scholarly journals An Elastic Collision Model for Impulsive Jumping by Small Planktonic Organisms

Fluids ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 154
Author(s):  
Houshuo Jiang
Keyword(s):  
High Efficiency ◽  
Time Series Data ◽  
Elastic Collision ◽  
Series Data ◽  
Small Scale ◽  
Maximum Speed ◽  
Acceleration Phase ◽  
Mechanics Model ◽  
Fluid Mechanics Model ◽  
Initial Acceleration

Many small marine planktonic organisms converge on similar propulsion mechanisms that involve impulsively generated viscous wake vortex rings, and small-scale fluid physics is key to mechanistically understanding the adaptive values of this important behavioral trait. Here, a theoretical fluid mechanics model is developed for plankton jumping, based on observations that the initial acceleration phase for a jumping plankter to attain its maximum speed is nearly impulsive, taking only a small fraction of the viscous timescale, and therefore can be regarded as nearly inviscid, analogous to a one-dimensional elastic collision. Flow circulation time-series data measured by particle image velocimetry (PIV) are input into the model and Froude propulsion efficiencies are calculated for several plankton species. Jumping by the tailed ciliate Pseudotontonia sp. has a high Froude propulsion efficiency ~0.9. Copepod jumping also has a very high efficiency, usually >0.95. Jumping by the squid Doryteuthis pealeii paralarvae has an efficiency of 0.44 ± 0.16 (SD). Jumping by the small medusa Sarsia tubulosa has an efficiency of 0.38 ± 0.26 (SD). Differences in the calculated efficiencies are attributed to the different ways by which these plankters impart momentum on the water during the initial acceleration phase as well as the accompanied different added mass coefficients.

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 Forecast Quality of Physics-Based and Data-Driven PV Performance Models for a Small-Scale PV System

2021 ◽  
Vol 9 ◽  
Author(s):  
Moritz Stüber ◽  
Felix Scherhag ◽  
Matthieu Deru ◽  
Alassane Ndiaye ◽  
Muhammad Moiz Sakha ◽  
...  
Keyword(s):  
Smart Grids ◽  
Time Series Data ◽  
Short Term Memory ◽  
Data Driven ◽  
Series Data ◽  
Small Scale ◽  
Pv System ◽  
Advantages And Disadvantages ◽  
Forecast Quality

In the context of smart grids, the need for forecasts of the power output of small-scale photovoltaic (PV) arrays increases as control processes such as the management of flexibilities in the distribution grid gain importance. However, there is often only very little knowledge about the PV systems installed: even fundamental system parameters such as panel orientation, the number of panels and their type, or time series data of past PV system performance are usually unknown to the grid operator. In the past, only forecasting models that attempted to account for cause-and-effect chains existed; nowadays, also data-driven methods that attempt to recognize patterns in past behavior are available. Choosing between physics-based or data-driven forecast methods requires knowledge about the typical forecast quality as well as the requirements that each approach entails. In this contribution, the achieved forecast quality for a typical scenario (day-ahead, based on numerical weather predictions [NWP]) is evaluated for one physics-based as well as five different data-driven forecast methods for a year at the same site in south-western Germany. Namely, feed-forward neural networks (FFNN), long short-term memory (LSTM) networks, random forest, bagging and boosting are investigated. Additionally, the forecast quality of the weather forecast is analyzed for key quantities. All evaluated PV forecast methods showed comparable performance; based on concise descriptions of the forecast approaches, advantages and disadvantages of each are discussed. The approaches are viable even though the forecasts regularly differ significantly from the observed behavior; the residual analysis performed offers a qualitative insight into the achievable forecast quality in a typical real-world scenario.

scholarly journals Comparative transcriptome analysis reveals the intensive early-stage responses of host cells to SARS-CoV-2 infection

2020 ◽  
Author(s):  
Jiya Sun ◽  
Fei Ye ◽  
Aiping Wu ◽  
Ren Yang ◽  
Mei Pan ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
High Efficiency ◽  
Time Series Data ◽  
Early Stage ◽  
Host Cells ◽  
Host Responses ◽  
Series Data ◽  
Antiviral Responses ◽  
Regulated Gene Expression ◽  
Host Genes

