Digital processing and data compilation approach for using remotely sensed imagery to identify geological lineaments in hard-rock terrains : an application for groundwater explorations in Nicaragua

Sustainable yields from water wells in hard-rock aquifers are achieved when the well bore intersects fracture networks. Fracture networks are often not readily discernable at the surface. Lineament analysis using remotely sensed satellite imagery has been employed to identify surface expressions of fracturing, and a variety of image-analysis techniques have been successfully applied in “ideal” settings. An ideal setting for lineament detection is where the influences of human development, vegetation, and climatic situations are minimal and hydrogeological conditions and geologic structure are known. There is not yet a well-accepted protocol for mapping lineaments nor have different approaches been compared in non-ideal settings. A new approach for image-processing/synthesis was developed to identify successful satellite imagery types for lineament analysis in non-ideal terrain. Four satellite sensors (ASTER, Landsat7 ETM+, QuickBird, RADARSAT-1) and a digital elevation model were evaluated for lineament analysis in Boaco, Nicaragua, where the landscape is subject to varied vegetative cover, a plethora of anthropogenic features, and frequent cloud cover that limit the availability of optical satellite data. A variety of digital image processing techniques were employed and lineament interpretations were performed to obtain 12 complementary image products that were evaluated subjectively to identify lineaments. The 12 lineament interpretations were synthesized to create a raster image of lineament zone coincidence that shows the level of agreement among the 12 interpretations. A composite lineament interpretation was made using the coincidence raster to restrict lineament observations to areas where multiple interpretations (at least 4) agree. Nine of the 11 previously mapped faults were identified from the coincidence raster. An additional 26 lineaments were identified from the coincidence raster, and the locations of 10 were confirmed by field observation. Four manual pumping tests suggest that well productivity is higher for wells proximal to lineament features. Interpretations from RADARSAT-1 products were superior to interpretations from other sensor products, suggesting that quality lineament interpretation in this region requires anthropogenic features to be minimized and topographic expressions to be maximized. The approach developed in this study has the potential to improve siting wells in non-ideal regions.

DEVELOPMENT OF AN OPTIMIZATION MODEL FOR BIOFUEL FACILITY SIZE AND LOCATION AND A SIMULATION MODEL FOR DESIGN OF A BIOFUEL SUPPLY CHAIN

A range of societal issues have been caused by fossil fuel consumption in the transportation sector in the United States (U.S.), including health related air pollution, climate change, the dependence on imported oil, and other oil related national security concerns. Biofuels production from various lignocellulosic biomass types such as wood, forest residues, and agriculture residues have the potential to replace a substantial portion of the total fossil fuel consumption. This research focuses on locating biofuel facilities and designing the biofuel supply chain to minimize the overall cost. For this purpose an integrated methodology was proposed by combining the GIS technology with simulation and optimization modeling methods. The GIS based methodology was used as a precursor for selecting biofuel facility locations by employing a series of decision factors. The resulted candidate sites for biofuel production served as inputs for simulation and optimization modeling. As a precursor to simulation or optimization modeling, the GIS-based methodology was used to preselect potential biofuel facility locations for biofuel production from forest biomass. Candidate locations were selected based on a set of evaluation criteria, including: county boundaries, a railroad transportation network, a state/federal road transportation network, water body (rivers, lakes, etc.) dispersion, city and village dispersion, a population census, biomass production, and no co-location with co-fired power plants. The simulation and optimization models were built around key supply activities including biomass harvesting/forwarding, transportation and storage. The built onsite storage served for spring breakup period where road restrictions were in place and truck transportation on certain roads was limited. Both models were evaluated using multiple performance indicators, including cost (consisting of the delivered feedstock cost, and inventory holding cost), energy consumption, and GHG emissions. The impact of energy consumption and GHG emissions were expressed in monetary terms to keep consistent with cost. Compared with the optimization model, the simulation model represents a more dynamic look at a 20-year operation by considering the impacts associated with building inventory at the biorefinery to address the limited availability of biomass feedstock during the spring breakup period. The number of trucks required per day was estimated and the inventory level all year around was tracked. Through the exchange of information across different procedures (harvesting, transportation, and biomass feedstock processing procedures), a smooth flow of biomass from harvesting areas to a biofuel facility was implemented. The optimization model was developed to address issues related to locating multiple biofuel facilities simultaneously. The size of the potential biofuel facility is set up with an upper bound of 50 MGY and a lower bound of 30 MGY. The optimization model is a static, Mathematical Programming Language (MPL)-based application which allows for sensitivity analysis by changing inputs to evaluate different scenarios. It was found that annual biofuel demand and biomass availability impacts the optimal results of biofuel facility locations and sizes.

