Properties and mechanisms of olfactory learning and memory

Directory of Open Access Journals (Sweden)

Michelle T Tong

2014-07-01

Full Text Available Memories are dynamic physical phenomena with psychometric forms as well as characteristic timescales. Most of our understanding of the cellular mechanisms underlying the neurophysiology of memory, however, derives from one-trial learning paradigms that, while powerful, do not fully embody the gradual, representational, and statistical aspects of cumulative learning. The early olfactory system -- particularly olfactory bulb -- comprises a reasonably well-understood and experimentally accessible neuronal network with intrinsic plasticity that underlies both one-trial (adult aversive, neonatal and cumulative (adult appetitive odor learning. These olfactory circuits employ many of the same molecular and structural mechanisms of memory as, for example, hippocampal circuits following inhibitory avoidance conditioning, but the temporal sequences of post-conditioning molecular events are likely to differ owing to the need to incorporate new information from ongoing learning events into the evolving memory trace. Moreover, the shapes of acquired odor representations, and their gradual transformation over the course of cumulative learning, also can be directly measured, adding an additional representational dimension to the traditional metrics of memory strength and persistence. In this review, we describe some established molecular and structural mechanisms of memory with a focus on the timecourses of post-conditioning molecular processes. We describe the properties of odor learning intrinsic to the olfactory bulb and review the utility of the olfactory system of adult rodents as a memory system in which to study the cellular mechanisms of cumulative learning.

Adaptive rival penalized competitive learning and combined linear predictor model for financial forecast and investment.

Science.gov (United States)

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

1997-01-01

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

MOLECULAR MECHANISMS OF FEAR LEARNING AND MEMORY

Science.gov (United States)

Johansen, Joshua P.; Cain, Christopher K.; Ostroff, Linnaea E.; LeDoux, Joseph E.

2011-01-01

Pavlovian fear conditioning is a useful behavioral paradigm for exploring the molecular mechanisms of learning and memory because a well-defined response to a specific environmental stimulus is produced through associative learning processes. Synaptic plasticity in the lateral nucleus of the amygdala (LA) underlies this form of associative learning. Here we summarize the molecular mechanisms that contribute to this synaptic plasticity in the context of auditory fear conditioning, the form of fear conditioning best understood at the molecular level. We discuss the neurotransmitter systems and signaling cascades that contribute to three phases of auditory fear conditioning: acquisition, consolidation, and reconsolidation. These studies suggest that multiple intracellular signaling pathways, including those triggered by activation of Hebbian processes and neuromodulatory receptors, interact to produce neural plasticity in the LA and behavioral fear conditioning. Together, this research illustrates the power of fear conditioning as a model system for characterizing the mechanisms of learning and memory in mammals, and potentially for understanding fear related disorders, such as PTSD and phobias. PMID:22036561

Molecular mechanisms of fear learning and memory.

Science.gov (United States)

Johansen, Joshua P; Cain, Christopher K; Ostroff, Linnaea E; LeDoux, Joseph E

2011-10-28

Pavlovian fear conditioning is a particularly useful behavioral paradigm for exploring the molecular mechanisms of learning and memory because a well-defined response to a specific environmental stimulus is produced through associative learning processes. Synaptic plasticity in the lateral nucleus of the amygdala (LA) underlies this form of associative learning. Here, we summarize the molecular mechanisms that contribute to this synaptic plasticity in the context of auditory fear conditioning, the form of fear conditioning best understood at the molecular level. We discuss the neurotransmitter systems and signaling cascades that contribute to three phases of auditory fear conditioning: acquisition, consolidation, and reconsolidation. These studies suggest that multiple intracellular signaling pathways, including those triggered by activation of Hebbian processes and neuromodulatory receptors, interact to produce neural plasticity in the LA and behavioral fear conditioning. Collectively, this body of research illustrates the power of fear conditioning as a model system for characterizing the mechanisms of learning and memory in mammals and potentially for understanding fear-related disorders, such as PTSD and phobias. Copyright © 2011 Elsevier Inc. All rights reserved.

Improving Nursing Students' Learning Outcomes in Fundamentals of Nursing Course through Combination of Traditional and e-Learning Methods.

Science.gov (United States)

Sheikhaboumasoudi, Rouhollah; Bagheri, Maryam; Hosseini, Sayed Abbas; Ashouri, Elaheh; Elahi, Nasrin

2018-01-01

Fundamentals of nursing course are prerequisite to providing comprehensive nursing care. Despite development of technology on nursing education, effectiveness of using e-learning methods in fundamentals of nursing course is unclear in clinical skills laboratory for nursing students. The aim of this study was to compare the effect of blended learning (combining e-learning with traditional learning methods) with traditional learning alone on nursing students' scores. A two-group post-test experimental study was administered from February 2014 to February 2015. Two groups of nursing students who were taking the fundamentals of nursing course in Iran were compared. Sixty nursing students were selected as control group (just traditional learning methods) and experimental group (combining e-learning with traditional learning methods) for two consecutive semesters. Both groups participated in Objective Structured Clinical Examination (OSCE) and were evaluated in the same way using a prepared checklist and questionnaire of satisfaction. Statistical analysis was conducted through SPSS software version 16. Findings of this study reflected that mean of midterm (t = 2.00, p = 0.04) and final score (t = 2.50, p = 0.01) of the intervention group (combining e-learning with traditional learning methods) were significantly higher than the control group (traditional learning methods). The satisfaction of male students in intervention group was higher than in females (t = 2.60, p = 0.01). Based on the findings, this study suggests that the use of combining traditional learning methods with e-learning methods such as applying educational website and interactive online resources for fundamentals of nursing course instruction can be an effective supplement for improving nursing students' clinical skills.

Neural robust stabilization via event-triggering mechanism and adaptive learning technique.

Science.gov (United States)

Wang, Ding; Liu, Derong

2018-06-01

The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties. Copyright © 2018 Elsevier Ltd. All rights reserved.

Learn new mechanisms from life

International Nuclear Information System (INIS)

Ji Qing; Luo Mingyan; Tong Xiaolin; Zhang Bo; Zhang Hui

2005-01-01

On the basis of the important experimental results of molecular motors, it was pointed out that the moving process of molecular motors is a coupling biological process of chemical-electrical-mechanical processes. This clever mechanism of energy conversion on the molecular level with several processes coupled together had never been observed before. The understanding of this new mechanism is an important step towards the understanding of life and an important content of what we can learn from life. The authors introduced here the status of the investigations on the mechanism for the force generation of kinesin and the studies of the authors in this field. (authors)

Virtual Learning Environment for Interactive Engagement with Advanced Quantum Mechanics

Science.gov (United States)

Pedersen, Mads Kock; Skyum, Birk; Heck, Robert; Müller, Romain; Bason, Mark; Lieberoth, Andreas; Sherson, Jacob F.

2016-06-01

A virtual learning environment can engage university students in the learning process in ways that the traditional lectures and lab formats cannot. We present our virtual learning environment StudentResearcher, which incorporates simulations, multiple-choice quizzes, video lectures, and gamification into a learning path for quantum mechanics at the advanced university level. StudentResearcher is built upon the experiences gathered from workshops with the citizen science game Quantum Moves at the high-school and university level, where the games were used extensively to illustrate the basic concepts of quantum mechanics. The first test of this new virtual learning environment was a 2014 course in advanced quantum mechanics at Aarhus University with 47 enrolled students. We found increased learning for the students who were more active on the platform independent of their previous performances.

Effects of Elicited Reflections combined with Tutor or Peer Feedback on Self-Regulated Learning and Learning Outcomes

NARCIS (Netherlands)

Van den Boom, Gerard; Paas, Fred; Van Merriënboer, Jeroen

2009-01-01

Van den Boom, G., Paas, F., & Van Merriënboer, J. J. G. (2007). Effects of elicited reflections combined with tutor or peer feedback on self-regulated learning and learning outcomes. Learning and Instruction, 17, 532-548.

Virtual learning environment for interactive engagement with advanced quantum mechanics

Directory of Open Access Journals (Sweden)

Mads Kock Pedersen

2016-04-01

Full Text Available A virtual learning environment can engage university students in the learning process in ways that the traditional lectures and lab formats cannot. We present our virtual learning environment StudentResearcher, which incorporates simulations, multiple-choice quizzes, video lectures, and gamification into a learning path for quantum mechanics at the advanced university level. StudentResearcher is built upon the experiences gathered from workshops with the citizen science game Quantum Moves at the high-school and university level, where the games were used extensively to illustrate the basic concepts of quantum mechanics. The first test of this new virtual learning environment was a 2014 course in advanced quantum mechanics at Aarhus University with 47 enrolled students. We found increased learning for the students who were more active on the platform independent of their previous performances.

Teaching Professionals Environmental Management: Combining Educational Learning and Practice Learning

DEFF Research Database (Denmark)

Jørgensen, Michael Søgaard; Jørgensen, Ulrik

2003-01-01

semesters. The target groups are professional environmental managers working in businesses including consultants, governmental institutions and organizations. To get access to the education the students must have a technical/nature science competence at master level or bachelor level combined with relevant...... job experience. Generally the participants have had 5-15 years of practical experience and many have been or are in the position of an internal or external job change towards new tasks that require new knowledge, methodologies or management skills. The education of "Masters of Environmental Management...... they can use in complex situations on the job is not simply a question of combining different university disciplines in the right blend and topping it with some experience. It involves combining science-based knowledge into thematic structures in carefully organized learning processes. The education...

Characterizing representational learning: A combined simulation and tutorial on perturbation theory

Directory of Open Access Journals (Sweden)

Antje Kohnle

2017-11-01

Full Text Available Analyzing, constructing, and translating between graphical, pictorial, and mathematical representations of physics ideas and reasoning flexibly through them (“representational competence” is a key characteristic of expertise in physics but is a challenge for learners to develop. Interactive computer simulations and University of Washington style tutorials both have affordances to support representational learning. This article describes work to characterize students’ spontaneous use of representations before and after working with a combined simulation and tutorial on first-order energy corrections in the context of quantum-mechanical time-independent perturbation theory. Data were collected from two institutions using pre-, mid-, and post-tests to assess short- and long-term gains. A representational competence level framework was adapted to devise level descriptors for the assessment items. The results indicate an increase in the number of representations used by students and the consistency between them following the combined simulation tutorial. The distributions of representational competence levels suggest a shift from perceptual to semantic use of representations based on their underlying meaning. In terms of activity design, this study illustrates the need to support students in making sense of the representations shown in a simulation and in learning to choose the most appropriate representation for a given task. In terms of characterizing representational abilities, this study illustrates the usefulness of a framework focusing on perceptual, syntactic, and semantic use of representations.

Characterizing representational learning: A combined simulation and tutorial on perturbation theory

Science.gov (United States)

Kohnle, Antje; Passante, Gina

2017-12-01

Analyzing, constructing, and translating between graphical, pictorial, and mathematical representations of physics ideas and reasoning flexibly through them ("representational competence") is a key characteristic of expertise in physics but is a challenge for learners to develop. Interactive computer simulations and University of Washington style tutorials both have affordances to support representational learning. This article describes work to characterize students' spontaneous use of representations before and after working with a combined simulation and tutorial on first-order energy corrections in the context of quantum-mechanical time-independent perturbation theory. Data were collected from two institutions using pre-, mid-, and post-tests to assess short- and long-term gains. A representational competence level framework was adapted to devise level descriptors for the assessment items. The results indicate an increase in the number of representations used by students and the consistency between them following the combined simulation tutorial. The distributions of representational competence levels suggest a shift from perceptual to semantic use of representations based on their underlying meaning. In terms of activity design, this study illustrates the need to support students in making sense of the representations shown in a simulation and in learning to choose the most appropriate representation for a given task. In terms of characterizing representational abilities, this study illustrates the usefulness of a framework focusing on perceptual, syntactic, and semantic use of representations.

A new semi-supervised learning model combined with Cox and SP-AFT models in cancer survival analysis.

Science.gov (United States)

Chai, Hua; Li, Zi-Na; Meng, De-Yu; Xia, Liang-Yong; Liang, Yong

2017-10-12

Gene selection is an attractive and important task in cancer survival analysis. Most existing supervised learning methods can only use the labeled biological data, while the censored data (weakly labeled data) far more than the labeled data are ignored in model building. Trying to utilize such information in the censored data, a semi-supervised learning framework (Cox-AFT model) combined with Cox proportional hazard (Cox) and accelerated failure time (AFT) model was used in cancer research, which has better performance than the single Cox or AFT model. This method, however, is easily affected by noise. To alleviate this problem, in this paper we combine the Cox-AFT model with self-paced learning (SPL) method to more effectively employ the information in the censored data in a self-learning way. SPL is a kind of reliable and stable learning mechanism, which is recently proposed for simulating the human learning process to help the AFT model automatically identify and include samples of high confidence into training, minimizing interference from high noise. Utilizing the SPL method produces two direct advantages: (1) The utilization of censored data is further promoted; (2) the noise delivered to the model is greatly decreased. The experimental results demonstrate the effectiveness of the proposed model compared to the traditional Cox-AFT model.

Combining traditional anatomy lectures with e-learning activities: how do students perceive their learning experience?

Science.gov (United States)

Lochner, Lukas; Wieser, Heike; Waldboth, Simone; Mischo-Kelling, Maria

2016-02-21

The purpose of this study was to investigate how students perceived their learning experience when combining traditional anatomy lectures with preparatory e-learning activities that consisted of fill-in-the-blank assignments, videos, and multiple-choice quizzes. A qualitative study was conducted to explore changes in study behaviour and perception of learning. Three group interviews with students were conducted and thematically analysed. Data was categorized into four themes: 1. Approaching the course material, 2. Understanding the material, 3. Consolidating the material, and 4. Perceived learning outcome. Students appreciated the clear structure of the course, and reported that online activities encouraged them towards a first engagement with the material. They felt that they were more active during in-class sessions, described self-study before the end-of-term exam as easier, and believed that contents would remain in their memories for a longer time. By adjusting already existing resources, lectures can be combined fairly easily and cost-effectively with preparatory e-learning activities. The creation of online components promote well-structured courses, can help minimize 'student passivity' as a characteristic element of lectures, and can support students in distributing their studies throughout the term, thus suggesting enhanced learning. Further research work should be designed to confirm the afore-mentioned findings through objective measurements of student learning outcomes.

Combining traditional anatomy lectures with e-learning activities: how do students perceive their learning experience?

Science.gov (United States)

Wieser, Heike; Waldboth, Simone; Mischo-Kelling, Maria

2016-01-01

Objectives The purpose of this study was to investigate how students perceived their learning experience when combining traditional anatomy lectures with preparatory e-learning activities that consisted of fill-in-the-blank assignments, videos, and multiple-choice quizzes. Methods A qualitative study was conducted to explore changes in study behaviour and perception of learning. Three group interviews with students were conducted and thematically analysed. Results Data was categorized into four themes: 1. Approaching the course material, 2. Understanding the material, 3. Consolidating the material, and 4. Perceived learning outcome.  Students appreciated the clear structure of the course, and reported that online activities encouraged them towards a first engagement with the material. They felt that they were more active during in-class sessions, described self-study before the end-of-term exam as easier, and believed that contents would remain in their memories for a longer time. Conclusions By adjusting already existing resources, lectures can be combined fairly easily and cost-effectively with preparatory e-learning activities. The creation of online components promote well-structured courses, can help minimize ‘student passivity’ as a characteristic element of lectures, and can support students in distributing their studies throughout the term, thus suggesting enhanced learning. Further research work should be designed to confirm the afore-mentioned findings through objective measurements of student learning outcomes. PMID:26897012

Assessing learning outcomes in middle-division classical mechanics: The Colorado Classical Mechanics and Math Methods Instrument

Science.gov (United States)

Caballero, Marcos D.; Doughty, Leanne; Turnbull, Anna M.; Pepper, Rachel E.; Pollock, Steven J.

2017-06-01

Reliable and validated assessments of introductory physics have been instrumental in driving curricular and pedagogical reforms that lead to improved student learning. As part of an effort to systematically improve our sophomore-level classical mechanics and math methods course (CM 1) at CU Boulder, we have developed a tool to assess student learning of CM 1 concepts in the upper division. The Colorado Classical Mechanics and Math Methods Instrument (CCMI) builds on faculty consensus learning goals and systematic observations of student difficulties. The result is a 9-question open-ended post test that probes student learning in the first half of a two-semester classical mechanics and math methods sequence. In this paper, we describe the design and development of this instrument, its validation, and measurements made in classes at CU Boulder and elsewhere.

Machine Learning and Quantum Mechanics

Science.gov (United States)

Chapline, George

The author has previously pointed out some similarities between selforganizing neural networks and quantum mechanics. These types of neural networks were originally conceived of as away of emulating the cognitive capabilities of the human brain. Recently extensions of these networks, collectively referred to as deep learning networks, have strengthened the connection between self-organizing neural networks and human cognitive capabilities. In this note we consider whether hardware quantum devices might be useful for emulating neural networks with human-like cognitive capabilities, or alternatively whether implementations of deep learning neural networks using conventional computers might lead to better algorithms for solving the many body Schrodinger equation.

The Effect of Known-and-Unknown Word Combinations on Intentional Vocabulary Learning

Science.gov (United States)

Kasahara, Kiwamu

2011-01-01

The purpose of this study is to examine whether learning a known-and-unknown word combination is superior in terms of retention and retrieval of meaning to learning a single unknown word. The term "combination" in this study means a two-word collocation of a familiar word and a word that is new to the participants. Following the results of…

Effectiveness of hands-on tutoring and guided self-directed learning versus self-directed learning alone to educate critical care fellows on mechanical ventilation - a pilot project.

Science.gov (United States)

Ramar, Kannan; De Moraes, Alice Gallo; Selim, Bernardo; Holets, Steven; Oeckler, Richard

2016-01-01

Physicians require extensive training to achieve proficiency in mechanical ventilator (MV) management of the critically ill patients. Guided self-directed learning (GSDL) is usually the method used to learn. However, it is unclear if this is the most proficient approach to teaching mechanical ventilation to critical care fellows. We, therefore, investigated whether critical care fellows achieve higher scores on standardized testing and report higher satisfaction after participating in a hands-on tutorial combined with GSDL compared to self-directed learning alone. First-year Pulmonary and Critical Care Medicine (PCCM) fellows ( n =6) and Critical Care Internal Medicine (CCIM) ( n =8) fellows participated. Satisfaction was assessed using the Likert scale. MV knowledge assessment was performed by administering a standardized 25-question multiple choice pre- and posttest. For 2 weeks the CCIM fellows were exposed to GSDL, while the PCCM fellows received hands-on tutoring combined with GSDL. Ninety-three percentage (6 PCCM and 7 CCIM fellows, total of 13 fellows) completed all evaluations and were included in the final analysis. CCIM and PCCM fellows scored similarly in the pretest (64% vs. 52%, p =0.13). Following interventions, the posttest scores increased in both groups. However, no significant difference was observed based on the interventions (74% vs. 77%, p =0.39). The absolute improvement with the hands-on-tutoring and GSDL group was higher than GSDL alone (25% vs. 10%, p =0.07). Improved satisfaction scores were noted with hands-on tutoring. Hands-on tutoring combined with GSDL and GSDL alone were both associated with an improvement in posttest scores. Absolute improvement in test and satisfaction scores both trended higher in the hands-on tutorial group combined with GSDL group.

Assessing learning outcomes in middle-division classical mechanics: The Colorado Classical Mechanics and Math Methods Instrument

Directory of Open Access Journals (Sweden)

Marcos D. Caballero

2017-04-01

Full Text Available Reliable and validated assessments of introductory physics have been instrumental in driving curricular and pedagogical reforms that lead to improved student learning. As part of an effort to systematically improve our sophomore-level classical mechanics and math methods course (CM 1 at CU Boulder, we have developed a tool to assess student learning of CM 1 concepts in the upper division. The Colorado Classical Mechanics and Math Methods Instrument (CCMI builds on faculty consensus learning goals and systematic observations of student difficulties. The result is a 9-question open-ended post test that probes student learning in the first half of a two-semester classical mechanics and math methods sequence. In this paper, we describe the design and development of this instrument, its validation, and measurements made in classes at CU Boulder and elsewhere.

The statistical mechanics of learning a rule

International Nuclear Information System (INIS)

Watkin, T.L.H.; Rau, A.; Biehl, M.

1993-01-01

A summary is presented of the statistical mechanical theory of learning a rule with a neural network, a rapidly advancing area which is closely related to other inverse problems frequently encountered by physicists. By emphasizing the relationship between neural networks and strongly interacting physical systems, such as spin glasses, the authors show how learning theory has provided a workshop in which to develop new, exact analytical techniques

Crack assessment of pipe under combined thermal and mechanical load

International Nuclear Information System (INIS)

Song, Tae Kwang; Kim, Yun Jae

2009-01-01

In this paper, J-integral and transient C(t)-integral, which were key parameters in low temperature and high temperature fracture mechanics, under combined thermal and mechanical load were estimated via 3-dimensional finite element analyses. Various type of thermal and mechanical load, material hardening were considered to decrease conservatism in existing solutions. As a results, V-factor and redistribution time for combined thermal and mechanical load were proposed to calculate J-integral and C(t)-integral, respectively.

Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control.

Science.gov (United States)

Dasgupta, Sakyasingha; Wörgötter, Florentin; Manoonpong, Poramate

2014-01-01

Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus, in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms.

Combining Formal, Non-Formal and Informal Learning for Workforce Skill Development

Science.gov (United States)

Misko, Josie

2008-01-01

This literature review, undertaken for Australian Industry Group, shows how multiple variations and combinations of formal, informal and non-formal learning, accompanied by various government incentives and organisational initiatives (including job redesign, cross-skilling, multi-skilling, diversified career pathways, action learning projects,…

Technology of combined chemical-mechanical fabrication of durable coatings

Science.gov (United States)

Smolentsev, V. P.; Ivanov, V. V.; Portnykh, A. I.

2018-03-01

The article presents the scientific fundamentals of methodology for calculating the modes and structuring the technological processes of combined chemical-mechanical fabrication of durable coatings. It is shown that they are based on classical patterns, describing the processes of simultaneous chemical and mechanical impact. The paper demonstrates the possibility of structuring a technological process, taking into account the systematic approach to impact management and strengthening the reciprocal positive influence of each impact upon the combined process. The combined processes have been planned for fabricating the model types of chemical-mechanical coatings of durable products in machine construction. The planning methodology is underpinned by a scientific hypothesis of a single source of impact management through energy potential of process components themselves, or by means of external energy supply through mechanical impact. The control of it is fairly thoroughly studied in the case of pulsed external strikes of hard pellets, similar to processes of vibroimpact hardening, thoroughly studied and mastered in many scientific schools of Russia.

Potentials of Industrie 4.0 and Machine Learning for Mechanical Joining

OpenAIRE

Jäckel, Mathias

2017-01-01

-Sensitivity analysis of the influence of component properties and joining parameters on the joining result for self-pierce riveting -Possibilities to link mechanical joining technologies with the automotive process chain for quality and flexibility improvements -Potential of using machine learning to reduce automotive product development cycles in relation to mechanical joining -Datamining for machine learning at mechanical joining

Examining the benefits of combining two learning strategies on recall of functional information in persons with multiple sclerosis.

Science.gov (United States)

Goverover, Yael; Basso, Michael; Wood, Hali; Chiaravalloti, Nancy; DeLuca, John

2011-12-01

Forgetfulness occurs commonly in people with multiple sclerosis (MS), but few treatments alleviate this problem. This study examined the combined effect of two cognitive rehabilitation strategies to improve learning and memory in MS: self-generation and spaced learning. The hypothesis was that the combination of spaced learning and self-generation would yield better learning and memory recall performance than spaced learning alone. Using a within groups design, 20 participants with MS and 18 healthy controls (HC) were presented with three tasks (learning names, appointment, and object location), each in three learning conditions (Massed, Spaced Learning, and combination of spaced and generated information). Participants were required to recall the information they learned in each of these conditions immediately and 30 min following the initial presentation. The combination of spaced learning and self-generation yielded better recall than did spaced learning alone. In turn, spaced learning resulted in better recall than the massed rehearsal condition. These findings reveal that the combination of these two learning strategies may possess utility as a cognitive rehabilitation strategy.

A blended learning approach to teach fluid mechanics in engineering

Science.gov (United States)

Rahman, Ataur

2017-05-01

This paper presents a case study on the teaching and learning of fluid mechanics at the University of Western Sydney (UWS), Australia, by applying a blended learning approach (BLA). In the adopted BLA, various flexible learning materials have been made available to the students such as online recorded lectures, online recorded tutorials, hand written tutorial solutions, discussion board and online practice quizzes. The lecture and tutorial class times have been primarily utilised to discuss confusing topics and engage students with practical issues in applying the theories learnt in fluid mechanics. Based on the data of over 734 students over a 4-year period, it has been shown that a BLA has improved the learning experience of the fluid mechanics students in UWS. The overall percentage of student satisfaction in this subject has increased by 18% in the BLA case compared with the traditional one.

Tuning Cell and Tissue Development by Combining Multiple Mechanical Signals.

Science.gov (United States)

Sinha, Ravi; Verdonschot, Nico; Koopman, Bart; Rouwkema, Jeroen

2017-10-01

Mechanical signals offer a promising way to control cell and tissue development. It has been established that cells constantly probe their mechanical microenvironment and employ force feedback mechanisms to modify themselves and when possible, their environment, to reach a homeostatic state. Thus, a correct mechanical microenvironment (external forces and mechanical properties and shapes of cellular surroundings) is necessary for the proper functioning of cells. In vitro or in the case of nonbiological implants in vivo, where cells are in an artificial environment, addition of the adequate mechanical signals can, therefore, enable the cells to function normally as in vivo. Hence, a wide variety of approaches have been developed to apply mechanical stimuli (such as substrate stretch, flow-induced shear stress, substrate stiffness, topography, and modulation of attachment area) to cells in vitro. These approaches have not just revealed the effects of the mechanical signals on cells but also provided ways for probing cellular molecules and structures that can provide a mechanistic understanding of the effects. However, they remain lower in complexity compared with the in vivo conditions, where the cellular mechanical microenvironment is the result of a combination of multiple mechanical signals. Therefore, combinations of mechanical stimuli have also been applied to cells in vitro. These studies have had varying focus-developing novel platforms to apply complex combinations of mechanical stimuli, observing the co-operation/competition between stimuli, combining benefits of multiple stimuli toward an application, or uncovering the underlying mechanisms of their action. In general, they provided new insights that could not have been predicted from previous knowledge. We present here a review of several such studies and the insights gained from them, thereby making a case for such studies to be continued and further developed.

Brain mechanisms of flavor learning

Directory of Open Access Journals (Sweden)

Takashi eYamamoto

2011-09-01

Full Text Available Once the flavor of the ingested food (conditioned stimulus, CS is associated with a preferable (e.g., good taste or nutritive satisfaction or aversive (e.g., malaise with displeasure signal (unconditioned stimulus, US, animals react to its subsequent exposure by increasing or decreasing ingestion to the food. These two types of association learning (preference learning vs. aversion learning are known as classical conditioned reactions which are basic learning and memory phenomena, leading selection of food and proper food intake. Since the perception of flavor is generated by interaction of taste and odor during food intake, taste and/or odor are mainly associated with bodily signals in the flavor learning. After briefly reviewing flavor learning in general, brain mechanisms of conditioned taste aversion is described in more detail. The CS-US association leading to long-term potentiation in the amygdala, especially in its basolateral nucleus, is the basis of establishment of conditioned taste aversion. The novelty of the CS detected by the cortical gustatory area may be supportive in CS-US association. After the association, CS input is conveyed through the amygdala to different brain regions including the hippocampus for contextual fear formation, to the supramammilary and thalamic paraventricular nuclei for stressful anxiety or memory dependent fearful or stressful emotion, to the reward system to induce aversive expression to the CS, or hedonic shift from positive to negative, and to the CS-responsive neurons in the gustatory system to enhance the responsiveness to facilitate to detect the harmful stimulus.

Brain mechanisms of flavor learning.

Science.gov (United States)

Yamamoto, Takashi; Ueji, Kayoko

2011-01-01

Once the flavor of the ingested food (conditioned stimulus, CS) is associated with a preferable (e.g., good taste or nutritive satisfaction) or aversive (e.g., malaise with displeasure) signal (unconditioned stimulus, US), animals react to its subsequent exposure by increasing or decreasing ingestion to the food. These two types of association learning (preference learning vs. aversion learning) are known as classical conditioned reactions which are basic learning and memory phenomena, leading selection of food and proper food intake. Since the perception of flavor is generated by interaction of taste and odor during food intake, taste and/or odor are mainly associated with bodily signals in the flavor learning. After briefly reviewing flavor learning in general, brain mechanisms of conditioned taste aversion is described in more detail. The CS-US association leading to long-term potentiation in the amygdala, especially in its basolateral nucleus, is the basis of establishment of conditioned taste aversion. The novelty of the CS detected by the cortical gustatory area may be supportive in CS-US association. After the association, CS input is conveyed through the amygdala to different brain regions including the hippocampus for contextual fear formation, to the supramammillary and thalamic paraventricular nuclei for stressful anxiety or memory dependent fearful or stressful emotion, to the reward system to induce aversive expression to the CS, or hedonic shift from positive to negative, and to the CS-responsive neurons in the gustatory system to enhance the responsiveness to facilitate to detect the harmful stimulus.

Combining University Student Self-Regulated Learning Indicators and Engagement with Online Learning Events to Predict Academic Performance

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Pardo, Abelardo; Han, Feifei; Ellis, Robert A.

2017-01-01

Self-regulated learning theories are used to understand the reasons for different levels of university student academic performance. Similarly, learning analytics research proposes the combination of detailed data traces derived from technology-mediated tasks with a variety of algorithms to predict student academic performance. The former approach…

Deep Learning Fluid Mechanics

Science.gov (United States)

Barati Farimani, Amir; Gomes, Joseph; Pande, Vijay

2017-11-01

We have developed a new data-driven model paradigm for the rapid inference and solution of the constitutive equations of fluid mechanic by deep learning models. Using generative adversarial networks (GAN), we train models for the direct generation of solutions to steady state heat conduction and incompressible fluid flow without knowledge of the underlying governing equations. Rather than using artificial neural networks to approximate the solution of the constitutive equations, GANs can directly generate the solutions to these equations conditional upon an arbitrary set of boundary conditions. Both models predict temperature, velocity and pressure fields with great test accuracy (>99.5%). The application of our framework for inferring and generating the solutions of partial differential equations can be applied to any physical phenomena and can be used to learn directly from experiments where the underlying physical model is complex or unknown. We also have shown that our framework can be used to couple multiple physics simultaneously, making it amenable to tackle multi-physics problems.

Combining Education and Practice Learning in Environmental Management and Cleaner Technology

DEFF Research Database (Denmark)

Jørgensen, Ulrik; Jørgensen, Michael Søgaard

2004-01-01

to overcome these problems are discussed. The educational principles are presented as a combination of educational learning and practice learning named as reflexive learning. The experience from working with reflexive learning is discussed and relation to the role it can play in creating profes......This chapter argues for a new role for universities in adding the training of (existing) professionals to the core agenda in parallel to academic education and scientific research. Based on experiences from Denmark new challenges both to academic knowledge and training are presented and way...

Combining theories to reach multi-faceted insights into learning opportunities in doctoral supervision

DEFF Research Database (Denmark)

Kobayashi, Sofie; Rump, Camilla Østerberg

The aim of this paper is to illustrate how theories can be combined to explore opportunities for learning in doctoral supervision. While our earlier research into learning dynamics in doctoral supervision in life science research (Kobayashi, 2014) has focused on illustrating learning opportunitie...

Effectiveness of hands-on tutoring and guided self-directed learning versus self-directed learning alone to educate critical care fellows on mechanical ventilation – a pilot project

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Kannan Ramar

2016-09-01

Full Text Available Background: Physicians require extensive training to achieve proficiency in mechanical ventilator (MV management of the critically ill patients. Guided self-directed learning (GSDL is usually the method used to learn. However, it is unclear if this is the most proficient approach to teaching mechanical ventilation to critical care fellows. We, therefore, investigated whether critical care fellows achieve higher scores on standardized testing and report higher satisfaction after participating in a hands-on tutorial combined with GSDL compared to self-directed learning alone. Methods: First-year Pulmonary and Critical Care Medicine (PCCM fellows (n=6 and Critical Care Internal Medicine (CCIM (n=8 fellows participated. Satisfaction was assessed using the Likert scale. MV knowledge assessment was performed by administering a standardized 25-question multiple choice pre- and posttest. For 2 weeks the CCIM fellows were exposed to GSDL, while the PCCM fellows received hands-on tutoring combined with GSDL. Results: Ninety-three percentage (6 PCCM and 7 CCIM fellows, total of 13 fellows completed all evaluations and were included in the final analysis. CCIM and PCCM fellows scored similarly in the pretest (64% vs. 52%, p=0.13. Following interventions, the posttest scores increased in both groups. However, no significant difference was observed based on the interventions (74% vs. 77%, p=0.39. The absolute improvement with the hands-on-tutoring and GSDL group was higher than GSDL alone (25% vs. 10%, p=0.07. Improved satisfaction scores were noted with hands-on tutoring. Conclusions: Hands-on tutoring combined with GSDL and GSDL alone were both associated with an improvement in posttest scores. Absolute improvement in test and satisfaction scores both trended higher in the hands-on tutorial group combined with GSDL group.

Technology of Rock Destruction by Combined Explosion-Mechanical Load

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Oleg M. Terentiev

2017-10-01

Full Text Available Background. Rock drilling is characterized by an energy capacity of more than 120 kWh/m3. This is due to the fact that about 90 % of the energy is expended on the “preparation” of rocks for destruction. This study proposes to combine explosive and mechanical loads to reduce specific energy consumption of rock destruction. Objective. The aim of the paper is energy effective technology development for rock destruction by combined explosive-mechanical loads. Methods. Analytical studies; regression analysis; math modeling; experimental research; technical and economic analysis. Results. Specific energy decreasing for explosive-mechanical rock drilling by 4–16 % was experimentally proved. Conclusions. As a result of the implementation of explosive-mechanical rock drilling on the created full-sized experimental device, the efficiency coefficient increased from 77 to 80 %.

Development of radiation oncology learning system combined with multi-institutional radiotherapy database (ROGAD)

International Nuclear Information System (INIS)

Takemura, Akihiro; Iinuma, Masahiro; Kou, Hiroko; Harauchi, Hajime; Inamura, Kiyonari

1999-01-01

We have constructed and are operating a multi-institutional radiotherapy database ROGAD (Radiation Oncology Greater Area Database) since 1992. One of it's purpose is 'to optimize individual radiotherapy plans'. We developed Radiation oncology learning system combined with ROGAD' which conforms to that purpose. Several medical doctors evaluated our system. According to those evaluations, we are now confident that our system is able to contribute to improvement of radiotherapy results. Our final target is to generate a good cyclic relationship among three components: radiotherapy results according to ''Radiation oncology learning system combined with ROGAD.'; The growth of ROGAD; and radiation oncology learning system. (author)

A Blended Learning Approach to Teach Fluid Mechanics in Engineering

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Rahman, Ataur

2017-01-01

This paper presents a case study on the teaching and learning of fluid mechanics at the University of Western Sydney (UWS), Australia, by applying a blended learning approach (BLA). In the adopted BLA, various flexible learning materials have been made available to the students such as online recorded lectures, online recorded tutorials, hand…

Application of ICT supported learning in fluid mechanics

DEFF Research Database (Denmark)

Brohus, Henrik; Svidt, Kjeld

2004-01-01

of tools for knowledge transfer facilitates deep understanding and increases learning efficiency. Air flow is by nature invisible and represents a further challenge in the effort of providing sufficient understanding of typical flow patterns and behaviour of room air flow. An example of visualisation......This paper focuses on the application of ICT, Information & Communication Technology, supported learning in the area of fluid mechanics education. Taking a starting point in a course in Ventilation Technology, including room air flow and contaminant distribution, it explains how ICT may be used...... actively in the learning environment to increase efficiency in the learning process. The paper comprises past experiences and lessons learnt as well as prospect for future development in the area. A model is presented that describes a high efficiency learning environment where ICT plays an important role...

Time to rethink the neural mechanisms of learning and memory.

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Gallistel, Charles R; Balsam, Peter D

2014-02-01

Most studies in the neurobiology of learning assume that the underlying learning process is a pairing - dependent change in synaptic strength that requires repeated experience of events presented in close temporal contiguity. However, much learning is rapid and does not depend on temporal contiguity, which has never been precisely defined. These points are well illustrated by studies showing that the temporal relations between events are rapidly learned- even over long delays- and that this knowledge governs the form and timing of behavior. The speed with which anticipatory responses emerge in conditioning paradigms is determined by the information that cues provide about the timing of rewards. The challenge for understanding the neurobiology of learning is to understand the mechanisms in the nervous system that encode information from even a single experience, the nature of the memory mechanisms that can encode quantities such as time, and how the brain can flexibly perform computations based on this information. Copyright © 2013 Elsevier Inc. All rights reserved.

Statistical learning: a powerful mechanism that operates by mere exposure.

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Aslin, Richard N

2017-01-01

How do infants learn so rapidly and with little apparent effort? In 1996, Saffran, Aslin, and Newport reported that 8-month-old human infants could learn the underlying temporal structure of a stream of speech syllables after only 2 min of passive listening. This demonstration of what was called statistical learning, involving no instruction, reinforcement, or feedback, led to dozens of confirmations of this powerful mechanism of implicit learning in a variety of modalities, domains, and species. These findings reveal that infants are not nearly as dependent on explicit forms of instruction as we might have assumed from studies of learning in which children or adults are taught facts such as math or problem solving skills. Instead, at least in some domains, infants soak up the information around them by mere exposure. Learning and development in these domains thus appear to occur automatically and with little active involvement by an instructor (parent or teacher). The details of this statistical learning mechanism are discussed, including how exposure to specific types of information can, under some circumstances, generalize to never-before-observed information, thereby enabling transfer of learning. WIREs Cogn Sci 2017, 8:e1373. doi: 10.1002/wcs.1373 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

Image Denoising Algorithm Combined with SGK Dictionary Learning and Principal Component Analysis Noise Estimation

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Wenjing Zhao

2018-01-01

Full Text Available SGK (sequential generalization of K-means dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental results show the following: (1 The SGK algorithm has the best denoising performance compared with the other three dictionary learning algorithms. (2 The SGK algorithm combined with PCA is superior to the SGK algorithm combined with other noise estimation algorithms. (3 Compared with the original SGK algorithm, the proposed algorithm has higher PSNR and better denoising performance.

Combined Treatment With Environmental Enrichment and (-)-Epigallocatechin-3-Gallate Ameliorates Learning Deficits and Hippocampal Alterations in a Mouse Model of Down Syndrome.

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Catuara-Solarz, Silvina; Espinosa-Carrasco, Jose; Erb, Ionas; Langohr, Klaus; Gonzalez, Juan Ramon; Notredame, Cedric; Dierssen, Mara

2016-01-01

Intellectual disability in Down syndrome (DS) is accompanied by altered neuro-architecture, deficient synaptic plasticity, and excitation-inhibition imbalance in critical brain regions for learning and memory. Recently, we have demonstrated beneficial effects of a combined treatment with green tea extract containing (-)-epigallocatechin-3-gallate (EGCG) and cognitive stimulation in young adult DS individuals. Although we could reproduce the cognitive-enhancing effects in mouse models, the underlying mechanisms of these beneficial effects are unknown. Here, we explored the effects of a combined therapy with environmental enrichment (EE) and EGCG in the Ts65Dn mouse model of DS at young age. Our results show that combined EE-EGCG treatment improved corticohippocampal-dependent learning and memory. Cognitive improvements were accompanied by a rescue of cornu ammonis 1 (CA1) dendritic spine density and a normalization of the proportion of excitatory and inhibitory synaptic markers in CA1 and dentate gyrus.

Development of radiation oncology learning system combined with multi-institutional radiotherapy database (ROGAD)

Energy Technology Data Exchange (ETDEWEB)

Takemura, Akihiro; Iinuma, Masahiro; Kou, Hiroko [Kanazawa Univ. (Japan). School of Medicine; Harauchi, Hajime; Inamura, Kiyonari

1999-09-01

We have constructed and are operating a multi-institutional radiotherapy database ROGAD (Radiation Oncology Greater Area Database) since 1992. One of it's purpose is 'to optimize individual radiotherapy plans'. We developed Radiation oncology learning system combined with ROGAD' which conforms to that purpose. Several medical doctors evaluated our system. According to those evaluations, we are now confident that our system is able to contribute to improvement of radiotherapy results. Our final target is to generate a good cyclic relationship among three components: radiotherapy results according to ''Radiation oncology learning system combined with ROGAD.'; The growth of ROGAD; and radiation oncology learning system. (author)

Mechanisms underlying the social enhancement of vocal learning in songbirds.

Science.gov (United States)

Chen, Yining; Matheson, Laura E; Sakata, Jon T

2016-06-14

Social processes profoundly influence speech and language acquisition. Despite the importance of social influences, little is known about how social interactions modulate vocal learning. Like humans, songbirds learn their vocalizations during development, and they provide an excellent opportunity to reveal mechanisms of social influences on vocal learning. Using yoked experimental designs, we demonstrate that social interactions with adult tutors for as little as 1 d significantly enhanced vocal learning. Social influences on attention to song seemed central to the social enhancement of learning because socially tutored birds were more attentive to the tutor's songs than passively tutored birds, and because variation in attentiveness and in the social modulation of attention significantly predicted variation in vocal learning. Attention to song was influenced by both the nature and amount of tutor song: Pupils paid more attention to songs that tutors directed at them and to tutors that produced fewer songs. Tutors altered their song structure when directing songs at pupils in a manner that resembled how humans alter their vocalizations when speaking to infants, that was distinct from how tutors changed their songs when singing to females, and that could influence attention and learning. Furthermore, social interactions that rapidly enhanced learning increased the activity of noradrenergic and dopaminergic midbrain neurons. These data highlight striking parallels between humans and songbirds in the social modulation of vocal learning and suggest that social influences on attention and midbrain circuitry could represent shared mechanisms underlying the social modulation of vocal learning.

Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations.

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Wu, Jingheng; Shen, Lin; Yang, Weitao

2017-10-28

Ab initio quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulation is a useful tool to calculate thermodynamic properties such as potential of mean force for chemical reactions but intensely time consuming. In this paper, we developed a new method using the internal force correction for low-level semiempirical QM/MM molecular dynamics samplings with a predefined reaction coordinate. As a correction term, the internal force was predicted with a machine learning scheme, which provides a sophisticated force field, and added to the atomic forces on the reaction coordinate related atoms at each integration step. We applied this method to two reactions in aqueous solution and reproduced potentials of mean force at the ab initio QM/MM level. The saving in computational cost is about 2 orders of magnitude. The present work reveals great potentials for machine learning in QM/MM simulations to study complex chemical processes.

Dual mechanisms governing reward-driven perceptual learning [version 1; referees: 2 approved

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Dongho Kim

2015-09-01

Full Text Available In this review, we explore how reward signals shape perceptual learning in animals and humans. Perceptual learning is the well-established phenomenon by which extensive practice elicits selective improvement in one’s perceptual discrimination of basic visual features, such as oriented lines or moving stimuli. While perceptual learning has long been thought to rely on ‘top-down’ processes, such as attention and decision-making, a wave of recent findings suggests that these higher-level processes are, in fact, not necessary.  Rather, these recent findings indicate that reward signals alone, in the absence of the contribution of higher-level cognitive processes, are sufficient to drive the benefits of perceptual learning. Here, we will review the literature tying reward signals to perceptual learning. Based on these findings, we propose dual underlying mechanisms that give rise to perceptual learning: one mechanism that operates ‘automatically’ and is tied directly to reward signals, and another mechanism that involves more ‘top-down’, goal-directed computations.

Use of the 5E learning cycle model combined with problem-based learning for a fundamentals of nursing course.

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Jun, Won Hee; Lee, Eun Ju; Park, Han Jong; Chang, Ae Kyung; Kim, Mi Ja

2013-12-01

The 5E learning cycle model has shown a positive effect on student learning in science education, particularly in courses with theory and practice components. Combining problem-based learning (PBL) with the 5E learning cycle was suggested as a better option for students' learning of theory and practice. The purpose of this study was to compare the effects of the traditional learning method with the 5E learning cycle model with PBL. The control group (n = 78) was subjected to a learning method that consisted of lecture and practice. The experimental group (n = 83) learned by using the 5E learning cycle model with PBL. The results showed that the experimental group had significantly improved self-efficacy, critical thinking, learning attitude, and learning satisfaction. Such an approach could be used in other countries to enhance students' learning of fundamental nursing. Copyright 2013, SLACK Incorporated.

