Literatura académica sobre el tema "Ceramics, multidimensional data analysis"
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Artículos de revistas sobre el tema "Ceramics, multidimensional data analysis"
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Puig-Castellví, Francesc, Yolanda Pérez, Benjamín Piña, Romà Tauler y Ignacio Alfonso. "Compression of multidimensional NMR spectra allows a faster and more accurate analysis of complex samples". Chemical Communications 54, n.º 25 (2018): 3090–93. http://dx.doi.org/10.1039/c7cc09891j.
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A variable-filtering approach of multidimensional NMR spectral data is proposed. Filtered data keeps resonance information and original spectral resolution. The denoised data can be easily peak-picked and integrated, allowing a fast and accurate NMR analysis.
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Boyd, C. Clifford y Donna C. Boyd. "A Multidimensional Investigation of Biocultural Relationships among Three Late Prehistoric Societies in Tennessee". American Antiquity 56, n.º 1 (enero de 1991): 75–88. http://dx.doi.org/10.2307/280974.
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Interrelations among three roughly contemporaneous late prehistoric Mississippian societies in Middle and East Tennessee are reexamined in terms of currently available biological, archaeological, and ethnohistoric data. Previous researchers have suggested a close relation between two of those cultures—Mouse Creek and Middle Cumberland—to the exclusion of the third, Dallas. However, multivariate analyses of craniofacial and mandibular dimensions of individuals from the three groups suggest a greater biological relation between Dallas and Mouse Creek than between Mouse Creek and Middle Cumberland. In addition, a comparison of intrasite settlement patterning, ceramic and mortuary variability, and ethnohistoric data across the three groups support the skeletal analysis. Relations between Dallas and Mouse Creek may mirror similar processes of sociopolitical reorganization occurring throughout the Southeast in the late prehistoric period.
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Munita, Casimiro S., Lúcia P. Barroso y Paulo M. S. Oliveira. "Variable selection study using Procrustes analysis". Open Journal of Archaeometry 1, n.º 1 (20 de diciembre de 2013): 7. http://dx.doi.org/10.4081/arc.2013.e7.
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Several analytical techniques are often used in archaeometric studies, and when used in combination, these techniques can be used to assess 30 or more elements. Multivariate statistical methods are frequently used to interpret archaeometric data, but their applications can be problematic or difficult to interpret due to the large number of variables. In general, the analyst first measures several variables, many of which may be found to be uninformative, this is naturally very time consuming and expensive. In subsequent studies the analyst may wish to measure fewer variables while attempting to minimize the loss of essential information. Such multidimensional data sets must be closely examined to draw useful information. This paper aims to describe and illustrate a stopping rule for the identification of redundant variables, and the selection of variables subsets, preserving multivariate data structure using Procrustes analysis, selecting those variables that are in some senses adequate for discrimination purposes. We provide an illustrative example of the procedure using a data set of 40 samples in which were determined the concentration of As, Ce, Cr, Eu, Fe, Hf, La, Na, Nd, Sc, Sm, Th, and U obtained via instrumental neutron activation analysis (INAA) on archaeological ceramic samples. The results showed that for this data set, only eight variables (As, Cr, Fe, Hf, La, Nd, Sm, and Th) are required to interpret the data without substantial loss information.
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Herr, Quentin, Alex Braun, Andrew Brownfield, Ed Rudman, Dan Dosch, Trent Josephsen y Anna Herr. "Measurement and data-assisted simulation of bit error rate in RQL circuits". Superconductor Science and Technology 35, n.º 2 (14 de enero de 2022): 025017. http://dx.doi.org/10.1088/1361-6668/ac45a1.
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Abstract A circuit-simulation-based method is used to determine the thermally-induced bit error rate of superconducting Single Flux Quantum logic circuits. Simulations are used to evaluate the multidimensional Gaussian integral across noise current sources attached to the active devices. The method is data-assisted and has predictive power. Measurement determines the value of a single parameter, effective noise bandwidth, for each error mechanism. The errors in the distributed networks of comparator-free Reciprocal Quantum Logic nucleate across multiple Josephson junctions, so the effective critical current is about three times that of the individual devices. The effective noise bandwidth is only 6%–23% of the junction plasma frequency at a modest clock rate of 3.4 GHz, which is 1% of the plasma frequency. This analysis shows the ways measured bit error rate comes out so much lower than simplistic estimates based on isolated devices.
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Barath, E., J. de Leeuw, W. Heiser, J. Meulman y F. Critchley. "Multidimensional Data Analysis". Biometrics 45, n.º 3 (septiembre de 1989): 1034. http://dx.doi.org/10.2307/2531708.
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Trajkovic, Dusan, Bratislav Mikaric y Marija Markovic-Blagojevic. "Multidimensional data analysis". Trendovi u poslovanju 3, n.º 2 (2015): 63–70. http://dx.doi.org/10.5937/trendpos1502063t.
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Iizuka, Masaya, Yuichi Mori, Tomoyuki Tarumi y Yutaka Tanaka. "9. Multidimensional Data Analysis". Journal of the Japanese Society of Computational Statistics 15, n.º 2 (2003): 337–45. http://dx.doi.org/10.5183/jjscs1988.15.2_337.
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Morita, Tsukio y Yutaka Tanaka. "9. Multidimensional Data Analysis". Journal of the Japanese Society of Computational Statistics 15, n.º 2 (2003): 347–55. http://dx.doi.org/10.5183/jjscs1988.15.2_347.
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Biebler, Karl-Ernst, Bernd J^|^auml;ger, Michael Wodny y Elke Below. "9. Multidimensional Data Analysis". Journal of the Japanese Society of Computational Statistics 15, n.º 2 (2003): 357–60. http://dx.doi.org/10.5183/jjscs1988.15.2_357.
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Tokairin, Tomoya y Yoshiharu Sato. "9. Multidimensional Data Analysis". Journal of the Japanese Society of Computational Statistics 15, n.º 2 (2003): 361–68. http://dx.doi.org/10.5183/jjscs1988.15.2_361.
Texto completoTesis sobre el tema "Ceramics, multidimensional data analysis"
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Passuti, Sara. "Electrοn crystallοgrathy οf nanοdοmains in functiοnal materials". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMC230.
