A broadband integrated time series (BITS) for longitudinal analyses of the digital divide

To evaluate actions taken to implement the Telecommunications Act of 1996, the primary goal of which was to foster competition in the industry, the FCC created a standardized form (Form 477) to collect information about broadband deployment and competition in local telephone service. These data represent the best publicly available record of broadband provision in the United States. Despite the potential benefits offered by this database, there are several nuances to these data related to shifting geographies and reporting requirements that uncorrected, prevent them from being used as an uninterrupted time series for longitudinal analyses. Given the analytical challenges associated with the FCC Form 477 data, the purpose of this paper is to present a solution to the fragmented nature of these data which prevents meaningful longitudinal analyses of the digital divide. Specifically, this paper develops and describes a procedure for producing an integrated broadband time series (BITS) for the last decade (2008–2018). This includes the procedures for using these data, their value to social and economic analysis, and their underlying limitations. The core contribution of this paper is the creation of data infrastructure for investigating the evolution of the digital divide.

Empirical Analysis of Relieving High-Speed Rail Freight Congestion in China

This paper discusses how to promote high-speed rail (HSR) freight business by solving the congestion problem. First, we define the existing operation modes in China and propose the idea of relieving congestion by reserving more carriages of HSR passenger trains for freight between cities with large potential volume or small capacity. Second, we take one HSR corridor as a case to study, and use predictive regression and integrated time series methods to forecast the growth of HSR freight volume along the corridor. Finally, combined with forecast results and available capacity during the peak month of 2018, we offer suggestions on the mode adoption in each segment during the peak month from 2019 to 2022. Results demonstrate: (1) Among all 84 Origin-Destination (OD) city flows, the percentage of those monthly volumes over 1 ton increases from 17.9% in 2018 to 84.6% in 2022, and those over 30 tons rise from 3.6% to 26.2%. (2) Among the segments between seven main cities in the HSR corridor, T-J should be given priority to operate trains with reserved mode; the segment between X and J deserves to reserve most carriages during the peak month in the future. Specifically, our model suggests reserving 5.3–10.1 carriages/day for J-X, and 4.8–16.3 carriages/day for X-J during the peak month from 2019 to 2022.

PERAMALAN MENGGUNAKAN METODE WEIGHTED FUZZY INTEGRATED TIME SERIES (Studi Kasus: Harga Beras di Indonesia Bulan Januari 2011 s/d Desember 2017)

Fuzzy Time Series (FTS) is a time series data forecasting technique that uses fuzzy theory concepts. Forecasting systems using FTS are useful for capturing patterns of past data and then to using it to produce information in the future. Initially in the FTS each pattern of relations formed was considered to have the same weight besides using only the first order. In its development the Weighted Fuzzy Integrated Time Series (WFITS) which gave a difference in the weight of each relation and high order usage has been appeared. Measuring the accuracy of forecasting results is used the value of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). In this study both the first-order and high-order WFITS methods were applied to forecast rice prices in Indonesia based on data from January 2011 to December 2017. In this regard, the results of the analysis obtained data forecasting using Lee's high-order model WFITS algorithm (1,2,3) giving the value of RMSE and MAPE on the data testing in a row as many as 69,898 and 0.47% while for the RMSE and MAPE on the training data is as many as 70.4039 and 0.54%. Keywords: Fuzzy Time Series, Weighted Fuzzy Integrated Time Series, RMSE, MAPE, High-Order, Rice Prices

Integrated Multi-omic Framework of the Plant Response to Jasmonic Acid

Understanding the systems-level actions of transcriptional responses to hormones provides insight into how the genome is reprogrammed in response to environmental stimuli. Here, we investigate the signaling pathway of the hormone jasmonic acid (JA), which controls a plethora of critically important processes in plants and is orchestrated by the transcription factor MYC2 and its closest relatives in Arabidopsis thaliana. We generated an integrated framework of the response to JA that spans from the activity of master and secondary-regulatory transcription factors, through gene expression outputs and alternative splicing to protein abundance changes, protein phosphorylation and chromatin remodeling. We integrated time series transcriptome analysis with (phospho)proteomic data to reconstruct gene regulatory network models. These enable us to predict previously unknown points of crosstalk from JA to other signaling pathways and to identify new components of the JA regulatory mechanism, which we validated through targeted mutant analysis. These results provide a comprehensive understanding of how a plant hormone remodels cellular functions and plant behavior, the general principles of which provide a framework for analysis of cross-regulation between other hormone and stress signaling pathways.