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Time Prediction (time + prediction)
Selected AbstractsPrediction of metabolite identity from accurate mass, migration time prediction and isotopic pattern information in CE-TOFMS dataELECTROPHORESIS, Issue 14 2010Masahiro Sugimoto Abstract CE-TOFMS is a powerful method for profiling charged metabolites. However, the limited availability of metabolite standards hinders the process of identifying compounds from detected features in CE-TOFMS data sets. To overcome this problem, we developed a method to identify unknown peaks based on the predicted migration time (tm) and accurate m/z values. We developed a predictive model using 375 standard cationic metabolites and support vector regression. The model yielded good correlations between the predicted and measured tm (R=0.952 and 0.905 using complete and cross-validation data sets, respectively). Using the trained model, we subsequently predicted the tm for 2938 metabolites available from the public databases and assigned tentative identities to noise-filtered features in human urine samples. While 38.9% of the peaks were assigned metabolite names by matching with the standard library alone, the proportion increased to 52.2%. The proposed methodology increases the value of metabolomic data sets obtained from CE-TOFMS profiling. [source] Accelerating seismicity of moderate-size earthquakes before the 1999 Chi-Chi, Taiwan, earthquake: Testing time-prediction of the self-organizing spinodal model of earthquakesGEOPHYSICAL JOURNAL INTERNATIONAL, Issue 1 2003Chien-chih Chen SUMMARY Seismic activation of moderate-size earthquakes for the 1999 Chi-Chi, Taiwan, earthquake has been found. A self-organizing spinodal (SOS) model can explain some observations concerning seismic activation, but the equal time durations of the mid and precursory periods during an earthquake cycle conjectured in the original, published, SOS model have not been supported in this case. The Chi-Chi test presented here shows unequal time durations of the mid and precursory periods of an earthquake cycle. This, in turn, makes the possibility of time prediction of a characteristic earthquake impossible in the context of the SOS model. In addition, comparisons with numerical simulations of the sliding-block model suggest the change in the system's stiffness is a potential mechanism of seismic activation. [source] Using automatic passenger counter data in bus arrival time predictionJOURNAL OF ADVANCED TRANSPORTATION, Issue 3 2007Mei Chen Artificial neural networks have been used in a variety of prediction models because of their flexibility in modeling complicated systems. Using the automatic passenger counter data collected by New Jersey Transit, a model based on a neural network was developed to predict bus arrival times. Test runs showed that the predicted travel times generated by the models are reasonably close to the actual arrival times. [source] A dynamic shortest path algorithm using multi-step ahead link travel time predictionJOURNAL OF ADVANCED TRANSPORTATION, Issue 1 2005Young-Ihn Lee Abstract In this paper, a multi-step ahead prediction algorithm of link travel speeds has been developed using a Kalman filtering technique in order to calculate a dynamic shortest path. The one-step and the multi-step ahead link travel time prediction models for the calculation of the dynamic shortest path have been applied to the directed test network that is composed of 16 nodes: 3 entrance nodes, 2 exit nodes and 11 internal nodes. Time-varying traffic conditions such as flows and travel time data for the test network have been generated using the CORSIM model. The results show that the multi-step ahead algorithm is compared more favorably for searching the dynamic shortest time path than the other algorithm. [source] Prediction of cooling time in injection molding by means of a simplified semianalytical equationADVANCES IN POLYMER TECHNOLOGY, Issue 3 2003D. M. Zarkadas Abstract A simplified semianalytical equation, used successfully in food freezing/chilling time prediction, is proposed as a potential simple alternative for cooling time prediction in injection molding of polymer parts, amorphous or semicrystalline. This equation is based on a convective boundary condition for the mold-part interface and requires information on the thermal contact resistance (TCR) or thermal contact conductance (TCC) at this interface, as well as information on the initial and final product temperatures, the mold surface temperature, and the thermal properties of the part. Eighty-five data points for four polymers, Polystyrene (PS), Polycarbonate (PC), Polypropylene (PP), and Polyethylene (PE) were generated with C-MOLDŌ, a commercial injection molding design software, and the performance of the proposed equation was tested. The % mean error and its standard deviation (SD) in cooling time prediction were, respectively, ,11.61 and 2.27 for PS, ,6.04 and 2.13 for PC, ,7.27 and 6.55 for PP, and ,8.88 and 2.93 for PE. It was also shown that the accuracy of the proposed equation is not affected significantly by the exact knowledge of the TCC, provided that the latter is not smaller than 1000,2000 W m,2 K,1. Since in this comparison all necessary temperatures were obtained from C-MOLDŌ, methods of using the proposed equation independently were tested. The use of the inlet melt temperature as the initial product temperature increased the % mean error by mostly 1.5% while its SD remained practically the same. By incorporating a literature based heat balance method in the proposed equation, it was possible to use it as a stand-alone predictor of polymer cooling time. The % mean error and its SD calculated this way were, respectively, ,9.44 and 0.97 for PS, ,9.44 and 0.83 for PC, ,14.22 and 5 for PP, and ,20.12 and 1.38 for PE. The proposed equation, at least in a preliminary stage, can be used successfully to predict the cooling time of the selected semicrystalline or amorphous polymers with the accuracy being higher for amorphous polymers. © 2003 Wiley Periodicals, Inc. Adv Polym Techn 22: 188,208, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/adv.10048 [source] Retention time prediction using the model of liquid chromatography of biomacromolecules at critical conditions in LC-MS phosphopeptide analysisPROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 19 2010Tatiana Yu Perlova Abstract