Constant-random practice and the adaptive process in motor learning with varying amounts of constant practice

The adaptive process in motor learning was examined in terms of effects of varying amounts of constant practice performed before random practice. Participants pressed five response keys sequentially, the last one coincident with the lighting of a final visual stimulus provided by a complex coincident timing apparatus. Different visual stimulus speeds were used during the random practice. 33 children (M age=11.6 yr.) were randomly assigned to one of three experimental groups: constant-random, constant-random 33%, and constant-random 66%. The constant-random group practiced constantly until they reached a criterion of performance stabilization three consecutive trials within 50 msec. of error. The other two groups had additional constant practice of 33 and 66%, respectively, of the number of trials needed to achieve the stabilization criterion. All three groups performed 36 trials under random practice; in the adaptation phase, they practiced at a different visual stimulus speed adopted in the stabilization phase. Global performance measures were absolute, constant, and variable errors, and movement pattern was analyzed by relative timing and overall movement time. There was no group difference in relation to global performance measures and overall movement time. However, differences between the groups were observed on movement pattern, since constant-random 66% group changed its relative timing performance in the adaptation phase.

Effect of bandwidth knowledge of results on the learning of a grip force control task

An experiment was conducted to investigate the persistence of the effect of ""bandwidth knowledge of results (KR)"" manipulated during the learning phase of performing a manual force-control task. The experiment consisted of two phases, an acquisition phase with the goal of maintaining 60% maximum force in 30 trials, and a second phase with the objective of maintaining 40% of maximum force in 20 further trials. There were four bandwidths of KR: when performance error exceeded 5, 10, or 15% of the target, and a control group (0% bandwidth). Analysis showed that 5, 10, and 15% bandwidth led to better performance than 0% bandwidth KR at the beginning of the second phase and persisted during the extended trials.

Combined photocatalytic and fungal processes for the treatment of nitrocellulose industry wastewater

The objective of this work was to characterize the delignification effluent originating from the delignification industry and evaluate the combination of the fungus and photocatalytic process (TiO(2)/UV system) for the treatment of this effluent. The delignification effluent has proven harmful to the environment because it presents high color (3516 CU), total phenol (876 mg/L and TOC (1599 mg/L) and is also highly toxic even in a low concentration. The results of photocatalysis were 11%, 25% and 13% higher for reductions in color, total phenol and TOC, respectively. The combined treatments presented benefits when compared to the non-combined treatments. Fungus and photocatalysis in combination proved to be the best treatment, reducing the color, total phenol, toxicity (inhibition of Escherichia coli growth) and TOC by 94.2%, 92.6%, 4.9% and 62%, respectively. (C) 2008 Elsevier B.V. All rights reserved.

High gravity batch and continuous processes for beer production: Evaluation of fermentation performance and beer quality

This study deals with two innovative brewing processes, high gravity batch and complete continuous beer fermentation systems. The results show a significant influence of the variables such as concentration and temperature on the yield factor of the substrate into ethanol and consequently on the productivity of the high gravity batch process. The technological feasibility of continuous production of beer based on yeast immobilization on cheap alternative carriers was also demonstrated. The influence of process parameters on fermentation performance and quality of the obtained beers was studied by sensorial analysis. No significant difference in the degree of acceptance between the obtained products and some traditional market brands was found. (c) 2008 Institute of Chemistry, Slovak Academy of Sciences.

An approach based on neural networks for estimation and generalization of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behaviour. In this paper, a system based on artificial neural networks is developed to overcome the problems usually found in the conventional mathematical models. More specifically, the developed system uses an artificial neural network that simulates the behaviour of the crossflow filtration process in a robust way. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the neural approach. Simulation results are presented to justify the validity of the proposed approach. (C) 2007 Elsevier B.V. All rights reserved.