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a widespread outbreak of highly pathogenic COVID-19. It is therefore important and timely to characterize interactions between the virus and host cell at the molecular level to understand its disease pathogenesis. To gain insights, we performed high-throughput sequencing that generated time-series data simultaneously for bioinformatics analysis of virus genomes and host transcriptomes implicated in SARS-CoV-2 infection. Our analysis results showed that the rapid growth of the virus was accompanied by an early intensive response of host genes. We also systematically compared the molecular footprints of the host cells in response to SARS-CoV-2, SARS-CoV and MERS-CoV. Upon infection, SARS-CoV-2 induced hundreds of up-regulated host genes hallmarked by a significant cytokine production followed by virus-specific host antiviral responses. While the cytokine and antiviral responses triggered by SARS-CoV and MERS-CoV were only observed during the late stage of infection, the host antiviral responses during the SARS-CoV-2 infection were gradually enhanced lagging behind the production of cytokine. The early rapid host responses were potentially attributed to the high efficiency of SARS-CoV-2 entry into host cells, underscored by evidence of a remarkably up-regulated gene expression of TPRMSS2 soon after infection. Taken together, our findings provide novel molecular insights into the mechanisms underlying the infectivity and pathogenicity of SARS-CoV-2.

scholarly journals Fisheries in the Mediterranean

2000 ◽  
Vol 1 (1) ◽  
pp. 5 ◽  
Author(s):  
C. PAPACONSTANTINOU ◽  
H. FARRUGIO
Keyword(s):  
Time Series Data ◽  
Early Stage ◽  
Eastern Mediterranean ◽  
Fishing Effort ◽  
Series Data ◽  
Small Scale ◽  
General Description ◽  
Stage Of Development ◽  
Mediterranean Countries ◽  
The Mediterranean

The aim of this paper is to give a description of the Mediterranean fisheries, and its level of exploitation and to address the main questions dealing with its management. The Mediterranean is a semi-enclosed marine area with generally narrow continental shelves. The primary production of the Mediterranean is among the lowest in the world (26-50g C m-2 y-1). The Mediterranean fisheries can be broken down into three main categories: small scale fisheries, trawling and seining fisheries, which operated on demersal, small pelagic and large pelagic resources. After a general description of the state of the resources in the different areas of the Mediterranean it is concluded that (a) the overall pictures from the western to the eastern Mediterranean are not considerably different, (b) the total landings in the Mediterranean have been increased the last decades, and (c) from the perspective of stock assessment, the very few available time series data show stable yield levels. In general fisheries management in the Mediterranean is at a rela- tively early stage of development, judging by the criteria of North Atlantic fisheries. Quota systems are generally not applied, mesh-size regulations usually are set at low levels relative to scientific advice, and effort limitation is not usually applied or, if it is, is not always based on a formal resource assessment. The conservation/management measures applied by the Mediterranean countries can be broadly separated into two major categories: those aiming to keep the fishing effort under control and those aiming to make the exploitation pattern more rational. The most acute problems in the management of the Mediterranean resources are the multispecificity of the catches and the lack of reliable official statistics.

scholarly journals Detecting Regime Transitions of the Nocturnal and Polar Near-Surface Temperature Inversion

2020 ◽  
Vol 77 (8) ◽  
pp. 2921-2940
Author(s):  
Amandine Kaiser ◽  
Davide Faranda ◽  
Sebastian Krumscheid ◽  
Danijel Belušić ◽  
Nikki Vercauteren
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Time Series Data ◽  
Temperature Inversion ◽  
Series Data ◽  
Small Scale ◽  
Dynamical Regime ◽  
Near Surface ◽  
Nonstationary Field ◽  
Regime Transitions

Abstract Many natural systems undergo critical transitions, i.e., sudden shifts from one dynamical regime to another. In the climate system, the atmospheric boundary layer can experience sudden transitions between fully turbulent states and quiescent, quasi-laminar states. Such rapid transitions are observed in polar regions or at night when the atmospheric boundary layer is stably stratified, and they have important consequences in the strength of mixing with the higher levels of the atmosphere. To analyze the stable boundary layer, many approaches rely on the identification of regimes that are commonly denoted as weakly and very stable regimes. Detecting transitions between the regimes is crucial for modeling purposes. In this work a combination of methods from dynamical systems and statistical modeling is applied to study these regime transitions and to develop an early warning signal that can be applied to nonstationary field data. The presented metric aims to detect nearing transitions by statistically quantifying the deviation from the dynamics expected when the system is close to a stable equilibrium. An idealized stochastic model of near-surface inversions is used to evaluate the potential of the metric as an indicator of regime transitions. In this stochastic system, small-scale perturbations can be amplified due to the nonlinearity, resulting in transitions between two possible equilibria of the temperature inversion. The simulations show such noise-induced regime transitions, successfully identified by the indicator. The indicator is further applied to time series data from nocturnal and polar meteorological measurements.

