Perceptual Learning Of Object Shape

Recognition of objects is accomplished through the use of cues that depend on internal representations of familiar shapes. We used a paradigm of perceptual learning during visual search to explore what features human observers use to identify objects. Human subjects were trained to search for a target object embedded in an array of distractors, until their performance improved from chance levels to over 80% of trials in an object specific manner. We determined the role of specific object components in the recognition of the object as a whole by measuring the transfer of learning from the trained object to other objects sharing components with it. Depending on the geometric relationship of the trained object with untrained objects, transfer to untrained objects was observed. Novel objects that shared a component with the trained object were identified at much higher levels than those that did not, and this could be used as an indicator of which features of the object were important for recognition. Training on an object transferred to the less complex components of the object when these components were embedded in an array of distractors of similar complexity. There was transfer to the different components of object, regardless of how well they distinguish the object from distractors. These results suggest that objects are not represented in a holistic manner during learning, but that their individual components are encoded. Transfer between objects was not complete, and occurred for more than one component, suggesting that a joint involvement of multiple components was necessary for full performance. The sequence of this learning indicated a possible underlying mechanism of the learning. Subjects improved first in a single quadrant of the visual field, and the improvement then spread out sequentially to the other quadrants. This location specificity of the improvement suggests that, with training, encoding information about object shape occurs in early, retinotopically mapped cortical areas. fMRI work suggests that the learning of novel objects in this manner involves a reciprocal switch between two cortical networks, one that involves the normally object-sensitive regions of LOC, and one that involves the temporal and parietal cortices

N′-[(E)-2-Hy­droxy-3,5-diiodo­benzyl­idene]cyclo­hexa­ne-1-carbohydrazide

In the title compound, C14H10I2N2O2, the two aromatic rings are inclined at a dihedral angle of 16.72 (33)°. The mol­ecular structure is stabilized by an intra­molecular O—H⋯N hydrogen bond. In the crystal, inter­molecular N—H⋯O inter­actions link the mol­ecules into chains running along the c axis. C—H⋯O inter­actions also occur. The crystal used for the structure determination was a non-merohedral twin with a domain ratio of 0.972 (2):0.028 (2)

Tris[4-(2-pyridylmethyl­eneamino)phenol]iron(II) bis­(perchlorate)

In the title compound, [Fe(C12H10N2O)3](ClO4)2, the metal center is coordinated by six N atoms from the three bidentate chelating ligands in a distorted octa­hedral coordination geometry, with overall formation of the meridional (OC-6-21) isomer. Inter­molecular O—H⋯O hydrogen bonds between the hydroxyl groups of the cation and the counter-anions form an infinite one-dimensional chain in the c-axis direction

N,N′-Bis(4-chloro­benzyl­idene)-3,3′-dimeth­oxy­biphenyl-4,4′-diamine

The title compound, C28H22Cl2N2O2, crystallized with two independent mol­ecules (A and B) in the asymmetric unit. The two mol­ecules differ essentially in the orientation of the outer aromatic rings. These dihedral angles are 56.07 (13) and 27.62 (15) Å for mol­ecules A and B, respectively. In the crystal, A mol­ecules are related as centrosymmetric pairs through a weak π–π inter­action [centroid–centroid distance = 3.6959 (15) Å]. There are also a number of inter­molecular C—H⋯O, C—H⋯N and C—H⋯π inter­actions present

Neural Mechanisms of Human Perceptual Learning: Electrophysiological Evidence for a Two-Stage Process

Artículo de publicación ISIBackground: Humans and other animals change the way they perceive the world due to experience. This process has been labeled as perceptual learning, and implies that adult nervous systems can adaptively modify the way in which they process sensory stimulation. However, the mechanisms by which the brain modifies this capacity have not been sufficiently analyzed. Methodology/Principal Findings: We studied the neural mechanisms of human perceptual learning by combining electroencephalographic (EEG) recordings of brain activity and the assessment of psychophysical performance during training in a visual search task. All participants improved their perceptual performance as reflected by an increase in sensitivity (d') and a decrease in reaction time. The EEG signal was acquired throughout the entire experiment revealing amplitude increments, specific and unspecific to the trained stimulus, in event-related potential (ERP) components N2pc and P3 respectively. P3 unspecific modification can be related to context or task-based learning, while N2pc may be reflecting a more specific attentional-related boosting of target detection. Moreover, bell and U-shaped profiles of oscillatory brain activity in gamma (30-60 Hz) and alpha (8-14 Hz) frequency bands may suggest the existence of two phases for learning acquisition, which can be understood as distinctive optimization mechanisms in stimulus processing.This research was supported by CONICYT doctoral grant to C.M.H. and by an ECOS-Sud/CONICYT grant C08S02 and FONDECYT 1090612 grant to D.C. and F.A

