Construct Time Series Research Articles

Vaccinia virus Tiantan strain blocks host antiviral innate immunity and programmed cell death by disrupting gene expression

The vaccinia virus Tiantan (VTT) is widely utilized as a smallpox vaccine in China and holds significant importance in the prevention of diseases stemming from poxvirus infections. Nevertheless, few studies have investigated the influence of VTT infection on host gene expression. In this study, we constructed time series transcriptomic profiles of HeLa cells infected with both VTT and western reserve (WR) strains. We observed similar patterns of viral gene expression, while the expression levels of host genes varied between the two strains. There was an immediate and significant repression of host gene expression, particularly in genes associated with oxidative phosphorylation. Conversely, genes involved in nerve growth factor (NGF)-stimulated transcription were significantly activated. The upregulation of genes linked to the ribonucleic acid (RNA)-induced silencing complex (RISC) suggested a potential role for posttranscriptional regulation in the interaction between the vaccinia virus and the host. In the later stages of infection, pathways such as extracellular matrix organization, neutrophil degranulation, complement and interferon responses, translation, and programmed cell death are largely inhibited. A significant number of host genes exhibit correlations with changes in the expression levels of viral genes. The host genes that are negatively correlated with viral genes are mainly enriched in pathways associated with translation and the response to viral infection. This study significantly contributes to advancing our understanding of the dynamics between the vaccinia virus and the host, improving the application of VTTs and facilitating the development of effective vaccines against diseases such as smallpox and monkeypox.

Productivity and Keynes’s 15-Hour Work Week Prediction for 2030: An Alternative, Macroeconomic Analysis for the United States

This paper analyses Keynes’s 1930 prediction that technical advances would cut people’s working week to 15 h by 2030 and investigates why actual working hours are significantly higher in the United States. Elaborating on Keynes’s forecast to provide a general productivity formula while keeping its simplicity, we ran tests on macro-data from 1929 to 2019 and on estimates for 2030, demonstrating that productivity is surprisingly still insufficient to allow for a reduction in working hours across the US economy. This finding represents a substantial contribution to the literature, which has mostly explained long working hours by means of new consumer needs. Even by using microdata, we show that consumption does not explain the stickiness of working hours to the bottom. Hence, this paper combines a macroeconomic, logical-analytical approach based on historical time series with rigorously constructed time series at the microeconomic level. Finally, we also provide policies to narrow the productivity differential to Keynes’s prediction for 2030 while fostering work-life balance and sustainable growth. To understand long working hours in the US despite technical advances—this being one of our main findings—productivity remains crucial.

A global time series of traffic volumes on extra-urban roads

Traffic on roads outside of urban areas (i.e. extra-urban roads) can have major ecological and environmental impacts on agricultural, forested, and natural areas. Yet, data on extra-urban traffic volumes is lacking in many regions. To address this data gap, we produced a global time-series of traffic volumes (Annual Average Daily Traffic; AADT) on all extra-urban highways, primary roads, and secondary roads for the years 1975, 1990, 2000 and 2015. We constructed time series of road networks from existing global datasets on roads, population density, and socio-economic indicators, and combined these with a large collection of empirical AADT data from all continents except Antarctica. We used quantile regression forests to predict the median and 5% and 95% prediction intervals of AADT on each road section. The validation accuracy of the model was high (pseudo-R2 = 0.7407) and AADT predictions from 1975 were also accurate. The resulting map series provides standardised and fine-scaled information on the development of extra-urban road traffic and has a wide variety of practical and scientific applications.

The Impact of Expert Opinions in COVID-19 News Coverage on Vaccination During the Fifth Outbreak in Hong Kong: A Time Series Analysis

