Готові списки джерел за темами / Computational Characterization

Добірка наукової літератури з теми "Computational Characterization"

Автор: Grafiati
Опубліковано: 28 вересня 2022
Оновлено: 28 січня 2023

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Зміст

  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Статті в журналах з теми "Computational Characterization":

1

Bienkowska, Jadwiga. "Computational characterization of proteins." Expert Review of Proteomics 2, no. 1 (January 2005): 129–38. http://dx.doi.org/10.1586/14789450.2.1.129.

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2

Zeng, Xiongzhi, Wei Hu, Xiao Zheng, Jin Zhao, Zhenyu Li, and Jinlong Yang. "Computational characterization of nanosystems." Chinese Journal of Chemical Physics 35, no. 1 (February 2022): 1–15. http://dx.doi.org/10.1063/1674-0068/cjcp2111233.

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Nanosystems play an important role in many applications. Due to their complexity, it is challenging to accurately characterize their structure and properties. An important means to reach such a goal is computational simulation, which is grounded on ab initio electronic structure calculations. Low scaling and accurate electronic-structure algorithms have been developed in recent years. Especially, the efficiency of hybrid density functional calculations for periodic systems has been significantly improved. With electronic structure information, simulation methods can be developed to directly obtain experimentally comparable data. For example, scanning tunneling microscopy images can be effectively simulated with advanced algorithms. When the system we are interested in is strongly coupled to environment, such as the Kondo effect, solving the hierarchical equations of motion turns out to be an effective way of computational characterization. Furthermore, the first principles simulation on the excited state dynamics rapidly emerges in recent years, and nonadiabatic molecular dynamics method plays an important role. For nanosystem involved chemical processes, such as graphene growth, multiscale simulation methods should be developed to characterize their atomic details. In this review, we review some recent progresses in methodology development for computational characterization of nanosystems. Advanced algorithms and software are essential for us to better understand of the nanoworld.
3

Larsson, Mats. "Computational characterization of drawbeads." Journal of Materials Processing Technology 209, no. 1 (January 2009): 376–86. http://dx.doi.org/10.1016/j.jmatprotec.2008.02.009.

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4

Khan, Ishita K., and Daisuke Kihara. "Computational characterization of moonlighting proteins." Biochemical Society Transactions 42, no. 6 (November 17, 2014): 1780–85. http://dx.doi.org/10.1042/bst20140214.

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Moonlighting proteins perform multiple independent cellular functions within one polypeptide chain. Moonlighting proteins switch functions depending on various factors including the cell-type in which they are expressed, cellular location, oligomerization status and the binding of different ligands at different sites. Although an increasing number of moonlighting proteins have been experimentally identified in recent years, the quantity of known moonlighting proteins is insufficient to elucidate their overall landscape. Moreover, most moonlighting proteins have been identified as a serendipitous discovery. Hence, characterization of moonlighting proteins using bioinformatics approaches can have a significant impact on the overall understanding of protein function. In this work, we provide a short review of existing computational approaches for illuminating the functional diversity of moonlighting proteins.
5

Politzer, Peter, Jane S. Murray, Jorge M. Seminario, Pat Lane, M. Edward Grice, and Monica C. Concha. "Computational characterization of energetic materials." Journal of Molecular Structure: THEOCHEM 573, no. 1-3 (October 2001): 1–10. http://dx.doi.org/10.1016/s0166-1280(01)00533-4.

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6

Gao, Haixiang, Chengfeng Ye, Crystal M. Piekarski, and Jean'ne M. Shreeve. "Computational Characterization of Energetic Salts." Journal of Physical Chemistry C 111, no. 28 (July 2007): 10718–31. http://dx.doi.org/10.1021/jp070702b.

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7

Abukhdeir, Nasser Mohieddin. "Computational characterization of ordered nanostructured surfaces." Materials Research Express 3, no. 8 (August 4, 2016): 082001. http://dx.doi.org/10.1088/2053-1591/3/8/082001.

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8

Wodo, Olga, Srikanta Tirthapura, Sumit Chaudhary, and Baskar Ganapathysubramanian. "Computational characterization of bulk heterojunction nanomorphology." Journal of Applied Physics 112, no. 6 (September 15, 2012): 064316. http://dx.doi.org/10.1063/1.4752864.

