Lesions impairing regular versus irregular past tense production

We investigated selective impairments in the production of regular and irregular past tense by examining language performance and lesion sites in a sample of twelve stroke patients. A disadvantage in regular past tense production was observed in six patients when phonological complexity was greater for regular than irregular verbs, and in three patients when phonological complexity was closely matched across regularity. These deficits were not consistently related to grammatical difficulties or phonological errors but were consistently related to lesion site. All six patients with a regular past tense disadvantage had damage to the left ventral pars opercularis (in the inferior frontal cortex), an area associated with articulatory sequencing in prior functional imaging studies. In addition, those that maintained a disadvantage for regular verbs when phonological complexity was controlled had damage to the left ventral supramarginal gyrus (in the inferior parietal lobe), an area associated with phonological short-term memory. When these frontal and parietal regions were spared in patients who had damage to subcortical (n = 2) or posterior temporo-parietal regions (n = 3), past tense production was relatively unimpaired for both regular and irregular forms. The remaining (12th) patient was impaired in producing regular past tense but was significantly less accurate when producing irregular past tense. This patient had frontal, parietal, subcortical and posterior temporo-parietal damage, but was distinguished from the other patients by damage to the left anterior temporal cortex, an area associated with semantic processing. We consider how our lesion site and behavioural observations have implications for theoretical accounts of past tense production

Vision-based Crack Identification on the Concrete Slab Surface using Fuzzy Reasoning Rules and Self-Organizing

Identifying cracks on the surface of concrete slab structure is important for structure stability maintenance. In order to avoid subjective visual inspection, it is necessary to develop an automated identification and measuring system by vision based method. Although there have been some intelligent computerized inspection methods, they are sensitive to noise due to the brightness contrast and objects such as forms and joints of certain size often falsely classified as cracks. In this paper, we propose a new fuzzy logic based image processing method that extracts cracks from concrete slab structure including small cracks that were often neglected as noise. We extract candidate crack areas by applying fuzzy method with three color channel values of concrete slab structure. Then further refinement processes are performed with Self Organizing Map algorithm and density based noise removal process to obtain basic crack characteristic attributes for further analysis. Experimental result verifies that the proposed method is sufficiently identified cracks with various sizes with high accuracy (97.3%) among 1319 ground truth cracks from 30 images

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

A problem-structuring model for analyzing transportation–environment relationships

This is the post-print version of the final paper published in European Journal of Operational Research. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2009 Elsevier B.V.This study discusses a decision support framework that guides policy makers in their strategic transportation related decisions by using multi-methodology. For this purpose, a methodology for analyzing the effects of transportation policies on environment, society, economy, and energy is proposed. In the proposed methodology, a three-stage problem structuring model is developed. Initially, experts’ opinions are structured by using a cognitive map to determine the relationships between transportation and environmental concepts. Then a structural equation model (SEM) is constructed, based on the cognitive map, to quantify the relations among external transportation and environmental factors. Finally the results of the SEM model are used to evaluate the consequences of possible policies via scenario analysis. In this paper a pilot study that covers only one module of the whole framework, namely transportation–environment interaction module, is conducted to present the applicability and usefulness of the methodology. This pilot study also reveals the impacts of transportation policies on the environment. To achieve a sustainable transportation system, the extent of the relationships between transportation and the environment must be considered. The World Development Indicators developed by the World Bank are used for this purpose

Lesion identification and the effect of lesion on motor mapping after stroke

Stroke is the most common cause of long-term severe disability and the motor system that is most commonly affected in stroke. One of the mechanisms that underlies recovery of motor deficits is reorganization or remapping of functional representations around the motor cortex. This mechanism has been shown in monkeys, but results in human subjects have been variable. In this thesis, I used a database that includes longitudinal behavioral and multimodal imaging data in both stroke patients and healthy controls for two research projects. Firstly, I improved an automatic lesion segmentation method to aid in the identification of the location and extent of the stroke in structural magnetic resonance imaging (MRI) images. I developed a point and click interface that allows for the automatic segmentation as well as selecting lesions generated at different thresholds based on the contrast of the T1 images. Second, I investigated the effect of subcortical strokes on motor representations by measuring changes in the topography of inter-hemispheric resting state functional connectivity (FC) MRI to track changes of the hand representation in the damaged hemisphere shows a higher variation across the medial-lateral axis, suggesting a shift in neighboring body representations along the motor strip. During recovery, however, there is a shift in an anterior-posterior direction suggesting a shift into sensory and premotor regions. Obtaining lesion profile and understanding its effect on the functional connectivity can provide us with useful information on the effects of stroke on brain structure and function, which in turn will help in the prognosis and rehabilitation of stroke patients

Recommender Thermometer for Measuring the Preparedness for Flood Resilience Management

