scholarly journals Multiyear trend in reproduction underpins interannual variation in gametogenic development of an Antarctic urchin

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rebecca De Leij ◽  
Lloyd S. Peck ◽  
Laura J. Grange
Keyword(s):  
Large Scale ◽  
Time Series Data ◽  
Gonad Index ◽  
Reproductive Investment ◽  
Series Data ◽  
Future Climate Change ◽  
Small Scale ◽  
Marine Biota ◽  
Climate Change Scenarios ◽  
Residual Variation

AbstractEcosystems and their biota operate on cyclic rhythms, often entrained by predictable, small-scale changes in their natural environment. Recording and understanding these rhythms can detangle the effect of human induced shifts in the climate state from natural fluctuations. In this study, we assess long-term patterns of reproductive investment in the Antarctic sea urchin, Sterechinus neumayeri, in relation to changes in the environment to identify drivers of reproductive processes. Polar marine biota are sensitive to small changes in their environment and so serve as a barometer whose responses likely mirror effects that will be seen on a wider global scale in future climate change scenarios. Our results indicate that seasonal reproductive periodicity in the urchin is underpinned by a multiyear trend in reproductive investment beyond and in addition to, the previously reported 18–24 month gametogenic cycle. Our model provides evidence that annual reproductive investment could be regulated by an endogenous rhythm since environmental factors only accounted for a small proportion of the residual variation in gonad index. This research highlights a need for multiyear datasets and the combination of biological time series data with large-scale climate metrics that encapsulate multi-factorial climate state shifts, rather than using single explanatory variables to inform changes in biological processes.

The Classic Maya Collapse: Testing Class Conflict Hypotheses

1980 ◽  
Vol 45 (2) ◽  
pp. 246-267 ◽  
Author(s):  
Robert L. Hamblin ◽  
Brian L. Pitcher
Keyword(s):  
Large Scale ◽  
Time Series Data ◽  
Classic Maya ◽  
Class Conflict ◽  
Series Data ◽  
Geographical Patterns ◽  
Historical Societies ◽  
Testing Class ◽  
Lowland Area ◽  
Maya Collapse

Several lines of archaeological evidence are presented in this paper to suggest the existence of class warfare among the Classic Maya and of issues that historically have been associated with class conflict. This evidence indicates that class warfare may have halted the rule of the monument-producing, or Classic, elites and precipitated the depopulation of the lowland area. The theory is evaluated quantitatively by testing for time-related mathematical patterns that have been found to characterize large-scale conflicts in historical societies. The information used in the evaluation involves the time series data on the duration of rule by Classic elites as inferred from the production of monuments with Long Count dates at a sample of 82 ceremonial centers. The analyses confirm that the Maya data do exhibit the temporal and geographical patterns predicted from the class conflict explanation of the Classic Maya collapse. Alternative predictions from the other theories are considered but generally not found to be supported by these data.

scholarly journals Beyond the implications of Grand Ethiopian Renaissance Dam filling policies

2021 ◽  
Vol 7 (3) ◽  
pp. 313-330
Author(s):  
Abay Yimere ◽  
◽  
Engdawork Assefa ◽  
Keyword(s):  
Time Series Data ◽  
Water Levels ◽  
Series Data ◽  
Future Climate Change ◽  
Policy Debate ◽  
International Treaty ◽  
Term Operation ◽  
Operating Level ◽  
Minimum Operating

<abstract> <p>The Grand Ethiopian Renaissance Dam (GERD) in Ethiopia and High Aswan Dam (HAD) in Egypt both operate on the Nile River, independent of a governing international treaty or agreement. As a result, the construction of the GERD, the Earth's eighth largest dam, ignited a furious debate among Ethiopia, Sudan, and Egypt on its filling policies and long-term operation. Ethiopia and Egypt's stance on the Nile River's water resources, combined with a nationalistic policy debate on the GERD's filling policies and long-term operation, has severely affected progress toward reaching agreeable terms before the first round of GERD filling was completed. These three countries continue to debate on the terms of agreement for the second round of GERD filling, scheduled to start by July 2021. We examined the GERD filling strategy for five- and six-year terms using time series data for the periods 1979–1987 and 1987–1992 to combine analyses for dry and wet seasons and investigate the potential impacts of filling the GERD above the downstream HAD using four HAD starting water levels. A model calibrated using MIKE Hydro results shows that during both five- and six-year terms of future GERD filling, Egypt would not need to invoke the HAD's minimum operating level. We pursued a narrative approach that appeals to both a technical and non-technical readership, and our results show the urgent need for cooperation at both policy and technical levels to mitigate and adapt to future climate change through the development of climate-proof agreements. Moreover, the results call for the riparian countries to move away from the current nationalistic policy debate approach and pursue a more cooperative, economically beneficial, and climate adaptive approach.</p> </abstract>

scholarly journals Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods and Results