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L’étude des matériaux fonctionnels se concentre de plus en plus sur des échantillonscaractérisés par des nano-domaines (allant de tailles submicroniques à des dizaines denanomètres) en raison de leurs propriétés physiques intéressantes, telles que celles observéesdans les films minces ou les matériaux céramiques. Lorsqu’il faut déterminer des phases inconnuesou obtenir des informations détaillées sur la structure cristalline de ces matériaux, ladiffraction des rayons X et la microscopie électronique à transmission (MET) se heurtent à desdifficultés. Pour résoudre ce problème, une nouvelle technique de diffraction électronique (ED),dite « Scanning Precession Electron Tomography » (SPET), a été employée. La SPET combinela méthode établie d’acquisition de données 3D ED assistée par la précession (également connuesous l’acronyme PEDT) avec un balayage du faisceau d’électrons sur une région d’intérêt (ROI)de l’échantillon et ce à chaque angle d’inclinaison du porte objet. Cette procédure permet decollecter des données 3D ED à partir de plusieurs ROIs en une seule acquisition, ce qui facilitela résolution et l’affinement précis de la structure cristalline de plusieurs nano-domaines ou dezones distinctes à l’intérieur d’un seul domaine. Dans cette thèse, les potentialités de la SPETsont explorées à la fois sur des films minces d’oxyde et sur des matériaux thermoélectriques(céramiques) préparés sous forme de lamelles TEM. En outre, une nouvelle méthodologie a étédéveloppée pour analyser efficacement la grande quantité de données collectées. Cette méthodeconsiste à trier les diagrammes de diffraction en fonction de leur région d’origine, à reconstruirela série 3D ED selon les différentes ROIs et à traiter automatiquement ces données pour la résolutionet l’obtention d’affinements précis de la structure. Ce travail démontre le potentiel de laSPET pour la caractérisation cristallographique fine de matériaux nano-structurés complexes.Cette approche est complémentaire de ce qui peut être fait en imagerie ou en spectroscopie par(S)TEM ou, en diffraction, par les approches dites 4D-STEM et ACOMThe investigation of functional materials has increasingly focused on samplescharacterized by nanodomains (ranging from submicron sizes to tens of nanometers) due totheir interesting physical properties, such as those observed in thin films and ceramic materials.When unknown phases need to be determined or detailed information on the crystallinestructure of these materials is required, this presents challenges for both X-ray diffraction andtransmission electron microscopy (TEM). To address this, a novel electron diffraction (ED) technique,Scanning Precession Electron Tomography (SPET), has been employed. SPET combinesthe established precession-assisted 3D ED data acquisition method (a.k.a. Precession ElectronDiffraction Tomography – PEDT) with a scan of the electron beam on a region of interest (ROI)of the specimen at each tilt step. This procedure allows to collect 3D ED data from multipleROIs with a single acquisition, facilitating structure solution and accurate structure refinementsof multiple nanodomains or distinct areas within a single domain, at once. In this thesis, thepotentialities of SPET are explored on both oxide thin films and ceramic thermoelectric materialsprepared as TEM lamellae. Additionally, a novel methodology was developed to efficientlyanalyze the large amount of data collected. This method involves sorting the diffraction patternsaccording to their region of origin, reconstructing the diffraction tilt series of the ROI, andautomatically processing the obtained tilt series for structure solution and accurate refinements.This work demonstrates the potential of SPET for the fine crystallographic characterization ofcomplex nanostructured materials. This approach appears to be complementary to what can bedone in imaging or spectroscopy by (S)TEM or, in diffraction, by the so-called 4D-STEM andACOM approaches
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Westerlund, Per. "Business Intelligence: Multidimensional Data Analysis". Thesis, Umeå universitet, Institutionen för datavetenskap, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-138758.
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The relational database model is probably the most frequently used database model today. It has its strengths, but it doesn’t perform very well with complex queries and analysis of very large sets of data. As computers have grown more potent, resulting in the possibility to store very large data volumes, the need for efficient analysis and processing of such data sets has emerged. The concept of Online Analytical Processing (OLAP) was developed to meet this need. The main OLAP component is the data cube, which is a multidimensional database model that with various techniques has accomplished an incredible speed-up of analysing and processing large data sets. A concept that is advancing in modern computing industry is Business Intelligence (BI), which is fully dependent upon OLAP cubes. The term refers to a set of tools used for multidimensional data analysis, with the main purpose to facilitate decision making. This thesis looks into the concept of BI, focusing on the OLAP technology and date cubes. Two different approaches to cubes are examined and compared; Multidimensiona lOnline Analytical Processing (MOLAP) and Relational Online Analytical Processing (ROLAP). As a practical part of the thesis, a BI project was implemented for the consulting company Sogeti Sverige AB. The aim of the project was to implement a prototype for easy access to, and visualisation of their internal economical data. There was no easy way for the consultants to view their reported data, such as how many hours they have been working every week, so the prototype was intended to propose a possible method. Finally, a performance study was conducted, including a small scale experiment comparing the performance of ROLAP, MOLAP and querying against the data warehouse. The results of the experiment indicates that ROLAP is generally the better choice for data cubing.
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Duch, Brown Amàlia. "Design and Analysis of Multidimensional Data Structures". Doctoral thesis, Universitat Politècnica de Catalunya, 2004. http://hdl.handle.net/10803/6647.
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Aquesta tesi està dedicada al disseny i a l'anàlisi d'estructures de dades multidimensionals, és a dir, estructures de dades que serveixen per emmagatzemar registres $K$-dimensionals que solen representar-se com a punts en l'espai $[0,1]^K$. Aquestes estructures tenen aplicacions en diverses àrees de la informàtica com poden ser els sistemes d'informació geogràfica, la robòtica, el processament d'imatges, la world wide web, el data mining, entre d'altres. Les estructures de dades multidimensionals també es poden utilitzar com a indexos d'estructures de dades que emmagatzemen, possiblement en memòria externa, dades més complexes que els punts.
Les estructures de dades multidimensionals han d'oferir la possibilitat de realitzar operacions d'inserció i esborrat de claus dinàmicament, a més de permetre realitzar cerques anomenades associatives. Exemples d'aquest tipus de cerques són les cerques per rangs ortogonals (quins punts cauen dintre d'un hiper-rectangle donat?) i les cerques del veí més proper (quin és el punt més proper a un punt donat?).
Podem dividir les contribucions d'aquesta tesi en dues parts:
La primera part està relacionada amb el disseny d'estructures de dades per a punts multidimensionals. Inclou el disseny d'arbres binaris $K$-dimensionals al·leatoritzats (Randomized $K$-d trees), el d'arbres quaternaris al·leatoritzats (Randomized quad trees) i el d'arbres multidimensionals amb punters de referència (Fingered multidimensional trees).
La segona part analitza el comportament de les estructures de dades multidimensionals. En particular, s'analitza el cost mitjà de les cerques parcials en arbres $K$-dimensionals relaxats, i el de les cerques per rang en diverses estructures de dades multidimensionals.
Respecte al disseny d'estructures de dades multidimensionals, proposem algorismes al·leatoritzats d'inserció i esborrat de registres per als arbres $K$-dimensionals i per als arbres quaternaris. Aquests algorismes produeixen arbres aleatoris, independentment de l'ordre d'inserció dels registres i desprès de qualsevol seqüència d'insercions i esborrats. De fet, el comportament esperat de les estructures produïdes mitjançant els algorismes al·leatoritzats és independent de la distribució de les dades d'entrada, tot i conservant la simplicitat i la flexibilitat dels arbres $K$-dimensionals i quaternaris estàndard. Introduïm també els arbres multidimensionals amb punters de referència. Això permet que les estructures multidimensionals puguin aprofitar l'anomenada localitat de referència en cerques associatives altament correlacionades.