Combining deep learning and satellite data to inform sustainable development

Science.gov (United States)

Lobell, D. B.

2017-12-01

Methods in machine learning, and in particular deep learning, are quickly advancing, in parallel with dramatic increases in the availability of fine resolution satellite data. The combination of both offers the possibility to improve understanding of some of the poorest regions of the world, where traditional data sources are limited. This talk will cover recent applications to track poverty at the village level in Africa, spot the onset of disease outbreaks in agriculture, and identify land use patterns and crop productivity.

Interpreting Students’ Perceptions in Fluid Mechanics Learning Outcomes

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Filomena SOARES

2015-11-01

Full Text Available The objective of this study is to analyse the impact of introducing a practical work in the learning process of the Fluid Transport Systems course in Chemical Engineering degree. The students, in groups of two or three elements, were free to choose the application case in order to develop the practical work proposed by the responsible teachers. The students selected a centrifugal pump to supply water to houses or buildings and designed the piping system. The practical work was evaluated through the written report. The students’ perceptions were analysed through a questionnaire. The learning outcomes were also considered in order to understand how the fluid mechanics concepts were acquired. In the teachers’ point of view the teamwork should enable the development of students’ soft skills and competencies, promoting the ability to integrate and work in teams. The students changed their learning processing and perception becoming more reflective and less accommodative, forcing them to think critically and share opinions. Regarding the Fluid Mechanics assessment, the practical work increased, in average, the final grade at least one value.

Combining lived experience with the facilitation of enquiry-based learning: a 'trigger' for transformative learning.

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Stacey, G; Oxley, R; Aubeeluck, A

2015-09-01

What is known on the subject The values underpinning recovery-orientated practice are recited in the literature and influential in the content of mental health nurse education internationally. However, scepticism exists regarding the degree to which students' assimilate the principles of recovery into their practice due to the troublesome and challenging nature of learning at a transformational level, also known as threshold concept learning. Evaluation suggests that this combination of educational approaches positively influences students' prior understandings, beliefs and values in relation to the prospect for people with significant mental health problems to recover. The components of threshold concepts are useful as a deductive framework for the evaluation of educational initiatives which attempt to initiate transformative learning. While this forum clearly holds significant potential for student development, support and preparation is needed for both the student and the facilitator in order to enable the possibility of learning which influences attitudes, beliefs and practice. The aim of this paper is to discuss the potential for combining lived experience of mental distress with the facilitation of enquiry-based learning (EBL) to act as a trigger for transformative learning in the context of promoting the understanding of mental health 'recovery' in nurse education.The values underpinning recovery-orientated practice are recited in the literature and influential in mental health nurse education internationally. However, scepticism exists regarding the degree to which students assimilate into their practice. An open-ended was distributed to a cohort of pre-registration nursing students receiving the co-facilitated EBL (n = 112). Data demonstrated how the specific attributes of this educational approach were identified by students as impacting positively on ill-informed preconceptions, understanding of complex theory and their future practice. Results were

Nonassociative learning promotes respiratory entrainment to mechanical ventilation.

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Shawna M MacDonald

Full Text Available BACKGROUND: Patient-ventilator synchrony is a major concern in critical care and is influenced by phasic lung-volume feedback control of the respiratory rhythm. Routine clinical application of positive end-expiratory pressure (PEEP introduces a tonic input which, if unopposed, might disrupt respiratory-ventilator entrainment through sustained activation of the vagally-mediated Hering-Breuer reflex. We suggest that this potential adverse effect may be averted by two differentiator forms of nonassociative learning (habituation and desensitization of the Hering-Breuer reflex via pontomedullary pathways. METHODOLOGY/PRINCIPAL FINDINGS: We tested these hypotheses in 17 urethane-anesthetized adult Sprague-Dawley rats under controlled mechanical ventilation. Without PEEP, phrenic discharge was entrained 1:1 to the ventilator rhythm. Application of PEEP momentarily dampened the entrainment to higher ratios but this effect was gradually adapted by nonassociative learning. Bilateral electrolytic lesions of the pneumotaxic center weakened the adaptation to PEEP, whereas sustained stimulation of the pneumotaxic center weakened the entrainment independent of PEEP. In all cases, entrainment was abolished after vagotomy. CONCLUSIONS/SIGNIFICANCE: Our results demonstrate an important functional role for pneumotaxic desensitization and extra-pontine habituation of the Hering-Breuer reflex elicited by lung inflation: acting as buffers or high-pass filters against tonic vagal volume input, these differentiator forms of nonassociative learning help to restore respiratory-ventilator entrainment in the face of PEEP. Such central sites-specific habituation and desensitization of the Hering-Breuer reflex provide a useful experimental model of nonassociative learning in mammals that is of particular significance in understanding respiratory rhythmogenesis and coupled-oscillator entrainment mechanisms, and in the clinical management of mechanical ventilation in

Statistical mechanics of learning orthogonal signals for general covariance models

International Nuclear Information System (INIS)

Hoyle, David C

2010-01-01

Statistical mechanics techniques have proved to be useful tools in quantifying the accuracy with which signal vectors are extracted from experimental data. However, analysis has previously been limited to specific model forms for the population covariance C, which may be inappropriate for real world data sets. In this paper we obtain new statistical mechanical results for a general population covariance matrix C. For data sets consisting of p sample points in R N we use the replica method to study the accuracy of orthogonal signal vectors estimated from the sample data. In the asymptotic limit of N,p→∞ at fixed α = p/N, we derive analytical results for the signal direction learning curves. In the asymptotic limit the learning curves follow a single universal form, each displaying a retarded learning transition. An explicit formula for the location of the retarded learning transition is obtained and we find marked variation in the location of the retarded learning transition dependent on the distribution of population covariance eigenvalues. The results of the replica analysis are confirmed against simulation

Improving students' meaningful learning on the predictive nature of quantum mechanics

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Rodolfo Alves de Carvalho Neto

2009-03-01

Full Text Available This paper deals with research about teaching quantum mechanics to 3rd year high school students and their meaningful learning of its predictive aspect; it is based on the Master’s dissertation of one of the authors (CARVALHO NETO, 2006. While teaching quantum mechanics, we emphasized its predictive and essentially probabilistic nature, based on Niels Bohr’s complementarity interpretation (BOHR, 1958. In this context, we have discussed the possibility of predicting measurement results in well-defined experimental contexts, even for individual events. Interviews with students reveal that they have used quantum mechanical ideas, suggesting their meaningful learning of the essentially probabilistic predictions of quantum mechanics.

Learner Characteristic Based Learning Effort Curve Mode: The Core Mechanism on Developing Personalized Adaptive E-Learning Platform

Science.gov (United States)

Hsu, Pi-Shan

2012-01-01

This study aims to develop the core mechanism for realizing the development of personalized adaptive e-learning platform, which is based on the previous learning effort curve research and takes into account the learner characteristics of learning style and self-efficacy. 125 university students from Taiwan are classified into 16 groups according…

Selective social learning in infancy: looking for mechanisms.

Science.gov (United States)

Crivello, Cristina; Phillips, Sara; Poulin-Dubois, Diane

2018-05-01

Although there is mounting evidence that selective social learning begins in infancy, the psychological mechanisms underlying this ability are currently a controversial issue. The purpose of this study is to investigate whether theory of mind abilities and statistical learning skills are related to infants' selective social learning. Seventy-seven 18-month-olds were first exposed to a reliable or an unreliable speaker and then completed a word learning task, two theory of mind tasks, and a statistical learning task. If domain-general abilities are linked to selective social learning, then infants who demonstrate superior performance on the statistical learning task should perform better on the selective learning task, that is, should be less likely to learn words from an unreliable speaker. Alternatively, if domain-specific abilities are involved, then superior performance on theory of mind tasks should be related to selective learning performance. Findings revealed that, as expected, infants were more likely to learn a novel word from a reliable speaker. Importantly, infants who passed a theory of mind task assessing knowledge attribution were significantly less likely to learn a novel word from an unreliable speaker compared to infants who failed this task. No such effect was observed for the other tasks. These results suggest that infants who possess superior social-cognitive abilities are more apt to reject an unreliable speaker as informant. A video abstract of this article can be viewed at: https://youtu.be/zuuCniHYzqo. © 2017 John Wiley & Sons Ltd.

Jointly Feature Learning and Selection for Robust Tracking via a Gating Mechanism.

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Bineng Zhong

Full Text Available To achieve effective visual tracking, a robust feature representation composed of two separate components (i.e., feature learning and selection for an object is one of the key issues. Typically, a common assumption used in visual tracking is that the raw video sequences are clear, while real-world data is with significant noise and irrelevant patterns. Consequently, the learned features may be not all relevant and noisy. To address this problem, we propose a novel visual tracking method via a point-wise gated convolutional deep network (CPGDN that jointly performs the feature learning and feature selection in a unified framework. The proposed method performs dynamic feature selection on raw features through a gating mechanism. Therefore, the proposed method can adaptively focus on the task-relevant patterns (i.e., a target object, while ignoring the task-irrelevant patterns (i.e., the surrounding background of a target object. Specifically, inspired by transfer learning, we firstly pre-train an object appearance model offline to learn generic image features and then transfer rich feature hierarchies from an offline pre-trained CPGDN into online tracking. In online tracking, the pre-trained CPGDN model is fine-tuned to adapt to the tracking specific objects. Finally, to alleviate the tracker drifting problem, inspired by an observation that a visual target should be an object rather than not, we combine an edge box-based object proposal method to further improve the tracking accuracy. Extensive evaluation on the widely used CVPR2013 tracking benchmark validates the robustness and effectiveness of the proposed method.

Associative Mechanisms Allow for Social Learning and Cultural Transmission of String Pulling in an Insect

Science.gov (United States)

Zhu, Xingfu; Ingraham, Thomas; Søvik, Eirik

2016-01-01

Social insects make elaborate use of simple mechanisms to achieve seemingly complex behavior and may thus provide a unique resource to discover the basic cognitive elements required for culture, i.e., group-specific behaviors that spread from “innovators” to others in the group via social learning. We first explored whether bumblebees can learn a nonnatural object manipulation task by using string pulling to access a reward that was presented out of reach. Only a small minority “innovated” and solved the task spontaneously, but most bees were able to learn to pull a string when trained in a stepwise manner. In addition, naïve bees learnt the task by observing a trained demonstrator from a distance. Learning the behavior relied on a combination of simple associative mechanisms and trial-and-error learning and did not require “insight”: naïve bees failed a “coiled-string experiment,” in which they did not receive instant visual feedback of the target moving closer when tugging on the string. In cultural diffusion experiments, the skill spread rapidly from a single knowledgeable individual to the majority of a colony’s foragers. We observed that there were several sequential sets (“generations”) of learners, so that previously naïve observers could first acquire the technique by interacting with skilled individuals and, subsequently, themselves become demonstrators for the next “generation” of learners, so that the longevity of the skill in the population could outlast the lives of informed foragers. This suggests that, so long as animals have a basic toolkit of associative and motor learning processes, the key ingredients for the cultural spread of unusual skills are already in place and do not require sophisticated cognition. PMID:27701411

Combination of biochemical and mechanical cues for tendon tissue engineering.

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Testa, Stefano; Costantini, Marco; Fornetti, Ersilia; Bernardini, Sergio; Trombetta, Marcella; Seliktar, Dror; Cannata, Stefano; Rainer, Alberto; Gargioli, Cesare

2017-11-01

Tendinopathies negatively affect the life quality of millions of people in occupational and athletic settings, as well as the general population. Tendon healing is a slow process, often with insufficient results to restore complete endurance and functionality of the tissue. Tissue engineering, using tendon progenitors, artificial matrices and bioreactors for mechanical stimulation, could be an important approach for treating rips, fraying and tissue rupture. In our work, C3H10T1/2 murine fibroblast cell line was exposed to a combination of stimuli: a biochemical stimulus provided by Transforming Growth Factor Beta (TGF-β) and Ascorbic Acid (AA); a three-dimensional environment represented by PEGylated-Fibrinogen (PEG-Fibrinogen) biomimetic matrix; and a mechanical induction exploiting a custom bioreactor applying uniaxial stretching. In vitro analyses by immunofluorescence and mechanical testing revealed that the proposed combined approach favours the organization of a three-dimensional tissue-like structure promoting a remarkable arrangement of the cells and the neo-extracellular matrix, reflecting into enhanced mechanical strength. The proposed method represents a novel approach for tendon tissue engineering, demonstrating how the combined effect of biochemical and mechanical stimuli ameliorates biological and mechanical properties of the artificial tissue compared to those obtained with single inducement. © 2017 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

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Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

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Yi Zhang

2015-01-01

Full Text Available Maximum likelihood classifier (MLC and support vector machines (SVM are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

[Learning and implicit memory: mechanisms and neuroplasticity].

Science.gov (United States)

Machado, S; Portella, C E; Silva, J G; Velasques, B; Bastos, V H; Cunha, M; Basile, L; Cagy, M; Piedade, R A; Ribeiro, P

Learning and memory are complex processes that researchers have been attempting to unravel for over a century in order to gain a clear view of the underlying mechanisms. To review the basic cellular and molecular mechanisms involved in the process of procedural retention, to offer an overall view of the fundamental mechanisms involved in storing information by means of theories and models of memory, and to discuss the different types of memory and the role played by the cerebellum as a modulator of procedural memory. Experimental results from recent decades have opened up new areas of study regarding the participation of the biochemical and cellular processes related to the consolidation of information in the nervous system. The neuronal circuits involved in acquiring and consolidating memory are still not fully understood and the exact location of memory in the nervous system remains unknown. A number of intrinsic and extrinsic factors interfere in these processes, such as molecular (long-term potentiation and depression) and cellular mechanisms, which respond to communication and transmission between nerve cells. There are also factors that have their origin in the outside environment, which use the association of events to bring about the formation of new memories or may divert the subject from his or her main focus. Memory is not a singular occurrence; it is sub-divided into declarative and non-declarative or, when talking about the time it lasts, into short and long-term memory. Moreover, given its relation with neuronal mechanisms of learning, memory cannot be said to constitute an isolated process.

Statistical-Mechanical Analysis of Pre-training and Fine Tuning in Deep Learning

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Ohzeki, Masayuki

2015-03-01

In this paper, we present a statistical-mechanical analysis of deep learning. We elucidate some of the essential components of deep learning — pre-training by unsupervised learning and fine tuning by supervised learning. We formulate the extraction of features from the training data as a margin criterion in a high-dimensional feature-vector space. The self-organized classifier is then supplied with small amounts of labelled data, as in deep learning. Although we employ a simple single-layer perceptron model, rather than directly analyzing a multi-layer neural network, we find a nontrivial phase transition that is dependent on the number of unlabelled data in the generalization error of the resultant classifier. In this sense, we evaluate the efficacy of the unsupervised learning component of deep learning. The analysis is performed by the replica method, which is a sophisticated tool in statistical mechanics. We validate our result in the manner of deep learning, using a simple iterative algorithm to learn the weight vector on the basis of belief propagation.

Development and validation of a reduced combined biodiesel–diesel reaction mechanism

DEFF Research Database (Denmark)

Ng, Hoon Kiat; Gan, Suyin; Ng, Jo-Han

2013-01-01

In this study, a compact combined biodiesel–diesel (CBD) reaction mechanism for diesel engine simulations is proposed through the combination of three component mechanisms using a chemical class-based approach. The proposed mechanism comprises the reaction mechanisms of methyl crotonate (MC...... to characterise the combustion of fossil diesel. Here, the MC and MB mechanisms are reduced before integrating with a compact n-heptane mechanism. CHEMKIN-PRO is used as the solver for the zero-dimensional, closed homogenous reactor with a constant volume in this study. In the first phase, the mechanisms of MC...... ranging from initial temperatures of 750–1350 K, pressures of 40–60 bar and equivalence ratios of 0.4–1.5. The mechanism is generally found to accurately predict the timing and duration of ID for the combustion of each surrogate fuel. This model is also shown to be feasible for use with multidimensional...

Learning as discourse change: A sociocultural mechanism

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Wickman, Per-Olof; Östman, Leif

2002-09-01

This paper deals with a theoretical mechanism for learning and a methodological approach for analyzing meaning making in classroom talk and action. It examines the potential of the approach for illuminating learning on a discursive level, i.e., how discourses change and how individuals become participants of new practices. Our approach involves a high-resolution analysis of how meaningful relations are built in encounters between individuals and between individuals and the world. The approach is based mainly on the work of the later Wittgenstein, but also on pragmatism and sociocultural research. To demonstrate how our approach can be used, we analyze what university students learn during a practical on insects. We specifically demonstrate how the encounters with physical pinned insects contribute to the meaning students make and how these encounters interact with other experiences during laboratory work.

Thermo-mechanical properties of SOFC components investigated by a combined method

DEFF Research Database (Denmark)

Teocoli, Francesca; Esposito, Vincenzo; Ramousse, Severine

, and differential thermo-mechanical behavior at each layer. The combination of such factors can have a critical effect on the final shape and microstructure, and on the mechanical integrity. Thermo-mechanical properties and sintering mechanisms of important SOFC materials (CGO, YSZ, ScYSZ) were systematically...

Studying the mechanisms of language learning by varying the learning environment and the learner.

Science.gov (United States)

Goldin-Meadow, Susan

Language learning is a resilient process, and many linguistic properties can be developed under a wide range of learning environments and learners. The first goal of this review is to describe properties of language that can be developed without exposure to a language model - the resilient properties of language - and to explore conditions under which more fragile properties emerge. But even if a linguistic property is resilient, the developmental course that the property follows is likely to vary as a function of learning environment and learner, that is, there are likely to be individual differences in the learning trajectories children follow. The second goal is to consider how the resilient properties are brought to bear on language learning when a child is exposed to a language model. The review ends by considering the implications of both sets of findings for mechanisms, focusing on the role that the body and linguistic input play in language learning.

Learning Similar Actions by Reinforcement or Sensory-Prediction Errors Rely on Distinct Physiological Mechanisms.

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Uehara, Shintaro; Mawase, Firas; Celnik, Pablo

2017-09-14

Humans can acquire knowledge of new motor behavior via different forms of learning. The two forms most commonly studied have been the development of internal models based on sensory-prediction errors (error-based learning) and success-based feedback (reinforcement learning). Human behavioral studies suggest these are distinct learning processes, though the neurophysiological mechanisms that are involved have not been characterized. Here, we evaluated physiological markers from the cerebellum and the primary motor cortex (M1) using noninvasive brain stimulations while healthy participants trained finger-reaching tasks. We manipulated the extent to which subjects rely on error-based or reinforcement by providing either vector or binary feedback about task performance. Our results demonstrated a double dissociation where learning the task mainly via error-based mechanisms leads to cerebellar plasticity modifications but not long-term potentiation (LTP)-like plasticity changes in M1; while learning a similar action via reinforcement mechanisms elicited M1 LTP-like plasticity but not cerebellar plasticity changes. Our findings indicate that learning complex motor behavior is mediated by the interplay of different forms of learning, weighing distinct neural mechanisms in M1 and the cerebellum. Our study provides insights for designing effective interventions to enhance human motor learning. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Function approximation using combined unsupervised and supervised learning.

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Andras, Peter

2014-03-01

Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimensional manifold. Here we propose the breaking of the function approximation task for high-dimensional data into two steps: (1) the mapping of the high-dimensional data onto a lower dimensional space corresponding to the manifold on which the data resides and (2) the approximation of the function using the mapped lower dimensional data. We use over-complete self-organizing maps (SOMs) for the mapping through unsupervised learning, and single hidden layer neural networks for the function approximation through supervised learning. We also extend the two-step procedure by considering support vector machines and Bayesian SOMs for the determination of the best parameters for the nonlinear neurons in the hidden layer of the neural networks used for the function approximation. We compare the approximation performance of the proposed neural networks using a set of functions and show that indeed the neural networks using combined unsupervised and supervised learning outperform in most cases the neural networks that learn the function approximation using the original high-dimensional data.

Cooperative Learning in a Soil Mechanics Course at Undergraduate Level

Science.gov (United States)

Pinho-Lopes, M.; Macedo, J.; Bonito, F.

2011-01-01

The implementation of the Bologna Process enforced a significant change on traditional learning models, which were focused mainly on the transmission of knowledge. The results obtained in a first attempt at implementation of a cooperative learning model in the Soil Mechanics I course of the Department of Civil Engineering of the University of…

Implicit and Explicit Learning Mechanisms Meet in Monkey Prefrontal Cortex.

Science.gov (United States)

Chafee, Matthew V; Crowe, David A

2017-10-11

In this issue, Loonis et al. (2017) provide the first description of unique synchrony patterns differentiating implicit and explicit forms of learning in monkey prefrontal networks. Their results have broad implications for how prefrontal networks integrate the two learning mechanisms to control behavior. Copyright © 2017 Elsevier Inc. All rights reserved.

A hypothesis on a role of oxytocin in the social mechanisms of speech and vocal learning.

Science.gov (United States)

Theofanopoulou, Constantina; Boeckx, Cedric; Jarvis, Erich D

2017-08-30

Language acquisition in humans and song learning in songbirds naturally happen as a social learning experience, providing an excellent opportunity to reveal social motivation and reward mechanisms that boost sensorimotor learning. Our knowledge about the molecules and circuits that control these social mechanisms for vocal learning and language is limited. Here we propose a hypothesis of a role for oxytocin (OT) in the social motivation and evolution of vocal learning and language. Building upon existing evidence, we suggest specific neural pathways and mechanisms through which OT might modulate vocal learning circuits in specific developmental stages. © 2017 The Authors.

Combining multi agent paradigm and memetic computing for personalized and adaptive learning experiences

NARCIS (Netherlands)

Acampora, G.; Gaeta, M.; Loia, V.

2011-01-01

Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering

On Combining Elements of Different Ways of Learning, Methods and Knowledge

Directory of Open Access Journals (Sweden)

Dušana Findeisen

2013-12-01

Full Text Available The paper deals with different thinkers' attitude towards methods in adult education. It examines the value of some elements of »trial and error learning« and »non-directive learning«. Like a multifaceted approach based on elements drawn from different methods, the way we learn can also be eclectic.  To illustrate this assertion, the author analyses the »anti method« used by Maurice Pialat, a French film director, contrasting it with methods in which the aim is set in advance and the process leading towards it is organised in sequences. This is most often the case in script-based shooting of films, directing a theatre performance or running adult education. Moreover, the author argues that learning about how to do something is combined with learning about how to be. She further emphasises that methods should not be used to impose one’s knowledge and one’s reality on the learner, thus destroying circumstances necessary for gaining or creating knowledge.

Value innovation, deliberate learning mechanisms and information from supply chain partners

NARCIS (Netherlands)

Berghman, L.A.; Matthyssens, P.; Vandenbempt, K.

2012-01-01

Although marketing scholars have emphasized both the importance of internal learning mechanisms and of external learning through supply chain partners research findings on how these factors influence each other are merely lacking. Analyzing survey data of 182 industrial firms, we examine how

Personal computer versus personal computer/mobile device combination users' preclinical laboratory e-learning activity.

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Kon, Haruka; Kobayashi, Hiroshi; Sakurai, Naoki; Watanabe, Kiyoshi; Yamaga, Yoshiro; Ono, Takahiro

2017-11-01

The aim of the present study was to clarify differences between personal computer (PC)/mobile device combination and PC-only user patterns. We analyzed access frequency and time spent on a complete denture preclinical website in order to maximize website effectiveness. Fourth-year undergraduate students (N=41) in the preclinical complete denture laboratory course were invited to participate in this survey during the final week of the course to track login data. Students accessed video demonstrations and quizzes via our e-learning site/course program, and were instructed to view online demonstrations before classes. When the course concluded, participating students filled out a questionnaire about the program, their opinions, and devices they had used to access the site. Combination user access was significantly more frequent than PC-only during supplementary learning time, indicating that students with mobile devices studied during lunch breaks and before morning classes. Most students had favorable opinions of the e-learning site, but a few combination users commented that some videos were too long and that descriptive answers were difficult on smartphones. These results imply that mobile devices' increased accessibility encouraged learning by enabling more efficient time use between classes. They also suggest that e-learning system improvements should cater to mobile device users by reducing video length and including more short-answer questions. © 2016 John Wiley & Sons Australia, Ltd.

Imitation Learning Based on an Intrinsic Motivation Mechanism for Efficient Coding

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Jochen eTriesch

2013-11-01

Full Text Available A hypothesis regarding the development of imitation learning is presented that is rooted in intrinsic motivations. It is derived from a recently proposed form of intrinsically motivated learning (IML for efficient coding in active perception, wherein an agent learns to perform actions with its sense organs to facilitate efficient encoding of the sensory data. To this end, actions of the sense organs that improve the encoding of the sensory data trigger an internally generated reinforcement signal. Here it is argued that the same IML mechanism might also support the development of imitation when general actions beyond those of the sense organs are considered: The learner first observes a tutor performing a behavior and learns a model of the the behavior's sensory consequences. The learner then acts itself and receives an internally generated reinforcement signal reflecting how well the sensory consequences of its own behavior are encoded by the sensory model. Actions that are more similar to those of the tutor will lead to sensory signals that are easier to encode and produce a higher reinforcement signal. Through this, the learner's behavior is progressively tuned to make the sensory consequences of its actions match the learned sensory model. I discuss this mechanism in the context of human language acquisition and bird song learning where similar ideas have been proposed. The suggested mechanism also offers an account for the development of mirror neurons and makes a number of predictions. Overall, it establishes a connection between principles of efficient coding, intrinsic motivations and imitation.

The Application of Problem-Based Learning in Mechanical Engineering

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Putra, Z. A.; Dewi, M.

2018-02-01

The course of Technology and Material Testing prepare students with the ability to do a variety of material testing in the study of mechanical engineering. Students find it difficult to understand the materials to make them unable to carry out the material testing in accordance with the purpose of study. This happens because they knowledge is not adequately supported by the competence to find and construct learning experience. In this study, quasy experiment research method with pre-post-test with control group design was used. The subjects of the study were students divided in two groups; control and experiment with twenty-two students in each group. Study result: their grades showed no difference in between the pre-test or post-test in control group, but the difference in grade existed between the pre-test and post-test in experiment group. Yet, there is no significant difference in the study result on both groups. The researcher recommend that it is necessary to develop Problem-Based Learning that suits need analysis on D3 Program for Mechanical Engineering Department at the State University of Padang, to ensure the compatibility between Model of Study and problems and need. This study aims to analyze how Problem-Based Learning effects on the course of Technology and Material Testing for the students of D3 Program of Mechanical Engineering of the State University of Padang.

Motor learning in childhood reveals distinct mechanisms for memory retention and re-learning.

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Musselman, Kristin E; Roemmich, Ryan T; Garrett, Ben; Bastian, Amy J

2016-05-01

Adults can easily learn and access multiple versions of the same motor skill adapted for different conditions (e.g., walking in water, sand, snow). Following even a single session of adaptation, adults exhibit clear day-to-day retention and faster re-learning of the adapted pattern. Here, we studied the retention and re-learning of an adapted walking pattern in children aged 6-17 yr. We found that all children, regardless of age, showed adult-like patterns of retention of the adapted walking pattern. In contrast, children under 12 yr of age did not re-learn faster on the next day after washout had occurred-they behaved as if they had never adapted their walking before. Re-learning could be improved in younger children when the adaptation time on day 1 was increased to allow more practice at the plateau of the adapted pattern, but never to adult-like levels. These results show that the ability to store a separate, adapted version of the same general motor pattern does not fully develop until adolescence, and furthermore, that the mechanisms underlying the retention and rapid re-learning of adapted motor patterns are distinct. © 2016 Musselman et al.; Published by Cold Spring Harbor Laboratory Press.

The Effect of Contextual Teaching and Learning Combined with Peer Tutoring towards Learning Achievement on Human Digestive System Concept

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Farhah Abadiyah

2017-11-01

Full Text Available This research aims to know the influence of contextual teaching and learning (CTL combined with peer tutoring toward learning achievement on human digestive system concept. This research was conducted at one of State Senior High School in South Tangerang in the academic year of 2016/2017. The research method was quasi experiment with nonequivalent pretest-postest control group design. The sample was taken by simple random sampling. The total of the sampels were 86 students which consisted of 44 students as a controlled group and 42 students as an experimental group. The research instrument was objective test which consisted of 25 multiple choice items of each pretest and posttest. The research also used observation sheets for teacher and students activity. The result of data analysis using t-test on the two groups show that the value of tcount was 2.40 and ttable was 1.99 on significant level α = 0,05, so that tcount > ttable.. This result indicated that there was influence of contextual teaching and learning (CTL combined with peer tutoring toward learning achievement on human digestive system concept.

Combining Project-Based Learning and Community-Based Research in a Research Methodology Course: The Lessons Learned

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Arantes do Amaral, João Alberto; Lino dos Santos, Rebeca Júlia Rodrigues

2018-01-01

In this article, we present our findings regarding the course "Research Methodology," offered to 22 first-year undergraduate students studying Administration at the Federal University of São Paulo, Osasco, Brazil. The course, which combined community-based research and project-based learning, was developed during the second semester of…

Sensorimotor Learning: Neurocognitive Mechanisms and Individual Differences.

Science.gov (United States)

Seidler, R D; Carson, R G

2017-07-13

Here we provide an overview of findings and viewpoints on the mechanisms of sensorimotor learning presented at the 2016 Biomechanics and Neural Control of Movement (BANCOM) conference in Deer Creek, OH. This field has shown substantial growth in the past couple of decades. For example it is now well accepted that neural systems outside of primary motor pathways play a role in learning. Frontoparietal and anterior cingulate networks contribute to sensorimotor adaptation, reflecting strategic aspects of exploration and learning. Longer term training results in functional and morphological changes in primary motor and somatosensory cortices. Interestingly, re-engagement of strategic processes once a skill has become well learned may disrupt performance. Efforts to predict individual differences in learning rate have enhanced our understanding of the neural, behavioral, and genetic factors underlying skilled human performance. Access to genomic analyses has dramatically increased over the past several years. This has enhanced our understanding of cellular processes underlying the expression of human behavior, including involvement of various neurotransmitters, receptors, and enzymes. Surprisingly our field has been slow to adopt such approaches in studying neural control, although this work does require much larger sample sizes than are typically used to investigate skill learning. We advocate that individual differences approaches can lead to new insights into human sensorimotor performance. Moreover, a greater understanding of the factors underlying the wide range of performance capabilities seen across individuals can promote personalized medicine and refinement of rehabilitation strategies, which stand to be more effective than "one size fits all" treatments.

Developmental Changes in Learning: Computational Mechanisms and Social Influences

Directory of Open Access Journals (Sweden)

Florian Bolenz

2017-11-01

Full Text Available Our ability to learn from the outcomes of our actions and to adapt our decisions accordingly changes over the course of the human lifespan. In recent years, there has been an increasing interest in using computational models to understand developmental changes in learning and decision-making. Moreover, extensions of these models are currently applied to study socio-emotional influences on learning in different age groups, a topic that is of great relevance for applications in education and health psychology. In this article, we aim to provide an introduction to basic ideas underlying computational models of reinforcement learning and focus on parameters and model variants that might be of interest to developmental scientists. We then highlight recent attempts to use reinforcement learning models to study the influence of social information on learning across development. The aim of this review is to illustrate how computational models can be applied in developmental science, what they can add to our understanding of developmental mechanisms and how they can be used to bridge the gap between psychological and neurobiological theories of development.

An approach for investigation of secure access processes at a combined e-learning environment

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Romansky, Radi; Noninska, Irina

2017-12-01

The article discuses an approach to investigate processes for regulation the security and privacy control at a heterogenous e-learning environment realized as a combination of traditional and cloud means and tools. Authors' proposal for combined architecture of e-learning system is presented and main subsystems and procedures are discussed. A formalization of the processes for using different types resources (public, private internal and private external) is proposed. The apparatus of Markovian chains (MC) is used for modeling and analytical investigation of the secure access to the resources is used and some assessments are presented.

PHYSIOLOGICAL QUALITY OF SOYBEAN SEEDS UNDER MECHANICAL INJURIES CAUSED BY COMBINES

OpenAIRE

FÁBIO PALCZEWSKI PACHECO; LÚCIA HELENA PEREIRA NÓBREGA; GISLAINE PICOLLO DE LIMA; MÁRCIA SANTORUM; WALTER BOLLER; LORIVAN FORMIGHIERI

2015-01-01

The mechanical harvesting causes injuries on seeds and may affect their quality. Different threshing mechanisms and their adjustments may also affect the intensity of impacts that machines cause on seeds. So, this study aimed at diagnosing and evaluating the effect of two combines: the first one with a threshing system of axial flow and the other one with a threshing system of tangential flow, under adjustments of concave opening (10 mm, 30 mm and 10 mm for a combine with axial ...

Elemental representation and configural mappings: combining elemental and configural theories of associative learning.

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McLaren, I P L; Forrest, C L; McLaren, R P

2012-09-01

In this article, we present our first attempt at combining an elemental theory designed to model representation development in an associative system (based on McLaren, Kaye, & Mackintosh, 1989) with a configural theory that models associative learning and memory (McLaren, 1993). After considering the possible advantages of such a combination (and some possible pitfalls), we offer a hybrid model that allows both components to produce the phenomena that they are capable of without introducing unwanted interactions. We then successfully apply the model to a range of phenomena, including latent inhibition, perceptual learning, the Espinet effect, and first- and second-order retrospective revaluation. In some cases, we present new data for comparison with our model's predictions. In all cases, the model replicates the pattern observed in our experimental results. We conclude that this line of development is a promising one for arriving at general theories of associative learning and memory.

A new approach to teaching and learning mechanics

NARCIS (Netherlands)

Westra, A.S.

2006-01-01

In this thesis a research project is described that took place from 2000 until 2004 in the Centre for Science and Mathematics Education in Utrecht. It involves a didactical research into the teaching and learning of an introduction to mechanics for fourth grade pre-university level students (Dutch:

Atomistic insight into the catalytic mechanism of glycosyltransferases by combined quantum mechanics/molecular mechanics (QM/MM) methods.

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Tvaroška, Igor

2015-02-11

Glycosyltransferases catalyze the formation of glycosidic bonds by assisting the transfer of a sugar residue from donors to specific acceptor molecules. Although structural and kinetic data have provided insight into mechanistic strategies employed by these enzymes, molecular modeling studies are essential for the understanding of glycosyltransferase catalyzed reactions at the atomistic level. For such modeling, combined quantum mechanics/molecular mechanics (QM/MM) methods have emerged as crucial. These methods allow the modeling of enzymatic reactions by using quantum mechanical methods for the calculation of the electronic structure of the active site models and treating the remaining enzyme environment by faster molecular mechanics methods. Herein, the application of QM/MM methods to glycosyltransferase catalyzed reactions is reviewed, and the insight from modeling of glycosyl transfer into the mechanisms and transition states structures of both inverting and retaining glycosyltransferases are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

A method of meta-mechanism combination and replacement based on motion study

Directory of Open Access Journals (Sweden)

Yadong Fang

2015-01-01

Full Text Available Lacking the effective methods to reduce labor and cost, many small- and medium-sized assembly companies are facing with the problem of high cost for a long time. In order to reduce costs of manual operations, the method of meta-mechanism combination and replacement is studied. In this paper, we mainly discuss assembling motion analysis, workpieces position information acquisition, motion library construction, assembling motion analysis by Maynard’s operation sequence technique, meta-mechanism database establishment, and match of motion and mechanism. At the same time, the principle, process, and system realization framework of mechanism replacement are introduced. Lastly, problems for low-cost automation of the production line are basically resolved by operator motion analysis and meta-mechanism combination and match.

Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.

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Gopnik, Alison; Wellman, Henry M

2012-11-01

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.

Obtaining Global Picture From Single Point Observations by Combining Data Assimilation and Machine Learning Tools

Science.gov (United States)

Shprits, Y.; Zhelavskaya, I. S.; Kellerman, A. C.; Spasojevic, M.; Kondrashov, D. A.; Ghil, M.; Aseev, N.; Castillo Tibocha, A. M.; Cervantes Villa, J. S.; Kletzing, C.; Kurth, W. S.

2017-12-01

Increasing volume of satellite measurements requires deployment of new tools that can utilize such vast amount of data. Satellite measurements are usually limited to a single location in space, which complicates the data analysis geared towards reproducing the global state of the space environment. In this study we show how measurements can be combined by means of data assimilation and how machine learning can help analyze large amounts of data and can help develop global models that are trained on single point measurement. Data Assimilation: Manual analysis of the satellite measurements is a challenging task, while automated analysis is complicated by the fact that measurements are given at various locations in space, have different instrumental errors, and often vary by orders of magnitude. We show results of the long term reanalysis of radiation belt measurements along with fully operational real-time predictions using data assimilative VERB code. Machine Learning: We present application of the machine learning tools for the analysis of NASA Van Allen Probes upper-hybrid frequency measurements. Using the obtained data set we train a new global predictive neural network. The results for the Van Allen Probes based neural network are compared with historical IMAGE satellite observations. We also show examples of predictions of geomagnetic indices using neural networks. Combination of machine learning and data assimilation: We discuss how data assimilation tools and machine learning tools can be combine so that physics-based insight into the dynamics of the particular system can be combined with empirical knowledge of it's non-linear behavior.

Unravelling salutogenic mechanisms in the workplace: the role of learning.

Science.gov (United States)

Pijpker, Roald; Vaandrager, Lenneke; Bakker, Evert Jan; Koelen, Maria

To explore the moderating and mediating role(s) of learning within the relationship between sense of coherence (SOC) and generalized resistance resources. Cross-sectional study (N=481), using a self-administered questionnaire, of employees working in the healthcare sector in the Netherlands in 2017. Four residential healthcare settings and one healthcare-related Facebook group were involved. Multiple linear regression models were used to test for moderating and mediating effects of learning. Social relations, task significance, and job control significantly explained variance in SOC. Conceptual, social, and instrumental learning, combined, moderated the relationship between SOC and task significance. Instrumental learning moderated the relationship between job control and SOC. Social learning also mediated this relationship. Conceptual learning did not show any moderating or mediating effect. The relationship between SOC and the three GRRs seems to be strengthened or explained-to a certain extent-by instrumental and social learning. Healthcare organizations are recommended to promote learning through formal activities as well as through cooperation, feedback, sharing experiences, and job challenges. This requires employee participation and a multilevel interdisciplinary approach. Copyright © 2018 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Modafinil combined with cognitive training is associated with improved learning in healthy volunteers--a randomised controlled trial.

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Gilleen, J; Michalopoulou, P G; Reichenberg, A; Drake, R; Wykes, T; Lewis, S W; Kapur, S

2014-04-01

Improving cognition in people with neuropsychiatric disorders remains a major clinical target. By themselves pharmacological and non-pharmacological approaches have shown only modest effects in improving cognition. In the present study we tested a recently-proposed methodology to combine CT with a 'cognitive-enhancing' drug to improve cognitive test scores and expanded on previous approaches by delivering combination drug and CT, over a long intervention of repeated sessions, and used multiple tasks to reveal the cognitive processes being enhanced. We also aimed to determine whether gains from this combination approach generalised to untrained tests. In this proof of principle randomised-controlled trial thirty-three healthy volunteers were randomised to receive either modafinil or placebo combined with daily cognitive training over two weeks. Volunteers were trained on tasks of new-language learning, working memory and verbal learning following 200 mg modafinil or placebo for ten days. Improvements in trained and untrained tasks were measured. Rate of new-language learning was significantly enhanced with modafinil, and effects were greatest over the first five sessions. Modafinil improved within-day learning rather than between-day retention. No enhancement of gains with modafinil was observed in working memory nor rate of verbal learning. Gains in all tasks were retained post drug-administration, but transfer effects to broad cognitive abilities were not seen. This study shows that combining CT with modafinil specifically elevates learning over early training sessions compared to CT with placebo and provides a proof of principle experimental paradigm for pharmacological enhancement of cognitive remediation. Copyright © 2014 Elsevier B.V. and ECNP. All rights reserved.

Neurocomputational mechanisms of prosocial learning and links to empathy.

Science.gov (United States)

Lockwood, Patricia L; Apps, Matthew A J; Valton, Vincent; Viding, Essi; Roiser, Jonathan P

2016-08-30

Reinforcement learning theory powerfully characterizes how we learn to benefit ourselves. In this theory, prediction errors-the difference between a predicted and actual outcome of a choice-drive learning. However, we do not operate in a social vacuum. To behave prosocially we must learn the consequences of our actions for other people. Empathy, the ability to vicariously experience and understand the affect of others, is hypothesized to be a critical facilitator of prosocial behaviors, but the link between empathy and prosocial behavior is still unclear. During functional magnetic resonance imaging (fMRI) participants chose between different stimuli that were probabilistically associated with rewards for themselves (self), another person (prosocial), or no one (control). Using computational modeling, we show that people can learn to obtain rewards for others but do so more slowly than when learning to obtain rewards for themselves. fMRI revealed that activity in a posterior portion of the subgenual anterior cingulate cortex/basal forebrain (sgACC) drives learning only when we are acting in a prosocial context and signals a prosocial prediction error conforming to classical principles of reinforcement learning theory. However, there is also substantial variability in the neural and behavioral efficiency of prosocial learning, which is predicted by trait empathy. More empathic people learn more quickly when benefitting others, and their sgACC response is the most selective for prosocial learning. We thus reveal a computational mechanism driving prosocial learning in humans. This framework could provide insights into atypical prosocial behavior in those with disorders of social cognition.

Reconciling genetic evolution and the associative learning account of mirror neurons through data-acquisition mechanisms.

Science.gov (United States)

Lotem, Arnon; Kolodny, Oren

2014-04-01

An associative learning account of mirror neurons should not preclude genetic evolution of its underlying mechanisms. On the contrary, an associative learning framework for cognitive development should seek heritable variation in the learning rules and in the data-acquisition mechanisms that construct associative networks, demonstrating how small genetic modifications of associative elements can give rise to the evolution of complex cognition.

Neuronal mechanisms of motor learning and motor memory consolidation in healthy old adults.

Science.gov (United States)

Berghuis, K M M; Veldman, M P; Solnik, S; Koch, G; Zijdewind, I; Hortobágyi, T

2015-06-01

It is controversial whether or not old adults are capable of learning new motor skills and consolidate the performance gains into motor memory in the offline period. The underlying neuronal mechanisms are equally unclear. We determined the magnitude of motor learning and motor memory consolidation in healthy old adults and examined if specific metrics of neuronal excitability measured by magnetic brain stimulation mediate the practice and retention effects. Eleven healthy old adults practiced a wrist extension-flexion visuomotor skill for 20 min (MP, 71.3 years), while a second group only watched the templates without movements (attentional control, AC, n = 11, 70.5 years). There was 40 % motor learning in MP but none in AC (interaction, p learn a new motor skill and consolidate the learned skill into motor memory, processes that are most likely mediated by disinhibitory mechanisms. These results are relevant for the increasing number of old adults who need to learn and relearn movements during motor rehabilitation.

Neurobiological mechanisms underlying the blocking effect in aversive learning.

Science.gov (United States)

Eippert, Falk; Gamer, Matthias; Büchel, Christian

2012-09-19

Current theories of classical conditioning assume that learning depends on the predictive relationship between events, not just on their temporal contiguity. Here we employ the classic experiment substantiating this reasoning-the blocking paradigm-in combination with functional magnetic resonance imaging (fMRI) to investigate whether human amygdala responses in aversive learning conform to these assumptions. In accordance with blocking, we demonstrate that significantly stronger behavioral and amygdala responses are evoked by conditioned stimuli that are predictive of the unconditioned stimulus than by conditioned stimuli that have received the same pairing with the unconditioned stimulus, yet have no predictive value. When studying the development of this effect, we not only observed that it was related to the strength of previous conditioned responses, but also that predictive compared with nonpredictive conditioned stimuli received more overt attention, as measured by fMRI-concurrent eye tracking, and that this went along with enhanced amygdala responses. We furthermore observed that prefrontal regions play a role in the development of the blocking effect: ventromedial prefrontal cortex (subgenual anterior cingulate) only exhibited responses when conditioned stimuli had to be established as nonpredictive for an outcome, whereas dorsolateral prefrontal cortex also showed responses when conditioned stimuli had to be established as predictive. Most importantly, dorsolateral prefrontal cortex connectivity to amygdala flexibly switched between positive and negative coupling, depending on the requirements posed by predictive relationships. Together, our findings highlight the role of predictive value in explaining amygdala responses and identify mechanisms that shape these responses in human fear conditioning.

