indistinguishable reversions in the expression of learning, indicating that LMAN contributes to learning through NMDA receptor-mediated glutamatergic transmission to RA. We next assessed how LMAN's role evolves over time by maintaining learned changes to song while periodically inactivating LMAN. The expression of learning consolidated to become LMAN independent over multiple days, indicating that this form of consolidation is not completed over one night, as previously suggested, and instead may occur gradually during singing. Subsequent cessation of reinforcement was followed by a gradual self-driven recovery of original song structure, indicating that consolidation does not correspond with the lasting retention of changes to song. Finally, for self-driven recovery, as for reinforcement-driven learning, LMAN was required for the expression of initial, but not later, changes to song. Our results indicate that NMDA receptor-dependent transmission from LMAN to RA plays an essential role in the initial expression of two distinct forms of vocal learning and that this role gradually wanes over a multiday process of consolidation. The results support an emerging view that cortical-basal ganglia circuits can direct the initial expression of learning via top-down influences on primary motor circuitry. PMID:21734110

Water stress, water salience, and the implications for water supply planning

NASA Astrophysics Data System (ADS)

Garcia, M. E.; Islam, S.

2017-12-01

Effectively addressing the water supply challenges posed by urbanization and climate change requires a holistic understanding of the water supply system, including the impact of human behavior on system dynamics. Decision makers have limits to available information and information processing capacity, and their attention is not equally distributed among risks. The salience of a given risk is higher when increased attention is directed to it and though perceived risk may increase, real risk does not change. Relevant to water supply planning is how and when water stress results in an increased salience of water risks. This work takes a socio-hydrological approach to develop a water supply planning model that includes water consumption as an endogenous variable, in the context of Las Vegas, NV. To understand the benefits and limitations of this approach, this model is compared to a traditional planning model that uses water consumption scenarios. Both models are applied to project system reliability and water stress under four streamflow and demographic scenarios, and to assess supply side responses to changing conditions. The endogenous demand model enables the identification of feedback between both supply and demand management decisions on future water consumption and system performance. This model, while specific to the Las Vegas case, demonstrates a prototypical modeling framework capable of examining water-supply demand interactions by incorporating water stress driven conservation.

Empirical Validation of Integrated Learning Performances for Hydrologic Phenomena: 3rd-Grade Students' Model-Driven Explanation-Construction

ERIC Educational Resources Information Center

Forbes, Cory T.; Zangori, Laura; Schwarz, Christina V.

2015-01-01

Water is a crucial topic that spans the K-12 science curriculum, including the elementary grades. Students should engage in the articulation, negotiation, and revision of model-based explanations about hydrologic phenomena. However, past research has shown that students, particularly early learners, often struggle to understand hydrologic…

Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model

PubMed Central

Jensen, Greg; Muñoz, Fabian; Alkan, Yelda; Ferrera, Vincent P.; Terrace, Herbert S.

2015-01-01

Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognitive processes, which performs transitive inference at low computational cost. This is accomplished by (1) representing stimulus positions along a unit span using beta distributions, (2) treating positive and negative feedback asymmetrically, and (3) updating the position of every stimulus during every trial, whether that stimulus was visible or not. Performance was compared for rhesus macaques, humans, and the betasort algorithm, as well as Q-learning, an established reward-prediction error (RPE) model. Of these, only Q-learning failed to respond above chance during critical test trials. Betasort’s success (when compared to RPE models) and its computational efficiency (when compared to full Markov decision process implementations) suggests that the study of reinforcement learning in organisms will be best served by a feature-driven approach to comparing formal models. PMID:26407227

Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model.

PubMed

Jensen, Greg; Muñoz, Fabian; Alkan, Yelda; Ferrera, Vincent P; Terrace, Herbert S

2015-01-01

Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognitive processes, which performs transitive inference at low computational cost. This is accomplished by (1) representing stimulus positions along a unit span using beta distributions, (2) treating positive and negative feedback asymmetrically, and (3) updating the position of every stimulus during every trial, whether that stimulus was visible or not. Performance was compared for rhesus macaques, humans, and the betasort algorithm, as well as Q-learning, an established reward-prediction error (RPE) model. Of these, only Q-learning failed to respond above chance during critical test trials. Betasort's success (when compared to RPE models) and its computational efficiency (when compared to full Markov decision process implementations) suggests that the study of reinforcement learning in organisms will be best served by a feature-driven approach to comparing formal models.

Andragogical Model in Language Training of Mining Specialists

NASA Astrophysics Data System (ADS)

Bondareva, Evgeniya; Chistyakova, Galina; Kleshevskyi, Yury; Sergeev, Sergey; Stepanov, Aleksey

2017-11-01

Nowadays foreign language competence is one of the main professional skills of mining engineers. Modern competitive conditions require the ability for meeting production challenges in a foreign language from specialists and managers of mining enterprises. This is the reason of high demand on foreign language training/retraining courses. Language training of adult learners fundamentally differs from children and adolescent education. The article describes the features of andragogical learning model. The authors conclude that distance learning is the most productive education form having a number of obvious advantages over traditional (in-class) one. Interactive learning method that involves active engagement of adult trainees appears to be of the greatest interest due to introduction of modern information and communication technologies for distance learning.

Assessing the Use of Remote Sensing and a Crop Growth Model to Improve Modeled Streamflow in Central Asia

NASA Astrophysics Data System (ADS)

Richey, A. S.; Richey, J. E.; Tan, A.; Liu, M.; Adam, J. C.; Sokolov, V.

2015-12-01

Central Asia presents a perfect case study to understand the dynamic, and often conflicting, linkages between food, energy, and water in natural systems. The destruction of the Aral Sea is a well-known environmental disaster, largely driven by increased irrigation demand on the rivers that feed the endorheic sea. Continued reliance on these rivers, the Amu Darya and Syr Darya, often place available water resources at odds between hydropower demands upstream and irrigation requirements downstream. A combination of tools is required to understand these linkages and how they may change in the future as a function of climate change and population growth. In addition, the region is geopolitically complex as the former Soviet basin states develop management strategies to sustainably manage shared resources. This complexity increases the importance of relying upon publically available information sources and tools. Preliminary work has shown potential for the Variable Infiltration Capacity (VIC) model to recreate the natural water balance in the Amu Darya and Syr Darya basins by comparing results to total terrestrial water storage changes observed from NASA's Gravity Recovery and Climate Experiment (GRACE) satellite mission. Modeled streamflow is well correlated to observed streamflow at upstream gauges prior to the large-scale expansion of irrigation and hydropower. However, current modeled results are unable to capture the human influence of water use on downstream flow. This study examines the utility of a crop simulation model, CropSyst, to represent irrigation demand and GRACE to improve modeled streamflow estimates in the Amu Darya and Syr Darya basins. Specifically we determine crop water demand with CropSyst utilizing available data on irrigation schemes and cropping patterns. We determine how this demand can be met either by surface water, modeled by VIC with a reservoir operation scheme, and/or by groundwater derived from GRACE. Finally, we assess how the inclusion of CropSyst and groundwater to model and meet irrigation demand improves modeled streamflow from VIC throughout the basins. The results of this work are integrated into a decision support platform to assist the basin states in understanding water availability and the impact of management decisions on available resources.

Learning tactile skills through curious exploration

PubMed Central

Pape, Leo; Oddo, Calogero M.; Controzzi, Marco; Cipriani, Christian; Förster, Alexander; Carrozza, Maria C.; Schmidhuber, Jürgen

2012-01-01

We present curiosity-driven, autonomous acquisition of tactile exploratory skills on a biomimetic robot finger equipped with an array of microelectromechanical touch sensors. Instead of building tailored algorithms for solving a specific tactile task, we employ a more general curiosity-driven reinforcement learning approach that autonomously learns a set of motor skills in absence of an explicit teacher signal. In this approach, the acquisition of skills is driven by the information content of the sensory input signals relative to a learner that aims at representing sensory inputs using fewer and fewer computational resources. We show that, from initially random exploration of its environment, the robotic system autonomously develops a small set of basic motor skills that lead to different kinds of tactile input. Next, the system learns how to exploit the learned motor skills to solve supervised texture classification tasks. Our approach demonstrates the feasibility of autonomous acquisition of tactile skills on physical robotic platforms through curiosity-driven reinforcement learning, overcomes typical difficulties of engineered solutions for active tactile exploration and underactuated control, and provides a basis for studying developmental learning through intrinsic motivation in robots. PMID:22837748

Learning Maximal Entropy Models from finite size datasets: a fast Data-Driven algorithm allows to sample from the posterior distribution

NASA Astrophysics Data System (ADS)

Ferrari, Ulisse

A maximal entropy model provides the least constrained probability distribution that reproduces experimental averages of an observables set. In this work we characterize the learning dynamics that maximizes the log-likelihood in the case of large but finite datasets. We first show how the steepest descent dynamics is not optimal as it is slowed down by the inhomogeneous curvature of the model parameters space. We then provide a way for rectifying this space which relies only on dataset properties and does not require large computational efforts. We conclude by solving the long-time limit of the parameters dynamics including the randomness generated by the systematic use of Gibbs sampling. In this stochastic framework, rather than converging to a fixed point, the dynamics reaches a stationary distribution, which for the rectified dynamics reproduces the posterior distribution of the parameters. We sum up all these insights in a ``rectified'' Data-Driven algorithm that is fast and by sampling from the parameters posterior avoids both under- and over-fitting along all the directions of the parameters space. Through the learning of pairwise Ising models from the recording of a large population of retina neurons, we show how our algorithm outperforms the steepest descent method. This research was supported by a Grant from the Human Brain Project (HBP CLAP).