BALTHMETRIC SURVEY IN THE KEWEENAW WATER WAY IN MICHIGAN, USA, MAKING 3D MODEL, AND COMPARING SONAR EQUIPMENT OF THE MODERN AUV AND THE TRADITIONAL SINGLE BEAM SONAR

This thesis represents the overview of hydrographic surveying and different types of modern and traditional surveying equipment, and data acquisition using the traditional single beam sonar system and a modern fully autonomous underwater vehicle, IVER3. During the thesis, the data sets were collected using the vehicles of the Great Lake Research Center at Michigan Technological University. This thesis also presents how to process and edit the bathymetric data on SonarWiz5. Moreover, the three dimensional models were created after importing the data sets in the same coordinate system. In these interpolated surfaces, the details and excavations can be easily seen on the surface models. In this study, the profiles are plotted on the surface models to compare the sensors and details on the seabed. It is shown that single beam sonar might miss some details, such as pipeline and quick elevation changes on the seabed when we compare to the side scan sonar of IVER3 because the single side scan sonar can acquire better resolution. However, sometimes using single beam sonar can save your project time and money because the single beam sonar is cheaper than side scan sonars and the processing might be easier than the side scan data.

Information processing in long delay memory-guided saccades: further insights from TMS

The performance of memory-guided saccades with two different delays (3 s and 30 s of memorisation) was studied in eight subjects. Single pulse transcranial magnetic stimulation (TMS) was applied simultaneously over the left and right dorsolateral prefrontal cortex (DLPFC) 1 s after target presentation. In both delays, stimulation significantly increased the percentage of error in amplitude of memory-guided saccades. Furthermore, the interfering effect of TMS was significantly higher in the short delay compared to that of the long delay paradigm. The results are discussed in the context of a mixed model of spatial working memory control including two components: First, serial information processing with a predominant role of the DLPFC during the early period of memorisation and, second, parallel information processing, which is independent from the DLPFC, operating during longer delays.

Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).

Stress effects on working memory, explicit memory, and implicit memory for neutral and emotional stimuli in healthy men

Stress is a strong modulator of memory function. However, memory is not a unitary process and stress seems to exert different effects depending on the memory type under study. Here, we explored the impact of social stress on different aspects of human memory, including tests for explicit memory and working memory (for neutral materials), as well as implicit memory (perceptual priming, contextual priming and classical conditioning for emotional stimuli). A total of 35 young adult male students were randomly assigned to either the stress or the control group, with stress being induced by the Trier Social Stress Test (TSST). Salivary cortisol levels were assessed repeatedly throughout the experiment to validate stress effects. The results support previous evidence indicating complex effects of stress on different types of memory: A pronounced working memory deficit was associated with exposure to stress. No performance differences between groups of stressed and unstressed subjects were observed in verbal explicit memory (but note that learning and recall took place within 1 h and immediately following stress) or in implicit memory for neutral stimuli. Stress enhanced classical conditioning for negative but not positive stimuli. In addition, stress improved spatial explicit memory. These results reinforce the view that acute stress can be highly disruptive for working memory processing. They provide new evidence for the facilitating effects of stress on implicit memory for negative emotional materials. Our findings are discussed with respect to their potential relevance for psychiatric disorders, such as post traumatic stress disorder.

The influence of emotion clarity on emotional prosody identification in paranoid schizophrenia

Identification of emotional facial expression and emotional prosody (i.e. speech melody) is often impaired in schizophrenia. For facial emotion identification, a recent study suggested that the relative deficit in schizophrenia is enhanced when the presented emotion is easier to recognize. It is unclear whether this effect is specific to face processing or part of a more general emotion recognition deficit.

Ventral striatum gray matter density reduction in patients with schizophrenia and psychotic emotional dysregulation