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9

Slanina, Zdenĕk, and Shigeru Nagase. "A computational characterization of N2@C60." Molecular Physics 104, no. 20-21 (October 20, 2006): 3167–71. http://dx.doi.org/10.1080/00268970601041131.

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10

Glossman-Mitnik, Daniel. "Computational molecular characterization of Coumarin-102." Journal of Molecular Structure: THEOCHEM 911, no. 1-3 (October 2009): 105–8. http://dx.doi.org/10.1016/j.theochem.2009.07.006.

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Статті в журналах Дисертації Книги

Дисертації з теми "Computational Characterization":

1

Lim, Chan-Ping Edwin. "Computational electromagnetic modeling for wireless channel characterization." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1158607443.

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2

Zerbe, Brandon S. "Computational characterization of protein hot spots." Thesis, Boston University, 2012. https://hdl.handle.net/2144/31628.

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Thesis (Ph.D.)--Boston University
PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.
Protein hot spots provide a large portion of the binding free energy during interact ions, and detecting and characterizing these hot spot regions provides insight that can be used in the development of novel drugs for t he purpose of regulating pathological pathways. In t his dissertation, I first compare t he FTMap algorithm, which detects hot spots by identifying locations where different simulated solvent-sized molecules are consistently found to have favorable interactions, to experimental methods that detect hot spots by alternative means. Specifically, I show that FTMap detects the hot spots detected by alanine scanning, and I discover two roles for residues near hot spots in protein-protein interaction (PPI) complexes. Furthermore, additional insights into the binding energetics of PPis are uniquely provided by FTMap, and these insights are important for drug-design. FTMap is then shown to detect the hot spots identified by successful fragment-screening experiments, and the additional sites detected by FTMap are shown to provide insight into the optimal regions for ligand extension for the molecules identified by t he fragment-screening experiment. Since binding sites are composed of multiple hot spots, we have recently used FTMap for binding site detection. I examine the highly accurate binding site detection algorithm, show that the success of this algorithm is a consequence of only a portion of the scoring protocol, and develop a faster protocol for binding site detection based on this insight. I also quantitate the improvement in precision obtained by using multiple probes and argue t hat the principle biophysical considerations in hot spot detection are hydrophobicity and complexity. Finally, I develop a functional-group clustering algorithm, which is informative for evaluation of the binding locations of pre-determined chemical moieties. I then provide evidence that other approaches employing FTMap results may lead to insight into selectivity. I conclude with a discussion on the nature of hot spots, and I suggest that evolutionary studies of protein divergence should provide insight into the emergence of chemical-selectivity thus providing biophysical insight into the factors that drive selectivity within hot spots.
2031-01-01
3

Igram, Dale J. "Computational Modeling and Characterization of Amorphous Materials." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1564347980986716.

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4

Wu, Shirley. "Characterization of protein function using automated computational methods /." May be available electronically:, 2009. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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5

Jones, Charlotte Louise. "Synthesis, characterization and computational studies of boron-oxygen compounds." Thesis, Bangor University, 2016. https://research.bangor.ac.uk/portal/en/theses/synthesis-characterization-and-computational-studies-of-boronoxygen-compounds(8c2d8db8-e5e0-4582-8e4d-2abd5b72fa97).html.