ABSTRACT The role of experts in news coverage has become increasingly prominent, but the evidence regarding the effectiveness of expert opinions in affecting public behavior remains mixed. This study seeks to examine the influence of expert opinions covered in the news on the public’s response to public health crises. By adopting a macro-level framing perspective, we investigated how framing consistency, a macro-level concept indicating the agreement between expert opinions in news coverage and government policies or among peer experts, evolves over time and its temporal causal relationship with public behavior. Specifically, this study collected all press news coverage in Hong Kong over four months during the fifth outbreak, including 1,416 articles with 650 expert opinions, as well as the vaccination data that paralleled with this period. We constructed time series of expert opinions and vaccination behavior, and then conducted Vector Autoregressive (VAR) models with Granger causality analysis to examine how framing consistency of expert opinions in news coverage influenced vaccination. The results indicate that the consistent framing between expert opinions and government policies increased COVID-19 vaccination during the fifth outbreak in Hong Kong, while conflicting opinions responding to government policies had no significant effect on vaccination. Opinions among medical experts on COVID-19 issues also did not significantly impact vaccination. The implications for designing communication strategies and enhancing public behavioral support during public health crises are discussed.

A two-step data-driven method for predicting the wear of train wheel treads using GA-BPNN and LSTM algorithms

ABSTRACT During train operations, the changing of wheel tread profile due to wheel-rail contact has a significant impact on the safe operation of the train and passenger comfort. Therefore, it is crucial to timely monitor the condition of the wheels and accurately predict wheel wear. In addition, wheel wear prediction can also guide wheel re-profiling, thereby extending the service life of wheels and minimizing operating costs. In this paper, a two-step data-driven wear prediction method was proposed to comprehensively predict wheel wear from two perspectives: the overall train and individual wheels. Firstly, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Radial Basis Function neural network (RBFNN) were used to preprocess historical data, remove outliers, and construct time series data. By analysing the wear trend of wheels, a two-step data-driven prediction method was proposed. The first step is to predict the average Wear of wheels in the future time, and the second step is to predict the deviation between each wheel Wear value and the mean in the future time. Different prediction models were utilized and compared to obtain the best predictive performance. Results show that back propagation neural network (BPNN) optimized by genetic algorithm (GA) and long short-term memory (LSTM) based model yield better performance for predicting the wear trend of the overall train and individual wheels respectively. In addition, comprehensive contour parameters were utilized in the prediction model to enhance the accuracy and robustness.

Traffic anomaly detection algorithm for CAN bus using similarity analysis

Recently, vehicles have experienced a rise in networking and informatization, leading to increased security concerns. As the most widely used automotive bus network, the Controller Area Network (CAN) bus is vulnerable to attacks, as security was not considered in its original design. This paper proposes SIDuBzip2, a traffic anomaly detection method for the CAN bus based on the bzip2 compression algorithm. The proposed method utilizes the pseudo-periodic characteristics of CAN bus traffic, constructing time series of CAN IDs and calculating the similarity between adjacent time series to identify abnormal traffic. The method consists of three parts: the conversion of CAN ID values to characters, the calculation of similarity based on bzip2 compression, and the optimal solution of model parameters. The experimental results demonstrate that the proposed SIDuBzip2 method effectively detects various attacks, including Denial of Service , replay, basic injection, mixed injection, and suppression attacks. In addition, existing CAN bus traffic anomaly detection methods are compared with the proposed method in terms of performance and delay, demonstrating the feasibility of the proposed method.

Characterizing the Water Storage Variation of Kusai Lake by Constructing Time Series from Multisource Remote Sensing Data

