Identifying Opportunities for Augmented Cognition During Live Flight Scenario: An Analysis of Pilot Mental Workload Using EEG

Augmented cognition is a form of human-systems interaction in which physiological sensing of a user’s cognitive state is used to precisely invoke system automations when needed. The present study monitored the in-flight physiological state of the pilot to determine the optimal combination of EEG indices to predict variations in workload, or opportunities for augmented cognition.The [sic] participants were 10 collegiate aviation students with FAA commercial pilot certificates and current medical certificates. Each participant performed a uniform flight scenario that included procedures that varied in workload demands. All maneuvers were performed while simultaneously acquiring EEG data in flight. The EEG data were divided into periods of high and low workload. Power spectral density values were computed and subjected to several machine learning methods to distinguish high and low workload periods. The results indicate excellent classification accuracy for distinguishing low and high workload. The present results further demonstrate the potential of augmented cognition

Decision support system for cardiovascular problems

The DISHEART project aims at developing a new computer based decision support system (DSS) integrating medical image data, modelling, simulation, computational Grid technologies and artificial intelligence methods for assisting clinical diagnosis and intervention in cardiovascular problems. The RTD goal is to improve and link existing state of the art technologies in order to build a computerised cardiovascular model for the analysis of the heart and blood vessels. The resulting DISHEART DSS interfaces computational biomechanical analysis tools with the information coming from multimodal medical images. The computational model is coupled to an artificial neural network (ANN) based decision model that can be educated for each particular patient with data coming from his/her images and/or analyses. The DISHEART DSS system is validated in trials of clinical diagnosis, surgical intervention and subject-specific design of medical devices in the cardiovascular domain. The DISHEART DSS also contributes to a better understanding of cardiovascular morphology and function as inferred from routine imaging examinations. Four reputable medical centers in Europe took an active role in the validation and dissemination of the DISHEART DSS as well as the elaboration of computational material and medical images. The integrated DISHEART DSS supports health professionals in taking promptly the best possible decision for prevention, diagnosis and treatment. Emphasis was put in the development of userfriendly, fast and reliable tools and interfaces providing access to heterogeneous health information sources, as well as on new methods for decision support and risk analysis. The use of Grid computing technology is essential in order to optimise and distribute the heavy computational work required for physical modelling and numerical simulations and especially for the parametric analysis required for educating the DSS for every particular application. The four end user SMEs participating in the project benefits from the new DISHEART DSS. The companies COMPASS, QUANTECH and Heartcore will market the DSS among public and private organizations related to the cardiovascular field. EndoArt will exploit the DISHEART DSS as a support for enhanced design and production of clinical devices. The partnership was sought in order to gather the maximum complementary of skills for the successful development of the project Disheart DSS, requiring experts in Mechanical sciences, Medical sciences, Informatic, and FEM technique to grow up the testes.Postprint (published version

Decision support system for cardiovascular problems

The two main lines of medical research in this project are vascular anatomy (large vessels around the heart, coronaries and peripheral arteries) and heart chambers. Geometric models will be constructed to aid clinical diagnosis or multiphysical modelling and simulation. Two levels of complexity will be considered. For heart modelling, the first level will concentrate on models of the left and right ventricular cavities for robust and efficient extraction of simple clinical indexes of geometry, volume, mass, and wall kinetics. The second level will aim at more complex, fourchambered models, which will be important in developing comprehensive solid and fluid models to assist the design of medical devices

State–of–the–art report on nonlinear representation of sources and channels

This report consists of two complementary parts, related to the modeling of two important sources of nonlinearities in a communications system. In the first part, an overview of important past work related to the estimation, compression and processing of sparse data through the use of nonlinear models is provided. In the second part, the current state of the art on the representation of wireless channels in the presence of nonlinearities is summarized. In addition to the characteristics of the nonlinear wireless fading channel, some information is also provided on recent approaches to the sparse representation of such channels

Multilayer network methodologies for brain data analysis and modelling

The term neuroscience includes in itself a plethora of research areas devoted to undercover the most fascinating complex organ of our body: the brain. A common denominator of neuroscience areas, is the need for the application of methodologies to integrate different features. In this thesis, we focused on the analysis of two types of brain data: brain data coming from Traumatic Brain Injury (TBI) patients and data collected for the study of neurocognitive healthy ageing. In both cases there was the need of applying computational techniques able to integrate different features. To do so we used multilayer networks. For two groups of TBI patients (adults and paediatrics), time series data were collected from the observations of IntraCranial Pressure (ICP) and Heart Rate (HR). We first detected events of simultaneous increase of HR and ICP, which we called brain-heart crosstalks. Subsequently time series were translated into graphs, and network measures, during brain-heart crosstalks, were obtained. These were then included as predictors in a mortality outcome model, with crosstalks. Causality measures were also investigated, using a Granger causality approach, to understand the dynamics of signals during these events. We further applied multilayer networks to study neurocognitive ageing. To do so, we implemented a pipeline for community detection, which we called NetRank, applying it to the Cam-CAN, a large cross-sectional cohort for the study of healthy neurocognitive ageing. Using multilayer networks modelling, we identified subgroups of individuals, with similar lifestyles, and we related them to structural and functional brain features. We believe that multilayer networks and their extensions represent a powerful tool to be used in integrative and cross modal neuroscience datasets. New insights on cognitive neuroscience and time series analysis, can in fact be gained trough multilayer network, possibly improving patients managements and allowing to develop new predictive tools.EPSR

