scholarly journals Inferring time-derivatives, including cell growth rates, using Gaussian processes

2016 ◽  
Author(s):  
Peter S. Swain ◽  
Keiran Stevenson ◽  
Allen Leary ◽  
Luis F. Montano-Gutierrez ◽  
Ivan B. N. Clark ◽  
...  
Keyword(s):  
Growth Rate ◽  
Gaussian Processes ◽  
Time Series Data ◽  
Parametric Method ◽  
Time Derivative ◽  
Spindle Pole ◽  
Series Data ◽  
Measured Variable ◽  
Wide Range

AbstractOften the time-derivative of a measured variable is of as much interest as the variable itself. For a growing population of biological cells, for example, the population's growth rate is typically more important than its size. Here we introduce a non-parametric method to infer first and second time-derivatives as a function of time from time-series data. Our approach is based on established properties of Gaussian processes and therefore applies to a wide range of data. In tests, the method is at least as accurate as others, but has several advantages: it estimates errors both in the inference and in any summary statistics, such as lag times, allows interpolation with the corresponding error estimation, and can be applied to any number of experimental replicates. As illustrations, we infer growth rate from measurements of the optical density of populations of microbial cells and estimate the rate of in vitro assembly of an amyloid fibril and both the speed and acceleration of two separating spindle pole bodies in a single yeast cell. Being accessible through both a GUI and from scripts, our algorithm should have broad application across the sciences.

scholarly journals The Economic Impact of Critical National Infrastructure Failure Due to Space Weather

2018 ◽  
Author(s):  
Edward J. Oughton
Keyword(s):  
Economic Impact ◽  
Space Weather ◽  
Time Series Data ◽  
Critical Infrastructure ◽  
Satellite Communications ◽  
Series Data ◽  
Future Research ◽  
Wide Range ◽  
Infrastructure Failure ◽  
National Infrastructure

Space weather is a collective term for different solar or space phenomena that can detrimentally affect technology. However, current understanding of space weather hazards is still relatively embryonic in comparison to terrestrial natural hazards such as hurricanes, earthquakes, or tsunamis. Indeed, certain types of space weather such as large Coronal Mass Ejections (CMEs) are an archetypal example of a low-probability, high-severity hazard. Few major events, short time-series data, and the lack of consensus regarding the potential impacts on critical infrastructure have hampered the economic impact assessment of space weather. Yet, space weather has the potential to disrupt a wide range of Critical National Infrastructure (CNI) systems including electricity transmission, satellite communications and positioning, aviation, and rail transportation. In the early 21st century, there has been growing interest in these potential economic and societal impacts. Estimates range from millions of dollars of equipment damage from the Quebec 1989 event, to some analysts asserting that losses will be in the billions of dollars in the wider economy from potential future disaster scenarios. Hence, the origin and development of the socioeconomic evaluation of space weather is tracked, from 1989 to 2017, and future research directions for the field are articulated. Since 1989, many economic analyzes of space weather hazards have often completely overlooked the physical impacts on infrastructure assets and the topology of different infrastructure networks. Moreover, too many studies have relied on qualitative assumptions about the vulnerability of CNI. By modeling both the vulnerability of critical infrastructure and the socioeconomic impacts of failure, the total potential impacts of space weather can be estimated, providing vital information for decision makers in government and industry. Efforts on this subject have historically been relatively piecemeal, which has led to little exploration of model sensitivities, particularly in relation to different assumption sets about infrastructure failure and restoration. Improvements may be expedited in this research area by open-sourcing model code, increasing the existing level of data sharing, and improving multidisciplinary research collaborations between scientists, engineers, and economists.

scholarly journals The Origins and Effects of Corporate Diversity Programs

2013 ◽  
Author(s):  
Frank Dobbin ◽  
Alexandra Kalev
Keyword(s):  
Equal Opportunity ◽  
Time Series Data ◽  
Diversity Training ◽  
Series Data ◽  
Mentoring Programs ◽  
Full Time ◽  
Wide Range ◽  
Diversity Programs ◽  
Corporate Diversity ◽  
The 1960S

