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[null,"1.","Record Nr.",null,"UNINA9911009379403321"]
[null,null,"Autore",null,"Windows-Yule Kit"]
[null,null,"Titolo",null,"Positron Emission Particle Tracking : A Comprehensive Guide"]
[null,null,"Pubbl/distr/stampa",null,"Bristol : , : Institute of Physics Publishing, , 2022"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"9780750344135"]
[{"$nulls":4},"075034413X"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (699 pages)"]
[null,null,"Collana",null,"IOP Ebooks Series"]
[null,null,"Soggetti",null,"Positron beams"]
[{"$nulls":4},"Tracers (Chemistry)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Acknowledgement -- Author biographies -- Kit Windows-Yule -- Leonard Nicuşan -- Matthew T Herald -- Samuel Manger -- David Parker -- Chapter 0 Using the book -- 0.1 The 'User' -- 0.2 The 'Researcher' -- 0.3 The 'Developer' -- 0.4 The 'Expert' -- Chapter 1 Imaging particulate and multiphase systems -- 1.1 Particulate and multiphase systems: why do they matter? -- 1.2 The importance of imaging -- 1.3 Particle and flow imaging: an overview -- References -- Chapter 2 The fundamentals of PEPT -- 2.1 Positron emission… -- 2.2 …particle tracking -- 2.2.1 Interactive example: PEPT-an idealised case -- PEPT: an idealised case -- Monte Carlo line of response generation -- Triangulate tracer's location -- Spatial error versus number of LoRs used -- 2.3 A more realistic picture -- 2.3.1 Issue 1: false coincidences -- 2.3.2 Issue 2: positron flight -- 2.3.3 Issue 3: imperfect detectors -- 2.3.4 Issue 4: finite detection rate -- 2.3.5 The real picture -- 2.3.6 Interactive example: sources of error in PEPT -- Sources of error in PEPT -- Monte Carlo line of response generation -- Adding noise: scattered events and spread -- Triangulate tracer's location -- Spatial error versus noise ratio -- 2.4 Not just particle tracking -- References -- Chapter 3 A history of PEPT -- 3.1 Adding the 'P': from PET to PEPT (origins to 1989) -- 3.2 MWPC PEPT (1989-1999) -- 3.3 Out with the old, in with the new (1999-2002) -- 3.4 Positron cameras of all shapes and sizes (2002-present) -- 3.5 PEPT elsewhere -- 3.6 The future-SuperPEPT, MicroPEPT and beyond -- References -- Chapter 4 Comparison with other techniques -- 4.1 "]
[{"$nulls":4},"Quasi-two-dimensional techniques -- 4.1.1 Particle tracking velocimetry (PTV) -- 4.1.2 Particle imaging velocimetry (PIV) -- 4.1.3 Photoelastic imaging -- 4.1.4 X-ray radiography -- 4.2 Tomographic techniques -- 4.2.1 X-ray computed tomography."]
[{"$nulls":4},"4.2.2 Single-photon emission computed tomography (SPECT) -- 4.2.3 Positron emission tomography (PET) -- 4.2.4 Electrical capacitance tomography (ECT) -- 4.2.5 Magnetic resonance imaging (MRI) -- 4.2.6 Refractive index matched scanning (RIMS) -- 4.3 Three-dimensional particle-tracking techniques -- 4.3.1 Radioactive particle tracking (RPT) -- 4.3.2 Magnetic particle tracking (MPT) -- 4.4 Non-imaging techniques -- Angle of repose (AOR) tests -- Tapped density tests-the Carr index and Hausner ratio -- Flow through an orifice -- Shear testing -- Powder rheometry -- 4.5 Numerical simulation -- 4.5.1 The discrete element method (DEM) -- A simple discrete element method simulation -- Define system of differential equations -- Numerically solving the ODE: Euler method -- Numerically solving the ODE: adaptive integration schemes -- 4.5.2 Computational fluid dynamics (CFD) -- 4.5.3 The Monte Carlo method -- A simple Monte Carlo simulation -- How many random samples? -- 4.6 Other techniques -- References -- Chapter 5 Tracers and detectors -- 5.1 Creating tracers -- 5.1.1 Introduction-the ideal tracer -- 5.1.2 Positron-emitting nuclides and direct activation -- 5.1.3 Indirect activation -- 5.1.4 Handling and coating -- 5.2 Detector systems -- 5.2.1 Introduction -- 5.2.2 Scintillation detectors -- 5.2.3 PEPT system geometries -- 5.2.4 Examples of PEPT systems -- 5.2.5 Future developments -- 5.3 Modelling PEPT systems -- 5.3.1 How it works -- 5.3.2 Existing GATE models -- 5.3.3 Modelling a PEPT detector -- 5.3.4 Defining a PEPT tracer -- 5.3.5 Recreating an experiment -- 5.3.6 Studying a PEPT system using GATE -- References -- Chapter 6 Pre-processing: PEPT data and algorithms -- 6.1 Understanding PEPT data -- 6.1.1 Interactive example: PEPT data format -- PEPT Data Format -- Initialise raw line of response data -- Visualising a sample of LoRs."]