2021 ◽  
Author(s):  
Sadnan Al Manir ◽  
Justin Niestroy ◽  
Maxwell Adam Levinson ◽  
Timothy Clark
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Series Data ◽  
Predictive Analytics ◽  
Defeasible Reasoning ◽  
Series Data ◽  
Inference Rules ◽  
Deep Networks ◽  
Evidence Graph ◽  
Over Time

Introduction: Transparency of computation is a requirement for assessing the validity of computed results and research claims based upon them; and it is essential for access to, assessment, and reuse of computational components. These components may be subject to methodological or other challenges over time. While reference to archived software and/or data is increasingly common in publications, a single machine-interpretable, integrative representation of how results were derived, that supports defeasible reasoning, has been absent. Methods: We developed the Evidence Graph Ontology, EVI, in OWL 2, with a set of inference rules, to provide deep representations of supporting and challenging evidence for computations, services, software, data, and results, across arbitrarily deep networks of computations, in connected or fully distinct processes. EVI integrates FAIR practices on data and software, with important concepts from provenance models, and argumentation theory. It extends PROV for additional expressiveness, with support for defeasible reasoning. EVI treats any com- putational result or component of evidence as a defeasible assertion, supported by a DAG of the computations, software, data, and agents that produced it. Results: We have successfully deployed EVI for very-large-scale predictive analytics on clinical time-series data. Every result may reference its own evidence graph as metadata, which can be extended when subsequent computations are executed. Discussion: Evidence graphs support transparency and defeasible reasoning on results. They are first-class computational objects, and reference the datasets and software from which they are derived. They support fully transparent computation, with challenge and support propagation. The EVI approach may be extended to include instruments, animal models, and critical experimental reagents.

scholarly journals Differentially Private Autocorrelation Time-Series Data Publishing Based on Sliding Window

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jing Zhao ◽  
Shubo Liu ◽  
Xingxing Xiong ◽  
Zhaohui Cai
Keyword(s):  
Time Series ◽  
Privacy Protection ◽  
Large Scale ◽  
Differential Privacy ◽  
Time Series Data ◽  
Sliding Window ◽  
Data Publishing ◽  
Series Data ◽  
Data Publication ◽  
Autocorrelation Time

Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.

A deep learning method for data recovery in sensor networks using effective spatio-temporal correlation data

Sensor Review ◽  
2019 ◽  
Vol 39 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Jinghan Du ◽  
Haiyan Chen ◽  
Weining Zhang
Keyword(s):  
Deep Learning ◽  
Sensor Networks ◽  
Large Scale ◽  
Time Series Data ◽  
Temporal Correlation ◽  
Data Recovery ◽  
Series Data ◽  
Learning Method ◽  
Content Type ◽  
Spatio Temporal

Purpose In large-scale monitoring systems, sensors in different locations are deployed to collect massive useful time-series data, which can help in real-time data analytics and its related applications. However, affected by hardware device itself, sensor nodes often fail to work, resulting in a common phenomenon that the collected data are incomplete. The purpose of this study is to predict and recover the missing data in sensor networks. Design/methodology/approach Considering the spatio-temporal correlation of large-scale sensor data, this paper proposes a data recover model in sensor networks based on a deep learning method, i.e. deep belief network (DBN). Specifically, when one sensor fails, the historical time-series data of its own and the real-time data from surrounding sensor nodes, which have high similarity with a failure observed using the proposed similarity filter, are collected first. Then, the high-level feature representation of these spatio-temporal correlation data is extracted by DBN. Moreover, to determine the structure of a DBN model, a reconstruction error-based algorithm is proposed. Finally, the missing data are predicted based on these features by a single-layer neural network. Findings This paper collects a noise data set from an airport monitoring system for experiments. Various comparative experiments show that the proposed algorithms are effective. The proposed data recovery model is compared with several other classical models, and the experimental results prove that the deep learning-based model can not only get a better prediction accuracy but also get a better performance in training time and model robustness. Originality/value A deep learning method is investigated in data recovery task, and it proved to be effective compared with other previous methods. This might provide a practical experience in the application of a deep learning method.