Neural network analysis of head-flow curves in deep well pumps

In impellers with splitter blades, the difficulty in calculation of the flow area of the impeller is because of the unknown flow rate occurring in the two separate areas when the splitter blades are added. Experimental studies were made to investigate the effects of splitter blade length on deep well pump performance for different numbers of blades. Head-flow curves of deep well pump impellers with splitter blades were investigated using artificial neural networks (ANNs). Gradient descent (GD), Gradient descent with momentum (GDM) and Levenberg-Marquardt (LM) learning algorithms were used in the networks. Experimental studies were completed to obtain training and test data. Blade number (z), non-dimensional splitter blade length ((L) over bar) and flow rate (Q) were used as the input layer, while the output is head (H-m). For the testing data, the root mean squared error (RMSE), fraction of variance (R-2) and mean absolute percentage error (MAPE) were found to be 0.1285, 0.9999 and 1.6821%, respectively. With these results, we believe that the ANN can be used for prediction of head-flow curves as an appropriate method in deep well pump impellers with splitter blades. (c) 2005 Elsevier Ltd. All rights reserved

of deep well pumps with splitter blade

Experimental studies were made to investigate the effects of splitter blade length (25%, 35%, 50%, 60% and 80% of the main blade length) on the pump characteristics of deep well Pumps for different blade numbers (z = 3, 4, 5, 6 and 7). In this study, an artificial neural network (ANN) was used for modeling the performance of deep well pumps with splitter blades. Two hundred and ten experimental results were used to train and test. Forty-two patterns have been randomly selected and used as the test data. The main parameters for the experiments are the blade number (z), non-dimensional splitter blade length ((L) over bar), flow rate (Q, 1/s), head (H-m, m), efficiency (eta, %) and power (P,, W). z, (L) over bar and Q have been used as the input layer, and H-m and eta have also been used as the output layer. The best training algorithm and number of neurons were obtained. Training of the network was performed using the Levenberg-Marquardt (LM) algorithm. To determine the effect of the transfer function, different ANN models are trained, and the results of these ANN models are compared. Some statistical methods; fraction of variance (R 2) and root mean squared error (RMSE) values, have been used for comparison. (c) 2006 Elsevier Ltd. All rightsC1 Pamukkale Univ, Tech Educ Fac, Dept Mech Educ, TR-20017 Kinikli, Denizli, Turkey

ENERGY SAVING BY USING AN AXIAL FLOW DEEP WELL PUMP: AN APPLICATION IN

Turkey is home to a large number of lakes, dam lakes and small lakes which have a great potential for growing fishery products. Growing trout in net frame systems manufactured in dam lakes is very common and economically very promising application. For growing trout, certain conditions such as certain water temperature range and minimum dissolved oxygen (DO) level must be satisfied. The water temperature required should be at 17-20 degrees C to grow trout. Also, the amount of dissolved oxygen (pc) should never fall below 6-7 mg/L. As the temperature rises during the months of June-September, water temperature rises above the acceptable limits to grow trout. Producers usually use different types of internal combustion engine-pump (ICE-P) systems to provide the circulation of the colder water in the deeper parts of the lake to lake surface to grow trout in these months. But, this method is not economically feasible. In this study, as an alternative to ICE-P system, an axial flow deep well pump has been proposed for energy saving purposes. To validate the feasibility of the system, total of 40 net frame systems each has dimensions of 5x5x8 meters have been installed to grow trout in net frames in Lake Karacaoren 2 in Burdur, Turkey. To grow trout in the hot seasons, it is necessary to circulate the colder water in the deeper parts of the lake to lake surface economically. For this aim, total of 10 an axial flow deep well pumps (AFDWPs) having a capacity of 300 m(3)/h, head of 4 m and running at 2850 rpm which is driven by 5.5 kW deep well motor (DWM) have been specially designed and manufactured. To compare the classical water circulation method with ICE-P and newly proposed AFDWP, after every two pumps (ICE/AFDWP) are installed, mean water temperatures were measured along the water column in net frame with the depth of 8 meters and also energy consumptions have been compared during the months of June to September. AFDWP and ICE-P were used only in these months where the water has to be circulated. According to the results energy consumption by using AFDWP was about 90 MWh/year, on the other hand, energy consumed by ICE-P was about 1949 MWh/year during the months of June-September. As a result, significant energy saving of 95.3 % (1906.8 MWh/year) can be obtained by using proposed AFDWP instead of the classical ICE-P during these months for total of 40 net frames