Molecular graph convolutions: moving beyond fingerprints

NASA Astrophysics Data System (ADS)

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-08-01

Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph—atoms, bonds, distances, etc.—which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

Molecular graph convolutions: moving beyond fingerprints.

PubMed

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-08-01

Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph-atoms, bonds, distances, etc.-which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

Characterising Wildlife Trade Market Supply-Demand Dynamics

PubMed Central

Rowcliffe, M.; Cowlishaw, G.; Alexander, J. S.; Ntiamoa-Baidu, Y.; Brenya, A.; Milner-Gulland, E. J.

2016-01-01

The trade in wildlife products can represent an important source of income for poor people, but also threaten wildlife locally, regionally and internationally. Bushmeat provides livelihoods for hunters, traders and sellers, protein to rural and urban consumers, and has depleted the populations of many tropical forest species. Management interventions can be targeted towards the consumers or suppliers of wildlife products. There has been a general assumption in the bushmeat literature that the urban trade is driven by consumer demand with hunters simply fulfilling this demand. Using the urban bushmeat trade in the city of Kumasi, Ghana, as a case study, we use a range of datasets to explore the processes driving the urban bushmeat trade. We characterise the nature of supply and demand by explicitly considering three market attributes: resource condition, hunter behaviour, and consumer behaviour. Our results suggest that bushmeat resources around Kumasi are becoming increasingly depleted and are unable to meet demand, that hunters move in and out of the trade independently of price signals generated by the market, and that, for the Kumasi bushmeat system, consumption levels are driven not by consumer choice but by shortfalls in supply and consequent price responses. Together, these results indicate that supply-side processes dominate the urban bushmeat trade in Kumasi. This suggests that future management interventions should focus on changing hunter behaviour, although complementary interventions targeting consumer demand are also likely to be necessary in the long term. Our approach represents a structured and repeatable method to assessing market dynamics in information-poor systems. The findings serve as a caution against assuming that wildlife markets are demand driven, and highlight the value of characterising market dynamics to inform appropriate management. PMID:27632169

Characterising Wildlife Trade Market Supply-Demand Dynamics.

PubMed

McNamara, J; Rowcliffe, M; Cowlishaw, G; Alexander, J S; Ntiamoa-Baidu, Y; Brenya, A; Milner-Gulland, E J

2016-01-01

The trade in wildlife products can represent an important source of income for poor people, but also threaten wildlife locally, regionally and internationally. Bushmeat provides livelihoods for hunters, traders and sellers, protein to rural and urban consumers, and has depleted the populations of many tropical forest species. Management interventions can be targeted towards the consumers or suppliers of wildlife products. There has been a general assumption in the bushmeat literature that the urban trade is driven by consumer demand with hunters simply fulfilling this demand. Using the urban bushmeat trade in the city of Kumasi, Ghana, as a case study, we use a range of datasets to explore the processes driving the urban bushmeat trade. We characterise the nature of supply and demand by explicitly considering three market attributes: resource condition, hunter behaviour, and consumer behaviour. Our results suggest that bushmeat resources around Kumasi are becoming increasingly depleted and are unable to meet demand, that hunters move in and out of the trade independently of price signals generated by the market, and that, for the Kumasi bushmeat system, consumption levels are driven not by consumer choice but by shortfalls in supply and consequent price responses. Together, these results indicate that supply-side processes dominate the urban bushmeat trade in Kumasi. This suggests that future management interventions should focus on changing hunter behaviour, although complementary interventions targeting consumer demand are also likely to be necessary in the long term. Our approach represents a structured and repeatable method to assessing market dynamics in information-poor systems. The findings serve as a caution against assuming that wildlife markets are demand driven, and highlight the value of characterising market dynamics to inform appropriate management.

Designing On-Demand Education for Simultaneous Development of Domain-Specific and Self-Directed Learning Skills

ERIC Educational Resources Information Center

Taminiau, E. M. C.; Kester, L.; Corbalan, G.; Spector, J. M.; Kirschner, P. A.; Van Merriënboer, J. J. G.

2015-01-01

On-demand education enables individual learners to choose their learning pathways according to their own learning needs. They must use self-directed learning (SDL) skills involving self-assessment and task selection to determine appropriate pathways for learning. Learners who lack these skills must develop them because SDL skills are prerequisite…

Imaging plus X: multimodal models of neurodegenerative disease.

PubMed

Oxtoby, Neil P; Alexander, Daniel C

2017-08-01

This article argues that the time is approaching for data-driven disease modelling to take centre stage in the study and management of neurodegenerative disease. The snowstorm of data now available to the clinician defies qualitative evaluation; the heterogeneity of data types complicates integration through traditional statistical methods; and the large datasets becoming available remain far from the big-data sizes necessary for fully data-driven machine-learning approaches. The recent emergence of data-driven disease progression models provides a balance between imposed knowledge of disease features and patterns learned from data. The resulting models are both predictive of disease progression in individual patients and informative in terms of revealing underlying biological patterns. Largely inspired by observational models, data-driven disease progression models have emerged in the last few years as a feasible means for understanding the development of neurodegenerative diseases. These models have revealed insights into frontotemporal dementia, Huntington's disease, multiple sclerosis, Parkinson's disease and other conditions. For example, event-based models have revealed finer graded understanding of progression patterns; self-modelling regression and differential equation models have provided data-driven biomarker trajectories; spatiotemporal models have shown that brain shape changes, for example of the hippocampus, can occur before detectable neurodegeneration; and network models have provided some support for prion-like mechanistic hypotheses of disease propagation. The most mature results are in sporadic Alzheimer's disease, in large part because of the availability of the Alzheimer's disease neuroimaging initiative dataset. Results generally support the prevailing amyloid-led hypothetical model of Alzheimer's disease, while revealing finer detail and insight into disease progression. The emerging field of disease progression modelling provides a natural mechanism to integrate different kinds of information, for example from imaging, serum and cerebrospinal fluid markers and cognitive tests, to obtain new insights into progressive diseases. Such insights include fine-grained longitudinal patterns of neurodegeneration, from early stages, and the heterogeneity of these trajectories over the population. More pragmatically, such models enable finer precision in patient staging and stratification, prediction of progression rates and earlier and better identification of at-risk individuals. We argue that this will make disease progression modelling invaluable for recruitment and end-points in future clinical trials, potentially ameliorating the high failure rate in trials of, e.g., Alzheimer's disease therapies. We review the state of the art in these techniques and discuss the future steps required to translate the ideas to front-line application.

USEPA Resistance Management Research

EPA Science Inventory

A significant increase in genetically modified corn planting driven by biofuel demand is expected for future planted acreages approaching 80% of total corn plantings in 2009. As demand increases, incidence of farmer non-compliance with mandated non-genetically modified refuge is...

Sustainability Factors for E-Learning Initiatives

ERIC Educational Resources Information Center

Gunn, Cathy

2010-01-01

This paper examines the challenges that "grass roots" e-learning initiatives face in trying to become sustainable. A cross-institutional study focused on local, rather than centrally driven, initiatives. A number of successful e-learning innovations were identified that had been driven by capable teachers seeking solutions to real…

Assessing Economic Modulation of Future Critical Materials Use: The Case of Automotive-Related Platinum Group Metals.

PubMed

Zhang, Jingshu; Everson, Mark P; Wallington, Timothy J; Field, Frank R; Roth, Richard; Kirchain, Randolph E

2016-07-19

Platinum-group metals (PGMs) are technological and economic enablers of many industrial processes. This important role, coupled with their limited geographic availability, has led to PGMs being labeled as "critical materials". Studies of future PGM flows have focused on trends within material flows or macroeconomic indicators. We complement the previous work by introducing a novel technoeconomic model of substitution among PGMs within the automotive sector (the largest user of PGMs) reflecting the rational response of firms to changing prices. The results from the model support previous conclusions that PGM use is likely to grow, in some cases strongly, by 2030 (approximately 45% for Pd and 5% for Pt), driven by the increasing sales of automobiles. The model also indicates that PGM-demand growth will be significantly influenced by the future Pt-to-Pd price ratio, with swings of Pt and Pd demand of as much as 25% if the future price ratio shifts higher or lower even if it stays within the historic range. Fortunately, automotive catalysts are one of the more effectively recycled metals. As such, with proper policy support, recycling can serve to meet some of this growing demand.

Executive Control Goes to School: Implications of Preschool Executive Performance for Observed Elementary Classroom Learning Engagement

PubMed Central

Nelson, Timothy D.; Nelson, Jennifer Mize; James, Tiffany D.; Clark, Caron A.C.; Kidwell, Katherine M.; Espy, Kimberly Andrews

2017-01-01

The transition to elementary school is accompanied by increasing demands for children to regulate their attention and behavior within the classroom setting. Executive control (EC) may be critical for meeting these demands; however, few studies have rigorously examined the association between EC and observed classroom behavior. This study examined EC in preschool (age 5 years, 3 months) as a predictor of classroom learning engagement behaviors in first grade, using a battery of performance-based EC tasks and live classroom observations in a longitudinal sample of 313 children. Multilevel modeling results indicated that stronger EC predicted more focused engagement and fewer task management and competing responses, controlling for socioeconomic status, child sex, and age at observations. Results suggest that early EC may support subsequent classroom engagement behaviors that are critical for successful transition to elementary school and long-term learning trajectories. PMID:28358540

Treatment of adolescent sexual offenders: theory-based practice.