College radio as a mechanism for participatory learning: Exploring the scope for online radio based learning among undergraduates

Directory of Open Access Journals (Sweden)

Bahaeldin Ibrahim

2016-03-01

Full Text Available This paper explores the prospects of online college radio at Sur College of Applied Sciences, its need among students and the possible scope of its contributions to student learning, engagement and community service. It explores the method of developing a holistic mechanism to capture the possibilities of maximizing learning experience by employing college radio as an educational tool to understand the micro-dynamics and localized necessities that deem it necessary or unnecessary. Through this, it attempts to locate an appropriate mechanism, and targeted use of the college radio in contributing to the learning outcomes and educational experience of the students. The study finds considerable scope for radio based learning at Sur College of Applied Sciences across a range of uses and gratification indicators consistent with the primary objectives of the college. The study discusses the theoretical and practical implications of the findings, and the pedagogical significance of the college radio as an alternative.

PHYSIOLOGICAL QUALITY OF SOYBEAN SEEDS UNDER MECHANICAL INJURIES CAUSED BY COMBINES

Directory of Open Access Journals (Sweden)

FÁBIO PALCZEWSKI PACHECO

2015-01-01

Full Text Available The mechanical harvesting causes injuries on seeds and may affect their quality. Different threshing mechanisms and their adjustments may also affect the intensity of impacts that machines cause on seeds. So, this study aimed at diagnosing and evaluating the effect of two combines: the first one with a threshing system of axial flow and the other one with a threshing system of tangential flow, under adjustments of concave opening (10 mm, 30 mm and 10 mm for a combine with axial flow and 3.0 mm, 15 mm and 3.0 mm for a combine with tangential flow and three cylinder rotations on the quality of soybean seeds harvested at two moisture contents. Soybean seeds of cultivar 'ND 4910' were harvested at 16.6% moisture (mid - morning and 13.7% moisture in the afternoon. The seeds quality was evaluated by germination tests, germination speed index (GSI, germination rate, moisture content, percentage of purity and vigor by tetrazolium test. Despite the combine, the results showed that the mechanical injury has most reduced seeds quality, at 16.6% moisture content, concave opening of 30 mm (axial and 10 mm (tangential and cylinder rotation of 1100 rpm (axial and 1000 (tangential, both with the highest rotations used. The combine with tangential flow had the highest degree of seeds purity. When seeds moisture content at harvest was close to 13.7%, there was the highest seed injury, while, at 16.6%, there was the highest number of crushed soybeans, regardless the combine adjustment.

Combined quantum mechanical and molecular mechanical reaction pathway calculation for aromatic hydroxylation by p-hydroxybenzoate-3-hydroxylase

NARCIS (Netherlands)

Ridder, L.; Mulholland, A.; Rietjens, I.M.C.M.; Vervoort, J.

1999-01-01

The reaction pathway for the aromatic 3-hydroxylation of p-hydroxybenzoate by the reactive C4a-hydroperoxyflavin cofactor intermediate in p-hydroxybenzoate hydroxylase (PHBH) has been investigated by a combined quantum mechanical and molecular mechanical (QM/MM) method. A structural model for the

Development of a web-based learning medium on mechanism of labour for nursing students.

Science.gov (United States)

Gerdprasert, Sailom; Pruksacheva, Tassanee; Panijpan, Bhinyo; Ruenwongsa, Pintip

2010-07-01

This study aimed to develop a web-based learning media on the process and mechanism of labour for the third-year university nursing and midwifery students. This media was developed based on integrating principles of the mechanism of labour with the 5Es inquiry cycle and interactive features of information technology. In this study, the web-based learning unit was used to supplement the conventional lecture as in the traditional teaching. Students' achievements were assessed by using the pre- and post-test on factual knowledge and semi-structured interviews on attitude to the unit. Supplementation with this learning unit made learning significantly more effective than the traditional lecture by itself. The students also showed positive attitude toward the learning unit. Copyright 2009 Elsevier Ltd. All rights reserved.

Space Mechanisms Lessons Learned and Accelerated Testing Studies

Science.gov (United States)

Fusaro, Robert L.

1997-01-01

A number of mechanism (mechanical moving component) failures and anomalies have recently occurred on satellites. In addition, more demanding operating and life requirements have caused mechanism failures or anomalies to occur even before some satellites were launched (e.g., during the qualification testing of GOES-NEXT, CERES, and the Space Station Freedom Beta Joint Gimbal). For these reasons, it is imperative to determine which mechanisms worked in the past and which have failed so that the best selection of mechanically moving components can be made for future satellites. It is also important to know where the problem areas are so that timely decisions can be made on the initiation of research to develop future needed technology. To chronicle the life and performance characteristics of mechanisms operating in a space environment, a Space Mechanisms Lessons Learned Study was conducted. The work was conducted by the NASA Lewis Research Center and by Mechanical Technologies Inc. (MTI) under contract NAS3-27086. The expectation of the study was to capture and retrieve information relating to the life and performance of mechanisms operating in the space environment to determine what components had operated successfully and what components had produced anomalies.

Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

Directory of Open Access Journals (Sweden)

Xu Chen

2018-01-01

Full Text Available Teaching-learning-based optimization (TLBO is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner’s learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO. In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others; by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems.

E-Learning Optimization: The Relative and Combined Effects of Mental Practice and Modeling on Enhanced Podcast-Based Learning--A Randomized Controlled Trial

Science.gov (United States)

Alam, Fahad; Boet, Sylvain; Piquette, Dominique; Lai, Anita; Perkes, Christopher P.; LeBlanc, Vicki R.

2016-01-01

Enhanced podcasts increase learning, but evidence is lacking on how they should be designed to optimize their effectiveness. This study assessed the impact two learning instructional design methods (mental practice and modeling), either on their own or in combination, for teaching complex cognitive medical content when incorporated into enhanced…

Bonding mechanisms in spot welded three layer combinations

DEFF Research Database (Denmark)

Moghadam, Marcel; Tiedje, Niels Skat; Seyyedian Choobi, Mahsa

2016-01-01

this interface. It has been shown previously that such a joint can reach relatively high strength resulting in plug failure in tensileshear testing. Additional strength due to these bonding mechanisms is also obtained in common spot welds in the so-called corona band around the weld nugget.......The strength of a spot weld generally stems from fusion bonding of the metal layers, but other solid state bonding mechanisms also contribute to the overall strength. Metallographic analyses are presented to identify the phases formed near and across the weld interfaces and to identify...... the occurring bonding mechanisms. When welding a combination of three galvanized steel layers where one outer layer is a thin low-carbon steel it is a common challenge to obtain nugget penetration into the thin low-carbon steel. It therefore happens in real production that no nugget is formed across...

Quantum opto-mechanics with micromirrors : combining nano-mechanics with quantum optics

International Nuclear Information System (INIS)

Groeblacher, S.

2010-01-01

This work describes more than four years of research on the effects of the radiation-pressure force of light on macroscopic mechanical structures. The basic system studied here is a mechanical oscillator that is highly reflective and part of an optical resonator. It interacts with the optical cavity mode via the radiation-pressure force. Both the dynamics of the mechanical oscillation and the properties of the light field are modified through this interaction. In our experiments we use quantum optical tools (such as homodyning and down-conversion) with the goal of ultimately showing quantum behavior of the mechanical center of mass motion. In this thesis we present several experiments that pave the way towards this goal and when combined should allow the demonstration of the envisioned quantum phenomena, including entanglement, teleportation and Schroeodinger cat states. The study of quantum behavior of truly macroscopic systems is a long outstanding goal, which will help to answer some of the most fundamental questions in quantum physics today: Why is the world around us classical and not quantum? Is there a size- or mass-limit to systems for them to behave according to quantum mechanics? Is quantum theory complete or do we have to extend it to include mechanisms such as decoherence? Can we use the quantum nature of macroscopic objects to, for example, improve the measurement precision of classical apparatuses? The experiments discussed in this thesis include the very first passive radiation-pressure cooling of a mechanical oscillator in a cryogenic optical resonator, as well as the experimental demonstration of radiation-pressure cooling close to the mechanical quantum ground state. Cooling of the mechanical motion is an important pre-condition for observing quantum effects of the mechanical oscillator. In another experiment, we have demonstrated that we are able to enter the strong-coupling regime of the optomechanical system a regime where coherent energy

Android Used in The Learning Innovation Atwood Machines on Lagrange Mechanics Methods

Directory of Open Access Journals (Sweden)

Shabrina Shabrina

2017-12-01

Full Text Available Android is one of the smartphone operating system platforms that is now widely developed in learning media. Android allows the learning process to be more flexible and not oriented to be teacher center, but it allows to be student center. The Atwood machines is an experimental tool that is often used to observe mechanical laws in constantly accelerated motion which can also be described by the Lagrange mechanics methods. As an innovative and alternative learning activity, Atwood Android-based learning apps are running for two experimental variations, which are variations in load in cart and load masses that are hung. The experiment of load-carrier mass variation found that the larger load mass in the cart, the smaller the acceleration experienced by the system. Meanwhile, the experiment on the variation of the loaded mass found that the larger the loaded mass, the greater the acceleration experienced by the system.

Mechanisms of radiation-induced conditioned taste aversion learning

International Nuclear Information System (INIS)

Rabin, B.M.; Hunt, W.A.

1986-01-01

The literature on taste aversion learning is reviewed and discussed, with particular emphasis on those studies that have used exposure to ionizing radiation as an unconditioned stimulus to produce a conditioned taste aversion. The primary aim of the review is to attempt to define the mechanisms that lead to the initiation of the taste aversion response following exposure to ionizing radiation. Studies using drug treatments to produce a taste aversion have been included to the extent that they are relevant to understanding the mechanisms by which exposure to ionizing radiation can affect the behavior of the organism. 141 references

Best practices for learning physiology: combining classroom and online methods.

Science.gov (United States)

Anderson, Lisa C; Krichbaum, Kathleen E

2017-09-01

Physiology is a requisite course for many professional allied health programs and is a foundational science for learning pathophysiology, health assessment, and pharmacology. Given the demand for online learning in the health sciences, it is important to evaluate the efficacy of online and in-class teaching methods, especially as they are combined to form hybrid courses. The purpose of this study was to compare two hybrid physiology sections in which one section was offered mostly in-class (85% in-class), and the other section was offered mostly online (85% online). The two sections in 2 yr ( year 1 and year 2 ) were compared in terms of knowledge of physiology measured in exam scores and pretest-posttest improvement, and in measures of student satisfaction with teaching. In year 1 , there were some differences on individual exam scores between the two sections, but no significant differences in mean exam scores or in pretest-posttest improvements. However, in terms of student satisfaction, the mostly in-class students in year 1 rated the instructor significantly higher than did the mostly online students. Comparisons between in-class and online students in the year 2 cohort yielded data that showed that mean exam scores were not statistically different, but pre-post changes were significantly greater in the mostly online section; student satisfaction among mostly online students also improved significantly. Education researchers must investigate effective combinations of in-class and online methods for student learning outcomes, while maintaining the flexibility and convenience that online methods provide. Copyright © 2017 the American Physiological Society.

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

Science.gov (United States)

Stclair, D. C.; Sabharwal, C. L.; Bond, W. E.; Hacke, Keith

1988-01-01

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

Effects of the Badge Mechanism on Self-Efficacy and Learning Performance in a Game-Based English Learning Environment

Science.gov (United States)

Yang, Jie Chi; Quadir, Benazir; Chen, Nian-Shing

2016-01-01

A growing number of studies have been conducted on digital game-based learning (DGBL). However, there has been a lack of attention paid to individuals' self-efficacy and learning performance in the implementation of DGBL. This study therefore investigated how the badge mechanism in DGBL enhanced users' self-efficacy in the subject domain of…

The Conceptual Mechanism for Viable Organizational Learning Based on Complex System Theory and the Viable System Model

Science.gov (United States)

Sung, Dia; You, Yeongmahn; Song, Ji Hoon

2008-01-01

The purpose of this research is to explore the possibility of viable learning organizations based on identifying viable organizational learning mechanisms. Two theoretical foundations, complex system theory and viable system theory, have been integrated to provide the rationale for building the sustainable organizational learning mechanism. The…

Exploring the mechanisms through which computers contribute to learning.

NARCIS (Netherlands)

Karasavvidis, I.; Karasavvidis, I.; Pieters, Julius Marie; Plomp, T.

2003-01-01

Even though it has been established that the incorporation of computers into the teaching and learning process enhances student performance, the underlying mechanisms through which this is accomplished have been largely unexplored. The present study aims to shed light on this issue. Two groups of 10

Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines

DEFF Research Database (Denmark)

van Tulder, Gijs; de Bruijne, Marleen

2016-01-01

The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann machines may...... outperform these standard filter banks because they learn a feature description directly from the training data. Like many other representation learning methods, restricted Boltzmann machines are unsupervised and are trained with a generative learning objective; this allows them to learn representations from...... unlabeled data, but does not necessarily produce features that are optimal for classification. In this paper we propose the convolutional classification restricted Boltzmann machine, which combines a generative and a discriminative learning objective. This allows it to learn filters that are good both...

How are learning strategies reflected in the eyes? Combining results from self-reports and eye-tracking.

Science.gov (United States)

Catrysse, Leen; Gijbels, David; Donche, Vincent; De Maeyer, Sven; Lesterhuis, Marije; Van den Bossche, Piet

2018-03-01

Up until now, empirical studies in the Student Approaches to Learning field have mainly been focused on the use of self-report instruments, such as interviews and questionnaires, to uncover differences in students' general preferences towards learning strategies, but have focused less on the use of task-specific and online measures. This study aimed at extending current research on students' learning strategies by combining general and task-specific measurements of students' learning strategies using both offline and online measures. We want to clarify how students process learning contents and to what extent this is related to their self-report of learning strategies. Twenty students with different generic learning profiles (according to self-report questionnaires) read an expository text, while their eye movements were registered to answer questions on the content afterwards. Eye-tracking data were analysed with generalized linear mixed-effects models. The results indicate that students with an all-high profile, combining both deep and surface learning strategies, spend more time on rereading the text than students with an all-low profile, scoring low on both learning strategies. This study showed that we can use eye-tracking to distinguish very strategic students, characterized using cognitive processing and regulation strategies, from low strategic students, characterized by a lack of cognitive and regulation strategies. These students processed the expository text according to how they self-reported. © 2017 The British Psychological Society.

Quantum Machine Learning

OpenAIRE

Romero García, Cristian

2017-01-01

[EN] In a world in which accessible information grows exponentially, the selection of the appropriate information turns out to be an extremely relevant problem. In this context, the idea of Machine Learning (ML), a subfield of Artificial Intelligence, emerged to face problems in data mining, pattern recognition, automatic prediction, among others. Quantum Machine Learning is an interdisciplinary research area combining quantum mechanics with methods of ML, in which quantum properties allow fo...

Cross-platform learning: on the nature of children's learning from multiple media platforms.

Science.gov (United States)

Fisch, Shalom M

2013-01-01

It is increasingly common for an educational media project to span several media platforms (e.g., TV, Web, hands-on materials), assuming that the benefits of learning from multiple media extend beyond those gained from one medium alone. Yet research typically has investigated learning from a single medium in isolation. This paper reviews several recent studies to explore cross-platform learning (i.e., learning from combined use of multiple media platforms) and how such learning compares to learning from one medium. The paper discusses unique benefits of cross-platform learning, a theoretical mechanism to explain how these benefits might arise, and questions for future research in this emerging field. Copyright © 2013 Wiley Periodicals, Inc., A Wiley Company.

Universal effect of dynamical reinforcement learning mechanism in spatial evolutionary games

International Nuclear Information System (INIS)

Zhang, Hai-Feng; Wu, Zhi-Xi; Wang, Bing-Hong

2012-01-01

One of the prototypical mechanisms in understanding the ubiquitous cooperation in social dilemma situations is the win–stay, lose–shift rule. In this work, a generalized win–stay, lose–shift learning model—a reinforcement learning model with dynamic aspiration level—is proposed to describe how humans adapt their social behaviors based on their social experiences. In the model, the players incorporate the information of the outcomes in previous rounds with time-dependent aspiration payoffs to regulate the probability of choosing cooperation. By investigating such a reinforcement learning rule in the spatial prisoner's dilemma game and public goods game, a most noteworthy viewpoint is that moderate greediness (i.e. moderate aspiration level) favors best the development and organization of collective cooperation. The generality of this observation is tested against different regulation strengths and different types of network of interaction as well. We also make comparisons with two recently proposed models to highlight the importance of the mechanism of adaptive aspiration level in supporting cooperation in structured populations

Learning to learn - intrinsic plasticity as a metaplasticity mechanism for memory formation.

Science.gov (United States)

Sehgal, Megha; Song, Chenghui; Ehlers, Vanessa L; Moyer, James R

2013-10-01

"Use it or lose it" is a popular adage often associated with use-dependent enhancement of cognitive abilities. Much research has focused on understanding exactly how the brain changes as a function of experience. Such experience-dependent plasticity involves both structural and functional alterations that contribute to adaptive behaviors, such as learning and memory, as well as maladaptive behaviors, including anxiety disorders, phobias, and posttraumatic stress disorder. With the advancing age of our population, understanding how use-dependent plasticity changes across the lifespan may also help to promote healthy brain aging. A common misconception is that such experience-dependent plasticity (e.g., associative learning) is synonymous with synaptic plasticity. Other forms of plasticity also play a critical role in shaping adaptive changes within the nervous system, including intrinsic plasticity - a change in the intrinsic excitability of a neuron. Intrinsic plasticity can result from a change in the number, distribution or activity of various ion channels located throughout the neuron. Here, we review evidence that intrinsic plasticity is an important and evolutionarily conserved neural correlate of learning. Intrinsic plasticity acts as a metaplasticity mechanism by lowering the threshold for synaptic changes. Thus, learning-related intrinsic changes can facilitate future synaptic plasticity and learning. Such intrinsic changes can impact the allocation of a memory trace within a brain structure, and when compromised, can contribute to cognitive decline during the aging process. This unique role of intrinsic excitability can provide insight into how memories are formed and, more interestingly, how neurons that participate in a memory trace are selected. Most importantly, modulation of intrinsic excitability can allow for regulation of learning ability - this can prevent or provide treatment for cognitive decline not only in patients with clinical disorders but

From Tootsie Rolls to Composites: Assessing a Spectrum of Active Learning Activities in Engineering Mechanics

Science.gov (United States)

2009-05-01

The introduction of active learning exercises into a traditional lecture has been shown to improve students’ learning. Hands-on learning...opportunities in labs and projects provide are additional tools in the active learning toolbox. This paper presents a series of innovative hands-on active ... learning activities for mechanics of materials topics. These activities are based on a Methodology for Developing Hands-on Active Learning Activities, a

Synergistic mechanism of combinative application of bensulfuron and urea

International Nuclear Information System (INIS)

Yu Liuqing; Huang Shiwen; Zhou Hongjie; Ye Guibiao

1998-01-01

Nutrient culture study was initiated to examine the synergistic mechanism of combinative application of bensulfuron and urea for weed control. The absorption of 14 C-bensulfuron and their distribution in Lindernia procumbens (Krock.) Philcox were also investigated to determine the variation between two methods (combinative use of 14 C-bensulfuron plus urea and 14 C-bensulfuron alone). One hour after combinative application of 14 C-bensulfuron plus urea, the highest amount of 14 C-activity in L. procumbens were obtained. However, when 14 C-bensulfuron applied alone, total absorption of 14 C-activity was much lower in the 1st hour and then it slowly increased with time. The distribution of 14 C-bensulfuron in root of L. procumbens plant was the highest and that in leaves was the lowest

Inner-Learning Mechanism Based Control Scheme for Manipulator with Multitasking and Changing Load

Directory of Open Access Journals (Sweden)

Fangzheng Xue

2014-05-01

Full Text Available With the rapid development of robot technology and its application, manipulators may face complex tasks and dynamic environments in the coming future, which leads to two challenges of control: multitasking and changing load. In this paper, a novel multicontroller strategy is presented to meet such challenges. The presented controller is composed of three parts: subcontrollers, inner-learning mechanism, and switching rules. Each subcontroller is designed with self-learning skills to fit the changing load under a special task. When a new task comes, switching rule reselects the most suitable subcontroller as the working controller to handle current task instead of the older one. Inner-learning mechanism makes the subcontrollers learn from the working controller when load changes so that the switching action causes smaller tracking error than the traditional switch controller. The results of the simulation experiments on two-degree manipulator show the proposed method effect.

Honeybees in a virtual reality environment learn unique combinations of colour and shape.

Science.gov (United States)

Rusch, Claire; Roth, Eatai; Vinauger, Clément; Riffell, Jeffrey A

2017-10-01

Honeybees are well-known models for the study of visual learning and memory. Whereas most of our knowledge of learned responses comes from experiments using free-flying bees, a tethered preparation would allow fine-scale control of the visual stimuli as well as accurate characterization of the learned responses. Unfortunately, conditioning procedures using visual stimuli in tethered bees have been limited in their efficacy. In this study, using a novel virtual reality environment and a differential training protocol in tethered walking bees, we show that the majority of honeybees learn visual stimuli, and need only six paired training trials to learn the stimulus. We found that bees readily learn visual stimuli that differ in both shape and colour. However, bees learn certain components over others (colour versus shape), and visual stimuli are learned in a non-additive manner with the interaction of specific colour and shape combinations being crucial for learned responses. To better understand which components of the visual stimuli the bees learned, the shape-colour association of the stimuli was reversed either during or after training. Results showed that maintaining the visual stimuli in training and testing phases was necessary to elicit visual learning, suggesting that bees learn multiple components of the visual stimuli. Together, our results demonstrate a protocol for visual learning in restrained bees that provides a powerful tool for understanding how components of a visual stimulus elicit learned responses as well as elucidating how visual information is processed in the honeybee brain. © 2017. Published by The Company of Biologists Ltd.

Representation and Integration: Combining Robot Control, High-Level Planning, and Action Learning

DEFF Research Database (Denmark)

Petrick, Ronald; Kraft, Dirk; Mourao, Kira

We describe an approach to integrated robot control, high-level planning, and action effect learning that attempts to overcome the representational difficulties that exist between these diverse areas. Our approach combines ideas from robot vision, knowledgelevel planning, and connectionist machine......-level action specifications, suitable for planning, from a robot’s interactions with the world. We present a detailed overview of our approach and show how it supports the learning of certain aspects of a high-level lepresentation from low-level world state information....... learning, and focuses on the representational needs of these components.We also make use of a simple representational unit called an instantiated state transition fragment (ISTF) and a related structure called an object-action complex (OAC). The goal of this work is a general approach for inducing high...

Criterion learning in rule-based categorization: simulation of neural mechanism and new data.

Science.gov (United States)

Helie, Sebastien; Ell, Shawn W; Filoteo, J Vincent; Maddox, W Todd

2015-04-01

In perceptual categorization, rule selection consists of selecting one or several stimulus-dimensions to be used to categorize the stimuli (e.g., categorize lines according to their length). Once a rule has been selected, criterion learning consists of defining how stimuli will be grouped using the selected dimension(s) (e.g., if the selected rule is line length, define 'long' and 'short'). Very little is known about the neuroscience of criterion learning, and most existing computational models do not provide a biological mechanism for this process. In this article, we introduce a new model of rule learning called Heterosynaptic Inhibitory Criterion Learning (HICL). HICL includes a biologically-based explanation of criterion learning, and we use new category-learning data to test key aspects of the model. In HICL, rule selective cells in prefrontal cortex modulate stimulus-response associations using pre-synaptic inhibition. Criterion learning is implemented by a new type of heterosynaptic error-driven Hebbian learning at inhibitory synapses that uses feedback to drive cell activation above/below thresholds representing ionic gating mechanisms. The model is used to account for new human categorization data from two experiments showing that: (1) changing rule criterion on a given dimension is easier if irrelevant dimensions are also changing (Experiment 1), and (2) showing that changing the relevant rule dimension and learning a new criterion is more difficult, but also facilitated by a change in the irrelevant dimension (Experiment 2). We conclude with a discussion of some of HICL's implications for future research on rule learning. Copyright © 2015 Elsevier Inc. All rights reserved.

Neuronal mechanisms of motor learning and motor memory consolidation in healthy old adults

NARCIS (Netherlands)

Berghuis, K. M. M.; Veldman, M. P.; Solnik, S.; Koch, G.; Zijdewind, I.; Hortobagyi, T.

It is controversial whether or not old adults are capable of learning new motor skills and consolidate the performance gains into motor memory in the offline period. The underlying neuronal mechanisms are equally unclear. We determined the magnitude of motor learning and motor memory consolidation

Capstone Teaching Models: Combining Simulation, Analytical Intuitive Learning Processes, History and Effectiveness

Science.gov (United States)

Reid, Maurice; Brown, Steve; Tabibzadeh, Kambiz

2012-01-01

For the past decade teaching models have been changing, reflecting the dynamics, complexities, and uncertainties of today's organizations. The traditional and the more current active models of learning have disadvantages. Simulation provides a platform to combine the best aspects of both types of teaching practices. This research explores the…

The Office Software Learning and Examination System Design Based on Fragmented Learning Idea

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Xu Ling

2016-01-01

Full Text Available Fragmented learning is that through the segmentation of learning content or learning time, make learners can use the fragmented time for learning fragmentated content, have the characteristics of time flexibility, learning targeted and high learning efficiency. Based on the fragmented learning ideas, combined with the teaching idea of micro class and interactive teaching, comprehensive utilization of flash animation design software, .NET development platform, VSTO technology, multimedia development technology and so on, design and develop a system integrated with learning, practice and examination of the Office software, which is not only conducive to the effective and personalized learning of students, but also conducive to the understanding the students’ situation of teachers, and liberate teachers from the heavy labor of mechanical, focus on promoting the formation of students’ knowledge system.

Orthographic learning in children with isolated and combined reading and spelling deficits.

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Mehlhase, Heike; Bakos, Sarolta; Landerl, Karin; Schulte-Körne, Gerd; Moll, Kristina

2018-05-07

Dissociations between reading and spelling problems are likely to be associated with different underlying cognitive deficits, and with different deficits in orthographic learning. In order to understand these differences, the current study examined orthographic learning using a printed-word learning paradigm. Children (4th grade) with isolated reading, isolated spelling and combined reading and spelling problems were compared to children with age appropriate reading and spelling skills on their performance during learning novel words and symbols (non-verbal control condition), and during immediate and delayed reading and spelling recall tasks. No group differences occurred in the non-verbal control condition. In the verbal condition, initial learning was intact in all groups, but differences occurred during recall tasks. Children with reading fluency deficits showed slower reading times, while children with spelling deficits were less accurate, both in reading and spelling recall. Children with isolated spelling problems showed no difficulties in immediate spelling recall, but had problems in remembering the spellings 2 hours later. The results suggest that different orthographic learning deficits underlie reading fluency and spelling problems: Children with isolated reading fluency deficits have no difficulties in building-up orthographic representations, but access to these representations is slowed down while children with isolated spelling deficits have problems in storing precise orthographic representations in long-term memory.

Automatic plankton image classification combining multiple view features via multiple kernel learning.

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Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

2017-12-28

Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system

Quantum Interactive Learning Tutorial on the Double-Slit Experiment to Improve Student Understanding of Quantum Mechanics

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Sayer, Ryan; Maries, Alexandru; Singh, Chandralekha

2017-01-01

Learning quantum mechanics is challenging, even for upper-level undergraduate and graduate students. Research-validated interactive tutorials that build on students' prior knowledge can be useful tools to enhance student learning. We have been investigating student difficulties with quantum mechanics pertaining to the double-slit experiment in…

Learning-based adaptive prescribed performance control of postcapture space robot-target combination without inertia identifications

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Wei, Caisheng; Luo, Jianjun; Dai, Honghua; Bian, Zilin; Yuan, Jianping

2018-05-01

In this paper, a novel learning-based adaptive attitude takeover control method is investigated for the postcapture space robot-target combination with guaranteed prescribed performance in the presence of unknown inertial properties and external disturbance. First, a new static prescribed performance controller is developed to guarantee that all the involved attitude tracking errors are uniformly ultimately bounded by quantitatively characterizing the transient and steady-state performance of the combination. Then, a learning-based supplementary adaptive strategy based on adaptive dynamic programming is introduced to improve the tracking performance of static controller in terms of robustness and adaptiveness only utilizing the input/output data of the combination. Compared with the existing works, the prominent advantage is that the unknown inertial properties are not required to identify in the development of learning-based adaptive control law, which dramatically decreases the complexity and difficulty of the relevant controller design. Moreover, the transient and steady-state performance is guaranteed a priori by designer-specialized performance functions without resorting to repeated regulations of the controller parameters. Finally, the three groups of illustrative examples are employed to verify the effectiveness of the proposed control method.

Self-Assessment Exercises in Continuum Mechanics with Autonomous Learning

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Marcé-Nogué, Jordi; Gil, LLuís; Pérez, Marco A.; Sánchez, Montserrat

2013-01-01

The main objective of this work is to generate a set of exercises to improve the autonomous learning in "Continuum Mechanics" through a virtual platform. Students will have to resolve four exercises autonomously related to the subject developed in class and they will post the solutions on the virtual platform within a deadline. Students…

TA Mentorship in Lecture significantly enhances students' learning in mechanics in large introductory physics classes

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Cheng, K.; Caglar, Mehmet

2011-10-01

Lab is an important component of students' learning in a traditional lecture-lab setting of introductory physics courses. Using standard mechanics concepts and baseline surveys as well as independent classroom observations, the effects of TA mentorship in Lecture on students' learning of physics concepts and problem-solving skills among different student subgroups taught by other TAs and lecturers using different level of student interactive engagement in classes have been analyzed. Our data indicate that in lecture training of TA promotes lecture/lab synergism in improvement students' learning of mechanics in large introductory physics classes.

Visual diet versus associative learning as mechanisms of change in body size preferences.

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Lynda G Boothroyd

Full Text Available Systematic differences between populations in their preferences for body size may arise as a result of an adaptive 'prepared learning' mechanism, whereby cues to health or status in the local population are internalized and affect body preferences. Alternatively, differences between populations may reflect their 'visual diet' as a cognitive byproduct of mere exposure. Here we test the relative importance of these two explanations for variation in body preferences. Two studies were conducted where female observers were exposed to pictures of high or low BMI women which were either aspirational (healthy, attractive models in high status clothes or non-aspirational (eating disordered patients in grey leotards, or to combinations thereof, in order to manipulate their body-weight preferences which were tested at baseline and at post-test. Overall, results showed good support for visual diet effects (seeing a string of small or large bodies resulted in a change from pre- to post-test whether the bodies were aspirational or not and also some support for the associative learning explanation (exposure to aspirational images of overweight women induced a towards preferring larger bodies, even when accompanied by equal exposure to lower weight bodies in the non-aspirational category. Thus, both influences may act in parallel.

Zebrafish and relational memory: Could a simple fish be useful for the analysis of biological mechanisms of complex vertebrate learning?

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Gerlai, Robert

2017-08-01

Analysis of the zebrafish allows one to combine two distinct scientific approaches, comparative ethology and neurobehavioral genetics. Furthermore, this species arguably represents an optimal compromise between system complexity and practical simplicity. This mini-review focuses on a complex form of learning, relational learning and memory, in zebrafish. It argues that zebrafish are capable of this type of learning, and it attempts to show how this species may be useful in the analysis of the mechanisms and the evolution of this complex brain function. The review is not intended to be comprehensive. It is a short opinion piece that reflects the author's own biases, and it draws some of its examples from the work coming from his own laboratory. Nevertheless, it is written in the hope that it will persuade those who have not utilized zebrafish and who may be interested in opening their research horizon to this relatively novel but powerful vertebrate research tool. Copyright © 2017 Elsevier B.V. All rights reserved.

Interactive Rhythm Learning System by Combining Tablet Computers and Robots

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Chien-Hsing Chou

2017-03-01

Full Text Available This study proposes a percussion learning device that combines tablet computers and robots. This device comprises two systems: a rhythm teaching system, in which users can compose and practice rhythms by using a tablet computer, and a robot performance system. First, teachers compose the rhythm training contents on the tablet computer. Then, the learners practice these percussion exercises by using the tablet computer and a small drum set. The teaching system provides a new and user-friendly score editing interface for composing a rhythm exercise. It also provides a rhythm rating function to facilitate percussion training for children and improve the stability of rhythmic beating. To encourage children to practice percussion exercises, a robotic performance system is used to interact with the children; this system can perform percussion exercises for students to listen to and then help them practice the exercise. This interaction enhances children’s interest and motivation to learn and practice rhythm exercises. The results of experimental course and field trials reveal that the proposed system not only increases students’ interest and efficiency in learning but also helps them in understanding musical rhythms through interaction and composing simple rhythms.

Integrating Organizational Learning and Business Praxis: A Case for Intelligent Project Management.

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Cavaleri, Steven A.; Fearon, David S.

2000-01-01

Project management provides a natural home for organizational learning, freeing it from mechanical processes. Organizational learning plays a critical role in intelligent project management, which combines manageability, performance outcomes of knowledge management, and innovation. Learning should be integrated into an organization's core…

Associative Learning in Invertebrates

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Hawkins, Robert D.; Byrne, John H.

2015-01-01

This work reviews research on neural mechanisms of two types of associative learning in the marine mollusk Aplysia, classical conditioning of the gill- and siphon-withdrawal reflex and operant conditioning of feeding behavior. Basic classical conditioning is caused in part by activity-dependent facilitation at sensory neuron–motor neuron (SN–MN) synapses and involves a hybrid combination of activity-dependent presynaptic facilitation and Hebbian potentiation, which are coordinated by trans-synaptic signaling. Classical conditioning also shows several higher-order features, which might be explained by the known circuit connections in Aplysia. Operant conditioning is caused in part by a different type of mechanism, an intrinsic increase in excitability of an identified neuron in the central pattern generator (CPG) for feeding. However, for both classical and operant conditioning, adenylyl cyclase is a molecular site of convergence of the two signals that are associated. Learning in other invertebrate preparations also involves many of the same mechanisms, which may contribute to learning in vertebrates as well. PMID:25877219

Learning to learn – intrinsic plasticity as a metaplasticity mechanism for memory formation

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Sehgal, Megha; Song, Chenghui; Ehlers, Vanessa L.; Moyer, James R.

2013-01-01

“Use it or lose it” is a popular adage often associated with use-dependent enhancement of cognitive abilities. Much research has focused on understanding exactly how the brain changes as a function of experience. Such experience-dependent plasticity involves both structural and functional alterations that contribute to adaptive behaviors, such as learning and memory, as well as maladaptive behaviors, including anxiety disorders, phobias, and posttraumatic stress disorder. With the advancing age of our population, understanding how use-dependent plasticity changes across the lifespan may also help to promote healthy brain aging. A common misconception is that such experience-dependent plasticity (e.g., associative learning) is synonymous with synaptic plasticity. Other forms of plasticity also play a critical role in shaping adaptive changes within the nervous system, including intrinsic plasticity – a change in the intrinsic excitability of a neuron. Intrinsic plasticity can result from a change in the number, distribution or activity of various ion channels located throughout the neuron. Here, we review evidence that intrinsic plasticity is an important and evolutionarily conserved neural correlate of learning. Intrinsic plasticity acts as a metaplasticity mechanism by lowering the threshold for synaptic changes. Thus, learning-related intrinsic changes can facilitate future synaptic plasticity and learning. Such intrinsic changes can impact the allocation of a memory trace within a brain structure, and when compromised, can contribute to cognitive decline during the aging process. This unique role of intrinsic excitability can provide insight into how memories are formed and, more interestingly, how neurons that participate in a memory trace are selected. Most importantly, modulation of intrinsic excitability can allow for regulation of learning ability – this can prevent or provide treatment for cognitive decline not only in patients with clinical

The evolution of social learning mechanisms and cultural phenomena in group foragers.

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van der Post, Daniel J; Franz, Mathias; Laland, Kevin N

2017-02-10

Advanced cognitive abilities are widely thought to underpin cultural traditions and cumulative cultural change. In contrast, recent simulation models have found that basic social influences on learning suffice to support both cultural phenomena. In the present study we test the predictions of these models in the context of skill learning, in a model with stochastic demographics, variable group sizes, and evolved parameter values, exploring the cultural ramifications of three different social learning mechanisms. Our results show that that simple forms of social learning such as local enhancement, can generate traditional differences in the context of skill learning. In contrast, we find cumulative cultural change is supported by observational learning, but not local or stimulus enhancement, which supports the idea that advanced cognitive abilities are important for generating this cultural phenomenon in the context of skill learning. Our results help to explain the observation that animal cultures are widespread, but cumulative cultural change might be rare.

Combining fMRI and behavioral measures to examine the process of human learning.

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Karuza, Elisabeth A; Emberson, Lauren L; Aslin, Richard N

2014-03-01

Prior to the advent of fMRI, the primary means of examining the mechanisms underlying learning were restricted to studying human behavior and non-human neural systems. However, recent advances in neuroimaging technology have enabled the concurrent study of human behavior and neural activity. We propose that the integration of behavioral response with brain activity provides a powerful method of investigating the process through which internal representations are formed or changed. Nevertheless, a review of the literature reveals that many fMRI studies of learning either (1) focus on outcome rather than process or (2) are built on the untested assumption that learning unfolds uniformly over time. We discuss here various challenges faced by the field and highlight studies that have begun to address them. In doing so, we aim to encourage more research that examines the process of learning by considering the interrelation of behavioral measures and fMRI recording during learning. Copyright © 2013 Elsevier Inc. All rights reserved.

Active Learning in Fluid Mechanics: Youtube Tube Flow and Puzzling Fluids Questions

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Hrenya, Christine M.

2011-01-01

Active-learning exercises appropriate for a course in undergraduate fluid mechanics are presented. The first exercise involves an experiment in gravity-driven tube flow, with small groups of students partaking in a contest to predict the experimental flow rates using the mechanical energy balance. The second exercise takes the form of an…

High Gain Antenna System Deployment Mechanism Integration, Characterization, and Lessons Learned

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Parong, Fil; Russell, Blair; Garcen, Walter; Rose, Chris; Johnson, Chris; Huber, Craig

2014-01-01

The integration and deployment testing of the High Gain Antenna System for the Global Precipitation Measurement mission is summarized. The HGAS deployment mechanism is described. The gravity negation system configuration and its influence on vertical, ground-based, deployment tests are presented with test data and model predictions. A focus is made on the late discovery and resolution of a potentially mission degrading deployment interference condition. The interaction of the flight deployment mechanism, gravity negation mechanism, and use of dynamic modeling is described and lessons learned presented.

Concurrence of rule- and similarity-based mechanisms in artificial grammar learning.

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Opitz, Bertram; Hofmann, Juliane

2015-03-01

A current theoretical debate regards whether rule-based or similarity-based learning prevails during artificial grammar learning (AGL). Although the majority of findings are consistent with a similarity-based account of AGL it has been argued that these results were obtained only after limited exposure to study exemplars, and performance on subsequent grammaticality judgment tests has often been barely above chance level. In three experiments the conditions were investigated under which rule- and similarity-based learning could be applied. Participants were exposed to exemplars of an artificial grammar under different (implicit and explicit) learning instructions. The analysis of receiver operating characteristics (ROC) during a final grammaticality judgment test revealed that explicit but not implicit learning led to rule knowledge. It also demonstrated that this knowledge base is built up gradually while similarity knowledge governed the initial state of learning. Together these results indicate that rule- and similarity-based mechanisms concur during AGL. Moreover, it could be speculated that two different rule processes might operate in parallel; bottom-up learning via gradual rule extraction and top-down learning via rule testing. Crucially, the latter is facilitated by performance feedback that encourages explicit hypothesis testing. Copyright © 2015 Elsevier Inc. All rights reserved.

Competency-Based Materials for the Florida Automotive Mechanics Curriculum

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Goodson, Ludy; And Others

1978-01-01

Describes Florida's new automotive mechanics curriculum, an individualized, self-paced learning sequence that combines text material, review exercises and actual work activities. Development of the materials, including incorporation of Florida's V-TECS catalog of performance objectives in auto mechanics, is described. A field-test experience of a…

Mind and activity. Psychic mechanism of learning

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Zoya A. Reshetova

2017-09-01

Full Text Available The paper is devoted to the issue of mechanisms of learning for understanding the nature of the human mind. Learning is regarded as a special activity that is important for developing the human mind in a specific cultural and historical setting and indirect activity. The author’s understanding of the ideas developed by the psychological theory of activity for establishing the principles of developing the human mind is highlighted. Interpretation of dialectical connections of brain processes and mind, and also the objective activity that emerges them is provided. According to the activity theory, the causes of the students’ psychological difficulties and the low efficacy of learning within predominant reproductive method or the use of the trial and error method are revealed. Thus, a new understanding of the renowned didactic principles of scientific rigour, accessibility, objectivity, the connection of learning with life and others is offered. The contribution of the psychological theory in organizing and managing the studies, increasing teaching activity and awareness, and the growth of the internal causes of motivation are shown. Particular attention is paid to the issue of intellectual development and creative abilities. The author believes the creative abilities of the student and the way the latter are taught are interconnected. At the same time, the developers and educators should make efforts to develop in the students a systemic orientation in the subject, primarily mastering the method of system analysis. Once the method of system analysis has been mastered, it becomes a general intellectual and developing tool through which activities are organized to solve any teaching problems with whatever type of content and difficulty level. Summing up, the organization and disclosure to the student of the process of learning as an activity with its social, consciously transformative and sense shaping meaning, the conditions of its development

Performance Evaluation of the Machine Learning Algorithms Used in Inference Mechanism of a Medical Decision Support System

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Mert Bal

2014-01-01

Full Text Available The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets.

Performance evaluation of the machine learning algorithms used in inference mechanism of a medical decision support system.

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Bal, Mert; Amasyali, M Fatih; Sever, Hayri; Kose, Guven; Demirhan, Ayse

2014-01-01

The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets.

Two different motor learning mechanisms contribute to learning reaching movements in a rotated visual environment [version 2; referees: 2 approved, 1 approved with reservations

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Virginia Way Tong Chu

2014-12-01

Full Text Available Practice of movement in virtual-reality and other artificially altered environments has been proposed as a method for rehabilitation following neurological injury and for training new skills in healthy humans.  For such training to be useful, there must be transfer of learning from the artificial environment to the performance of desired skills in the natural environment.  Therefore an important assumption of such methods is that practice in the altered environment engages the same learning and plasticity mechanisms that are required for skill performance in the natural environment.  We test the hypothesis that transfer of learning may fail because the learning and plasticity mechanism that adapts to the altered environment is different from the learning mechanism required for improvement of motor skill.  In this paper, we propose that a model that separates skill learning and environmental adaptation is necessary to explain the learning and aftereffects that are observed in virtual reality experiments.  In particular, we studied the condition where practice in the altered environment should lead to correct skill performance in the original environment. Our 2-mechanism model predicts that aftereffects will still be observed when returning to the original environment, indicating a lack of skill transfer from the artificial environment to the original environment. To illustrate the model prediction, we tested 10 healthy participants on the interaction between a simple overlearned motor skill (straight hand movements to targets in different directions and an artificially altered visuomotor environment (rotation of visual feedback of the results of movement.  As predicted by the models, participants show adaptation to the altered environment and after-effects on return to the baseline environment even when practice in the altered environment should have led to correct skill performance.  The presence of aftereffect under all conditions that

Models of intracellular mechanisms of plant bioelectrical potentials caused by combined stimulation

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D. V. Chernetchenko

2014-10-01

Full Text Available This paper deals with bioelectrical potentials of the plants recorded during different types of stimuli and combined stimulus as well. All registrations were observed on the leaves of the corn. We used different stimuli, such as cold, heat, photo- and electrical stimulation, and certain combination of this stimuli. Hardware and software system for automated recording of bioelectrical potentials has been successfully used in this work. We proposed the universal pattern of bioelectrical potentials’ recording which allowed to detect the response of the biological object to different stimuli and various combinations of these stimuli. This pattern can be used for the deeper understanding of biological mechanisms of electrical potentials’ generation in cells and discovering of processes of accommodation of whole organisms to these stimuli. Integrated system of recording and biometrical processing was used for analysis of corn leaves electrical responses to the thermal stimuli. The dynamics of these potentials was studied, with the quantitative analysis of the potential level stabilization.We calculated the ratio of amplitude of response potentials to the first response amplitude. Mathematical models of the plant cell were used for studying of intracellular mechanisms of biopotentials gereration. As a result of modeling, we revealed that electrical response of the cells was based on selectiveconductivity of cell membrane for Н+ and Ca2+ ions. Therefore, we showed the biophysical relation of plant potentials to underlying intracellular biophysical mechanisms during thermal and combined stimulation.

eLearning course may shorten the duration of mechanical restraint among psychiatric inpatients: a cluster-randomized trial.

