135 research outputs found
sj-docx-1-eso-10.1177_23969873231204127 – Supplemental material for Changes in stroke and TIA admissions during the COVID-19 pandemic: A meta-analysis
No full textSupplemental material, sj-docx-1-eso-10.1177_23969873231204127 for Changes in stroke and TIA admissions during the COVID-19 pandemic: A meta-analysis by Karin Gunnarsson, Avin Tofiq, Alen Mathew, Yang Cao, Mia von Euler and Jakob O Ström in European Stroke Journal</p
sj-docx-2-eso-10.1177_23969873231204127 – Supplemental material for Changes in stroke and TIA admissions during the COVID-19 pandemic: A meta-analysis
No full textSupplemental material, sj-docx-2-eso-10.1177_23969873231204127 for Changes in stroke and TIA admissions during the COVID-19 pandemic: A meta-analysis by Karin Gunnarsson, Avin Tofiq, Alen Mathew, Yang Cao, Mia von Euler and Jakob O Ström in European Stroke Journal</p
sj-docx-3-eso-10.1177_23969873231204127 – Supplemental material for Changes in stroke and TIA admissions during the COVID-19 pandemic: A meta-analysis
No full textSupplemental material, sj-docx-3-eso-10.1177_23969873231204127 for Changes in stroke and TIA admissions during the COVID-19 pandemic: A meta-analysis by Karin Gunnarsson, Avin Tofiq, Alen Mathew, Yang Cao, Mia von Euler and Jakob O Ström in European Stroke Journal</p
sj-docx-4-eso-10.1177_23969873231204127 – Supplemental material for Changes in stroke and TIA admissions during the COVID-19 pandemic: A meta-analysis
No full textSupplemental material, sj-docx-4-eso-10.1177_23969873231204127 for Changes in stroke and TIA admissions during the COVID-19 pandemic: A meta-analysis by Karin Gunnarsson, Avin Tofiq, Alen Mathew, Yang Cao, Mia von Euler and Jakob O Ström in European Stroke Journal</p
[William Allen, half-length portrait, nearly facing front]
No full textDemocratic Senator from Ohio, 1837-1849; Governor of Ohio, 1874-1876.Alternative identification: Hon. Elisha H. Alen, Hawaiian minister.Scratched on back of plate: 69.Original served by appointment only.Produced by Mathew Brady's studio.Transfer; U.S. War College; 1920; (DLC/PP-1920:46153).Forms part of: Daguerreotype collection (Library of Congress)
Computing Least Common Subsumers in ALEN
No full textComputing the least common subsumer (lcs) in description logics is an inference task first introduced for sublanguages of CLASSIC. Roughly speaking, the lcs of a set of concept descriptions is the most specific concept description that subsumes all of the input descriptions. As such, the lcs allows to extract the commonalities from given concept descriptions, a task essential for several applications like, e.g., inductive learning, information retrieval, or the bottom-up construction of KR-knowledge bases. Previous work on the lcs has concentrated on description logics that either allow for number restrictions or for existential restrictions. Many applications, however, require to combine these constructors. In this work, we present an lcs algorithm for the description logic ALEN, which allows for both constructors (as well as concept conjunction, primitive negation, and value restrictions). The proof of correctness of our lcs algorithm is based on an appropriate structural characterization of subsumption in ALEN also introduced in this paper.This research was carried out while the second author was still at the LuFG Theoretical Computer Science, RWTH Aachen
Liberal Democracy in Crisis Rethinking Resistance under Neoliberal Governmentality (2019), Palgrave Macmillan by Alen Toplišek
No full textThis book is one of the interesting books related to the study of democracy that focuses on the phenomena of liberal democracy crisis. This book is based on Project carried out by the School of Political and International Studies, Queen Mary University of London, between 2012 and 2016. The main motivation of this project is to understand and reveal the phenomenon of increasing waves of protest activities occurring in several parts of the world during the period 2012 and 2013. In conveying his ideas and thoughts, the author of this book, Alen Toplišek divides the structure of this book into three main topics and 7 chapters. Each chapter has its specific analysis that is arranged to show the writer\u27s logical frame
Analyzing CO2 Emission Intensity: A Comprehensive Study of Clean and Unclean Energy Sources using ML Techniques
No full textThis thesis presents a comprehensive analysis of CO2 emission intensity, examining the intricate interplay of energy sources and their contributions to greenhouse gas emissions. The study evaluates the impact of various energy sources, categorizing them into two main groups: clean energy (wind, solar, hydro, bioenergy, and nuclear) and unclean energy (coal, gas, and other fossil fuels). By
utilizing Generalized Linear Models (GLM), this research offers a robust prediction of CO2 emission intensity, providing insights into the relative contributions of different energy sources on a regional and global scale. This analysis, which includes the percentage of energy usage from each source, allows for a more accurate quantification of CO2 intensity, simplifying th e process of rebalancing energy dependence to reduce environmental impact. Furthermore, this research employs Time Series Forecasting Techniques, specifically the AutoRegressive Integrated Moving Average (ARIMA) model, to forecast the trends in CO2 intensity across various regions. These forecasting methods facilitate a deeper understanding of how CO2 emissions are expected to evolve over time and allow for the identification of critical points for intervention and mitigation strategies. Findings reveal significant variations in CO2 emission intensity across energy sources and regions, shedding light on the key players in our environmental challenges. The study's data -driven analysis, incorporating energy usage percentages, offers insights into the relative contributions of different energy sources to CO2 emission intensity and underscores the critical importance of transitioning toward cleaner, more sustainable energy alternatives. This research serves as a valuable resource for policymakers, energy industry stakeholders, and environmental advocates, providing empirical guidance for mitigating the environmental impact of energy production and offering a quantifiable basis for rebalancing energy dependence