ANALISIS PROSPEK PERKEMBANGAN MODAL DAN TENAGA KERJA PADA INDUSTRI KECIL DI KABUPATEN SIDENRENG RAPPANG

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Agus Syam
Keyword(s):  
Time Series Data ◽  
Small And Medium Enterprises ◽  
Descriptive Analysis ◽  
Series Data ◽  
Small Scale ◽  
Quantitative Descriptive Analysis ◽  
Capital Growth ◽  
Small Industry ◽  
Small Industries ◽  
Small Scale Industries

Abstract Analysis of Prospect Capital Growth and Employment in Small Industries in The District Sidenreng Rappang. This research was conducted to answer the question "what are the prospects of capital growth and labor in the District Small Industries Sidenreng Rappang 5 (five) years from (2012-2016)". Thus, this study aims to determine how the prospect of the development of capital and labor in the District Small Industries Sidenreng Rappang 5 (five) years from (2012-2016). To that end, this study expected to be useful: (1) provide information for Local Government and the Department of Cooperatives and Small and Medium Enterprises in order to foster small-scale industries in the district Sidenreng Rappang, (2) as a reference material other researchers who study small-scale industries in the district Sidenreng Rappang. This research is a descriptive study using data time series (data year) only selected sub-populations in 2007-2011 for the development of capital and labor. Data collection techniques used are; documentation, interviews, and observations. Analysis of the data used, namely: qualitative and quantitative descriptive analysis. Result of research show the; 1) Capital developments in small industry in the District Sidenreng Rappang past five years (2007-2011) has increased by an average of 10.31 percent annually, 2) The development of labor in small-scale industries in the district Sidenreng Rappang past five years (2007-2011) has increased by an average of 1.04 percent annually, and 3) Development prospects of capital and labor in small-scale industries in the district Sidenreng Rappang for the coming five years (2012-2016) amounted to 3.93 percent.Kata Kunci: Modal, Tenaga Kerja dan Industri Kecil

scholarly journals Multivariate analysis of nonlinearity in sandbar behavior

2008 ◽  
Vol 15 (1) ◽  
pp. 145-158 ◽  
Author(s):  
L. Pape ◽  
B. G. Ruessink
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Nonlinear Models ◽  
Multivariate Time Series ◽  
Series Data ◽  
Small Scale ◽  
Synthetic Datasets ◽  
Long Term Prediction ◽  
Sandbar Migration

Abstract. Alongshore sandbars are often present in the nearshore zones of storm-dominated micro- to mesotidal coasts. Sandbar migration is the result of a large number of small-scale physical processes that are generated by the incoming waves and the interaction between the wave-generated processes and the morphology. The presence of nonlinearity in a sandbar system is an important factor determining its predictability. However, not all nonlinearities in the underlying system are equally expressed in the time-series of sandbar observations. Detecting the presence of nonlinearity in sandbar data is complicated by the dependence of sandbar migration on the external wave forcings. Here, a method for detecting nonlinearity in multivariate time-series data is introduced that can reveal the nonlinear nature of the dependencies between system state and forcing variables. First, this method is applied to four synthetic datasets to demonstrate its ability to qualify nonlinearity for all possible combinations of linear and nonlinear relations between two variables. Next, the method is applied to three sandbar datasets consisting of daily-observed cross-shore sandbar positions and hydrodynamic forcings, spanning between 5 and 9 years. Our analysis reveals the presence of nonlinearity in the time-series of sandbar and wave data, and the relative importance of nonlinearity for each variable. The relation between the results of each sandbar case and patterns in bar behavior are discussed, together with the effects of noise. The small effect of nonlinearity implies that long-term prediction of sandbar positions based on wave forcings might not require sophisticated nonlinear models.