PubMed

Sermabeikian, P; Martinez, D

1994-11-01

The treatment of adolescent sexual offenders (ASO) has its theoretical underpinnings in social learning theory. Although social learning theory has been frequently cited in literature, a comprehensive application of this theory, as applied to practice, has not been mapped out. The social learning and social cognitive theories of Bandura appear to be particularly relevant to the group treatment of this population. The application of these theories to practice, as demonstrated in a program model, is discussed as a means of demonstrating how theory-driven practice methods can be developed.

Attention to the Second Language

ERIC Educational Resources Information Center

Wickens, Christopher D.

2007-01-01

Attention to a task, and the language it requires to be performed, can be described in relation to two theoretical models which have prompted research into the effects of task demands on learning and performance outside the field of second language acquisition (SLA). These are the SEEV (selection, effort, expectancy and value) model of selective…

Blended learning in anesthesia education: current state and future model.

PubMed

Kannan, Jaya; Kurup, Viji

2012-12-01

Educators in anesthesia residency programs across the country are facing a number of challenges as they attempt to integrate blended learning techniques in their curriculum. Compared with the rest of higher education, which has made advances to varying degrees in the adoption of online learning anesthesiology education has been sporadic in the active integration of blended learning. The purpose of this review is to discuss the challenges in anesthesiology education and relevance of the Universal Design for Learning framework in addressing them. There is a wide chasm between student demand for online education and the availability of trained faculty to teach. The design of the learning interface is important and will significantly affect the learning experience for the student. This review examines recent literature pertaining to this field, both in the realm of higher education in general and medical education in particular, and proposes the application of a comprehensive learning model that is new to anesthesiology education and relevant to its goals of promoting self-directed learning.

Supporting Semantic Annotation of Educational Content by Automatic Extraction of Hierarchical Domain Relationships

ERIC Educational Resources Information Center

Vrablecová, Petra; Šimko, Marián

2016-01-01

The domain model is an essential part of an adaptive learning system. For each educational course, it involves educational content and semantics, which is also viewed as a form of conceptual metadata about educational content. Due to the size of a domain model, manual domain model creation is a challenging and demanding task for teachers or…

The relationships among nurses' job characteristics and attitudes toward web-based continuing learning.

PubMed

Chiu, Yen-Lin; Tsai, Chin-Chung; Fan Chiang, Chih-Yun

2013-04-01

The purpose of this study was to explore the relationships between job characteristics (job demands, job control and social support) and nurses' attitudes toward web-based continuing learning. A total of 221 in-service nurses from hospitals in Taiwan were surveyed. The Attitudes toward Web-based Continuing Learning Survey (AWCL) was employed as the outcome variables, and the Chinese version Job Characteristic Questionnaire (C-JCQ) was administered to assess the predictors for explaining the nurses' attitudes toward web-based continuing learning. To examine the relationships among these variables, hierarchical regression was conducted. The results of the regression analysis revealed that job control and social support positively associated with nurses' attitudes toward web-based continuing learning. However, the relationship of job demands to such learning was not significant. Moreover, a significant demands×job control interaction was found, but the job demands×social support interaction had no significant relationships with attitudes toward web-based continuing learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings.

PubMed

Mahapatra, Chinmaya; Moharana, Akshaya Kumar; Leung, Victor C M

2017-12-05

Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q -learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q -learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand.

Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings

PubMed Central

Moharana, Akshaya Kumar

2017-01-01

Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q-learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q-learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand. PMID:29206159

Lessons Learned from Stakeholder-Driven Modeling in the Western Lake Erie Basin

NASA Astrophysics Data System (ADS)

Muenich, R. L.; Read, J.; Vaccaro, L.; Kalcic, M. M.; Scavia, D.

2017-12-01

Lake Erie's history includes a great environmental success story. Recognizing the impact of high phosphorus loads from point sources, the United States and Canada 1972 Great Lakes Water Quality Agreement set load reduction targets to reduce algae blooms and hypoxia. The Lake responded quickly to those reductions and it was declared a success. However, since the mid-1990s, Lake Erie's algal blooms and hypoxia have returned, and this time with a dominant algae species that produces toxins. Return of the algal blooms and hypoxia is again driven by phosphorus loads, but this time a major source is the agriculturally-dominated Maumee River watershed that covers NW Ohio, NE Indiana, and SE Michigan, and the hypoxic extent has been shown to be driven by Maumee River loads plus those from the bi-national and multiple land-use St. Clair - Detroit River system. Stakeholders in the Lake Erie watershed have a long history of engagement with environmental policy, including modeling and monitoring efforts. This talk will focus on the application of interdisciplinary, stakeholder-driven modeling efforts aimed at understanding the primary phosphorus sources and potential pathways to reduce these sources and the resulting algal blooms and hypoxia in Lake Erie. We will discuss the challenges, such as engaging users with different goals, benefits to modeling, such as improvements in modeling data, and new research questions emerging from these modeling efforts that are driven by end-user needs.

Effective Management of High-Use/High-Demand Space Using Restaurant-Style Pagers

ERIC Educational Resources Information Center

Gonzalez, Adriana

2012-01-01

The library landscape is changing at a fast pace, with an increase in the demand for study space including quiet, individualized study space; open group study space; and as enclosed group study space. In large academic libraries, managing limited high-demand resources is crucial and is partially being driven by the greater emphasis on group…

Data Science in Supply Chain Management: Data-Related Influences on Demand Planning

ERIC Educational Resources Information Center

Jin, Yao

2013-01-01

Data-driven decisions have become an important aspect of supply chain management. Demand planners are tasked with analyzing volumes of data that are being collected at a torrential pace from myriad sources in order to translate them into actionable business intelligence. In particular, demand volatilities and planning are vital for effective and…

Model-based learning and the contribution of the orbitofrontal cortex to the model-free world.

PubMed

McDannald, Michael A; Takahashi, Yuji K; Lopatina, Nina; Pietras, Brad W; Jones, Josh L; Schoenbaum, Geoffrey

2012-04-01

Learning is proposed to occur when there is a discrepancy between reward prediction and reward receipt. At least two separate systems are thought to exist: one in which predictions are proposed to be based on model-free or cached values; and another in which predictions are model-based. A basic neural circuit for model-free reinforcement learning has already been described. In the model-free circuit the ventral striatum (VS) is thought to supply a common-currency reward prediction to midbrain dopamine neurons that compute prediction errors and drive learning. In a model-based system, predictions can include more information about an expected reward, such as its sensory attributes or current, unique value. This detailed prediction allows for both behavioral flexibility and learning driven by changes in sensory features of rewards alone. Recent evidence from animal learning and human imaging suggests that, in addition to model-free information, the VS also signals model-based information. Further, there is evidence that the orbitofrontal cortex (OFC) signals model-based information. Here we review these data and suggest that the OFC provides model-based information to this traditional model-free circuitry and offer possibilities as to how this interaction might occur. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Finding new facts; thinking new thoughts.

PubMed

Schulz, Laura

2012-01-01

The idea of the child as an active learner is one of Piaget's enduring legacies. In this chapter, I discuss the ways in which contemporary computational models of learning do, and do not, address learning as an active, child-driven process. In Part 1, I discuss the problem of search and exploration. In Part 2, I discuss the (harder and more interesting) problem of hypothesis generation. I conclude by proposing some possible new directions for research.

The Application of Magnesium Alloys in Aircraft Interiors — Changing the Rules

NASA Astrophysics Data System (ADS)

Davis, Bruce

The commercial aircraft market is forecast to steadily grow over the next two decades. Part of this growth is driven by the desire of airlines to replace older models in their fleet with newer, more fuel efficient designs, to realize lower operating costs and to address the rising cost of aviation fuel. As such the aircraft OEMs are beginning to set more and more demanding mass targets on their new platforms.

Role of dopamine D2 receptors in human reinforcement learning.

PubMed

Eisenegger, Christoph; Naef, Michael; Linssen, Anke; Clark, Luke; Gandamaneni, Praveen K; Müller, Ulrich; Robbins, Trevor W

2014-09-01

Influential neurocomputational models emphasize dopamine (DA) as an electrophysiological and neurochemical correlate of reinforcement learning. However, evidence of a specific causal role of DA receptors in learning has been less forthcoming, especially in humans. Here we combine, in a between-subjects design, administration of a high dose of the selective DA D2/3-receptor antagonist sulpiride with genetic analysis of the DA D2 receptor in a behavioral study of reinforcement learning in a sample of 78 healthy male volunteers. In contrast to predictions of prevailing models emphasizing DA's pivotal role in learning via prediction errors, we found that sulpiride did not disrupt learning, but rather induced profound impairments in choice performance. The disruption was selective for stimuli indicating reward, whereas loss avoidance performance was unaffected. Effects were driven by volunteers with higher serum levels of the drug, and in those with genetically determined lower density of striatal DA D2 receptors. This is the clearest demonstration to date for a causal modulatory role of the DA D2 receptor in choice performance that might be distinct from learning. Our findings challenge current reward prediction error models of reinforcement learning, and suggest that classical animal models emphasizing a role of postsynaptic DA D2 receptors in motivational aspects of reinforcement learning may apply to humans as well.