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A total of twenty-one new non-metal cation polyborate salts are reported; nineteen of these contain the pentaborate anion, [B5O6(OH)4]-, one contains the triborate monoanion, [B3O3(OH)4]-, and one contains the heptaborate dianion, [B7O9(OH)5]2-. The crystal structures of ten salts containing these polyborate anions are reported: pyrrolidinium pentaborate (1), N-methylpyrrolidinium pentaborate.½ acetone (2.½CH3COCH3), N,N-dimethylpyrrolidinium pentaborate (3), 2-hydroxymethylpyrrolidinium pentaborate hemihydrate (4.½H2O), (2-hydroxyethyl)-N-methylpyrrolidinium pentaborate.0.3 hydrate (5.0.3H2O), 4-aminobenzylammonium pentaborate hemihydrate (9.½H2O), N,N-dimethyl-1-adamantylammonium pentaborate sesquihydrate (14.1.5H2O), N,N,N-trimethyl-1-adamantylammonium pentaborate trihydrate (15.3H2O), N,N,N-trimethyl-2-adamantylammonium pentaborate trihydrate (17.3H2O) and 4,4’-bipiperidinium heptaborate dihydrate (20.2H2O). The crystal structure of an additional amine-boric acid co-crystallized species is also reported. All of the synthesized compounds reported in Chapter 3 were characterized using spectroscopic (IR, multi-element NMR) and analytical (melting point, elemental analysis, thermal analysis, powder X-ray diffraction) techniques. The solid-state structures of the polyborate salts form giant H-bonded anionic lattices, with the ‘brickwall’ structure found to be sufficiently flexible to accommodate larger cations (within limits). When these limits are approached, they may be stretched further to accommodate the size of the cations by using ‘spacer’ molecules which increase the size of the lattice. It is only once the lattice cannot be stretched any further and/or when there are sufficient cation-anion interactions to dominate the energetics that polyborates other than pentaborates may be observed. The strength of the H-bonds found within polyborate salts were calculated using DFT theory; the results show that the α-reciprocal R22(8) H-bond interaction is the most energetically favoured (-21 kJ mol-1), which is also the most commonly observed interaction within the solid-state structures of pentaborate salts. The synthesis and characterization of Lewis base adducts of triorganoboroxines is also described within this thesis. Eight new adducts are reported, including the crystal structures of the triorganoboroxine.amine adducts of tri(4-chlorophenyl)boroxine.morpholine (30) and tri(4-chlorophenyl)boroxine.benzylamine (33). The Lewis acidity of the triorganoboroxines were also investigated and it was discovered that strongly electron-withdrawing pentafluorophenyl substituents greatly increase the Lewis acidity of the B atoms, resulting in a higher acceptor number.
6

Kalaitzopoulos, Dimitrios. "Molecular characterization and computational analysis of constitutional chromosome rearrangements." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614054.

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7

Briscoe, Adam. "Characterization and computational modelling of acrylic bone cement polymerisation." Thesis, University of Southampton, 2006. https://eprints.soton.ac.uk/64795/.

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Total joint replacement is one of the most successful surgical procedures and is a proven treatment for arthritis. Despite low failure rates, the wide application of the treatment means that large numbers of prostheses fail and must be revised. Improved pre-clinical testing methods for these orthopaedic devices may assist in developing new prostheses with improved clinical results. Computational modelling of biological systems is becoming increasingly accurate and is a much quicker and cheaper alternative to physical testing, but continued development is necessary to ensure computational models produce accurate and reliable predictions of implant behaviour. Acrylic bone cements have been used as a method of fixation for over 50 years but despite improvements in cement handling techniques and numerous attempts to improve the mechanical properties of the cement in other ways, the cement is often highlighted as the weak link in the joint replacement system. Aseptic loosening is cited as the cause for the majority of revision operations and cement degradation has been shown to be a contributor to the loosening process. In-vivo, cement is subject to cyclic loads and these are the primary cause of cement damage. Residual stresses generated during the polymerisation of the cement are now thought to play a significant role in cement failure. This thesis examines the development of residual stresses as a result of thermal and chemical changes during polymerisation of the cement. Experimental techniques for characterising the evolution of materials properties during the polymerisation reaction are discussed. Differential scanning calorimetry was used to measure the reaction variables such as the activation energy of polymerisation. The development of an ultrasonic rheometry technique for monitoring the mechanical property evolution within a bone cement specimen is discussed. Computational models were generated to predict the reaction behaviour of the cement in terms of the heat produced and the evolution of the physical properties of the curing mass. Some advantages and disadvantages of candidate mathematical models have been evaluated and are discussed, along with applications in several implant fixation scenarios. The model compared well with experimental data and was used to predict thermal necrosis in the bone surrounding both a hip resurfacing implant and a knee replacement. Using the output reaction path produced by the thermal model a mechanical model was also produced simulating the shrinkage and mechanical property evolution exhibited by the polymerising cement. Two material models were compared with and without the effects of plasticity. Residual stress magnitudes were assessed in comparison with published values and showed better agreement when plasticity was included. Peak stresses were observed to occur during polymerisation. The location of the peak stresses were compared with experimental data on pre-load crack locations in the literature and showed good agreement.
8

Gaspar, Paulo Miguel da Silva. "Computational methods for gene characterization and genomic knowledge extraction." Doctoral thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13949.

