The generation effect and word learning: a test of the effect of the language experience approach versus the text approach in the acquisition of new reading vocabulary

A class of twenty-two grade one children was tested to determine their reading levels using the Stanford Diagnostic Reading Achievement Test. Based on these results and teacher input the students were paired according to reading ability. The students ages ranged from six years four months to seven years four months at the commencement of the study. Eleven children were assigned to the language experience group and their partners became the text group. Each member of the language experience group generated a list of eight to be learned words. The treatment consisted of exposing the student to a given word three times per session for ten sessions, over a period of five days. The dependent variables consisted of word identification speed, word identification accuracy, and word recognition accuracy. Each member of the text group followed the same procedure using his/her partner's list of words. Upon completion of this training, the entire process was repeated with members of the text group from the first part becoming members of the language experience group and vice versa. The results suggest that generally speaking language experience words are identified faster than text words but that there is no difference in the rate at which these words are learned. Language experience words may be identified faster because the auditory-semantic information is more readily available in them than in text words. The rate of learning in both types of words, however, may be dictated by the orthography of the to be learned word.

Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant

Artificial neural networks have a good potential to be employed for fault diagnosis and condition monitoring problems in complex processes. In this paper, the applicability of the fuzzy ARTMAP (FAM) neural network as an intelligent learning system for fault detection and diagnosis in a power generation plant is described. The process under scrutiny is the circulating water (CW) system, with specific attention to the conditions of heat transfer and tube blockage in the CW system. A series of experiments has been conducted systematically to investigate the effectiveness of FAM in fault detection and diagnosis tasks. In addition, a set of domain rules has been extracted from the trained FAM network so that its predictions can be explained and justified. The outcomes demonstrate the benefits of employing FAM as an intelligent fault detection and diagnosis tool with an explanatory capability for monitoring and diagnosing complex processes in power generation plants.

Automatic acceptance test case generation from essential use cases

Requirements validation is a crucial process to determine whether client-stakeholders' needs and expectations of a product are sufficiently correct and complete. Various requirements validation techniques have been used to evaluate the correctness and quality of requirements, but most of these techniques are tedious, expensive and time consuming. Accordingly, most project members are reluctant to invest their time and efforts in the requirements validation process. Moreover, automated tool supports that promote effective collaboration between the client-stakeholders and the engineers are still lacking. In this paper, we describe a novel approach that combines prototyping and test-based requirements techniques to improve the requirements validation process and promote better communication and collaboration between requirements engineers and clientstakeholders. To justify the potential of this prototype tool, we also present three types of evaluation conducted on the prototpye tool, which are the usability survey, 3-tool comparison analysis and expert reviews.

Monitoring thrombin generation by electrochemistry: development of an amperometric biosensor screening test for plasma and whole blood

BACKGROUND: Complete investigation of thrombophilic or hemorrhagic clinical presentations is a time-, apparatus-, and cost-intensive process. Sensitive screening tests for characterizing the overall function of the hemostatic system, or defined parts of it, would be very useful. For this purpose, we are developing an electrochemical biosensor system that allows measurement of thrombin generation in whole blood as well as in plasma. METHODS: The measuring system consists of a single-use electrochemical sensor in the shape of a strip and a measuring unit connected to a personal computer, recording the electrical signal. Blood is added to a specific reagent mixture immobilized in dry form on the strip, including a coagulation activator (e.g., tissue factor or silica) and an electrogenic substrate specific to thrombin. RESULTS: Increasing thrombin concentrations gave standard curves with progressively increasing maximal current and decreasing time to reach the peak. Because the measurement was unaffected by color or turbidity, any type of blood sample could be analyzed: platelet-poor plasma, platelet-rich plasma, and whole blood. The test strips with the predried reagents were stable when stored for several months before testing. Analysis of the combined results obtained with different activators allowed discrimination between defects of the extrinsic, intrinsic, and common coagulation pathways. Activated protein C (APC) predried on the strips allowed identification of APC-resistance in plasma and whole blood samples. CONCLUSIONS: The biosensor system provides a new method for assessing thrombin generation in plasma or whole blood samples as small as 10 microL. The assay is easy to use, thus allowing it to be performed in a point-of-care setting.

Verb generation in patients with focal frontal lesions: A neuropsychological test of neuroimaging findings

What are the neural bases of semantic memory? Traditional beliefs that the temporal lobes subserve the retrieval of semantic knowledge, arising from lesion studies, have been recently called into question by functional neuroimaging studies finding correlations between semantic retrieval and activity in left prefrontal cortex. Has neuroimaging taught us something new about the neural bases of cognition that older methods could not reveal or has it merely identified brain activity that is correlated with but not causally related to the process of semantic retrieval? We examined the ability of patients with focal frontal lesions to perform a task commonly used in neuroimaging experiments, the generation of semantically appropriate action words for concrete nouns, and found evidence of the necessity of the left inferior frontal gyrus for certain components of the verb generation task. Notably, these components did not include semantic retrieval per se.

UML approach to the generation of test sequences for Java-based concurrent systems

Starting with a UML specification that captures the underlying functionality of some given Java-based concurrent system, we describe a systematic way to construct, from this specification, test sequences for validating an implementation of the system. The approach is to first extend the specification to create UML state machines that directly address those aspects of the system we wish to test. To be specific, the extended UML state machines can capture state information about the number of waiting threads or the number of threads blocked on a given object. Using the SAL model checker we can generate from the extended UML state machines sequences that cover all the various possibilities of events and states. These sequences can then be directly transformed into test sequences suitable for input into a testing tool such as ConAn. As an illustration, the methodology is applied to generate sequences for testing a Java implementation of the producer-consumer system. © 2005 IEEE

Automatized Generation of Metadata and Calibrated Test Item Banks for E-learning

Report published in the Proceedings of the National Conference on "Education in the Information Society", Plovdiv, May, 2013

Generation of a 3D proximal femur shape from a single projection 2D radiographic image.

Summary Generalized Procrustes analysis and thin plate splines were employed to create an average 3D shape template of the proximal femur that was warped to the size and shape of a single 2D radiographic image of a subject. Mean absolute depth errors are comparable with previous approaches utilising multiple 2D input projections. Introduction Several approaches have been adopted to derive volumetric density (g cm-3) from a conventional 2D representation of areal bone mineral density (BMD, g cm-2). Such approaches have generally aimed at deriving an average depth across the areal projection rather than creating a formal 3D shape of the bone. Methods Generalized Procrustes analysis and thin plate splines were employed to create an average 3D shape template of the proximal femur that was subsequently warped to suit the size and shape of a single 2D radiographic image of a subject. CT scans of excised human femora, 18 and 24 scanned at pixel resolutions of 1.08 mm and 0.674 mm, respectively, were equally split into training (created 3D shape template) and test cohorts. Results The mean absolute depth errors of 3.4 mm and 1.73 mm, respectively, for the two CT pixel sizes are comparable with previous approaches based upon multiple 2D input projections. Conclusions This technique has the potential to derive volumetric density from BMD and to facilitate 3D finite element analysis for prediction of the mechanical integrity of the proximal femur. It may further be applied to other anatomical bone sites such as the distal radius and lumbar spine.