The development of domain-specific and domain-general metacognitive monitoring

Metacognitive monitoring may be a critical element in self-regulated learning. Two types of metacognitive monitoring have been identified: domain-specific and domain-general. Domain-specific metacognitive monitoring occurs when an individual is monitoring content-specific knowledge. Domain-general metacognitive monitoring occurs in situations when content-specific knowledge is not available. Currently no research is available that examines the developmental differences between domain-specific and domain-general metacognitive monitoring in children. This study attempted to address this issue by asking children in first, fourth, and seventh grade to make item-by-item confidence judgments while providing answers in two domain-specific tasks and two domain-general tasks. Two working memory spans tasks were also employed to control for maturational processes. Domain-specific metacognitive monitoring appeared earlier than domain-general metacognitive monitoring. Both domain-specific and domain-general metacognitive monitoring appear to benefit from experience because older students were more accurate metacognitive monitors and less overconfident than younger students. Maturational processes likely play a less significant role than experience in student improvement at metacognitive monitoring than previously thought

The Differential Contributions of Auditory-verbal and Visuospatial Working Memory on Decoding Skills in Children Who Are Poor Decoders

This study investigated the differential contribution of auditory-verbal and visuospatial working memory (WM) on decoding skills in second- and fifth-grade children identified with poor decoding. Thirty-two second-grade students and 22 fifth-grade students completed measures that assessed simple and complex auditory-verbal and visuospatial memory, phonological awareness, orthographic knowledge, listening comprehension and verbal and nonverbal intelligence. Bivariate correlations revealed that complex auditory-verbal WM was moderately and significantly correlated to word attack at second grade. The simple auditory-verbal WM measure was moderately and significantly correlated to word identification in fifth grade. The complex visuospatial WM measures were not correlated to word identification or word attack for second-grade students. However, for fifth-grade participants, there was a negative correlation between a complex visuospatial WM measure and word attack and a positive correlation between orthographic knowledge and word identification. Different types of WM measures predicted word identification and word attack ability in second and fifth graders. We wondered whether the processes involved in visuospatial memory (the visuospatial sketchpad) or auditory-verbal memory (the phonological loop), acting alone, would predict decoding skills. They did not. Similarly, the cognitive control abilities related to executive functions (measured by our complex memory tasks), acting alone, did not predict decoding at either grade. The optimal prediction models for each grade involved various combinations of storage, cognitive control, and retrieval processes. Second graders appeared to rely more on the processes involved in auditory-verbal WM when identifying words, while fifth-grade students relied on the visuospatial domains to identify words. For second-grade students, both complex visuospatial and auditory-verbal WM predicted word attack ability, but by fifth grade, only the visual domains predicted word attack. This study has implications for training instruction in reading. It was not the individual contributions of auditory-verbal or visuospatial WM that best predicted reading ability in second and fifth grade decoders, but rather, a combination of factors. Training WM in isolation of other skills does not increase reading ability. In fact, for young students, too much WM storage can interfere with learning to decode

The Cognitive and Neural Underpinnings of Language Learning and Processing

Language is at the epicenter of our existence, allowing us to communicate and think about complex ideas, feelings, and anything else we want. While language is often regarded as an isolated, unique phenomenon when looking across taxa, it is important to realize that this apparent detachment from the rest of cognition is illusory – our human language abilities are in fact highly dependent upon more basic cognitive processes. Much of this dissertation focuses on the ways in which the process of statistical learning both allows for and constrains language learning and processing. Statistical learning can be thought of as a cognitive process by which learners implicitly form associations between stimuli by tracking and storing the underlying statistical relationships between such elements. To provide insight into the relationship between statistical learning and language, two studies are reported here. I first demonstrate the reliability of paradigms that are frequently used to test this construct, while also showing how individual differences in statistical learning are correlated with biases in language processing. In the second study, I characterize how constraints on the input available to learners can affect their ability to acquire statistically learned grammatical regularities. I also establish that such knowledge is retained over time, by examining performance at a follow-up session two-weeks after training. The third study puts long-held assumptions about the modularity of the brain’s language network to the test by examining neuroplasticity in adult patients with brain tumors. The results of this study show that the right frontal lobe is capable of maintaining language function when there is damage to the left frontal lobe. Together, the findings reported within offer evidence for a language system that is highly sensitive to the distributional properties of the input, and is characterized by processes of entrenchment and plasticity