I respecte de l'anàlisi d'estructures de dades multidimensionals, primer analitzem el cost esperat de las cerques parcials en els arbres $K$-dimensionals relaxats. Seguidament utilitzem aquest resultat com a base per a l'anàlisi de les cerques per rangs ortogonals, juntament amb arguments combinatoris i geomètrics. D'aquesta manera obtenim un estimat asimptòtic precís del cost de les cerques per rangs ortogonals en els arbres $K$-dimensionals aleatoris. Finalment, mostrem que les tècniques utilitzades es poden estendre fàcilment a d'altres estructures de dades i per tant proporcionem una anàlisi exacta del cost mitjà de cerques per rang en estructures de dades com són els arbres $K$-dimensionals estàndard, els arbres quaternaris, els tries quaternaris i els tries $K$-dimensionals.
Esta tesis está dedicada al diseño y al análisis de estructuras de datos multidimensionales; es decir, estructuras de datos específicas para almacenar registros $K$-dimensionales que suelen representarse como puntos en el espacio $[0,1]^K$. Estas estructuras de datos tienen aplicaciones en diversas áreas de la informática como son: los sistemas de información geográfica, la robótica, el procesamiento de imágenes, la world wide web o data mining, entre otras.
Las estructuras de datos multidimensionales suelen utilizarse también como índices de estructuras que almacenan, posiblemente en memoria externa, datos complejos.
Las estructuras de datos multidimensionales deben ofrecer la posibilidad de realizar operaciones de inserción y borrado de llaves de manera dinámica, pero además deben permitir realizar búsquedas asociativas en los registros almacenados. Ejemplos de búsquedas asociativas son las búsquedas por rangos ortogonales (¿qué puntos de la estructura de datos están dentro de un hiper-rectángulo dado?) y las búsquedas del vecino más cercano (¿cuál es el punto de la estructura de datos más cercano a un punto dado?).
Las contribuciones de esta tesis se dividen en dos partes:
La primera parte está dedicada al diseño de estructuras de datos para puntos multidimensionales, que incluye el diseño de los árboles binarios $K$-dimensionales aleatorios (Randomized $K$-d trees), el de los árboles cuaternarios aleatorios (Randomized quad trees), y el de los árboles multidimensionales con punteros de referencia (Fingered multidimensional trees).
La segunda parte contiene contribuciones al análisis del comportamiento de las estructuras de datos para puntos multidimensionales. En particular, damos el análisis del costo promedio de las búsquedas parciales en los árboles $K$-dimensionales relajados y el de las búsquedas por rango en varias estructuras de datos multidimensionales.
Con respecto al diseño de estructuras de datos multidimensionales, proponemos algoritmos aleatorios de inserción y borrado de registros para los árboles $K$-dimensionales y los árboles cuaternarios que producen árboles aleatorios independientemente del orden de inserción de los registros y después de cualquier secuencia de inserciones y borrados intercalados. De hecho, con la aleatorización garantizamos un buen rendimiento esperado de las estructuras de datos resultantes, que es independiente de la distribución de los datos de entrada, conservando la flexibilidad y la simplicidad de los árboles $K$-dimensionales y de los árboles cuaternarios estándar. También proponemos los árboles multidimensionales con punteros de referencia, una técnica que permite que las estructuras de datos multidimensionales exploten la localidad de referencia en búsquedas asociativas que se presentan altamente correlacionadas.
Con respecto al análisis de estructuras de datos multidimensionales, comenzamos dando un análisis preciso del costo esperado de las búsquedas parciales en los árboles $K$-dimensionales relajados. A continuación, utilizamos este resultado como base para el análisis de las búsquedas por rangos ortogonales, combinándolo con argumentos combinatorios y geométricos. Como resultado obtenemos un estimado asintótico preciso del costo de las búsquedas por rango en los árboles $K$-dimensionales relajados. Finalmente, mostramos que las técnicas utilizadas pueden extenderse fácilmente a otras estructuras de datos y por tanto proporcionamos un análisis preciso del costo promedio de búsquedas por rango en estructuras de datos como los árboles $K$-dimensionales estándar, los árboles cuaternarios, los tries cuaternarios y los tries $K$-dimensionales.
This thesis is about the design and analysis of point multidimensional data structures: data structures that store $K$-dimensional keys which we may abstract as points in $[0,1]^K$. These data structures are present in many applications of geographical information systems, image processing or robotics, among others. They are also frequently used as indexes of more complex data structures, possibly stored in external memory.
Point multidimensional data structures must have capabilities such as insertion, deletion and (exact) search of items, but in addition they must support the so called {em associative queries}. Examples of these queries are orthogonal range queries (which are the items that fall inside a given hyper-rectangle?) and nearest neighbour queries (which is the closest item to some given point?).
The contributions of this thesis are two-fold:
Contributions to the design of point multidimensional data structures: the design of randomized $K$-d trees, the design of randomized quad trees and the design of fingered multidimensional search trees;
Contributions to the analysis of the performance of point multidimensional data structures: the average-case analysis of partial match queries in relaxed $K$-d trees and the average-case analysis of orthogonal range queries in various multidimensional data structures.
Concerning the design of randomized point multidimensional data structures, we propose randomized insertion and deletion algorithms for $K$-d trees and quad trees that produce random $K$-d trees and quad trees independently of the order in which items are inserted into them and after any sequence of interleaved insertions and deletions. The use of randomization provides expected performance guarantees, irrespective of any assumption on the data distribution, while retaining the simplicity and flexibility of standard $K$-d trees and quad trees.
Also related to the design of point multidimensional data structures is the proposal of fingered multidimensional search trees, a new technique that enhances point multidimensional data structures to exploit locality of reference in associative queries.
With regards to performance analysis, we start by giving a precise analysis of the cost of partial matches in randomized $K$-d trees. We use these results as a building block in our analysis of orthogonal range queries, together with combinatorial and geometric arguments and we provide a tight asymptotic estimate of the cost of orthogonal range search in randomized $K$-d trees. We finally show that the techniques used apply easily to other data structures, so we can provide an analysis of the average cost of orthogonal range search in other data structures such as standard $K$-d trees, quad trees, quad tries, and $K$-d tries.
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Schroeder, Michael Philipp 1986. "Analysis and visualization of multidimensional cancer genomics data". Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/301436.
