Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model

Availability of a validated, realistic fuel cost model is a prerequisite to the development and validation of new optimization methods and control tools. This paper uses an autoregressive integrated moving average (ARIMA) model with historical fuel cost data in development of a three-step-ahead fuel cost distribution prediction. First, the data features of Form EIA-923 are explored and the natural gas fuel costs of Texas generating facilities are used to develop and validate the forecasting algorithm for the Texas example. Furthermore, the spot price associated with the natural gas hub in Texas is utilized to enhance the fuel cost prediction. The forecasted data is fit to a normal distribution and the Kullback-Leibler divergence is employed to evaluate the difference between the real fuel cost distributions and the estimated distributions. The comparative evaluation suggests the proposed forecasting algorithm is effective in general and is worth pursuing further.Comment: Accepted by IEEE PES 2018 General Meetin

Prevalence of resistance in non-type-specific <i>E</i>. <i>coli</i> recovered from feedlot cattle, by sampling date.

<p>Marginal (adjusted) means estimates of the prevalence of resistance to various antimicrobial drugs among non-type specific <i>E</i>. <i>coli</i> isolates obtained from individual fecal samples at the first and second samplings. These estimates have been adjusted for isolate, individual, and pen effects. Due to a large variation in second sampling relative to days on feed, estimates have been categorized and presented at 33–75 days on feed, 75–120 days on feed, and >120 days on feed. Error bars represent 95% confidence intervals. Dashed lines differentiate which antimicrobial drugs were tested by one or both susceptibility testing methods. Number of isolates in legend indicate how many were tested by each susceptibility test (n = number tested by disk diffusion / number tested by both tests / number tested by broth microdilution). <i>P</i>-values relate to differences in adjusted prevalence among the 4 days-on-feed categories, and were not adjusted for multiple comparisons among AMDs. * = unadjusted prevalence with “plus four 95% confidence interval for a proportion".</p

Multivariate correlation between resistance outcomes.

<p>Pairwise correlation between 2 antimicrobial resistance outcomes obtained from a multivariate regression model including resistances to tetracycline, streptomycin, sulfisoxazole, ampicillin, and chloramphenicol.</p

Number of antimicrobial drugs to which non-type specific <i>E</i>. <i>coli</i> isolates were resistant.

<p>Number of antimicrobial drugs to which non-type specific <i>E</i>. <i>coli</i> isolates were resistant.</p

Selection of study pens, individuals and <i>E</i>. <i>coli</i> isolates at arrival and second sampling.

<p>Selection of study pens, individuals and <i>E</i>. <i>coli</i> isolates at arrival and second sampling.</p

Exposures to antimicrobial drugs in individual cattle from which NTSEC were recovered for this study (n = 923).

<p>Exposures to antimicrobial drugs in individual cattle from which NTSEC were recovered for this study (n = 923).</p

Dependence at different levels of clustering for NTSEC isolates in the second sample set as estimated using alternating logistic regression^a.

<p>Dependence at different levels of clustering for NTSEC isolates in the second sample set as estimated using alternating logistic regression<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143995#t006fn001" target="_blank">^a</a>.</p

Resistance patterns for non-type specific <i>E</i>. <i>coli</i> isolates recovered from the arrival and second sample sets.

<p>Resistance patterns for non-type specific <i>E</i>. <i>coli</i> isolates recovered from the arrival and second sample sets.</p

Pen-level exposures to antimicrobial drugs for groups which housed cattle that were used (n = 215 pens).

<p>Pen-level exposures to antimicrobial drugs for groups which housed cattle that were used (n = 215 pens).</p

Data_Sheet_2_Investigating Effects of Tulathromycin Metaphylaxis on the Fecal Resistome and Microbiome of Commercial Feedlot Cattle Early in the Feeding Period.XLSX

<p>The objective was to examine effects of treating commercial beef feedlot cattle with therapeutic doses of tulathromycin, a macrolide antimicrobial drug, on changes in the fecal resistome and microbiome using shotgun metagenomic sequencing. Two pens of cattle were used, with all cattle in one pen receiving metaphylaxis treatment (800 mg subcutaneous tulathromycin) at arrival to the feedlot, and all cattle in the other pen remaining unexposed to parenteral antibiotics throughout the study period. Fecal samples were collected from 15 selected cattle in each group just prior to treatment (Day 1), and again 11 days later (Day 11). Shotgun sequencing was performed on isolated metagenomic DNA, and reads were aligned to a resistance and a taxonomic database to identify alignments to antimicrobial resistance (AMR) gene accessions and microbiome content. Overall, we identified AMR genes accessions encompassing 9 classes of AMR drugs and encoding 24 unique AMR mechanisms. Statistical analysis was used to identify differences in the resistome and microbiome between the untreated and treated groups at both timepoints, as well as over time. Based on composition and ordination analyses, the resistome and microbiome were not significantly different between the two groups on Day 1 or on Day 11. However, both the resistome and microbiome changed significantly between these two sampling dates. These results indicate that the transition into the feedlot—and associated changes in diet, geography, conspecific exposure, and environment—may exert a greater influence over the fecal resistome and microbiome of feedlot cattle than common metaphylactic antimicrobial drug treatment.</p