Role of Dopamine D2 Receptors in Human Reinforcement Learning

PubMed Central

Eisenegger, Christoph; Naef, Michael; Linssen, Anke; Clark, Luke; Gandamaneni, Praveen K; Müller, Ulrich; Robbins, Trevor W

2014-01-01

Influential neurocomputational models emphasize dopamine (DA) as an electrophysiological and neurochemical correlate of reinforcement learning. However, evidence of a specific causal role of DA receptors in learning has been less forthcoming, especially in humans. Here we combine, in a between-subjects design, administration of a high dose of the selective DA D2/3-receptor antagonist sulpiride with genetic analysis of the DA D2 receptor in a behavioral study of reinforcement learning in a sample of 78 healthy male volunteers. In contrast to predictions of prevailing models emphasizing DA's pivotal role in learning via prediction errors, we found that sulpiride did not disrupt learning, but rather induced profound impairments in choice performance. The disruption was selective for stimuli indicating reward, whereas loss avoidance performance was unaffected. Effects were driven by volunteers with higher serum levels of the drug, and in those with genetically determined lower density of striatal DA D2 receptors. This is the clearest demonstration to date for a causal modulatory role of the DA D2 receptor in choice performance that might be distinct from learning. Our findings challenge current reward prediction error models of reinforcement learning, and suggest that classical animal models emphasizing a role of postsynaptic DA D2 receptors in motivational aspects of reinforcement learning may apply to humans as well. PMID:24713613

Predicting the trajectories and intensities of hurricanes by applying machine learning techniques

NASA Astrophysics Data System (ADS)

Sujithkumar, A.; King, A. W.; Kovilakam, M.; Graves, D.

2017-12-01

The world has witnessed an escalation of devastating hurricanes and tropical cyclones over the last three decades. Hurricanes and tropical cyclones of very high magnitude will likely be even more frequent in a warmer world. Thus, precise forecasting of the track and intensity of hurricane/tropical cyclones remains one of the meteorological community's top priorities. However, comprehensive prediction of hurricane/ tropical cyclone is a difficult problem due to the many complexities of underlying physical processes with many variables and complex relations. The availability of global meteorological and hurricane/tropical storm climatological data opens new opportunities for data-driven approaches to hurricane/tropical cyclone modeling. Here we report initial results from two data-driven machine learning techniques, specifically, random forest (RF) and Bayesian learning (BL) to predict the trajectory and intensity of hurricanes and tropical cyclones. We used International Best Track Archive for Climate Stewardship (IBTrACS) data along with weather data from NOAA in a 50 km buffer surrounding each of the reported hurricane and tropical cyclone tracts to train the model. Initial results reveal that both RF and BL are skillful in predicting storm intensity. We will also present results for the more complicated trajectory prediction.

Future Climate Impacts on Crop Water Demand and Groundwater Longevity in Agricultural Regions

NASA Astrophysics Data System (ADS)

Russo, T. A.; Sahoo, S.; Elliott, J. W.; Foster, I.

2016-12-01

Improving groundwater management practices under future drought conditions in agricultural regions requires three steps: 1) estimating the impacts of climate and drought on crop water demand, 2) projecting groundwater availability given climate and demand forcing, and 3) using this information to develop climate-smart policy and water use practices. We present an innovative combination of models to address the first two steps, and inform the third. Crop water demand was simulated using biophysical crop models forced by multiple climate models and climate scenarios, with one case simulating climate adaptation (e.g. modify planting or harvest time) and another without adaptation. These scenarios were intended to represent a range of drought projections and farm management responses. Nexty, we used projected climate conditions and simulated water demand across the United States as inputs to a novel machine learning-based groundwater model. The model was applied to major agricultural regions relying on the High Plains and Mississippi Alluvial aquifer systems in the US. The groundwater model integrates input data preprocessed using single spectrum analysis, mutual information, and a genetic algorithm, with an artificial neural network model. Model calibration and test results indicate low errors over the 33 year model run, and strong correlations to groundwater levels in hundreds of wells across each aquifer. Model results include a range of projected groundwater level changes from the present to 2050, and in some regions, identification and timeframe of aquifer depletion. These results quantify aquifer longevity under climate and crop scenarios, and provide decision makers with the data needed to compare scenarios of crop water demand, crop yield, and groundwater response, as they aim to balance water sustainability with food security.

Resistance Management Research for PIP Crops

EPA Science Inventory

A significant increase in genetically modified corn planting driven by biofuel demand is expected for future planted acreages approaching 80% of total corn plantings in 2009. As demand increases, incidence of farmer non-compliance with mandated non-genetically modified refuge is...

The Five Families of Cognitive Learning: A Context in Which To Conduct Cognitive Demands Analyses of Innovative Technologies.

ERIC Educational Resources Information Center

Klein, Davina C. D.; O'Neil, Harold F., Jr.; Dennis, Robert A.; Baker, Eva L.

A cognitive demands analysis of a learning technology, a term that includes the hardware and the computer software products that form learning environments, attempts to describe the types of cognitive learning expected of the individual by the technology. This paper explores the context of cognitive learning, suggesting five families of cognitive…

How music training enhances working memory: a cerebrocerebellar blending mechanism that can lead equally to scientific discovery and therapeutic efficacy in neurological disorders.

PubMed

Vandervert, Larry

2015-01-01

Following in the vein of studies that concluded that music training resulted in plastic changes in Einstein's cerebral cortex, controlled research has shown that music training (1) enhances central executive attentional processes in working memory, and (2) has also been shown to be of significant therapeutic value in neurological disorders. Within this framework of music training-induced enhancement of central executive attentional processes, the purpose of this article is to argue that: (1) The foundational basis of the central executive begins in infancy as attentional control during the establishment of working memory, (2) In accordance with Akshoomoff, Courchesne and Townsend's and Leggio and Molinari's cerebellar sequence detection and prediction models, the rigors of volitional control demands of music training can enhance voluntary manipulation of information in thought and movement, (3) The music training-enhanced blending of cerebellar internal models in working memory as can be experienced as intuition in scientific discovery (as Einstein often indicated) or, equally, as moments of therapeutic advancement toward goals in the development of voluntary control in neurological disorders, and (4) The blending of internal models as in (3) thus provides a mechanism by which music training enhances central executive processes in working memory that can lead to scientific discovery and improved therapeutic outcomes in neurological disorders. Within the framework of Leggio and Molinari's cerebellar sequence detection model, it is determined that intuitive steps forward that occur in both scientific discovery and during therapy in those with neurological disorders operate according to the same mechanism of adaptive error-driven blending of cerebellar internal models. It is concluded that the entire framework of the central executive structure of working memory is a product of the cerebrocerebellar system which can, through the learning of internal models, incorporate the multi-dimensional rigor and volitional-control demands of music training and, thereby, enhance voluntary control. It is further concluded that this cerebrocerebellar view of the music training-induced enhancement of central executive control in working memory provides a needed mechanism to explain both the highest level of scientific discovery and the efficacy of music training in the remediation of neurological impairments.

[Learning how to learn for specialist further education].

PubMed

Breuer, G; Lütcke, B; St Pierre, M; Hüttl, S

2017-02-01

The world of medicine is becoming from year to year more complex. This necessitates efficient learning processes, which incorporate the principles of adult education but with unchanged periods of further education. The subject matter must be processed, organized, visualized, networked and comprehended. The learning process should be voluntary and self-driven with the aim of learning the profession and becoming an expert in a specialist field. Learning is an individual process. Despite this, the constantly cited learning styles are nowadays more controversial. An important factor is a healthy mixture of blended learning methods, which also use new technical possibilities. These include a multitude of e‑learning options and simulations, which partly enable situative learning in a "shielded" environment. An exemplary role model of the teacher and feedback for the person in training also remain core and sustainable aspects in medical further education.

Developing a Global Database of Historic Flood Events to Support Machine Learning Flood Prediction in Google Earth Engine

NASA Astrophysics Data System (ADS)

Tellman, B.; Sullivan, J.; Kettner, A.; Brakenridge, G. R.; Slayback, D. A.; Kuhn, C.; Doyle, C.

2016-12-01

There is an increasing need to understand flood vulnerability as the societal and economic effects of flooding increases. Risk models from insurance companies and flood models from hydrologists must be calibrated based on flood observations in order to make future predictions that can improve planning and help societies reduce future disasters. Specifically, to improve these models both traditional methods of flood prediction from physically based models as well as data-driven techniques, such as machine learning, require spatial flood observation to validate model outputs and quantify uncertainty. A key dataset that is missing for flood model validation is a global historical geo-database of flood event extents. Currently, the most advanced database of historical flood extent is hosted and maintained at the Dartmouth Flood Observatory (DFO) that has catalogued 4320 floods (1985-2015) but has only mapped 5% of these floods. We are addressing this data gap by mapping the inventory of floods in the DFO database to create a first-of- its-kind, comprehensive, global and historical geospatial database of flood events. To do so, we combine water detection algorithms on MODIS and Landsat 5,7 and 8 imagery in Google Earth Engine to map discrete flood events. The created database will be available in the Earth Engine Catalogue for download by country, region, or time period. This dataset can be leveraged for new data-driven hydrologic modeling using machine learning algorithms in Earth Engine's highly parallelized computing environment, and we will show examples for New York and Senegal.

