The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

Discrete versus multiple word displays: A re-analysis of studies comparing dyslexic and typically developing children

The study examines whether impairments in reading a text can be explained by a deficit in word decoding or an additional deficit in the processes governing the integration of reading subcomponents (including eye movement programming and pronunciation) should also be postulated. We report a re-analysis of data from eleven previous experiments conducted in our lab where the reading performance on single, discrete word displays as well multiple displays (texts, and in few cases also word lists) was investigated in groups of dyslexic children and typically developing readers. The analysis focuses on measures of time and not accuracy. Across experiments, dyslexic children are slower and more variable than typically developing readers in reading texts as well as vocal RTs to singly presented words; the dis-homogeneity in variability between groups points to the inappropriateness of standard measures of size effect (such as Cohen’s d), and suggests the use of the ratio between groups’ performance. The mean ratio for text reading is 1.95 across experiments. Mean ratio for vocal RTs for singly presented words is considerably smaller (1.52). Furthermore, this latter value is probably an overestimation as considering total reading times (i.e., a measure including also the pronunciation component) considerably reduces the group difference in vocal RTs (1.19 according to Martelli et al., 2014). The ratio difference between single and multiple displays does not depend upon the presence of a semantic context in the case of texts as large ratios are also observed with lists of unrelated words (though studies testing this aspect were few). We conclude that, if care is taken in using appropriate comparisons, the deficit in reading texts or lists of words is appreciably greater than that revealed with discrete word presentations. Thus, reading multiple stimuli present a specific, additional challenge to dyslexic children indicating that models of reading should incorporate this aspect

Fault detection in operating helicopter drive train components based on support vector data description

The objective of the paper is to develop a vibration-based automated procedure dealing with early detection of mechanical degradation of helicopter drive train components using Health and Usage Monitoring Systems (HUMS) data. An anomaly-detection method devoted to the quantification of the degree of deviation of the mechanical state of a component from its nominal condition is developed. This method is based on an Anomaly Score (AS) formed by a combination of a set of statistical features correlated with specific damages, also known as Condition Indicators (CI), thus the operational variability is implicitly included in the model through the CI correlation. The problem of fault detection is then recast as a one-class classification problem in the space spanned by a set of CI, with the aim of a global differentiation between normal and anomalous observations, respectively related to healthy and supposedly faulty components. In this paper, a procedure based on an efficient one-class classification method that does not require any assumption on the data distribution, is used. The core of such an approach is the Support Vector Data Description (SVDD), that allows an efficient data description without the need of a significant amount of statistical data. Several analyses have been carried out in order to validate the proposed procedure, using flight vibration data collected from a H135, formerly known as EC135, servicing helicopter, for which micro-pitting damage on a gear was detected by HUMS and assessed through visual inspection. The capability of the proposed approach of providing better trade-off between false alarm rates and missed detection rates with respect to individual CI and to the AS obtained assuming jointly-Gaussian-distributed CI has been also analysed

The perils of automaticity

Classical theories of skill acquisition propose that automatization (i.e., performance requires progressively less attention as experience is acquired) is a defining characteristic of expertise in a variety of domains (e.g., Fitts & Posner, 1967). Automaticity is believed to enhance smooth and efficient skill execution by allowing performers to focus on strategic elements of performance rather than on the mechanical details that govern task implementation (Williams & Ford, 2008). By contrast, conscious processing (i.e., paying conscious attention to one’s action during motor execution) has been found to disrupt skilled movement and performance proficiency (e.g., Beilock & Carr, 2001). On the basis of this evidence, researchers have tended to extol the virtues of automaticity. However, few researchers have considered the wide range of empirical evidence which indicates that highly automated behaviors can, on occasion, lead to a series of errors that may prove deleterious to skilled performance. Therefore, the purpose of the current paper is to highlight the perils, rather than the virtues, of automaticity. We draw on Reason’s (1990) classification scheme of everyday errors to show how an overreliance on automated procedures may lead to 3 specific performance errors (i.e., mistakes, slips, and lapses) in a variety of skill domains (e.g., sport, dance, music). We conclude by arguing that skilled performance requires the dynamic interplay of automatic processing and conscious processing in order to avoid performance errors and to meet the contextually contingent demands that characterize competitive environments in a range of skill domains

Motor Control and Reading Fluency: Contributions beyond Phonological Awareness and Rapid Automatized Naming in Children with Reading Disabilities.

Multiple domains of deficit have been proposed to account for the apparent reading failure of children with a reading disability. Deficits in both phonological awareness and rapid automatized naming are consistently linked with the development of a reading disability in young school age children. Less research, however, has sought to connect these two reading related processes to global theories of deficit, such as temporal processing deficits, in the explanation of reading fluency difficulties. This study sought to explore the relationship between aspects of temporal processing, as indexed through measures of motor fluency and control, and measures of reading related processes, phonological awareness and rapid automatized naming, to word reading fluency. Using structural equation modeling, measures of patterned motor movement were found to be negatively and significantly related to measures of phonological awareness. Measures of oral and repetitive movement were found to be positively and significantly related to measures of patterned movement. Finally, phonological awareness was found to be a significant predictor of word reading fluency both independently and through rapid automatized naming. No direct relationship between measures of motor control and fluency and word reading fluency was found. These findings suggest that temporal processing, as indexed by measures of motor fluency and control, are moderately predictive of the facility with which a child with a reading disability can access, manipulate, and reproduce phonetically based information. Implications for the inclusion of motor based measures in the assessment of children with reading disabilities and future directions for research are discussed