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Cancer is a complex disease caused by somatic alterations of the genome and epigenome in tumor cells. Increased investments and cheaper access to various technologies have built momentum for the generation of cancer genomics data. The availability of such large datasets offers many new possibilities to gain insight into cancer molecular properties. Within this scope I present two methods that exploit the broad availability of cancer genomic data: OncodriveROLE, an approach to classify mutational cancer driver genes into activating and loss of function mode of actions and MutEx, a statistical measure to assess the trend of the somatic alterations in a set of genes to be mutually exclusive across tumor samples. Nevertheless, the unprecedented dimension of the available data raises new complications for its accessibility and exploration which we try to solve with new visualization solutions: i) Gitools interactive heatmaps with prepared large scale cancer genomics datasets ready to be explored, ii) jHeatmap, an interactive heatmap browser for the web capable of displaying multidimensional cancer genomics data and designed for its inclusion into web portals, and iii) SVGMap, a web server to project data onto customized SVG figures useful for mapping experimental measurements onto the model.El cancer és una malaltia complexa causada per alteracions somàtiques del genoma i epigenoma de les cèl•lules tumorals. Un augment d’inversions i l'accés a tecnologies de baix cost ha provocat un increment important en la generació de dades genòmiques de càncer. La disponibilitat d’aquestes dades ofereix noves possibilitats per entendre millor les propietats moleculars del càncer. En aquest àmbit, presento dos mètodes que aprofiten aquesta gran disponibilitat de dades genòmiques de càncer: OncodriveROLE, un procediment per a classificar gens “drivers” del càncer segons si el seu mode d’acció ésl'activació o la pèrdua de funció del producte gènic; i MutEx, un estadístic per a mesurar la tendència de les mutacions somàtiques a l’exclusió mútua. Tanmateix, la manca de precedents d’aquesta gran dimensió de dades fa sorgir nous problemes en quant a la seva accessibilitat i exploració, els quals intentem solventar amb noves eines de visualització: i) Heatmaps interactius de Gitools amb dades genòmiques de càncer a gran escala, a punt per ser explorades, ii) jHeatmap, un heatmap interactiu per la web capaç de mostrar dades genòmiques de cancer multidimensionals i dissenyat per la seva inclusió a portals web; i iii) SVGMap, un servidor web per traslladar dades en figures SVG customitzades, útil per a la transl•lació de mesures experimentals en un model visual.
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Nam, Beomseok. "Distributed multidimensional indexing for scientific data analysis applications". College Park, Md. : University of Maryland, 2007. http://hdl.handle.net/1903/6795.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Palmas, Gregorio. "Visual Analysis of Multidimensional Data for Biomechanics and HCI". Doctoral thesis, KTH, Beräkningsvetenskap och beräkningsteknik (CST), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-193713.
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Multidimensional analysis is performed in many scientific fields.Its main tasks involve the identification of correlations between data dimensions,the investigation of data clusters, and the identification of outliers. Visualization techniques often help in getting a better understanding. In this thesis, we present our work on improving visual multidimensional analysis by exploiting the semantics of the data and enhancing the perception of existing visualizations. Firstly, we exploit the semantics of the data by creating new visualizations which present visual encodings specifically tailoredto the analyzed dimensions. We consider the resulting visual analysis to be more intuitive for the user asit provides a more easily understandable idea of the data. In this thesis we concentrate on the visual analysis of multidimensional biomechanical data for Human-Computer Interaction (HCI).To this end, we present new visualizations tackling the specific features of different aspectsof biomechanical data such as movement ergonomics, leading to a more intuitive analysis. Moreover, by integrating drawings or sketches of the physical setup of a case study as new visualizations, we allow for a fast and effective case-specific analysis. The creation of additional visualizations for communicating trends of clusters of movements enables a cluster-specific analysis which improves our understanding of postures and muscular co-activation.Moreover, we create a new visualization which addresses the specificity of the multidimensional data related to permutation-based optimization problems. Each permutation of a given set of n elements represents a point defined in an n-dimensional space. Our method approximates the topologyof the problem-related optimization landscape inferring the minima basins and their properties and visualizing them organized in a quasi-landscape. We show the variability of the solutions in a basin using heat maps generated from permutation matrices.Furthermore, we continue improving our visual multidimensional analysis by enhancing the perceptual encoding of existing well-known multidimensional visualizations. We focus on Parallel Coordinates Plots (PCP) and its derivative Continuous Parallel Coordinates (CPC). The main perceptual issues of PCP are visual clutter and overplotting which hamper the recognition of patterns in large data sets. In this thesis, we present an edge-bundling method for PCP which uses density-based clustering for each dimension. This reduces clutter and provides a faster overview of clusters and trends. Moreover, it allows for a fast rendering of the clustered lines using polygons. Furthermore, we present the first bundling approach for Continuous Parallel Coordinates where classic edge-bundling fails due to the absence of lines. Our method performs a deformation of the visualization space of CPC leading to similar results as those obtained through classic edge-bundling.Our work involved 10 HCI case studies and helped to establisha new research methodology in this field. This led to publications in internationally peer-reviewed journals and conference proceedings.QC 20161011
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Odondi, Maurice Jacob. "Multidimensional analysis of successive categories (rating) data by dual scaling". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ28031.pdf.
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Weherage, Pradeep Peiris. "BigDataCube: Distributed Multidimensional Data Cube Over Apache Spark : An OLAP framework that brings Multidimensional Data Analysis to modern Distributed Storage Systems". Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215696.