Data-driven Applications for the Sun-Earth System

NASA Astrophysics Data System (ADS)

Kondrashov, D. A.

2016-12-01

Advances in observational and data mining techniques allow extracting information from the large volume of Sun-Earth observational data that can be assimilated into first principles physical models. However, equations governing Sun-Earth phenomena are typically nonlinear, complex, and high-dimensional. The high computational demand of solving the full governing equations over a large range of scales precludes the use of a variety of useful assimilative tools that rely on applied mathematical and statistical techniques for quantifying uncertainty and predictability. Effective use of such tools requires the development of computationally efficient methods to facilitate fusion of data with models. This presentation will provide an overview of various existing as well as newly developed data-driven techniques adopted from atmospheric and oceanic sciences that proved to be useful for space physics applications, such as computationally efficient implementation of Kalman Filter in radiation belts modeling, solar wind gap-filling by Singular Spectrum Analysis, and low-rank procedure for assimilation of low-altitude ionospheric magnetic perturbations into the Lyon-Fedder-Mobarry (LFM) global magnetospheric model. Reduced-order non-Markovian inverse modeling and novel data-adaptive decompositions of Sun-Earth datasets will be also demonstrated.

New Roads for Patron-Driven E-Books: Collection Development and Technical Services Implications of a Patron-Driven Acquisitions Pilot at Rutgers

ERIC Educational Resources Information Center

De Fino, Melissa; Lo, Mei Ling

2011-01-01

Collection development librarians have long struggled to meet user demands for new titles. Too often, required resources are not purchased, whereas some purchased resources do not circulate. E-books selected through patron-driven plans are a solution but present new challenges for both selectors and catalogers. Radical changes to traditional…

Data-driven heterogeneity in mathematical learning disabilities based on the triple code model.

PubMed

Peake, Christian; Jiménez, Juan E; Rodríguez, Cristina

2017-12-01

Many classifications of heterogeneity in mathematical learning disabilities (MLD) have been proposed over the past four decades, however no empirical research has been conducted until recently, and none of the classifications are derived from Triple Code Model (TCM) postulates. The TCM proposes MLD as a heterogeneous disorder, with two distinguishable profiles: a representational subtype and a verbal subtype. A sample of elementary school 3rd to 6th graders was divided into two age cohorts (3rd - 4th grades, and 5th - 6th grades). Using data-driven strategies, based on the cognitive classification variables predicted by the TCM, our sample of children with MLD clustered as expected: a group with representational deficits and a group with number-fact retrieval deficits. In the younger group, a spatial subtype also emerged, while in both cohorts a non-specific cluster was produced whose profile could not be explained by this theoretical approach. Copyright © 2017 Elsevier Ltd. All rights reserved.

Youths Teaching Youths: Learning to Code as an Example of Interest-Driven Learning

ERIC Educational Resources Information Center

Vickery, Jacqueline

2014-01-01

This column examines a case study focusing on web design as an example of interest-driven learning and the acquisition of (digital media) literacies. A summer workshop was offered at a working-class public library, led by a self-taught seventeen year old girl. Nine students (ages 8-16) learned basic HTML and CSS and designed their own websites in…

Modeling Geomagnetic Variations using a Machine Learning Framework

NASA Astrophysics Data System (ADS)

Cheung, C. M. M.; Handmer, C.; Kosar, B.; Gerules, G.; Poduval, B.; Mackintosh, G.; Munoz-Jaramillo, A.; Bobra, M.; Hernandez, T.; McGranaghan, R. M.

2017-12-01

We present a framework for data-driven modeling of Heliophysics time series data. The Solar Terrestrial Interaction Neural net Generator (STING) is an open source python module built on top of state-of-the-art statistical learning frameworks (traditional machine learning methods as well as deep learning). To showcase the capability of STING, we deploy it for the problem of predicting the temporal variation of geomagnetic fields. The data used includes solar wind measurements from the OMNI database and geomagnetic field data taken by magnetometers at US Geological Survey observatories. We examine the predictive capability of different machine learning techniques (recurrent neural networks, support vector machines) for a range of forecasting times (minutes to 12 hours). STING is designed to be extensible to other types of data. We show how STING can be used on large sets of data from different sensors/observatories and adapted to tackle other problems in Heliophysics.

Cancer cell metabolism: one hallmark, many faces.

PubMed

Cantor, Jason R; Sabatini, David M

2012-10-01

Cancer cells must rewire cellular metabolism to satisfy the demands of growth and proliferation. Although many of the metabolic alterations are largely similar to those in normal proliferating cells, they are aberrantly driven in cancer by a combination of genetic lesions and nongenetic factors such as the tumor microenvironment. However, a single model of altered tumor metabolism does not describe the sum of metabolic changes that can support cell growth. Instead, the diversity of such changes within the metabolic program of a cancer cell can dictate by what means proliferative rewiring is driven, and can also impart heterogeneity in the metabolic dependencies of the cell. A better understanding of this heterogeneity may enable the development and optimization of therapeutic strategies that target tumor metabolism.

Solitary pulse-on-demand production by optical injection locking of passively Q-switched InGaN diode laser near lasing threshold

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

Zeng, X., E-mail: xi.zeng@csem.ch, E-mail: dmitri.boiko@csem.ch; Stadelmann, T.; Grossmann, S.

2015-02-16

In this letter, we investigate the behavior of a Q-switched InGaN multi-section laser diode (MSLD) under optical injection from a continuous wave external cavity diode laser. We obtain solitary optical pulse generation when the slave MSLD is driven near free running threshold, and the peak output power is significantly enhanced with respect to free running configuration. When the slave laser is driven well above threshold, optical injection reduces the peak power. Using standard semiconductor laser rate equation model, we find that both power enhancement and suppression effects are the result of partial bleaching of the saturable absorber by externally injectedmore » photons.« less

Varying execution discipline to increase performance

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

Campbell, P.L.; Maccabe, A.B.

1993-12-22

This research investigates the relationship between execution discipline and performance. The hypothesis has two parts: 1. Different execution disciplines exhibit different performance for different computations, and 2. These differences can be effectively predicted by heuristics. A machine model is developed that can vary its execution discipline. That is, the model can execute a given program using either the control-driven, data-driven or demand-driven execution discipline. This model is referred to as a ``variable-execution-discipline`` machine. The instruction set for the model is the Program Dependence Web (PDW). The first part of the hypothesis will be tested by simulating the execution of themore » machine model on a suite of computations, based on the Livermore Fortran Kernel (LFK) Test (a.k.a. the Livermore Loops), using all three execution disciplines. Heuristics are developed to predict relative performance. These heuristics predict (a) the execution time under each discipline for one iteration of each loop and (b) the number of iterations taken by that loop; then the heuristics use those predictions to develop a prediction for the execution of the entire loop. Similar calculations are performed for branch statements. The second part of the hypothesis will be tested by comparing the results of the simulated execution with the predictions produced by the heuristics. If the hypothesis is supported, then the door is open for the development of machines that can vary execution discipline to increase performance.« less

A learning scheme for reach to grasp movements: on EMG-based interfaces using task specific motion decoding models.

PubMed

Liarokapis, Minas V; Artemiadis, Panagiotis K; Kyriakopoulos, Kostas J; Manolakos, Elias S

2013-09-01

A learning scheme based on random forests is used to discriminate between different reach to grasp movements in 3-D space, based on the myoelectric activity of human muscles of the upper-arm and the forearm. Task specificity for motion decoding is introduced in two different levels: Subspace to move toward and object to be grasped. The discrimination between the different reach to grasp strategies is accomplished with machine learning techniques for classification. The classification decision is then used in order to trigger an EMG-based task-specific motion decoding model. Task specific models manage to outperform "general" models providing better estimation accuracy. Thus, the proposed scheme takes advantage of a framework incorporating both a classifier and a regressor that cooperate advantageously in order to split the task space. The proposed learning scheme can be easily used to a series of EMG-based interfaces that must operate in real time, providing data-driven capabilities for multiclass problems, that occur in everyday life complex environments.

The dark side of incremental learning: a model of cumulative semantic interference during lexical access in speech production.

PubMed

Oppenheim, Gary M; Dell, Gary S; Schwartz, Myrna F

2010-02-01

Naming a picture of a dog primes the subsequent naming of a picture of a dog (repetition priming) and interferes with the subsequent naming of a picture of a cat (semantic interference). Behavioral studies suggest that these effects derive from persistent changes in the way that words are activated and selected for production, and some have claimed that the findings are only understandable by positing a competitive mechanism for lexical selection. We present a simple model of lexical retrieval in speech production that applies error-driven learning to its lexical activation network. This model naturally produces repetition priming and semantic interference effects. It predicts the major findings from several published experiments, demonstrating that these effects may arise from incremental learning. Furthermore, analysis of the model suggests that competition during lexical selection is not necessary for semantic interference if the learning process is itself competitive. Copyright 2009 Elsevier B.V. All rights reserved.

Effectively managing consumer fuel price driven transit demand.

DOT National Transportation Integrated Search

2013-05-01

This study presents a literature review of transit demand elasticities with respect to gas prices, describes features of a transit service area population that may be more sensitive to fuel prices, identifies where stress points in the family of tran...

