The Relation of Ongoing Brain Activity, Evoked Neural Responses, and Cognition

Ongoing brain activity has been observed since the earliest neurophysiological recordings and is found over a wide range of temporal and spatial scales. It is characterized by remarkably large spontaneous modulations. Here, we review evidence for the functional role of these ongoing activity fluctuations and argue that they constitute an essential property of the neural architecture underlying cognition. The role of spontaneous activity fluctuations is probably best understood when considering both their spatiotemporal structure and their functional impact on cognition. We first briefly argue against a “segregationist” view on ongoing activity, both in time and space, which would selectively associate certain frequency bands or levels of spatial organization with specific functional roles. Instead, we emphasize the functional importance of the full range, from differentiation to integration, of intrinsic activity within a hierarchical spatiotemporal structure. We then highlight the flexibility and context-sensitivity of intrinsic functional connectivity that suggest its involvement in functionally relevant information processing. This role in information processing is pursued by reviewing how ongoing brain activity interacts with afferent and efferent information exchange of the brain with its environment. We focus on the relationship between the variability of ongoing and evoked brain activity, and review recent reports that tie ongoing brain activity fluctuations to variability in human perception and behavior. Finally, these observations are discussed within the framework of the free-energy principle which – applied to human brain function – provides a theoretical account for a non-random, coordinated interaction of ongoing and evoked activity in perception and behavior

CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract meaningful patterns without prior information. However, ICA is not robust to mild data variation and remains a parameter-sensitive algorithm. The validity of the extracted patterns is hard to establish, as well as the significance of differences between patterns extracted from different groups of subjects. We start from a generative model of the fMRI group data to introduce a probabilistic ICA pattern-extraction algorithm, called CanICA (Canonical ICA). Thanks to an explicit noise model and canonical correlation analysis, our method is auto-calibrated and identifies the group-reproducible data subspace before performing ICA. We compare our method to state-of-the-art multi-subject fMRI ICA methods and show that the features extracted are more reproducible

Micro/Nano-engineered techniques for enhanced pool boiling heat transfer

Environmental aspects such as water treatment as well as military applications and thermal management emphasize on the need for next generation cooling technologies based on boiling heat transfer. Micro/nano enhanced surfaces have shown a great potential for the performance enhancement in the systems involving boiling phenomena. The lack of fully understanding the mechanisms responsible for the enhancement on these surfaces and scalability of these technologies for large and complex geometries over the wide range of materials are two main issues. The goals of this dissertation are to provide an understanding about the fundamentals of pool boiling heat transfer (BHT) and critical heat flux (CHF) mechanisms on engineered surfaces, to develop new techniques for surface alteration for BHT and CHF enhancement, and to propose novel, facile and scalable surfaces modification techniques for related industries. Surfaces with artificial cavities, surfaces with different wettability, and surfaces with different porosities were fabricated and tested to shed light into the fundamentals of surface/boiling interaction. In addition, 3-D foam-liked graphene and crenarchaeon Sulfolobus solfataricus P2 bio-coating surface modification techniques were proposed for BHT and CHF enhancement. For artificial cavities it was shown that CHF occurrence on the hydrophilic surfaces is mainly due to hydrodynamic instability, while dry-out is the dominant CHF mechanism on the hydrophobic surfaces. The obtained results imply that although the increase in hole diameter enhances CHF for all the fabricated samples, the effect of pitch size depends on surface wettability such that CHF increases and decreases with pitch size on the hydrophobic and hydrophilic surfaces, respectively. For biphilic surfaces, a novel and facile process flow for the fabrication of biphilic surfaces was proposed. It was shown that boiling heat transfer coefficient and CHF increased with A*=AHydrophobic/ATotal up to 38.46%. Surfaces with A*>38.46% demonstrated a decreasing trend in CHF and heat transfer coefficient enhancement, which is caused by earlier interaction of nucleated bubbles, thereby triggering the generation of vapor blanket at lower wall superheat temperatures. This ratio could serve as a valuable design guideline in the design and development of new generation thermal systems. Pool boiling on pHEMA coated surfaces with thicknesses of 50, 100 and 200 nm were used to study the effect of surface porosity and inclination angle on heat transfer and bubble departure process. According to obtained results, combination of the effects of the interaction between active nucleation sites, the increase in bubble generation frequency, and the increase in bubble interactions were presented as the reasons behind the enhancement in heat transfer on coated surfaces. It was observed that under an optimum condition for the inclination angle, the porous coating provides a suitable escape path for vapor phase, which results in space to be filled by the liquid phase thereby enabling liquid replenishment. Pool boiling experiments conducted on 3D foam-like graphene coated surfaces to show the effect of graphene coating thickness on the pool boiling heat transfer performance. According to the obtained results, 3D structure of the coating has a significant effect on pool boiling heat transfer mechanism. Factors such as pore shape and mechanical resonance of the 3D structure could be possible reasons for bubbling behavior in developed nucleate boiling. Furthermore it was found that there exists an optimum thickness of 3D graphene coatings, where the maximum heat transfer coefficient were achieved. This is mainly due to the trapped bubbles inside the porous medium, which affects the bubble dynamics involving bubble departure diameter and frequency. A novel coating, crenarchaeon Sulfolobus solfataricus P2 biocoatings, were proposed for the performance enhancement of heating and cooling devices, thermofluidic systems, batteries, and micro- and nanofluidic devices. These biocoatings have the potential for addressing high heat removal requirements in many applications involving heat and fluid flows. Pool boiling experiments were performed on biocoated surfaces with thicknesses of 1 and 2μm. The obtained results indicated that biocoated surfaces enhance boiling heat transfer by providing numerous nucleation site densities and by increasing bubble interaction on the superheated surface. Interconnected channels inside the porous coating, and capillary pumping enhance liquid transportation and reduce the liquid-vapor counter flow resistance, thereby delating CHF condition. There is a strong potential economic value of research performed in the framework of this thesis. Refrigeration, automotive/aerospace engineering, thermal management companies will benefit from the commercial development of the performed researc