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Kontio, Raija; Pitkänen, Anneli; Joffe, Grigori; Katajisto, Jouko; Välimäki, Maritta

2014-10-01

The management of psychiatric inpatients exhibiting severely disturbed and aggressive behaviour is an important educational topic. Well structured, IT-based educational programmes (eLearning) often ensure quality and may make training more affordable and accessible. The aim of this study was to explore the impact of an eLearning course for personnel on the rates and duration of seclusion and mechanical restraint among psychiatric inpatients. In a cluster-randomized intervention trial, the nursing personnel on 10 wards were randomly assigned to eLearning (intervention) or training-as-usual (control) groups. The eLearning course comprised six modules with specific topics (legal and ethical issues, behaviour-related factors, therapeutic relationship and self-awareness, teamwork and integrating knowledge with practice) and specific learning methods. The rates (incidents per 1000 occupied bed days) and durations of the coercion incidents were examined before and after the course. A total of 1283 coercion incidents (1143 seclusions [89%] and 140 incidents involving the use of mechanical restraints [11%]) were recorded on the study wards during the data collection period. On the intervention wards, there were no statistically significant changes in the rates of seclusion and mechanical restraint. However, the duration of incidents involving mechanical restraints shortened from 36.0 to 4.0 h (median) (P eLearning course, the duration of incidents involving the use of mechanical restraints decreased. However, more studies are needed to ensure that the content of the course focuses on the most important factors associated with the seclusion-related elements. The eLearning course deserves further development and further studies. The duration of coercion incidents merits attention in future research.

Learning biology through connecting mathematics to scientific mechanisms: Student outcomes and teacher supports

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Schuchardt, Anita

Integrating mathematics into science classrooms has been part of the conversation in science education for a long time. However, studies on student learning after incorporating mathematics in to the science classroom have shown mixed results. Understanding the mixed effects of including mathematics in science has been hindered by a historical focus on characteristics of integration tangential to student learning (e.g., shared elements, extent of integration). A new framework is presented emphasizing the epistemic role of mathematics in science. An epistemic role of mathematics missing from the current literature is identified: use of mathematics to represent scientific mechanisms, Mechanism Connected Mathematics (MCM). Building on prior theoretical work, it is proposed that having students develop mathematical equations that represent scientific mechanisms could elevate their conceptual understanding and quantitative problem solving. Following design and implementation of an MCM unit in inheritance, a large-scale quantitative analysis of pre and post implementation test results showed MCM students, compared to traditionally instructed students) had significantly greater gains in conceptual understanding of mathematically modeled scientific mechanisms, and their ability to solve complex quantitative problems. To gain insight into the mechanism behind the gain in quantitative problem solving, a small-scale qualitative study was conducted of two contrasting groups: 1) within-MCM instruction: competent versus struggling problem solvers, and 2) within-competent problem solvers: MCM instructed versus traditionally instructed. Competent MCM students tended to connect their mathematical inscriptions to the scientific phenomenon and to switch between mathematical and scientifically productive approaches during problem solving in potentially productive ways. The other two groups did not. To address concerns about teacher capacity presenting barriers to scalability of MCM

Linking Individual Learning Styles to Approach-Avoidance Motivational Traits and Computational Aspects of Reinforcement Learning.

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Kristoffer Carl Aberg

Full Text Available Learning how to gain rewards (approach learning and avoid punishments (avoidance learning is fundamental for everyday life. While individual differences in approach and avoidance learning styles have been related to genetics and aging, the contribution of personality factors, such as traits, remains undetermined. Moreover, little is known about the computational mechanisms mediating differences in learning styles. Here, we used a probabilistic selection task with positive and negative feedbacks, in combination with computational modelling, to show that individuals displaying better approach (vs. avoidance learning scored higher on measures of approach (vs. avoidance trait motivation, but, paradoxically, also displayed reduced learning speed following positive (vs. negative outcomes. These data suggest that learning different types of information depend on associated reward values and internal motivational drives, possibly determined by personality traits.

Linking Individual Learning Styles to Approach-Avoidance Motivational Traits and Computational Aspects of Reinforcement Learning

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Carl Aberg, Kristoffer; Doell, Kimberly C.; Schwartz, Sophie

2016-01-01

Learning how to gain rewards (approach learning) and avoid punishments (avoidance learning) is fundamental for everyday life. While individual differences in approach and avoidance learning styles have been related to genetics and aging, the contribution of personality factors, such as traits, remains undetermined. Moreover, little is known about the computational mechanisms mediating differences in learning styles. Here, we used a probabilistic selection task with positive and negative feedbacks, in combination with computational modelling, to show that individuals displaying better approach (vs. avoidance) learning scored higher on measures of approach (vs. avoidance) trait motivation, but, paradoxically, also displayed reduced learning speed following positive (vs. negative) outcomes. These data suggest that learning different types of information depend on associated reward values and internal motivational drives, possibly determined by personality traits. PMID:27851807

Auto Mechanics I. Learning Activity Packets (LAPs). Section E--Brakes.

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Oklahoma State Board of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This document contains two learning activity packets (LAPs) that outline the study activities for the "brakes" instructional area for an Auto Mechanics I course. The two LAPs cover the following topics: brake systems and power disc brakes. Each LAP contains a cover sheet that describes its purpose, an introduction, and the tasks included…

Talker-specific learning in amnesia: Insight into mechanisms of adaptive speech perception.

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Trude, Alison M; Duff, Melissa C; Brown-Schmidt, Sarah

2014-05-01

A hallmark of human speech perception is the ability to comprehend speech quickly and effortlessly despite enormous variability across talkers. However, current theories of speech perception do not make specific claims about the memory mechanisms involved in this process. To examine whether declarative memory is necessary for talker-specific learning, we tested the ability of amnesic patients with severe declarative memory deficits to learn and distinguish the accents of two unfamiliar talkers by monitoring their eye-gaze as they followed spoken instructions. Analyses of the time-course of eye fixations showed that amnesic patients rapidly learned to distinguish these accents and tailored perceptual processes to the voice of each talker. These results demonstrate that declarative memory is not necessary for this ability and points to the involvement of non-declarative memory mechanisms. These results are consistent with findings that other social and accommodative behaviors are preserved in amnesia and contribute to our understanding of the interactions of multiple memory systems in the use and understanding of spoken language. Copyright © 2014 Elsevier Ltd. All rights reserved.

Different mechanisms in learning different second languages: Evidence from English speakers learning Chinese and Spanish.

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Cao, Fan; Sussman, Bethany L; Rios, Valeria; Yan, Xin; Wang, Zhao; Spray, Gregory J; Mack, Ryan M

2017-03-01

phonology in Chinese. In summary, our study suggests different mechanisms in learning different L2s, providing important insights into neural plasticity and important implications in second language education. Copyright © 2017 Elsevier Inc. All rights reserved.

Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

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Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V

2015-01-01

Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

Using Optimal Combination of Teaching-Learning Methods (Open Book Assignment and Group Tutorials) as Revision Exercises to Improve Learning Outcome in Low Achievers in Biochemistry

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Rajappa, Medha; Bobby, Zachariah; Nandeesha, H.; Suryapriya, R.; Ragul, Anithasri; Yuvaraj, B.; Revathy, G.; Priyadarssini, M.

2016-01-01

Graduate medical students of India are taught Biochemistry by didactic lectures and they hardly get any opportunity to clarify their doubts and reinforce the concepts which they learn in these lectures. We used a combination of teaching-learning (T-L) methods (open book assignment followed by group tutorials) to study their efficacy in improving…

Teacher Opinions on the Innovation Management Skills of School Administrators and Organizational Learning Mechanisms

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Omur, Yunus Emre; Argon, Turkan

2016-01-01

Problem Statement: In modern society, schools, just as other institutions, are required to be innovative organizations. For this purpose, they must not only be learning organizations, they must also be innovative. In this sense, the purpose of this study is to discover the relationship between organizational learning mechanisms at schools and…

Using video games to combine learning and assessment in mathematics education

OpenAIRE

Kristian Juha Mikael Kiili; Keith Devlin; Arttu Perttula; Pauliina Tuomi; Antero Lindstedt

2015-01-01

One problem with most education systems is that learning and (summative) assessment are generally treated as quite separate things in schools. We argue that video games can provide an opportunity to combine these processes in an engaging and effective way. The present study focuses on investigating the effectiveness and the assessment power of two different mathematics video games, Semideus and Wuzzit Trouble. In the current study, we validated the Semideus game as a rational number test inst...

Vicarious neural processing of outcomes during observational learning.

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Elisabetta Monfardini

Full Text Available Learning what behaviour is appropriate in a specific context by observing the actions of others and their outcomes is a key constituent of human cognition, because it saves time and energy and reduces exposure to potentially dangerous situations. Observational learning of associative rules relies on the ability to map the actions of others onto our own, process outcomes, and combine these sources of information. Here, we combined newly developed experimental tasks and functional magnetic resonance imaging (fMRI to investigate the neural mechanisms that govern such observational learning. Results show that the neural systems involved in individual trial-and-error learning and in action observation and execution both participate in observational learning. In addition, we identified brain areas that specifically activate for others' incorrect outcomes during learning in the posterior medial frontal cortex (pMFC, the anterior insula and the posterior superior temporal sulcus (pSTS.

Vicarious neural processing of outcomes during observational learning.

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Monfardini, Elisabetta; Gazzola, Valeria; Boussaoud, Driss; Brovelli, Andrea; Keysers, Christian; Wicker, Bruno

2013-01-01

Learning what behaviour is appropriate in a specific context by observing the actions of others and their outcomes is a key constituent of human cognition, because it saves time and energy and reduces exposure to potentially dangerous situations. Observational learning of associative rules relies on the ability to map the actions of others onto our own, process outcomes, and combine these sources of information. Here, we combined newly developed experimental tasks and functional magnetic resonance imaging (fMRI) to investigate the neural mechanisms that govern such observational learning. Results show that the neural systems involved in individual trial-and-error learning and in action observation and execution both participate in observational learning. In addition, we identified brain areas that specifically activate for others' incorrect outcomes during learning in the posterior medial frontal cortex (pMFC), the anterior insula and the posterior superior temporal sulcus (pSTS).

Physicochemical Mechanisms of Synergistic Biological Action of Combinations of Aromatic Heterocyclic Compounds

OpenAIRE

Evstigneev, Maxim P.

2013-01-01

The mechanisms of synergistic biological effects observed in the simultaneous use of aromatic heterocyclic compounds in combination are reviewed, and the specific biological role of heteroassociation of aromatic molecules is discussed.

Implementation of Simulation Based-Concept Attainment Method to Increase Interest Learning of Engineering Mechanics Topic

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Sultan, A. Z.; Hamzah, N.; Rusdi, M.

2018-01-01

The implementation of concept attainment method based on simulation was used to increase student’s interest in the subjects Engineering of Mechanics in second semester of academic year 2016/2017 in Manufacturing Engineering Program, Department of Mechanical PNUP. The result of the implementation of this learning method shows that there is an increase in the students’ learning interest towards the lecture material which is summarized in the form of interactive simulation CDs and teaching materials in the form of printed books and electronic books. From the implementation of achievement method of this simulation based concept, it is noted that the increase of student participation in the presentation and discussion as well as the deposit of individual assignment of significant student. With the implementation of this method of learning the average student participation reached 89%, which before the application of this learning method only reaches an average of 76%. And also with previous learning method, for exam achievement of A-grade under 5% and D-grade above 8%. After the implementation of the new learning method (simulation based-concept attainment method) the achievement of Agrade has reached more than 30% and D-grade below 1%.

Implementation of Project Based Learning in Mechatronic Lab Course at Bandung State Polytechnic

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Basjaruddin, Noor Cholis; Rakhman, Edi

2016-01-01

Mechatronics is a multidisciplinary that includes a combination of mechanics, electronics, control systems, and computer science. The main objective of mechatronics learning is to establish a comprehensive mindset in the development of mechatronic systems. Project Based Learning (PBL) is an appropriate method for use in the learning process of…

Introductory Education for Mechanical Engineering by Exercise in Mechanical Disassembly

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Matsui, Yoshio; Asakawa, Naoki; Iwamori, Satoru

An introductory program “Exercise for engineers in mechanical disassembly” is an exercise that ten students of every team disassemble a motor scooter to the components and then assemble again to the initial form in 15 weeks. The purpose of this program is to introduce mechanical engineering by touching the real machine and learning how it is composed from various mechanical parts to the students at the early period after the entrance into the university. Additional short lectures by young teachers and a special lecture by a top engineer in the industry encourage the students to combine the actual machine and the mechanical engineering subjects. Furthermore, various educations such as group leader system, hazard prediction training, parts filing are included in this program. As a result, students recognize the importance of the mechanical engineering study and the way of group working.

Latent memory facilitates relearning through molecular signaling mechanisms that are distinct from original learning.

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Menges, Steven A; Riepe, Joshua R; Philips, Gary T

2015-09-01

A highly conserved feature of memory is that it can exist in a latent, non-expressed state which is revealed during subsequent learning by its ability to significantly facilitate (savings) or inhibit (latent inhibition) subsequent memory formation. Despite the ubiquitous nature of latent memory, the mechanistic nature of the latent memory trace and its ability to influence subsequent learning remains unclear. The model organism Aplysia californica provides the unique opportunity to make strong links between behavior and underlying cellular and molecular mechanisms. Using Aplysia, we have studied the mechanisms of savings due to latent memory for a prior, forgotten experience. We previously reported savings in the induction of three distinct temporal domains of memory: short-term (10min), intermediate-term (2h) and long-term (24h). Here we report that savings memory formation utilizes molecular signaling pathways that are distinct from original learning: whereas the induction of both original intermediate- and long-term memory in naïve animals requires mitogen activated protein kinase (MAPK) activation and ongoing protein synthesis, 2h savings memory is not disrupted by inhibitors of MAPK or protein synthesis, and 24h savings memory is not dependent on MAPK activation. Collectively, these findings reveal that during forgetting, latent memory for the original experience can facilitate relearning through molecular signaling mechanisms that are distinct from original learning. Copyright © 2015 Elsevier Inc. All rights reserved.

Monocular perceptual learning of contrast detection facilitates binocular combination in adults with anisometropic amblyopia.

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Chen, Zidong; Li, Jinrong; Liu, Jing; Cai, Xiaoxiao; Yuan, Junpeng; Deng, Daming; Yu, Minbin

2016-02-01

Perceptual learning in contrast detection improves monocular visual function in adults with anisometropic amblyopia; however, its effect on binocular combination remains unknown. Given that the amblyopic visual system suffers from pronounced binocular functional loss, it is important to address how the amblyopic visual system responds to such training strategies under binocular viewing conditions. Anisometropic amblyopes (n = 13) were asked to complete two psychophysical supra-threshold binocular summation tasks: (1) binocular phase combination and (2) dichoptic global motion coherence before and after monocular training to investigate this question. We showed that these participants benefited from monocular training in terms of binocular combination. More importantly, the improvements observed with the area under log CSF (AULCSF) were found to be correlated with the improvements in binocular phase combination.

Using Formal Game Design Methods to Embed Learning Outcomes into Game Mechanics and Avoid Emergent Behaviour

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Grey, Simon; Grey, David; Gordon, Neil; Purdy, Jon

2017-01-01

This paper offers an approach to designing game-based learning experiences inspired by the Mechanics-Dynamics-Aesthetics (MDA) model (Hunicke et al., 2004) and the elemental tetrad model (Schell, 2008) for game design. A case for game based learning as an active and social learning experience is presented including arguments from both teachers and…

Combining bimodal presentation schemes and buzz groups improves clinical reasoning and learning at morning report.

Science.gov (United States)

Balslev, Thomas; Rasmussen, Astrid Bruun; Skajaa, Torjus; Nielsen, Jens Peter; Muijtjens, Arno; De Grave, Willem; Van Merriënboer, Jeroen

2014-12-11

Abstract Morning reports offer opportunities for intensive work-based learning. In this controlled study, we measured learning processes and outcomes with the report of paediatric emergency room patients. Twelve specialists and 12 residents were randomised into four groups and discussed the same two paediatric cases. The groups differed in their presentation modality (verbal only vs. verbal + text) and the use of buzz groups (with vs. without). The verbal interactions were analysed for clinical reasoning processes. Perceptions of learning and judgment of learning were reported in a questionnaire. Diagnostic accuracy was assessed by a 20-item multiple-choice test. Combined bimodal presentation and buzz groups increased the odds ratio of clinical reasoning to occur in the discussion of cases by a factor of 1.90 (p = 0.013), indicating superior reasoning for buzz groups working with bimodal materials. For specialists, a positive effect of bimodal presentation was found on perceptions of learning (p presentation on diagnostic accuracy was noted in the specialists (p presentation and buzz group discussion of emergency cases improves clinicians' clinical reasoning and learning.

Learning from doing: the case for combining normalisation process theory and participatory learning and action research methodology for primary healthcare implementation research.

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de Brún, Tomas; O'Reilly-de Brún, Mary; O'Donnell, Catherine A; MacFarlane, Anne

2016-08-03

The implementation of research findings is not a straightforward matter. There are substantive and recognised gaps in the process of translating research findings into practice and policy. In order to overcome some of these translational difficulties, a number of strategies have been proposed for researchers. These include greater use of theoretical approaches in research focused on implementation, and use of a wider range of research methods appropriate to policy questions and the wider social context in which they are placed. However, questions remain about how to combine theory and method in implementation research. In this paper, we respond to these proposals. Focussing on a contemporary social theory, Normalisation Process Theory, and a participatory research methodology, Participatory Learning and Action, we discuss the potential of their combined use for implementation research. We note ways in which Normalisation Process Theory and Participatory Learning and Action are congruent and may therefore be used as heuristic devices to explore, better understand and support implementation. We also provide examples of their use in our own research programme about community involvement in primary healthcare. Normalisation Process Theory alone has, to date, offered useful explanations for the success or otherwise of implementation projects post-implementation. We argue that Normalisation Process Theory can also be used to prospectively support implementation journeys. Furthermore, Normalisation Process Theory and Participatory Learning and Action can be used together so that interventions to support implementation work are devised and enacted with the expertise of key stakeholders. We propose that the specific combination of this theory and methodology possesses the potential, because of their combined heuristic force, to offer a more effective means of supporting implementation projects than either one might do on its own, and of providing deeper understandings of

Assessing Cognitive Load Theory to Improve Student Learning for Mechanical Engineers

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Impelluso, Thomas J.

2009-01-01

A computer programming class for students of mechanical engineering was redesigned and assessed: Cognitive Load Theory was used to redesign the content; online technologies were used to redesign the delivery. Student learning improved and the dropout rate was reduced. This article reports on both attitudinal and objective assessment: comparing…

E-learning optimization: the relative and combined effects of mental practice and modeling on enhanced podcast-based learning-a randomized controlled trial.

Science.gov (United States)

Alam, Fahad; Boet, Sylvain; Piquette, Dominique; Lai, Anita; Perkes, Christopher P; LeBlanc, Vicki R

2016-10-01

Enhanced podcasts increase learning, but evidence is lacking on how they should be designed to optimize their effectiveness. This study assessed the impact two learning instructional design methods (mental practice and modeling), either on their own or in combination, for teaching complex cognitive medical content when incorporated into enhanced podcasts. Sixty-three medical students were randomised to one of four versions of an airway management enhanced podcast: (1) control: narrated presentation; (2) modeling: narration with video demonstration of skills; (3) mental practice: narrated presentation with guided mental practice; (4) combined: modeling and mental practice. One week later, students managed a manikin-based simulated airway crisis. Knowledge acquisition was assessed by baseline and retention multiple-choice quizzes. Two blinded raters assessed all videos obtained from simulated crises to measure the students' skills using a key-elements scale, critical error checklist, and the Ottawa global rating scale (GRS). Baseline knowledge was not different between all four groups (p = 0.65). One week later, knowledge retention was significantly higher for (1) both the mental practice and modeling group than the control group (p = 0.01; p = 0.01, respectively) and (2) the combined mental practice and modeling group compared to all other groups (all ps = 0.01). Regarding skills acquisition, the control group significantly under-performed in comparison to all other groups on the key-events scale (all ps ≤ 0.05), the critical error checklist (all ps ≤ 0.05), and the Ottawa GRS (all ps ≤ 0.05). The combination of mental practice and modeling led to greater improvement on the key events checklist (p = 0.01) compared to either strategy alone. However, the combination of the two strategies did not result in any further learning gains on the two other measures of clinical performance (all ps > 0.05). The effectiveness of enhanced podcasts for

Improvement of Learning and Memory Induced by Cordyceps Polypeptide Treatment and the Underlying Mechanism

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Guangxin Yuan

2018-01-01

Full Text Available Our previous research revealed that Cordyceps militaris can improve the learning and memory, and although the main active ingredient should be its polypeptide complexes, the underlying mechanism of its activity remains poorly understood. In this study, we explored the mechanisms by which Cordyceps militaris improves learning and memory in a mouse model. Mice were given scopolamine hydrobromide intraperitoneally to establish a mouse model of learning and memory impairment. The effects of Cordyceps polypeptide in this model were tested using the Morris water maze test; serum superoxide dismutase activity; serum malondialdehyde levels; activities of acetyl cholinesterase, Na+-k+-ATPase, and nitric oxide synthase; and gamma aminobutyric acid and glutamate contents in brain tissue. Moreover, differentially expressed genes and the related cellular signaling pathways were screened using an mRNA expression profile chip. The results showed that the genes Pik3r5, Il-1β, and Slc18a2 were involved in the effects of Cordyceps polypeptide on the nervous system of these mice. Our findings suggest that Cordyceps polypeptide may improve learning and memory in the scopolamine-induced mouse model of learning and memory impairment by scavenging oxygen free radicals, preventing oxidative damage, and protecting the nervous system.

Combining Face-to-Face Learning with Online Learning in Virtual Worlds

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Berns, Anke; Gonzalez-Pardo, Antonio; Camacho, David

2012-01-01

This paper focuses on the development of videogame-like applications in a 3D virtual environment as a complement to the face-to-face teaching and learning. With the changing role of teaching and learning and the increasing use of "blended learning," instructors are increasingly expected to explore new ways to attend to the needs of their…

Achieving effective learning effects in the blended course: a combined approach of online self-regulated learning and collaborative learning with initiation.

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Tsai, Chia-Wen

2011-09-01

In many countries, undergraduates are required to take at least one introductory computer course to enhance their computer literacy and computing skills. However, the application software education in Taiwan can hardly be deemed as effective in developing students' practical computing skills. The author applied online self-regulated learning (SRL) and collaborative learning (CL) with initiation in a blended computing course and examined the effects of different combinations on enhancing students' computing skills. Four classes, comprising 221 students, participated in this study. The online SRL and CL with initiation (G1, n = 53), online CL with initiation (G2, n = 68), and online CL without initiation (G3, n = 68) were experimental groups, and the last class, receiving traditional lecture (G4, n = 32), was the control group. The results of this study show that students who received the intervention of online SRL and CL with initiation attained significantly best grades for practical computing skills, whereas those that received the traditional lectures had statistically poorest grades among the four classes. The implications for schools and educators who plan to provide online or blended learning for their students, particularly in computing courses, are also provided in this study.

Neural oscillatory mechanisms during novel grammar learning underlying language analytical abilities.

Science.gov (United States)

Kepinska, Olga; Pereda, Ernesto; Caspers, Johanneke; Schiller, Niels O

2017-12-01

The goal of the present study was to investigate the initial phases of novel grammar learning on a neural level, concentrating on mechanisms responsible for individual variability between learners. Two groups of participants, one with high and one with average language analytical abilities, performed an Artificial Grammar Learning (AGL) task consisting of learning and test phases. During the task, EEG signals from 32 cap-mounted electrodes were recorded and epochs corresponding to the learning phases were analysed. We investigated spectral power modulations over time, and functional connectivity patterns by means of a bivariate, frequency-specific index of phase synchronization termed Phase Locking Value (PLV). Behavioural data showed learning effects in both groups, with a steeper learning curve and higher ultimate attainment for the highly skilled learners. Moreover, we established that cortical connectivity patterns and profiles of spectral power modulations over time differentiated L2 learners with various levels of language analytical abilities. Over the course of the task, the learning process seemed to be driven by whole-brain functional connectivity between neuronal assemblies achieved by means of communication in the beta band frequency. On a shorter time-scale, increasing proficiency on the AGL task appeared to be supported by stronger local synchronisation within the right hemisphere regions. Finally, we observed that the highly skilled learners might have exerted less mental effort, or reduced attention for the task at hand once the learning was achieved, as evidenced by the higher alpha band power. Copyright © 2017 Elsevier Inc. All rights reserved.

Learning mechanisms in multidisciplinary teamwork with real customers and open-ended problems

Science.gov (United States)

Heikkinen, Juho; Isomöttönen, Ville

2015-11-01

Recently, there has been a trend towards adding a multidisciplinary or multicultural element to traditional monodisciplinary project courses in computing and engineering. In this article, we examine the implications of multidisciplinarity for students' learning experiences during a one-semester project course for real customers. We use a qualitative research approach and base our analysis on students' learning reports on three instances of a project course titled Multidisciplinary working life project. The main contribution of this article is the unified theoretical picture of the learning mechanisms stemming from multidisciplinarity. Our main conclusions are that (1) students generally have a positive view of multidisciplinarity; (2) multidisciplinary teams enable students to better identify their own expertise, which leads to increased occupational identity; and (3) learning experiences are not fixed, as team spirit and student attitude play an important role in how students react to challenging situations arising from introduction of the multidisciplinarity.

Simultaneous Learning and Filtering without Delusions: A Bayes-Optimal Derivation of Combining Predictive Inference and AdaptiveFiltering

Directory of Open Access Journals (Sweden)

Jan eKneissler

2015-04-01

Full Text Available Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF. PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than ten-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

A Learning Log Analysis of an English-Reading e-Book System Combined with a Guidance Mechanism

Science.gov (United States)

Wu, Ting-Ting

2016-01-01

Learning English by reading articles on multimedia e-book devices can assist students in improving their vocabulary and in understanding the associations among vocabulary, textual meaning, and paragraph composition. Adaptive integration of reading technologies and strategies not only strengthens their language ability and reading comprehension,…

Effectiveness of teaching and learning mathematics for Thai university engineering students through a combination of activity and lecture based classroom

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Parinya S. Ngiamsunthorn

2014-04-01

Full Text Available There are concerns of developing effective pedagogical practices for teaching mathematics for engineering students as many engineering students experience difficulties in learning compulsory mathematics subjects in their first and second years of the degree. This paper aims to investigate the effectiveness of using a variety of teaching and learning approaches including lecture based learning, activity based learning, e-learning via learning management system (LMS and practice or tutorial session in mathematics subjects for engineering students. This study was carried out on 160 students who need to enroll three basic mathematics subjects (MTH101, MTH102 and MTH201 for an engineering degree during academic year 2011 – 2012. The students were divided into three groups according to their majors of study. The first two groups of students were given a combination of various teaching approaches for only one semester (either MTH102 or MTH201, while the last group was given a combination of various teaching approaches for two semesters (both MTH102 and MTH201. To evaluate the effectiveness of teaching and learning, examination results, questionnaires on attitude towards teaching and learning, and a formal university teaching evaluation by students were collected and analyzed. It is found that different students perceive mathematics contents from different teaching methods according to their preferred learning styles. Moreover, most students in all groups performed at least the same or better in their final subject (MTH201. However, there is an interesting finding that low proficiency students in earlier mathematics subjects who received a combination of various teaching approaches for two semesters can improve their examination results better than other groups, on average. This is also reflected from an increasing average score on teaching evaluation from this group of students about teaching techniques.

Modulatory mechanisms of cortisol effects on emotional learning and memory: novel perspectives.

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van Ast, Vanessa A; Cornelisse, Sandra; Marin, Marie-France; Ackermann, Sandra; Garfinkel, Sarah N; Abercrombie, Heather C

2013-09-01

It has long been known that cortisol affects learning and memory processes. Despite a wealth of research dedicated to cortisol effects on learning and memory, the strength or even directionality of the effects often vary. A number of the factors that alter cortisol's effects on learning and memory are well-known. For instance, effects of cortisol can be modulated by emotional arousal and the memory phase under study. Despite great advances in understanding factors that explain variability in cortisol's effects, additional modulators of cortisol effects on memory exist that are less widely acknowledged in current basic experimental research. The goal of the current review is to disseminate knowledge regarding less well-known modulators of cortisol effects on learning and memory. Since several models for the etiology of anxiety, such as post-traumatic stress disorder (PTSD), incorporate stress and the concomitant release of cortisol as important vulnerability factors, enhanced understanding of mechanisms by which cortisol exerts beneficial as opposed to detrimental effects on memory is very important. Further elucidation of the factors that modulate (or alter) cortisol's effects on memory will allow reconciliation of seemingly inconsistent findings in the basic and clinical literatures. The present review is based on a symposium as part of the 42nd International Society of Psychoneuroendocrinology Conference, New York, USA, that highlighted some of those modulators and their underlying mechanisms. Copyright © 2013 Elsevier Ltd. All rights reserved.

System level mechanisms of adaptation, learning, memory formation and evolvability: the role of chaperone and other networks.

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Gyurko, David M; Soti, Csaba; Stetak, Attila; Csermely, Peter

2014-05-01

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here, we describe first the protein structure networks of molecular chaperones, then characterize chaperone containing sub-networks of interactomes called as chaperone-networks or chaperomes. We review the role of molecular chaperones in short-term adaptation of cellular networks in response to stress, and in long-term adaptation discussing their putative functions in the regulation of evolvability. We provide a general overview of possible network mechanisms of adaptation, learning and memory formation. We propose that changes of network rigidity play a key role in learning and memory formation processes. Flexible network topology provides ' learning-competent' state. Here, networks may have much less modular boundaries than locally rigid, highly modular networks, where the learnt information has already been consolidated in a memory formation process. Since modular boundaries are efficient filters of information, in the 'learning-competent' state information filtering may be much smaller, than after memory formation. This mechanism restricts high information transfer to the 'learning competent' state. After memory formation, modular boundary-induced segregation and information filtering protect the stored information. The flexible networks of young organisms are generally in a 'learning competent' state. On the contrary, locally rigid networks of old organisms have lost their 'learning competent' state, but store and protect their learnt information efficiently. We anticipate that the above mechanism may operate at the level of both protein-protein interaction and neuronal networks.

Collaborative Game-based Learning - Automatized Adaptation Mechanics for Game-based Collaborative Learning using Game Mastering Concepts

OpenAIRE

Wendel, Viktor Matthias

2015-01-01

Learning and playing represent two core aspects of the information and communication society nowadays. Both issues are subsumed in Digital Education Games, one major field of Serious Games. Serious Games combine concepts of gaming with a broad range of application fields: among others, educational sectors and training or health and sports, but also marketing, advertisement, political education, and other societally relevant areas such as climate, energy, and safety. This work focuses on colla...

Shared mechanisms of perceptual learning and decision making.

Science.gov (United States)

Law, Chi-Tat; Gold, Joshua I

2010-04-01

Perceptual decisions require the brain to weigh noisy evidence from sensory neurons to form categorical judgments that guide behavior. Here we review behavioral and neurophysiological findings suggesting that at least some forms of perceptual learning do not appear to affect the response properties of neurons that represent the sensory evidence. Instead, improved perceptual performance results from changes in how the sensory evidence is selected and weighed to form the decision. We discuss the implications of this idea for possible sites and mechanisms of training-induced improvements in perceptual processing in the brain. Copyright © 2009 Cognitive Science Society, Inc.

Enhanced Mechanical Properties of Poplar Wood by a Combined-Hydro-Thermo-Mechanical (CHTM) Modification

OpenAIRE

Houri Sharifnia; Behbood Mohebbi

2011-01-01

The current research explains an innovated technique to enhanced mechanice properties of poplar wood by combination of two modification techniques, hydrothermal and mechanical. Blocks of 50×55×500mm3 were cut from poplar wood and treated in a reactor at 120, 150 and 180°C for 30 min. Afterwards, the blocks were pressed at 180°C for 20 min at a pressure of 80 bar to achieve a compression set of 60% in radial direction. Density and bending properties (moduli of elasticity and rupture) as well a...

From inverse problems to learning: a Statistical Mechanics approach

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Baldassi, Carlo; Gerace, Federica; Saglietti, Luca; Zecchina, Riccardo

2018-01-01

We present a brief introduction to the statistical mechanics approaches for the study of inverse problems in data science. We then provide concrete new results on inferring couplings from sampled configurations in systems characterized by an extensive number of stable attractors in the low temperature regime. We also show how these result are connected to the problem of learning with realistic weak signals in computational neuroscience. Our techniques and algorithms rely on advanced mean-field methods developed in the context of disordered systems.

Introducing Innovative Approaches to Learning in Fluid Mechanics: A Case Study

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Gynnild, Vidar; Myrhaug, Dag; Pettersen, Bjornar

2007-01-01

The purpose of the current article is to examine the impact of laboratory demonstrations and computer visualizations on learning in a third-year fluid mechanics course at Norwegian University of Science and Technology (NTNU). As a first step, on entering the course, students were exposed to a laboratory demonstration focusing on the nature of…

Sharpened cortical tuning and enhanced cortico-cortical communication contribute to the long-term neural mechanisms of visual motion perceptual learning.

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Chen, Nihong; Bi, Taiyong; Zhou, Tiangang; Li, Sheng; Liu, Zili; Fang, Fang

2015-07-15

Much has been debated about whether the neural plasticity mediating perceptual learning takes place at the sensory or decision-making stage in the brain. To investigate this, we trained human subjects in a visual motion direction discrimination task. Behavioral performance and BOLD signals were measured before, immediately after, and two weeks after training. Parallel to subjects' long-lasting behavioral improvement, the neural selectivity in V3A and the effective connectivity from V3A to IPS (intraparietal sulcus, a motion decision-making area) exhibited a persistent increase for the trained direction. Moreover, the improvement was well explained by a linear combination of the selectivity and connectivity increases. These findings suggest that the long-term neural mechanisms of motion perceptual learning are implemented by sharpening cortical tuning to trained stimuli at the sensory processing stage, as well as by optimizing the connections between sensory and decision-making areas in the brain. Copyright © 2015 Elsevier Inc. All rights reserved.

Quantum Mechanics/Molecular Mechanics Method Combined with Hybrid All-Atom and Coarse-Grained Model: Theory and Application on Redox Potential Calculations.

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Shen, Lin; Yang, Weitao

2016-04-12

We developed a new multiresolution method that spans three levels of resolution with quantum mechanical, atomistic molecular mechanical, and coarse-grained models. The resolution-adapted all-atom and coarse-grained water model, in which an all-atom structural description of the entire system is maintained during the simulations, is combined with the ab initio quantum mechanics and molecular mechanics method. We apply this model to calculate the redox potentials of the aqueous ruthenium and iron complexes by using the fractional number of electrons approach and thermodynamic integration simulations. The redox potentials are recovered in excellent accordance with the experimental data. The speed-up of the hybrid all-atom and coarse-grained water model renders it computationally more attractive. The accuracy depends on the hybrid all-atom and coarse-grained water model used in the combined quantum mechanical and molecular mechanical method. We have used another multiresolution model, in which an atomic-level layer of water molecules around redox center is solvated in supramolecular coarse-grained waters for the redox potential calculations. Compared with the experimental data, this alternative multilayer model leads to less accurate results when used with the coarse-grained polarizable MARTINI water or big multipole water model for the coarse-grained layer.

Combining Unsupervised and Supervised Statistical Learning Methods for Currency Exchange Rate Forecasting

OpenAIRE

Vasiljeva, Polina

2016-01-01

In this thesis we revisit the challenging problem of forecasting currency exchange rate. We combine machine learning methods such as agglomerative hierarchical clustering and random forest to construct a two-step approach for predicting movements in currency exchange prices of the Swedish krona and the US dollar. We use a data set with over 200 predictors comprised of different financial and macro-economic time series and their transformations. We perform forecasting for one week ahead with d...

Using Game Mechanics to Measure What Students Learn from Programming Games

Science.gov (United States)

Denner, Jill; Werner, Linda; Campe, Shannon; Ortiz, Eloy

2014-01-01

Despite the growing popularity of teaching children to program games, little is known about the benefits for learning. In this article, the authors propose that game mechanics can be used as a window into how the children are thinking and describe a strategy for using them to analyze students' games. The study involved sixty 10-14 year old…

Adaptive learning in a compartmental model of visual cortex—how feedback enables stable category learning and refinement

Science.gov (United States)

Layher, Georg; Schrodt, Fabian; Butz, Martin V.; Neumann, Heiko

2014-01-01

The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, both of which are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in computational neuroscience. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of additional (sub-) category representations. We demonstrate the temporal evolution of such learning and show how the proposed combination of an associative memory with a modulatory feedback integration successfully establishes category and subcategory representations

micro-mechanical experimental investigation and modelling of strain and damage of argillaceous rocks under combined hydric and mechanical loads

International Nuclear Information System (INIS)

Wang, L.

2012-01-01

The hydro-mechanical behavior of argillaceous rocks, which are possible host rocks for underground radioactive nuclear waste storage, is investigated by means of micro-mechanical experimental investigations and modellings. Strain fields at the micrometric scale of the composite structure of this rock, are measured by the combination of environmental scanning electron microscopy, in situ testing and digital image correlation technique. The evolution of argillaceous rocks under pure hydric loading is first investigated. The strain field is strongly heterogeneous and manifests anisotropy. The observed nonlinear deformation at high relative humidity (RH) is related not only to damage, but also to the nonlinear swelling of the clay mineral itself, controlled by different local mechanisms depending on RH. Irreversible deformations are observed during hydric cycles, as well as a network of microcracks located in the bulk of the clay matrix and/or at the inclusion-matrix interface. Second, the local deformation field of the material under combined hydric and mechanical loadings is quantified. Three types of deformation bands are evidenced under mechanical loading, either normal to stress direction (compaction), parallel (microcracking) or inclined (shear). Moreover, they are strongly controlled by the water content of the material: shear bands are in particular prone to appear at high RH states. In view of understanding the mechanical interactions a local scale, the material is modeled as a composite made of non-swelling elastic inclusions embedded in an elastic swelling clay matrix. The internal stress field induced by swelling strain incompatibilities between inclusions and matrix, as well as the overall deformation, is numerically computed at equilibrium but also during the transient stage associated with a moisture gradient. An analytical micro-mechanical model based on Eshelby's solution is proposed. In addition, 2D finite element computations are performed. Results

Unpacking "Active Learning": A Combination of Flipped Classroom and Collaboration Support Is More Effective but Collaboration Support Alone Is Not

Science.gov (United States)

Rau, Martina A.; Kennedy, Kristopher; Oxtoby, Lucas; Bollom, Mark; Moore, John W.

2017-01-01

Much evidence shows that instruction that actively engages students with learning materials is more effective than traditional, lecture-centric instruction. These "active learning" models comprise an extremely heterogeneous set of instructional methods: they often include collaborative activities, flipped classrooms, or a combination of…

Learning with multiple representations: an example of a revision lesson in mechanics

Science.gov (United States)

Wong, Darren; Poo, Sng Peng; Eng Hock, Ng; Loo Kang, Wee

2011-03-01

We describe an example of learning with multiple representations in an A-level revision lesson on mechanics. The context of the problem involved the motion of a ball thrown vertically upwards in air and studying how the associated physical quantities changed during its flight. Different groups of students were assigned to look at the ball's motion using various representations: motion diagrams, vector diagrams, free-body diagrams, verbal description, equations and graphs, drawn against time as well as against displacement. Overall, feedback from students about the lesson was positive. We further discuss the benefits of using computer simulation to support and extend student learning.

Combined impact of exercise and temperature in learning and memory performance of fluoride toxicated rats.

Science.gov (United States)

Basha, P Mahaboob; Sujitha, N S

2012-12-01

In previous studies, we investigated a link between high fluoride exposure and functional IQ deficits in rats. This study is an extension conducted to explore the combined influence of physical exercise and temperature stress on the learning ability and memory in rats and to assess whether any positive modulation could be attenuated due to exercise regimen subjected to F-toxicated animals at different temperatures. Accumulation of ingested fluoride resulted significant inhibition in acetylcholinesterase activity (P learning phase [F (5, 35) = 19.065; P temperatures, high (35 °C) and low temperatures (20 °C) led to a slower acquisition and poor retention of the task when compared to thermo neutral temperatures (25 and 30 °C). Thus exercise up-regulate antioxidant defenses and promote learning abilities in fluorotic population.

Combining Digital Archives Content with Serious Game Approach to Create a Gamified Learning Experience

Directory of Open Access Journals (Sweden)

D.-T. Shih

2015-08-01

Full Text Available This paper presents an interdisciplinary to develop content-aware application that combines game with learning on specific categories of digital archives. The employment of content-oriented game enhances the gamification and efficacy of learning in culture education on architectures and history of Hsinchu County, Taiwan. The gamified form of the application is used as a backbone to support and provide a strong stimulation to engage users in learning art and culture, therefore this research is implementing under the goal of “The Digital ARt/ARchitecture Project”. The purpose of the abovementioned project is to develop interactive serious game approaches and applications for Hsinchu County historical archives and architectures. Therefore, we present two applications, “3D AR for Hukou Old ” and “Hsinchu County History Museum AR Tour” which are in form of augmented reality (AR. By using AR imaging techniques to blend real object and virtual content, the users can immerse in virtual exhibitions of Hukou Old Street and Hsinchu County History Museum, and to learn in ubiquitous computing environment. This paper proposes a content system that includes tools and materials used to create representations of digitized cultural archives including historical artifacts, documents, customs, religion, and architectures. The Digital ARt / ARchitecture Project is based on the concept of serious game and consists of three aspects: content creation, target management, and AR presentation. The project focuses on developing a proper approach to serve as an interactive game, and to offer a learning opportunity for appreciating historic architectures by playing AR cards. Furthermore, the card game aims to provide multi-faceted understanding and learning experience to help user learning through 3D objects, hyperlinked web data, and the manipulation of learning mode, and then effectively developing their learning levels on cultural and historical archives in

Combining Digital Archives Content with Serious Game Approach to Create a Gamified Learning Experience

Science.gov (United States)

Shih, D.-T.; Lin, C. L.; Tseng, C.-Y.

2015-08-01

This paper presents an interdisciplinary to develop content-aware application that combines game with learning on specific categories of digital archives. The employment of content-oriented game enhances the gamification and efficacy of learning in culture education on architectures and history of Hsinchu County, Taiwan. The gamified form of the application is used as a backbone to support and provide a strong stimulation to engage users in learning art and culture, therefore this research is implementing under the goal of "The Digital ARt/ARchitecture Project". The purpose of the abovementioned project is to develop interactive serious game approaches and applications for Hsinchu County historical archives and architectures. Therefore, we present two applications, "3D AR for Hukou Old " and "Hsinchu County History Museum AR Tour" which are in form of augmented reality (AR). By using AR imaging techniques to blend real object and virtual content, the users can immerse in virtual exhibitions of Hukou Old Street and Hsinchu County History Museum, and to learn in ubiquitous computing environment. This paper proposes a content system that includes tools and materials used to create representations of digitized cultural archives including historical artifacts, documents, customs, religion, and architectures. The Digital ARt / ARchitecture Project is based on the concept of serious game and consists of three aspects: content creation, target management, and AR presentation. The project focuses on developing a proper approach to serve as an interactive game, and to offer a learning opportunity for appreciating historic architectures by playing AR cards. Furthermore, the card game aims to provide multi-faceted understanding and learning experience to help user learning through 3D objects, hyperlinked web data, and the manipulation of learning mode, and then effectively developing their learning levels on cultural and historical archives in Hsinchu County.

QSAR modelling using combined simple competitive learning networks and RBF neural networks.

Science.gov (United States)

Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

2018-04-01

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

Combining Self-Explaining with Computer Architecture Diagrams to Enhance the Learning of Assembly Language Programming

Science.gov (United States)

Hung, Y.-C.

2012-01-01

This paper investigates the impact of combining self explaining (SE) with computer architecture diagrams to help novice students learn assembly language programming. Pre- and post-test scores for the experimental and control groups were compared and subjected to covariance (ANCOVA) statistical analysis. Results indicate that the SE-plus-diagram…

Isobolographic analysis of the mechanisms of action of anticonvulsants from a combination effect.

Science.gov (United States)

Matsumura, Nobuko; Nakaki, Toshio

2014-10-15

The nature of the pharmacodynamic interactions of drugs is influenced by the drugs׳ mechanisms of action. It has been hypothesized that drugs with different mechanisms are likely to interact synergistically, whereas those with similar mechanisms seem to produce additive interactions. In this review, we describe an extensive investigation of the published literature on drug combinations of anticonvulsants, the nature of the interaction of which has been evaluated by type I and II isobolographic analyses and the subthreshold method. The molecular targets of antiepileptic drugs (AEDs) include Na(+) and Ca(2+) channels, GABA type-A receptor, and glutamate receptors such as NMDA and AMPA/kainate receptors. The results of this review indicate that the nature of interactions evaluated by type I isobolographic analyses but not by the two other methods seems to be consistent with the above hypothesis. Type I isobolographic analyses may be used not only for evaluating drug combinations but also for predicting the targets of new drugs. Copyright © 2014 Elsevier B.V. All rights reserved.