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Doutoramento conjunto MAPi em Ciências da Computação
Motivation: Medicine and health sciences are changing from the classical symptom-based to a more personalized and genetics-based paradigm, with an invaluable impact in health-care. While advancements in genetics were already contributing significantly to the knowledge of the human organism, the breakthrough achieved by several recent initiatives provided a comprehensive characterization of the human genetic differences, paving the way for a new era of medical diagnosis and personalized medicine. Data generated from these and posterior experiments are now becoming available, but its volume is now well over the humanly feasible to explore. It is then the responsibility of computer scientists to create the means for extracting the information and knowledge contained in that data. Within the available data, genetic structures contain significant amounts of encoded information that has been uncovered in the past decades. Finding, reading and interpreting that information are necessary steps for building computational models of genetic entities, organisms and diseases; a goal that in due course leads to human benefits. Aims: Numerous patterns can be found within the human variome and exome. Exploring these patterns enables the computational analysis and manipulation of digital genomic data, but requires specialized algorithmic approaches. In this work we sought to create and explore efficient methodologies to computationally calculate and combine known biological patterns for various purposes, such as the in silico optimization of genetic structures, analysis of human genes, and prediction of pathogenicity from human genetic variants. Results: We devised several computational strategies to evaluate genes, explore genomes, manipulate sequences, and analyze patients’ variomes. By resorting to combinatorial and optimization techniques we were able to create and combine sequence redesign algorithms to control genetic structures; by combining the access to several web-services and external resources we created tools to explore and analyze available genetic data and patient data; and by using machine learning we developed a workflow for analyzing human mutations and predicting their pathogenicity.
Motivação: A medicina e as ciências da saúde estão atualmente num processo de alteração que muda o paradigma clássico baseado em sintomas para um personalizado e baseado na genética. O valor do impacto desta mudança nos cuidados da saúde é inestimável. Não obstante as contribuições dos avanços na genética para o conhecimento do organismo humano até agora, as descobertas realizadas recentemente por algumas iniciativas forneceram uma caracterização detalhada das diferenças genéticas humanas, abrindo o caminho a uma nova era de diagnóstico médico e medicina personalizada. Os dados gerados por estas e outras iniciativas estão disponíveis mas o seu volume está muito para lá do humanamente explorável, e é portanto da responsabilidade dos cientistas informáticos criar os meios para extrair a informação e conhecimento contidos nesses dados. Dentro dos dados disponíveis estão estruturas genéticas que contêm uma quantidade significativa de informação codificada que tem vindo a ser descoberta nas últimas décadas. Encontrar, ler e interpretar essa informação são passos necessários para construir modelos computacionais de entidades genéticas, organismos e doenças; uma meta que, em devido tempo, leva a benefícios humanos. Objetivos: É possível encontrar vários padrões no varioma e exoma humano. Explorar estes padrões permite a análise e manipulação computacional de dados genéticos digitais, mas requer algoritmos especializados. Neste trabalho procurámos criar e explorar metodologias eficientes para o cálculo e combinação de padrões biológicos conhecidos, com a intenção de realizar otimizações in silico de estruturas genéticas, análises de genes humanos, e previsão da patogenicidade a partir de diferenças genéticas humanas. Resultados: Concebemos várias estratégias computacionais para avaliar genes, explorar genomas, manipular sequências, e analisar o varioma de pacientes. Recorrendo a técnicas combinatórias e de otimização criámos e conjugámos algoritmos de redesenho de sequências para controlar estruturas genéticas; através da combinação do acesso a vários web-services e recursos externos criámos ferramentas para explorar e analisar dados genéticos, incluindo dados de pacientes; e através da aprendizagem automática desenvolvemos um procedimento para analisar mutações humanas e prever a sua patogenicidade.
9

Dashti, Zahra Jalali Sefid. "Computational characterization of IRE-regulated genes in Glossina morsitans." Thesis, University of Western Cape, 2013. http://hdl.handle.net/11394/3325.

