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3. Thermal and Elastic Characterization of Nanostructured Fe2O3 Polymorphs and TiO2‑Coated Fe2O3 Using Open Photoacoustic CellN Jovančić, D K Markushev, Dragan D. Markushev, S M Aleksić, D S Pantić, Dorota Korte, Mladen Franko, 2020, original scientific article Keywords: Elastic, Nanostructure, Photoacoustic, Polymorphs, Thermal · TiO2-coated Fe2O3
Published in RUNG: 19.05.2020; Views: 2702; Downloads: 0
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5. Meteor ali topova krogljaDragan Božič, Zoran Božič, Miran Lola Božič, 2007, independent professional component part or a chapter in a monograph Abstract: Prispevek o padcu meteorita Avče, njegovih značilnostih in nadaljnji usodi.
Keywords: meteoriti, Avče, zgodovina, Slovenija, astronomija, meteorit Avče
Published in RUNG: 18.12.2017; Views: 3282; Downloads: 0
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7. Neznani in zamolčani LinhartDragan Božič, Zoran Božič, 2010, professional article Abstract: Članek o odkritju Linhartovega portreta in o zapostavljanju prvega slovenskega dramatika v srednješolskih berilih v 19. stoletju.
Keywords: slovenski dramatiki, miniaturni portreti, Slovenija, kulturna zgodovina, čitanke, razsvetljenstvo, srednješolska berila, dramatika, slovenska književnost, komedija
Published in RUNG: 15.12.2017; Views: 4112; Downloads: 0
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8. Prostor, vržen iz tira : Pogovor s Srečom DraganomNarvika Bovcon, Aleš Vaupotič, Srečo Dragan, 2017, interview Abstract: Prostor, vržen iz tira, razstava Sreča Dragana, likovnega
umetnika, videasta, konceptualista in novomedijskega
umetnika, tudi energičnega univerzitetnega profesorja,
mentorja ter producenta in organizatorja odmevnih dogodkov
in sodelovanj, pokriva pet desetletij umetniškega delovanja
in ustvarjanja. V zasnove in izvedbe včasih tehnično
izjemno zahtevnih projektov so bili vpleteni mnogi sodelavci
in ustvarjalci. Do leta 1987 je Srečo Dragan umetniško
deloval v tandemu z Nušo Dragan. Razstava se v
naslovu navezuje na Draganovo retrospektivo v Moderni
galeriji v Ljubljani leta 2000 z naslovom Čas, vržen iz tira:
1968–2000, ki privzema del Hamletovega verza: »The time
is out of joint.« Takrat je razstavo pospremil programski
zapis: »Inštitucija tehno-modificiranega pogleda kot
primarnega okolja Draganovega medijskega ustvarjanja
je od vseh, ki smo to predstavitev pripravljali, zahtevala
spremembo klasične pregledne razstave v obliko kot-darazstave.
To je bil osnovni model-ideja, ki omogoča polje
za testne in problematične transferje za nove tehnologije,
tehno-znanosti in elektronske, še posebej pa kibernetične
umetnosti. / Trije interaktivni razstavni prostori-teritoriji,
ki jih zasedajo nematerialne strukture kot-da-umetniška
dela, predstavljajo aktivne točke komunikacije« (black.fri.
uni-lj.si/sdmg2001). Tako kot na prelomu tisočletja se tudi
letošnja retrospektiva vzpostavlja iz avtorskega urejevalnega
in reaktualizirajočega postopka. Dela niso preprosto
dana na ogled, ampak na novo aktivirana prek preteklih in
sodobnih kontekstov. Naslov namiguje, da se nekaj zanimivega
tokrat dogaja s prostorom. Pomembno vlogo igra pet
stopenj rekonceptualizacije projektov, označene so bile z
napisi na tleh galerije: konceptna tabla 1.0 vsebuje izvirni
koncept, rekonceptualizacija 2.0 je instalacija-dokument
dela kot novo avtorsko delo, tabla 3.0 rekonceptualizira kot
modul za razstavljanje zgodovinsko kontekstualiziranih
projektov na skupinskih razstavah, 4.0 konceptualizira
robotsko vodenje obiskovalca po razstavi in končna faza
5.0 – to je živa podatkovna baza komentarjev obiskovalcev
Draganovih razstav ter udeležencev v njegovih projektih.
Keywords: Srečo Dragan, "Prostor, vržen iz tira", novomedijska umetnost, intervju z umetnikom
Published in RUNG: 30.08.2017; Views: 4178; Downloads: 0
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9. Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer’s diseaseDragan Gamberger, Nada Lavrač, Shantanu Srivatsa, Rudolph E. Tanzi, Murali Doraiswamy, 2017, original scientific article Abstract: The heterogeneity of Alzheimer’s disease contributes to the high failure rate of prior clinical trials. We analyzed 5-year longitudinal outcomes and biomarker data from 562 subjects with mild cognitive impairment (MCI) from two national studies (ADNI) using a novel multilayer clustering algorithm. The algorithm identified homogenous clusters of MCI subjects with markedly different prognostic cognitive
trajectories. A cluster of 240 rapid decliners had 2-fold greater atrophy and progressed to dementia at almost 5 times the rate of a cluster of 184 slow decliners. A classifier for identifying rapid decliners in one study showed high sensitivity and specificity in the second study. Characterizing subgroups of at risk subjects, with diverse prognostic outcomes, may provide novel mechanistic insights and facilitate clinical trials of drugs to delay the onset of AD.
Keywords: Alzheimer's disease, Rapid decliners, Data clustering, Mild cognitive impairment
Published in RUNG: 17.08.2017; Views: 4224; Downloads: 349
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10. Explicit Feature Construction and Manipulation for Covering Rule Learning AlgorithmsNada Lavrač, Johannes Fuernkranz, Dragan Gamberger, 2010, independent scientific component part or a chapter in a monograph Abstract: Features are the main rule building blocks for rule learning algorithms. They can be simple tests for attribute values or complex logical terms representing available domain knowledge. In contrast to common practice in classification rule learning, we argue that separation of the feature construction and rule construction processes has theoretical and practical justification. Explicit usage of features enables a unifying framework of both propositional and relational rule learning and we present and analyze procedures for feature construction in both types of domains. It is demonstrated that the presented procedure for constructing a set of simple features has the property that the resulting set enables construction of complete and consistent rules whenever it is possible, and that the set does not include obviously irrelevant features. Additionally, the concept of feature relevancy is important for the effectiveness of rule learning. It this work, we illustrate the concept in the coverage space and
prove that the relative relevancy has the quality-preserving property in respect to the resulting rules. Moreover, we show that the transformation from the attribute to the feature space enables a novel, theoretically justified way of handling unknown attribute values. The same approach
enables that estimated imprecision of continuous attributes can be taken into account, resulting in construction of robust features in respect to this imprecision.
Keywords: Machine learning, Feature construction, Rule learning, Unknown attribute values
Published in RUNG: 14.07.2017; Views: 4297; Downloads: 0
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