Improving Learning Analytics--Combining Observational and Self-Report Data on Student Learning

Science.gov (United States)

Ellis, Robert A.; Han, Feifei; Pardo, Abelardo

2017-01-01

The field of education technology is embracing a use of learning analytics to improve student experiences of learning. Along with exponential growth in this area is an increasing concern of the interpretability of the analytics from the student experience and what they can tell us about learning. This study offers a way to address some of the…

Opioid Mechanism Involvement in the Synergism Produced by the Combination of Diclofenac and Caffeine in the Formalin Model

OpenAIRE

Flores-Ramos, Jos? Mar?a; D?az-Reval, M. Irene

2013-01-01

Analgesics can be administered in combination with caffeine for improved analgesic effectiveness in a process known as synergism. The mechanisms by which these combinations produce synergism are not yet fully understood. The aim of this study was to analyze whether the administration of diclofenac combined with caffeine produced antinociceptive synergism and whether opioid mechanisms played a role in this event. The formalin model was used to evaluate the antinociception produced by the oral ...

Mechanism for Promoting Motivation, Confidence, and Autonomy through Synchronic Communication Sessions in Virtual Learning Environments

Science.gov (United States)

Valencia, Jorge Andrick Parra; Dallos, Adriana Rocío Lizcano; Ballesteros, Eliécer Pineda

2017-01-01

This study presents a mechanism which explains the effect of synchronous communication on students' perception of the training process in virtual learning methodology used in a postgraduate programme at the University of Santander. We use System Dynamics to design a mechanism that integrates motivation, confidence, trust, and autonomy in students.…

Dense Neighborhoods and Mechanisms of Learning: Evidence from Children with Phonological Delay

Science.gov (United States)

Gierut, Judith A.; Morrisette, Michele L.

2015-01-01

There is a noted advantage of dense neighborhoods in language acquisition, but the learning mechanism that drives the effect is not well understood. Two hypotheses--long-term auditory word priming and phonological working memory--have been advanced in the literature as viable accounts. These were evaluated in two treatment studies enrolling twelve…

IMPLEMENTATION OF MULTIAGENT REINFORCEMENT LEARNING MECHANISM FOR OPTIMAL ISLANDING OPERATION OF DISTRIBUTION NETWORK

DEFF Research Database (Denmark)

Saleem, Arshad; Lind, Morten

2008-01-01

among electric power utilities to utilize modern information and communication technologies (ICT) in order to improve the automation of the distribution system. In this paper we present our work for the implementation of a dynamic multi-agent based distributed reinforcement learning mechanism...

Long-term potentiation in the amygdala: a cellular mechanism of fear learning and memory.

Science.gov (United States)

Sigurdsson, Torfi; Doyère, Valérie; Cain, Christopher K; LeDoux, Joseph E

2007-01-01

Much of the research on long-term potentiation (LTP) is motivated by the question of whether changes in synaptic strength similar to LTP underlie learning and memory. Here we discuss findings from studies on fear conditioning, a form of associative learning whose neural circuitry is relatively well understood, that may be particularly suited for addressing this question. We first review the evidence suggesting that fear conditioning is mediated by changes in synaptic strength at sensory inputs to the lateral nucleus of the amygdala. We then discuss several outstanding questions that will be important for future research on the role of synaptic plasticity in fear learning. The results gained from these studies may shed light not only on fear conditioning, but may also help unravel more general cellular mechanisms of learning and memory.

Mirror reversal and visual rotation are learned and consolidated via separate mechanisms: recalibrating or learning de novo?

Science.gov (United States)

Telgen, Sebastian; Parvin, Darius; Diedrichsen, Jörn

2014-10-08

Motor learning tasks are often classified into adaptation tasks, which involve the recalibration of an existing control policy (the mapping that determines both feedforward and feedback commands), and skill-learning tasks, requiring the acquisition of new control policies. We show here that this distinction also applies to two different visuomotor transformations during reaching in humans: Mirror-reversal (left-right reversal over a mid-sagittal axis) of visual feedback versus rotation of visual feedback around the movement origin. During mirror-reversal learning, correct movement initiation (feedforward commands) and online corrections (feedback responses) were only generated at longer latencies. The earliest responses were directed into a nonmirrored direction, even after two training sessions. In contrast, for visual rotation learning, no dependency of directional error on reaction time emerged, and fast feedback responses to visual displacements of the cursor were immediately adapted. These results suggest that the motor system acquires a new control policy for mirror reversal, which initially requires extra processing time, while it recalibrates an existing control policy for visual rotations, exploiting established fast computational processes. Importantly, memory for visual rotation decayed between sessions, whereas memory for mirror reversals showed offline gains, leading to better performance at the beginning of the second session than in the end of the first. With shifts in time-accuracy tradeoff and offline gains, mirror-reversal learning shares common features with other skill-learning tasks. We suggest that different neuronal mechanisms underlie the recalibration of an existing versus acquisition of a new control policy and that offline gains between sessions are a characteristic of latter. Copyright © 2014 the authors 0270-6474/14/3413768-12$15.00/0.

Collaborative Learning in Architectural Education: Benefits of Combining Conventional Studio, Virtual Design Studio and Live Projects

Science.gov (United States)

Rodriguez, Carolina; Hudson, Roland; Niblock, Chantelle

2018-01-01

Combinations of Conventional Studio and Virtual Design Studio (VDS) have created valuable learning environments that take advantage of different instruments of communication and interaction. However, past experiences have reported limitations in regards to student engagement and motivation, especially when the studio projects encourage abstraction…

Change in catalase and peroxidase activity in rat blood in case of combined radiation and mechanical injuries

International Nuclear Information System (INIS)

Volkovaya, T.A.

1982-01-01

Changes of catalase and peroxide activity of blood in rats in case of irradiation at 2.0 and 7.0 Gy, mechanical injury of animal chest and combined radiation injury were studied. The given data testify to considerable increase of the above enzymes activity in case of all these effects. The less decrease of catalase and peroxide activity was observed after infliction of mechanical injury alone. Aggravating effect of mechanical injury on the irradiated organism leads to more noticeable decrease of catalase activity (at early periods of observation) in comparison with radiation effect. Peroxide changes in case of combined radiation and mechanical injury of rats differ slightly from similar factors observed in case of irradiation alone

Combination of low energy and mechanical cooling technologies for buildings in Central Europe

NARCIS (Netherlands)

Lain, M.; Hensen, J.L.M.

2004-01-01

This paper discusses options for incorporating low energy cooling technologies combined with standard mechanical cooling in buildings in central Europe. Case studies, design recommendations and role of computer simulation of building and system in the design process are presented. Applicability of

Assessing the Effectiveness of a Hybrid-Flipped Model of Learning on Fluid Mechanics Instruction: Overall Course Performance, Homework, and Far- and Near-Transfer of Learning

Science.gov (United States)

Harrison, David J.; Saito, Laurel; Markee, Nancy; Herzog, Serge

2017-01-01

To examine the impact of a hybrid-flipped model utilising active learning techniques, the researchers inverted one section of an undergraduate fluid mechanics course, reduced seat time, and engaged in active learning sessions in the classroom. We compared this model to the traditional section on four performance measures. We employed a propensity…

Combining brain stimulation and video game to promote long-term transfer of learning and cognitive enhancement.

Science.gov (United States)

Looi, Chung Yen; Duta, Mihaela; Brem, Anna-Katharine; Huber, Stefan; Nuerk, Hans-Christoph; Cohen Kadosh, Roi

2016-02-23

Cognitive training offers the potential for individualised learning, prevention of cognitive decline, and rehabilitation. However, key research challenges include ecological validity (training design), transfer of learning and long-term effects. Given that cognitive training and neuromodulation affect neuroplasticity, their combination could promote greater, synergistic effects. We investigated whether combining transcranial direct current stimulation (tDCS) with cognitive training could further enhance cognitive performance compared to training alone, and promote transfer within a short period of time. Healthy adults received real or sham tDCS over their dorsolateral prefrontal cortices during two 30-minute mathematics training sessions involving body movements. To examine the role of training, an active control group received tDCS during a non-mathematical task. Those who received real tDCS performed significantly better in the game than the sham group, and showed transfer effects to working memory, a related but non-numerical cognitive domain. This transfer effect was absent in active and sham control groups. Furthermore, training gains were more pronounced amongst those with lower baseline cognitive abilities, suggesting the potential for reducing cognitive inequalities. All effects associated with real tDCS remained 2 months post-training. Our study demonstrates the potential benefit of this approach for long-term enhancement of human learning and cognition.

Monocular perceptual learning of contrast detection facilitates binocular combination in adults with anisometropic amblyopia

OpenAIRE

Chen, Zidong; Li, Jinrong; Liu, Jing; Cai, Xiaoxiao; Yuan, Junpeng; Deng, Daming; Yu, Minbin

2016-01-01

Perceptual learning in contrast detection improves monocular visual function in adults with anisometropic amblyopia; however, its effect on binocular combination remains unknown. Given that the amblyopic visual system suffers from pronounced binocular functional loss, it is important to address how the amblyopic visual system responds to such training strategies under binocular viewing conditions. Anisometropic amblyopes (n?=?13) were asked to complete two psychophysical supra-threshold binoc...

COMBINING COOPERATIVE LEARNING WITH READING ALOUD BY TEACHERS

Directory of Open Access Journals (Sweden)

George Jacobs

2004-06-01

Full Text Available This article begins with a section that describes cooperative learning and explains eight cooperative learning principles. The second section discusses the interface between cooperative learning and language pedagogy. Next is a section about the why and how of reading aloud by teachers. The heart of the article resides in the last and longest section which describes techniques for integrating cooperative learning with reading aloud by teachers. These techniques include ones that can be used before, while and after the teacher has read aloud to the class.

Evolution of social versus individual learning in a subdivided population revisited: comparative analysis of three coexistence mechanisms using the inclusive-fitness method.

Science.gov (United States)

Kobayashi, Yutaka; Ohtsuki, Hisashi

2014-03-01

Learning abilities are categorized into social (learning from others) and individual learning (learning on one's own). Despite the typically higher cost of individual learning, there are mechanisms that allow stable coexistence of both learning modes in a single population. In this paper, we investigate by means of mathematical modeling how the effect of spatial structure on evolutionary outcomes of pure social and individual learning strategies depends on the mechanisms for coexistence. We model a spatially structured population based on the infinite-island framework and consider three scenarios that differ in coexistence mechanisms. Using the inclusive-fitness method, we derive the equilibrium frequency of social learners and the genetic load of social learning (defined as average fecundity reduction caused by the presence of social learning) in terms of some summary statistics, such as relatedness, for each of the three scenarios and compare the results. This comparative analysis not only reconciles previous models that made contradictory predictions as to the effect of spatial structure on the equilibrium frequency of social learners but also derives a simple mathematical rule that determines the sign of the genetic load (i.e. whether or not social learning contributes to the mean fecundity of the population). Copyright © 2013 Elsevier Inc. All rights reserved.

Exploring Student-Generated Animations, Combined with a Representational Pedagogy, as a Tool for Learning in Chemistry

Science.gov (United States)

Yaseen, Zeynep; Aubusson, Peter

2018-02-01

This article describes an investigation into teaching and learning with student-generated animations combined with a representational pedagogy. In particular, it reports on interactive discussions that were stimulated by the students' own animations as well as their critiques of experts' animations. Animations representing views of states of matter provided a vehicle by which to investigate learning in a series of lessons. The study was implemented with Year 11 high school students. After students constructed, presented and discussed their animations, they watched and critiqued experts' animations. They were then interviewed about the teaching-learning process. Most students (91%) spoke positively about follow-up discussion classes, saying that their previous conceptions and understanding of states of matter had improved. They explained that they had identified some alternative conceptions, which they had held regarding states of matter and explained how their conceptions had changed. They reported that the teaching/learning process had helped them to develop a deeper understanding of the changing states of matter.

A duetting perspective on avian song learning.

Science.gov (United States)

Rivera-Cáceres, Karla D; Templeton, Christopher N

2017-12-25

Avian song learning has a rich history of study and has become the preeminent system for understanding the ontogeny of vocal communication in animals. Song learning in birds has many parallels with human language learning, ranging from the neural mechanisms involved to the importance of social factors in shaping signal acquisition. While much has been learned about the process of song learning, virtually all of the research done to date has focused on temperate species, where often only one sex (the male) sings. Duetting species, in which both males and females learn to sing and learn to combine their songs into temporally coordinated joint displays, could provide many insights into the processes by which vocal learning takes place. Here we highlight three key features of song learning-neuroendocrine control mechanisms, timing and life history stages of song acquisition, and the role of social factors in song selection and use-that have been elucidated from species where only males sing, and compare these with duetting species. We summarize what is known about song learning in duetting species and then provide several suggestions for fruitful directions for future research. We suggest that focusing research efforts on duetting species could significantly advance our understanding of vocal learning in birds and further cement the importance of avian species as models for understanding human conversations and the processes of vocal learning more broadly. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting protein complexes using a supervised learning method combined with local structural information.

Science.gov (United States)

Dong, Yadong; Sun, Yongqi; Qin, Chao

2018-01-01

The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient for guiding the detection of complexes. Therefore, we propose a new robust score function that combines the classification model with local structural information. Based on the score function, we provide a search method that works both forwards and backwards. The results from experiments on six benchmark PPI datasets and three protein complex datasets show that our approach can achieve better performance compared with the state-of-the-art supervised, semi-supervised and unsupervised methods for protein complex detection, occasionally significantly outperforming such methods.

Outsourcing and supplier learning

DEFF Research Database (Denmark)

Lema, Rasmus

2012-01-01

that learning from customers was important but insufficient for making the transition. Capability formation depended significantly on other channels and mechanisms outside or independent of outsourcing relationships. This paper shows how firms actively mobilised and combined internal and external sources......There is increasing agreement that Indian software firms are making the transition from competitive advantage based on low cost to competitive advantage based on innovation. However, there are few insights about how this transition process works. This paper seeks to bring together the outsourcing......, global value chains and firm capability literatures. It draws on empirical material focused on learning and innovation ‘events’ in Indian software firms – their inputs and sources – and makes headway in opening the ‘black box’ of supplier learning in outsourcing relationships. This paper suggests...

Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code

Directory of Open Access Journals (Sweden)

Guillermo Vigueras

2017-01-01

Full Text Available The current trends in next-generation exascale systems go towards integrating a wide range of specialized (co-processors into traditional supercomputers. Due to the efficiency of heterogeneous systems in terms of Watts and FLOPS per surface unit, opening the access of heterogeneous platforms to a wider range of users is an important problem to be tackled. However, heterogeneous platforms limit the portability of the applications and increase development complexity due to the programming skills required. Program transformation can help make programming heterogeneous systems easier by defining a step-wise transformation process that translates a given initial code into a semantically equivalent final code, but adapted to a specific platform. Program transformation systems require the definition of efficient transformation strategies to tackle the combinatorial problem that emerges due to the large set of transformations applicable at each step of the process. In this paper we propose a machine learning-based approach to learn heuristics to define program transformation strategies. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of this approach.

Parametrization of Combined Quantum Mechanical and Molecular Mechanical Methods: Bond-Tuned Link Atoms

Directory of Open Access Journals (Sweden)

Xin-Ping Wu

2018-05-01

Full Text Available Combined quantum mechanical and molecular mechanical (QM/MM methods are the most powerful available methods for high-level treatments of subsystems of very large systems. The treatment of the QM−MM boundary strongly affects the accuracy of QM/MM calculations. For QM/MM calculations having covalent bonds cut by the QM−MM boundary, it has been proposed previously to use a scheme with system-specific tuned fluorine link atoms. Here, we propose a broadly parametrized scheme where the parameters of the tuned F link atoms depend only on the type of bond being cut. In the proposed new scheme, the F link atom is tuned for systems with a certain type of cut bond at the QM−MM boundary instead of for a specific target system, and the resulting link atoms are call bond-tuned link atoms. In principle, the bond-tuned link atoms can be as convenient as the popular H link atoms, and they are especially well adapted for high-throughput and accurate QM/MM calculations. Here, we present the parameters for several kinds of cut bonds along with a set of validation calculations that confirm that the proposed bond-tuned link-atom scheme can be as accurate as the system-specific tuned F link-atom scheme.

Parametrization of Combined Quantum Mechanical and Molecular Mechanical Methods: Bond-Tuned Link Atoms.

Science.gov (United States)

Wu, Xin-Ping; Gagliardi, Laura; Truhlar, Donald G

2018-05-30

Combined quantum mechanical and molecular mechanical (QM/MM) methods are the most powerful available methods for high-level treatments of subsystems of very large systems. The treatment of the QM-MM boundary strongly affects the accuracy of QM/MM calculations. For QM/MM calculations having covalent bonds cut by the QM-MM boundary, it has been proposed previously to use a scheme with system-specific tuned fluorine link atoms. Here, we propose a broadly parametrized scheme where the parameters of the tuned F link atoms depend only on the type of bond being cut. In the proposed new scheme, the F link atom is tuned for systems with a certain type of cut bond at the QM-MM boundary instead of for a specific target system, and the resulting link atoms are call bond-tuned link atoms. In principle, the bond-tuned link atoms can be as convenient as the popular H link atoms, and they are especially well adapted for high-throughput and accurate QM/MM calculations. Here, we present the parameters for several kinds of cut bonds along with a set of validation calculations that confirm that the proposed bond-tuned link-atom scheme can be as accurate as the system-specific tuned F link-atom scheme.

Exploration of problem-based learning combined with standardized patient in the teaching of basic science of ophthalmology

Directory of Open Access Journals (Sweden)

Jin Yan

2015-08-01

Full Text Available AIM:To investigate the effect of problem-based learning(PBLcombined with standardized patient(SPin the teaching of basic science of ophthalmology. METHODS: Sixty-four students of Optometry in grade 2012 were randomly divided into experimental group(n=32and control group(n=32. Traditional teaching method was implemented in control group while PBL combined with SP was applied in experimental group. At the end of term students were interviewed using self-administered questionnaire to obtain their evaluation for teaching effect. Measurement data were expressed as (-overx±s and analyzed by independent samples t test. Enumeration data were analyzed by χ2 test, and PRESULTS:The mean scores of theory test(83.22±3.75and experimental test(94.28±2.20in experimental group were significantly higher than theory test(70.72±3.95and experimental test(85.44±3.52in control group(all PPPCONCLUSION:Using PBL combined with SP teaching mode in basic science of ophthalmology can highly improve learning enthusiasm of students and cultivate self-learning ability of students, practice ability and ability of clinical analysis.

Middle school students' learning of mechanics concepts through engagement in different sequences of physical and virtual experiments

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Sullivan, Sarah; Gnesdilow, Dana; Puntambekar, Sadhana; Kim, Jee-Seon

2017-08-01

Physical and virtual experimentation are thought to have different affordances for supporting students' learning. Research investigating the use of physical and virtual experiments to support students' learning has identified a variety of, sometimes conflicting, outcomes. Unanswered questions remain about how physical and virtual experiments may impact students' learning and for which contexts and content areas they may be most effective. Using a quasi-experimental design, we examined eighth grade students' (N = 100) learning of physics concepts related to pulleys depending on the sequence of physical and virtual labs they engaged in. Five classes of students were assigned to either the: physical first condition (PF) (n = 55), where students performed a physical pulley experiment and then performed the same experiment virtually, or virtual first condition (VF) (n = 45), with the opposite sequence. Repeated measures ANOVA's were conducted to examine how physical and virtual labs impacted students' learning of specific physics concepts. While we did not find clear-cut support that one sequence was better, we did find evidence that participating in virtual experiments may be more beneficial for learning certain physics concepts, such as work and mechanical advantage. Our findings support the idea that if time or physical materials are limited, using virtual experiments may help students understand work and mechanical advantage.

Learning "Math on the Move": Effectiveness of a Combined Numeracy and Physical Activity Program for Primary School Children.

Science.gov (United States)

Vetter, Melanie; O'Connor, Helen; O'Dwyer, Nicholas; Orr, Rhonda

2018-03-27

Physically active learning that combines physical activity with core curriculum areas is emerging in school-based health interventions. This study investigates the effectiveness of learning an important numeracy skill of times tables (TT) while concurrently engaging in aerobic activity compared with a seated classroom approach. Grade-4 primary school students were randomly allocated to physical activity (P) or classroom (C) groups and received the alternate condition in the following term. P group received moderate to vigorous exercise (20 min, 3 times per week, 6 wk) while simultaneously learning selected TT. C group received similar learning, but seated. Changes in TT accuracy, general numeracy, aerobic fitness, and body mass index were assessed. Data were expressed as mean (SEM) and between-condition effect size (ES; 95% confidence interval). Participants [N = 85; 55% male, 9.8 (0.3) y, 36.4% overweight/obese] improved similarly on TT in both conditions [C group: 2.2% (1.1%); P group: 2.5% (1.3%); ES = 0.03; -0.30 to 0.36; P = .86]. Improvement in general numeracy was significantly greater for P group than C group [C group: 0.7% (1.2%); P group: 5.3% (1.4%); ES = 0.42; 0.08 to 0.75; P < .03]. An improvement in aerobic fitness for P group (P < .01) was not significantly greater than C group [C group: 0.8 (0.6); P group: 2.2 (0.5) mL·kg·min -1 ; ES = 0.32; -0.01 to 0.66; P = .06]. Body mass index was unchanged. Combined movement with learning TT was effective. Physically active learning paradigms may contribute to meeting daily physical activity guidelines while supporting or even boosting learning.

Learning the mechanisms of chemical disequilibria

Energy Technology Data Exchange (ETDEWEB)

Nicholson, Schuyler B.; Alaghemandi, Mohammad [Department of Chemistry, University of Massachusetts Boston, Boston, Massachusetts 02125 (United States); Green, Jason R., E-mail: jason.green@umb.edu [Department of Chemistry, University of Massachusetts Boston, Boston, Massachusetts 02125 (United States); Department of Physics, University of Massachusetts Boston, Boston, Massachusetts 02125 (United States); Center for Quantum and Nonequilibrium Systems, University of Massachusetts Boston, Boston, Massachusetts 02125 (United States)

2016-08-28

When at equilibrium, large-scale systems obey thermodynamics because they have microscopic configurations that are typical. “Typical” states are a fraction of those possible with the majority of the probability. A more precise definition of typical states underlies the transmission, coding, and compression of information. However, this definition does not apply to natural systems that are transiently away from equilibrium. Here, we introduce a variational measure of typicality and apply it to atomistic simulations of a model for hydrogen oxidation. While a gaseous mixture of hydrogen and oxygen combusts, reactant molecules transform through a variety of ephemeral species en route to the product, water. Out of the exponentially growing number of possible sequences of chemical species, we find that greater than 95% of the probability concentrates in less than 1% of the possible sequences. Overall, these results extend the notion of typicality across the nonequilibrium regime and suggest that typical sequences are a route to learning mechanisms from experimental measurements. They also open up the possibility of constructing ensembles for computing the macroscopic observables of systems out of equilibrium.

Learning the mechanisms of chemical disequilibria

International Nuclear Information System (INIS)

Nicholson, Schuyler B.; Alaghemandi, Mohammad; Green, Jason R.

2016-01-01

When at equilibrium, large-scale systems obey thermodynamics because they have microscopic configurations that are typical. “Typical” states are a fraction of those possible with the majority of the probability. A more precise definition of typical states underlies the transmission, coding, and compression of information. However, this definition does not apply to natural systems that are transiently away from equilibrium. Here, we introduce a variational measure of typicality and apply it to atomistic simulations of a model for hydrogen oxidation. While a gaseous mixture of hydrogen and oxygen combusts, reactant molecules transform through a variety of ephemeral species en route to the product, water. Out of the exponentially growing number of possible sequences of chemical species, we find that greater than 95% of the probability concentrates in less than 1% of the possible sequences. Overall, these results extend the notion of typicality across the nonequilibrium regime and suggest that typical sequences are a route to learning mechanisms from experimental measurements. They also open up the possibility of constructing ensembles for computing the macroscopic observables of systems out of equilibrium.

Virtual laboratory learning media development to improve science literacy skills of mechanical engineering students on basic physics concept of material measurement

Science.gov (United States)

Jannati, E. D.; Setiawan, A.; Siahaan, P.; Rochman, C.

2018-05-01

This study aims to determine the description of virtual laboratory learning media development to improve science literacy skills of Mechanical Engineering students on the concept of basic Physics. Quasi experimental method was employed in this research. The participants of this research were first semester students of mechanical engineering in Majalengka University. The research instrument was readability test of instructional media. The results of virtual laboratory learning media readability test show that the average score is 78.5%. It indicates that virtual laboratory learning media development are feasible to be used in improving science literacy skill of Mechanical Engineering students in Majalengka University, specifically on basic Physics concepts of material measurement.

Neural mechanisms of reinforcement learning in unmedicated patients with major depressive disorder.

Science.gov (United States)

Rothkirch, Marcus; Tonn, Jonas; Köhler, Stephan; Sterzer, Philipp

2017-04-01

According to current concepts, major depressive disorder is strongly related to dysfunctional neural processing of motivational information, entailing impairments in reinforcement learning. While computational modelling can reveal the precise nature of neural learning signals, it has not been used to study learning-related neural dysfunctions in unmedicated patients with major depressive disorder so far. We thus aimed at comparing the neural coding of reward and punishment prediction errors, representing indicators of neural learning-related processes, between unmedicated patients with major depressive disorder and healthy participants. To this end, a group of unmedicated patients with major depressive disorder (n = 28) and a group of age- and sex-matched healthy control participants (n = 30) completed an instrumental learning task involving monetary gains and losses during functional magnetic resonance imaging. The two groups did not differ in their learning performance. Patients and control participants showed the same level of prediction error-related activity in the ventral striatum and the anterior insula. In contrast, neural coding of reward prediction errors in the medial orbitofrontal cortex was reduced in patients. Moreover, neural reward prediction error signals in the medial orbitofrontal cortex and ventral striatum showed negative correlations with anhedonia severity. Using a standard instrumental learning paradigm we found no evidence for an overall impairment of reinforcement learning in medication-free patients with major depressive disorder. Importantly, however, the attenuated neural coding of reward in the medial orbitofrontal cortex and the relation between anhedonia and reduced reward prediction error-signalling in the medial orbitofrontal cortex and ventral striatum likely reflect an impairment in experiencing pleasure from rewarding events as a key mechanism of anhedonia in major depressive disorder. © The Author (2017). Published by Oxford

Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

Science.gov (United States)

Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

2009-01-01

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data

Directory of Open Access Journals (Sweden)

Zhaodi Wang

2018-04-01

Full Text Available Deep learning has become a widely used powerful tool in many research fields, although not much so yet in agriculture technologies. In this work, two deep convolutional neural networks (CNN, viz. Residual Network (ResNet and its improved version named ResNeXt, are used to detect internal mechanical damage of blueberries using hyperspectral transmittance data. The original structure and size of hypercubes are adapted for the deep CNN training. To ensure that the models are applicable to hypercube, we adjust the number of filters in the convolutional layers. Moreover, a total of 5 traditional machine learning algorithms, viz. Sequential Minimal Optimization (SMO, Linear Regression (LR, Random Forest (RF, Bagging and Multilayer Perceptron (MLP, are performed as the comparison experiments. In terms of model assessment, k-fold cross validation is used to indicate that the model performance does not vary with the different combination of dataset. In real-world application, selling damaged berries will lead to greater interest loss than discarding the sound ones. Thus, precision, recall, and F1-score are also used as the evaluation indicators alongside accuracy to quantify the false positive rate. The first three indicators are seldom used by investigators in the agricultural engineering domain. Furthermore, ROC curves and Precision-Recall curves are plotted to visualize the performance of classifiers. The fine-tuned ResNet/ResNeXt achieve average accuracy and F1-score of 0.8844/0.8784 and 0.8952/0.8905, respectively. Classifiers SMO/ LR/RF/Bagging/MLP obtain average accuracy and F1-score of 0.8082/0.7606/0.7314/0.7113/0.7827 and 0.8268/0.7796/0.7529/0.7339/0.7971, respectively. Two deep learning models achieve better classification performance than the traditional machine learning methods. Classification for each testing sample only takes 5.2 ms and 6.5 ms respectively for ResNet and ResNeXt, indicating that the deep learning framework has great

Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data

Science.gov (United States)

Hu, Menghan; Zhai, Guangtao

2018-01-01

Deep learning has become a widely used powerful tool in many research fields, although not much so yet in agriculture technologies. In this work, two deep convolutional neural networks (CNN), viz. Residual Network (ResNet) and its improved version named ResNeXt, are used to detect internal mechanical damage of blueberries using hyperspectral transmittance data. The original structure and size of hypercubes are adapted for the deep CNN training. To ensure that the models are applicable to hypercube, we adjust the number of filters in the convolutional layers. Moreover, a total of 5 traditional machine learning algorithms, viz. Sequential Minimal Optimization (SMO), Linear Regression (LR), Random Forest (RF), Bagging and Multilayer Perceptron (MLP), are performed as the comparison experiments. In terms of model assessment, k-fold cross validation is used to indicate that the model performance does not vary with the different combination of dataset. In real-world application, selling damaged berries will lead to greater interest loss than discarding the sound ones. Thus, precision, recall, and F1-score are also used as the evaluation indicators alongside accuracy to quantify the false positive rate. The first three indicators are seldom used by investigators in the agricultural engineering domain. Furthermore, ROC curves and Precision-Recall curves are plotted to visualize the performance of classifiers. The fine-tuned ResNet/ResNeXt achieve average accuracy and F1-score of 0.8844/0.8784 and 0.8952/0.8905, respectively. Classifiers SMO/ LR/RF/Bagging/MLP obtain average accuracy and F1-score of 0.8082/0.7606/0.7314/0.7113/0.7827 and 0.8268/0.7796/0.7529/0.7339/0.7971, respectively. Two deep learning models achieve better classification performance than the traditional machine learning methods. Classification for each testing sample only takes 5.2 ms and 6.5 ms respectively for ResNet and ResNeXt, indicating that the deep learning framework has great potential for

Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data.

Science.gov (United States)

Wang, Zhaodi; Hu, Menghan; Zhai, Guangtao

2018-04-07

Deep learning has become a widely used powerful tool in many research fields, although not much so yet in agriculture technologies. In this work, two deep convolutional neural networks (CNN), viz. Residual Network (ResNet) and its improved version named ResNeXt, are used to detect internal mechanical damage of blueberries using hyperspectral transmittance data. The original structure and size of hypercubes are adapted for the deep CNN training. To ensure that the models are applicable to hypercube, we adjust the number of filters in the convolutional layers. Moreover, a total of 5 traditional machine learning algorithms, viz. Sequential Minimal Optimization (SMO), Linear Regression (LR), Random Forest (RF), Bagging and Multilayer Perceptron (MLP), are performed as the comparison experiments. In terms of model assessment, k-fold cross validation is used to indicate that the model performance does not vary with the different combination of dataset. In real-world application, selling damaged berries will lead to greater interest loss than discarding the sound ones. Thus, precision, recall, and F1-score are also used as the evaluation indicators alongside accuracy to quantify the false positive rate. The first three indicators are seldom used by investigators in the agricultural engineering domain. Furthermore, ROC curves and Precision-Recall curves are plotted to visualize the performance of classifiers. The fine-tuned ResNet/ResNeXt achieve average accuracy and F1-score of 0.8844/0.8784 and 0.8952/0.8905, respectively. Classifiers SMO/ LR/RF/Bagging/MLP obtain average accuracy and F1-score of 0.8082/0.7606/0.7314/0.7113/0.7827 and 0.8268/0.7796/0.7529/0.7339/0.7971, respectively. Two deep learning models achieve better classification performance than the traditional machine learning methods. Classification for each testing sample only takes 5.2 ms and 6.5 ms respectively for ResNet and ResNeXt, indicating that the deep learning framework has great potential for

Transfer Learning for SSVEP Electroencephalography Based Brain–Computer Interfaces Using Learn++.NSE and Mutual Information

Directory of Open Access Journals (Sweden)

Matthew Sybeldon

2017-01-01

Full Text Available Brain–Computer Interfaces (BCI using Steady-State Visual Evoked Potentials (SSVEP are sometimes used by injured patients seeking to use a computer. Canonical Correlation Analysis (CCA is seen as state-of-the-art for SSVEP BCI systems. However, this assumes that the user has full control over their covert attention, which may not be the case. This introduces high calibration requirements when using other machine learning techniques. These may be circumvented by using transfer learning to utilize data from other participants. This paper proposes a combination of ensemble learning via Learn++ for Nonstationary Environments (Learn++.NSEand similarity measures such as mutual information to identify ensembles of pre-existing data that result in higher classification. Results show that this approach performed worse than CCA in participants with typical SSVEP responses, but outperformed CCA in participants whose SSVEP responses violated CCA assumptions. This indicates that similarity measures and Learn++.NSE can introduce a transfer learning mechanism to bring SSVEP system accessibility to users unable to control their covert attention.

Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation.

Science.gov (United States)

Pearce, Marcus T

2018-05-11

Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) probabilistic prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of probabilistic prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of probabilistic prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

Cooperative learning combined with short periods of lecturing: A good alternative in teaching biochemistry.

Science.gov (United States)

Fernández-Santander, Ana

2008-01-01

The informal activities of cooperative learning and short periods of lecturing has been combined and used in the university teaching of biochemistry as part of the first year course of Optics and Optometry in the academic years 2004-2005 and 2005-2006. The lessons were previously elaborated by the teacher and included all that is necessary to understand the topic (text, figures, graphics, diagrams, pictures, etc.). Additionally, a questionnaire was prepared for every chapter. All lessons contained three parts: objectives, approach and development, and the assessment of the topic. Team work, responsibility, and communication skills were some of the abilities developed with this new methodology. Students worked collaboratively in small groups of two or three following the teacher's instructions with short periods of lecturing that clarified misunderstood concepts. Homework was minimized. On comparing this combined methodology with the traditional one (only lecture), students were found to exhibit a higher satisfaction with the new method. They were more involved in the learning process and had a better attitude toward the subject. The use of this new methodology showed a significant increase in the mean score of the students' academic results. The rate of students who failed the subject was significantly inferior in comparison with those who failed in the previous years when only lecturing was applied. This combined methodology helped the teacher to observe the apprenticeship process of students better and to act as a facilitator in the process of building students' knowledge. Copyright © 2008 International Union of Biochemistry and Molecular Biology, Inc.

Immuno-pharmacodynamics for evaluating mechanism of action and developing immunotherapy combinations.

Science.gov (United States)

Parchment, Ralph E; Voth, Andrea Regier; Doroshow, James H; Berzofsky, Jay A

2016-08-01

Immunotherapy has become a major modality of cancer treatment, with multiple new classes of immunotherapeutics recently entering the clinic and obtaining market approval from regulatory agencies. While the promise of these therapies is great, so is the number of possible combinations not only with each other but also with small molecule therapeutics. Furthermore, the observation of unusual dose-response relationships suggests a critical dependency of drug effectiveness on the dosage regimen (dose and schedule). Clinical pharmacodynamic (PD) biomarkers will be useful endpoints for confirming drug mechanism of action, evaluating combination therapies for synergy or antagonism, and identifying optimal dosage regimens. In contrast to conventional PD in which drug action occurs entirely within a single target cell (ie, is self-contained within the malignant cell), immunotherapy involves a complex mechanism of action with sequential steps that propagate through multiple cell types, both normal and malignant. Its intercellular pharmacology begins with molecular target engagement either on an immune effector cell or a malignant cell, followed by stimulatory biochemical and biological signals in immune effector cells, and then finally ends with activation of cell death mechanisms in malignant cells lying within a certain distance from the activated effector cells (immune cell-tumor cell proximity). Evaluating such "trans-cellular pharmacology," in which different steps of drug action are distributed across multiple cell types, requires novel microscopy and image analysis tools capable of quantifying PD-biomarker responses, mapping the responses onto the cellular geography of the tumor using phenotypic biomarkers to identify specific cell types, and finally analyzing the spatial relationships between biomarkers in the context of each cell's biological role. We have termed this form of nearest neighbor image analysis of drug action "proximity PD microscopy," to indicate the

Learning Human Actions by Combining Global Dynamics and Local Appearance.

Science.gov (United States)

Luo, Guan; Yang, Shuang; Tian, Guodong; Yuan, Chunfeng; Hu, Weiming; Maybank, Stephen J

2014-12-01

In this paper, we address the problem of human action recognition through combining global temporal dynamics and local visual spatio-temporal appearance features. For this purpose, in the global temporal dimension, we propose to model the motion dynamics with robust linear dynamical systems (LDSs) and use the model parameters as motion descriptors. Since LDSs live in a non-Euclidean space and the descriptors are in non-vector form, we propose a shift invariant subspace angles based distance to measure the similarity between LDSs. In the local visual dimension, we construct curved spatio-temporal cuboids along the trajectories of densely sampled feature points and describe them using histograms of oriented gradients (HOG). The distance between motion sequences is computed with the Chi-Squared histogram distance in the bag-of-words framework. Finally we perform classification using the maximum margin distance learning method by combining the global dynamic distances and the local visual distances. We evaluate our approach for action recognition on five short clips data sets, namely Weizmann, KTH, UCF sports, Hollywood2 and UCF50, as well as three long continuous data sets, namely VIRAT, ADL and CRIM13. We show competitive results as compared with current state-of-the-art methods.

Combining different Technologies in a Funerary Archaeology content and language integrated Learning (CLIL) Course

OpenAIRE

Cignoni, Laura; Fornaciari, Gino

2009-01-01

The aim of this paper is to describe a project in which Italian undergraduate students at the Palaeopathology Division of Pisa University will attend a two-year Content and Language Integrated Learning (CLIL) course combining the study of funerary archaeology with English as vehicular language. At the presence of a subject and language teacher working together, the trainees will use different types of technology including devices such as electronic blackboards and Word applications with user-...

Approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems

International Nuclear Information System (INIS)

Zhang, Xiaoshun; Yu, Tao; Yang, Bo; Zheng, Limin; Huang, Linni

2015-01-01

Highlights: • A novel optimal carbon-energy combined-flow (OCECF) model is firstly established. • A novel approximate ideal multi-objective solution Q(λ) learning is designed. • The proposed algorithm has a high convergence stability and reliability. • The proposed algorithm can be applied for OCECF in a large-scale power grid. - Abstract: This paper proposes a novel approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems. The carbon emissions, fuel cost, active power loss, voltage deviation and carbon emission loss are chosen as the optimization objectives, which are simultaneously optimized by five different Q-value matrices. The dynamic optimal weight of each objective is calculated online from the entire Q-value matrices such that the greedy action policy can be obtained. Case studies are carried out to evaluate the optimization performance for carbon-energy combined-flow in an IEEE 118-bus system and the regional power grid of southern China.

Probabilistic Simulation of Combined Thermo-Mechanical Cyclic Fatigue in Composites

Science.gov (United States)

Chamis, Christos C.

2011-01-01

A methodology to compute probabilistically-combined thermo-mechanical fatigue life of polymer matrix laminated composites has been developed and is demonstrated. Matrix degradation effects caused by long-term environmental exposure and mechanical/thermal cyclic loads are accounted for in the simulation process. A unified time-temperature-stress-dependent multifactor-interaction relationship developed at NASA Glenn Research Center has been used to model the degradation/aging of material properties due to cyclic loads. The fast probability-integration method is used to compute probabilistic distribution of response. Sensitivities of fatigue life reliability to uncertainties in the primitive random variables (e.g., constituent properties, fiber volume ratio, void volume ratio, ply thickness, etc.) computed and their significance in the reliability-based design for maximum life is discussed. The effect of variation in the thermal cyclic loads on the fatigue reliability for a (0/+/-45/90)s graphite/epoxy laminate with a ply thickness of 0.127 mm, with respect to impending failure modes has been studied. The results show that, at low mechanical-cyclic loads and low thermal-cyclic amplitudes, fatigue life for 0.999 reliability is most sensitive to matrix compressive strength, matrix modulus, thermal expansion coefficient, and ply thickness. Whereas at high mechanical-cyclic loads and high thermal-cyclic amplitudes, fatigue life at 0.999 reliability is more sensitive to the shear strength of matrix, longitudinal fiber modulus, matrix modulus, and ply thickness.

Effects of Online Synchronous Instruction with an Attention Monitoring and Alarm Mechanism on Sustained Attention and Learning Performance

Science.gov (United States)

Chen, Chih-Ming; Wang, Jung-Ying

2018-01-01

Many studies have shown that learners' sustained attention strongly affects e-learning performance, particularly during online synchronous instruction. This work thus develops a novel attention monitoring and alarm mechanism (AMAM) based on brainwave signals to improve learning performance via monitoring the attention state of individual learners…

Providing pervasive Learning eXperiences by Combining Internet of Things and e-Learning standards

Directory of Open Access Journals (Sweden)

Aroua TAAMALLAH

2015-12-01

Full Text Available Nowadays, learning is more and more taking place anywhere and anytime. This implies that e-learning environments are expanded from only virtual learning environments to both virtual and physical ones. Thanks to the evolution of Internet, ICT (Information and Communication Technology and Internet of Things, new learning scenarios could be experienced by learners either individually or collaboratively. These learning scenarios are Pervasive in such a way that they allow to mix virtual and physical learning environments as well. They are therefore characterized by possible interactions of the learner with the physical environment, the Learner's contextual data detection as well as the adaptation of pedagogical strategies and services according to this context. This paper aims to take advantage of this trend and keep up also with existing e-Learning standards such as IMS LD and LOM. The solution proposed is therefore to extend these standards models with that of Internet of Things and to provide an adaptation approach of learning activities based on learner's context and her/his track using the eXperience API. In this context and in order to allow both reasoning capabilities and interoperability between the proposed models Ontological representations and implementation are therefore proposed. Moreover a technical architecture highlighting the required software components and their interactions is provided. And finally, a relevant pervasive learning scenario is implemented and experimented.

Learning Activities That Combine Science Magic Activities with the 5E Instructional Model to Influence Secondary-School Students' Attitudes to Science

Science.gov (United States)

Lin, Jang-Long; Cheng, Meng-Fei; Chang, Ying-Chi; Li, Hsiao-Wen; Chang, Jih-Yuan; Lin, Deng-Min

2014-01-01

The purpose of this study was to investigate how learning materials based on Science Magic activities affect student attitudes to science. A quasi-experimental design was conducted to explore the combination of Science Magic with the 5E Instructional Model to develop learning materials for teaching a science unit about friction. The participants…

Exploring the Learning Mechanism in Educational Games

OpenAIRE

Kiili, Kristian; Ketamo, Harri

2007-01-01

The main aim of this paper is to evaluate the problem based gaming model that tries to explain the learning process in educational games. The model was studied through Geometry game aimed for pre-school children (N = 24). The game relays on learning by teaching approach and involves AI-engine modeling the human concept learning structures. The qualitative analyses were used to explore participants learning processes and gaming strategies. The results indicated that the model well describes th...

Multi-fidelity machine learning models for accurate bandgap predictions of solids

International Nuclear Information System (INIS)

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

2016-01-01

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelity quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.

Failing to learn from negative prediction errors: Obesity is associated with alterations in a fundamental neural learning mechanism.

Science.gov (United States)

Mathar, David; Neumann, Jane; Villringer, Arno; Horstmann, Annette

2017-10-01

Prediction errors (PEs) encode the difference between expected and actual action outcomes in the brain via dopaminergic modulation. Integration of these learning signals ensures efficient behavioral adaptation. Obesity has recently been linked to altered dopaminergic fronto-striatal circuits, thus implying impairments in cognitive domains that rely on its integrity. 28 obese and 30 lean human participants performed an implicit stimulus-response learning paradigm inside an fMRI scanner. Computational modeling and psycho-physiological interaction (PPI) analysis was utilized for assessing PE-related learning and associated functional connectivity. We show that human obesity is associated with insufficient incorporation of negative PEs into behavioral adaptation even in a non-food context, suggesting differences in a fundamental neural learning mechanism. Obese subjects were less efficient in using negative PEs to improve implicit learning performance, despite proper coding of PEs in striatum. We further observed lower functional coupling between ventral striatum and supplementary motor area in obese subjects subsequent to negative PEs. Importantly, strength of functional coupling predicted task performance and negative PE utilization. These findings show that obesity is linked to insufficient behavioral adaptation specifically in response to negative PEs, and to associated alterations in function and connectivity within the fronto-striatal system. Recognition of neural differences as a central characteristic of obesity hopefully paves the way to rethink established intervention strategies: Differential behavioral sensitivity to negative and positive PEs should be considered when designing intervention programs. Measures relying on penalization of unwanted behavior may prove less effective in obese subjects than alternative approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

Combining D-cycloserine with appetitive extinction learning modulates amygdala activity during recall.