A range of various thermometers and similar scales are employed in different human and resilience management activities: Distress Thermometer, Panic Thermometer, Fear Thermometer, fire danger rating, hurricane scales, earthquake scales (Richter Magnitude Scale, Mercalli Scale), Anxiety Thermometer, Help Thermometer, Problem Thermometer, Emotion Thermometer, Depression Thermometer, the Torino scale (assessing asteroid/comet impact prediction), Excessive Heat Watch, etc. Extensive financing of the preparedness for flood resilience management with overheated full-scale resilience management might be compared to someone ill running a fever of 41°C. As the financial crisis hits and resilience management financing cools down it reminds a sick person whose body temperature is too low. The degree indicated by the Recommender Thermometer for Measuring the Preparedness for Flood Resilience Management with a scale between Tmin=34,0° and Tmax=42,0° shows either cool or overheated preparedness for flood resilience management. The formalized presentation of this research shows how changes in the micro, meso and macro environment of resilience management and the extent to which the goals pursued by various interested parties are met cause corresponding changes in the “temperature” of the preparedness for resilience management. Global innovative aspects of the Recommender Thermometer developed by the authors of this paper are, primarily, its capacity to measure the “temperature” of the preparedness for flood resilience management automatically, to compile multiple alternative recommendations (preparedness for floods, including preparing your home for floods, taking precautions against a threat of floods, retrofitting for flood-prone areas, checking your house insurance; preparedness for bushfires, preparedness for cyclones, preparedness for severe storms, preparedness for heat waves, etc.) customised for a specific user, to perform multiple criteria analysis of the recommendations, and to select the ten most rational ones for that user. Across the world, no other system offers these functions yet. The Recommender Thermometer was developed and fine-tuned in the course of the Android (Academic Network for Disaster Resilience to Optimise educational Development) project

Use of COTS functional analysis software as an IVHM design tool for detection and isolation of UAV fuel system faults

This paper presents a new approach to the development of health management solutions which can be applied to both new and legacy platforms during the conceptual design phase. The approach involves the qualitative functional modelling of a system in order to perform an Integrated Vehicle Health Management (IVHM) design – the placement of sensors and the diagnostic rules to be used in interrogating their output. The qualitative functional analysis was chosen as a route for early assessment of failures in complex systems. Functional models of system components are required for capturing the available system knowledge used during various stages of system and IVHM design. MADe™ (Maintenance Aware Design environment), a COTS software tool developed by PHM Technology, was used for the health management design. A model has been built incorporating the failure diagrams of five failure modes for five different components of a UAV fuel system. Thus an inherent health management solution for the system and the optimised sensor set solution have been defined. The automatically generated sensor set solution also contains a diagnostic rule set, which was validated on the fuel rig for different operation modes taking into account the predicted fault detection/isolation and ambiguity group coefficients. It was concluded that when using functional modelling, the IVHM design and the actual system design cannot be done in isolation. The functional approach requires permanent input from the system designer and reliability engineers in order to construct a functional model that will qualitatively represent the real system. In other words, the physical insight should not be isolated from the failure phenomena and the diagnostic analysis tools should be able to adequately capture the experience bases. This approach has been verified on a laboratory bench top test rig which can simulate a range of possible fuel system faults. The rig is fully instrumented in order to allow benchmarking of various sensing solution for fault detection/isolation that were identified using functional analysis

The left superior temporal gyrus is a shared substrate for auditory short-term memory and speech comprehension: evidence from 210 patients with stroke

Competing theories of short-term memory function make specific predictions about the functional anatomy of auditory short-term memory and its role in language comprehension. We analysed high-resolution structural magnetic resonance images from 210 stroke patients and employed a novel voxel based analysis to test the relationship between auditory short-term memory and speech comprehension. Using digit span as an index of auditory short-term memory capacity we found that the structural integrity of a posterior region of the superior temporal gyrus and sulcus predicted auditory short-term memory capacity, even when performance on a range of other measures was factored out. We show that the integrity of this region also predicts the ability to comprehend spoken sentences. Our results therefore support cognitive models that posit a shared substrate between auditory short-term memory capacity and speech comprehension ability. The method applied here will be particularly useful for modelling structure–function relationships within other complex cognitive domains

Multi-Sensor Feature Extraction and Data Fusion Using ANFIS and 2D Wavelet Transform in Structural Health Monitoring

In this chapter, a novel feature extraction and data fusion approach for structural damage detection and localisation is presented. This approach combines adaptive network-based fuzzy inference systems (ANFIS) and two-dimensional wavelet transform (2D-WT) technologies. Simultaneous multi-sensor feature extraction and data fusion based on 2D-WT is carried out by forming a 2D multivariate signal, which is used to analyse the structure vibration response by measuring all sensors jointly. Energy values obtained from two-level db3 wavelet decomposition are arranged in a so-called energy percentage matrix (EPM), which is taken as an input for the ANFIS. The system is further trained by defining its output as the structural condition represented by a condition index. A set of output index patterns are defined depending on the level of damage assessment performed. The proposed method was tested through experiments using a cantilever beam structure. The testing results showed that the method is successful in detecting and localising damage by vibration analysis in structural health monitoring