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Temporal analysis of products statistical analysis package (tapsap) is a package for reading, cleaning, analyzing and making inference of temporal analysis of products (TAP) information. This package is designed with the intent for collaboration within the TAP community for advancing TAP analysis.
The rapid transient method records time stamps of individual ion arrival for accurate identification and quantification of nanoparticles.
Understanding a regional-scale impact of the weather on the transportation system and how that impact varies geographically, is important from a sustainability standpoint. In this work, we have performed a city-scale analysis on the impact of weather on the traffic speed for the City of Chicago. We have found that there is a significant variation in the average hourly speed due to different weather patterns which also varies geographically. We have also observed that the rainfall has an evident impact on the average hourly speed on the freeways that are influenced by urbanized residential and commercial areas, and non-urbanized areas. Also, low visibility has shown a significant reduction in the average hourly traffic speed during the congested hours on the freeways that are influenced by non urbanized areas. We anticipate the contributions of this work in estimating emission and fuel economy at a regional scale which are important sustainability measures for transportation.
Cicadas are insects that belong to the order Hemiptera (i.e., true bugs). They feed on plant fluids above and below ground. Cicada species fall into two categories: periodical and annual. Seven species of periodical cicadas have been identified, emerging in 13-, 17-, and 21-year cycles [1]. The largest brood (i.e., periodical cicadas of the same cycle) of 17-year cicadas is Brood X, examples of which are shown in Fig. 1. Male cicadas produce the loudest sounds of the insect world. A single male can produce a clicking sound that exceeds 100 dB sound pressure level. This male cicada sound emanates from an organ called the tympanic membrane, which is located on both sides of the body near the base of the wing [2]. The vibrations from this organ generate the sound. Female cicadas make a similar— although less loud—clicking sound with their wings. Figure 2 shows the locations of active cicadas within the United States.
This report summarizes the work performed under the Sandia LDRD project "Adverse Event Prediction Using Graph-Augmented Temporal Analysis." The goal of the project was to develop a method for analyzing multiple time-series data streams to identify precursors providing advance warning of the potential occurrence of events of interest. The proposed approach combined temporal analysis of each data stream with reasoning about relationships between data streams using a geospatial-temporal semantic graph. This class of problems is relevant to several important topics of national interest. In the course of this work we developed new temporal analysis techniques, including temporal analysis using Markov Chain Monte Carlo techniques, temporal shift algorithms to refine forecasts, and a version of Ripley's K-function extended to support temporal precursor identification. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication submissions and reports that were prepared as part of this work. We then describe work in progress that is not yet ready for publication.
This report summarizes the work performed under the Sandia LDRD project "Adverse Event Prediction Using Graph-Augmented Temporal Analysis." The goal of the project was to develop a method for analyzing multiple time-series data streams to identify precursors providing advance warning of the potential occurrence of events of interest. The proposed approach combined temporal analysis of each data stream with reasoning about relationships between data streams using a geospatial-temporal semantic graph. This class of problems is relevant to several important topics of national interest. In the course of this work we developed new temporal analysis techniques, including temporal analysis using Markov Chain Monte Carlo techniques, temporal shift algorithms to refine forecasts, and a version of Ripley's K-function extended to support temporal precursor identification. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication sub-missions and reports that were prepared as part of this work. We then describe work in progress that is not yet ready for publication.
This work was presented at the American Center for Life Cycle Assessment 2019 Conference (LCAXIX) in Tucson, AZ on September 24, 2019. Subpart W of the Environmental Protection Agency’s (EPA) Greenhouse Gas Reporting Program (GHGRP) consists of facility level greenhouse gas data for petroleum and natural gas systems that emit 25,000 metric tons or more of GHGs per year. Envirofacts is a tool that provides all publicly available data reported to GHGRP and it can be used to customize and download the data. This is one of the most granular datasets available that can be used to perform a detailed cradle-to-gate Life Cycle Analysis (LCA) of the United States natural gas supply chain. However, as robust as this data is, it is not available in a format that can be analyzed easily, and it is especially difficult to perform a temporal analysis on it. NETL's python script downloads all the .csv files for the desired reporting years from the subpart W of GHGRP.
The temporal analysis of products (TAP) reactor provides a wealth of data for transient studies of gas phase heterogeneous reactions, with the ability to provide a comprehensive picture of intrinsic reaction kinetics pertaining to complex catalytic mechanisms. A novel framework called the Y-Procedure was previously developed by Yablonsky and coworkers to extract information from this non-steady-state kinetic data. The Y-Procedure reconstructs the chemical transformation rate and gas composition in the catalytic zone with no a priori assumptions on the kinetic model and is used to relate changes in catalytic reaction rates and kinetic parameters to changes in a catalyst’s surface composition. This code provides the required steps to pre-process, analyze, and perform the Y-Procedure on experiments derived from the TAP reactor.
The temporal analysis of products (TAP) reactor, a transient kinetic tool, provides users with information as the catalyst state evolves. However, the state of the art for TAP uncertainty quantification only considers the experimental noise present in the outlet flow signal. Additional sources of uncertainty, including, but not limited to, initial surface coverages, the catalyst zone location, the inert void fraction, and the gas pulse intensity and pulse delay, are not included. For this reason, a framework for quantifying all uncertainty sources present in TAP experiments is presented and applied to a carbon monoxide oxidation case study. Herein, two methods for quantifying these sources of uncertainty are introduced. The first utilizes initial state sensitivities to approximate the parameter variances, as well as to provide insights into the structural certainty of the model. The second generates parameter confidence distributions through an ensemble-based sampling algorithm. This initial state covariance matrix can ultimately be merged with the experimental noise covariance matrix, providing a unified description of the parameter uncertainties for a single TAP experiment.