Science.gov (United States)

Ebrahimi, Claudia; Koch, Stefan P; Friedel, Eva; Crespo, Ilsoray; Fydrich, Thomas; Ströhle, Andreas; Heinz, Andreas; Schlagenhauf, Florian

2017-07-01

Appetitive Pavlovian conditioning plays a crucial role in the pathogenesis of drug addiction and conditioned reward cues can trigger craving and relapse even after long phases of abstinence. Promising preclinical work showed that the NMDA-receptor partial agonist D-cycloserine (DCS) facilitates Pavlovian extinction learning of fear and drug cues. Furthermore, DCS-augmented exposure therapy seems to be beneficial in various anxiety disorders, while the supposed working mechanism of DCS during human appetitive or aversive extinction learning is still not confirmed. To test the hypothesis that DCS administration before extinction training improves extinction learning, healthy adults (n=32) underwent conditioning, extinction, and extinction recall on three successive days in a randomized, double-blind, placebo-controlled fMRI design. Monetary wins and losses served as unconditioned stimuli during conditioning to probe appetitive and aversive learning. An oral dose of 50mg of DCS or placebo was administered 1h before extinction training and DCS effects during extinction recall were evaluated on a behavioral and neuronal level. We found attenuated amygdala activation in the DCS compared to the placebo group during recall of the extinguished appetitive cue, along with evidence for enhanced functional amygdala-vmPFC coupling in the DCS group. While the absence of additional physiological measures of conditioned responses during recall in this study prevent the evaluation of a behavioral DCS effect, our neuronal findings are in accordance with recent theories linking successful extinction recall in humans to modulatory top-down influences from the vmPFC that inhibit amygdala activation. Our results should encourage further translational studies concerning the usefulness of DCS to target maladaptive Pavlovian reward associations. Copyright © 2017 Elsevier Inc. All rights reserved.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

DEFF Research Database (Denmark)

Pinto, Tiago; Vale, Zita; Sousa, Tiago M.

2014-01-01

decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology...... that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management...... allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides...

Unsupervised Learning of Digit Recognition Using Spike-Timing-Dependent Plasticity

Directory of Open Access Journals (Sweden)

Peter U. Diehl

2015-08-01

Full Text Available In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns, since most of such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e. conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks.

Influences of combined traffic noise on the ability of learning and memory in mice

Directory of Open Access Journals (Sweden)

Guo-Qing Di

2018-01-01

Full Text Available Objective: The present study aimed to evaluate the influences of combined traffic noise (CTN on the ability of learning and memory in mice. Materials and Methods: The Institute of Cancer Research (ICR mice were exposed to CTN from highways and high-speed railways for 42 days, whose day–night equivalent continuous A-weighted sound pressure level (Ldn was 70 dB(A. On the basis of behavioral reactions in Morris water maze (MWM and the concentrations of amino acid neurotransmitters in the hippocampus, the impacts of CTN on learning and memory in mice were examined. Results: The MWM test showed that the ability of learning and memory in mice was improved after short-term exposure (6–10 days, the first batch to 70 dB(A CTN, which showed the excitatory effect of stimuli. Long-term exposure (26–30 days, the third batch; 36–40 days, the fourth batch led to the decline of learning and memory ability, which indicated the inhibitory effect of stimuli. Assays testing amino acid neurotransmitters showed that the glutamate level of the experimental group was higher than that of the control group in the first batch. However, the former was lower than the latter in the third and fourth batches. Both, behavioral reactions and the concentrations of amino acid neurotransmitters, testified that short-term exposure and long-term exposure resulted in excitatory effect and inhibitory effect on the ability of learning and memory, respectively. Conclusion: The effects of 70 dB(A CTN on the ability of learning and memory were closely related to the exposure duration. Furthermore, those effects were regulated and controlled by the level of glutamate in the hippocampus.

Self-control over combined video feedback and modeling facilitates motor learning.

Science.gov (United States)

Post, Phillip G; Aiken, Christopher A; Laughlin, David D; Fairbrother, Jeffrey T

2016-06-01

Allowing learners to control the video presentation of knowledge of performance (KP) or an expert model during practice has been shown to facilitate motor learning (Aiken, Fairbrother, & Post, 2012; Wulf, Raupach, & Pfeiffer, 2005). Split-screen replay features now allow for the simultaneous presentation of these modes of instructional support. It is uncertain, however, if such a combination incorporated into a self-control protocol would yield similar benefits seen in earlier self-control studies. Therefore, the purpose of the present study was to examine the effects of self-controlled split-screen replay on the learning of a golf chip shot. Participants completed 60 practice trials, three administrations of the Intrinsic Motivation Inventory, and a questionnaire on day one. Retention and transfer tests and a final motivation inventory were completed on day two. Results revealed significantly higher form and accuracy scores for the self-control group during transfer. The self-control group also had significantly higher scores on the perceived competence subscale, reported requesting feedback mostly after perceived poor trials, and recalled a greater number of critical task features compared to the yoked group. The findings for the performance measures were consistent with previous self-control research. Copyright © 2016 Elsevier B.V. All rights reserved.

N-methyl-d-aspartate receptors, learning and memory: chronic intraventricular infusion of the NMDA receptor antagonist d-AP5 interacts directly with the neural mechanisms of spatial learning.

Science.gov (United States)

Morris, R G M; Steele, R J; Bell, J E; Martin, S J

2013-03-01

Three experiments were conducted to contrast the hypothesis that hippocampal N-methyl-d-aspartate (NMDA) receptors participate directly in the mechanisms of hippocampus-dependent learning with an alternative view that apparent impairments of learning induced by NMDA receptor antagonists arise because of drug-induced neuropathological and/or sensorimotor disturbances. In experiment 1, rats given a chronic i.c.v. infusion of d-AP5 (30 mm) at 0.5 μL/h were selectively impaired, relative to aCSF-infused animals, in place but not cued navigation learning when they were trained during the 14-day drug infusion period, but were unimpaired on both tasks if trained 11 days after the minipumps were exhausted. d-AP5 caused sensorimotor disturbances in the spatial task, but these gradually worsened as the animals failed to learn. Histological assessment of potential neuropathological changes revealed no abnormalities in d-AP5-treated rats whether killed during or after chronic drug infusion. In experiment 2, a deficit in spatial learning was also apparent in d-AP5-treated rats trained on a spatial reference memory task involving two identical but visible platforms, a task chosen and shown to minimise sensorimotor disturbances. HPLC was used to identify the presence of d-AP5 in selected brain areas. In Experiment 3, rats treated with d-AP5 showed a delay-dependent deficit in spatial memory in the delayed matching-to-place protocol for the water maze. These data are discussed with respect to the learning mechanism and sensorimotor accounts of the impact of NMDA receptor antagonists on brain function. We argue that NMDA receptor mechanisms participate directly in spatial learning. © 2013 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Maze learning by a hybrid brain-computer system.

Science.gov (United States)

Wu, Zhaohui; Zheng, Nenggan; Zhang, Shaowu; Zheng, Xiaoxiang; Gao, Liqiang; Su, Lijuan

2016-09-13

The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation.

Maze learning by a hybrid brain-computer system

Science.gov (United States)

Wu, Zhaohui; Zheng, Nenggan; Zhang, Shaowu; Zheng, Xiaoxiang; Gao, Liqiang; Su, Lijuan

2016-09-01

The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation.

Engaging students as partners in developing online learning and feedback activities for first-year fluid mechanics

Science.gov (United States)

Brown, Alan

2018-01-01

Much learning takes place outside of formal class settings, yet students starting in higher education are not always well equipped with independent learning skills, appropriate self-knowledge or the required levels of intrinsic motivation This project used students as partners to develop resources that could be used by first-year undergraduates in fluid mechanics, using activities and receiving feedback through the virtual learning environment (VLE), in order to build these three attributes of independent learners. While there were significant benefits to the students who developed the resources, the target students saw much lower benefits as a result of poorer than expected engagement. The challenge this research presents is to develop activities that maximise engagement in large classes, as well as develop appropriate independent learning skills.

Evolving autonomous learning in cognitive networks.

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Sheneman, Leigh; Hintze, Arend

2017-12-01

There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning and could be another step towards autonomously learning machines.

E-Learning 2.0: Learning Redefined

OpenAIRE

Kumar, Rupesh

2009-01-01

The conventional e-learning approach emphasizes a learning system more than a learning environment. While traditional e-learning systems continue to be significant, there is a new set of services emerging, embracing the philosophy of Web 2.0. Known as e-learning 2.0, it aims to create a personalized learning environment. E-learning 2.0 combines the use of discrete but complementary tools and web services to support the creation of ad-hoc learning communities. This paper discusses the influenc...

Supporting Learning from Illustrated Texts: Conceptualizing and Evaluating a Learning Strategy

Science.gov (United States)

Schlag, Sabine; Ploetzner, Rolf

2011-01-01

Texts and pictures are often combined in order to improve learning. Many students, however, have difficulty to appropriately process text-picture combinations. We have thus conceptualized a learning strategy which supports learning from illustrated texts. By inducing the processes of information selection, organization, integration, and…

Combined Mechanical Destruction and Alkaline Pretreatment of Wheat Straw for Enhanced Enzymatic Saccharification

Directory of Open Access Journals (Sweden)

Qianqian Wang

2014-09-01

Full Text Available Wheat straw was pretreated by combined mechanical destruction and alkaline pretreatments to enhance enzymatic saccharification. Four strategies were employed to evaluate the potential of wheat straw as a feedstock for fermentable sugar production. The effects of the pretreatments on the substrate morphology, size distribution, chemical composition, and cellulose crystallinity, along with the subsequent enzymatic digestibility, were investigated. Optical microscope images showed that mechanical pretreatment alone resulted in poor fiber defibrillation, wherein samples mostly consisted of rigid fiber bundles, while integrated mechanical destruction and alkaline pretreatment led to relatively good fiber defibrillation. Low temperature NaOH/urea pretreatment can fibrillate the rigid fiber bundles into a relatively loose network and alter the structure of the treated substrate to make cellulose more accessible. The glucan conversion rates were 77% and 95% for integrated mechanical destruction and alkaline pretreatments and mechanical destruction followed by low temperature NaOH/urea and ammonium/urea pretreatments, respectively, after 72 h of enzymatic hydrolysis with enzyme loadings of 10 FPU cellulase per g of oven-dry substrate.

Single or in Combination Antimicrobial Resistance Mechanisms of Klebsiella pneumoniae Contribute to Varied Susceptibility to Different Carbapenems

Science.gov (United States)

Tsai, Yu-Kuo; Liou, Ci-Hong; Fung, Chang-Phone; Lin, Jung-Chung; Siu, L. Kristopher

2013-01-01

Resistance to carbapenems has been documented by the production of carbapenemase or the loss of porins combined with extended-spectrum β-lactamases or AmpC β-lactamases. However, no complete comparisons have been made regarding the contributions of each resistance mechanism towards carbapenem resistance. In this study, we genetically engineered mutants of Klebsiella pneumoniae with individual and combined resistance mechanisms, and then compared each resistance mechanism in response to ertapenem, imipenem, meropenem, doripenem and other antibiotics. Among the four studied carbapenems, ertapenem was the least active against the loss of porins, cephalosporinases and carbapenemases. In addition to the production of KPC-2 or NDM-1 alone, resistance to all four carbapenems could also be conferred by the loss of two major porins, OmpK35 and OmpK36, combined with CTX-M-15 or DHA-1 with its regulator AmpR. Because the loss of OmpK35/36 alone or the loss of a single porin combined with bla CTX-M-15 or bla DHA-1-ampR expression was only sufficient for ertapenem resistance, our results suggest that carbapenems other than ertapenem should still be effective against these strains and laboratory testing for non-susceptibility to other carbapenems should improve the accurate identification of these isolates. PMID:24265784

Effects of ginsenoside of stem and leaf combined with choline on learning and memory ability of rat models with Alzheimer diseases

Institute of Scientific and Technical Information of China (English)

Xiaomin Zhao; Xianglin Xie; Zuoli Xia; Yunsheng Gao; Yuyun Zhu; Hongxia Gu

2006-01-01

phase: (0.38±0.74), (2.63±1.06) times, P ＜ 0.01]; moreover, effect was stronger than that inGSL group and choline group. The Q value was 1.07 and 1.59, respectively and it showed synergistic effect. ② Spatial localization task: Training times were more in model group than sham operation group, and there was significant difference [(2.9±2.5), (12.6±3.5) times; P ＜ 0.01]. Training times were less in combination group than model group, and there was significant difference [(11.8±2.4), (27.9±2.5) times, P ＜ 0.01]; moreover, effect was stronger than that in GSL group and choline group. The Q value was 1.07 and it showed synergistic effect. ③ Activity of choilne acetylase: Activity was lower in model group than sham operation group, and there was significant difference [(30.56±8.33), (61.11 ±8.33) nkat/g; P ＜ 0.01]. Activity was higher in combination group than model group and there was significant difference [(50.00±8.33), (30.56±8.33) nkat/g, P ＜ 0.01];moreover, effect was stronger than that in GSL group and choline group. The Q value was 1.5 and it showed synergistic effect.CONCLUSION: GSL in combination with choline can synergically improve the disorder of learning and memory of AD model rats. Its mechanism may be involved in enhancing the function of central cholinergic system.

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

Directory of Open Access Journals (Sweden)

Yoonsik Shim

2016-10-01

Full Text Available 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.

Science.gov (United States)

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.

Mechanical system reliability analysis using a combination of graph theory and Boolean function

International Nuclear Information System (INIS)

Tang, J.

2001-01-01

A new method based on graph theory and Boolean function for assessing reliability of mechanical systems is proposed. The procedure for this approach consists of two parts. By using the graph theory, the formula for the reliability of a mechanical system that considers the interrelations of subsystems or components is generated. Use of the Boolean function to examine the failure interactions of two particular elements of the system, followed with demonstrations of how to incorporate such failure dependencies into the analysis of larger systems, a constructive algorithm for quantifying the genuine interconnections between the subsystems or components is provided. The combination of graph theory and Boolean function provides an effective way to evaluate the reliability of a large, complex mechanical system. A numerical example demonstrates that this method an effective approaches in system reliability analysis

Reification in the Learning of Square Roots in a Ninth Grade Classroom: Combining Semiotic and Discursive Approaches

Science.gov (United States)

Shinno, Yusuke

2018-01-01

This paper reports on combining semiotic and discursive approaches to reification in classroom interactions. It focuses on the discursive characteristics and semiotic processes involved in the teaching and learning of square roots in a ninth grade classroom in Japan. The purpose of this study is to characterize the development of mathematical…

Combined Effects of Note-Taking/-Reviewing on Learning and the Enhancement through Interventions: A Meta-Analytic Review

Science.gov (United States)

Kobayashi, Keiichi

2006-01-01

Meta-analyses of 33 studies were conducted to examine (1) how much the combination of taking and reviewing notes contributes to school learning, and (2) whether interventions in the note-taking/-reviewing procedure enhance note-taking/-reviewing effects, and if so, how much and under what conditions. Syntheses of findings from…

Promise of combined hydrothermal/chemical and mechanical refining for pretreatment of woody and herbaceous biomass.

Science.gov (United States)

Kim, Sun Min; Dien, Bruce S; Singh, Vijay

2016-01-01

Production of advanced biofuels from woody and herbaceous feedstocks is moving into commercialization. Biomass needs to be pretreated to overcome the physicochemical properties of biomass that hinder enzyme accessibility, impeding the conversion of the plant cell walls to fermentable sugars. Pretreatment also remains one of the most costly unit operations in the process and among the most critical because it is the source of chemicals that inhibit enzymes and microorganisms and largely determines enzyme loading and sugar yields. Pretreatments are categorized into hydrothermal (aqueous)/chemical, physical, and biological pretreatments, and the mechanistic details of which are briefly outlined in this review. To leverage the synergistic effects of different pretreatment methods, conducting two or more pretreatments consecutively has gained attention. Especially, combining hydrothermal/chemical pretreatment and mechanical refining, a type of physical pretreatment, has the potential to be applied to an industrial plant. Here, the effects of the combined pretreatment (combined hydrothermal/chemical pretreatment and mechanical refining) on energy consumption, physical structure, sugar yields, and enzyme dosage are summarized.

Fabrication of microfibrillated cellulose gel from waste pulp sludge via mild maceration combined with mechanical shearing

Science.gov (United States)

Nusheng Chen; Junyong Zhu; Zhaohui Tong

2016-01-01

This article describes a facile route, which combines mild maceration of waste pulp sludge and a mechanical shearing process, to prepare microfibrillated cellulose (MFC) with a high storage modulus. In the maceration, the mixture of glacial acetic acid and hydrogen peroxide was used to extract cellulose from never-dried waste pulp sludge. Then, two different mechanical...

Efficient learning mechanisms hold in the social domain and are implemented in the medial prefrontal cortex.

Science.gov (United States)

Seid-Fatemi, Azade; Tobler, Philippe N

2015-05-01

When we are learning to associate novel cues with outcomes, learning is more efficient if we take advantage of previously learned associations and thereby avoid redundant learning. The blocking effect represents this sort of efficiency mechanism and refers to the phenomenon in which a novel stimulus is blocked from learning when it is associated with a fully predicted outcome. Although there is sufficient evidence that this effect manifests itself when individuals learn about their own rewards, it remains unclear whether it also does when they learn about others' rewards. We employed behavioral and neuroimaging methods to address this question. We demonstrate that blocking does indeed occur in the social domain and it does so to a similar degree as observed in the individual domain. On the neural level, activations in the medial prefrontal cortex (mPFC) show a specific contribution to blocking and learning-related prediction errors in the social domain. These findings suggest that the efficiency principle that applies to reward learning in the individual domain also applies to that in the social domain, with the mPFC playing a central role in implementing it. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Combining Graphic Arts, Hollywood and the Internet to Improve Distance Learning in Science and Math

Science.gov (United States)

Tso-Varela, S.; Friedberg, R.; Lipnick, D.

We on the Navajo Reservation face the daunting problem of trying to educate a widely scattered student population over a landmass (25,000+ sq. miles) larger than all the New England states combined. Compounding this problem is the fact that English is a second language for many students and that many of our students lack basic foundation skills. One of the obvious answers is Distance Learning Programs. But, in the past Distance Learning Programs have been notably ineffective on the Navajo Reservation. An experimental Internet Astronomy that we taught last summer showed conclusively that we must specifically tailor our Distance Learning courses to a Navajo audience. As with many college level science courses, our experimental course was English intensive and there lies the crux of the problem. With the help of our colleague institutions, Los Alamos National Laboratory, University of California at Berkeley, University of New Mexico, Kennesaw State University, and New Mexico Highlands University, we undertook to replace 90% of the traditional verbiage with art, an idiom much accepted on the Navajo Reservation. We used the Walt Disney Studios as a model. Specifically, we studied the Pvt. Snafu cartoons used by the War Department in World War II. We tried to emulate their style and techniques. We developed our own cartoon characters, Astroboy, Professor Tso and Roxanne. We combined high quality graphic art, animation, cartooning, Navajo cultural elements, Internet hyperlinks and voiceovers to tell the story of Astronomy 101 Lab. In addition we have added remedial math resources and other helpful resources to our web site. We plan to test initial efforts in an experimental Internet course this summer.

Lifetimes of organic photovoltaics: Combining chemical and physical characterisation techniques to study degradation mechanisms

DEFF Research Database (Denmark)

Norrman, K.; Larsen, N.B.; Krebs, Frederik C

2006-01-01

Degradation mechanisms of a photovoltaic device with an Al/C-60/C-12-PSV/PEDOT:PSS/ITO/glass geometry was studied using a combination of in-plane physical and chemical analysis techniques: TOF-SIMS, AFM, SEM, interference microscopy and fluorescence microscopy. A comparison was made between...

Implementation of a Modular Hands-on Learning Pedagogy: Student Attitudes in a Fluid Mechanics and Heat Transfer Course

Science.gov (United States)

Burgher, J. K.; Finkel, D.; Adesope, O. O.; Van Wie, B. J.

2015-01-01

This study used a within-subjects experimental design to compare the effects of learning with lecture and hands-on desktop learning modules (DLMs) in a fluid mechanics and heat transfer class. The hands-on DLM implementation included the use of worksheets and one of two heat exchangers: an evaporative cooling device and a shell and tube heat…

[Construction of research system for processing mechanism of traditional Chinese medicine based on chemical composition transformation combined with intestinal absorption barrier].

Science.gov (United States)

Sun, E; Xu, Feng-Juan; Zhang, Zhen-Hai; Wei, Ying-Jie; Tan, Xiao-Bin; Cheng, Xu-Dong; Jia, Xiao-Bin

2014-02-01

Based on practice of Epimedium processing mechanism for many years and integrated multidisciplinary theory and technology, this paper initially constructs the research system for processing mechanism of traditional Chinese medicine based on chemical composition transformation combined with intestinal absorption barrier, which to form an innovative research mode of the " chemical composition changes-biological transformation-metabolism in vitro and in vivo-intestinal absorption-pharmacokinetic combined pharmacodynamic-pharmacodynamic mechanism". Combined with specific examples of Epimedium and other Chinese herbal medicine processing mechanism, this paper also discusses the academic thoughts, research methods and key technologies of this research system, which will be conducive to systematically reveal the modem scientific connotation of traditional Chinese medicine processing, and enrich the theory of Chinese herbal medicine processing.

When does social learning become cultural learning?

Science.gov (United States)

Heyes, Cecilia

2017-03-01

Developmental research on selective social learning, or 'social learning strategies', is currently a rich source of information about when children copy behaviour, and who they prefer to copy. It also has the potential to tell us when and how human social learning becomes cultural learning; i.e. mediated by psychological mechanisms that are specialized, genetically or culturally, to promote cultural inheritance. However, this review article argues that, to realize its potential, research on the development of selective social learning needs more clearly to distinguish functional from mechanistic explanation; to achieve integration with research on attention and learning in adult humans and 'dumb' animals; and to recognize that psychological mechanisms can be specialized, not only by genetic evolution, but also by associative learning and cultural evolution. © 2015 John Wiley & Sons Ltd.

"Gamestar Mechanic": Learning a Designer Mindset through Communicational Competence with the Language of Games

Science.gov (United States)

Games, Ivan Alex

2010-01-01

This article presents the results of a three-year study of "Gamestar Mechanic" (www.gamestarmechanic.com), a flash-based multiplayer online role-playing game developed for the MacArthur Foundation's digital media learning initiative by the University of Wisconsin-Madison, and Gamelab in New York. The game's objective is to help children…

An Opportunistic Routing Mechanism Combined with Long-Term and Short-Term Metrics for WMN

Directory of Open Access Journals (Sweden)

Weifeng Sun

2014-01-01

Full Text Available WMN (wireless mesh network is a useful wireless multihop network with tremendous research value. The routing strategy decides the performance of network and the quality of transmission. A good routing algorithm will use the whole bandwidth of network and assure the quality of service of traffic. Since the routing metric ETX (expected transmission count does not assure good quality of wireless links, to improve the routing performance, an opportunistic routing mechanism combined with long-term and short-term metrics for WMN based on OLSR (optimized link state routing and ETX is proposed in this paper. This mechanism always chooses the highest throughput links to improve the performance of routing over WMN and then reduces the energy consumption of mesh routers. The simulations and analyses show that the opportunistic routing mechanism is better than the mechanism with the metric of ETX.

An opportunistic routing mechanism combined with long-term and short-term metrics for WMN.

Science.gov (United States)

Sun, Weifeng; Wang, Haotian; Piao, Xianglan; Qiu, Tie

2014-01-01

WMN (wireless mesh network) is a useful wireless multihop network with tremendous research value. The routing strategy decides the performance of network and the quality of transmission. A good routing algorithm will use the whole bandwidth of network and assure the quality of service of traffic. Since the routing metric ETX (expected transmission count) does not assure good quality of wireless links, to improve the routing performance, an opportunistic routing mechanism combined with long-term and short-term metrics for WMN based on OLSR (optimized link state routing) and ETX is proposed in this paper. This mechanism always chooses the highest throughput links to improve the performance of routing over WMN and then reduces the energy consumption of mesh routers. The simulations and analyses show that the opportunistic routing mechanism is better than the mechanism with the metric of ETX.

Taste aversion learning produced by combined treatment with subthreshold radiation and lithium chloride

International Nuclear Information System (INIS)

Rabin, B.M.; Hunt, W.A.; Lee, J.

1987-01-01

These experiments were designed to determine whether treatment with two subthreshold doses of radiation or lithium chloride, either alone or in combination, could lead to taste aversion learning. The first experiment determined the thresholds for a radiation-induced taste aversion at 15-20 rad and for lithium chloride at 0.30-0.45 mEq/kg. In the second experiment it was shown that exposing rats to two doses of 15 rad separated by up to 3 hr produced a taste aversion. Treatment with two injections of lithium chloride (0.30 mEq/kg) did not produce a significant reduction in preference. Combined treatment with radiation and lithium chloride did produce a taste aversion when the two treatments were administered within 1 hr of each other. The results are discussed in terms of the implications of these findings for understanding the nature of the unconditioned stimuli leading to the acquisition of a conditioned taste aversion

Mechanisms of social avoidance learning can explain the emergence of adaptive and arbitrary behavioral traditions in humans.

Science.gov (United States)

Lindström, Björn; Olsson, Andreas

2015-06-01

Many nonhuman animals preferentially copy the actions of others when the environment contains predation risk or other types of danger. In humans, the role of social learning in avoidance of danger is still unknown, despite the fundamental importance of social learning for complex social behaviors. Critically, many social behaviors, such as cooperation and adherence to religious taboos, are maintained by threat of punishment. However, the psychological mechanisms allowing threat of punishment to generate such behaviors, even when actual punishment is rare or absent, are largely unknown. To address this, we used both computer simulations and behavioral experiments. First, we constructed a model where simulated agents interacted under threat of punishment and showed that mechanisms' (a) tendency to copy the actions of others through social learning, together with (b) the rewarding properties of avoiding a threatening punishment, could explain the emergence, maintenance, and transmission of large-scale behavioral traditions, both when punishment is common and when it is rare or nonexistent. To provide empirical support for our model, including the 2 mechanisms, we conducted 4 experiments, showing that humans, if threatened with punishment, are exceptionally prone to copy and transmit the behavior observed in others. Our results show that humans, similar to many nonhuman animals, use social learning if the environment is perceived as dangerous. We provide a novel psychological and computational basis for a range of human behaviors characterized by the threat of punishment, such as the adherence to cultural norms and religious taboos. (c) 2015 APA, all rights reserved).

The Combined Use of Hypnosis and Sensory and Motor Stimulation in Assisting Children with Developmental Learning Problems.

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Jampolsky, Gerald G.

Hypnosis was combined with sensory and motor stimulation to remediate reversal problems in five children (6 1/2- 9-years-old). Under hypnosis Ss were given the suggestion that they learn their numbers through feel and then given 1 hour of structured instruction daily for 10 days. Instruction stressed conditioning, vibratory memory, touch memory,…

Designing instruction to support mechanical reasoning: Three alternatives in the simple machines learning environment

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McKenna, Ann Frances

2001-07-01

Creating a classroom environment that fosters a productive learning experience and engages students in the learning process is a complex endeavor. A classroom environment is dynamic and requires a unique synergy among students, teacher, classroom artifacts and events to achieve robust understanding and knowledge integration. This dissertation addresses this complex issue by developing, implementing, and investigating the simple machines learning environment (SIMALE) to support students' mechanical reasoning and understanding. SIMALE was designed to support reflection, collaborative learning, and to engage students in generative learning through multiple representations of concepts and successive experimentation and design activities. Two key components of SIMALE are an original web-based software tool and hands-on Lego activities. A research study consisting of three treatment groups was created to investigate the benefits of hands-on and web-based computer activities on students' analytic problem solving ability, drawing/modeling ability, and conceptual understanding. The study was conducted with two populations of students that represent a diverse group with respect to gender, ethnicity, academic achievement and social/economic status. One population of students in this dissertation study participated from the Mathematics, Engineering, and Science Achievement (MESA) program that serves minorities and under-represented groups in science and mathematics. The second group was recruited from the Academic Talent Development Program (ATDP) that is an academically competitive outreach program offered through the University of California at Berkeley. Results from this dissertation show success of the SIMALE along several dimensions. First, students in both populations achieved significant gains in analytic problem solving ability, drawing/modeling ability, and conceptual understanding. Second, significant differences that were found on pre-test measures were eliminated

Students' learning processes during school-based learning and workplace learning in vocational education : a review

NARCIS (Netherlands)

Dr. Harmen Schaap; Dr. Liesbeth Baartman; Prof.Dr. Elly de Bruijn

2012-01-01

This article reviews 24 articles in order to get a structured view on student's learning processes when dealing with a combination of school-based learning and workplace learning in vocational education. It focuses on six main themes: students' expertise development, students' learning styles,

Development of a Mechanical Engineering Test Item Bank to promote learning outcomes-based education in Japanese and Indonesian higher education institutions

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Jeffrey S. Cross

2017-11-01

Full Text Available Following on the 2008-2012 OECD Assessment of Higher Education Learning Outcomes (AHELO feasibility study of civil engineering, in Japan a mechanical engineering learning outcomes assessment working group was established within the National Institute of Education Research (NIER, which became the Tuning National Center for Japan. The purpose of the project is to develop among engineering faculty members, common understandings of engineering learning outcomes, through the collaborative process of test item development, scoring, and sharing of results. By substantiating abstract level learning outcomes into concrete level learning outcomes that are attainable and assessable, and through measuring and comparing the students’ achievement of learning outcomes, it is anticipated that faculty members will be able to draw practical implications for educational improvement at the program and course levels. The development of a mechanical engineering test item bank began with test item development workshops, which led to a series of trial tests, and then to a large scale test implementation in 2016 of 348 first semester master’s students in 9 institutions in Japan, using both multiple choice questions designed to measure the mastery of basic and engineering sciences, and a constructive response task designed to measure “how well students can think like an engineer.” The same set of test items were translated from Japanese into to English and Indonesian, and used to measure achievement of learning outcomes at Indonesia’s Institut Teknologi Bandung (ITB on 37 rising fourth year undergraduate students. This paper highlights how learning outcomes assessment can effectively facilitate learning outcomes-based education, by documenting the experience of Japanese and Indonesian mechanical engineering faculty members engaged in the NIER Test Item Bank project.First published online: 30 November 2017

Mechanisms of P-Glycoprotein Modulation by Semen Strychni Combined with Radix Paeoniae Alba

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Li-Li Liu

2017-01-01

Full Text Available Semen Strychni has been extensively used as a Chinese herb, but its therapeutic window is narrowed by the strong toxicity of the compound, which limits its effectiveness. Radix Paeoniae Alba has been reported to reduce the toxic effects and increase the therapeutic effects of Semen Strychni, but the underlying mechanism remains unknown. This research aimed to explore the mechanism through which P-glycoprotein (P-gp is modulated by Semen Strychni combined with Radix Paeoniae Alba in vitro. An MTT assay was used to study cytotoxicity in an MDCK-MDR1 cell model. Rh123 efflux and accumulation were measured to assess P-gp function. The expression levels of MDR1 mRNA and P-gp protein in MDCK-MDR1 cells were investigated. A P-gp ATPase activity assay kit was applied to detect the effect on P-gp ATPase activity. Semen Strychni combined with Radix Paeoniae Alba could induce P-gp-mediated drug transport by inhibiting brucine and strychnine transport in MDCK-MDR1 cells, enhancing the P-gp efflux function, upregulating the P-gp expression and MDR1 mRNA levels, and stimulating P-gp ATPase activity.

Identifying Human Phenotype Terms by Combining Machine Learning and Validation Rules

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Manuel Lobo

2017-01-01

Full Text Available Named-Entity Recognition is commonly used to identify biological entities such as proteins, genes, and chemical compounds found in scientific articles. The Human Phenotype Ontology (HPO is an ontology that provides a standardized vocabulary for phenotypic abnormalities found in human diseases. This article presents the Identifying Human Phenotypes (IHP system, tuned to recognize HPO entities in unstructured text. IHP uses Stanford CoreNLP for text processing and applies Conditional Random Fields trained with a rich feature set, which includes linguistic, orthographic, morphologic, lexical, and context features created for the machine learning-based classifier. However, the main novelty of IHP is its validation step based on a set of carefully crafted manual rules, such as the negative connotation analysis, that combined with a dictionary can filter incorrectly identified entities, find missed entities, and combine adjacent entities. The performance of IHP was evaluated using the recently published HPO Gold Standardized Corpora (GSC, where the system Bio-LarK CR obtained the best F-measure of 0.56. IHP achieved an F-measure of 0.65 on the GSC. Due to inconsistencies found in the GSC, an extended version of the GSC was created, adding 881 entities and modifying 4 entities. IHP achieved an F-measure of 0.863 on the new GSC.

Neurocognitive mechanisms underlying social learning in infancy: infants' neural processing of the effects of others' actions.

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Paulus, Markus; Hunnius, Sabine; Bekkering, Harold

2013-10-01

Social transmission of knowledge is one of the reasons for human evolutionary success, and it has been suggested that already human infants possess eminent social learning abilities. However, nothing is known about the neurocognitive mechanisms that subserve infants' acquisition of novel action knowledge through the observation of other people's actions and their consequences in the physical world. In an electroencephalogram study on social learning in infancy, we demonstrate that 9-month-old infants represent the environmental effects of others' actions in their own motor system, although they never achieved these effects themselves before. The results provide first insights into the neurocognitive basis of human infants' unique ability for social learning of novel action knowledge.

Perceptual learning shapes multisensory causal inference via two distinct mechanisms.

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McGovern, David P; Roudaia, Eugenie; Newell, Fiona N; Roach, Neil W

2016-04-19

To accurately represent the environment, our brains must integrate sensory signals from a common source while segregating those from independent sources. A reasonable strategy for performing this task is to restrict integration to cues that coincide in space and time. However, because multisensory signals are subject to differential transmission and processing delays, the brain must retain a degree of tolerance for temporal discrepancies. Recent research suggests that the width of this 'temporal binding window' can be reduced through perceptual learning, however, little is known about the mechanisms underlying these experience-dependent effects. Here, in separate experiments, we measure the temporal and spatial binding windows of human participants before and after training on an audiovisual temporal discrimination task. We show that training leads to two distinct effects on multisensory integration in the form of (i) a specific narrowing of the temporal binding window that does not transfer to spatial binding and (ii) a general reduction in the magnitude of crossmodal interactions across all spatiotemporal disparities. These effects arise naturally from a Bayesian model of causal inference in which learning improves the precision of audiovisual timing estimation, whilst concomitantly decreasing the prior expectation that stimuli emanate from a common source.

Using repetitive transcranial magnetic stimulation to study the underlying neural mechanisms of human motor learning and memory.

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Censor, Nitzan; Cohen, Leonardo G

2011-01-01

In the last two decades, there has been a rapid development in the research of the physiological brain mechanisms underlying human motor learning and memory. While conventional memory research performed on animal models uses intracellular recordings, microfusion of protein inhibitors to specific brain areas and direct induction of focal brain lesions, human research has so far utilized predominantly behavioural approaches and indirect measurements of neural activity. Repetitive transcranial magnetic stimulation (rTMS), a safe non-invasive brain stimulation technique, enables the study of the functional role of specific cortical areas by evaluating the behavioural consequences of selective modulation of activity (excitation or inhibition) on memory generation and consolidation, contributing to the understanding of the neural substrates of motor learning. Depending on the parameters of stimulation, rTMS can also facilitate learning processes, presumably through purposeful modulation of excitability in specific brain regions. rTMS has also been used to gain valuable knowledge regarding the timeline of motor memory formation, from initial encoding to stabilization and long-term retention. In this review, we summarize insights gained using rTMS on the physiological and neural mechanisms of human motor learning and memory. We conclude by suggesting possible future research directions, some with direct clinical implications.

Action Reflected and Project Based Combined Methodology for the Appropriate Comprehension of Mechanisms in Industrial Design Education

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Yavuzcan, H. Güçlü; Sahin, Damla

2017-01-01

In industrial design (ID) education, mechanics-based courses are mainly based on a traditional lecture approach and they are highly abstract for ID students to comprehend. The existing studies highlight the requirement of a new approach for mechanics-based courses in ID departments. This study presents a combined teaching model for mechanisms…

Combined quantum mechanical and molecular mechanical method for metal-organic frameworks: proton topologies of NU-1000.

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Wu, Xin-Ping; Gagliardi, Laura; Truhlar, Donald G

2018-01-17

Metal-organic frameworks (MOFs) are materials with applications in catalysis, gas separations, and storage. Quantum mechanical (QM) calculations can provide valuable guidance to understand and predict their properties. In order to make the calculations faster, rather than modeling these materials as periodic (infinite) systems, it is useful to construct finite models (called cluster models) and use subsystem methods such as fragment methods or combined quantum mechanical and molecular mechanical (QM/MM) methods. Here we employ a QM/MM methodology to study one particular MOF that has been of widespread interest because of its wide pores and good solvent and thermal stability, namely NU-1000, which contains hexanuclear zirconium nodes and 1,3,6,8-tetrakis(p-benzoic acid)pyrene (TBAPy 4- ) linkers. A modified version of the Bristow-Tiana-Walsh transferable force field has been developed to allow QM/MM calculations on NU-1000; we call the new parametrization the NU1T force field. We consider isomeric structures corresponding to various proton topologies of the [Zr 6 (μ 3 -O) 8 O 8 H 16 ] 8+ node of NU-1000, and we compute their relative energies using a QM/MM scheme designed for the present kind of problem. We compared the results to full quantum mechanical (QM) energy calculations and found that the QM/MM models can reproduce the full QM relative energetics (which span a range of 334 kJ mol -1 ) with a mean unsigned deviation (MUD) of only 2 kJ mol -1 . Furthermore, we found that the structures optimized by QM/MM are nearly identical to their full QM optimized counterparts.

MECHANICAL BEHAVIOR OF PRESTRESSED VISCOELASTIC ADHESIVE AREAS UNDER COMBINING LOADINGS

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Halil Murat Enginsoy

2017-12-01

Full Text Available In this article, mechanical behaviors of adhesive tape VHB 4950 elastomeric material, which is an element of acrylic polymer group and which is in viscoelastic behavior, under different pre-stress conditions and complex forces of different geometric parameters created by combining loadings have been experimentally and numerically investigated. In experimental studies, loading-unloading cyclic tests, one of the different standardized tests for the mechanical characterization of viscoelastic material, have been applied which give the most suitable convergent optimization parameters for the finite element model. Different material models were also investigated by using the data obtained from loading-unloading test results in all numerical models. According to the experimental results, the most suitable material parameters were determined with the Abaqus Parallel Rheological Framework Model (PRF for 4 Yeoh Networks with Bergstrom-Boyce Flow model created in the Mcalibration software for finite element analysis. Subsequently, using these material parameters, finite element analysis was performed as three dimension non-linear viscoelastic with a commercial finite element software Abaqus. The finite element analysis results showed good correlation to the Force (N-Displacement (mm experimental data for maximum load-carrying capacity of structural specimens.

Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease.

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Shamir, Reuben R; Dolber, Trygve; Noecker, Angela M; Walter, Benjamin L; McIntyre, Cameron C

2015-01-01

Deep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinson's disease (PD). However, patients often require time-intensive post-operative management to balance their coupled stimulation and medication treatments. Given the large and complex parameter space associated with this task, we propose that clinical decision support systems (CDSS) based on machine learning algorithms could assist in treatment optimization. Develop a proof-of-concept implementation of a CDSS that incorporates patient-specific details on both stimulation and medication. Clinical data from 10 patients, and 89 post-DBS surgery visits, were used to create a prototype CDSS. The system was designed to provide three key functions: (1) information retrieval; (2) visualization of treatment, and; (3) recommendation on expected effective stimulation and drug dosages, based on three machine learning methods that included support vector machines, Naïve Bayes, and random forest. Measures of medication dosages, time factors, and symptom-specific pre-operative response to levodopa were significantly correlated with post-operative outcomes (P < 0.05) and their effect on outcomes was of similar magnitude to that of DBS. Using those results, the combined machine learning algorithms were able to accurately predict 86% (12/14) of the motor improvement scores at one year after surgery. Using patient-specific details, an appropriately parameterized CDSS could help select theoretically optimal DBS parameter settings and medication dosages that have potential to improve the clinical management of PD patients. Copyright © 2015 Elsevier Inc. All rights reserved.

Combined Ultrasonic Elliptical Vibration and Chemical Mechanical Polishing of Monocrystalline Silicon

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Liu Defu

2016-01-01

Full Text Available An ultrasonic elliptical vibration assisted chemical mechanical polishing(UEV-CMP is employed to achieve high material removal rate and high surface quality in the finishing of hard and brittle materials such as monocrystalline silicon, which combines the functions of conventional CMP and ultrasonic machining. In theultrasonic elliptical vibration aided chemical mechanical polishingexperimental setup developed by ourselves, the workpiece attached at the end of horn can vibrate simultaneously in both horizontal and vertical directions. Polishing experiments are carried out involving monocrystalline silicon to confirm the performance of the proposed UEV-CMP. The experimental results reveal that the ultrasonic elliptical vibration can increase significantly the material removal rate and reduce dramatically the surface roughness of monocrystalline silicon. It is found that the removal rate of monocrystalline silicon polished by UEV-CMP is increased by approximately 110% relative to that of conventional CMP because a passive layer on the monocrystalline silicon surface, formed by the chemical action of the polishing slurry, will be removed not only by the mechanical action of CMP but also by ultrasonic vibration action. It indicates that the high efficiency and high quality CMP of monocrystalline silicon can be performed with the proposed UEV-CMP technique.

Design Learning of Teaching Factory in Mechanical Engineering

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Putra, R. C.; Kusumah, I. H.; Komaro, M.; Rahayu, Y.; Asfiyanur, E. P.

2018-02-01

The industrial world that is the target of the process and learning outcomes of vocational high school (SMK) has its own character and nuance. Therefore, vocational education institutions in the learning process should be able to make the appropriate learning approach and in accordance with the industrial world. One approach to learning that is based on production and learning in the world of work is by industry-based learning or known as Teaching Factory, where in this model apply learning that involves direct students in goods or service activities are expected to have the quality so it is worth selling and accepted by consumers. The method used is descriptive approach. The purpose of this research is to get the design of the teaching factory based on the competency requirements of the graduates of the spouse industry, especially in the engineering department. The results of this study is expected to be one of the choice of model factory teaching in the field of machinery engineering in accordance with the products and competencies of the graduates that the industry needs.

Improved probabilistic inference as a general learning mechanism with action video games.

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Green, C Shawn; Pouget, Alexandre; Bavelier, Daphne

2010-09-14

Action video game play benefits performance in an array of sensory, perceptual, and attentional tasks that go well beyond the specifics of game play [1-9]. That a training regimen may induce improvements in so many different skills is notable because the majority of studies on training-induced learning report improvements on the trained task but limited transfer to other, even closely related, tasks ([10], but see also [11-13]). Here we ask whether improved probabilistic inference may explain such broad transfer. By using a visual perceptual decision making task [14, 15], the present study shows for the first time that action video game experience does indeed improve probabilistic inference. A neural model of this task [16] establishes how changing a single parameter, namely the strength of the connections between the neural layer providing the momentary evidence and the layer integrating the evidence over time, captures improvements in action-gamers behavior. These results were established in a visual, but also in a novel auditory, task, indicating generalization across modalities. Thus, improved probabilistic inference provides a general mechanism for why action video game playing enhances performance in a wide variety of tasks. In addition, this mechanism may serve as a signature of training regimens that are likely to produce transfer of learning. Copyright © 2010 Elsevier Ltd. All rights reserved.

Hospitals as learning organizations: fostering innovation through interactive learning.

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Dias, Casimiro; Escoval, Ana

2015-01-01

The article aims to provide an analytical understanding of hospitals as "learning organizations." It further analyzes the development of learning organizations as a way to enhance innovation and performance in the hospital sector. The article pulls together primary data on organizational flexibility, innovation, and performance from 95 administrators from hospital boards in Portugal, collected through a survey, interviews with hospital's boards, and a nominal group technique with a panel of experts on health systems. Results show that a combination of several organizational traits of the learning organization enhances its capacity for innovation development. The logistic model presented reveals that hospitals classified as "advanced learning organizations" have 5 times more chance of developing innovation than "basic learning organizations." Empirical findings further pointed out incentives, standards, and measurement requirements as key elements for integration of service delivery systems and expansion of the current capacity for structured and real-time learning in the hospital sector. The major implication arising from this study is that policy needs to combine instruments that promote innovation opportunities and incentives, with instruments stimulating the further development of the core components of learning organizations. Such a combination of policy instruments has the potential to ensure a wide external cooperation through a learning infrastructure.