PROGRAM, THE NEBRASKA ACADEMY OF SCIENCES: One Hundred-Thirty-First Annual Meeting, APRIL 23-24, 2021. ONLINE

AFFILIATED SOCIETIES OF THE NEBRASKA ACADEMY OF SCIENCES, INC. 1.American Association of Physics Teachers, Nebraska Section: Web site: http://www.aapt.org/sections/officers.cfm?section=Nebraska 2.Friends of Loren Eiseley: Web site: http://www.eiseley.org/ 3.Lincoln Gem & Mineral Club: Web site: http://www.lincolngemmineralclub.org/ 4.Nebraska Chapter, National Council for Geographic Education 5.Nebraska Geological Society: Web site: http://www.nebraskageologicalsociety.org Sponsors of a $50 award to the outstanding student paper presented at the Nebraska Academy of SciencesAnnual Meeting, Earth Science /Nebraska Chapter, National Council Sections 6.Nebraska Graduate Women in Science 7.Nebraska Junior Academy of Sciences: Web site: http://www.nebraskajunioracademyofsciences.org/ 8.Nebraska Ornithologists’ Union: Web site: http://www.noubirds.org/ 9.Nebraska Psychological Association: http://www.nebpsych.org/ 10.Nebraska-Southeast South Dakota Section Mathematical Association of America: Web site: http://sections.maa.org/nesesd/ 11.Nebraska Space Grant Consortium: Web site: http://www.ne.spacegrant.org/ CONTENTS AERONAUTICS & SPACE SCIENCE ANTHROPOLOGY APPLIED SCIENCE & TECHNOLOGY BIOLOGICAL & MEDICAL SCIENCES COLLEGIATE ACADEMY: BIOLOGY COLLEGIATE ACADEMY: CHEMISTRY & PHYSICS EARTH SCIENCES ENVIRONMENTAL SCIENCES GENERAL CHEMISTRY GENERAL PHYSICS TEACHING OF SCIENCE & MATHEMATICS 2020-2021 PROGRAM COMMITTEE 2020-2021 EXECUTIVE COMMITTEE FRIENDS OF THE ACADEMY NEBRASKA ACADEMY OF SCIENCS FRIEND OF SCIENCE AWARD WINNERS FRIEND OF SCIENCE AWARD TO DR PAUL KAR

Predictive Learning with Heterogeneity in Populations

University of Minnesota Ph.D. dissertation. October 2017. Major: Computer Science. Advisor: Vipin Kumar. 1 computer file (PDF); x, 119 pages.Predictive learning forms the backbone of several data-driven systems powering scientific as well as commercial applications, e.g., filtering spam messages, detecting faces in images, forecasting health risks, and mapping ecological resources. However, one of the major challenges in applying standard predictive learning methods in real-world applications is the heterogeneity in populations of data instances, i.e., different groups (or populations) of data instances show different nature of predictive relationships. For example, different populations of human subjects may show different risks for a disease even if they have similar diagnosis reports, depending on their ethnic profiles, medical history, and lifestyle choices. In the presence of population heterogeneity, a central challenge is that the training data comprises of instances belonging from multiple populations, and the instances in the test set may be from a different population than that of the training instances. This limits the effectiveness of standard predictive learning frameworks that are based on the assumption that the instances are independent and identically distributed (i.i.d), which are ideally true only in simplistic settings. This thesis introduces several ways of learning predictive models with heterogeneity in populations, by incorporating information about the context of every data instance, which is available in varying types and formats in different application settings. It introduces a novel multi-task learning framework for problems where we have access to some ancillary variables that can be grouped to produce homogeneous partitions of data instances, thus addressing the heterogeneity in populations. This thesis also introduces a novel strategy for constructing mode-specific ensembles in binary classification settings, where each class shows multi-modal distribution due to the heterogeneity in their populations. When the context of data instances is implicitly defined such that the test data is known to comprise of contextually similar groups, this thesis presents a novel framework for adapting classification decisions using the group-level properties of test instances. This thesis also builds the foundations of a novel paradigm of scientific discovery, termed as theory-guided data science, that seeks to explore the full potential of data science methods but without ignoring the treasure of knowledge contained in scientific theories and principles