Microbial processes and bacterial populations associated to anaerobic treatment of sulfate-rich wastewater

A pilot-scale (1.2 m(3)) anaerobic sequencing batch biofilm reactor (ASBBR) containing mineral coal for biomass attachment was fed with sulfate-rich wastewater at increasing sulfate concentrations. Ethanol was used as the main organic source. Tested COD/sulfate ratios were of 1.8 and 1.5 for sulfate loading rates of 0.65-1.90 kgSO(4)(2-)/cycle (48 h-cycle) or of 1.0 in the trial with 3.0 gSO(4)(2-) l(-1). Sulfate removal efficiencies observed in all trials were as high as 99%. Molecular inventories indicated a shift on the microbial composition and a decrease on species diversity with the increase of sulfate concentration. Beta-proteobacteria species affiliated with Aminomonas spp. and Thermanaerovibrio spp. predominated at 1.0 gSO(4)(2-) l(-1). At higher sulfate concentrations the predominant bacterial group was Delta-proteobacteria mainly Desulfovibrio spp. and Desulfomicrobium spp. at 2.0 gSO(4)(2-) l(-1), whereas Desulfurella spp. and Coprothermobacter spp. predominated at 3.0 gSO(4)(2-) l(-1). These organisms have been commonly associated with sulfate reduction producing acetate, sulfide and sulfur. Methanogenic archaea(Methanosaeta spp.)was found at 1.0 and 2.0 gSO(4)(2-) l(-1). Additionally, a simplified mathematical model was used to infer on metabolic pathways of the biomass involved in sulfate reduction. (C) 2009 Elsevier Ltd. All rights reserved.

Feasibility of a Sequencing Reactor Operated in Batch and Fed-batch Mode Applied to Nitrification and Denitrification Processes

The objective of this work was to study the operational feasibility of nitrification and denitrification processes in a mechanically stirred sequencing batch reactor (SBR) operated in batch and fed-batch mode. The reactor was equipped with a draft-tube to improve mass transfer and contained dispersed (aerobic) and granulated (anaerobic) biomass. The following reactor variables were adjusted: aeration time during the nitrification step; dissolved oxygen concentration, feed time defining batch and fed-batch phases, concentration of external carbon source used as electron donor during the denitrification stage and volumetric ammonium nitrogen load in the influent. The reactor (5 L volume) was maintained at 30 +/- 1 degrees C and treated either 1.0 or 1.5 L wastewater in 8-h cycles. Ammonium nitrogen concentrations assessed were: 50 (condition 1) and 100 mgN-NH(4)(+).L(-1) (condition 2), resulting in 29 and 67 mgN-NH(4)(+).L-1-d(-1), respectively. A synthetic medium and ethanol were used as external carbon sources (ECS). Total nitrogen removal efficiencies were 94.4 and 95.9% when the reactor was operated under conditions 1 and 2, respectively. Low nitrite (0.2 and 0.3 mgN-NO(2)(-).L(-1), respectively) and nitrate (0.01 and 0.3 mgN-NO(3)(-).L(-1), respectively) concentrations were detected in the effluent and ammonium nitrogen removal efficiencies were 97.6% and 99.6% under conditions 1 and 2, respectively.

Learning Profile Identification Based on the Analysis of the User Context of Interaction

One of the e-learning environment goal is to attend the individual needs of students during the learning process. The adaptation of contents, activities and tools into different visualization or in a variety of content types is an important feature of this environment, bringing to the user the sensation that there are suitable workplaces to his profile in the same system. Nevertheless, it is important the investigation of student behaviour aspects, considering the context where the interaction happens, to achieve an efficient personalization process. The paper goal is to present an approach to identify the student learning profile analyzing the context of interaction. Besides this, the learning profile could be analyzed in different dimensions allows the system to deal with the different focus of the learning.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

Accelerating autonomous learning by using heuristic selection of actions

This paper investigates how to make improved action selection for online policy learning in robotic scenarios using reinforcement learning (RL) algorithms. Since finding control policies using any RL algorithm can be very time consuming, we propose to combine RL algorithms with heuristic functions for selecting promising actions during the learning process. With this aim, we investigate the use of heuristics for increasing the rate of convergence of RL algorithms and contribute with a new learning algorithm, Heuristically Accelerated Q-learning (HAQL), which incorporates heuristics for action selection to the Q-Learning algorithm. Experimental results on robot navigation show that the use of even very simple heuristic functions results in significant performance enhancement of the learning rate.