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Philosophiae Doctor - PhD
Blood feeding is a habit exhibited by many insects. Considering the devastating impact of these insects on human health, it is important to focus research on understanding the biology behind blood-feeding, disease transmission and host-pathogen interactions. Such knowledge would pave the way for developing efficient preventative measures. Iron an important element for species survival, is at the center of events controlling tsetse’s fitness and reproductive success. Hence, targeting genes involved in iron trafficking and sequestration would present possible means of preventing disease transmission. Considering the dynamic and multi-factorial nature of iron metabolism, a well-coordinated regulatory system is expected to be at work. Despite extensive literature on the mechanism of iron regulation and key factors responsible in maintaining its homeostasis in human, less attention has been given to understand such system in insects, especially the blood-feeding insects. The availability of the genome sequences for several insect disease vectors allows for a more detailed analysis on the identification and characterization of events controlling and preventing iron-induced toxicity following a blood-meal. The International Glossina Genome Initiative (IGGI) has coordinated the sequencing and annotation of the Glossina morsitans genome that has led to the identification of 12220 genes. This knowledge-base along with current understanding of the IRE system in regulating iron metabolism, allowed for investigating the UTRs of Glossina genes for the presence of these elements. Using a combination of motif enrichment and IRE-stem loop structure prediction, an IRE-mediated regulation was inferred for 150 genes, among which, 72 were identified with 5’-IREs and 78 with 3’-IREs. Of the identified IRE-regulated genes, the ferritin heavy chain and MRCK-alpha are the only known genes to have IREs, while the rest are novel genes for which putative roles in regulating iron levels in tsetse fly have been assigned in this study. Moreover, the functional inference of the identified genes further points to the enrichment of transcription and translation. Furthermore, several hypothetical proteins with no defined functions were identified to be IRE-regulated. These include TMP007137, TMP009128, TMP002546, TMP002921, TMP003628, TMP004581, TMP008259, TMP012389, TMP005219, TMP005827, TMP007908, TMP009332, TMP01- 3384, TMP009102, TMP010544, TMP010707, TMP004292, TMP006517, TMP014030, TMP009821 and TMP003060 for which an iron-regulatory mechanism of action may be inferred. We further report 26 IRE-regulated secreted proteins in Glossina, that present good candidates for further investigation pertaining to the development of novel vector control strategies. Using the predicted data on the identified IRE-regulated genes and their functional classification, we derived at 29 genes with putative roles in iron trafficking, where several unknown and hypothetical proteins are included. Thus a novel role is inferred for these genes in cellular binding and transport in the context of iron metabolism. It is therefore possible that these genes may have evolved in Glossina, such that they compensate for the absence of an IRE- regulated mechanism for transferrin. Additionally, we propose 14 IRE-regulated genes involved in immune and stress response, which may indeed play crucial roles at the host pathogen interface through their possible mechanisms of iron sequestration. Using the subcellular localization analysis, we further categorized the putative IRE regulated genes into several subcellular localizations, where the majority of genes were found within the nucleus and the cytosol. The detection of the conserved motifs in a set of genes, is an interesting yet sophisticated area of research, that allows for identifying either co-regulated or orthologous genes, while further providing support for the putative function of a set of genes that would otherwise remain uncharacterized. This is based on the notion that co-regulated genes are often coexpressed to carry out a specific function. As such, 14 regulatory elements were identified in the 5’- and 3’-UTRs of IRE-regulated genes, involved in embryonic development and reproduction, inflammation and immune response, signaling pathways and neurogenesis as well as DNA repair. This study further proposes several IRE-regulated genes as targets for micro-RNA regulation through identifying micro-RNA binding sites in their 3’UTRs. Using a motif clustering approach we clustered IRE-regulated genes based on the number of motifs they share. Significantly co-regulated genes sharing two or more motifs were determined as critical targets for future investigation. The expression map of IRE-regulated genes was analyzed to better understand the events taking place from 3 hours to 15 days following a blood meal. Re-analysis of Anopheles microarray chip showed the significant expression of three cell envelope and transport genes as early response and six as late response to a blood meal, which could indeed be assigned a putative role in iron trafficking. Genes identified in this study with implications in iron metabolism, whose timely expression allows for maintaining iron homeostasis, represent good targets for future work. Considering the important role of evolution in species adaptation to habits such as Hematophagy, it is of importance to identify evolutionary signatures associated with these changes. To distinguish between evolutionary forces that are specific to iron-metabolism in blood-feeding insects and those that are found in other insects, the IRE-regulated genes were clustered into orthologous groups using several blood feeding and non-blood feeding insect species. Assessment of different evolutionary scenarios using the Maximum Likelihood (ML) approach, points to variations in the evolution of IRE-regulated genes between the two insect groups, whereby several genes indicate an increased mutation rate in the BF-insect group relative to their non-blood feeding insect counterparts. These include TMP003602 (phosphoinositide3-kinase), TMP009157 (ubiquitin-conjugating enzyme9), TMP010317 (general transcription factor IIH subunit1), TMP011104 (serine-pyruvate mitochondrial), TMP013137 (pentatricopeptide Transcription and translation), TMP013886 (tRNA(uridine-2-o-)-methyl-transferase-trm7) and TMP014187 (mediator 100kD). Additionally, we have indicated the presence of positively selected sites within seven blood-feeding IRE-regulated genes namely TMP002520 (nucleoporin), TMP008942 (eukaryotic translation initiation factor 3), TMP009871(bruno-3 transcript) , TMP010317 (general transcription factor IIH subunit1), TMP010673 (ferritin heavy-chain protein), TMP011104 (serine-pyruvate mitochondrial) and TMP011448 (brain chitinase and chia). Thus the results of this study provides an in depth understanding of iron metabolism in Glossina morsitans and confers important targets for future validations based on which innovative control strategies may be designed.
10