Corporations have implemented a wide range of equal opportunity and diversity programs since the 1960s. This chapter reviews studies of the origins of these programs, surveys that assess the popularity of different programs, and research on the effects of programs on the workforce. Human resources managers championed several waves of innovations: corporate equal opportunity policies and recruitment and training programs in the 1960s; bureaucratic hiring and promotion policies and grievance mechanisms in the 1970s; diversity training, networking, and mentoring programs in the 1980s; and work/family and sexual harassment programs in the 1990s and beyond. It was those managers who designed equal opportunity and diversity programs, not lawyers or judges or government bureaucrats, thus corporate take-up of the programs remains very uneven. Statistical analyses of time-series data on the effects of corporate diversity measures reveal several patterns. Initiatives designed to quash managerial bias, through diversity training, diversity performance evaluations, and bureaucratic rules, have been broadly ineffective. By contrast, innovations designed to engage managers in promoting workforce integration—mentoring programs, diversity taskforces, and full-time diversity staffers—have led to increases in diversity in the most difficult job to integrate, management. The research has clear implications for corporate and public policy.

scholarly journals Co-eye: a multi-resolution ensemble classifier for symbolically approximated time series

2020 ◽  
Vol 109 (11) ◽  
pp. 2029-2061
Author(s):  
Zahraa S. Abdallah ◽  
Mohamed Medhat Gaber
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Ensemble Classifier ◽  
Series Data ◽  
New Classification ◽  
Symbolic Representations ◽  
Classification Technique ◽  
Field Of Vision ◽  
Main Challenge ◽  
Wide Range

Abstract Time series classification (TSC) is a challenging task that attracted many researchers in the last few years. One main challenge in TSC is the diversity of domains where time series data come from. Thus, there is no “one model that fits all” in TSC. Some algorithms are very accurate in classifying a specific type of time series when the whole series is considered, while some only target the existence/non-existence of specific patterns/shapelets. Yet other techniques focus on the frequency of occurrences of discriminating patterns/features. This paper presents a new classification technique that addresses the inherent diversity problem in TSC using a nature-inspired method. The technique is stimulated by how flies look at the world through “compound eyes” that are made up of thousands of lenses, called ommatidia. Each ommatidium is an eye with its own lens, and thousands of them together create a broad field of vision. The developed technique similarly uses different lenses and representations to look at the time series, and then combines them for broader visibility. These lenses have been created through hyper-parameterisation of symbolic representations (Piecewise Aggregate and Fourier approximations). The algorithm builds a random forest for each lens, then performs soft dynamic voting for classifying new instances using the most confident eyes, i.e., forests. We evaluate the new technique, coined Co-eye, using the recently released extended version of UCR archive, containing more than 100 datasets across a wide range of domains. The results show the benefits of bringing together different perspectives reflecting on the accuracy and robustness of Co-eye in comparison to other state-of-the-art techniques.

scholarly journals Thermal Infrared and Ionospheric Anomalies of the 2017 Mw6.5 Jiuzhaigou Earthquake

2020 ◽  
Vol 12 (17) ◽  
pp. 2843
Author(s):  
Meijiao Zhong ◽  
Xinjian Shan ◽  
Xuemin Zhang ◽  
Chunyan Qu ◽  
Xiao Guo ◽  
...  
Keyword(s):  
Time Series Data ◽  
Longwave Radiation ◽  
Thermal Infrared ◽  
Geological Structure ◽  
Series Data ◽  
Electron Content ◽  
Jiuzhaigou Earthquake ◽  
Total Electron ◽  
Wide Range ◽  
First Time

Taking the 2017 Mw6.5 Jiuzhaigou earthquake as a case study, ionospheric disturbances (i.e., total electron content and TEC) and thermal infrared (TIR) anomalies were simultaneously investigated. The characteristics of the temperature of brightness blackbody (TBB), medium-wave infrared brightness (MIB), and outgoing longwave radiation (OLR) were extracted and compared with the characteristics of ionospheric TEC. We observed different relationships among the three types of TIR radiation according to seismic or aseismic conditions. A wide range of positive TEC anomalies occurred southern to the epicenter. The area to the south of the Huarong mountain fracture, which contained the maximum TEC anomaly amplitudes, overlapped one of the regions with notable TIR anomalies. We observed three stages of increasing TIR radiation, with ionospheric TEC anomalies appearing after each stage, for the first time. There was also high spatial correspondence between both TIR and TEC anomalies and the regional geological structure. Together with the time series data, these results suggest that TEC anomaly genesis might be related to increasing TIR.

scholarly journals Aβ42 fibril formation from predominantly oligomeric samples suggests a link between oligomer heterogeneity and fibril polymorphism

2019 ◽  
Vol 6 (7) ◽  
pp. 190179 ◽  
Author(s):  
Christine Xue ◽  
Joyce Tran ◽  
Hongsu Wang ◽  
Giovanna Park ◽  
Frederick Hsu ◽  
...  
Keyword(s):  
Growth Rate ◽  
Green Tea ◽  
Epigallocatechin Gallate ◽  
Amyloid Β ◽  
Lag Time ◽  
Aβ Oligomers ◽  
Wide Range ◽  
Aβ Aggregation