Equivalent Carbon Dioxide Capture and Storage Processes in Offshore Petroleum Production Facilities

The present study approaches the economic and technical evaluation of equivalent carbon dioxide (CO(2) eqv.) capture and storage processes, considered in a proposal case compared to a base case. The base case considers an offshore petroleum production facility, with high CO(2) content (4 vol%) in the composition of the produced gas and both CO(2) and natural gas emissions to the atmosphere, called CO(2) eqv. emissions. The results obtained with this study, by using a Hysys process simulator, showed a CO(2) emission reduction of 65% comparing the proposal case in relation to the base case.

Knowledge transfer in product development processes: A case study in small and medium enterprises (SMEs) of the metal-mechanic sector from Sao Paulo, Brazil

This paper reports a research that evaluated the product development methodologies used in Brazilian small and medium-sized metal-mechanic enterprises (SMEs), in a specific region of Sao Paulo. The tool used for collecting the data was a questionnaire, which was developed and applied through interviews conducted by the researchers in 32 companies. The main focus of this paper can be condensed in the synthesis-question ""Is only the company responsible for the development?"" which was analyzed thoroughly. The results obtained from this analysis were evaluated directly (through the respective percentages of answers) and statistically (through the search of an index which demonstrates if two questions are related). The results point to a degree of maturity in SMEs, which allows product development to be conducted in cooperation networks. (C) 2007 Elsevier Ltd. All rights reserved.

Learning about knowledge management for improving environmental impact assessment in a government agency: The Western Australian experience

How does knowledge management (KM) by a government agency responsible for environmental impact assessment (EIA) potentially contribute to better environmental assessment and management practice? Staff members at government agencies in charge of the EIA process are knowledge workers who perform judgement-oriented tasks highly reliant on individual expertise, but also grounded on the agency`s knowledge accumulated over the years. Part of an agency`s knowledge can be codified and stored in an organizational memory, but is subject to decay or loss if not properly managed. The EIA agency operating in Western Australia was used as a case study. Its KM initiatives were reviewed, knowledge repositories were identified and staff surveyed to gauge the utilisation and effectiveness of such repositories in enabling them to perform EIA tasks. Key elements of KM are the preparation of substantive guidance and spatial information management. It was found that treatment of cumulative impacts on the environment is very limited and information derived from project follow-up is not properly captured and stored, thus not used to create new knowledge and to improve practice and effectiveness. Other opportunities for improving organizational learning include the use of after-action reviews. The learning about knowledge management in EIA practice gained from Western Australian experience should be of value to agencies worldwide seeking to understand where best to direct their resources for their own knowledge repositories and environmental management practice. (C) 2011 Elsevier Ltd. All rights reserved.

MODELLING AND CONTROL OF ERSW PROCESSES BY NEURONAL NETWORK

A study on the use of artificial intelligence (AI) techniques for the modelling and subsequent control of an electric resistance spot welding process (ERSW) is presented. The ERSW process is characterized by the coupling of thermal, electrical, mechanical, and metallurgical phenomena. For this reason, early attempts to model it using computational methods established as the methods of finite differences, finite element, and finite volumes, ask for simplifications that lead the model obtained far from reality or very costly in terms of computational costs, to be used in a real-time control system. In this sense, the authors have developed an ERSW controller that uses fuzzy logic to adjust the energy transferred to the weld nugget. The proposed control strategies differ in the speed with which it reaches convergence. Moreover, their application for a quality control of spot weld through artificial neural networks (ANN) is discussed.

Microstructure and texture assessment of Al-Mn-Fe-Si (3003) aluminum alloy produced by continuous and semicontinuous casting processes

Aluminum sheets are currently produced by the direct-chill process (DC). The need for low-cost aluminum sheets is a challenge for the development of new materials produced by the twin roll caster (TRC) process. It is expected that sheets produced from these different casting procedures will differ in their microstructure. These differences in microstructure and in the crystallographic texture have great impact on sheet mechanical properties and formability. The present study investigated microstructure and evaluated texture of two strips of Al-Mn-Fe-Si (3003) aluminum alloy produced by TRC and by hot-rolling processes. It was possible to notice that the microstructure, morphology, and grain size of the TRC sample were more homogenous than those found in hot-rolled samples. Both strips, obtained by the two processes, showed strong texture gradient across the thickness.