Abstract not provided.
Detailed understanding of mechanisms and reaction kinetics are required in order to develop and optimize catalysts and catalytic processes. While steady state investigations are known to give a global view of the catalytic system, transient studies are invaluable since they can provide more detailed insight into elementary steps. For almost thirty years temporal analysis of products (TAP) has been successfully utilized for transient studies of gas phase heterogeneous catalysis, and there have been a number of advances in instrumentation and numerical modeling methods in that time. In the current work, the range of available TAP apparatus will be discussed while detailed explanations of the types of TAP experiment, the information that can be determined from these experiments and the analysis methods are also included. TAP is a complex methodology and is often viewed as a niche specialty. Here, part of the intention of this work is to highlight the significant contributions TAP can make to catalytic research, while also discussing the issues which will make TAP more relevant and approachable to a wider segment of the catalytic research community. With this in mind, an outlook is also disclosed for the technique in terms of what is needed to revitalize the field and make it more applicable to the recent advances in catalyst characterization (e.g. operando modes ).
Kinetics-informed neural networks improve fit quality for multi-pulse and noisy temporal analysis of products datasets.
The HPC system consists of a set of layers of software and hardware for I/O and networking. System logs are helpful resources to understand what is going on in the system. A challenge is that it is non-trivial to analyze the logs maintained in various levels of the stack. Independent analysis might lead to an incomplete conclusion due to the limited coverage of each log. This work takes a comprehensive approach to analysis that incorporates the logs in the multiple layers and components, in order to facilitate the detection of anomalous activities. This research aims to identify and predict potential performance bottlenecks in the HPC system, by capturing the temporal variation patterns from heterogeneous, high-dimensional, and non-linear log data. In this paper, we share our preliminary efforts for spatio-temporal analysis of HPC I/O and connection data, with our initial observations from the analysis of one-week HPC log data sets collected from one of NERSC systems.
As the energy sector experiences a paradigm shift towards cleaner and greener generation sources, the need for evaluating the applicability of marine energy for grid-based applications has intensified. This is because marine energy (ocean, wave and tidal) may be utilized to provide unique benefits to electric grid operations, especially in conjunction with other established renewable technologies (such as wind and solar) or technologies like energy storage. In this work, we focus our attention on some specific characteristics of two types of marine energy resources i.e. tidal and ocean current based resources. Specifically, we perform a comprehensive temporal analysis using a suite of metrics on resource data to characterize the inherent predictability of the resource across various time scales of interest. We also investigate the correlation of tidal power generation profiles with energy demand and comment on the effectiveness of having such resources in the overall generation portfolio.
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Particulate matter (PM) has been recognized as one of the key risk factors of lung cancer. However, spatial and temporal patterns of this association remain unclear. Spatiotemporal analyses incorporate the spatial and temporal structure of the data within random effects models, generating more accurate evaluations of PM-lung cancer associations at a scale that can better inform lung cancer prevention programs.
Although subsidence has been observed at the San Emidio geothermal field in Nevada using interferometric synthetic aperture radar since the early 1990s, the spatial extent and temporal evolution of the subsidence have not heretofore been quantified. Furthermore, the weather conditions and geographic location of San Emidio negatively affect interferometric image quality, causing low correlation amongst pairs. To address this, we introduce a new method for selecting pairs in areas of low correlation and small deformation signal using a minimum spanning tree method with a measure of image quality as the weighting criterion. We validate our pair selection approach by comparing our data products to SqueeSAR TM data products from a previous study at San Emidio. We also develop a deformation model which characterizes the spatial extent of subsidence at San Emidio in terms of volume change of the reservoir. After applying this deformation model to our data set of interferometric pairs, we examine the temporal relationship of the observed deformation with production and injection operations associated with geothermal power production.
System biology and bioprocess technology can be better understood using shotgun proteomics as a monitoring system during the fermentation. We demonstrated a shotgun proteomic method to monitor the temporal yeast proteome in early, middle and late exponential phases. Our study identified a total of 1389 proteins combining all 2D-LC-MS/MS runs. The temporal Saccharomyces cerevisiae proteome was enriched with proteolysis, radical detoxification, translation, one-carbon metabolism, glycolysis and TCA cycle. Heat shock proteins and proteins associated with oxidative stress response were found throughout the exponential phase. The most abundant proteins observed were translation elongation factors, ribosomal proteins, chaperones and glycolytic enzymes. The high abundance of the H-protein of the glycine decarboxylase complex (Gcv3p) indicated the availability of glycine in the environment. We observed differentially expressed proteins and the induced proteins at mid-exponential phase were involved in ribosome biogenesis, mitochondria DNA binding/replication and transcriptional activator. Induction of tryptophan synthase (Trp5p) indicated the abundance of tryptophan during the fermentation. As fermentation progressed toward late exponential phase, a decrease in cell proliferation was implied from the repression of ribosomal proteins, transcription coactivators, methionine aminopeptidase and translation-associated proteins.