Ontology-Driven Disability-Aware E-Learning Personalisation with ONTODAPS

ERIC Educational Resources Information Center

Nganji, Julius T.; Brayshaw, Mike; Tompsett, Brian

2013-01-01

Purpose: The purpose of this paper is to show how personalisation of learning resources and services can be achieved for students with and without disabilities, particularly responding to the needs of those with multiple disabilities in e-learning systems. The paper aims to introduce ONTODAPS, the Ontology-Driven Disability-Aware Personalised…

Learning for Understanding: A Faculty-Driven Paradigm Shift in Learning, Imaginative Teaching, and Creative Assessment

ERIC Educational Resources Information Center

Diaz-Lefebvre, Rene

2006-01-01

This article describes an experimental pilot study begun in 1994 in the Glendale Community College (Glendale, Arizona) psychology department. The faculty-driven idea incorporated Howard Gardner's multiple intelligences theory (MI) into a new paradigm--one where creative forms of learning resulted in real understanding. The pilot study, Multiple…

Problem-Based Learning and Creative Instructional Approaches for Laboratory Exercises in Introductory Crop Science

ERIC Educational Resources Information Center

Teplitski, Max; McMahon, Margaret J.

2006-01-01

The implementation of problem-based learning (PBL) and other inquiry-driven educational techniques is often resisted by both faculty and students, who may not be comfortable with this learning/instructional style. We present here a hybrid approach, which combines elements of expository education with inquiry-driven laboratory exercises and…

Safety belt promotion: theory and practice.

PubMed

Nelson, G D; Moffit, P B

1988-02-01

The purpose of this paper is to provide practitioners a rationale and description of selected theoretically based approaches to safety belt promotion. Theory failure is a threat to the integrity and effectiveness of safety belt promotion. The absence of theory driven programs designed to promote safety belt use is a concern of this paper. Six theoretical models from the social and behavioral sciences are reviewed with suggestions for application to promoting safety belt use and include Theory of Reasoned Action, the Health Belief Model, Fear Arousal, Operant Learning, Social Learning Theory, and Diffusion of Innovations. Guidelines for the selection and utilization of theory are discussed.

Generative Models in Deep Learning: Constraints for Galaxy Evolution

NASA Astrophysics Data System (ADS)

Turp, Maximilian Dennis; Schawinski, Kevin; Zhang, Ce; Weigel, Anna K.

2018-01-01

New techniques are essential to make advances in the field of galaxy evolution. Recent developments in the field of artificial intelligence and machine learning have proven that these tools can be applied to problems far more complex than simple image recognition. We use these purely data driven approaches to investigate the process of star formation quenching. We show that Variational Autoencoders provide a powerful method to forward model the process of galaxy quenching. Our results imply that simple changes in specific star formation rate and bulge to disk ratio cannot fully describe the properties of the quenched population.

Computational Psychiatry and the Challenge of Schizophrenia.

PubMed

Krystal, John H; Murray, John D; Chekroud, Adam M; Corlett, Philip R; Yang, Genevieve; Wang, Xiao-Jing; Anticevic, Alan

2017-05-01

Schizophrenia research is plagued by enormous challenges in integrating and analyzing large datasets and difficulties developing formal theories related to the etiology, pathophysiology, and treatment of this disorder. Computational psychiatry provides a path to enhance analyses of these large and complex datasets and to promote the development and refinement of formal models for features of this disorder. This presentation introduces the reader to the notion of computational psychiatry and describes discovery-oriented and theory-driven applications to schizophrenia involving machine learning, reinforcement learning theory, and biophysically-informed neural circuit models. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center 2017.

Learning to Predict Demand in a Transport-Resource Sharing Task

DTIC Science & Technology

2015-09-01

exhaustive manner. We experimented with the scikit- learn machine- learning library for Python and a range of R packages before settling on R. We...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Approved for public release; distribution is unlimited LEARNING TO...COVERED Master’s thesis 4. TITLE AND SUBTITLE LEARNING TO PREDICT DEMAND IN A TRANSPORT-RESOURCE SHARING TASK 5. FUNDING NUMBERS 6. AUTHOR

District Central Offices as Learning Organizations: How Sociocultural and Organizational Learning Theories Elaborate District Central Office Administrators' Participation in Teaching and Learning Improvement Efforts

ERIC Educational Resources Information Center

Honig, Meredith I.

2008-01-01

School district central office administrators face unprecedented demands to become key supporters of efforts to improve teaching and learning districtwide. Some suggest that these demands mean that central offices, especially in midsized and large districts, should become learning organizations but provide few guides for how central offices might…

A tale of two slinkies: learning about scientific models in a student-driven classroom

NASA Astrophysics Data System (ADS)

Gandhi, Punit; Berggren, Calvin; Livezey, Jesse; Olf, Ryan

2014-11-01

We describe a set of conceptual activities and hands-on experiments based around understanding the dynamics of a slinky that is hung vertically and released from rest. The motion, or lack thereof, of the bottom of the slinky after the top is dropped sparks students' curiosity by challenging their expectations and provides context for learning about scientific model building. This curriculum helps students learn about the model building process by giving them an opportunity to enlist their collective intellectual and creative resources to develop and explore two different physical models of the falling slinky system. By engaging with two complementary models, students not only have the opportunity to understand an intriguing phenomenon from multiple perspectives, but also learn deeper lessons about the nature of scientific understanding, the role of physical models, and the experience of doing science. The activities we present were part of a curriculum developed for a week-long summer program for incoming freshmen as a part of the Compass Project at UC Berkeley, but could easily be implemented in a wide range of classrooms at the high school or introductory college level.

Attack of the Killer Fungus: A Hypothesis-Driven Lab Module †

PubMed Central

Sato, Brian K.

2013-01-01

Discovery-driven experiments in undergraduate laboratory courses have been shown to increase student learning and critical thinking abilities. To this end, a lab module involving worm capture by a nematophagous fungus was developed. The goals of this module are to enhance scientific understanding of the regulation of worm capture by soil-dwelling fungi and for students to attain a set of established learning goals, including the ability to develop a testable hypothesis and search for primary literature for data analysis, among others. Students in a ten-week majors lab course completed the lab module and generated novel data as well as data that agrees with the published literature. In addition, learning gains were achieved as seen through a pre-module and post-module test, student self-assessment, class exam, and lab report. Overall, this lab module enables students to become active participants in the scientific method while contributing to the understanding of an ecologically relevant model organism. PMID:24358387

Climate change is projected to have severe impacts on the frequency and intensity of peak electricity demand across the United States

PubMed Central

Auffhammer, Maximilian; Baylis, Patrick; Hausman, Catherine H.

2017-01-01

It has been suggested that climate change impacts on the electric sector will account for the majority of global economic damages by the end of the current century and beyond [Rose S, et al. (2014) Understanding the Social Cost of Carbon: A Technical Assessment]. The empirical literature has shown significant increases in climate-driven impacts on overall consumption, yet has not focused on the cost implications of the increased intensity and frequency of extreme events driving peak demand, which is the highest load observed in a period. We use comprehensive, high-frequency data at the level of load balancing authorities to parameterize the relationship between average or peak electricity demand and temperature for a major economy. Using statistical models, we analyze multiyear data from 166 load balancing authorities in the United States. We couple the estimated temperature response functions for total daily consumption and daily peak load with 18 downscaled global climate models (GCMs) to simulate climate change-driven impacts on both outcomes. We show moderate and heterogeneous changes in consumption, with an average increase of 2.8% by end of century. The results of our peak load simulations, however, suggest significant increases in the intensity and frequency of peak events throughout the United States, assuming today’s technology and electricity market fundamentals. As the electricity grid is built to endure maximum load, our findings have significant implications for the construction of costly peak generating capacity, suggesting additional peak capacity costs of up to 180 billion dollars by the end of the century under business-as-usual. PMID:28167756

Innovation Diffusion Model in Higher Education: Case Study of E-Learning Diffusion

ERIC Educational Resources Information Center

Buc, Sanjana; Divjak, Blaženka

2015-01-01

The diffusion of innovation (DOI) is critical for any organization and especially nowadays for higher education institutions (HEIs) in the light of vast pressure of emerging educational technologies as well as of the demand of economy and society. DOI takes into account the initial and the implementation phase. The conceptual model of DOI in…

AIDS: An ICT Model for Integrating Teaching, Learning and Research in Technical University Education in Ghana

ERIC Educational Resources Information Center

Asabere, Nana; Togo, Gilbert; Acakpovi, Amevi; Torby, Wisdom; Ampadu, Kwame

2017-01-01

Information and Communication Technologies (ICT) has changed the way we communicate and carry out certain daily activities. Globally, ICT has become an essential means for disseminating information. Using Accra Technical University in Ghana as a case study, this paper proposes an ICT model called Awareness Incentives Demand and Support (AIDS). Our…

Why Engaging in Mathematical Practices May Explain Stronger Outcomes in Affect and Engagement: Comparing Student-Driven with Highly Guided Inquiry

ERIC Educational Resources Information Center

Sengupta-Irving, Tesha; Enyedy, Noel

2015-01-01

This article investigates why students reported liking a student-driven learning design better than a highly guided design despite equivalent gains in knowledge assessments in both conditions. We created two learning designs based on the distinction in the literature between student-driven and teacher-led approaches. One teacher assigned each of…

The Leadership Roles of Distance Learning Administrators (DLAs) in Increasing Educational Value and Quality Perceptions

ERIC Educational Resources Information Center

McFarlane, Donovan A.