In September 2011, Zhuonai Lake (ZL) in the northeast of Hoh Xil (HX) on the Qinghai–Tibet Plateau (QTP) broke out. The outburst event seriously changed the environmental hydraulics in this region. Due to the insufficient temporal resolution of observations, it is challenging to assess the impact of this event on short-period variations of water volumes in three lakes downstream of ZL. Combining multisource remote sensing data, we constructed long and high-temporal-resolution time series for the lake level, area, and lake water storage (LWS) of Kusai Lake (KL) to characterize the variabilities before and after the outburst. The water level, area, and LWS time series contain 1051 samples from 1990 to 2022, with uncertainties of 0.16 m, 2.5 km2, and 0.016 km3, respectively. The accuracies verified using the Database for Hydrological Time Series of Inland Waters (DAHITI) are 0.26 m, 2.64 km2, and 0.08 km3 for water level, area, and LWS, respectively. We characterized the LWS variations during the past 30 years based on the high temporal resolution LWS time series. Before the outburst, the 1-year and 3.5-year variations dominated the LWS time series, and there was no obvious semi-annual signal. After the outburst, the 3.5-year variation disappeared, and a strong semi-annual oscillation was observed. From 2012 to 2015, the periodic LWS variations in KL were disturbed by the ZL outburst and the subsequent outflow of KL led by the outburst. Regular cyclic signals have been restored since 2016, with an amplified annual fluctuation. By analysis, precipitation, evaporation, and glacier area change are excluded as driving factors of the pattern change in LWS variations of KL. It can be concluded that the altered recharge pattern of KL triggered by the outburst directly resulted in the observed changes in TWS behavior. For the first time, we identified the periodic patterns of LWS variations of KL during the past 30 years and revealed that the ZL outburst event significantly influenced these patterns. This finding contributes to the comprehensive understanding of the effects of the ZL outburst on downstream lake dynamics. Furthermore, the presented procedure for constructing long and high-resolution time series of LWS allows for monitoring and characterizing the short-period variabilities of Tibetan lakes that lack hydrological data.

Paints, paper, and the “scissors” of prices: On economic backwardness of eighteenth-century Russia

The author of this article systematizes archival data on the prices of paper and paints in Russia between the 1710s and 1780s and consider the development of their production in the country. We use more than 160 archival sources, which allows to construct time series. The resulting numbers make the author doubt the “price revolution” in eighteenth-century Russia. The price jump was observed in the agricultural market, the prices of the considered industrial goods remained stable. Thus we can talk about the “scissors” of prices, the socioeconomic consequences of which are studied in the article. The author aims to explain the reasons for the success of replacing foreign products with domestically produced goods in Petrine Russia, as well as the reasons for the technological lag at the end of the century

Выявление перспективных способов преобразования параметров электрической энергии

This paper performs an analysis of the electric power conversion methods, and also examines the development trends of DC-to-DC, AC-to-AC, AC-to-DC and DC-to-AC power converters with the use of the International Patent Classification (2023). A comparison has been made of such different kinds of converters as power converters with intermediate conversion and without intermediate conversion into AC or DC, converters using devices of a transistor and thyristor type, and specific circuit implements corresponding to the IPC classification. Perspective directions of such converters’ development have been revealed. The analysis was carried out on the basis of the US patents databases which were formed by the authors for the IPC groups H02M3, H02M5, H02M7 within 2012 to 2023. The perspective directions were revealed by constructing time series of the US patents for the analyzed power converters and their further comparison by the issued patents number and growth patterns. As a result of research, the advantage of transistor converters circuits has been proved, the perspective development directions of the electric power parameters conversion methods as well as their implementation have been described. There was found a decrease of the issued US invention patents in the considered IPC groups by 40% within 2019 to 2022.

Spatio-temporal characteristics of human activities using location big data in Qilian Mountain National Park

ABSTRACT Human activities significantly impact the environment. Understanding the patterns and distribution of these activities is crucial for ecological protection. With location-based technology advancement, big data such as location and trajectory data can be used to analyze human activities on finer temporal and spatial scales than traditional remote sensing data. In this study, Qilian Mountain National Park (QMNP) was chosen as the research area, and Tencent location data were used to construct time series data. Time series clustering and decomposition were performed, and the spatio-temporal distribution characteristics of human activities in the study area were analyzed in conjunction with GPS trajectory data and land use data. The study found two distinct human activity patterns, Pattern A and Pattern B, in QMNP. Compared to Pattern B, Pattern A had a higher volume of location data and clear nighttime peaks. By incorporating land use and trajectory data, we conclude that Pattern A and Pattern B represent the activity patterns of the resident and tourist populations, respectively. Moreover, the study identified seasonal variations in human activities, with human activity in summer being approximately two hours longer than in winter. We also conducted an analysis of human activities in different counties within the study area.