Start Time Control of Micro Gas Turbine CHP System With Heat Storage

2011 ◽  
Author(s):  
Osamu Kurata ◽  
Norihiko Iki ◽  
Takayuki Matsunuma ◽  
Tetsuhiko Maeda ◽  
Satoshi Hirano ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Room Temperature ◽  
High Efficiency ◽  
Heat Storage ◽  
Time Series Data ◽  
Global Climate ◽  
Series Data ◽  
Start Time ◽  
Micro Gas Turbine ◽  
Chp System

Combined heat and power (CHP) systems are widely used considering the prevention of global climate change and the reduction of energy costs. In distributed CHP systems, both high efficiency of elements and good coordination of the systems are considered as the points to solve. We had been researched and demonstrated the micro gas turbine CHP system with heat storage at Sapporo City University from April 2006 to March 2010. At first, the start times of microturbine (MGT) and heat storage system (HST) was set up by schedule timers. In 2008 the schedule timers were substituted to a new programmable logic controller (PLC) and the start times of MGT and HST were calculated as the function of temperature outside and room temperature. Setting the start time of MGT at maximum 5 hours before 8:00 and interlocking relays of HST on MGT, the start times were calculated from temperature outside and room temperature at 21:00 the day before. Control of start time using PLC was demonstrated from Feb. 21, 2009 to June 1 and from Nov. 16 to Jan. 7, 2010. It is shown the time series data of temperature and analysis of the CHP with the original boiler heating system.

scholarly journals Banking system stability: A prerequisite for financing the Sustainable Development Goals in Nigeria

2021 ◽  
Vol 16 (2) ◽  
pp. 103-118
Author(s):  
Agatha Amadi ◽  
Kehinde A. Adetiloye ◽  
Abiola Babajide ◽  
Idimmachi Amadi
Keyword(s):  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Time Series Data ◽  
Banking System ◽  
System Stability ◽  
Series Data ◽  
Small Scale ◽  
The Sustainable Development ◽  
Distributed Lag ◽  
Development Goals

The banking system, which has been the fulcrum of funding for Nigeria’s economy, is plagued by instability in the face of a growing amount of non-performing loans. This is examined in the current milieu of the need for funding the Sustainable Development Goals (SDGs). Using a number of proxies for SDGs 8 and 9, annual time series data covering 1992 to 2019 were used with variables such as GDP per capita, commercial banks’ loans to small-scale enterprises, banking system stability indicators and liquid assets to total assets of banks. The study utilized the Autoregressive Distributed Lag. Findings showed that banking system stability has a significant positive effect on funding the SDGs 8 and 9 beyond the five per cent level of significance within the study period. Non-performing loans remained negative throughout the study. The result suggests that banking stability would enhance funding of the SDGs, and banks would be stable if they finance the SDGs. The policy implication explains the importance of banks actively pursuing opportunities to build sustainable enterprises and developing strategies that will enable their core banking business to be more venture-driven rather than consumer-oriented. In conclusion, there is a need to completely eliminate or reduce the quantum of non-performing loans from the system and establish a regulatory framework that will facilitate its expected role of intermediation in the economy profitably and successfully. AcknowledgmentThe authors would like to appreciate Covenant University for financial support to publish this paper.

scholarly journals Let's Do the Time Warp Again: Non-linear time series matching as a tool for sequentially structured data in ecology

2021 ◽  
Author(s):  
Jens C Hegg ◽  
Brian P Kennedy
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Linear Time ◽  
Temporal Structure ◽  
Structured Data ◽  
Series Data ◽  
Small Scale ◽  
Temporal Data ◽  
Ecological Data ◽  
Aggregation Techniques

Ecological patterns are often fundamentally chronological. However, generalization of data is necessarily accompanied by a loss of detail or resolution. Temporal data in particular contains information not only in data values but in the temporal structure, which is lost when these values are aggregated to provide point estimates. Dynamic Time Warping (DTW) is a time series comparison method that is capable of efficiently comparing series despite temporal offsets that confound other methods. The DTW method is both efficient and remarkably flexible, capable of efficiently matching not only time series but any sequentially structured dataset, which has made it a popular technique in machine learning, artificial intelligence, and big data analytical tasks. DTW is rarely used in ecology despite the ubiquity of temporally structured data. As technological advances have increased the richness of small-scale ecological data, DTW may be an attractive analysis technique because it is able to utilize the additional information contained in the temporal structure of many ecological datasets. In this study we use an example dataset of high-resolution fish movement records obtained from otolith microchemistry to compare traditional analysis techniques with DTW clustering. Our results suggest that DTW is capable of detecting subtle behavioral patterns within otolith datasets which traditional data aggregation techniques cannot. These results provide evidence that the DTW method may be useful across many of the temporal data types commonly collected in ecology, as well other sequentially ordered "pseudo time series" data such as classification of species by shape.

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