Iyer, Kaushik A. "Quantitative characterization of thermophysical properties in computational heat transfer." Full text open access at:, 1993. http://content.ohsu.edu/u?/etd,273.

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Книги з теми "Computational Characterization":

1

Marques, Severino P. C. Computational Viscoelasticity. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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2

Jha, Prafulla K., and Arun Pratap. Synthesis, Characterization and Properties of Nanostructures: Computational and Experimental Approach. Switzerland: ttp, TRANS TECH PUBLICATIONS LTD, 2009.

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3

Sansour, Carlo. Generalized Continua and Dislocation Theory: Theoretical Concepts, Computational Methods and Experimental Verification. Vienna: Springer Vienna, 2012.

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4

Sabbagh, Harold A. Computational Electromagnetics and Model-Based Inversion: A Modern Paradigm for Eddy-Current Nondestructive Evaluation. New York, NY: Springer New York, 2013.

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5

Öchsner, Andreas. Materials with Complex Behaviour II: Properties, Non-Classical Materials and New Technologies. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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6

Hu, Wenbing. Polymer Physics: A Molecular Approach. Vienna: Springer Vienna, 2013.

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7

Vogt, Thomas. Modeling Nanoscale Imaging in Electron Microscopy. Boston, MA: Springer US, 2012.

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8

Tamin, Mohd Nasir. Damage and Fracture of Composite Materials and Structures. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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9

Scarpas, Tom. 7th RILEM International Conference on Cracking in Pavements: Mechanisms, Modeling, Testing, Detection and Prevention Case Histories. Dordrecht: Springer Netherlands, 2012.

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10

Shuai, Zhigang. Theory of Charge Transport in Carbon Electronic Materials. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Частини книг з теми "Computational Characterization":

1

Price, Sarah L., and Louise S. Price. "Computational Polymorph Prediction." In Solid State Characterization of Pharmaceuticals, 427–50. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470656792.ch12.

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2

Militký, Jiří, and Sayed Ibrahim. "Complex Characterization of Yarn Unevenness." In Computational Textile, 57–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-70658-8_4.

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3

Blanco, Enrique. "Computational Characterization of Regulatory Regions." In Algorithms in Computational Molecular Biology, 397–424. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470892107.ch19.

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4

Clark, Adrian F., and Patrick Courtney. "Databases for Performance Characterization." In Computational Imaging and Vision, 29–40. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-015-9538-4_3.

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5

Halwachs, Bettina, Gregor Gorkiewicz, and Gerhard G. Thallinger. "High-Throughput Characterization and Comparison of Microbial Communities." In Computational Medicine, 37–57. Vienna: Springer Vienna, 2012. http://dx.doi.org/10.1007/978-3-7091-0947-2_3.