Forecasting husbandry development using time series

Building time series models based on historical data is a pressing challenge in the agricultural sector. This is essential, as analysing and predicting processes related to the food security of the state, region, and business entities are of paramount importance in management. With the help of forecasts, enterprises can adjust their production activities in such a way as to satisfy demand and deliver products to consumers on time. The research aims to predict the trends in the growth of cattle and cow populations and identify the most suitable forecasting timeframe. Statistical methods related to autoregression are used for this type of analysis: autoregressive models, moving average models or a combination of both, integrated variable structure models, and models that include seasonal effects and exogenous factors with an autoregressive and moving average component in the model. Monthly statistical data on the number of cattle and cows are used, among them mean, standard deviation, minimum and maximum values, asymmetry, and kurtosis. The dynamics of the decrease in the number of cattle and cows are shown. The studied series were checked for stationarity. The time series data for the cattle population underwent a Box-Cox transformation. The optimal parameters of the models used are given. Predictive values for periods (months) were obtained and the change in the number of cattle over the last 15 years was analysed. Constructed time series are compared with the actual values, which are illustrated in the graphs. Estimates of rootmean-square deviation, and mean absolute percentage error for different forecasting terms are given. By comparing these estimates for different time intervals, the optimal period for the forecast (24 months) was determined. This study allows farms and enterprises in the industry to predict a possible number of products (milk, meat) that could be collected or obtained in the future. It helps to take the necessary management steps: plan resource needs, improve efficiency, increase profits, reduce costs, and adapt to changes in the market

Time series analysis of groundwater quality at selected sites of Purba and Paschim Burdwan, West Bengal, India.

The Water Quality Index (WQI) is used to monitor the health and usability of a water body. In this study, we aimed to construct time series prediction models using groundwater WQI (GW-WQI) at four sites: IISCO-Asansol, Durgapur Town, Burdwan University, and Burdwan Station. While statistical spatio-temporal analysis has been reported earlier, no time series analysis of the data or predictive modelling has been done. Pre-monsoon and post-monsoon physico-chemical data from 2010 to 2022 were obtained from the West Bengal Pollution Control Board website to calculate the GW-WQI. Prediction modelling was performed using R 4.1.3 software. Best fit forecast models were selected to predict future trends of GW-WQI with 80% of the data. Subsequently, the models were validated using R-squared, root mean square error (RMSE), mean absolute error (MAE), maximum absolute percentage error (MAPE), and Thiel's U for the model using 20% of the data. Our results show that GW-WQI was good in pre-monsoon but unfit for drinking in post-monsoon in IISCO-Asansol, Durgapur Town, Burdwan University, and Burdwan Station. Arsenic, fluoride, and mercury were the major contaminants resulting in poor GW-WQI. Seasonal ARIMA was the best model for Burdwan University and IISCO-Asansol, ETSfor Durgapur Station, and BaggedARIMA for Burdwan Station. The forecast model for Durgapur and Burdwan Station predicted a sharp increase until 2027 but was fluctuating for IISCO-Asansol and Burdwan University. Thus, GW-WQI is a major problem in the industrial belt of West Bengal that is likely to remain high or worsen in the future.

Алгоритм обработки информации для прогнозирования развития технологий на примере теле- и радиовещания

Today, many Russian companies working in the field of software development for television and radio broadcasting can extract the necessary data on trends in their field of activity by analyzing information received from foreign competitors, for example, studying specialized popular science literature posted on the official websites of foreign competitors, when analyzing information obtained from exhibitions and presentations. However, the opportunity to comprehensively analyze the novelties of foreign developers patented in the world patent databases will give Russian companies competitive advantages in the formation of strategic development plans. It is proposed to develop a patent landscape based on the data of the global patent array (in English) in the field of television and radio broadcasting, analyze patent trends and predict the development of technologies in this area. The novelty of the developed method for predicting the development of technologies lies in the cooperation of USPTO (USA) patent clustering methods based on the international patent classification and forecasting technological trends using the generated time series. The developed method is implemented in Python; in parallelizing the process of parsing patents, the Multiprocessing library was used, Statsmodels was used to analyze data and carry out the forecasting process, and Matplotlib was used to visualize data. The MySQL database management system was chosen to organize information storage. There were processed 28,591 patents, the process of forecasting the development of television and radio technologies was tested based on the constructed time series in the interval of 2015–2020.