[{"$nulls":4},"Temporal resolution? -- 6.2 Available algorithms -- 6.2.1 The Birmingham algorithm -- 6.2.2 Interactive example: the Birmingham algorithm -- Interactive PEPT analysis example using the Birmingham method [1] -- This Jupyter Notebook -- Initialise raw line of response data -- Find minimum distance point -- Remove the farthest lines of response -- Iteratively remove the farthest LoRs and recompute MDP -- Complete Birmingham Method code -- High-performance Birmingham Method implementation -- 6.2.3 The line-density method -- 6.2.4 Interactive example: the line-density method -- Interactive PEPT analysis example using the line density algorithm [4] -- This Jupyter Notebook -- Initialise raw line of response data -- Voxelise the lines of response -- Fit 1D Gaussians around the peak -- Complete line density method code -- 6.2.5 The G-means clustering algorithm -- 6.2.6 Interactive example: the G-means algorithm -- Interactive PEPT analysis example using the clustering (G-means) algorithm [6] -- This Jupyter Notebook -- Initialise raw line of response data -- Voxelise the lines of response -- High pass filter -- Cluster voxels with G-means -- Complete clustering (G-means) algorithm code -- Multiple particle tracking -- 6.2.7 Feature-point identification (FPI) -- 6.2.8 Interactive example: FPI -- Interactive PEPT analysis example using the feature point identification algorithm [17] -- This Jupyter Notebook -- Initialise raw line of response data -- Voxelise the lines of response -- Subtract convolved matrix and blur -- Extract voxel peaks -- Complete FPI algorithm code -- Multiple particle tracking -- High-performance FPI algorithm implementation -- 6.2.9 Spatiotemporal B-spline reconstruction (SBSR) -- 6.2.10 Voronoi tesselation method -- 6.2.11 "]
[{"$nulls":4},"Interactive example: Voronoi tesselation."]
[{"$nulls":4},"Interactive PEPT analysis example using the Voronoi tesselation method [30] -- This Jupyter Notebook -- Initialise raw line of response data -- Discretise the lines of response -- Voronoi tesselation -- Gather points under consideration (PUCs) -- Local filtering based on the local outlier factor -- Global filtering -- Clustering the remaining PUCs -- Extract final tracer locations -- Complete Voronoi tesselation algorithm code -- 6.2.12 The triangulation method -- 6.2.13 Interactive example: triangulation method -- Interactive PEPT analysis example using the triangulation method [37] -- This Jupyter Notebook -- Initialise raw line of response data -- Calculate the LoR distance matrix -- Cluster LoRs closer than the tracer radius -- Find centroids of clustered LoRs' cutpoints -- Complete triangulation method code -- 6.2.14 PEPT using machine learning (PEPT-ML) -- 6.2.15 Interactive example: PEPT-ML -- Interactive PEPT analysis example using the PEPT-ML algorithm [38] -- This Jupyter Notebook -- Initialise raw line of response data -- Find cutpoints -- Cluster cutpoints with HDBSCAN -- Compute cluster centres -- Complete PEPT-ML algorithm code -- Second pass of clustering -- Multiple particle tracking -- High-performance PEPT-ML algorithm implementation -- 6.2.16 PEPT using expectation-maximisation (PEPT-EM) -- 6.2.17 Interactive example: PEPT-EM -- Interactive PEPT analysis examples using PEPT-EM [44] -- This Jupyter Notebook -- Initialise Raw Line of Response Data -- Calculate MDP and assign a weight to each LoR -- Recalculate MDP with previous weights -- Complete PEPT-EM algorithm code -- 6.2.18 The K-medoids method -- 6.2.19 Interactive example: K-medoids method -- Interactive PEPT analysis example using the K-medoids method [45] -- This Jupyter Notebook -- Initialise raw line of response data -- Find cutpoints."]