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6

Uetz, Peter, Björn Titz, and Gerard Cagney. "Experimental Methods for Protein Interaction Identification and Characterization." In Computational Biology, 1–32. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-125-1_1.

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7

Rohr, Karl, H. Siegfried Stiehl, Sönke Frantz, and Thomas Hartkens. "Performance Characterization of Landmark Operators." In Computational Imaging and Vision, 285–97. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-015-9538-4_23.

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8

Rahman, Ashfaqur, and Manzur Murshed. "Temporal Texture Characterization: A Review." In Studies in Computational Intelligence, 291–316. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76827-2_12.

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9

Rohr, Karl. "Performance Characterization of Landmark Operators." In Computational Imaging and Vision, 109–77. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9787-6_3.

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10

Quiroz, Marcela, Laura Cruz-Reyes, Jose Torres-Jimenez, Claudia Gómez Santillán, Héctor J. Fraire Huacuja, and Patricia Melin. "Characterization of the Optimization Process." In Studies in Computational Intelligence, 493–507. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05170-3_34.

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Тези доповідей конференцій з теми "Computational Characterization":

1

Rad, Leili Baghaei, Hanying Feng, Jun Ye, R. F. W. Pease, David G. Seiler, Alain C. Diebold, Robert McDonald, et al. "Computational Scanning Electron Microscopy." In CHARACTERIZATION AND METROLOGY FOR NANOELECTRONICS: 2007 International Conference on Frontiers of Characterization and Metrology. AIP, 2007. http://dx.doi.org/10.1063/1.2799427.

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2

Kambadur, Melanie, Sunpyo Hong, Juan Cabral, Harish Patil, Chi-Keung Luk, Sohaib Sajid, and Martha A. Kim. "Fast Computational GPU Design with GT-Pin." In 2015 IEEE International Symposium on Workload Characterization (IISWC). IEEE, 2015. http://dx.doi.org/10.1109/iiswc.2015.14.

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3

Sokolnikov, Andre. "Computational methods for THz material characterization." In SPIE Defense, Security, and Sensing, edited by Mehdi Anwar, Nibir K. Dhar, and Thomas W. Crowe. SPIE, 2011. http://dx.doi.org/10.1117/12.885647.

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4

Kim, Sang-Kon. "Plasmonic computational lithography for below 10-nm patterning." In Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XVIII, edited by Takuo Tanaka and Din Ping Tsai. SPIE, 2020. http://dx.doi.org/10.1117/12.2570752.

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5

Jongerius, Rik, Phillip Stanley-Marbell, and Henk Corporaal. "Quantifying the common computational problems in contemporary applications." In 2011 IEEE International Symposium on Workload Characterization (IISWC). IEEE, 2011. http://dx.doi.org/10.1109/iiswc.2011.6114199.

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6

Pickering, C., J. Russell, D. A. O. Hope, R. T. Carline, A. D. Marrs, D. J. Robbins, and A. Dann. "Instrumental and computational advances for real-time process control using spectroscopic ellipsometry." In CHARACTERIZATION AND METROLOGY FOR ULSI TECHNOLOGY. ASCE, 1998. http://dx.doi.org/10.1063/1.56817.

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7

Gowri, R., Sribidhya Mohanty, Anurag Vidyarthi, and Sumit Tripati. "Characterization of various coplanar waveguide discontinuities." In Computational Electromagnetics (ICMTCE). IEEE, 2011. http://dx.doi.org/10.1109/icmtce.2011.5915184.

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8

Kumar, Ajeet, Ayush Paliwal, Jorawar S. Dham, and Lakshay Gautam. "Optical modeling and computational analysis of tapered dome shaped nanoantenna." In Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XVI, edited by Takuo Tanaka and Din Ping Tsai. SPIE, 2018. http://dx.doi.org/10.1117/12.2320890.

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9

Valdes, Julio J., Ljubomir Nikolic, Simone Disabato, and Manual Roveri. "A Computational Intelligence Characterization of Solar Magnetograms." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206596.