Mathematical Modeling of Hydraulic Fracturing In Shale Gas Reservoirs

During the past few years, hydraulic fracturing and horizontal drilling have facilitated the production of gas from shale reserves that were uneconomic to produce in the past. Each shale formation has a specific nature, therefore every basin or well may need to be treated differently. Additionally, shales have characteristics such as extremely low permeability, sensitivity to contacting fluids, and existing micro fractures which cause complications while evaluating them. There is also an absence of a clear explanation for the application of 2D models and the effect of various parameters on the fracture in shale formations. Therefore, the objective of this study is to analyze different 2D hydraulic fracture geometry models while examining these models for their application in shale gas formations and to identify a 2D model that is most suitable to be used in the hydraulic fracture treatment design of shale gas reservoirs. It is also intended to investigate the effect of fracture height, fluid loss and rock stiffness on the fracture geometry and the well. In this study the two most commonly used hydraulic fracture geometry models in the oil and gas industry, PKN and KGD, have been discussed and based on these models two mathematical computer codes were developed in order to calculate various parameters such as fracture length, average fracture width, wellbore net pressure, pumping time, and maximum fracture width at wellbore. The PKN-C model is identified as the most suitable 2D model to be used in shale gas reservoirs due to its more acceptable vertical plane strain assumption. Low permeability formations such as shale reservoirs require narrower and longer fractures for a higher productivity. Thus, using a model that would predict longer and narrower fractures, such as the PKN-C model, would be more suitable. The KGD-C model predicts a higher dimensionless fracture conductivity compared to the PKN-C model. However, the fracture geometry predicted by the PKN-C model results in higher post-fracture productivity. Additionally, it was observed that longer and narrower fractures are produced in rocks with a high Young’s modulus (such as shale). Additionally, increasing the leak off coefficient when fluid loss is small will result in slightly shorter fracture lengths, while increasing the leak off coefficients when fluid loss is high will result in significantly shorter fracture lengths

Revisiting Cybersecurity Awareness in the Midst of Disruptions

The awareness of cybersecurity and knowledge about risks from a variety of threats, which present harm or steal private information in internetworking could help in mitigation of vulnerabilities to risks of threats in safeguarding information from malware and bots. Revisiting cybersecurity awareness of every member and evaluation of organization’s posture might help to protect sensitive or private information from a network of computers, working together and forming into botnets. The purpose of the qualitative case study narrative was to explore prospects for integrating cybersecurity education into elementary school children’s curriculum through interviews of elementary schoolteachers, IT experts, and parents to gain feedback about perceptions on cybersecurity knowledge and awareness. The analysis of schools’ organizational security postures related to all levels of education, recommending in raising awareness of the underlying and unprecedented security vulnerabilities. One area of greatest need is in protecting the wellbeing of people in securing private or protected assets and sensitive information, most valuable and vulnerable amid disruption. The possible lack of cybersecurity awareness in online settings could increase an organizational vulnerability to risks of threats and outsider attempts to install malware during a variety of cyber-attacks. Organizations with online ambiguity face a threat from botnets to infect networks. This qualitative exploratory single case-study into perceptions of teachers and leaders, information technology (IT) experts, and parents of elementary school children about cybersecurity awareness level of children in elementary schools helped to reinforce the important role of education in building foundational cyber-safety practices

A group model for stable multi-subject ICA on fMRI datasets

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study

A review on recent advances on knowledge management implementations

Knowledge management plays an essential role on developing efficient systems in educational systems. However, there are different factors influencing the success of knowledge management. In this paper, we review recent advances on implementation of knowledge management (KM) in different areas and discuss why some of KM implementations fail and how they could turn to a successful one. The review focus more on recently published papers in different perspective from the implementation of KM in educational units to KM implementation on project management field

Variability of the grain size distribution of a soil related to suffusion

Risk and Reliability in Geotechnical Engineerin

Development and Uses of Upper-division Conceptual Assessment

The use of validated conceptual assessments alongside more standard course exams has become standard practice for the introductory courses in many physics departments. These assessments provide a more standard measure of certain learning goals, allowing for comparisons of student learning across instructors, semesters, and institutions. Researchers at the University of Colorado Boulder have developed several similar assessments designed to target the more advanced physics content of upper-division classical mechanics, electrostatics, quantum mechanics, and electrodynamics. Here, we synthesize the existing research on our upper-division assessments and discuss some of the barriers and challenges associated with developing, validating, and implementing these assessments as well as some of the strategies we have used to overcome these barriers.Comment: 12 pages, 5 figures, submitted to the Phys. Rev. ST - PER Focused collection on Upper-division PE