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Multidimensional Data Analysis is an important subdivision of Data Analytic paradigm. Data Cube provides the base abstraction for Multidimensional Data Analysis and helps in discovering useful insights of a dataset. On-Line Analytical Processing (OLAP) enhanced it to the next level supporting online responses to analytical queries with the underlying technique that precomputes (materializes) the data cubes. Data Cube Materialization is significant for OLAP, but it is an expensive task in term of data processing and storage. Most of the early decision support system benefits the value of multidimensional data analysis with a standard data architecture that extract, transform and load data from multiple data sources into a centralized database called Data Warehouse, on which OLAP engines provides the data cube abstraction. But this architecture and traditional OLAP engines do not hold with modern intensive datasets. Today, we have distributed data storage systems that keep data on a cluster of computer nodes, in which distributed data processing engines like MapReduce, Spark, Storm, etc. provide more ad-hoc style data analytical capabilities. Yet, there is no proper distributed system approach available for multidimensional data analysis, nor any distributed OLAP engine is available that follows distributed data cube materialization. It is essential to have a proper Distributed Data Cube Materialization mechanism to support multidimensional data analysis over the present distributed storage systems. Various research work available today which considered MapReduce for data cube materialization. Also, Apache Spark recently enabled CUBE operator as part of their DataFrame API. The thesis raises the problem statement, the best-distributed system approach for Data Cube Materialization, MapReduce or Spark? and contributes with experiments that compare the two distributed systems in materializing data cubes over the number of records, dimensions and cluster size. The results confirm Spark is more scalable and efficient in data cube materialization than MapReduce. The thesis further contributed with a novel framework, BigDataCube, which uses Spark DataFrames underneath for materializing data cubes and fulfills the need of multidimensional data analysis for modern distributed storage systems.Multidimensional Data Analysis är en viktig del av Data Analytic paradigm. Data Cube tillhandahåller den grundläggade abstraktionen för Multidimensional Data Analysis och hjälper till att hitta användningsbara observationer av ett dataset. OnLine Analytical Processing (OLAP) lyfter det till nästa nivå och stödjer resultat från analytiska frågor i realtid med en underliggande teknik som materliserar Data Cubes. Data Cube Materialization är signifikant för OLAP, men är en kostsam uppgift vad gäller processa och lagra datat.De flesta av tidiga beslutssystem uppfyller Multidimensional Data Analysis med en standarddataarkitektur som extraherar, transformerar och läser data från flera datakällor in I en central databas, s.k. Data Warehouse, som exekveras av OLAP och tillhandahåller en Data Cube-abstraktion. Men denna arkitektur och tradionella OLAP-motorer klarar inte att hantera moderna högbelastade datasets. Idag har vi system med distribuerad datalagring, som har data på ett kluster av datornoder, med distribuerade dataprocesser, så som MapReduce, Spark, Storm etc. Dessa tillåter en mer ad-hoc dataanalysfunktionalitet. Än så länge så finns det ingen korrekt angreppsätt tillgänlig för Multidimensional Data Analysis eller någon distribuerad OLAP-motor som följer Distributed Data Cube Materialization.Det är viktigt att ha en korrekt Distributed Data Cube Materializationmekanism för att stödja Multidimensional Data Analysis för dagens distribuerade lagringssystem. Det finns många forskningarar idag som tittar på MapReduce för Data Cube Materialization. Nyligen har även Apache Spark tillgänglitgjort CUBE-operationer som en del av deras DataFrame API. Detta examensarbete tar upp frågeställningen, vilket som är det bästa angrepssättet för distribuerade system för Data Cube Materialization, MapReduce eller Spark. Arbetet bidrar dessutom med experiment som jämför de två distribuerade systemen i materialiserande datakubar över antalet poster, dimensioner och klusterstorlek. Examensarbetet bidrar även med ett mindre ramverk BigDataCube, som använder Spark DataFramesi bakgrunden för Data Cube Materialization och uppfyller behovet av Multidimensional Data Analysis av distribuerade lagringssystem.
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Jernberg, Robert y Tobias Hultgren. "Flexible Data Extraction for Analysis using Multidimensional Databases and OLAP Cubes". Thesis, KTH, Data- och elektroteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-123393.
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Bright is a company that provides customer and employee satisfaction surveys, and uses this information to provide feedback to their customers. Data from the surveys are stored in a relational database and information is generated both by directly querying the database as well as doing analysis on extracted data. As the amount of data grows, generating this information takes increasingly more time. Extracting the data requires significant manual work and is in practice avoided. As this is not an uncommon issue, there is a substantial theoretical framework around the area. The aim of this degree project is to explore the different methods for achieving flexible and efficient data analysis on large amounts of data. This was implemented using a multidimensional database designed for analysis as well as an OnLine Analytical Processing (OLAP) cube built using Microsoft's SQL Server Analysis Services (SSAS). The cube was designed with the possibility to extract data on an individual level through PivotTables in Excel. The implemented prototype was analyzed, showing that the prototype consistently delivers correct results severalfold as efficient as the current solution as well as making new types of analysis possible and convenient. It is concluded that the use of an OLAP cube was a good choice for the issue at hand, and that the use of SSAS provided the necessary features for a functional prototype. Finally, recommendations on possible further developments were discussed.Bright är ett företag som tillhandahåller undersökningar för kund- och medarbetarnöjdhet, och använder den informationen för att ge återkoppling till sina kunder. Data från undersökningarna sparas i en relationsdatabas och information genereras både genom att direkt fråga databasen såväl som att göra manuell analys på extraherad data. När mängden data ökar så ökar även tiden som krävs för att generera informationen. För att extrahera data krävs en betydande mängd manuellt arbete och i praktiken undviks det. Då detta inte är ett ovanligt problem finns det ett gediget teoretiskt ramverk kring området. Målet med detta examensarbete är att utforska de olika metoderna för att uppnå flexibel och effektiv dataanalys på stora mängder data. Det implementerades genom att använda en multidimensionell databas designad för analys samt en OnLine Analytical Processing (OLAP)-kub byggd med Microsoft SQL Server Analysis Services (SSAS). Kuben designades med möjligheten att extrahera data på en individuell nivå med PivotTables i Excel. Den implementerade prototypen analyserades vilket visade att prototypen konsekvent levererar korrekta resultat flerfaldigt så effektivt som den nuvarande lösningen såväl som att göra nya typer av analys möjliga och lättanvända. Slutsatsen dras att användandet av en OLAP-kub var ett bra val för det aktuella problemet, samt att valet att använda SSAS tillhandahöll de nödvändiga funktionaliteterna för en funktionell prototyp. Slutligen diskuterades rekommendationer av möjliga framtida utvecklingar.
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Johnson, Kevin J. "Strategies for chemometric analysis of gas chromatographic data /". Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/8513.
Texto completoLibros sobre el tema "Ceramics, multidimensional data analysis"
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Murtagh, Fionn. Multidimensional clustering algorithms. Vienna: Physica-Verlag, 1985.
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1952-, Blakely Jeffrey A. y Bennett W. J, eds. Analysis and publication of ceramics: The computer data-base in archaeology. Oxford, England: B.A.R., 1989.
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Sivertun, Åke. Geographical Information Systems (GIS) as a tool for analysis and communication of multidimensional data. Umeå, Sweden: Dept. of Geography, University of Umeå, 1993.
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Adam, Pintz y United States. National Aeronautics and Space Administration., eds. Ceramic material life prediction: A program to translate ANSYS results to CARES/LIFE reliability analysis : final report. [Washington, DC: National Aeronautics and Space Administration, 1994.
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Grigor'ev, Anatoliy y Evgeniy Isaev. Methods and algorithms of data processing. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.
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The tutorial deals with selected methods and algorithms of data processing, the sequence of solving problems of processing and analysis of data to create models behavior of the object taking into account all the components of its mathematical model. Describes the types of technological methods for the use of software and hardware for solving problems in this area. The algorithms of distributions, regressions vremenny series, transform them with the aim of obtaining mathematical models and prediction of the behavior information and economic systems (objects).
The second edition is supplemented by materials that are in demand by researchers in the part of the correct use of clustering algorithms. Are elements of the classification algorithms to identify their capabilities, strengths and weaknesses. Are the procedures of justification and verify the adequacy of the results of the cluster analysis, conducted a comparison and evaluation of different clustering techniques, given information about visualization of multidimensional data and examples of practical application of clustering algorithms.
Meets the requirements of Federal state educational standards of higher education of the last generation.
For students of economic specialties, specialists, and graduate students.
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Babeshko, Lyudmila y Irina Orlova. Econometrics and econometric modeling in Excel and R. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.