2011-01-01

This paper examines the leadership roles of distance learning administrators (DLAs) in light of the demand and need for value and quality in educational distance learning programs and schools. The author explores the development of distance learning using available and emerging technologies in relation to increased demand for education, training,…

Youth Climate Summits: Empowering & Engaging Youth to Lead on Climate Change

NASA Astrophysics Data System (ADS)

Kretser, J.

2017-12-01

The Wild Center's Youth Climate Summits is a program that engages youth in climate literacy from knowledge and understanding to developing action in their schools and communities. Each Youth Climate Summit is a one to three day event that brings students and teachers together to learn about climate change science, impacts and solutions at a global and local level. Through speakers, workshops and activities, the Summit culminates in a student-driven Climate Action Plan that can be brought back to schools and communities. The summits have been found to be powerful vehicles for inspiration, learning, community engagement and youth leadership development. Climate literacy with a focus on local climate impacts and solutions is a key component of the Youth Climate Summit. The project-based learning surrounding the creation of a unique, student driven, sustainability and Climate Action Plan promotes leadership skills applicable and the tools necessary for a 21st Century workforce. Student driven projects range from school gardens and school energy audits to working with NYS officials to commit to going 100% renewable electricty at the three state-owned downhill ski facilities. The summit model has been scaled and replicated in other communities in New York State, Vermont, Ohio, Michigan and Washington states as well as internationally in Finland, Germany and Sri Lanka.

Formulation and demonstration of a robust mean variance optimization approach for concurrent airline network and aircraft design

NASA Astrophysics Data System (ADS)

Davendralingam, Navindran

Conceptual design of aircraft and the airline network (routes) on which aircraft fly on are inextricably linked to passenger driven demand. Many factors influence passenger demand for various Origin-Destination (O-D) city pairs including demographics, geographic location, seasonality, socio-economic factors and naturally, the operations of directly competing airlines. The expansion of airline operations involves the identificaion of appropriate aircraft to meet projected future demand. The decisions made in incorporating and subsequently allocating these new aircraft to serve air travel demand affects the inherent risk and profit potential as predicted through the airline revenue management systems. Competition between airlines then translates to latent passenger observations of the routes served between OD pairs and ticket pricing---this in effect reflexively drives future states of demand. This thesis addresses the integrated nature of aircraft design, airline operations and passenger demand, in order to maximize future expected profits as new aircraft are brought into service. The goal of this research is to develop an approach that utilizes aircraft design, airline network design and passenger demand as a unified framework to provide better integrated design solutions in order to maximize expexted profits of an airline. This is investigated through two approaches. The first is a static model that poses the concurrent engineering paradigm above as an investment portfolio problem. Modern financial portfolio optimization techniques are used to leverage risk of serving future projected demand using a 'yet to be introduced' aircraft against potentially generated future profits. Robust optimization methodologies are incorporated to mitigate model sensitivity and address estimation risks associated with such optimization techniques. The second extends the portfolio approach to include dynamic effects of an airline's operations. A dynamic programming approach is employed to simulate the reflexive nature of airline supply-demand interactions by modeling the aggregate changes in demand that would result from tactical allocations of aircraft to maximize profit. The best yet-to-be-introduced aircraft maximizes profit by minimizing the long term fleetwide direct operating costs.

Complex Constructivism: A Theoretical Model of Complexity and Cognition

ERIC Educational Resources Information Center

Doolittle, Peter E.

2014-01-01

Education has long been driven by its metaphors for teaching and learning. These metaphors have influenced both educational research and educational practice. Complexity and constructivism are two theories that provide functional and robust metaphors. Complexity provides a metaphor for the structure of myriad phenomena, while constructivism…

Peer Learning and Support of Technology in an Undergraduate Biology Course to Enhance Deep Learning

PubMed Central

Tsaushu, Masha; Tal, Tali; Sagy, Ornit; Kali, Yael; Gepstein, Shimon; Zilberstein, Dan

2012-01-01

This study offers an innovative and sustainable instructional model for an introductory undergraduate course. The model was gradually implemented during 3 yr in a research university in a large-lecture biology course that enrolled biology majors and nonmajors. It gives priority to sources not used enough to enhance active learning in higher education: technology and the students themselves. Most of the lectures were replaced with continuous individual learning and 1-mo group learning of one topic, both supported by an interactive online tutorial. Assessment included open-ended complex questions requiring higher-order thinking skills that were added to the traditional multiple-choice (MC) exam. Analysis of students’ outcomes indicates no significant difference among the three intervention versions in the MC questions of the exam, while students who took part in active-learning groups at the advanced version of the model had significantly higher scores in the more demanding open-ended questions compared with their counterparts. We believe that social-constructivist learning of one topic during 1 mo has significantly contributed to student deep learning across topics. It developed a biological discourse, which is more typical to advanced stages of learning biology, and changed the image of instructors from “knowledge transmitters” to “role model scientists.” PMID:23222836

Peer learning and support of technology in an undergraduate biology course to enhance deep learning.

PubMed

Tsaushu, Masha; Tal, Tali; Sagy, Ornit; Kali, Yael; Gepstein, Shimon; Zilberstein, Dan

2012-01-01

This study offers an innovative and sustainable instructional model for an introductory undergraduate course. The model was gradually implemented during 3 yr in a research university in a large-lecture biology course that enrolled biology majors and nonmajors. It gives priority to sources not used enough to enhance active learning in higher education: technology and the students themselves. Most of the lectures were replaced with continuous individual learning and 1-mo group learning of one topic, both supported by an interactive online tutorial. Assessment included open-ended complex questions requiring higher-order thinking skills that were added to the traditional multiple-choice (MC) exam. Analysis of students' outcomes indicates no significant difference among the three intervention versions in the MC questions of the exam, while students who took part in active-learning groups at the advanced version of the model had significantly higher scores in the more demanding open-ended questions compared with their counterparts. We believe that social-constructivist learning of one topic during 1 mo has significantly contributed to student deep learning across topics. It developed a biological discourse, which is more typical to advanced stages of learning biology, and changed the image of instructors from "knowledge transmitters" to "role model scientists."

Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval

PubMed Central

Karisani, Payam; Qin, Zhaohui S; Agichtein, Eugene

2018-01-01

Abstract The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie PMID:29688379

Exploring the oxygen supply and demand framework as a learning tool in undergraduate nursing education.

PubMed

Gillespie, Mary; Shackell, Eileen

2017-11-01

In nursing education, physiological concepts are typically presented within a body 'systems' framework yet learners are often challenged to apply this knowledge in the holistic and functional manner needed for effective clinical decision-making and safe patient care. A nursing faculty addressed this learning challenge by developing an advanced organizer as a conceptual and integrative learning tool to support learners in diverse learning environments and practice settings. A mixed methods research study was conducted that explored the effectiveness of the Oxygen Supply and Demand Framework as a learning tool in undergraduate nursing education. A pretest/post-test assessment and reflective journal were used to gather data. Findings indicated the Oxygen Supply and Demand Framework guided the development of pattern recognition and thinking processes and supported knowledge development, knowledge application and clinical decision-making. The Oxygen Supply and Demand Framework supports undergraduate students learning to provide safe and effective nursing care. Copyright © 2017 Elsevier Ltd. All rights reserved.

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

PubMed Central

2011-01-01

Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements. PMID:21798025

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment.

PubMed

Stålring, Jonna C; Carlsson, Lars A; Almeida, Pedro; Boyer, Scott

2011-07-28

Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.

Molecular graph convolutions: moving beyond fingerprints

PubMed Central

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-01-01

Molecular “fingerprints” encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph—atoms, bonds, distances, etc.—which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement. PMID:27558503

Modeling Future Land Use Scenarios in South Korea: Applying the IPCC Special Report on Emissions Scenarios and the SLEUTH Model on a Local Scale

NASA Astrophysics Data System (ADS)

Han, Haejin; Hwang, YunSeop; Ha, Sung Ryong; Kim, Byung Sik

2015-05-01

This study developed three scenarios of future land use/land cover on a local level for the Kyung-An River Basin and its vicinity in South Korea at a 30-m resolution based on the two scenario families of the Intergovernmental Panel on Climate Change (IPCC) Special Report Emissions Scenarios (SRES): A2 and B1, as well as a business-as-usual scenario. The IPCC SRES A2 and B1 were used to define future local development patterns and associated land use change. We quantified the population-driven demand for urban land use for each qualitative storyline and allocated the urban demand in geographic space using the SLEUTH model. The model results demonstrate the possible land use/land cover change scenarios for the years from 2000 to 2070 by examining the broad narrative of each SRES within the context of a local setting, such as the Kyoungan River Basin, constructing narratives of local development shifts and modeling a set of `best guess' approximations of the future land use conditions in the study area. This study found substantial differences in demands and patterns of land use changes among the scenarios, indicating compact development patterns under the SRES B1 compared to the rapid and dispersed development under the SRES A2.

Simulating water markets with transaction costs

NASA Astrophysics Data System (ADS)

Erfani, Tohid; Binions, Olga; Harou, Julien J.