Layered graphene nanomaterials for gas separation membrane applications
No full textMembrane-based gas separation technologies have attracted great interest because of their potential to improve separation performance, lower energy and capital costs, reduce the equipment size, minimize the environmental footprint, and provide easier operation. The performance of gas separation membranes, which can be characterized in terms of permeability and selectivity, is primarily dependent on the physical and chemical properties of the membrane materials. The performance of membrane based separation technologies is limited by several factors, including the minimum thickness of the membrane (to maximize the flux), the maximum achievable selectivity, and their robustness at higher temperatures and pressures or under corrosive and reactive conditions. Among the emerging membrane materials, graphene oxide (GO) is a promising material that can dramatically enhance the gas separation performance of the membrane technology beyond the limits of conventional membrane materials in terms of both permeability and selectivity. This performance enhancement is due to the ultimate thinness, superior mechanical strength, large surface area, and unique two-dimensional layered structure of GO. Monolayer GO can be engineered in several ways to form ultrathin layered GO membranes with a narrow pore size distribution. Graphene oxide membranes can allow extremely high fluxes because of their ultimate thinness and unique layered structure. In addition, the high selectivity is due to the molecular sieving or diffusion effect resulting from their narrow pore size distribution or their unique surface chemistry.
Graphene oxide membranes can be prepared in several forms: as supported, self-standing, and nanocomposite materials. Self-standing GO membranes show promising selectivity with higher permeability because of the ultimate thinness of the membrane. However, supported GO membranes are more mechanically robust and may be better options for practical separation conditions. In this research, we have developed a facile preparation method of fabricating supported ultra-thin GO membrane and thin self-standing GO membrane. First, to achieve a better exfoliation of the graphene-oxide, we have adopted several approaches, such as using surfactants with GO solution, optimizing sonication and centrifugation parameters, etc. We have observed that the size of the GO particle (lateral dimension of exfoliated GO flake) greatly influences the formation of the membrane and hence impacts the gas separation characteristics of the GO membrane. Furthermore, we have developed a facile preparation of ultra-thin supported GO-PES membrane, which can block the transport of almost all the gas particle through the membrane. To achieve selectivity toward target gas, selective pores are developed in the completely stacked graphene-oxide chain by vacuum-drying, heating, or partial reduction of the graphene-oxide. The effect of moisture content in the structural integrity of the membrane is also realized with several controlled drying approaches of the ultra-thin membranes. Finally, the hydrocarbon (methane, propane, and butane) mixture separation performance of the GO membranes is evaluated by analyzing the gas mixture composition in both feed and permeate side using gas chromatography. We have characterized the gas separation performance for thin GO-PES, self-standing GO, ultra-thin GO-PES, reduced GO-PES membranes. Moreover, aside from understanding the transport mechanism through the interlayer spaces of GO membrane, we have analyzed the compared performances of different types of GO membranes to separate a hydrocarbon gas mixture of methane, propane, and butane. In all the membranes, two different mechanisms of hydrocarbon gas separation are observed, adsorption-desorption based and molecular sieving based, depending on the pressure difference between the feed and permeate side of the GO membrane. Because of its thick support material, very thin GO membrane layer, and controllable interlayer spacing, the ultra-thin GO-PES gas separation membranes are simultaneously robust, highly permeable, and highly selective.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-12-01The student, Saif Khan Alen, accepted the attached license on 2018-12-14 at 10:32.The student, Saif Khan Alen, submitted this Thesis for approval on 2018-12-14 at 10:43.This Thesis was approved for publication on 2018-12-14 at 12:25.DSpace SAF Submission Ingestion Package generated from Vireo submission #13331 on 2019-02-07 at 14:23:43Made available in DSpace on 2019-02-07T20:44:31Z (GMT). No. of bitstreams: 3
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Previous issue date: 2018-12-14Embargo set by: Seth Robbins for item 109895
Lift date: 2021-02-07T20:44:35Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 109895 on 2021-02-08T10:15:22Z
Thialkalivibrio nitratireducens sp. nov., a nitrate-reducing member of an autotrophic denitrifying consortium from a soda lake
No full textStrain ALEN 2(T) was isolated from a mixed culture capable of complete autotrophic denitrification with thiosulfate as electron donor at pH 10; the mixed culture was enriched from sediment from Lake Fazda (Wadi Natrun, Egypt), a hypersaline alkaline lake. The isolate had large, non-motile, coccoid or barrel-shaped cells with intracellular sulfur globules. The bacterium was obligately chemolithoautotrophic. It grew with reduced sulfur compounds aerobically and anaerobically with nitrate as electron acceptor, nitrate being reduced to nitrite. It was moderately halophilic and obligately alkaliphilic. On the basis of genetic analysis and its unique phenotype, strain ALEN 2 T ( = DSM 14787(T) = UNIQEM 213(T)) is proposed as the type strain of a novel species of the genus Thialkalivibrio, Thialkalivibrio nitratireducens
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