Spontaneous brain activity predicts learning ability of foreign sounds.

Science.gov (United States)

Ventura-Campos, Noelia; Sanjuán, Ana; González, Julio; Palomar-García, María-Ángeles; Rodríguez-Pujadas, Aina; Sebastián-Gallés, Núria; Deco, Gustavo; Ávila, César

2013-05-29

Can learning capacity of the human brain be predicted from initial spontaneous functional connectivity (FC) between brain areas involved in a task? We combined task-related functional magnetic resonance imaging (fMRI) and resting-state fMRI (rs-fMRI) before and after training with a Hindi dental-retroflex nonnative contrast. Previous fMRI results were replicated, demonstrating that this learning recruited the left insula/frontal operculum and the left superior parietal lobe, among other areas of the brain. Crucially, resting-state FC (rs-FC) between these two areas at pretraining predicted individual differences in learning outcomes after distributed (Experiment 1) and intensive training (Experiment 2). Furthermore, this rs-FC was reduced at posttraining, a change that may also account for learning. Finally, resting-state network analyses showed that the mechanism underlying this reduction of rs-FC was mainly a transfer in intrinsic activity of the left frontal operculum/anterior insula from the left frontoparietal network to the salience network. Thus, rs-FC may contribute to predict learning ability and to understand how learning modifies the functioning of the brain. The discovery of this correspondence between initial spontaneous brain activity in task-related areas and posttraining performance opens new avenues to find predictors of learning capacities in the brain using task-related fMRI and rs-fMRI combined.

Research on Motivation in Collaborative Learning: Moving beyond the Cognitive-Situative Divide and Combining Individual and Social Processes

Science.gov (United States)

Jarvela, Sanna; Volet, Simone; Jarvenoja, Hanna

2010-01-01

In this article we propose that in order to advance our understanding of motivation in collaborative learning we should move beyond the cognitive-situative epistemological divide and combine individual and social processes. Our claim is that although recent research has recognized the importance of social aspects in emerging and sustained…

Brain and behavioral evidence for altered social learning mechanisms among women with assault-related posttraumatic stress disorder.

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Cisler, Josh M; Bush, Keith; Scott Steele, J; Lenow, Jennifer K; Smitherman, Sonet; Kilts, Clinton D

2015-04-01

Current neurocircuitry models of PTSD focus on the neural mechanisms that mediate hypervigilance for threat and fear inhibition/extinction learning. Less focus has been directed towards explaining social deficits and heightened risk of revictimization observed among individuals with PTSD related to physical or sexual assault. The purpose of the present study was to foster more comprehensive theoretical models of PTSD by testing the hypothesis that assault-related PTSD is associated with behavioral impairments in a social trust and reciprocity task and corresponding alterations in the neural encoding of social learning mechanisms. Adult women with assault-related PTSD (n = 25) and control women (n = 15) completed a multi-trial trust game outside of the MRI scanner. A subset of these participants (15 with PTSD and 14 controls) also completed a social and non-social reinforcement learning task during 3T fMRI. Brain regions that encoded the computationally modeled parameters of value expectation, prediction error, and volatility (i.e., uncertainty) were defined and compared between groups. The PTSD group demonstrated slower learning rates during the trust game and social prediction errors had a lesser impact on subsequent investment decisions. PTSD was also associated with greater encoding of negative expected social outcomes in perigenual anterior cingulate cortex and bilateral middle frontal gyri, and greater encoding of social prediction errors in the left temporoparietal junction. These data suggest mechanisms of PTSD-related deficits in social functioning and heightened risk for re-victimization in assault victims; however, comorbidity in the PTSD group and the lack of a trauma-exposed control group temper conclusions about PTSD specifically. Copyright © 2015 Elsevier Ltd. All rights reserved.

Trends in mechanical ventilation: are we ventilating our patients in the best possible way?

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Dellaca', Raffaele L; Veneroni, Chiara; Farre', Ramon

2017-06-01

This review addresses how the combination of physiology, medicine and engineering principles contributed to the development and advancement of mechanical ventilation, emphasising the most urgent needs for improvement and the most promising directions of future development. Several aspects of mechanical ventilation are introduced, highlighting on one side the importance of interdisciplinary research for further development and, on the other, the importance of training physicians sufficiently on the technological aspects of modern devices to exploit properly the great complexity and potentials of this treatment. To learn how mechanical ventilation developed in recent decades and to provide a better understanding of the actual technology and practice.To learn how and why interdisciplinary research and competences are necessary for providing the best ventilation treatment to patients.To understand which are the most relevant technical limitations in modern mechanical ventilators that can affect their performance in delivery of the treatment.To better understand and classify ventilation modes.To learn the classification, benefits, drawbacks and future perspectives of automatic ventilation tailoring algorithms.

Combining extreme learning machines using support vector machines for breast tissue classification.

Science.gov (United States)

Daliri, Mohammad Reza

2015-01-01

In this paper, we present a new approach for breast tissue classification using the features derived from electrical impedance spectroscopy. This method is composed of a feature extraction method, feature selection phase and a classification step. The feature extraction phase derives the features from the electrical impedance spectra. The extracted features consist of the impedivity at zero frequency (I0), the phase angle at 500 KHz, the high-frequency slope of phase angle, the impedance distance between spectral ends, the area under spectrum, the normalised area, the maximum of the spectrum, the distance between impedivity at I0 and the real part of the maximum frequency point and the length of the spectral curve. The system uses the information theoretic criterion as a strategy for feature selection and the combining extreme learning machines (ELMs) for the classification phase. The results of several ELMs are combined using the support vector machines classifier, and the result of classification is reported as a measure of the performance of the system. The results indicate that the proposed system achieves high accuracy in classification of breast tissues using the electrical impedance spectroscopy.

Quantum interactive learning tutorial on the double-slit experiment to improve student understanding of quantum mechanics

Science.gov (United States)

Sayer, Ryan; Maries, Alexandru; Singh, Chandralekha

2017-06-01

Learning quantum mechanics is challenging, even for upper-level undergraduate and graduate students. Research-validated interactive tutorials that build on students' prior knowledge can be useful tools to enhance student learning. We have been investigating student difficulties with quantum mechanics pertaining to the double-slit experiment in various situations that appear to be counterintuitive and contradict classical notions of particles and waves. For example, if we send single electrons through the slits, they may behave as a "wave" in part of the experiment and as a "particle" in another part of the same experiment. Here we discuss the development and evaluation of a research-validated Quantum Interactive Learning Tutorial (QuILT) which makes use of an interactive simulation to improve student understanding of the double-slit experiment and strives to help students develop a good grasp of foundational issues in quantum mechanics. We discuss common student difficulties identified during the development and evaluation of the QuILT and analyze the data from the pretest and post test administered to the upper-level undergraduate and first-year physics graduate students before and after they worked on the QuILT to assess its effectiveness. These data suggest that on average, the QuILT was effective in helping students develop a more robust understanding of foundational concepts in quantum mechanics that defy classical intuition using the context of the double-slit experiment. Moreover, upper-level undergraduates outperformed physics graduate students on the post test. One possible reason for this difference in performance may be the level of student engagement with the QuILT due to the grade incentive. In the undergraduate course, the post test was graded for correctness while in the graduate course, it was only graded for completeness.

Quantum interactive learning tutorial on the double-slit experiment to improve student understanding of quantum mechanics

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Ryan Sayer

2017-05-01

Full Text Available Learning quantum mechanics is challenging, even for upper-level undergraduate and graduate students. Research-validated interactive tutorials that build on students’ prior knowledge can be useful tools to enhance student learning. We have been investigating student difficulties with quantum mechanics pertaining to the double-slit experiment in various situations that appear to be counterintuitive and contradict classical notions of particles and waves. For example, if we send single electrons through the slits, they may behave as a “wave” in part of the experiment and as a “particle” in another part of the same experiment. Here we discuss the development and evaluation of a research-validated Quantum Interactive Learning Tutorial (QuILT which makes use of an interactive simulation to improve student understanding of the double-slit experiment and strives to help students develop a good grasp of foundational issues in quantum mechanics. We discuss common student difficulties identified during the development and evaluation of the QuILT and analyze the data from the pretest and post test administered to the upper-level undergraduate and first-year physics graduate students before and after they worked on the QuILT to assess its effectiveness. These data suggest that on average, the QuILT was effective in helping students develop a more robust understanding of foundational concepts in quantum mechanics that defy classical intuition using the context of the double-slit experiment. Moreover, upper-level undergraduates outperformed physics graduate students on the post test. One possible reason for this difference in performance may be the level of student engagement with the QuILT due to the grade incentive. In the undergraduate course, the post test was graded for correctness while in the graduate course, it was only graded for completeness.

Observational Word Learning: Beyond Propose-But-Verify and Associative Bean Counting.

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Roembke, Tanja; McMurray, Bob

2016-04-01

Learning new words is difficult. In any naming situation, there are multiple possible interpretations of a novel word. Recent approaches suggest that learners may solve this problem by tracking co-occurrence statistics between words and referents across multiple naming situations (e.g. Yu & Smith, 2007), overcoming the ambiguity in any one situation. Yet, there remains debate around the underlying mechanisms. We conducted two experiments in which learners acquired eight word-object mappings using cross-situational statistics while eye-movements were tracked. These addressed four unresolved questions regarding the learning mechanism. First, eye-movements during learning showed evidence that listeners maintain multiple hypotheses for a given word and bring them all to bear in the moment of naming. Second, trial-by-trial analyses of accuracy suggested that listeners accumulate continuous statistics about word/object mappings, over and above prior hypotheses they have about a word. Third, consistent, probabilistic context can impede learning, as false associations between words and highly co-occurring referents are formed. Finally, a number of factors not previously considered in prior analysis impact observational word learning: knowledge of the foils, spatial consistency of the target object, and the number of trials between presentations of the same word. This evidence suggests that observational word learning may derive from a combination of gradual statistical or associative learning mechanisms and more rapid real-time processes such as competition, mutual exclusivity and even inference or hypothesis testing.

Impact of Combined Prenatal Ethanol and Prenatal Stress Exposures on Markers of Activity-Dependent Synaptic Plasticity in Rat Dentate Gyrus

OpenAIRE

Staples, Miranda C.; Porch, Morgan W.; Savage, Daniel D.

2014-01-01

Prenatal ethanol exposure and prenatal stress can each cause long-lasting deficits in hippocampal synaptic plasticity and disrupt learning and memory processes. However, the mechanisms underlying these perturbations following a learning event are still poorly understood. We examined the effects of prenatal ethanol exposure and prenatal stress exposure, either alone or in combination, on the cytosolic expression of activity-regulated cytoskeletal (ARC) protein and the synaptosomal expression o...

Contextual Approach with Guided Discovery Learning and Brain Based Learning in Geometry Learning

Science.gov (United States)

Kartikaningtyas, V.; Kusmayadi, T. A.; Riyadi

2017-09-01

The aim of this study was to combine the contextual approach with Guided Discovery Learning (GDL) and Brain Based Learning (BBL) in geometry learning of junior high school. Furthermore, this study analysed the effect of contextual approach with GDL and BBL in geometry learning. GDL-contextual and BBL-contextual was built from the steps of GDL and BBL that combined with the principles of contextual approach. To validate the models, it uses quasi experiment which used two experiment groups. The sample had been chosen by stratified cluster random sampling. The sample was 150 students of grade 8th in junior high school. The data were collected through the student’s mathematics achievement test that given after the treatment of each group. The data analysed by using one way ANOVA with different cell. The result shows that GDL-contextual has not different effect than BBL-contextual on mathematics achievement in geometry learning. It means both the two models could be used in mathematics learning as the innovative way in geometry learning.

Win-stay-lose-learn promotes cooperation in the prisoner's dilemma game with voluntary participation.

Directory of Open Access Journals (Sweden)

Chen Chu

Full Text Available Voluntary participation, demonstrated to be a simple yet effective mechanism to promote persistent cooperative behavior, has been extensively studied. It has also been verified that the aspiration-based win-stay-lose-learn strategy updating rule promotes the evolution of cooperation. Inspired by this well-known fact, we combine the Win-Stay-Lose-Learn updating rule with voluntary participation: Players maintain their strategies when they are satisfied, or players attempt to imitate the strategy of one randomly chosen neighbor. We find that this mechanism maintains persistent cooperative behavior, even further promotes the evolution of cooperation under certain conditions.

Manpower allocation in a cellular manufacturing system considering the impact of learning, training and combination of learning and training in operator skills

Directory of Open Access Journals (Sweden)

Masoud

2017-01-01

Full Text Available In this article, a manpower allocation and cell loading problem is studied, where demand is sto-chastic. The inter-cell and intra-cell movements are considered and attention is focused on as-signing operators with different skill levels to operations, because cell performance in addition to load cell is dependent on manpower. The purpose of this article is manpower allocation in cellu-lar manufacturing with consideration to learning and training policies. The manpower skill levels are determined in order to enhance production rate. The main contribution of this approach is the scenarios of training and learning in addition to the combination of training and learning being simulated. By using these three scenarios, the skill level of workers increase which reduces the processing time. In this regard cell layout is static where processing times and customer demand follow a normal distribution. As one of the significant costs of industrial unit is related to pro-duction cost, this study has attempted to reduce these costs by increasing the skill level of opera-tor which causes to reduce the processing time. Scenarios are evaluated by using a simulation method that finally attained results indicate this simulation provides better manpower assign-ments.

Analyzing Learning in Professional Learning Communities: A Conceptual Framework

Science.gov (United States)

Van Lare, Michelle D.; Brazer, S. David

2013-01-01

The purpose of this article is to build a conceptual framework that informs current understanding of how professional learning communities (PLCs) function in conjunction with organizational learning. The combination of sociocultural learning theories and organizational learning theories presents a more complete picture of PLC processes that has…

Combining Service and Learning in Higher Education

National Research Council Canada - National Science Library

Gray, Maryann

1999-01-01

.... Hundreds of college and university presidents, most of the major higher education associations, and a number of highly influential scholars actively support the development of service-learning...

Cognitive neuroepigenetics: the next evolution in our understanding of the molecular mechanisms underlying learning and memory?

Science.gov (United States)

Marshall, Paul; Bredy, Timothy W.

2016-07-01

A complete understanding of the fundamental mechanisms of learning and memory continues to elude neuroscientists. Although many important discoveries have been made, the question of how memories are encoded and maintained at the molecular level remains. So far, this issue has been framed within the context of one of the most dominant concepts in molecular biology, the central dogma, and the result has been a protein-centric view of memory. Here, we discuss the evidence supporting a role for neuroepigenetic mechanisms, which constitute dynamic and reversible, state-dependent modifications at all levels of control over cellular function, and their role in learning and memory. This neuroepigenetic view suggests that DNA, RNA and protein each influence one another to produce a holistic cellular state that contributes to the formation and maintenance of memory, and predicts a parallel and distributed system for the consolidation, storage and retrieval of the engram.

Combining metric episodes with semantic event concepts within the Symbolic and Sub-Symbolic Robotics Intelligence Control System (SS-RICS)

Science.gov (United States)

Kelley, Troy D.; McGhee, S.

2013-05-01

This paper describes the ongoing development of a robotic control architecture that inspired by computational cognitive architectures from the discipline of cognitive psychology. The Symbolic and Sub-Symbolic Robotics Intelligence Control System (SS-RICS) combines symbolic and sub-symbolic representations of knowledge into a unified control architecture. The new architecture leverages previous work in cognitive architectures, specifically the development of the Adaptive Character of Thought-Rational (ACT-R) and Soar. This paper details current work on learning from episodes or events. The use of episodic memory as a learning mechanism has, until recently, been largely ignored by computational cognitive architectures. This paper details work on metric level episodic memory streams and methods for translating episodes into abstract schemas. The presentation will include research on learning through novelty and self generated feedback mechanisms for autonomous systems.

Infant Statistical Learning

Science.gov (United States)

Saffran, Jenny R.; Kirkham, Natasha Z.

2017-01-01

Perception involves making sense of a dynamic, multimodal environment. In the absence of mechanisms capable of exploiting the statistical patterns in the natural world, infants would face an insurmountable computational problem. Infant statistical learning mechanisms facilitate the detection of structure. These abilities allow the infant to compute across elements in their environmental input, extracting patterns for further processing and subsequent learning. In this selective review, we summarize findings that show that statistical learning is both a broad and flexible mechanism (supporting learning from different modalities across many different content areas) and input specific (shifting computations depending on the type of input and goal of learning). We suggest that statistical learning not only provides a framework for studying language development and object knowledge in constrained laboratory settings, but also allows researchers to tackle real-world problems, such as multilingualism, the role of ever-changing learning environments, and differential developmental trajectories. PMID:28793812

Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text.

Science.gov (United States)

Bravo, Àlex; Li, Tong Shu; Su, Andrew I; Good, Benjamin M; Furlong, Laura I

2016-01-01

Drug toxicity is a major concern for both regulatory agencies and the pharmaceutical industry. In this context, text-mining methods for the identification of drug side effects from free text are key for the development of up-to-date knowledge sources on drug adverse reactions. We present a new system for identification of drug side effects from the literature that combines three approaches: machine learning, rule- and knowledge-based approaches. This system has been developed to address the Task 3.B of Biocreative V challenge (BC5) dealing with Chemical-induced Disease (CID) relations. The first two approaches focus on identifying relations at the sentence-level, while the knowledge-based approach is applied both at sentence and abstract levels. The machine learning method is based on the BeFree system using two corpora as training data: the annotated data provided by the CID task organizers and a new CID corpus developed by crowdsourcing. Different combinations of results from the three strategies were selected for each run of the challenge. In the final evaluation setting, the system achieved the highest Recall of the challenge (63%). By performing an error analysis, we identified the main causes of misclassifications and areas for improving of our system, and highlighted the need of consistent gold standard data sets for advancing the state of the art in text mining of drug side effects.Database URL: https://zenodo.org/record/29887?ln¼en#.VsL3yDLWR_V. © The Author(s) 2016. Published by Oxford University Press.

Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

Science.gov (United States)

Chua, Huey Eng; Bhowmick, Sourav S; Tucker-Kellogg, Lisa

2017-10-01

Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms

OpenAIRE

Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-e; Zhang, Xiaochu

2017-01-01

Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs’ appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers’ attention from different fields and many studies have validated MMORPGs’ positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational app...

Combining Service and Learning in Higher Education

National Research Council Canada - National Science Library

Gray, Maryann

1999-01-01

The Policy Debate In the past decade, colleges and universities have made greater efforts to involve students in community service, particularly service-learning, a special form of community service...

Nonlinear mechanical response of the extracellular matrix: learning from articular cartilage

Science.gov (United States)

Kearns, Sarah; Das, Moumita

2015-03-01

We study the mechanical structure-function relations in the extracellular matrix (ECM) with focus on nonlinear shear and compression response. As a model system, our study focuses on the ECM in articular cartilage tissue which has two major mechanobiological components: a network of the biopolymer collagen that acts as a stiff, reinforcing matrix, and a flexible aggrecan network that facilitates deformability. We model this system as a double network hydrogel made of interpenetrating networks of stiff and flexible biopolymers respectively. We study the linear and nonlinear mechanical response of the model ECM to shear and compression forces using a combination of rigidity percolation theory and energy minimization approaches. Our results may provide useful insights into the design principles of the ECM as well as biomimetic hydrogels that are mechanically robust and can, at the same time, easily adapt to cues in their surroundings.

Quinolinic Acid, an Endogenous Molecule Combining Excitotoxicity, Oxidative Stress and Other Toxic Mechanisms

Directory of Open Access Journals (Sweden)

Verónica Pérez-De La Cruz

2012-01-01

Full Text Available Quinolinic acid (QUIN, an endogenous metabolite of the kynurenine pathway, is involved in several neurological disorders, including Huntington's disease, Alzheimer's disease, schizophrenia, HIV associated dementia (HAD etc. QUIN toxicity involves several mechanisms which trigger various metabolic pathways and transcription factors. The primary mechanism exerted by this excitotoxin in the central nervous system (CNS has been largely related with the overactivation of N-methyl-D-aspartate receptors and increased cytosolic Ca 2+ concentrations, followed by mitochondrial dysfunction, cytochrome c release, ATP exhaustion, free radical formation and oxidative damage. As a result, this toxic pattern is responsible for selective loss of middle size striatal spiny GABAergic neurons and motor alterations in lesioned animals. This toxin has recently gained attention in biomedical research as, in addition to its proven excitotoxic profile, a considerable amount of evidence suggests that oxidative stress and energetic disturbances are major constituents of its toxic pattern in the CNS. Hence, this profile has changed our perception of how QUIN-related disorders combine different toxic mechanisms resulting in brain damage. This review will focus on the description and integration of recent evidence supporting old and suggesting new mechanisms to explain QUIN toxicity.

The Purdue Mechanics Freeform Classroom: A New Approach to Engineering Mechanics Education

OpenAIRE

Rhoads, Jeffrey F.; Nauman, Eric; Holloway, Beth M; Krousgrill, Charles Morton

2014-01-01

The [REMOVED] Mechanics Freeform Classroom: A New Approach to Engineering Mechanics EducationMotivated by the need to address the broad spectrum of learning styles embraced by today’sengineering students, a desire to encourage active, peer-to-peer, and self-learning, and a goal ofinteracting with every student despite ever-expanding enrollments, the mechanics faculty at[REMOVED] University have developed the [REMOVED] Mechanics Freeform Classroom(PMFC) -- a new approach to engineering mechani...

Maize water status and physiological traits as affected by root endophytic fungus Piriformospora indica under combined drought and mechanical stresses.

Science.gov (United States)

Hosseini, Fatemeh; Mosaddeghi, Mohammad Reza; Dexter, Anthony Roger; Sepehri, Mozhgan

2018-05-01

Under combined drought and mechanical stresses, mechanical stress primarily controlled physiological responses of maize. Piriformospora indica mitigated the adverse effects of stresses, and inoculated maize experienced less oxidative damage and had better adaptation to stressful conditions. The objective of this study was to investigate the effect of maize root colonization by an endophytic fungus P. indica on plant water status, physiological traits and root morphology under combined drought and mechanical stresses. Seedlings of inoculated and non-inoculated maize (Zea mays L., cv. single cross 704) were cultivated in growth chambers filled with moistened siliceous sand at a matric suction of 20 hPa. Drought stress was induced using PEG 6000 solution with osmotic potentials of 0, - 0.3 and - 0.5 MPa. Mechanical stress (i.e., penetration resistances of 1.05, 4.23 and 6.34 MPa) was exerted by placing weights on the surface of the sand medium. After 30 days, leaf water potential (LWP) and relative water content (RWC), root and shoot fresh weights, root volume (RV) and diameter (RD), leaf proline content, leaf area (LA) and catalase (CAT) and ascorbate peroxidase (APX) activities were measured. The results show that exposure to individual drought and mechanical stresses led to higher RD and proline content and lower plant biomass, RV and LA. Moreover, increasing drought and mechanical stress severity increased APX activity by about 1.9- and 3.1-fold compared with the control. When plants were exposed to combined stresses, mechanical stress played the dominant role in controlling plant responses. P. indica-inoculated plants are better adapted to individual and combined stresses. The inoculated plants had greater RV, LA, RWC, LWP and proline content under stressful conditions. In comparison with non-inoculated plants, inoculated plants showed lower CAT and APX activities which means that they experienced less oxidative stress induced by stressful conditions.

Adhesive wear mechanism under combined electric diamond grinding

Directory of Open Access Journals (Sweden)

Popov Vyacheslav

2017-01-01

Full Text Available The article provides a scientific substantiation of loading of metal-bond diamond grinding wheels and describes the mechanism of contact interaction (interlocking of wheels with tool steel as well as its general properties having an influence on combined electric diamond grinding efficiency. The study concluded that a loaded layer can be formed in a few stages different by nature. It is known, that one of the causes of grinding degradation is a continuous loading of active grits (abrasive grinding tool by workpiece chips. It all affects the diamond grinding wheels efficiency and grinding ability with a result in increase of tool pressure, contact temperature and wheels specific removal rate. Science has partially identified some various methods to minimize grinding wheel loading, however, as to loading of metal-bond diamond grinding wheels the search is still in progress. Therefore, research people have to state, that in spite of the fact that the wheels made of cubic boron nitride are of little use as applied to ceramic, ultrahard, hard-alloyed hard-to-machine and nano-materials of the time, but manufactures have to apply cubic boron nitride wheels wherein diamond ones preferable.

The local enhancement conundrum: in search of the adaptive value of a social learning mechanism.

Science.gov (United States)

Arbilly, Michal; Laland, Kevin N

2014-02-01

Social learning mechanisms are widely thought to vary in their degree of complexity as well as in their prevalence in the natural world. While learning the properties of a stimulus that generalize to similar stimuli at other locations (stimulus enhancement) prima facie appears more useful to an animal than learning about a specific stimulus at a specific location (local enhancement), empirical evidence suggests that the latter is much more widespread in nature. Simulating populations engaged in a producer-scrounger game, we sought to deploy mathematical models to identify the adaptive benefits of reliance on local enhancement and/or stimulus enhancement, and the alternative conditions favoring their evolution. Surprisingly, we found that while stimulus enhancement readily evolves, local enhancement is advantageous only under highly restricted conditions: when generalization of information was made unreliable or when error in social learning was high. Our results generate a conundrum over how seemingly conflicting empirical and theoretical findings can be reconciled. Perhaps the prevalence of local enhancement in nature is due to stimulus enhancement costs independent of the learning task itself (e.g. predation risk), perhaps natural habitats are often characterized by unreliable yet highly rewarding payoffs, or perhaps local enhancement occurs less frequently, and stimulus enhancement more frequently, than widely believed. Copyright © 2013 Elsevier Inc. All rights reserved.

Blended learning – integrating E-learning with traditional learning methods in teaching basic medical science

OpenAIRE

J.G. Bagi; N.K. Hashilkar

2014-01-01

Background: Blended learning includes an integration of face to face classroom learning with technology enhanced online material. It provides the convenience, speed and cost effectiveness of e-learning with the personal touch of traditional learning. Objective: The objective of the present study was to assess the effectiveness of a combination of e-learning module and traditional teaching (Blended learning) as compared to traditional teaching alone to teach acid base homeostasis to Phase I MB...

Combining Formal Logic and Machine Learning for Sentiment Analysis

DEFF Research Database (Denmark)

Petersen, Niklas Christoffer; Villadsen, Jørgen

2014-01-01

This paper presents a formal logical method for deep structural analysis of the syntactical properties of texts using machine learning techniques for efficient syntactical tagging. To evaluate the method it is used for entity level sentiment analysis as an alternative to pure machine learning...

Liraglutide, leptin and their combined effects on feeding: additive intake reduction through common intracellular signalling mechanisms.

Science.gov (United States)

Kanoski, S E; Ong, Z Y; Fortin, S M; Schlessinger, E S; Grill, H J

2015-03-01

To investigate the behavioural and intracellular mechanisms by which the glucagon like peptide-1 (GLP-1) receptor agonist, liraglutide, and leptin in combination enhance the food intake inhibitory and weight loss effects of either treatment alone. We examined the effects of liraglutide (a long-acting GLP-1 analogue) and leptin co-treatment, delivered in low or moderate doses subcutaneously (s.c.) or to the third ventricle, respectively, on cumulative intake, meal patterns and hypothalamic expression of intracellular signalling proteins [phosphorylated signal transducer and activator of transcription-3 (pSTAT3) and protein tyrosine phosphatase-1B (PTP1B)] in lean rats. A low-dose combination of liraglutide (25 µg/kg) and leptin (0.75 µg) additively reduced cumulative food intake and body weight, a result mediated predominantly through a significant reduction in meal frequency that was not present with either drug alone. Liraglutide treatment alone also reduced meal size; an effect not enhanced with leptin co-administration. Moderate doses of liraglutide (75 µg/kg) and leptin (4 µg), examined separately, each reduced meal frequency, cumulative food intake and body weight; only liraglutide reduced meal size. In combination these doses did not further enhance the anorexigenic effects of either treatment alone. Ex vivo immunoblot analysis showed elevated pSTAT3 in the hypothalamic tissue after liraglutide-leptin co-treatment, an effect which was greater than that of leptin treatment alone. In addition, s.c. liraglutide reduced the expression of PTP1B (a negative regulator of leptin receptor signalling), revealing a potential mechanism for the enhanced pSTAT3 response after liraglutide-leptin co-administration. Collectively, these results show novel behavioural and molecular mechanisms underlying the additive reduction in food intake and body weight after liraglutide-leptin combination treatment. © 2014 John Wiley & Sons Ltd.

Adaptive learning in a compartmental model of visual cortex - how feedback enables stable category learning and refinement

Directory of Open Access Journals (Sweden)

Georg eLayher

2014-12-01

Full Text Available The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, but both belong to the category of felines. In other words, tigers and leopards are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in the computational neurosciences. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of (sub- category representations. We demonstrate the temporal evolution of such learning and show how the approach successully establishes category and subcategory

Essential Features of Serious Games Design in Higher Education: Linking Learning Attributes to Game Mechanics

Science.gov (United States)

Lameras, Petros; Arnab, Sylvester; Dunwell, Ian; Stewart, Craig; Clarke, Samantha; Petridis, Panagiotis

2017-01-01

This paper consolidates evidence and material from a range of specialist and disciplinary fields to provide an evidence-based review and synthesis on the design and use of serious games in higher education. Search terms identified 165 papers reporting conceptual and empirical evidence on how learning attributes and game mechanics may be planned,…

Not Deep Learning but Autonomous Learning of Open Innovation for Sustainable Artificial Intelligence

Directory of Open Access Journals (Sweden)

JinHyo Joseph Yun

2016-08-01

Full Text Available What do we need for sustainable artificial intelligence that is not harmful but beneficial human life? This paper builds up the interaction model between direct and autonomous learning from the human’s cognitive learning process and firms’ open innovation process. It conceptually establishes a direct and autonomous learning interaction model. The key factor of this model is that the process to respond to entries from external environments through interactions between autonomous learning and direct learning as well as to rearrange internal knowledge is incessant. When autonomous learning happens, the units of knowledge determinations that arise from indirect learning are separated. They induce not only broad autonomous learning made through the horizontal combinations that surpass the combinations that occurred in direct learning but also in-depth autonomous learning made through vertical combinations that appear so that new knowledge is added. The core of the interaction model between direct and autonomous learning is the variability of the boundary between proven knowledge and hypothetical knowledge, limitations in knowledge accumulation, as well as complementarity and conflict between direct and autonomous learning. Therefore, these should be considered when introducing the interaction model between direct and autonomous learning into navigations, cleaning robots, search engines, etc. In addition, we should consider the relationship between direct learning and autonomous learning when building up open innovation strategies and policies.

Social learning and the replication process: an experimental investigation.

Science.gov (United States)

Derex, Maxime; Feron, Romain; Godelle, Bernard; Raymond, Michel

2015-06-07

Human cultural traits typically result from a gradual process that has been described as analogous to biological evolution. This observation has led pioneering scholars to draw inspiration from population genetics to develop a rigorous and successful theoretical framework of cultural evolution. Social learning, the mechanism allowing information to be transmitted between individuals, has thus been described as a simple replication mechanism. Although useful, the extent to which this idealization appropriately describes the actual social learning events has not been carefully assessed. Here, we used a specifically developed computer task to evaluate (i) the extent to which social learning leads to the replication of an observed behaviour and (ii) the consequences it has for fitness landscape exploration. Our results show that social learning does not lead to a dichotomous choice between disregarding and replicating social information. Rather, it appeared that individuals combine and transform information coming from multiple sources to produce new solutions. As a consequence, landscape exploration was promoted by the use of social information. These results invite us to rethink the way social learning is commonly modelled and could question the validity of predictions coming from models considering this process as replicative. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Design, Qualification and Lessons Learned of the Shutter Calibration Mechanism for EnMAP Mission

Science.gov (United States)

Schmidt, Tilo; Muller, Silvio; Bergander, Arvid; Zajac, Kai; Seifart, Klaus

2015-09-01

The Shutter Calibration Mechanism (SCM) Assembly is one of three mechanisms which are developed by HTS for the EnMAP instrument in subcontract to OHB System AG Munich. EnMAP is the Environmental Mapping and Analysis Program of the German Space Agency DLR.The binary rotary encoder of the SCM using hall-effect sensors was already presented during ESMATS 2011. This paper summarizes the main functions and design features of the Hardware and focuses on qualification testing which has finished successfully in 2014. Of particular interest is the functional testing of the main drive including the precise hall-effect position sensing system and the test of the fail safe mechanism. In addition to standard test campaign required for QM also a shock emission measurement of the fail safe mechanism activation was conducted.Test conduction and results will be presented with focus on deviations from the expected behaviour, mitigation measures and on lessons learned.

A Hybrid Approach to Composite Damage and Failure Analysis Combining Synergistic Damage Mechanics and Peridynamics

Science.gov (United States)

2017-09-30

other provision of law, no person shall be subject to any penalty for fai ling to comply with a collection of information if it does not display a...to Composite Damage and Fai lure Analysis Combining Synergistic Damage Mechanics and Peridynamics Sb. GRANT NUMBER NOOO 14-16-1-2173 Sc. PROGRAM

Exploring Bhavana samskara using Tinospora cordifolia and Phyllanthus emblica combination for learning and memory in mice

Directory of Open Access Journals (Sweden)

Harshad Onkarrao Malve

2015-01-01

Full Text Available Background: Current medications for dementia and enhancement of learning and memory are limited hence we need to explore traditional medicinal systems like Ayurveda to investigate agents that can improve learning and enhance memory. Objective: The present study was carried out to evaluate effects and mechanisms of Ayurveda drug formulations, Tinospora cordifolia (Tc and Phyllanthus emblica (Pe with and without Bhavana samskara on learning and memory of mice. Materials and Methods: After approval of Animal Ethics Committee, Swiss albino mice were divided into seven groups, administered orally: Distilled water, Rivastigmine (2.4 mg/kg, Tc (100 mg/kg, Pe (300 mg/kg, formulation 1 (Tc + Pe: 400 mg/kg and formulation 2 (Tc + Pe + Ocimum sanctum: 400 mg/kg daily for 15 days. Piracetam (200 mg/kg was injected daily intraperitoneally for 8 days. The mice underwent a learning session using elevated plus maze. Memory was tested 24 hours later. Results: Mice pretreated with all the drugs showed a trend toward reducing transfer latencies but values were comparable to vehicle control. In all drug-treated groups, a significant reduction in transfer latency was observed after 24 h. Improvement in learning and memory by both formulations were comparable to individual plant drugs, Tc and Pe. Conclusion: The plant drugs showed improvements in learning and memory. The fixed-dose formulations with Bhavana samskara, showed encouraging results as compared to individual agents but the difference was not statistically significant. Hence, the concept of Bhavana samskara could not be explored in the present study. However, these drugs showed comparable or better effects than the modern medicinal agents thus, their therapeutic potential as nootropics needs to be explored further.

Empowerment of Students Critical Thinking Skills Through Implementation of Think Talk Write Combined Problem Based Learning

OpenAIRE

Yanuarta, Lidya; Gofur, Abdul; Indriwati, Sri Endah

2016-01-01

Critical thinking is a complex reflection process that helps individuals become more analytical in their thinking. Empower critical thinking in students need to be done so that students can resolve the problems that exist in their life and are able to apply alternative solutions to problems in a different situations. Therefore, Think Talk Write (TTW) combined Problem Based Learning (PBL) were needed to empowered the critical thinking skills so that students were able to face the challenges of...

REMEMBERING TO LEARN: INDEPENDENT PLACE AND JOURNEY CODING MECHANISMS CONTRIBUTE TO MEMORY TRANSFER

OpenAIRE

Bahar, Amir S.; Shapiro, Matthew L.

2012-01-01

The neural mechanisms that integrate new episodes with established memories are unknown. When rats explore an environment, CA1 cells fire in place fields that indicate locations. In goal-directed spatial memory tasks, some place fields differentiate behavioral histories (journey-dependent place fields) while others do not (journey-independent place fields). To investigate how these signals inform learning and memory for new and familiar episodes, we recorded CA1 and CA3 activity in rats train...

Intelligent Web-Based Learning System with Personalized Learning Path Guidance

Science.gov (United States)

Chen, C. M.

2008-01-01

Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths…

Maladaptive learning and memory in hybrids as a reproductive isolating barrier.

Science.gov (United States)

Rice, Amber M; McQuillan, Michael A

2018-05-30

Selection against hybrid offspring, or postzygotic reproductive isolation, maintains species boundaries in the face of gene flow from hybridization. In this review, we propose that maladaptive learning and memory in hybrids is an important, but overlooked form of postzygotic reproductive isolation. Although a role for learning in premating isolation has been supported, whether learning deficiencies can contribute to postzygotic isolation has rarely been tested. We argue that the novel genetic combinations created by hybridization have the potential to impact learning and memory abilities through multiple possible mechanisms, and that any displacement from optima in these traits is likely to have fitness consequences. We review evidence supporting the potential for hybridization to affect learning and memory, and evidence of links between learning abilities and fitness. Finally, we suggest several avenues for future research. Given the importance of learning for fitness, especially in novel and unpredictable environments, maladaptive learning and memory in hybrids may be an increasingly important source of postzygotic reproductive isolation. © 2018 The Author(s).

Comparing Learning Outcomes of Blended Learning and Traditional Face-to-Face Learning of University Students in ESL Courses

Science.gov (United States)

Zhang, Wei; Zhu, Chang

2018-01-01

Combining elements of online and face-to-face education, blended learning is emerging as an important teaching and learning model in higher education. In order to examine the effectiveness of blended learning, as compared to the traditional face-to-face learning mode, this research investigated the learning outcomes of students following English…

Learning-dependent plasticity with and without training in the human brain.

Science.gov (United States)

Zhang, Jiaxiang; Kourtzi, Zoe

2010-07-27

Long-term experience through development and evolution and shorter-term training in adulthood have both been suggested to contribute to the optimization of visual functions that mediate our ability to interpret complex scenes. However, the brain plasticity mechanisms that mediate the detection of objects in cluttered scenes remain largely unknown. Here, we combine behavioral and functional MRI (fMRI) measurements to investigate the human-brain mechanisms that mediate our ability to learn statistical regularities and detect targets in clutter. We show two different routes to visual learning in clutter with discrete brain plasticity signatures. Specifically, opportunistic learning of regularities typical in natural contours (i.e., collinearity) can occur simply through frequent exposure, generalize across untrained stimulus features, and shape processing in occipitotemporal regions implicated in the representation of global forms. In contrast, learning to integrate discontinuities (i.e., elements orthogonal to contour paths) requires task-specific training (bootstrap-based learning), is stimulus-dependent, and enhances processing in intraparietal regions implicated in attention-gated learning. We propose that long-term experience with statistical regularities may facilitate opportunistic learning of collinear contours, whereas learning to integrate discontinuities entails bootstrap-based training for the detection of contours in clutter. These findings provide insights in understanding how long-term experience and short-term training interact to shape the optimization of visual recognition processes.

Multi-Drug Resistance Transporters and a Mechanism-Based Strategy for Assessing Risks of Pesticide Combinations to Honey Bees.

Science.gov (United States)

Guseman, Alex J; Miller, Kaliah; Kunkle, Grace; Dively, Galen P; Pettis, Jeffrey S; Evans, Jay D; vanEngelsdorp, Dennis; Hawthorne, David J

2016-01-01

Annual losses of honey bee colonies remain high and pesticide exposure is one possible cause. Dangerous combinations of pesticides, plant-produced compounds and antibiotics added to hives may cause or contribute to losses, but it is very difficult to test the many combinations of those compounds that bees encounter. We propose a mechanism-based strategy for simplifying the assessment of combinations of compounds, focusing here on compounds that interact with xenobiotic handling ABC transporters. We evaluate the use of ivermectin as a model substrate for these transporters. Compounds that increase sensitivity of bees to ivermectin may be inhibiting key transporters. We show that several compounds commonly encountered by honey bees (fumagillin, Pristine, quercetin) significantly increased honey bee mortality due to ivermectin and significantly reduced the LC50 of ivermectin suggesting that they may interfere with transporter function. These inhibitors also significantly increased honey bees sensitivity to the neonicotinoid insecticide acetamiprid. This mechanism-based strategy may dramatically reduce the number of tests needed to assess the possibility of adverse combinations among pesticides. We also demonstrate an in vivo transporter assay that provides physical evidence of transporter inhibition by tracking the dynamics of a fluorescent substrate of these transporters (Rhodamine B) in bee tissues. Significantly more Rhodamine B remains in the head and hemolymph of bees pretreated with higher concentrations of the transporter inhibitor verapamil. Mechanism-based strategies for simplifying the assessment of adverse chemical interactions such as described here could improve our ability to identify those combinations that pose significantly greater risk to bees and perhaps improve the risk assessment protocols for honey bees and similar sensitive species.

Mechanisms of n-3 fatty acid-mediated development and maintenance of learning memory performance.

Science.gov (United States)

Su, Hui-Min

2010-05-01

Docosahexaenoic acid (DHA, 22:6n-3) is specifically enriched in the brain and mainly anchored in the neuronal membrane, where it is involved in the maintenance of normal neurological function. Most DHA accumulation in the brain takes place during brain development in the perinatal period. However, hippocampal DHA levels decrease with age and in the brain disorder Alzheimer's disease (AD), and this decrease is associated with reduced hippocampal-dependent spatial learning memory ability. A potential mechanism is proposed by which the n-3 fatty acids DHA and eicosapentaenoic acid (20:5n-3) aid the development and maintenance of spatial learning memory performance. The developing brain or hippocampal neurons can synthesize and take up DHA and incorporate it into membrane phospholipids, especially phosphatidylethanolamine, resulting in enhanced neurite outgrowth, synaptogenesis and neurogenesis. Exposure to n-3 fatty acids enhances synaptic plasticity by increasing long-term potentiation and synaptic protein expression to increase the dendritic spine density, number of c-Fos-positive neurons and neurogenesis in the hippocampus for learning memory processing. In aged rats, n-3 fatty acid supplementation reverses age-related changes and maintains learning memory performance. n-3 fatty acids have anti-oxidative stress, anti-inflammation, and anti-apoptosis effects, leading to neuron protection in the aged, damaged, and AD brain. Retinoid signaling may be involved in the effects of DHA on learning memory performance. Estrogen has similar effects to n-3 fatty acids on hippocampal function. It would be interesting to know if there is any interaction between DHA and estrogen so as to provide a better strategy for the development and maintenance of learning memory. Copyright 2010 Elsevier Inc. All rights reserved.

Learning outcomes afforded by self-assessed, segmented video–print combinations

OpenAIRE

Jack Koumi

2015-01-01

Learning affordances of video and print are examined in order to assess the learning outcomes afforded by hybrid video–print learning packages. The affordances discussed for print are: navigability, surveyability and legibility. Those discussed for video are: design for constructive reflection, provision of realistic experiences, presentational attributes, motivational influences and teacher personalisation. The video affordances are examined through a framework of pedagogic design principles...

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Directory of Open Access Journals (Sweden)

Pontil Massimiliano

2009-10-01

Full Text Available Abstract Background Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (ΔΔG measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots" at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition. Results We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which ΔΔG ≥ 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%. Conclusion We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been

FACTORS INFLUENCING VICARIOUS LEARNING MECHANISM EFFECTIVENESS WITHIN ORGANIZATIONS

OpenAIRE

JOHN R. VOIT; COLIN G. DRURY

2013-01-01

As organizations become larger it becomes increasingly difficult to share lessons-learned across their disconnected units allowing individuals to learn vicariously from each other's experiences. This lesson-learned information is often unsolicited by the recipient group or individual and required an individual or group to react to the information to yield benefits for the organization. Data was collected using 39 interviews and 582 survey responses that proved the effects of information usefu...

Developing a learning analytics tool

DEFF Research Database (Denmark)

Wahl, Christian; Belle, Gianna; Clemmensen, Anita Lykke

This poster describes how learning analytics and collective intelligence can be combined in order to develop a tool for providing support and feedback to learners and teachers regarding students self-initiated learning activities.......This poster describes how learning analytics and collective intelligence can be combined in order to develop a tool for providing support and feedback to learners and teachers regarding students self-initiated learning activities....