Integrated performance prediction and quality control in manufacturing systems

textPredicting the condition of a degrading dynamic system is critical for implementing successful control and designing the optimal operation and maintenance strategies throughout the lifetime of the system. In many situations, especially in manufacturing, systems experience multiple degradation cycles, failures, and maintenance events throughout their lifetimes. In such cases, historical records of sensor readings observed during the lifecycle of a machine can yield vital information about degradation patterns of the monitored machine, which can be used to formulate dynamic models for predicting its future performance. Besides the ability to predict equipment failures, another major component of cost effective and high-throughput manufacturing is tight control of product quality. Quality control is assured by taking periodic measurements of the products at various stages of production. Nevertheless, quality measurements of the product require time and are often executed on costly measurement equipment, which increases the cost of manufacturing and slows down production. One possible way to remedy this situation is to utilize the inherent link between the manufacturing equipment condition, mirrored in the readings of sensors mounted on that machine, and the quality of products coming out of it. The concept of Virtual Metrology (VM) addresses the quality control problem by using data-driven models that relate the product quality to the equipment sensors, enabling continuous estimation of the quality characteristics of the product, even when physical measurements of product quality are not available. VM can thus bring significant production benefits, including improved process control, reduced quality losses and higher productivity. In this dissertation, new methods are formulated that will combine long-term performance prediction of sensory signatures from a degrading manufacturing machine with VM quality estimation, which enables integration of predictive condition monitoring (prediction of sensory signatures) with predictive manufacturing process control (predictive VM model). The recently developed algorithm for prediction of sensory signatures is capable of predicting the system condition by comparing the similarity of the most recent performance signatures with the known degradation patterns available in the historical records. The method accomplishes the prediction of non-Gaussian and non-stationary time-series of relevant performance signatures with analytical tractability, which enables calculations of predicted signature distributions with significantly greater speeds than what can be found in literature. VM quality estimation is implemented using the recently introduced growing structure multiple model system paradigm (GSMMS), based on the use of local linear dynamic models. The concept of local models enables representation of complex, non-linear dependencies with non-Gaussian and non-stationary noise characteristics, using a locally tractable model representation. Localized modeling enables a VM that can detect situations when the VM model is not adequate and needs to be improved, which is one of the main challenges in VM. Finally, uncertainty propagation with Monte Carlo simulation is pursued in order to propagate the predicted distributions of equipment signatures through the VM model to enable prediction of distributions of the quality variables using the readily available sensor readings streaming from the monitored manufacturing machine. The newly developed methods are applied to long-term production data coming from an industrial plasma-enhanced chemical vapor deposition (PECVD) tool operating in a major semiconductor manufacturing fab.Mechanical Engineerin

Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots

Brain-Targeted Early Childhood Beginnings: A Case Study in India

The purpose of this study was to describe the implementation of a neuroeducational curriculum model in one early childhood setting in India and to examine the efficacy of the translational curriculum model from the perceptions of administrators, teachers, and parents. An explanatory single case study model was used to shed light on the applied and contextual phenomenon of brain-compatible education within a critical case. This case study used a causal-process tracing approach, which begins with an interest in a specific outcome and focuses on questions that ask which preconditions are necessary and sufficient to make a specific kind of outcome possible. Additionally, this case study employed survey research to understand the roles of several dimensions of efficacy in the implementation process. These dimensions of efficacy include personal and general teaching efficacy, collective efficacy, and Brain-Targeted Teaching efficacy. The main findings from the research center around trust in the efficacy of the translational model and collective efficacy as the primary normative factor that contributed to the successful implementation of the neuroeducational curriculum model