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The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluation, analysis, and practical implementation of Box — Jenkins stationary time series models, as well as multidimensional time series models: vector autoregressive models and vector error correction models. It includes basic econometric models for panel data that have been widely used in recent decades, as well as formal tests for selecting models based on their hierarchical structure. Each section provides examples of evaluating, analyzing, and testing models in the R software environment. Meets the requirements of the Federal state educational standards of higher education of the latest generation.
It is addressed to master's students studying in the Field of Economics, the curriculum of which includes the disciplines Econometrics (advanced course)", "Econometric modeling", "Econometric research", and graduate students."
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Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.
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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge".
The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems.
Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
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Tihonova, Elena, Elena Dianina, Vasiliy Egorov y Elena Karpova. Social management of career guidance activities in educational institutions of a modern metropolis. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1872851.
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The monograph is devoted to the analysis of the theoretical and methodological foundations of the study of the management system of professional orientation of high school students of general education institutions in the conditions of a modern metropolis. The central place in the work is occupied by the author's structural and functional model of management of the system of career guidance. A large theoretical material based on the results of the author's research is presented, which allows to formulate and substantiate a number of new scientific provisions that open up a promising direction in the study of the problems of career guidance in the mainstream of management sociology. The results of a multidimensional analysis of the data of the author's empirical studies of the influence of macro- and microsocial factors on the effectiveness of career guidance management in educational institutions of the modern Russian metropolis on the example of Moscow are presented.
It is intended for a wide range of specialists in the field of sociology of management, pedagogy, sociology of education, as well as for heads of sociological services, teachers, graduate students, students, anyone interested in the issues of professional orientation of young people in a modern metropolis.
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M, Amabile Teresa, Singer Jill, Chedd-Angier Production Company (U.S.)., Consort for Mathematics and its Applications, Inc. y Magic Lantern Communications, eds. Multidimensional data analysis [videorecording]. Oakville, Ont. :bMagic Lantern Communications Ltd, 1989.
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Bhattacharyya, Siddhartha, Sourav De, Paramartha Dutta y Indrajit Pan. Intelligent Multidimensional Data Clustering and Analysis. IGI Global, 2017.
Buscar texto completoCapítulos de libros sobre el tema "Ceramics, multidimensional data analysis"
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Everitt, Brian S. y Graham Dunn. "Multidimensional Scaling". En Applied Multivariate Data Analysis, 93–124. West Sussex, United Kingdom: John Wiley & Sons, Ltd,., 2013. http://dx.doi.org/10.1002/9781118887486.ch5.
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Adachi, Kohei. "Multidimensional Scaling". En Matrix-Based Introduction to Multivariate Data Analysis, 247–58. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4103-2_16.
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Adachi, Kohei. "Multidimensional Scaling". En Matrix-Based Introduction to Multivariate Data Analysis, 243–53. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2341-5_16.
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Samarabandu, Jagath K., Raj Acharya y Ping-chin Cheng. "Analysis and Presentation of Three Dimensional Data Sets". En Multidimensional Microscopy, 231–50. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4613-8366-6_13.
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Borg, I. y J. Lingoes. "Mappings of Data in Distances". En Multidimensional Similarity Structure Analysis, 292–308. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4612-4768-5_18.
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Terada, Yoshikazu y Patrick J. F. Groenen. "Multidimensional Scaling of Distributional Data". En Analysis of Distributional Data, 273–92. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781315370545-12.
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Groenen, Patrick J. F., Rudolf Mathar y Javier Trejos. "Global Optimization Methods for Multidimensional Scaling Applied to Mobile Communications". En Data Analysis, 459–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58250-9_37.
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Mathar, Rudolf. "Algorithms in Multidimensional Scaling". En Conceptual and Numerical Analysis of Data, 159–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-75040-3_10.
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Suerdem, Ahmet. "Multidimensional Scaling of Qualitative Data". En The Routledge Reviewer's Guide to Mixed Methods Analysis, 45–56. New York: Routledge, 2021. http://dx.doi.org/10.4324/9780203729434-4.
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van Wezel, Michiel C., Walter A. Kosters, Peter van der Putten y Joost N. Kok. "Nonmetric Multidimensional Scaling with Neural Networks". En Advances in Intelligent Data Analysis, 145–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44816-0_15.
Texto completoActas de conferencias sobre el tema "Ceramics, multidimensional data analysis"
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Chang-Hui Lu, Dong-Fen Hou, Xu-Chong Liu, Qing-Bao Liu y Su Deng. "Imprecise information in multidimensional data analysis". En 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccsit.2010.5565108.
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Doreswamy y K. S. Harishkumar. "Multidimensional Data Model for Air Pollution Data Analysis". En 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2018. http://dx.doi.org/10.1109/icacci.2018.8554621.
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Petruska, Igor. "STATISTICAL ANALYSIS OF MULTIDIMENSIONAL DATA IN FINANCIAL ANALYSIS". En 5th International Multidisciplinary Scientific Conferences on SOCIAL SCIENCES and ARTS SGEM2018. STEF92 Technology, 2018. http://dx.doi.org/10.5593/sgemsocial2018/1.3/s03.038.
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Bochicchio, Mario A., Antonella Longo, Lucia Vaira, Antonio Malvasi y Andrea Tinelli. "Multidimensional analysis of fetal growth curves". En 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691754.
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Valis, D., L. Zak y Z. Vintr. "Perspective Exploratory Methods for Multidimensional Data Analysis". En 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2019. http://dx.doi.org/10.1109/ieem44572.2019.8978643.
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"Multidimensional Analysis of RFID Data in Logistics". En 2009 42nd Hawaii International Conference on System Sciences. IEEE, 2009. http://dx.doi.org/10.1109/hicss.2009.315.
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MORHÁČ, M., J. KLIMAN, V. MATOUŠEK, I. TURZO, L. KRUPA y M. JANDEL. "ADVANCED ANALYSIS OF MULTIDIMENSIONAL EXPERIMENTAL NUCLEAR DATA". En Proceedings of the 5th International Conference. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812776723_0042.
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Hilal, Median, Christoph G. Schuetz y Michael Schrefl. "Superimposed multidimensional schemas for RDF data analysis". En 2017 IEEE 14th International Scientific Conference on Informatics. IEEE, 2017. http://dx.doi.org/10.1109/informatics.2017.8327230.
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Goil, S. y A. Choudhary. "High Performance Multidimensional Analysis and Data Mining". En SC98 - High Performance Networking and Computing Conference. IEEE, 1998. http://dx.doi.org/10.1109/sc.1998.10043.
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BONDAREV, A. E. "VISUALIZATION, CONSTRUCTING, AND ANALYSIS OF MULTIDIMENSIONAL DATA". En GRAPHICON 2024, 23–25. Omsk State Technical University, 2024. http://dx.doi.org/10.25206/978-5-8149-3873-2-2024-23-25.