[{"$nulls":4},"Filter cutpoints with far nearest neighbors -- Cluster filtered cutpoints using K-medoids -- Compute clusters' centroids -- Complete K-medoids method mode -- Multiple particle pracking -- 6.2.20 The multiple location-allocation algorithm (MLAA) -- 6.2.21 Interactive example: the multiple location-allocation algorithm (MLAA) -- Interactive PEPT analysis example using the multiple location-allocation algorithm (MLAA) [49] -- This Jupyter Notebook -- Initialise raw line of response data -- Voxelise the lines of response -- Voxel global thresholding -- The location-allocation algorithm -- Complete multiple location-allocation algorithm code -- Multiple particle tracking -- 6.3 From finding tracers to tracking trajectories -- 6.3.1 Interactive example: the effects of sample size and overlap -- Effect of sample size and overlap -- Prelude -- Initialise raw line of response data -- Effect of sample size -- Effect of overlap -- 6.3.2 Trajectory extraction -- 6.3.3 Interactive example: filtering trajectories -- Filtering trajectories -- Initialising lines of response -- Filtering based on spatial error -- Filtering based on nearest neighbours -- Filtering using the PEPT Library -- 6.3.4 Interactive example: separating trajectories -- Separating trajectories -- Initialising lines of response -- PTV-based trajectory separation -- Clustering-based trajectory separation -- 6.4 Horses for courses: comparing algorithm capability for differing tasks -- References -- Chapter 7 Post-processing: extracting physical information from PEPT data -- 7.1 Particle trajectories -- 7.1.1 Single-particle trajectories and their interpretation -- 7.1.2 Interactive example: plotting single particle trajectories -- Tutorial: Using PEPT data to plot single particle trajectories -- Setting up -- Importing data -- Plotting data -- Using the pept library."]
[{"$nulls":4},"7.1.3 Interactive example: multiple-particle data."]
[null,null,null,"Sommario/riassunto","This book provides both an accessible introduction to, and a "]
[{"$nulls":4},"comprehensive overview of, the PEPT technique, replete with interactive examples, usable algorithms and real PEPT data, allowing the reader to gain a deep understanding and practical, working knowledge of the methodology."]
[null,"2.","Record Nr.",null,"UNINA9910367739203321"]
[null,null,"Autore",null,"Gaio Ana"]
[null,null,"Titolo",null,"Sustainable Cultural Management"]
[null,null,"Pubbl/distr/stampa",null,"MDPI - Multidisciplinary Digital Publishing Institute, 2019"]
[null,null,"ISBN",null,"3-03921-599-X"]
[null,null,"Descrizione fisica",null,"1 online resource (304 p.)"]
[null,null,"Soggetti",null,"Information technology industries"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","The connections between culture and sustainability have been in the public agenda since the 20th century. However, whilst global sustainability programmes at international institutional levels are yet to recognise the role of culture in their sustainability policies, the bid (albeit failed) in the early 2000s to formally add \"culture\" to the trilogy of sustainability pillars (economic, social, and environmental) mobilised a new discourse for the reframing of cultural policy narrative, which in turn urged a reassessment of methods of cultural management reflecting the same concerns among the sector's grassroots. The idea of sustainability and culture working together and their envisioned role in future-proofing society and human development captured the imagination of cultural commentators, policy makers and practitioners alike, keen to fulfil these principles \"out there\"-in cultural organizations and events mega and small, in cities and regions, local and global. The papers in this Special Issue reflect this appeal. This publication covers a wide selection of issues related to sustainable cultural management, which means that it can be recommended to a varied audience. First of all, it can be recommended to managers experienced in cultural management, where success is measured more by the degree of mission accomplishment and the social benefits achieved rather than by profit. Another group comprises the employees of cultural organizations who want to improve their knowledge of sustainable cultural management. This Special Issue can also be recommended to artists, researchers, students, state and local government employees, founders and patrons of art, and all those who want to understand the importance of sustainable cultural management."]