LC combined with MS/MS analysis of complex mixtures of protein digests is a reliable and sensitive method for characterization of protein phosphorylation. Peptide retention times (RTs) measured during an LC-MS/MS run depend on both the peptide sequence and the location of modified amino acids. These RTs can be predicted using the LC of biomacromolecules at critical conditions model (BioLCCC). Comparing the observed RTs to those obtained from the BioLCCC model can provide additional validation of MS/MS-based peptide identifications to reduce the false discovery rate and to improve the reliability of phosphoproteome profiling. In this study, energies of interaction between phosphorylated residues and the surface of RP separation media for both "classic" alkyl C18 and polar-embedded C18 stationary phases were experimentally determined and included in the BioLCCC model extended for phosphopeptide analysis. The RTs for phosphorylated peptides and their nonphosphorylated analogs were predicted using the extended BioLCCC model and compared with their experimental RTs. The extended model was evaluated using literary data and a complex phosphoproteome data set distributed through the Association of Biomolecular Resource Facilities Proteome Informatics Research Group 2010 study. The reported results demonstrate the capability of the extended BioLCCC model to predict RTs which may lead to improved sensitivity and reliability of LC-MS/MS-based phosphoproteome profiling. [source] Predictions of peptides' retention times in reversed-phase liquid chromatography as a new supportive tool to improve protein identification in proteomicsPROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 4 2009Tomasz B, czek Dr. Abstract One of the initial steps of proteomic analysis is peptide separation. However, little information from RP-HPLC, employed for peptides separation, is utilized in proteomics. Meanwhile, prediction of the retention time for a given peptide, combined with routine MS/MS data analysis, could help to improve the confidence of peptide identifications. Recently, a number of models has been proposed to characterize quantitatively the structure of a peptide and to predict its gradient RP-HPLC retention at given separation conditions. The chromatographic behavior of peptides has usually been related to their amino acid composition. However, different values of retention coefficients of the same amino acid in different peptides at different neighborhoods were commonly observed. Therefore, specific retention coefficients were derived by regression analysis or by artificial neural networks (ANNs) with the use of a set of peptides retention. In the review, various approaches for peptide elution time prediction in RP-HPLC are presented and critically discussed. The contribution of sequence dependent parameters (e.g., amphipathicity or peptide sequence) and peptide physicochemical descriptors (e.g., hydrophobicity or peptide length) that have been shown to affect the peptide retention time in LC are considered and analyzed. The predictive capability of the retention time prediction models based on quantitative structure,retention relationships (QSRRs) are discussed in details. Advantages and limitations of various retention prediction strategies are identified. It is concluded that proper processing of chromatographic data by statistical learning techniques can result in information of direct use for proteomics, which is otherwise wasted. [source] Predicting creep crack initiation in austenitic and ferritic steels using the creep toughness parameter and time-dependent failure assessment diagramFATIGUE & FRACTURE OF ENGINEERING MATERIALS AND STRUCTURES, Issue 10 2009C. M. DAVIES ABSTRACT Methods for evaluating the creep toughness parameter, Kmatc, are reviewed and Kmatc data are determined for a ferritic P22 steel from creep crack growth tests on compact tension, C(T), specimens of homogenous parent material (PM) and heterogeneous specimen weldments at 565 °C and compared to similar tests on austenitic type 316H stainless steel at 550 °C. Appropriate relations describing the time dependency of Kmatc are determined accounting for data scatter. Considerable differences are observed in the form of the Kmatc data and the time-dependent failure assessment diagrams (TDFADs) for both the 316H and P22 steel. The TDFAD for P22 shows a strong time dependency, but is insensitive to time for 316H. Creep crack initiation (CCI) time predictions are obtained using the TDFAD approach and compared to experimental results from C(T) specimens and feature components. The TDFAD based on parent material properties can be used to obtain conservative predictions of CCI on weldments. Conservative predictions are almost always obtained when lower bound Kmatc values are employed. Long-term test are generally more relevant to industrial component lifetimes. The different trends between long- and short-term CCI time and growth data indicate that additional long-term test are required to further validate the procedure to predict the lifetimes of high temperature components. [source] Rotational molding cycle time reduction through surface enhanced molds: Part A,Theoretical studyPOLYMER ENGINEERING & SCIENCE, Issue 9 2007M.Z. Abdullah Rotational molding has been regarded as a plastic molding method with great potential. The process offers virtually stress-free products having no weld lines or material wastage, and utilizes relatively inexpensive molds. Yet its widespread growth is hindered due to long production cycle times, which are limited by the time required to heat up and cool down the mold and the product. To address this issue, efforts have been made to enhance heat transfer to and from molds, ultimately reducing cycle times. The application of extended and rough surfaces to molds is investigated here. The aim of this study is to predict reductions in cycle time due to the enhancement of mold surfaces (i.e. roughness-enhanced and pin-enhanced molds). By utilizing a combination of heat transfer correlations, numerical analysis, and an existing rotational molding process simulation, cycle time predictions were made. The average predicted cycle time reductions were ,21 and 32% for the roughness-enhanced and pin-enhanced molds considered, under a variety of conditions. POLYM. ENG. SCI., 47:1406,1419, 2007. © 2007 Society of Plastics Engineers [source] | |||||||||||||