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Nowadays, the study of multidimensional data is one of the most important areas of data analysis. However, multidimensional solutions can be obtained as a result of parametric solution of mathematical modeling problems. The technology of generalized computational experiment is used to construct such solutions. Mathematical modeling problems are considered in the space of defining parameters, where the defining parameters can be characteristic numbers of a particular problem under consideration, geometric characteristics, and control parameters of the numerical method. For each parameter a partitioning in a certain range is carried out, and the problem is solved at each point of such partitioning. As a result of application of such an approach, the solution is obtained not for a single, separately taken problem, but for a class of problems specified by the ranges of changes in the determining parameters. The questions of application of parallel technologies for realization of such approach are considered. Examples of implementation of construction and analysis of multidimensional solutions for different types of problems are presented.
Informes sobre el tema "Ceramics, multidimensional data analysis"
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Grady, Dennis E. Analysis of Shock and High-Rate Data for Ceramics: Equation of State Properties and Fragmentation in the Ballistic Environment. Fort Belvoir, VA: Defense Technical Information Center, mayo de 2009. http://dx.doi.org/10.21236/ada503449.
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Crowe. PR-261-15609-R01 Machine Learning Algorithms for Smart Meter Diagnostics Part II (TR2701). Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), diciembre de 2015. http://dx.doi.org/10.55274/r0010862.
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Modern smart meters provide an abundance of diagnostic data. Detecting abnormalities in this data can be difficult given the sheer quantity of information. Determining what kind of readings constitute normal operation versus an impending problem has been the subject of significant research; however, there is still room for improvement in real-time fault monitoring. Statistical models known as Machine Learning Algorithms (MLAs) have been identified as a potential solution. A new feature set was selected that allowed for extension of MLAs to ultrasonic meters with different path arrangements. Principal Component Analysis was used to give structure to and visualize multidimensional ultrasonic meter data. The results showed that MLAs may be extended to meters of different sizes, manufacturers, and from different flow facilities.
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Panchenko, Liubov y Andrii Khomiak. Education Statistics: Looking for Case-Study for Modeling. [б. в.], noviembre de 2020. http://dx.doi.org/10.31812/123456789/4461.
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The article deals with the problem of using modeling in social statistics courses. It allows the student-researcher to build one-dimensional and multidimensional models of the phenomena and processes that are being studied. Social Statistics course programs from foreign universities (University of Arkansas; Athabasca University; HSE University, Russia; McMaster University, Canada) are analyzed. The article provides an example using the education data set – Guardian UK universities ranking in Social Statistics course. Examples of research questions are given, data analysis for these questions is performed (correlation, hypothesis testing, discriminant analysis). During the research the discriminant model with group variable – modified Guardian score – and 9 predictors: course satisfaction, teaching quality, feedback, staff-student ratio, money spent on each student and other) was built. Lower student’s satisfaction with feedback was found to be significantly different from the satisfaction with teaching. The article notes the modeling and statistical analysis should be accompanied by a meaningful interpretation of the results. In this example, we discussed the essence of university ratings, the purpose of Guardian rating, the operationalization and measurement of such concepts as satisfaction with teaching, feedback; ways to use statistics in education, data sources etc. with students. Ways of using this education data in group and individual work of students are suggested.
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Generalao, Ian Nicole, Pauline Joy Lorenzo, Chrislyn Joanna Eñate, Frances Camille Dumalaog, John Joseph Ocbina, Kimberly Librero y Aniceto Jr Orbeta. An Assessment of the Enterprise-Based Training Modality in the Philippines: Barriers, Incentives, and Policy Gaps. Philippine Institute for Development Studies, mayo de 2024. http://dx.doi.org/10.62986/dp2024.05.
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Demand-driven technical and vocational education and training (TVET) has been associated with positive economic outcomes for the trainees and the participating firms and enterprises. Despite these potential positive implications, numerous studies consistently show the limited involvement of the private sector in training provision and curriculum development in the Philippines. Thus, this study examines the enterprise-based training (EBT) modality in the country by identifying the barriers to entry of industries in co-developing and offering upskilling programs and by evaluating existing incentive mechanisms. A series of key informant interviews and site visits with various stakeholders were conducted to identify prevailing issues and challenges that firms and industries face. To better contextualize the EBT situation, these were complemented by a desk review of the literature and the policy and legal framework governing EBT and an analysis of recent data. This study uncovers the multidimensional and interconnected issues surrounding the alarmingly dismal contribution of EBT to training provision. This study finds clear evidence that despite the abundance of financial and non-financial incentives, existing mechanisms have not been effective in attracting private actors, including firms/enterprises and technical vocational institutions (TVIs), to implement EBT programs. Other factors include gaps in the definition, scope, and measure of EBT, limited capacity of implementers, unresponsiveness of training programs, and intersectoral and interagency coordination concerns. This study has three main contributions. First, the elaborate discussion of the various EBT programs and the associated incentives can facilitate a better understanding and deeper appreciation of EBT. Second, policymakers can learn from and adopt good practices on industry involvement in TVET highlighted throughout the study. Finally, policy recommendations are formulated and presented to address the identified barriers and disincentives, which have persistently discouraged industry involvement in skills development.
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Ramm-Granberg, Tynan, F. Rocchio, Catharine Copass, Rachel Brunner y Eric Nelsen. Revised vegetation classification for Mount Rainier, North Cascades, and Olympic national parks: Project summary report. National Park Service, febrero de 2021. http://dx.doi.org/10.36967/nrr-2284511.
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Field crews recently collected more than 10 years of classification and mapping data in support of the North Coast and Cascades Inventory and Monitoring Network (NCCN) vegetation maps of Mount Rainier (MORA), Olympic (OLYM), and North Cascades (NOCA) National Parks. Synthesis and analysis of these 6000+ plots by Washington Natural Heritage Program (WNHP) and Institute for Natural Resources (INR) staff built on the foundation provided by the earlier classification work of Crawford et al. (2009). These analyses provided support for most of the provisional plant associations in Crawford et al. (2009), while also revealing previously undescribed vegetation types that were not represented in the United States National Vegetation Classification (USNVC). Both provisional and undescribed types have since been submitted to the USNVC by WNHP staff through a peer-reviewed process. NCCN plots were combined with statewide forest and wetland plot data from the US Forest Service (USFS) and other sources to create a comprehensive data set for Washington. Analyses incorporated Cluster Analysis, Nonmetric Multidimensional Scaling (NMS), Multi-Response Permutation Procedure (MRPP), and Indicator Species Analysis (ISA) to identify, vet, and describe USNVC group, alliance, and association distinctions. The resulting revised classification contains 321 plant associations in 99 alliances. A total of 54 upland associations were moved through the peer review process and are now part of the USNVC. Of those, 45 were provisional or preliminary types from Crawford et al. (2009), with 9 additional new associations that were originally identified by INR. WNHP also revised the concepts of 34 associations, wrote descriptions for 2 existing associations, eliminated/archived 2 associations, and created 4 new upland alliances. Finally, WNHP created 27 new wetland alliances and revised or clarified an additional 21 as part of this project (not all of those occur in the parks). This report and accompanying vegetation descriptions, keys and synoptic and environmental tables (all products available from the NPS Data Store project reference: https://irma.nps.gov/DataStore/Reference/Profile/2279907) present the fruit of these combined efforts: a comprehensive, up-to-date vegetation classification for the three major national parks of Washington State.
