197,079 research outputs found
New and little-known Meligethes species from Yugoslavia (Coleoptera, Nitidulidae, Meligethinae)
No full textNew floristic records in the Balkans: 2
No full textUdgivelsesdato: August 2006New chorological data are presented for 100 species and subspecies from Bosnia & Herzegovina (reports no.29, 72), Bulgaria (30-36, 38-53, 61-71, 74-87), Croatia (28), Greece (1-27, 88-95), Montenegro (54-56, 58,73), Serbia (54, 55, 57, 59, 60, 96-100) and Turkey-in-Europe (37). The taxa belong to the following families:Adoxaceae (75), Amaryllidaceae (16), Apiaceae (1, 76), Aspleniaceae (54), Asteraceae (2, 20, 21, 35, 59, 72, 77),(75), (16), (1, 76), (54), (2, 20, 21, 35, 59, 72, 77), Boraginaceae (78, 79, 89), Brassicaceae (3-5, 30), Caryophyllaceae (55, 61), Convolvulaceae (36), Crassulaceae(78, 79, 89), (3-5, 30), (55, 61), (36), (39), Cucurbitaceae (6), Cyperaceae (52, 60, 68, 69, 84), Ephedraceae (74), Equisetaceae (88), Fabaceae (7, 22,23, 37, 49, 62, 80-83, 87, 90, 91), Gentianaceae (8), Geraniaceae (28), Grossulariaceae (96), Lamiaceae (9, 31,40-42), Liliaceae (17, 18, 26, 27, 45, 58, 85, 92-94, 98-100), Linaceae (63), Oleaceae (43), Orchidaceae (53),Oxalidaceae (24), Plantaginaceae (38), Poaceae (19, 34, 46-48, 70, 71, 86, 95), Polygalaceae (64), Polygonaceae(24), (38), (19, 34, 46-48, 70, 71, 86, 95), (64), (10, 25, 44), Potamogetonaceae (29), Primulaceae (11), Ranunculaceae (12-14, 32, 65), Rosaceae (33, 66,97), Rubiaceae (56, 73), Salicaceae (50), Scrophulariaceae (51), Valerianaceae (15) and Violaceae (57, 67).First reports for countries are: Bosnia & Herzegovina - Lactuca visianii (72), Potamogeton rutilus (29);Bulgaria - Convolvulus pilosellifolius (36), Deschampsia caespitosa subsp. alpina (34), Plantago maritimasubsp. serpentina (38), Thymus callieri subsp. callieri (31); Montenegro - Asperula hercegovina (73); Serbia -Allium paniculatum subsp. villosum (98), Viola obliqua (57); Turkey-in-Europe - Chamaecytisus jankae (37).subsp. (98), (57); Turkey-in-Europe - (37). The publication includes contributions by B. Biel & Kit Tan (1-19), N. Böhling (20-27), F. Conti & D.Uzunov (28), B. Davidovic, J. Blažencic & V. Stevanovic (29), D. Dimitrov (30-34), D. Dimitrov & V. Trifonov(35-38), V. Goranova & K. Vassilev (39-48), M. Hájek, P. Hájková, I. Apostolova, D. Sopotlieva & N. Velev(49-52), N. Grozeva (53), D. Lakušic, V. Stevanovic, S. Jovanovic & G. Tomovic (54-58), P. Lazarevic (59-60),H. Pedashenko (61-71), V. Stevanovic & D. Lakušic (72-73), S. Stoyanov (74-86), S. Stoyanov, V. Goranova &D. Stoykov (87), Kit Tan, G. Vold & G. Sfikas (88-95), and G. Tomovic, M. Niketic, B. Zlatkovic, S. Vukojicic& V. Stevanovic (96-100).</p
Morphological and biogeographical reexamination of the Meligethes squamosus species complex (Coleoptera: Nitidulidae: Meligethinae).