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10

Cabrera, Jan-Michael, Kristopher J. Overholt, Mustafa Abbasi, Howard N. Granzow, Derek J. Gordon, and Ofodike A. Ezekoye. "Glovebox Fire Suppression Experimental and Computational Characterization." In ASME 2013 Heat Transfer Summer Conference collocated with the ASME 2013 7th International Conference on Energy Sustainability and the ASME 2013 11th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/ht2013-17549.

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Анотація:
Gloveboxes are generally used to protect workers from the potential hazards that arise in the materials they are working with. In the middle of the 20th century gloveboxes were mainly used to protect workers against radioactive materials. Factory Mutual investigated accidents involved in gloveboxes at nuclear facilities from 1956 to 1965 that resulted in damages and contamination clean up efforts reported at $1,232,000 ($8.5 million in 2012 dollars) [1]. Fire suppression systems are therefore a key aspect to glovebox safety. Use of water based systems generally result in undesired transport of hazardous materials [2]. Halon suppression systems were used but due to its large potential to deplete ozone was phased out in the latter part of the 20th century. Activation tests of a commercial automatic fire suppression system (Fire Foe™) containing heptaflouropropane (FE-36) fire suppressant were conducted within a glovebox at the University of Texas Fire Research Group’s burn structure. Ten tests were conducted at four different fire sizes: three 13 kW, one 20 kW, three 25 kW, and three 50 kW. Activation times and gas temperatures were recorded. Gas temperatures from experiments were compared against NIST’s Fire Dynamics Simulator (FDS) gas temperatures with good agreement. More stratification was observed in experimental temperatures compared to FDS temperatures. The time and spatially averaged net heat flux on a virtual Fire Foe™ tube from the FDS simulations were passed to a thermo-physical semi-empirical submodel to predict activation. The submodel did not capture the exponential nature observed in the data of activation times versus fire heat release rate. Work is continuing on improving the submodel to capture the observed events.

Звіти організацій з теми "Computational Characterization":

1

Lightstone, F., and B. Bennion. Computational Biology for Drug Discovery and Characterization. Office of Scientific and Technical Information (OSTI), February 2009. http://dx.doi.org/10.2172/948962.

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2

Gord, James R. Experimental and Computational Characterization of Combustion Phenomena. Fort Belvoir, VA: Defense Technical Information Center, May 2006. http://dx.doi.org/10.21236/ada474982.

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3

Demirel, Melik Cumhar. Linking Experimental Characterization and Computational Modeling in Microstructural Evolution. Office of Scientific and Technical Information (OSTI), June 2002. http://dx.doi.org/10.2172/821471.

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4

Demirel, Melik Cumhur. Linking Experimental Characterization and Computational Modeling in Microstructural Evolution. Office of Scientific and Technical Information (OSTI), June 2002. http://dx.doi.org/10.2172/821477.

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5

Demirel, Melik Cumhur. Linking Experimental Characterization and Computational Modeling in Microstructural Evolution. Office of Scientific and Technical Information (OSTI), June 2002. http://dx.doi.org/10.2172/821484.

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6

Albrecht, Karl O., Vassiliki Alexandra Glezakou, Roger J. Rousseau, Mark H. Engelhard, Tamas Varga, Robert J. Colby, John E. Jaffe, et al. Rh-Based Mixed Alcohol Synthesis Catalysts: Characterization and Computational Report. Office of Scientific and Technical Information (OSTI), August 2013. http://dx.doi.org/10.2172/1095435.

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7

Basak, Subnash C. Quantitative Characterization of Molecular Similarity Spaces: Tools for Computational Toxicology. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada374363.

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8

Bennion, B., K. Kulp, M. Cosman, and F. Lightstone. Computational Characterization and Prediction of Estrogen Receptor Coactivator Binding Site Inhibitors. Office of Scientific and Technical Information (OSTI), August 2005. http://dx.doi.org/10.2172/900142.

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9

Bennion, Brian J., Kris Kulp, Monique Cosman, and Felice Lightstone. Computational Characterization and Prediction of Estrogen Receptor Coactivator Binding Site Inhibitors. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada446323.

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10

Rimsza, Jessica, Eric Sorte, and Todd Alam. Computational and Experimental Characterization of Intermediate Amorphous Phases in Geological Materials. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1734484.

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