Technological learning in bioenergy systems

International Nuclear Information System (INIS)

Junginger, Martin; Visser, Erika de; Hjort-Gregersen, Kurt; Koornneef, Joris; Raven, Rob; Faaij, Andre; Turkenburg, Wim

2006-01-01

The main goal of this article is to determine whether cost reductions in different bioenergy systems can be quantified using the experience curve approach, and how specific issues (arising from the complexity of biomass energy systems) can be addressed. This is pursued by case studies on biofuelled combined heat and power (CHP) plants in Sweden, global development of fluidized bed boilers and Danish biogas plants. As secondary goal, the aim is to identify learning mechanisms behind technology development and cost reduction for the biomass energy systems investigated. The case studies reveal large difficulties to devise empirical experience curves for investment costs of biomass-fuelled power plants. To some extent, this is due to lack of (detailed) data. The main reason, however, are varying plant costs due to differences in scale, fuel type, plant layout, region etc. For fluidized bed boiler plants built on a global level, progress ratios (PRs) for the price of entire plants lies approximately between 90-93% (which is typical for large plant-like technologies). The costs for the boiler section alone was found to decline much faster. The experience curve approach delivers better results, when the production costs of the final energy carrier are analyzed. Electricity from biofuelled CHP-plants yields PRs of 91-92%, i.e. an 8-9% reduction of electricity production costs with each cumulative doubling of electricity production. The experience curve for biogas production displays a PR of 85% from 1984 to the beginning of 1990, and then levels to approximately 100% until 2002. For technologies developed on a local level (e.g. biogas plants), learning-by-using and learning-by-interacting are important learning mechanism, while for CHP plants utilizing fluidized bed boilers, upscaling is probably one of the main mechanisms behind cost reductions

Multidimensionality of Teachers' Graded Responses for Preschoolers' Stylistic Learning Behavior: The Learning-to-Learn Scales

Science.gov (United States)

McDermott, Paul A.; Fantuzzo, John W.; Warley, Heather P.; Waterman, Clare; Angelo, Lauren E.; Gadsden, Vivian L.; Sekino, Yumiko

2011-01-01

Assessment of preschool learning behavior has become very popular as a mechanism to inform cognitive development and promote successful interventions. The most widely used measures offer sound predictions but distinguish only a few types of stylistic learning and lack sensitive growth detection. The Learning-to-Learn Scales was designed to…

The Evolution of Learning Mechanisms.

Science.gov (United States)

Garcia, John; Garcia y Robertson, Rodrigo

This paper introduces seven principles of learning, enduring over the last five centuries of psychological thought, to discuss the evolution of the "Biophyche" (the brain in action) in the development of humans and other large organisms. It describes the conditioning theories of Darwin, Pavlov, and Thorndike and critically reviews the…

Effect and Mechanism of Mitomycin C Combined with Recombinant Adeno-Associated Virus Type II against Glioma

Directory of Open Access Journals (Sweden)

Hong Ma

2013-12-01

Full Text Available The effect of chemotherapy drug Mitomycin C (MMC in combination with recombinant adeno-associated virus II (rAAV2 in cancer therapy was investigated, and the mechanism of MMC affecting rAAV2’s bioactivity was also studied. The combination effect was evaluated by the level of GFP and TNF expression in a human glioma cell line, and the mechanism of MMC effects on rAAV mediated gene expression was investigated by AAV transduction related signal molecules. C57 and BALB/c nude mice were injected with rAAV-EGFP or rAAV-TNF alone, or mixed with MMC, to evaluate the effect of MMC on AAV-mediated gene expression and tumor suppression. MMC was shown to improve the infection activity of rAAV2 both in vitro and in vivo. Enhancement was found to be independent of initial rAAV2 receptor binding stage or subsequent second-strand synthesis of target DNA, but was related to cell cycle retardation followed by blocked genome degradation. In vivo injection of MMC combined with rAAV2 into the tumors of the animals resulted in significant suppression of tumor growth. It was thus demonstrated for the first time that MMC could enhance the expression level of the target gene mediated by rAAV2. The combination of rAAV2 and MMC may be a promising strategy in cancer therapy.

Peer-Assisted Learning Programme: Supporting Students in High-Risk Subjects at the Mechanical Engineering Department at Walter Sisulu University

Directory of Open Access Journals (Sweden)

Makala Qonda

2017-12-01

Full Text Available The majority of the students who enroll at the Walter Sisulu University (WSU in South Africa are not equipped with the necessary academic/learning skills to cope with the university environment, especially in Mechanical Engineering. The Department of Higher Education and Training (2013, p. 17, further states that “students’ support is crucial to ensure that students adapt to the demands of college life and that they can meet the demands of college programmes”. Particularly in South Africa, the school environment might also contribute to poor student performance as a result of insufficient student support, and a lack of facilities and resources. In order to address this gap, a Peer-Assisted Learning (PAL programme was implemented to provide support targeting high-risk subjects for at-risk students in Mechanical Engineering at WSU. The programme therefore is pro-active and student-driven in that senior students assist junior students with their academic work and learning processes. The programme is designed to encourage collaborative and cooperative learning approaches during group sessions and active student engagement to support student learning (Laal & Laal, 2012. The programme requires substantial resources and time commitments. It is important from an operational, learning, and student perspective to understand in what ways the PAL programme assists students (if at all. Eliciting the experiences of students also helps the department to design interventions from a student-centred perspective using the lens of learning theories.  This qualitative case study explores the student experience of the Peer-Assisted Learning (PAL programme. Open-ended questionnaires/survey from 20 first-year students elicited their perceptions and experiences of the PAL programme. Responses were analysed thematically. Findings indicated that the students had useful insights that may contribute to revising the programme. Aspects mentioned were improved study

Multisensory perceptual learning of temporal order: audiovisual learning transfers to vision but not audition.

Directory of Open Access Journals (Sweden)

David Alais

2010-06-01

Full Text Available An outstanding question in sensory neuroscience is whether the perceived timing of events is mediated by a central supra-modal timing mechanism, or multiple modality-specific systems. We use a perceptual learning paradigm to address this question.Three groups were trained daily for 10 sessions on an auditory, a visual or a combined audiovisual temporal order judgment (TOJ. Groups were pre-tested on a range TOJ tasks within and between their group modality prior to learning so that transfer of any learning from the trained task could be measured by post-testing other tasks. Robust TOJ learning (reduced temporal order discrimination thresholds occurred for all groups, although auditory learning (dichotic 500/2000 Hz tones was slightly weaker than visual learning (lateralised grating patches. Crossmodal TOJs also displayed robust learning. Post-testing revealed that improvements in temporal resolution acquired during visual learning transferred within modality to other retinotopic locations and orientations, but not to auditory or crossmodal tasks. Auditory learning did not transfer to visual or crossmodal tasks, and neither did it transfer within audition to another frequency pair. In an interesting asymmetry, crossmodal learning transferred to all visual tasks but not to auditory tasks. Finally, in all conditions, learning to make TOJs for stimulus onsets did not transfer at all to discriminating temporal offsets. These data present a complex picture of timing processes.The lack of transfer between unimodal groups indicates no central supramodal timing process for this task; however, the audiovisual-to-visual transfer cannot be explained without some form of sensory interaction. We propose that auditory learning occurred in frequency-tuned processes in the periphery, precluding interactions with more central visual and audiovisual timing processes. Functionally the patterns of featural transfer suggest that perceptual learning of temporal order

Multisensory perceptual learning of temporal order: audiovisual learning transfers to vision but not audition.

Science.gov (United States)

Alais, David; Cass, John

2010-06-23

An outstanding question in sensory neuroscience is whether the perceived timing of events is mediated by a central supra-modal timing mechanism, or multiple modality-specific systems. We use a perceptual learning paradigm to address this question. Three groups were trained daily for 10 sessions on an auditory, a visual or a combined audiovisual temporal order judgment (TOJ). Groups were pre-tested on a range TOJ tasks within and between their group modality prior to learning so that transfer of any learning from the trained task could be measured by post-testing other tasks. Robust TOJ learning (reduced temporal order discrimination thresholds) occurred for all groups, although auditory learning (dichotic 500/2000 Hz tones) was slightly weaker than visual learning (lateralised grating patches). Crossmodal TOJs also displayed robust learning. Post-testing revealed that improvements in temporal resolution acquired during visual learning transferred within modality to other retinotopic locations and orientations, but not to auditory or crossmodal tasks. Auditory learning did not transfer to visual or crossmodal tasks, and neither did it transfer within audition to another frequency pair. In an interesting asymmetry, crossmodal learning transferred to all visual tasks but not to auditory tasks. Finally, in all conditions, learning to make TOJs for stimulus onsets did not transfer at all to discriminating temporal offsets. These data present a complex picture of timing processes. The lack of transfer between unimodal groups indicates no central supramodal timing process for this task; however, the audiovisual-to-visual transfer cannot be explained without some form of sensory interaction. We propose that auditory learning occurred in frequency-tuned processes in the periphery, precluding interactions with more central visual and audiovisual timing processes. Functionally the patterns of featural transfer suggest that perceptual learning of temporal order may be

High concentration of vitamin E decreases thermosensation and thermotaxis learning and the underlying mechanisms in the nematode Caenorhabditis elegans.

Science.gov (United States)

Li, Yiping; Li, Yinxia; Wu, Qiuli; Ye, Huayue; Sun, Lingmei; Ye, Boping; Wang, Dayong

2013-01-01

α-tocopherol is a powerful liposoluble antioxidant and the most abundant isoform of vitamin E in the body. Under normal physiological conditions, adverse effects of relatively high concentration of vitamin E on organisms and the underlying mechanisms are still largely unclear. In the present study, we used the nematode Caenorhabditis elegans as an in vivo assay system to investigate the possible adverse effects of high concentration of vitamin E on thermosensation and thermotaxis learning and the underlying mechanisms. Our data show that treatment with 100-200 µg/mL of vitamin E did not noticeably influence both thermosensation and thermotaxis learning; however, treatment with 400 µg/mL of vitamin E altered both thermosensation and thermotaxis learning. The observed decrease in thermotaxis learning in 400 µg/mL of vitamin E treated nematodes might be partially due to the moderate but significant deficits in thermosensation, but not due to deficits in locomotion behavior or perception to food and starvation. Treatment with 400 µg/mL of vitamin E did not noticeably influence the morphology of GABAergic neurons, but significantly decreased fluorescent intensities of the cell bodies in AFD sensory neurons and AIY interneurons, required for thermosensation and thermotaxis learning control. Treatment with 400 µg/mL of vitamin E affected presynaptic function of neurons, but had no remarkable effects on postsynaptic function. Moreover, promotion of synaptic transmission by activating PKC-1 effectively retrieved deficits in both thermosensation and thermotaxis learning induced by 400 µg/mL of vitamin E. Therefore, relatively high concentrations of vitamin E administration may cause adverse effects on thermosensation and thermotaxis learning by inducing damage on the development of specific neurons and presynaptic function under normal physiological conditions in C. elegans.

Learning Groups in MOOCs: Lessons for Online Learning in Higher Education

Directory of Open Access Journals (Sweden)

Godfrey Mayende

2017-06-01

Full Text Available when there is interaction within online learning groups, meaningful learning is achieved. Motivating and sustaining effective student interactions requires planning, coordination and implementation of curriculum, pedagogy and technology. For our aim to understand online learning group processes to identify effective online learning group mechanisms, comparative analysis was used on a massive open online course (MOOC run in 2015 and 2016. Qualitative (interaction on the platform and quantitative (survey methods were used. The findings revealed several possible ways to improve online learning group processes. This paper concludes that course organization helped in increasing individual participation in the groups. Motivation by peers helped to increase sustainability of interaction in the learning groups. Applying these mechanisms in higher education can make online learning groups more effective.

Mechanism of Sporicidal Activity for the Synergistic Combination of Peracetic Acid and Hydrogen Peroxide.

Science.gov (United States)

Leggett, Mark J; Schwarz, J Spencer; Burke, Peter A; McDonnell, Gerald; Denyer, Stephen P; Maillard, Jean-Yves

2016-02-15

There is still great interest in controlling bacterial endospores. The use of chemical disinfectants and, notably, oxidizing agents to sterilize medical devices is increasing. With this in mind, hydrogen peroxide (H2O2) and peracetic acid (PAA) have been used in combination, but until now there has been no explanation for the observed increase in sporicidal activity. This study provides information on the mechanism of synergistic interaction of PAA and H2O2 against bacterial spores. We performed investigations of the efficacies of different combinations, including pretreatments with the two oxidizers, against wild-type spores and a range of spore mutants deficient in the spore coat or small acid-soluble spore proteins. The concentrations of the two biocides were also measured in the reaction vessels, enabling the assessment of any shift from H2O2 to PAA formation. This study confirmed the synergistic activity of the combination of H2O2 and PAA. However, we observed that the sporicidal activity of the combination is largely due to PAA and not H2O2. Furthermore, we observed that the synergistic combination was based on H2O2 compromising the spore coat, which was the main spore resistance factor, likely allowing better penetration of PAA and resulting in the increased sporicidal activity. Copyright © 2016 Leggett et al.

Separation of atmospheric, oceanic and hydrological polar motion excitation mechanisms based on a combination of geometric and gravimetric space observations

Science.gov (United States)

Göttl, F.; Schmidt, M.; Seitz, F.; Bloßfeld, M.

2015-04-01

The goal of our study is to determine accurate time series of geophysical Earth rotation excitations to learn more about global dynamic processes in the Earth system. For this purpose, we developed an adjustment model which allows to combine precise observations from space geodetic observation systems, such as Satellite Laser Ranging (SLR), Global Navigation Satellite Systems, Very Long Baseline Interferometry, Doppler Orbit determination and Radiopositioning Integrated on Satellite, satellite altimetry and satellite gravimetry in order to separate geophysical excitation mechanisms of Earth rotation. Three polar motion time series are applied to derive the polar motion excitation functions (integral effect). Furthermore we use five time variable gravity field solutions from Gravity Recovery and Climate Experiment to determine not only the integral mass effect but also the oceanic and hydrological mass effects by applying suitable filter techniques and a land-ocean mask. For comparison the integral mass effect is also derived from degree 2 potential coefficients that are estimated from SLR observations. The oceanic mass effect is also determined from sea level anomalies observed by satellite altimetry by reducing the steric sea level anomalies derived from temperature and salinity fields of the oceans. Due to the combination of all geodetic estimated excitations the weaknesses of the individual processing strategies can be reduced and the technique-specific strengths can be accounted for. The formal errors of the adjusted geodetic solutions are smaller than the RMS differences of the geophysical model solutions. The improved excitation time series can be used to improve the geophysical modeling.

Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT.

Science.gov (United States)

Lavassani, Mehrzad; Forsström, Stefan; Jennehag, Ulf; Zhang, Tingting

2018-05-12

Digitalization is a global trend becoming ever more important to our connected and sustainable society. This trend also affects industry where the Industrial Internet of Things is an important part, and there is a need to conserve spectrum as well as energy when communicating data to a fog or cloud back-end system. In this paper we investigate the benefits of fog computing by proposing a novel distributed learning model on the sensor device and simulating the data stream in the fog, instead of transmitting all raw sensor values to the cloud back-end. To save energy and to communicate as few packets as possible, the updated parameters of the learned model at the sensor device are communicated in longer time intervals to a fog computing system. The proposed framework is implemented and tested in a real world testbed in order to make quantitative measurements and evaluate the system. Our results show that the proposed model can achieve a 98% decrease in the number of packets sent over the wireless link, and the fog node can still simulate the data stream with an acceptable accuracy of 97%. We also observe an end-to-end delay of 180 ms in our proposed three-layer framework. Hence, the framework shows that a combination of fog and cloud computing with a distributed data modeling at the sensor device for wireless sensor networks can be beneficial for Industrial Internet of Things applications.

Simulation of Semi-Solid Material Mechanical Behavior Using a Combined Discrete/Finite Element Method

Science.gov (United States)

Sistaninia, M.; Phillion, A. B.; Drezet, J.-M.; Rappaz, M.

2011-01-01

As a necessary step toward the quantitative prediction of hot tearing defects, a three-dimensional stress-strain simulation based on a combined finite element (FE)/discrete element method (DEM) has been developed that is capable of predicting the mechanical behavior of semisolid metallic alloys during solidification. The solidification model used for generating the initial solid-liquid structure is based on a Voronoi tessellation of randomly distributed nucleation centers and a solute diffusion model for each element of this tessellation. At a given fraction of solid, the deformation is then simulated with the solid grains being modeled using an elastoviscoplastic constitutive law, whereas the remaining liquid layers at grain boundaries are approximated by flexible connectors, each consisting of a spring element and a damper element acting in parallel. The model predictions have been validated against Al-Cu alloy experimental data from the literature. The results show that a combined FE/DEM approach is able to express the overall mechanical behavior of semisolid alloys at the macroscale based on the morphology of the grain structure. For the first time, the localization of strain in the intergranular regions is taken into account. Thus, this approach constitutes an indispensible step towards the development of a comprehensive model of hot tearing.

Embedding Number-Combinations Practice Within Word-Problem Tutoring

Science.gov (United States)

Powell, Sarah R.; Fuchs, Lynn S.; Fuchs, Douglas

2012-01-01

Two aspects of mathematics with which students with mathematics learning difficulty (MLD) often struggle are word problems and number-combination skills. This article describes a math program in which students receive instruction on using algebraic equations to represent the underlying problem structure for three word-problem types. Students also learn counting strategies for answering number combinations that they cannot retrieve from memory. Results from randomized-control trials indicated that embedding the counting strategies for number combinations produces superior word-problem and number-combination outcomes for students with MLD beyond tutoring programs that focus exclusively on number combinations or word problems. PMID:22661880

Deciphering the Cognitive and Neural Mechanisms Underlying ...

International Development Research Centre (IDRC) Digital Library (Canada)

Deciphering the Cognitive and Neural Mechanisms Underlying Auditory Learning. This project seeks to understand the brain mechanisms necessary for people to learn to perceive sounds. Neural circuits and learning. The research team will test people with and without musical training to evaluate their capacity to learn ...

An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms

International Nuclear Information System (INIS)

Aliabadi, Danial Esmaeili; Kaya, Murat; Şahin, Güvenç

2017-01-01

Deregulated electricity markets are expected to provide affordable electricity for consumers through promoting competition. Yet, the results do not always fulfill the expectations. The regulator's market-clearing mechanism is a strategic choice that may affect the level of competition in the market. We conceive of the market-clearing mechanism as composed of two components: pricing rules and rationing policies. We investigate the strategic behavior of power generation companies under different market-clearing mechanisms using an agent-based simulation model which integrates a game-theoretical understanding of the auction mechanism in the electricity market and generation companies' learning mechanism. Results of our simulation experiments are presented using various case studies representing different market settings. The market in simulations is observed to converge to a Nash equilibrium of the stage game or to a similar state under most parameter combinations. Compared to pay-as-bid pricing, bid prices are closer to marginal costs on average under uniform pricing while GenCos' total profit is also higher. The random rationing policy of the ISO turns out to be more successful in achieving lower bid prices and lower GenCo profits. In minimizing GenCos' total profit, a combination of pay-as-bid pricing rule and random rationing policy is observed to be the most promising. - Highlights: • An agent-based simulation of generation company behavior in electricity markets is developed. • Learning dynamics of companies is modeled with an extended Q-learning algorithm. • Different market clearing mechanisms of the regulator are compared. • Convergence to Nash equilibria is analyzed under different cases. • The level of competition in the market is studied.

ONE PROBABLE MECHANISM OF THE LEARNING-MEMORY DAMAGE BY LEAD: THE CHANGES OF NOS IN HIPPOCAMPUS

Institute of Scientific and Technical Information of China (English)

王静; 赵义; 杨章民; 张进; 李积胜; 司履生; 王一理

2003-01-01

Objective To study the effects of lead on the activity and expression of nitric oxide synthase (NOS) and relationship between the effects of lead on learning-memory and changes of NOS in subfields of hippocampus. Methods Y-maze test was used to study the effects of lead on ability of learning-memory; NADPH-d histochemistry and immunohistochemistry methods were used to investigate the changes of NOS in subfields of hippocampus. Results Compared with the control group, the ability of learning- memory in lead-exposed rats was significantly decreased (P＜0.05); the number of NOS positive neurons in CA1 region and dentate gyrus of lead-exposed rats was significantly decreased(P＜0.05), but no marked changes in CA3 region; the number of nNOS positive neurons in CA1 of lead-exposed rats was also significantly decreased(P＜0.05), but no obvious changes in CA3. Conclusion Lead could damage the ability of learning-memory in rats. Lead could decrease the activity and expression of NOS in hippocampus and had different effects on NOS in different subfields of hippocampus. The changes of NOS in hippocampus induced by lead may be the mechanism of the learning-memory damage by lead.

Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms

Science.gov (United States)

Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-e; Zhang, Xiaochu

2017-01-01

Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs’ appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers’ attention from different fields and many studies have validated MMORPGs’ positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational application of the MMORPGs based on relevant macroscopic and microscopic studies, showing that gamers’ overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives. We suggest that attentional bias makes gamers/learners allocate more cognitive resources toward task-related stimuli in a controlled or an automatic way. Moreover, with a moderating role played by activation of reward circuit, playing the MMORPGs may strengthen or increase functional connectivity from seed regions such as left anterior insular/frontal operculum (AI/FO) and visual word form area to other language-related brain areas. PMID:28303097

Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms.

Science.gov (United States)

Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-E; Zhang, Xiaochu

2017-01-01

Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs' appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers' attention from different fields and many studies have validated MMORPGs' positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational application of the MMORPGs based on relevant macroscopic and microscopic studies, showing that gamers' overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives. We suggest that attentional bias makes gamers/learners allocate more cognitive resources toward task-related stimuli in a controlled or an automatic way. Moreover, with a moderating role played by activation of reward circuit, playing the MMORPGs may strengthen or increase functional connectivity from seed regions such as left anterior insular/frontal operculum (AI/FO) and visual word form area to other language-related brain areas.

Global view of the mechanisms of improved learning and memory capability in mice with music-exposure by microarray.

Science.gov (United States)

Meng, Bo; Zhu, Shujia; Li, Shijia; Zeng, Qingwen; Mei, Bing

2009-08-28

Music has been proved beneficial to improve learning and memory in many species including human in previous research work. Although some genes have been identified to contribute to the mechanisms, it is believed that the effect of music is manifold, behind which must concern a complex regulation network. To further understand the mechanisms, we exposed the mice to classical music for one month. The subsequent behavioral experiments showed improvement of spatial learning capability and elevation of fear-motivated memory in the mice with music-exposure as compared to the naïve mice. Meanwhile, we applied the microarray to compare the gene expression profiles of the hippocampus and cortex between the mice with music-exposure and the naïve mice. The results showed approximately 454 genes in cortex (200 genes up-regulated and 254 genes down-regulated) and 437 genes in hippocampus (256 genes up-regulated and 181 genes down-regulated) were significantly affected in music-exposing mice, which mainly involved in ion channel activity and/or synaptic transmission, cytoskeleton, development, transcription, hormone activity. Our work may provide some hints for better understanding the effects of music on learning and memory.

Combining Benford's Law and machine learning to detect money laundering. An actual Spanish court case.

Science.gov (United States)

Badal-Valero, Elena; Alvarez-Jareño, José A; Pavía, Jose M

2018-01-01

This paper is based on the analysis of the database of operations from a macro-case on money laundering orchestrated between a core company and a group of its suppliers, 26 of which had already been identified by the police as fraudulent companies. In the face of a well-founded suspicion that more companies have perpetrated criminal acts and in order to make better use of what are very limited police resources, we aim to construct a tool to detect money laundering criminals. We combine Benford's Law and machine learning algorithms (logistic regression, decision trees, neural networks, and random forests) to find patterns of money laundering criminals in the context of a real Spanish court case. After mapping each supplier's set of accounting data into a 21-dimensional space using Benford's Law and applying machine learning algorithms, additional companies that could merit further scrutiny are flagged up. A new tool to detect money laundering criminals is proposed in this paper. The tool is tested in the context of a real case. Copyright © 2017 Elsevier B.V. All rights reserved.

Mechanism of red mud combined with Fenton's reagent in sewage sludge conditioning.

Science.gov (United States)

Zhang, Hao; Yang, Jiakuan; Yu, Wenbo; Luo, Sen; Peng, Li; Shen, Xingxing; Shi, Yafei; Zhang, Shinan; Song, Jian; Ye, Nan; Li, Ye; Yang, Changzhu; Liang, Sha

2014-08-01

Red mud was evaluated as an alternative skeleton builder combined with Fenton's reagent in sewage sludge conditioning. The results show that red mud combined with Fenton's reagent showed good conditioning capability with the pH of the filtrate close to neutrality, indicating that red mud acted as a neutralizer as well as a skeleton builder when jointly used with Fenton's reagent. Through response surface methodology (RSM), the optimal dosages of Fe(2+), H2O2 and red mud were proposed as 31.9, 33.7 and 275.1 mg/g DS (dry solids), respectively. The mechanism of the composite conditioner could be illuminated as follows: (1) extracellular polymeric substances (EPS), including loosely bound EPS and tightly bound EPS, were degraded into dissolved organics, e.g., proteins and polysaccharides; (2) bound water was released and converted into free water due to the degradation of EPS; and (3) morphology of the conditioned sludge exhibited a porous structure in contrast with the compact structure of raw sludge, and the addition of red mud formed new mineral phases and a rigid lattice structure in sludge, allowing the outflow of free water. Thus, sludge dewatering performance was effectively improved. The economic assessment for a wastewater treatment plant of 370,000 equivalent inhabitants confirms that using red mud conditioning, combined with Fenton's reagent, leads to a saving of approximately 411,000 USD/y or 50.8 USD/t DS comparing with using lime and ordinary Portland cement combined with Fenton's reagent, and approximately 612,000 USD/y or 75.5 USD/t DS comparing with the traditional treatment. Copyright © 2014 Elsevier Ltd. All rights reserved.

Angular approach combined to mechanical model for tool breakage detection by eddy current sensors

OpenAIRE

Ritou , Mathieu; Garnier , Sébastien; Furet , Benoît; Hascoët , Jean-Yves

2014-01-01

International audience; The paper presents a new complete approach for Tool Condition Monitoring (TCM) in milling. The aim is the early detection of small damages so that catastrophic tool failures are prevented. A versatile in-process monitoring system is introduced for reliability concerns. The tool condition is determined by estimates of the radial eccentricity of the teeth. An adequate criterion is proposed combining mechanical model of milling and angular approach. Then, a new solution i...

A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity.

Directory of Open Access Journals (Sweden)

Quan Wang

2017-08-01

Full Text Available The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP and synaptic normalization (SN. When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network's changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network's sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that

Evolving Stochastic Learning Algorithm based on Tsallis entropic index

Science.gov (United States)

Anastasiadis, A. D.; Magoulas, G. D.

2006-03-01

In this paper, inspired from our previous algorithm, which was based on the theory of Tsallis statistical mechanics, we develop a new evolving stochastic learning algorithm for neural networks. The new algorithm combines deterministic and stochastic search steps by employing a different adaptive stepsize for each network weight, and applies a form of noise that is characterized by the nonextensive entropic index q, regulated by a weight decay term. The behavior of the learning algorithm can be made more stochastic or deterministic depending on the trade off between the temperature T and the q values. This is achieved by introducing a formula that defines a time-dependent relationship between these two important learning parameters. Our experimental study verifies that there are indeed improvements in the convergence speed of this new evolving stochastic learning algorithm, which makes learning faster than using the original Hybrid Learning Scheme (HLS). In addition, experiments are conducted to explore the influence of the entropic index q and temperature T on the convergence speed and stability of the proposed method.

Treating electrostatics with Wolf summation in combined quantum mechanical and molecular mechanical simulations.

Science.gov (United States)

Ojeda-May, Pedro; Pu, Jingzhi

2015-11-07

The Wolf summation approach [D. Wolf et al., J. Chem. Phys. 110, 8254 (1999)], in the damped shifted force (DSF) formalism [C. J. Fennell and J. D. Gezelter, J. Chem. Phys. 124, 234104 (2006)], is extended for treating electrostatics in combined quantum mechanical and molecular mechanical (QM/MM) molecular dynamics simulations. In this development, we split the QM/MM electrostatic potential energy function into the conventional Coulomb r(-1) term and a term that contains the DSF contribution. The former is handled by the standard machinery of cutoff-based QM/MM simulations whereas the latter is incorporated into the QM/MM interaction Hamiltonian as a Fock matrix correction. We tested the resulting QM/MM-DSF method for two solution-phase reactions, i.e., the association of ammonium and chloride ions and a symmetric SN2 reaction in which a methyl group is exchanged between two chloride ions. The performance of the QM/MM-DSF method was assessed by comparing the potential of mean force (PMF) profiles with those from the QM/MM-Ewald and QM/MM-isotropic periodic sum (IPS) methods, both of which include long-range electrostatics explicitly. For ion association, the QM/MM-DSF method successfully eliminates the artificial free energy drift observed in the QM/MM-Cutoff simulations, in a remarkable agreement with the two long-range-containing methods. For the SN2 reaction, the free energy of activation obtained by the QM/MM-DSF method agrees well with both the QM/MM-Ewald and QM/MM-IPS results. The latter, however, requires a greater cutoff distance than QM/MM-DSF for a proper convergence of the PMF. Avoiding time-consuming lattice summation, the QM/MM-DSF method yields a 55% reduction in computational cost compared with the QM/MM-Ewald method. These results suggest that, in addition to QM/MM-IPS, the QM/MM-DSF method may serve as another efficient and accurate alternative to QM/MM-Ewald for treating electrostatics in condensed-phase simulations of chemical reactions.

Cellular and oscillatory substrates of fear extinction learning.

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Davis, Patrick; Zaki, Yosif; Maguire, Jamie; Reijmers, Leon G

2017-11-01

The mammalian brain contains dedicated circuits for both the learned expression and suppression of fear. These circuits require precise coordination to facilitate the appropriate expression of fear behavior, but the mechanisms underlying this coordination remain unclear. Using a combination of chemogenetics, activity-based neuronal-ensemble labeling and in vivo electrophysiology, we found that fear extinction learning confers on parvalbumin-expressing (PV) interneurons in the basolateral amygdala (BLA) a dedicated role in the selective suppression of a previously encoded fear memory and BLA fear-encoding neurons. In addition, following extinction learning, PV interneurons enable a competing interaction between a 6-12 Hz oscillation and a fear-associated 3-6 Hz oscillation within the BLA. Loss of this competition increases a 3-6 Hz oscillatory signature, with BLA→medial prefrontal cortex directionality signaling the recurrence of fear expression. The discovery of cellular and oscillatory substrates of fear extinction learning that critically depend on BLA PV interneurons could inform therapies aimed at preventing the pathological recurrence of fear following extinction learning.

Deep Learning in Open Source Learning Streams

DEFF Research Database (Denmark)

Kjærgaard, Thomas

2016-01-01

This chapter presents research on deep learning in a digital learning environment and raises the question if digital instructional designs can catalyze deeper learning than traditional classroom teaching. As a theoretical point of departure the notion of ‘situated learning’ is utilized...... and contrasted to the notion of functionalistic learning in a digital context. The mechanism that enables deep learning in this context is ‘The Open Source Learning Stream’. ‘The Open Source Learning Stream’ is the notion of sharing ‘learning instances’ in a digital space (discussion board, Facebook group......, unistructural, multistructural or relational learning. The research concludes that ‘The Open Source Learning Stream’ can catalyze deep learning and that there are four types of ‘Open Source Learning streams’; individual/ asynchronous, individual/synchronous, shared/asynchronous and shared...

Optimal design of planar slider-crank mechanism using teaching-learning-based optimization algorithm

International Nuclear Information System (INIS)

Chaudhary, Kailash; Chaudhary, Himanshu

2015-01-01

In this paper, a two stage optimization technique is presented for optimum design of planar slider-crank mechanism. The slider crank mechanism needs to be dynamically balanced to reduce vibrations and noise in the engine and to improve the vehicle performance. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of crank and connecting rod using the equipemental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e., cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the shaking force and the shaking moment is solved using Teaching-learning-based optimization algorithm (TLBO) and its computational performance is compared with Genetic algorithm (GA).

Optimal design of planar slider-crank mechanism using teaching-learning-based optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Chaudhary, Kailash; Chaudhary, Himanshu [Malaviya National Institute of Technology, Jaipur (Malaysia)

2015-11-15

In this paper, a two stage optimization technique is presented for optimum design of planar slider-crank mechanism. The slider crank mechanism needs to be dynamically balanced to reduce vibrations and noise in the engine and to improve the vehicle performance. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of crank and connecting rod using the equipemental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e., cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the shaking force and the shaking moment is solved using Teaching-learning-based optimization algorithm (TLBO) and its computational performance is compared with Genetic algorithm (GA).

Learning Crisis Unit through Post-Crisis: Characteristics and Mechanisms

Science.gov (United States)

Chebbi, Hela; Pündrich, Aline Pereira

2015-01-01

Purpose: This paper aims to identify the characteristics that a crisis unit should have to achieve effective learning after crisis. Literature has identified many relations between learning organizations and crisis; yet, there is a dearth of research on specific studies about crisis units and their post-crisis learning features. Thus, this paper…

Distributed Open and Distance Learning: How Does E-Learning Fit? LSDA Reports.

Science.gov (United States)

Fletcher, Mick

The distinctions between types of open and distance learning broadly equate to the concept of learning at a time, place, and pace that best suits the learner. Distance learning refers to geography, whereas open learning refers to time. Flexible learning is a generic term referring either to geography or time. Combining these distinctions allows…

Transcriptome Analysis of Sunflower Genotypes with Contrasting Oxidative Stress Tolerance Reveals Individual- and Combined- Biotic and Abiotic Stress Tolerance Mechanisms.

Directory of Open Access Journals (Sweden)

Vemanna S Ramu

Full Text Available In nature plants are often simultaneously challenged by different biotic and abiotic stresses. Although the mechanisms underlying plant responses against single stress have been studied considerably, plant tolerance mechanisms under combined stress is not understood. Also, the mechanism used to combat independently and sequentially occurring many number of biotic and abiotic stresses has also not systematically studied. From this context, in this study, we attempted to explore the shared response of sunflower plants to many independent stresses by using meta-analysis of publically available transcriptome data and transcript profiling by quantitative PCR. Further, we have also analyzed the possible role of the genes so identified in contributing to combined stress tolerance. Meta-analysis of transcriptomic data from many abiotic and biotic stresses indicated the common representation of oxidative stress responsive genes. Further, menadione-mediated oxidative stress in sunflower seedlings showed similar pattern of changes in the oxidative stress related genes. Based on this a large scale screening of 55 sunflower genotypes was performed under menadione stress and those contrasting in oxidative stress tolerance were identified. Further to confirm the role of genes identified in individual and combined stress tolerance the contrasting genotypes were individually and simultaneously challenged with few abiotic and biotic stresses. The tolerant hybrid showed reduced levels of stress damage both under combined stress and few independent stresses. Transcript profiling of the genes identified from meta-analysis in the tolerant hybrid also indicated that the selected genes were up-regulated under individual and combined stresses. Our results indicate that menadione-based screening can identify genotypes not only tolerant to multiple number of individual biotic and abiotic stresses, but also the combined stresses.

The mechanism of combination with hemocoagulase and pantoprazole in upper gastrointestinal bleeding

Directory of Open Access Journals (Sweden)

Ming-Ke Yan

2017-03-01

Full Text Available Objective: Through the combination with hemocoagulase and pantoprazole on gastrointestinal bleeding, to observe the changes of serum BUN (blood urea nitrogen, LPO (LPO, NO (nitric oxide, TNF-α(TNF alpha, hs-CRP (high sensitivity C reactive protein and cortisol levels, and to explore the mechanism of combination. Methods: 110 cases of upper gastrointestinal bleeding in our hospital from January 2015 to September 2016 were selected and divided into the control group and the observation group, 55 cases for each group. Patients were treated with bed rest, fasting, intravenous nutrition, oxygen, and according to the individual situation actively supplement blood capacity, and the control group were treated with 40 mg intravenous pantoprazole treatment, 2 times/d; the patients in the observation group were treated with 2 kU hemocoagulase injection based on the treatment of control group, 2 times of intravenous injection per day, and all patients were treated for 3 d, and then the BUN, LPO, NO, TNF-α, hs-CRP and cortisol were detected. Results: (1 There were no significantly differences of the serum levels of BUN, LPO, and NO of the two groups before treatment (P>0.05. After treatment, the serum levels the two groups were significantly lower than before treatment, and LPO, BUN, and NO levels in the observation group were significantly better than the control group (P0.05. After treatment, the serum levels in the two groups were significantly lower than before treatment, and TNF-α, hs-CRP, and cortisol levels in the observation group were significantly better than the control group (P<0.05. Conclusions: The treatment of patients with combined use of hemocoagulase and pantoprazole on gastrointestinal bleeding, can significantly improve the serum levels of BUN, LPO, NO, TNF-α, hs-CRP and cortisol levels, and further illustrates the synergistic effect of the combination, also shows that the combination of two drugs for patients with upper

Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human-like learning.

Science.gov (United States)

Oudeyer, Pierre-Yves

2017-01-01

Autonomous lifelong development and learning are fundamental capabilities of humans, differentiating them from current deep learning systems. However, other branches of artificial intelligence have designed crucial ingredients towards autonomous learning: curiosity and intrinsic motivation, social learning and natural interaction with peers, and embodiment. These mechanisms guide exploration and autonomous choice of goals, and integrating them with deep learning opens stimulating perspectives.

Mechanical Fault Diagnosis Using Color Image Recognition of Vibration Spectrogram Based on Quaternion Invariable Moment

Directory of Open Access Journals (Sweden)

Liang Hua

2015-01-01

Full Text Available Automatic extraction of time-frequency spectral image of mechanical faults can be achieved and faults can be identified consequently when rotating machinery spectral image processing technology is applied to fault diagnosis, which is an advantage. Acquired mechanical vibration signals can be converted into color time-frequency spectrum images by the processing of pseudo Wigner-Ville distribution. Then a feature extraction method based on quaternion invariant moment was proposed, combining image processing technology and multiweight neural network technology. The paper adopted quaternion invariant moment feature extraction method and gray level-gradient cooccurrence matrix feature extraction method and combined them with geometric learning algorithm and probabilistic neural network algorithm, respectively, and compared the recognition rates of rolling bearing faults. The experimental results show that the recognition rates of quaternion invariant moment are higher than gray level-gradient cooccurrence matrix in the same recognition method. The recognition rates of geometric learning algorithm are higher than probabilistic neural network algorithm in the same feature extraction method. So the method based on quaternion invariant moment geometric learning and multiweight neural network is superior. What is more, this algorithm has preferable generalization performance under the condition of fewer samples, and it has practical value and acceptation on the field of fault diagnosis for rotating machinery as well.

Virtual Learning Environment for Interactive Engagement with Advanced Quantum Mechanics

Science.gov (United States)

Pedersen, Mads Kock; Skyum, Birk; Heck, Robert; Müller, Romain; Bason, Mark; Lieberoth, Andreas; Sherson, Jacob F.

2016-01-01

A virtual learning environment can engage university students in the learning process in ways that the traditional lectures and lab formats cannot. We present our virtual learning environment "StudentResearcher," which incorporates simulations, multiple-choice quizzes, video lectures, and gamification into a learning path for quantum…

Using video games to combine learning and assessment in mathematics education

Directory of Open Access Journals (Sweden)

Kristian Juha Mikael Kiili

2015-12-01

Full Text Available One problem with most education systems is that learning and (summative assessment are generally treated as quite separate things in schools. We argue that video games can provide an opportunity to combine these processes in an engaging and effective way. The present study focuses on investigating the effectiveness and the assessment power of two different mathematics video games, Semideus and Wuzzit Trouble. In the current study, we validated the Semideus game as a rational number test instrument. We used it as a pre- and a post-test for a three-hour intervention in which we studied the effectiveness of Wuzzit Trouble, a game built on whole number arithmetic and designed to enhance mathematical thinking and problem solving skills. The results showed that (1 games can be used to assess mathematical knowledge validly, and (2 even short game-based interventions can be very effective. Based on the results, we argue that game-based assessment can create a more complete picture of mathematical knowledge than simply measuring students' accuracy, providing indicators of student misconceptions and conceptual change processes

Dissociable Learning Processes Underlie Human Pain Conditioning.

Science.gov (United States)

Zhang, Suyi; Mano, Hiroaki; Ganesh, Gowrishankar; Robbins, Trevor; Seymour, Ben

2016-01-11

Pavlovian conditioning underlies many aspects of pain behavior, including fear and threat detection [1], escape and avoidance learning [2], and endogenous analgesia [3]. Although a central role for the amygdala is well established [4], both human and animal studies implicate other brain regions in learning, notably ventral striatum and cerebellum [5]. It remains unclear whether these regions make different contributions to a single aversive learning process or represent independent learning mechanisms that interact to generate the expression of pain-related behavior. We designed a human parallel aversive conditioning paradigm in which different Pavlovian visual cues probabilistically predicted thermal pain primarily to either the left or right arm and studied the acquisition of conditioned Pavlovian responses using combined physiological recordings and fMRI. Using computational modeling based on reinforcement learning theory, we found that conditioning involves two distinct types of learning process. First, a non-specific "preparatory" system learns aversive facial expressions and autonomic responses such as skin conductance. The associated learning signals-the learned associability and prediction error-were correlated with fMRI brain responses in amygdala-striatal regions, corresponding to the classic aversive (fear) learning circuit. Second, a specific lateralized system learns "consummatory" limb-withdrawal responses, detectable with electromyography of the arm to which pain is predicted. Its related learned associability was correlated with responses in ipsilateral cerebellar cortex, suggesting a novel computational role for the cerebellum in pain. In conclusion, our results show that the overall phenotype of conditioned pain behavior depends on two dissociable reinforcement learning circuits. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Statistical Mechanics of On-line Learning When a Moving Teacher Goes around an Unlearnable True Teacher

Science.gov (United States)

Urakami, Masahiro; Miyoshi, Seiji; Okada, Masato

2007-04-01

In the framework of on-line learning, a learning machine might move around a teacher due to the differences in structures or output functions between the teacher and the learning machine. In this paper we analyze the generalization performance of a new student supervised by a moving machine. A model composed of a fixed true teacher, a moving teacher, and a student is treated theoretically using statistical mechanics, where the true teacher is a nonmonotonic perceptron and the others are simple perceptrons. Calculating the generalization errors numerically, we show that the generalization errors of a student can temporarily become smaller than that of a moving teacher and can reach the lowest value, even if the student only uses examples from the moving teacher. However, the generalization error of the student eventually becomes the same value with that of the moving teacher. This behavior is qualitatively different from that of a linear model.

Students’ learning processes during school-based learning and workplace learning in vocational education : a review

NARCIS (Netherlands)

Schaap, H.; Baartman, L.K.J.; Bruijn, de E.

2012-01-01

Learning in vocational schools and workplaces are the two main components of vocational education. Students have to develop professional competences by building meaningful relations between knowledge, skills and attitudes. There are, however, some major concerns about the combination of learning in

The mechanical design and dynamic testing of the IBEX-H1 electrostatic analyzer spacecraft instrument

Energy Technology Data Exchange (ETDEWEB)

Bernardin, John D [Los Alamos National Laboratory; Baca, Allen G [SNL

2009-01-01

This paper presents the mechanical design, fabrication and dynamic testing of an electrostatic analyzer spacecraft instrument. The functional and environmental requirements combined with limited spacecraft accommodations, resulted in complex component geometries, unique material selections, and difficult fabrication processes. The challenging aspects of the mechanical design and several of the more difficult production processes are discussed. In addition, the successes, failures, and lessons learned from acoustic and random vibration testing of a full-scale prototype instrument are presented.

Autophosphorylation of [alpha]CaMKII is Differentially Involved in New Learning and Unlearning Mechanisms of Memory Extinction

Science.gov (United States)

Kimura, Ryoichi; Silva, Alcino J.; Ohno, Masuo

2008-01-01

Accumulating evidence indicates the key role of [alpha]-calcium/calmodulin-dependent protein kinase II ([alpha]CaMKII) in synaptic plasticity and learning, but it remains unclear how this kinase participates in the processing of memory extinction. Here, we investigated the mechanism by which [alpha]CaMKII may mediate extinction by using…

Combined therapeutic effect and molecular mechanisms of metformin and cisplatin in human lung cancer xenografts in nude mice

OpenAIRE

Yu-Qin Chen; Gang Chen

2015-01-01

Objective: This work was aimed at studying the inhibitory activity of metformin combined with the commonly used chemotherapy drug cisplatin in human lung cancer xenografts in nude mice. We also examined the combined effects of these drugs on the molecular expression of survivin, matrix metalloproteinase-2 (MMP-2), vascular endothelial growth factor-C (VEGF-C), and vascular endothelial growth factorreceptor-3 (VEGFR-3) to determine the mechanism of action and to explore the potential applicati...

Collective Learning in Games through Social Networks

NARCIS (Netherlands)

Kosterman, S.; Gierasimczuk, N.; Armentano, M.G.; Monteserin, A.; Tang, J.; Yannibelli, V.

2015-01-01

This paper argues that combining social networks communication and games can positively influence the learning behavior of players. We propose a computational model that combines features of social network learning (communication) and game-based learning (strategy reinforcement). The focus is on

Learning Object Metadata in a Web-Based Learning Environment