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Nikula, Blair y Robert Cook. Status and distribution of Odonates at Cape Cod National Seashore. National Park Service, 2024. http://dx.doi.org/10.36967/2303254.
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Odonates are significant components of most wetland habitats and important indicators of their health. At Cape Cod National Seashore (CACO), we compiled odonate records dating back to the 1980s and, based partly on that data, identified 41 wetland sites for sampling, representing six freshwater habitats (kettle pond, inter-dune pond, dune slack, riparian marsh, vernal pool, and bog). We surveyed these sites for adult odonates during the 2016?2018 field seasons. Ten sites were surveyed all three years (total 19-20 surveys/site); all ten had at least some historical data. The remaining 31 sites were surveyed for one field season, a total of 6-8 times each. We conducted 391 surveys, recording 53,435 individuals and 74 species (45 dragonflies and 29 damselflies); not all individuals were identified to species. Abundance and species richness varied significantly between habitats. For all individuals recorded, abundance was greatest at vernal pools and kettle ponds. Riparian sites had the lowest abundance. Species richness was highest at kettle ponds, including several species of conservation concern, two listed as Threatened by the state of Massachusetts. Riparian marshes and dune slacks had relatively low richness. Among the 10 sites surveyed three years, we found significant annual variation in abundance and species richness. There was significant and generally greater between-site variation in abundance within a year than between years at sites. Community analysis found pond depth, habitat type, and presence of predaceous fish were significant factors explaining between-site variation in community composition. Habitats also differed significantly in community composition. Multidimensional scaling showed sites tend to cluster together by habitat type. Vernal ponds have the highest average community similarity to all other habitats (53.5%), with dune slack (52.9%), bog (52.0%) and inter-dune (51.5%) close behind. In contrast, riparian sites (46.3%) and kettle ponds (39.5%) are least similar to other habitats. Overall, 86 species of odonates have been recorded at CACO, a rich and diverse assemblage reflecting the variety and quality of freshwater habitats present. Although these habitats are relatively well-protected, stressors include climate change, nutrient inflow from adjacent development, road runoff, and trampling of emergent vegetation. A plan for monitoring is beyond the scope of this project. Ideally, it would be best to use the insight into odonate variation obtained from these surveys to develop a monitoring program designed to meet standards of statistical confidence and power currently employed in NPS monitoring programs.
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Sett, Dominic, Florian Waldschmidt, Alvaro Rojas-Ferreira, Saut Sagala, Teresa Arce Mojica, Preeti Koirala, Patrick Sanady et al. Climate and disaster risk analytics tool for adaptive social protection. United Nations University - Institute for Environment and Human Security, marzo de 2022. http://dx.doi.org/10.53324/wnsg2302.
Texto completoResumen
Adaptive Social Protection (ASP) as discussed in this report is an approach to enhance the well-being of communities at risk. As an integrated approach, ASP builds on the interface of Disaster Risk Management (DRM), Climate Change Adaptation (CCA) and Social Protection (SP) to address interconnected risks by building resilience, thereby overcoming the shortcomings of traditionally sectoral approaches. The design of meaningful ASP measures needs to be informed by specific information on risk, risk drivers and impacts on communities at risk. In contrast, a limited understanding of risk and its drivers can potentially lead to maladaptation practices. Therefore, multidimensional risk assessments are vital for the successful implementation of ASP. Although many sectoral tools to assess risks exist, available integrated risk assessment methods across sectors are still inadequate in the context of ASP, presenting an important research and implementation gap. ASP is now gaining international momentum, making the timely development of a comprehensive risk analytics tool even more important, including in Indonesia, where nationwide implementation of ASP is currently under way. OBJECTIVE: To address this gap, this study explores the feasibility of a climate and disaster risk analytics tool for ASP (CADRAT-ASP), combining sectoral risk assessment in the context of ASP with a more comprehensive risk analytics approach. Risk analytics improve the understanding of risks by locating and quantifying the potential impacts of disasters. For example, the Economics of Climate Adaptation (ECA) framework quantifies probable current and expected future impacts of extreme events and determines the monetary cost and benefits of specific risk management and adaptation measures. Using the ECA framework, this report examines the viability and practicality of applying a quantitative risk analytics approach for non-financial and non-tangible assets that were identified as central to ASP. This quantitative approach helps to identify cost-effective interventions to support risk-informed decision making for ASP. Therefore, we used Nusa Tenggara, Indonesia, as a case study, to identify potential entry points and examples for the further development and application of such an approach. METHODS & RESULTS: The report presents an analysis of central risks and related impacts on communities in the context of ASP. In addition, central social protection dimensions (SPD) necessary for the successful implementation of ASP and respective data needs from a theoretical perspective are identified. The application of the quantitative ECA framework is tested for tropical storms in the context of ASP, providing an operational perspective on technical feasibility. Finally, recommendations on further research for the potential application of a suitable ASP risk analytics tool in Indonesia are proposed. Results show that the ECA framework and its quantitative modelling platform CLIMADA successfully quantified the impact of tropical storms on four SPDs. These SPDs (income, access to health, access to education and mobility) were selected based on the results from the Hazard, Exposure and Vulnerability Assessment (HEVA) conducted to support the development of an ASP roadmap for the Republic of Indonesia (UNU-EHS 2022, forthcoming). The SPDs were modelled using remote sensing, gridded data and available global indices. The results illustrate the value of the outcome to inform decision making and a better allocation of resources to deliver ASP to the case study area. RECOMMENDATIONS: This report highlights strong potential for the application of the ECA framework in the ASP context. The impact of extreme weather events on four social protection dimensions, ranging from access to health care and income to education and mobility, were successfully quantified. In addition, further developments of CADRAT-ASP can be envisaged to improve modelling results and uptake of this tool in ASP implementation. Recommendations are provided for four central themes: mainstreaming the CADRAT approach into ASP, data and information needs for the application of CADRAT-ASP, methodological advancements of the ECA framework to support ASP and use of CADRAT-ASP for improved resilience-building. Specific recommendations are given, including the integration of additional hazards, such as flood, drought or heatwaves, for a more comprehensive outlook on potential risks. This would provide a broader overview and allow for multi-hazard risk planning. In addition, high-resolution local data and stakeholder involvement can increase both ownership and the relevance of SPDs. Further recommendations include the development of a database and the inclusion of climate and socioeconomic scenarios in analyses.