INTRODUCTION: Substantial heterogeneity remains across studies investigating changes in gray matter in schizophrenia. Differences in methodology, heterogeneous symptom patterns and symptom trajectories may contribute to inconsistent findings. To address this problem, we recently proposed to group patients by symptom dimensions, which map on the language, the limbic and the motor systems. The aim of the present study was to investigate whether patients with prevalent symptoms of emotional dysregulation would show structural neuronal abnormalities in the limbic system. METHOD: 43 right-handed medicated patients with schizophrenia were assessed with the Bern Psychopathology Scale (BPS). The patients and a control group of 34 healthy individuals underwent structural imaging at a 3T MRI scanner. Whole brain voxel-based morphometry (VBM) was compared between patient subgroups with different severity of emotional dysregulation. Group comparisons (comparison between patients with severe emotional dysregulation, patients with mild emotional dysregulation, patients with no emotional dysregulation and healthy controls) were performed using a one way ANOVA and ANCOVA respectively. RESULTS: Patients with severe emotional dysregulation had significantly decreased gray matter density in a large cluster including the right ventral striatum and the head of the caudate compared to patients without emotional dysregulation. Comparing patients with severe emotional dysregulation and healthy controls, several clusters of significant decreased GM density were detected in patients, including the right ventral striatum, head of the caudate, left hippocampus, bilateral thalamus, dorsolateral prefrontal and orbitofrontal cortex. The significant effect in the ventral striatum was lost when patients with and without emotional dysregulation were pooled and compared with controls. DISCUSSION: Decreased gray matter density in a large cluster including the right ventral striatum was associated with severe symptoms of emotional dysregulation in patients with schizophrenia. The ventral striatum is an important part of the limbic system, and was indicated to be involved in the generation of incentive salience and psychotic symptoms. Only patients with severe emotional dysregulation had decreased gray matter in several brain structures associated with emotion and reward processing compared to healthy controls. The results support the hypothesis that grouping patients according to specific clinical symptoms matched to the limbic system allows identifying patient subgroups with structural abnormalities in the limbic network.

Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale

The development of susceptibility maps for debris flows is of primary importance due to population pressure in hazardous zones. However, hazard assessment by process-based modelling at a regional scale is difficult due to the complex nature of the phenomenon, the variability of local controlling factors, and the uncertainty in modelling parameters. A regional assessment must consider a simplified approach that is not highly parameter dependant and that can provide zonation with minimum data requirements. A distributed empirical model has thus been developed for regional susceptibility assessments using essentially a digital elevation model (DEM). The model is called Flow-R for Flow path assessment of gravitational hazards at a Regional scale (available free of charge under http://www.flow-r.org) and has been successfully applied to different case studies in various countries with variable data quality. It provides a substantial basis for a preliminary susceptibility assessment at a regional scale. The model was also found relevant to assess other natural hazards such as rockfall, snow avalanches and floods. The model allows for automatic source area delineation, given user criteria, and for the assessment of the propagation extent based on various spreading algorithms and simple frictional laws. We developed a new spreading algorithm, an improved version of Holmgren's direction algorithm, that is less sensitive to small variations of the DEM and that is avoiding over-channelization, and so produces more realistic extents. The choices of the datasets and the algorithms are open to the user, which makes it compliant for various applications and dataset availability. Amongst the possible datasets, the DEM is the only one that is really needed for both the source area delineation and the propagation assessment; its quality is of major importance for the results accuracy. We consider a 10 m DEM resolution as a good compromise between processing time and quality of results. However, valuable results have still been obtained on the basis of lower quality DEMs with 25 m resolution.

The power of emotional valence – from cognitive to affective processes in reading

The comprehension of stories requires the reader to imagine the cognitive and affective states of the characters. The content of many stories is unpleasant, as they often deal with conflict, disturbance or crisis. Nevertheless, unpleasant stories can be liked and enjoyed. In this fMRI study, we used a parametric approach to examine (1) the capacity of increasing negative valence of story contents to activate the mentalizing network (cognitive and affective theory of mind, ToM), and (2) the neural substrate of liking negatively valenced narratives. A set of 80 short narratives was compiled, ranging from neutral to negative emotional valence. For each story mean rating values on valence and liking were obtained from a group of 32 participants in a prestudy, and later included as parametric regressors in the fMRI analysis. Another group of 24 participants passively read the narratives in a three Tesla MRI scanner. Results revealed a stronger engagement of affective ToM-related brain areas with increasingly negative story valence. Stories that were unpleasant, but simultaneously liked, engaged the medial prefrontal cortex (mPFC), which might reflect the moral exploration of the story content. Further analysis showed that the more the mPFC becomes engaged during the reading of negatively valenced stories, the more coactivation can be observed in other brain areas related to the neural processing of affective ToM and empathy.

Atypical visual processing in posttraumatic stress disorder

BACKGROUND: Many patients with Posttraumatic Stress Disorder (PTSD) feel overwhelmed in situations with high levels of sensory input, as in crowded situations with complex sensory characteristics. These difficulties might be related to subtle sensory processing deficits similar to those that have been found for sounds in electrophysiological studies. METHOD: Visual processing was investigated with functional magnetic resonance imaging in trauma-exposed participants with (N = 18) and without PTSD (N = 21) employing a picture-viewing task. RESULTS: Activity observed in response to visual scenes was lower in PTSD participants 1) in the ventral stream of the visual system, including striate and extrastriate, inferior temporal, and entorhinal cortices, and 2) in dorsal and ventral attention systems (P < 0.05, FWE-corrected). These effects could not be explained by the emotional salience of the pictures. CONCLUSIONS: Visual processing was substantially altered in PTSD in the ventral visual stream, a component of the visual system thought to be responsible for object property processing. Together with previous reports of subtle auditory deficits in PTSD, these findings provide strong support for potentially important sensory processing deficits, whose origins may be related to dysfunctional attention processes.