Amyloid-β (Aβ) oligomers play a central role in the pathogenesis of Alzheimer's disease. Oligomers of different sizes, morphology and structures have been reported in both in vivo and in vitro studies, but there is a general lack of understanding about where to place these oligomers in the overall process of Aβ aggregation and fibrillization. Here, we show that Aβ42 spontaneously forms oligomers with a wide range of sizes in the same sample. These Aβ42 samples contain predominantly oligomers, and they quickly form fibrils upon incubation at 37°C. When fractionated using ultrafiltration filters, the samples enriched with smaller oligomers form fibrils at a faster rate than the samples enriched with larger oligomers, with both a shorter lag time and faster fibril growth rate. This observation is independent of Aβ42 batches and hexafluoroisopropanol treatment. Furthermore, the fibrils formed by the samples enriched with larger oligomers are more readily solubilized by epigallocatechin gallate, a main catechin component of green tea. These results suggest that the fibrils formed by larger oligomers may adopt a different structure from fibrils formed by smaller oligomers, pointing to a link between oligomer heterogeneity and fibril polymorphism.

A Study on the performance of Agricultural Sector in India

2017 ◽  
Vol 51 (03) ◽  
Author(s):  
Laishram Priscilla ◽  
Arsha Balakrishnan ◽  
Lalrinsangpuii Lalrinsangpuii ◽  
A. K. Chauhan
Keyword(s):  
Growth Rate ◽  
Agricultural Sector ◽  
Time Series Data ◽  
Decomposition Analysis ◽  
Series Data ◽  
Meat Production ◽  
Area Effect ◽  
Agriculture Sector ◽  
Per Capita ◽  
Vegetables And Fruits

<span>The time series data at all India level on area, production and productivity of foodgrains, production and per capita availability of milk and eggs and production of meat were compiled and a decade wise analysis of growth rate, instability index and decomposition analysis was done to study the performance of agriculture sector. During the overall period, the area under food grains showed negative growth whereas production and productivity growth was positive. For milk and egg, both production and per capita availability showed positive growth. Meat production showed a positively significant growth rate. Growth rate in area, production and productivity of both vegetables and fruits was positive. In general, for foodgrains, the yield effect was higher than the area effect which could be attributed to increased use of high yielding varieties. For vegetables and fruits, the contribution of area effect was more than that of yield and the interaction effect suggesting that measures should be taken to improve their productivity. </span>

Applying Circular Statistics to the Analysis of Monitoring Data

2007 ◽  
Vol 23 (4) ◽  
pp. 227-237 ◽  
Author(s):  
Thomas Kubiak ◽  
Cornelia Jonas
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Ambulatory Monitoring ◽  
Circular Data ◽  
Circular Statistics ◽  
Series Data ◽  
Cross Sectional ◽  
Psychological Variables ◽  
Wide Range ◽  
Time Series Approach

Abstract. Patterns of psychological variables in time have been of interest to research from the beginning. This is particularly true for ambulatory monitoring research, where large (cross-sectional) time-series datasets are often the matter of investigation. Common methods for identifying cyclic variations include spectral analyses of time-series data or time-domain based strategies, which also allow for modeling cyclic components. Though the prerequisites of these sophisticated procedures, such as interval-scaled time-series variables, are seldom met, their usage is common. In contrast to the time-series approach, methods from a different field of statistics, directional or circular statistics, offer another opportunity for the detection of patterns in time, where fewer prerequisites have to be met. These approaches are commonly used in biology or geostatistics. They offer a wide range of analytical strategies to examine “circular data,” i.e., data where period of measurement is rotationally invariant (e.g., directions on the compass or daily hours ranging from 0 to 24, 24 being the same as 0). In psychology, however, circular statistics are hardly known at all. In the present paper, we intend to give a succinct introduction into the rationale of circular statistics and describe how this approach can be used for the detection of patterns in time, contrasting it with time-series analysis. We report data from a monitoring study, where mood and social interactions were assessed for 4 weeks in order to illustrate the use of circular statistics. Both the results of periodogram analyses and circular statistics-based results are reported. Advantages and possible pitfalls of the circular statistics approach are highlighted concluding that ambulatory assessment research can benefit from strategies borrowed from circular statistics.

scholarly journals Ownership protection of outsourced biomedical time series data based on optimal watermarking scheme in data mining