2014-06-01

This paper presents an optimization model to simulate short-term pair-wise spot-market trading of surface water abstraction licenses (water rights). The approach uses a node-arc multicommodity formulation that tracks individual supplier-receiver transactions in a water resource network. This enables accounting for transaction costs between individual buyer-seller pairs and abstractor-specific rules and behaviors using constraints. Trades are driven by economic demand curves that represent each abstractor's time-varying water demand. The purpose of the proposed model is to assess potential hydrologic and economic outcomes of water markets and aid policy makers in designing water market regulations. The model is applied to the Great Ouse River basin in Eastern England. The model assesses the potential weekly water trades and abstractions that could occur in a normal and a dry year. Four sectors (public water supply, energy, agriculture, and industrial) are included in the 94 active licensed water diversions. Each license's unique environmental restrictions are represented and weekly economic water demand curves are estimated. Rules encoded as constraints represent current water management realities and plausible stakeholder-informed water market behaviors. Results show buyers favor sellers who can supply large volumes to minimize transactions. The energy plant cooling and agricultural licenses, often restricted from obtaining water at times when it generates benefits, benefit most from trades. Assumptions and model limitations are discussed. This article was corrected on 13 JUN 2014. See the end of the full text for details.

Modeling future land use scenarios in South Korea: applying the IPCC special report on emissions scenarios and the SLEUTH model on a local scale.

PubMed

Han, Haejin; Hwang, YunSeop; Ha, Sung Ryong; Kim, Byung Sik

2015-05-01

This study developed three scenarios of future land use/land cover on a local level for the Kyung-An River Basin and its vicinity in South Korea at a 30-m resolution based on the two scenario families of the Intergovernmental Panel on Climate Change (IPCC) Special Report Emissions Scenarios (SRES): A2 and B1, as well as a business-as-usual scenario. The IPCC SRES A2 and B1 were used to define future local development patterns and associated land use change. We quantified the population-driven demand for urban land use for each qualitative storyline and allocated the urban demand in geographic space using the SLEUTH model. The model results demonstrate the possible land use/land cover change scenarios for the years from 2000 to 2070 by examining the broad narrative of each SRES within the context of a local setting, such as the Kyoungan River Basin, constructing narratives of local development shifts and modeling a set of 'best guess' approximations of the future land use conditions in the study area. This study found substantial differences in demands and patterns of land use changes among the scenarios, indicating compact development patterns under the SRES B1 compared to the rapid and dispersed development under the SRES A2.

Intrinsically motivated action-outcome learning and goal-based action recall: a system-level bio-constrained computational model.

PubMed

Baldassarre, Gianluca; Mannella, Francesco; Fiore, Vincenzo G; Redgrave, Peter; Gurney, Kevin; Mirolli, Marco

2013-05-01

Reinforcement (trial-and-error) learning in animals is driven by a multitude of processes. Most animals have evolved several sophisticated systems of 'extrinsic motivations' (EMs) that guide them to acquire behaviours allowing them to maintain their bodies, defend against threat, and reproduce. Animals have also evolved various systems of 'intrinsic motivations' (IMs) that allow them to acquire actions in the absence of extrinsic rewards. These actions are used later to pursue such rewards when they become available. Intrinsic motivations have been studied in Psychology for many decades and their biological substrates are now being elucidated by neuroscientists. In the last two decades, investigators in computational modelling, robotics and machine learning have proposed various mechanisms that capture certain aspects of IMs. However, we still lack models of IMs that attempt to integrate all key aspects of intrinsically motivated learning and behaviour while taking into account the relevant neurobiological constraints. This paper proposes a bio-constrained system-level model that contributes a major step towards this integration. The model focusses on three processes related to IMs and on the neural mechanisms underlying them: (a) the acquisition of action-outcome associations (internal models of the agent-environment interaction) driven by phasic dopamine signals caused by sudden, unexpected changes in the environment; (b) the transient focussing of visual gaze and actions on salient portions of the environment; (c) the subsequent recall of actions to pursue extrinsic rewards based on goal-directed reactivation of the representations of their outcomes. The tests of the model, including a series of selective lesions, show how the focussing processes lead to a faster learning of action-outcome associations, and how these associations can be recruited for accomplishing goal-directed behaviours. The model, together with the background knowledge reviewed in the paper, represents a framework that can be used to guide the design and interpretation of empirical experiments on IMs, and to computationally validate and further develop theories on them. Copyright © 2012 Elsevier Ltd. All rights reserved.

Risk Management of GM Crops

EPA Science Inventory

Driven by biofuel demand, a significant increase in GM corn acreage is anticipated for the 2007 growing season with future planted GM corn acreage approaching 80% of the corn crop by 2009. As demand increases, grower non-compliance with mandated planting requirements is likely to...

Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network

NASA Astrophysics Data System (ADS)

Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli

2013-03-01

Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.

Understanding MRI: basic MR physics for physicians.

PubMed

Currie, Stuart; Hoggard, Nigel; Craven, Ian J; Hadjivassiliou, Marios; Wilkinson, Iain D

2013-04-01

More frequently hospital clinicians are reviewing images from MR studies of their patients before seeking formal radiological opinion. This practice is driven by a multitude of factors, including an increased demand placed on hospital services, the wide availability of the picture archiving and communication system, time pressures for patient treatment (eg, in the management of acute stroke) and an inherent desire for the clinician to learn. Knowledge of the basic physical principles behind MRI is essential for correct image interpretation. This article, written for the general hospital physician, describes the basic physics of MRI taking into account the machinery, contrast weighting, spin- and gradient-echo techniques and pertinent safety issues. Examples provided are primarily referenced to neuroradiology reflecting the subspecialty for which MR currently has the greatest clinical application.

Predictors of Outcomes in Autism Early Intervention: Why Don’t We Know More?

PubMed Central

Vivanti, Giacomo; Prior, Margot; Williams, Katrina; Dissanayake, Cheryl

2014-01-01

Response to early intervention programs in autism is variable. However, the factors associated with positive versus poor treatment outcomes remain unknown. Hence the issue of which intervention/s should be chosen for an individual child remains a common dilemma. We argue that lack of knowledge on “what works for whom and why” in autism reflects a number of issues in current approaches to outcomes research, and we provide recommendations to address these limitations. These include: a theory-driven selection of putative predictors; the inclusion of proximal measures that are directly relevant to the learning mechanisms demanded by the specific educational strategies; the consideration of family characteristics. Moreover, all data on associations between predictor and outcome variables should be reported in treatment studies. PMID:24999470

Importance of food-demand management for climate mitigation

NASA Astrophysics Data System (ADS)

Bajželj, Bojana; Richards, Keith S.; Allwood, Julian M.; Smith, Pete; Dennis, John S.; Curmi, Elizabeth; Gilligan, Christopher A.

2014-10-01

Recent studies show that current trends in yield improvement will not be sufficient to meet projected global food demand in 2050, and suggest that a further expansion of agricultural area will be required. However, agriculture is the main driver of losses of biodiversity and a major contributor to climate change and pollution, and so further expansion is undesirable. The usual proposed alternative--intensification with increased resource use--also has negative effects. It is therefore imperative to find ways to achieve global food security without expanding crop or pastureland and without increasing greenhouse gas emissions. Some authors have emphasized a role for sustainable intensification in closing global `yield gaps' between the currently realized and potentially achievable yields. However, in this paper we use a transparent, data-driven model, to show that even if yield gaps are closed, the projected demand will drive further agricultural expansion. There are, however, options for reduction on the demand side that are rarely considered. In the second part of this paper we quantify the potential for demand-side mitigation options, and show that improved diets and decreases in food waste are essential to deliver emissions reductions, and to provide global food security in 2050.

Model based manipulator control

NASA Technical Reports Server (NTRS)

Petrosky, Lyman J.; Oppenheim, Irving J.

1989-01-01

The feasibility of using model based control (MBC) for robotic manipulators was investigated. A double inverted pendulum system was constructed as the experimental system for a general study of dynamically stable manipulation. The original interest in dynamically stable systems was driven by the objective of high vertical reach (balancing), and the planning of inertially favorable trajectories for force and payload demands. The model-based control approach is described and the results of experimental tests are summarized. Results directly demonstrate that MBC can provide stable control at all speeds of operation and support operations requiring dynamic stability such as balancing. The application of MBC to systems with flexible links is also discussed.

Acetylcholine-modulated plasticity in reward-driven navigation: a computational study.

PubMed

Zannone, Sara; Brzosko, Zuzanna; Paulsen, Ole; Clopath, Claudia

2018-06-21

Neuromodulation plays a fundamental role in the acquisition of new behaviours. In previous experimental work, we showed that acetylcholine biases hippocampal synaptic plasticity towards depression, and the subsequent application of dopamine can retroactively convert depression into potentiation. We also demonstrated that incorporating this sequentially neuromodulated Spike-Timing-Dependent Plasticity (STDP) rule in a network model of navigation yields effective learning of changing reward locations. Here, we employ computational modelling to further characterize the effects of cholinergic depression on behaviour. We find that acetylcholine, by allowing learning from negative outcomes, enhances exploration over the action space. We show that this results in a variety of effects, depending on the structure of the model, the environment and the task. Interestingly, sequentially neuromodulated STDP also yields flexible learning, surpassing the performance of other reward-modulated plasticity rules.