No full textNew floristic records in the Balkans: 1
No full textUdgivelsesdato: April 2006New chorological data are presented for 95 species and subspecies from Albania (report no. 80), Bosnia &Herzegovina (70, 82), Bulgaria (15-30, 72-75, 83-95), Greece (1-14, 31-69), and Serbia & Montenegro (70,71, 76-79, 81, 82). The taxa belong to the following families: Amaranthaceae (2, 15, 16), Amaryllidaceae(38), Apiaceae (17, 18, 47, 80, 83, 84), Asclepiadaceae (85), Asteraceae (29, 31, 86-91), Brassicaceae(3, 48, 70), Campanulaceae (49, 71, 76), Cannabaceae (72), Caprifoliaceae (4), Caryophyllaceae (19,20, 32, 33, 50-52), Chenopodiaceae (5), Cyperaceae (28, 78), Dipsacaceae (34, 53, 54), Fabaceae (6-8, 21, 22, 55, 69), Guttiferae (56), Iridaceae (39-41), Juncaceae (13), Lamiaceae (57-61), Liliaceae (42-46, 68), Linaceae (62), Ophioglossaceae (1), Orchidaceae (94), Orobanchaceae (63), Papaveraceae (92),Plantaginaceae (9), Poaceae (79, 95), Polygonaceae (93), Ranunculaceae (14, 23, 24, 35), Rhamnaceae(9), (79, 95), (93), (14, 23, 24, 35), (64), Rosaceae (65), Rubiaceae (66), Scrophulariaceae (25, 26, 36, 73, 77, 81), Solanaceae (10, 11, 74),Thymelaeaceae (30, 67, 82), Valerianaceae (37), Verbenaceae (12), Violaceae (27) and Vitaceae (75).(30, 67, 82), (37), (12), (27) and (75).First reports for countries are: Albania - Eryngium serbicum (80), Bulgaria - Parthenocissus quinquefolia (75), Greece - Gonocytisus dirmilensis (69), Littorella uniflora (9) and Verbena aristigera(75), Greece - (69), (9) and (12); Serbia & Montenegro - Campanula moravica (76), Daphne malyana (82), Lindernia dubia(77) and Poa timoleontis (79). Gonocytisus dirmilensis and Verbena aristigera are new for Europe.The publication includes contributions by B. Biel & Kit Tan (1-13), N. Böhling (14), D. Dimitrov & V.Vutov (15-28), R. Dimova & V. Vladimirov (29-30), Kit Tan & G. Vold (31-46), Kit Tan, G. Vold, G. Iatrou &G. Sfikas (47-68), Kit Tan, M. Vural & A. Strid (69), D. Lakušic & V. Stevanovic (70-71), A. Petrova (72-75),V. Randelovic, B. Zlatkovic, N. Randelovic & M. Juškovic (76-79), V. Stevanovic & S. Vukoijicic (80-81), V.Stevanovic & B. Zlatkovic (82) and V. Vladimirov (83-95).</p
Effect of atmospheric ageing on volatility and ROS of biodiesel exhaust nano-particles
No full textGenerally, the magnitude of pollutant emissions from diesel engines is ultimately coupled to the structure of fuel molecules. The presence of oxygen, level of unsaturation and the carbon chain length of respective molecules influence the combustion chemistry. It is speculated that increased oxygen content in the fuel may lead to the increased oxidative potential (Stevanovic, S. 2013). Also, upon the exposure to UV and ozone in the atmosphere, the chemical composition of the exhaust is changed. The presence of an oxidant and UV is triggering the cascade of photochemical reactions as well as the partitioning of semi-volatile compounds between the gas and particle phase. To gain an insight into the relationship between the molecular structures of the esters, their volatile organic content and the potential toxicity of diesel exhaust particulate matter, measurements were conducted on a modern common rail diesel engine. This research also investigates the contribution of atmospheric conditions on the transfer of semi-volatile fraction of diesel exhaust from the gas phase to the particle phase and the extent to which semi-volatile compounds (SVOCs) are related to the oxidative potential, expressed through the concentration of reactive oxygen species (ROS) (Stevanovic, S. 2013)..
Anaesthesia Management for Awake Craniotomy: Systematic Review and Meta-Analysis
No full textBackground
Awake craniotomy (AC) renders an expanded role in functional neurosurgery. Yet, evidence
for optimal anaesthesia management remains limited. We aimed to summarise the latest
clinical evidence of AC anaesthesia management and explore the relationship of AC fail ures on the used anaesthesia techniques.
Methods
Two authors performed independently a systematic search of English articles in PubMed
and EMBASE database 1/2007-12/2015. Search included randomised controlled trials
(RCTs), observational trials, and case reports (n>4 cases), which reported anaesthetic
approach for AC and at least one of our pre-specified outcomes: intraoperative seizures,
hypoxia, arterial hypertension, nausea and vomiting, neurological dysfunction, conversion
into general anaesthesia and failure of AC. Random effects meta-analysis was used to esti mate event rates for four outcomes. Relationship with anaesthesia technique was explored
using logistic meta-regression, calculating the odds ratios (OR) and 95% confidence inter vals [95%CI].
Results
We have included forty-seven studies. Eighteen r
A Deep Learning Architecture for Augmented Shape Reconstruction via Microwave Imaging
No full textIn this paper, an innovative microwave imaging approach that combines deep learning techniques and qualitative inversion methods is presented. In particular, the proposed approach is meant for imaging piece-wise homogeneous targets and aims at providing an augmented morphological reconstruction, which not only retrieves the shape of the targets, but also the spatial variations of the permittivity values. Such an information is not displayed by qualitative inversion methods; however it is efficiently encoded in the gradient of the unknown contrast. In particular in this paper, a physics-assisted deep learning technique, where domain knowledge is given in the inputs of a U-Net architecture, is developed. The domain knowledge is provided by the qualitative image of the unknown targets obtained using the orthogonality sampling method, thus allowing the architecture to provide, once trained, a fully automated and real-time prediction. An initial assessment for the approach with synthetic data is provided
A deep learning enhanced inverse scattering framework for microwave imaging of piece-wise homogeneous targets
No full textIn this paper, we present a framework for the solution of inverse scattering problems that integrates traditional imaging methods and deep learning. The goal is to image piece-wise homogeneous targets and it is pursued in three steps. First, raw-data are processed via orthogonality sampling method to obtain a qualitative image of the targets. Then, such an image is fed into a U-Net. In order to take advantage of the implicitly sparse nature of the information to be retrieved, the network is trained to retrieve a map of the spatial gradient of the unknown contrast. Finally, such an augmented shape is turned into a map of the unknown permittivity by means of a simple post-processing. The framework is computationally effective, since all processing steps are performed in real-time. To provide an example of the achievable performance, Fresnel experimental data have been used as a validation
- …