scholarly journals How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 95 ◽  
Author(s):  
Johannes Stübinger ◽  
Katharina Adler
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Structural Breaks ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Real Data ◽  
Linear Mapping ◽  
Series Data ◽  
Lag Effects ◽  
Silver Metal

This paper develops the generalized causality algorithm and applies it to a multitude of data from the fields of economics and finance. Specifically, our parameter-free algorithm efficiently determines the optimal non-linear mapping and identifies varying lead–lag effects between two given time series. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences—structural breaks in their relationship are also captured. A large-scale simulation study validates the outperformance in the vast majority of parameter constellations in terms of efficiency, robustness, and feasibility. Finally, the presented methodology is applied to real data from the areas of macroeconomics, finance, and metal. Highest similarity show the pairs of gross domestic product and consumer price index (macroeconomics), S&P 500 index and Deutscher Aktienindex (finance), as well as gold and silver (metal). In addition, the algorithm takes full use of its flexibility and identifies both various structural breaks and regime patterns over time, which are (partly) well documented in the literature.

scholarly journals Lagrangian and spectral analysis of the forced flow past a circular cylinder using pulsed tangential jets

2012 ◽  
Vol 696 ◽  
pp. 285-300 ◽  
Author(s):  
T. Jardin ◽  
Y. Bury
Keyword(s):  
Circular Cylinder ◽  
Time Series Data ◽  
Excitation Frequency ◽  
Shear Layers ◽  
Forced Flow ◽  
Series Data ◽  
Small Scale ◽  
Characteristic Time Scale ◽  
Momentum Coefficient ◽  
Flow Past

AbstractWe numerically investigate the influence of pulsed tangential jets on the flow past a circular cylinder. To this end a spectral-Lagrangian dual approach is used on the basis of time-series data. The analysis reveals that the flow response to unsteady forcing is driven by strong interactions between shear layers and pulsed jets. The latter preferentially lead to either the lock-on regime or the quasi-steady vortex feeding regime whether the excitation frequency is of the order of, or significantly greater than, the frequency of the natural instability. The intensity of the wake vortices is mainly influenced by the momentum coefficient through the introduction of opposite-sign vorticity in the shear layers. This feature is emphasized using a modal-based time reconstruction, i.e. by reconstructing the flow field upon a specific harmonic spectrum associated with a characteristic time scale. The quasi-steady regime exhibits small-scale counter-rotating vortices that circumscribe the separated region. In the lock-on regime, atypical wake patterns such as 2P or $\mathrm{P} + \mathrm{S} $ can be observed, depending on the forcing frequency and the momentum coefficient, highlighting remarkable analogies with oscillating cylinders.

scholarly journals AstroCatR: a mechanism and tool for efficient time series reconstruction of large-scale astronomical catalogues

2020 ◽  
Vol 496 (1) ◽  
pp. 629-637
Author(s):  
Ce Yu ◽  
Kun Li ◽  
Shanjiang Tang ◽  
Chao Sun ◽  
Bin Ma ◽  
...  
Keyword(s):  
Time Series ◽  
High Performance ◽  
Large Scale ◽  
Extrasolar Planets ◽  
Time Series Data ◽  
Series Data ◽  
Data Sets ◽  
Observation Data ◽  
Data Volume ◽  
And Performance

ABSTRACT Time series data of celestial objects are commonly used to study valuable and unexpected objects such as extrasolar planets and supernova in time domain astronomy. Due to the rapid growth of data volume, traditional manual methods are becoming extremely hard and infeasible for continuously analysing accumulated observation data. To meet such demands, we designed and implemented a special tool named AstroCatR that can efficiently and flexibly reconstruct time series data from large-scale astronomical catalogues. AstroCatR can load original catalogue data from Flexible Image Transport System (FITS) files or data bases, match each item to determine which object it belongs to, and finally produce time series data sets. To support the high-performance parallel processing of large-scale data sets, AstroCatR uses the extract-transform-load (ETL) pre-processing module to create sky zone files and balance the workload. The matching module uses the overlapped indexing method and an in-memory reference table to improve accuracy and performance. The output of AstroCatR can be stored in CSV files or be transformed other into formats as needed. Simultaneously, the module-based software architecture ensures the flexibility and scalability of AstroCatR. We evaluated AstroCatR with actual observation data from The three Antarctic Survey Telescopes (AST3). The experiments demonstrate that AstroCatR can efficiently and flexibly reconstruct all time series data by setting relevant parameters and configuration files. Furthermore, the tool is approximately 3× faster than methods using relational data base management systems at matching massive catalogues.

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