The Rise and Fall of Alaska and Yukon Glaciers Detected by TOPEX/Poseidon and Jason‐2 Altimeters Using a Novel Glacier‐Threshold Method

AbstractRadar altimetry has been used to monitor sea level changes and ice sheet elevation changes for decades. Over mountain glaciers, radar altimetry has limited applications due to contaminated waveforms caused by complex glacier surfaces and steep terrains. In this study, we develop a glacier‐threshold method (GTM) to determine glacier elevation changes in Alaska and Yukon. The GTM retracks waveforms, reduces terrain effect and detects invalid elevation observations from the TOPEX/Poseidon (T/P) and Jason‐2 (J2) altimeters, resulting in an average usable rate of 35% from original altimeter‐measured heights. The selected measurements are used to construct time series of glacier elevation changes over 1993–2002 (T/P) and 2008–2016 (J2) at 47 sites. A crossover analysis, validation by airborne laser altimetry observations, and comparisons with recent studies confirm the estimated glacier elevation changes. Our findings suggest that the thinning of glaciers in Alaska and Yukon has been ubiquitous in recent years. The site near Walsh Glacier has the highest thinning rate of −5.71 ± 0.09 m/yr, followed by Chitina Glacier at −4.51 ± 0.21 m/yr. Only 17% of sites show glacier thickening due to surges and mass accumulations. Using a sophisticated data processing algorithm like the GTM, we show that altimeter data from the repeat TOPEX‐Jason missions can be used to monitor long‐term glacier elevation changes at inaccessible spots, creating an additional value for altimeter missions originally purposed for monitoring long‐term sea level change.

Доказательное и профессиональное суждение в аудите

The article proposes and substantiates new formulations of the concepts of professional and evidence-based judgment. The concept of the application of such judgments when working with numerical estimates is developed. The usefulness of using the method of expert assessments in the audit is shown. Time series are built — in current and comparable prices — of average revenues for audits for audit organizations in general and in three sections: large-scale, regional, type of economic entities. Considered — both in general terms and with examples — the possibility of using the constructed time series through a combination of professional and evidence-based judgments when making various decisions in the audit. Suggestions for improving this combination are formulated. Th e possibility of combining professional and evidence-based judgments in assessing audit risks and in determining the size of audit samples is shown.

Comprehensive commodity price forecasting framework using text mining methods

AbstractExploiting advanced and appropriate methods to construct high‐quality features from different types of data becomes crucial in agricultural futures price forecasting. Thus, this study develops a comprehensive commodity price forecasting framework using text mining methods. First, the modal features of the price series are extracted using the proposed Integrated‐EEMD‐VMD‐SE method, and dynamic topic sentiment features are constructed from Weibo texts using the proposed dynamic topic model joint sentiment analysis method. Second, combined with statistical variables, lag order selection and feature selection are performed on these comprehensive factors. Finally, 12 comparative prediction models are designed based on random forest (RF), long short‐term memory (LSTM), and multilayer perceptron (MLP), and empirical analysis is carried out on two cases of pork prices and soybean futures prices. The experimental results show that the proposed prediction framework has high prediction accuracy, and the mean absolute percentage error (MAPE) values are 1.00 and 0.92, respectively. The constructed time series modal features and dynamic topic sentiment features can significantly improve the performance of the prediction model.

Effects of warming and fishing on Atlantic sea scallop (Placopecten magellanicus) size structure in the Mid-Atlantic rotationally closed areas

Abstract The Atlantic sea scallop supports one of the most lucrative fisheries on the Northeast U.S. shelf. Understanding the interannual variability of sea scallop size structure and associated drivers is critically important for projecting the response of population dynamics to climate change and designing coherent fishery management strategies. In this study, we constructed time series of sea scallop size structures in three rotationally closed areas in the Mid-Atlantic Bight (MAB) and decomposed their total variances using the variance partitioning method. The results suggested that the interannual variances in sea scallop size structures were associated more with thermal stress in regions shallower than 60 m but more with fishing mortality in regions deeper than 60 m. The percentages of small (large) size groups increased (decreased) with elevated thermal stress and fishing pressure. We adopted a scope for growth model to build a mechanistic link between temperature and sea scallop size. Model results suggested a gradual decrease in maximum shell height and habitat contraction under warming. This study quantified the relative contributions of thermal stress and fishing mortality to the variance of scallop size structure and discussed the need for adaptive management plans to mitigate potential socioeconomic impacts caused by size structure changes.