2017 ◽  
Vol 21 ◽  
Author(s):  
Trung Duy Pham ◽  
Dat Tran ◽  
Wanli Ma
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Digital Data ◽  
Series Data ◽  
Biomedical Data ◽  
Protection Mechanism ◽  
Wide Range ◽  
Watermarking Scheme ◽  
Ownership Protection

In the biomedical and healthcare fields, the ownership protection of the outsourced data is becoming a challenging issue in sharing the data between data owners and data mining experts to extract hidden knowledge and patterns. Watermarking has been proved as a right-protection mechanism that provides detectable evidence for the legal ownership of a shared dataset, without compromising its usability under a wide range of data mining for digital data in different formats such as audio, video, image, relational database, text and software. Time series biomedical data such as Electroencephalography (EEG) or Electrocardiography (ECG) is valuable and costly in healthcare, which need to have owner protection when sharing or transmission in data mining application. However, this issue related to kind of data has only been investigated in little previous research as its characteristics and requirements. This paper proposes an optimized watermarking scheme to protect ownership for biomedical and healthcare systems in data mining. To achieve the highest possible robustness without losing watermark transparency, Particle Swarm Optimization (PSO) technique is used to optimize quantization steps to find a suitable one. Experimental results on EEG data show that the proposed scheme provides good imperceptibility and more robust against various signal processing techniques and common attacks such as noise addition, low-pass filtering, and re-sampling.

scholarly journals TREND, GROWTH RATE AND INSTABILITY ANALYSIS OF PRODUCTION, PRODUCTIVITY, AREA AND SUPPLY OF POTATO IN KAVREPALANCHOK DISTRICT, NEPAL

2021 ◽  
Vol 1 (2) ◽  
pp. 88-87
Author(s):  
Mahesh Rijal ◽  
Rabin Thapa ◽  
Arvind Srivastava ◽  
Gunakeshari Lamsal
Keyword(s):  
Growth Rate ◽  
Time Series Data ◽  
Annual Growth ◽  
Series Data ◽  
Annual Growth Rate ◽  
Wholesale Market ◽  
White Potato ◽  
Instability Analysis ◽  
Compound Annual Growth Rate ◽  
Future Production

A study was carried out to assess the trend of area, production, productivity and supply of potato in Kavre district, Nepal. The time-series data (1999/00 to 2017/18) were collected from the “Statistical Information on Nepalese Agriculture” published yearly by the Ministry of Agriculture and Livestock Development, Nepal and the data of potato (red and white) supply from Kavre to Kalimati wholesale market from 2000/01 to 2019/20 was collected from the official website of Kalimati market. The data were entered and analyzed using Microsoft Excel and XLSTAT. Mann-Kendall test (M-K) and Sen’s slope method were used for trend analysis. The results showed that the potato cultivation area increased by 341.786 ha/year, production increased by 8323.933 Mt/year and productivity increased by 0.231 Mt/ha/year from 1999/00 to 2017/18. Similarly, the red potato supply from Kavre to the Kalimati market increased by 13.412 Mt/year and the white potato supply decreased by 234.174 Mt/year during the period from 2000/01 to 2019/20. The instability analysis showed 34.41%, 41.36% and 11.16%. coefficient of variation for area, production and productivity while red potato and white potato supply showed 11.64% and 107.86% variation. The average annual growth rates for area, production and productivity of potato were 6.02%, 8.83% and 2.43%, respectively. Similarly, growth rate of red potato supply was 3.91% per annum while white potato supply decreased at the compound annual growth rate of 19.61%. Thus, an increasing trend of area, production and productivity and supply of potato along with a positive growth rate for the potato can be seen in the Kavre district. Findings from this study could be used to suggest necessary policy guidelines for future production and marketing strategies of potato in Kavre.

scholarly journals Impact of Industrial Hollowing on Regional Economic Growth Rate: An Empirical Analysis based on Shanghai’s Data

2018 ◽  
Vol 14 (22) ◽  
pp. 223
Author(s):  
Yajie Bai ◽  
Maoguo Wu
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Time Series Data ◽  
Negative Impact ◽  
Empirical Test ◽  
Ordinary Least Squares ◽  
Series Data ◽  
Producer Price ◽  
Economic Growth Rate ◽  
Labor Resources

The relation between industrial hollowing-out and Shanghai’s economic growth rate was analyzed by using ordinary least squares and ECM regression model. Data used in the empirical test was a monthly time series data from January 2003 to February 2017. Empirical results show Industrial producer price index, and the total amount of imports has a positive relationship with economic growth rate. However, fixed asset investment, land use cost, and labor resources cost have a negative impact on economic growth rate.

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