The structure of psychopathological symptoms and the associations with DSM-diagnoses in treatment seeking individuals

BACKGROUND: Research on comorbidity of psychiatric disorders identifies broad superordinate dimensions as underlying structure of psychopathology. While a syndrome-level approach informs diagnostic systems, a symptom-level approach is more likely to represent the dimensional components within existing diagnostic categories. It may capture general emotional, cognitive or physiological processes as underlying liabilities of different disorders and thus further develop dimensional-spectrum models of psychopathology. METHODS: Exploratory and confirmatory factor analyses were used to examine the structure of psychopathological symptoms assessed with the Brief Symptom Inventory in two outpatient samples (n=3171), including several correlated-factors and bifactor models. The preferred models were correlated with DSM-diagnoses. RESULTS: A model containing eight correlated factors for depressed mood, phobic fear, aggression, suicidal ideation, nervous tension, somatic symptoms, information processing deficits, and interpersonal insecurity, as well a bifactor model fit the data best. Distinct patterns of correlations with DSM-diagnoses identified a) distress-related disorders, i.e., mood disorders, PTSD, and personality disorders, which were associated with all correlated factors as well as the underlying general distress factor; b) anxiety disorders with more specific patterns of correlations; and c) disorders defined by behavioural or somatic dysfunctions, which were characterised by non-significant or negative correlations with most factors. CONCLUSIONS: This study identified emotional, somatic, cognitive, and interpersonal components of psychopathology as transdiagnostic psychopathological liabilities. These components can contribute to a more accurate description and taxonomy of psychopathology, may serve as phenotypic constructs for further aetiological research, and can inform the development of tailored general and specific interventions to treat mental disorders.

A Stratified Model for Psychosis Prediction in Clinical Practice

Objective: Impaired cognition is an important dimension in psychosis and its at-risk states. Research on the value of impaired cognition for psychosis prediction in at-risk samples, however, mainly relies on study-specific sample means of neurocognitive tests, which unlike widely available general test norms are difficult to translate into clinical practice. The aim of this study was to explore the combined predictive value of at-risk criteria and neurocognitive deficits according to test norms with a risk stratification approach. Method: Potential predictors of psychosis (neurocognitive deficits and at-risk criteria) over 24 months were investigated in 97 at-risk patients. Results: The final prediction model included (1) at-risk criteria (attenuated psychotic symptoms plus subjective cognitive disturbances) and (2) a processing speed deficit (digit symbol test). The model was stratified into 4 risk classes with hazard rates between 0.0 (both predictors absent) and 1.29 (both predictors present). Conclusions: The combination of a processing speed deficit and at-risk criteria provides an optimized stratified risk assessment. Based on neurocognitive test norms, the validity of our proposed 3 risk classes could easily be examined in independent at-risk samples and, pending positive validation results, our approach could easily be applied in clinical practice in the future.

Short-Term Plasticity in a Computational Model of the Tail-Withdrawal Circuit in Aplysia.

The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit responded preferentially to long-duration, low-frequency inputs.

From school to work: A comprehensive model of work-related motivation in the transition to working life.

Motivation is a core concept to understand work related outcomes and vocational pursuits. However, existing research mostly focused on specific aspects of motivation, such as goals or self-efficacy beliefs, while falling short of adequately addressing more complex and integrative notions of motivation. Advancing the current state of research, we draw from Motivational Systems Theory and a model of proactive motivation to propose a comprehensive model of work-related motivation. Specifically, we define motivation as a system of mutually related factors consisting of goals, emotions, and personal agency beliefs, comprised by capability beliefs and context evaluations. Adapting this model of motivation to the school-to-work transition, we postulate that this motivational system is affected by different social, personal, and environmental variables, for example social support, the presence of role-models, personality traits, and scholastic achievement. We further expect that students with more autonomous work-related goals, expectations of more positive emotional experiences in their future working life, fewer perceived barriers to their career development, and higher work-related self-efficacy beliefs would be more successful in their transition from school to work. We also propose that goal-directed engagement acts as a partial mediator in the relationship between motivation and a successful transition. Finally, we hypothesize that work-related motivation while in school will have meaningful effects on positive outcomes while in vocational training, as represented by more work engagement, higher career commitment, job satisfaction, and lower intentions to quit training. In sum, we advance the point that the adaptation of a broader concept of work-related motivation in the school-to-work transition would result in more powerful predictions of success in this transition and would enhance scientific research and interventions in career development and counselling practice.