SAR time series despeckling via nonlocal matrix decomposition in logarithm domain

Advanced satellite and signal processing techniques have made Synthetic Aperture Radar (SAR) capable of monitoring the dynamic processes of the earth with high temporal frequency through co-registered time series. There are several challenges in interpreting these three-dimensional data cubes, including the inherent speckle effect and outliers caused by abrupt changes or sporadically appearing objects. In this paper, we propose a novel despeckling method, LogSAR-NLMD, based on nonlocal matrix decomposition in the logarithm domain to despeckle SAR time series without producing the artifacts caused by outliers. A maximum a posteriori (MAP) problem is developed by taking into account smoothness and low-rank prior knowledge in addition to the likelihood of SAR reflectivities following the Fisher-Tippett distribution. Additionally, nonlocal self-similarity is exploited to construct time series matrices that satisfy the prior better than the original data. In order to solve the MAP problem, the variable splitting procedure and the alternating direction method of multipliers (ADMM) framework are used. The proposed method has been validated by multiple experiments on synthetic and real data by comparing it with other state-of-the-art methods for despeckling SAR time series.

Densifying and Optimizing the Water Level Series for Large Lakes from Multi-Orbit ICESat-2 Observations

Satellite laser altimetry has been widely used for monitoring surface height changes in inland waters. However, constructing time series of water levels is partially limited in temporal resolution only based on the individual orbit of altimeter observations. To densify and optimize the time series of altimetry-based water levels is crucial to the scientific understanding of lake hydrologic dynamics. This paper focuses on synthesizing the multi-orbit on-lake observations from the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) to densify and refine the water level time series for large lakes. The approach of synthesizing water level time series has been validated through experiments applied to 18 large lakes worldwide, resulting in an average R of 0.93, RMSE of 0.14 m, MAE of 0.12 m, NSE of 0.67, and CV of 2.86, according to the hydrologic gauge stations. The evaluation results demonstrate that our approach can provide an effective solution for densifying the water level series of large lakes covered by multi-orbit ICESat-2 observations. Further, the approach can be extended to monitor the high-frequency variation of other lakes covered by the multiple ICESat-2 orbits. This approach provides the potential of generating higher-frequency estimates of water levels based on satellite altimetry, which could not only help to reveal the characteristics of the seasonal dynamics of lakes but also be used to investigate the abrupt water level changes due to hydrological extreme events (e.g., floods, droughts, etc.).

Time Series Construction of Oregon and Washington Snowfall since 1890 and an Update of California Snowfall through 2020

Abstract Time series of snowfall observations from over 500 stations in Oregon (OR) and Washington (WA) were generated for subregions of these states. Data problems encountered were as follows: 1) monthly totals in printed reports prior to 1940 that were not in the digital archive, 2) archived data listed as “missing” that were available, 3) digitized reports after 2010 eliminated good data, and 4) “zero” totals incorrectly entered in the official archive rather than “missing,” especially after 1980. Though addressing these was done, there is reduced confidence that some regional time series are representative of true long-term trends, especially for regions with few systematically reporting stations. For most regions characterized by consistent monitoring and with the most robust statistical reproducibility, we find no statistically significant trends in their periods of record (up to 131 years) for November–April seasonal totals through April 2020. This result includes the main snowfall regions of the Cascade Range. However, snowfall in some lower-elevation areas of OR and WA appear to have experienced declining trends, consistent with an increase in northeastern Pacific Ocean temperatures. Finally, previously constructed time series through April 2011 for regions in California are updated through April 2020 to include the recent, exceptionally low seasonal totals on the western slopes of the Sierra Nevada. This update indicates 2014/15 was the record lowest, 2013/14 was the 5th lowest, and 2012/13 was the 14th lowest of 142 years. Even so, the 1879–2020 linear trend in this key watershed region, though −2.6% decade−1, was not significantly different from zero due to high interannual variability and reconstruction uncertainty.