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Institution

University of Bologna

EducationBologna, Emilia-Romagna, Italy
About: University of Bologna is a education organization based out in Bologna, Emilia-Romagna, Italy. It is known for research contribution in the topics: Population & Galaxy. The organization has 38387 authors who have published 115176 publications receiving 3460869 citations. The organization is also known as: Università di Bologna & UNIBO.


Papers
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Journal ArticleDOI
TL;DR: The major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomographic, and magnetic resonance imaging are summarised.
Abstract: Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Each step needs careful evaluation for the construction of robust and reliable models to be transferred into clinical practice for the purposes of prognosis, non-invasive disease tracking, and evaluation of disease response to treatment. After the definition of texture parameters (shape features; first-, second-, and higher-order features), we briefly discuss the origin of the term radiomics and the methods for selecting the parameters useful for a radiomic approach, including cluster analysis, principal component analysis, random forest, neural network, linear/logistic regression, and other. Reproducibility and clinical value of parameters should be firstly tested with internal cross-validation and then validated on independent external cohorts. This article summarises the major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomography, and magnetic resonance imaging.

579 citations

Journal ArticleDOI
TL;DR: Using this approach, it can be verified that the space-vector modulation technique, owing to the intrinsic two degrees of freedom, represents the general solution of the modulation problem of matrix converters.
Abstract: In this paper, a novel representation of the switch state of a three-phase to three-phase matrix converter is presented. This approach, based on space-vector notation, simplifies the study of the modulation strategies, leading to a complete general solution of the problem and providing a very useful unitary point of view. The already established strategies can be considered as particular cases of the proposed general solution. Using this approach, it can be verified that the space-vector modulation technique, owing to the intrinsic two degrees of freedom, represents the general solution of the modulation problem of matrix converters. This technique can be considered the best solution for the possibility to achieve the highest voltage transfer ratio and to optimize the switching pattern through a suitable use of the zero configurations.

579 citations

Journal ArticleDOI
TL;DR: In this paper, the authors combine ultradeep optical spectroscopy from the GMASS project with multi-band photometry and HST imaging to study a sample of passive galaxies at 1.39 2.
Abstract: We combine ultradeep optical spectroscopy from the GMASS project ("Galaxy Mass Assembly ultradeep Spectroscopic Survey") with GOODS multi-band photometry and HST imaging to study a sample of passive galaxiesat 1.39 2. These galaxies have morphologies that are predominantly compact and spheroidal.However, their sizes (R_e 1, and absent if R_e 2. The results are compared with theoretical models and the main implications discussed in the framework of massive galaxy formation and evolution.

578 citations

Journal ArticleDOI
TL;DR: In this article, the authors assess the progress, opportunities, and challenges in this emerging field, which consists of a geochemical reaction regulated by subsurface microbiology, including mineral precipitation, gas generation, biofilm formation and biopolymer generation.
Abstract: Consideration of soil as a living ecosystem offers the potential for innovative and sustainable solutions to geotechnical problems. This is a new paradigm for many in geotechnical engineering. Realising the potential of this paradigm requires a multidisciplinary approach that embraces biology and geochemistry to develop techniques for beneficial ground modification. This paper assesses the progress, opportunities, and challenges in this emerging field. Biomediated geochemical processes, which consist of a geochemical reaction regulated by subsurface microbiology, currently being explored include mineral precipitation, gas generation, biofilm formation and biopolymer generation. For each of these processes, subsurface microbial processes are employed to create an environment conducive to the desired geochemical reactions among the minerals, organic matter, pore fluids, and gases that constitute soil. Geotechnical applications currently being explored include cementation of sands to enhance bearing capacity and liquefaction resistance, sequestration of carbon, soil erosion control, groundwater flow control, and remediation of soil and groundwater impacted by metals and radionuclides. Challenges in biomediated ground modification include upscaling processes from the laboratory to the field, in situ monitoring of reactions, reaction products and properties, developing integrated biogeochemical and geotechnical models, management of treatment by-products, establishing the durability and longevity/reversibility of the process, and education of engineers and researchers.

578 citations

Journal ArticleDOI
TL;DR: In this paper, two-particle angular correlations for charged particles emitted in pPb collisions at a nucleon-nucleon center-of-mass energy of 5.02 TeV are presented.

575 citations


Authors

Showing all 39076 results

NameH-indexPapersCitations
Anil K. Jain1831016192151
H. S. Chen1792401178529
Stefan Schreiber1781233138528
Alvio Renzini16290895452
David H. Adams1551613117783
Roberto Romero1511516108321
Thomas E. Starzl150162591704
Paolo Boffetta148145593876
Kypros H. Nicolaides147130287091
J. Fraser Stoddart147123996083
Fabio Finelli147542111128
Jack Hirsh14673486332
Kjell Fuxe142147989846
Andrew Ivanov142181297390
Peter Lang140113698592
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023398
20221,031
20217,486
20207,099
20196,390
20185,737