iURBAN

iURBAN: Intelligent Urban Energy Tool introduces an urban energy tool integrating different ICT energy management systems (both hardware and software) in two European cities, providing useful data to a novel decision support system that makes available the necessary parameters for the generation and further operation of associated business models. The business models contribute at a global level to efficiently manage and distribute the energy produced and consumed at a local level (city or neighbourhood), incorporating behavioural aspects of the users into the software platform and in general prosumers. iURBAN integrates a smart Decision Support System (smartDSS) that collects real-time or near real-time data, aggregates, analyses and suggest actions of energy consumption and production from different buildings, renewable energy production resources, combined heat and power plants, electric vehicles (EV) charge stations, storage systems, sensors and actuators. The consumption and production data is collected via a heterogeneous data communication protocols and networks. The iURBAN smartDSS through a Local Decision Support System allows the citizens to analyse the consumptions and productions that they are generating, receive information about CO2 savings, advises in demand response and the possibility to participate actively in the energy market. Whilst, through a Centralised Decision Support System allow to utilities, ESCOs, municipalities or other authorised third parties to: Get a continuous snapshot of city energy consumption and productionManage energy consumption and productionForecasting of energy consumptionPlanning of new energy "producers" for the future needs of the cityVisualise, analyse and take decisions of all the end points that are consuming or producing energy in a city level, permitting them to forecast and planning renewable power generation available in the city

Yale Medicine : Alumni Bulletin of the School of Medicine, Autumn 2003- Summer 2004

This volume contains Yale medicine: alumni bulletin of the School of Medicine, v.38 (Autumn 2003-Summer 2004). Prepared in cooperation with the alumni and development offices at the School of Medicine. Earlier volumes are called Yale School of Medicine alumni bulletins, dating from v.1 (1953) through v.13 (1965). Digitized with funding from the Arcadia fund, 2017.https://elischolar.library.yale.edu/yale_med_alumni_newsletters/1019/thumbnail.jp

Acute Exercise and Creativity: Embodied Cognition Approaches

This dissertation manuscript is the culmination of three years of research examining several unique, exercise-induced mechanisms underlying creativity. This collection of work addresses historical and current empirical concepts of creativity in a narrative review, providing recommendations for future research. Several reviews follow this introduction, highlighting the proposed effects of exercise on creativity, putative mechanisms for creativity, and the effects of exercise and embodied manipulations on creative behavior. Multiple experiments utilizing moderate-intensity exercise as a theoretical stimulus for higher-order cognitions were conducted to investigate associations between exercise and creativity, which lead to the final dissertation experiment. The dissertation experiment was the first to provide statistically significant evidence for acute, moderate-intensity treadmill exercise coupled with anagram problem-solving to prime subsequent RAT completion compared to a non-exercise, priming only condition. We emphasize that the additive effects of exercise plus priming may be a viable strategy for enhancing verbal convergent creativity. Future research is warranted to explore a variety of priming effects on the relationship between exercise, embodied interventions, and creativityThis dissertation manuscript is the culmination of three years of research examining several unique, exercise-induced mechanisms underlying creativity. This collection of work addresses historical and current empirical concepts of creativity in a narrative review, providing recommendations for future research. Several reviews follow this introduction, highlighting the proposed effects of exercise on creativity, putative mechanisms for creativity, and the effects of exercise and embodied manipulations on creative behavior. Multiple experiments utilizing moderate-intensity exercise as a theoretical stimulus for higher-order cognitions were conducted to investigate associations between exercise and creativity, which lead to the final dissertation experiment. The dissertation experiment was the first to provide statistically significant evidence for acute, moderate-intensity treadmill exercise coupled with anagram problem-solving to prime subsequent RAT completion compared to a non-exercise, priming only condition. We emphasize that the additive effects of exercise plus priming may be a viable strategy for enhancing verbal convergent creativity. Future research is warranted to explore a variety of priming effects on the relationship between exercise, embodied interventions, and creativit