Nearest-Neighbor Distances Change Over Short Time Intervals in Foraging Flocks of Shorebirds

2020 ◽  
Vol 19 (1) ◽  
pp. 107
Author(s):  
Ivana Novcic
Keyword(s):  
Nearest Neighbor ◽  
Time Intervals ◽  
Short Time ◽  
Nearest Neighbor Distances

Application of Lattice Imaging Techniques to Amorphous Films

1975 ◽  
Vol 33 ◽  
pp. 8-9
Author(s):  
S. R. Herd ◽  
P. Chaudhari
Keyword(s):  
Nearest Neighbor ◽  
Random Network ◽  
Imaging Techniques ◽  
Network Models ◽  
Accurate Method ◽  
Direct Transmission ◽  
Lattice Imaging ◽  
Transmission Electron ◽  
Lattice Planes ◽  
Nearest Neighbor Distances

Electron diffraction and direct transmission have been used extensively to study the local atomic arrangement in amorphous solids and in particular Ge. Nearest neighbor distances had been calculated from E.D. profiles and the results have been interpreted in terms of the microcrystalline or the random network models. Direct transmission electron microscopy appears the most direct and accurate method to resolve this issue since the spacial resolution of the better instruments are of the order of 3Å. In particular the tilted beam interference method is used regularly to show fringes corresponding to 1.5 to 3Å lattice planes in crystals as resolution tests.

Proposal of PV Output Estimate Method in Short Time Intervals

2016 ◽  
Vol 136 (12) ◽  
pp. 891-897 ◽  
Author(s):  
Katsuhiro Matsuda ◽  
Kazuhiro Misawa ◽  
Hirotaka Takahashi ◽  
Kenta Furukawa ◽  
Satoshi Uemura
Keyword(s):  
Time Intervals ◽  
Estimate Method ◽  
Short Time

EVALUATION OF SHORT TIME INTERVALS IN NORMAL AGING AND LATE-LIFE DEPRESSIONS

2020 ◽  
pp. 54-69
Author(s):  
Elena Yu. Balashova ◽  
◽  
Lika I. Mikeladze ◽  
Elena K. Kozlova ◽  
◽  
...  
Keyword(s):  
Late Life ◽  
Normal Aging ◽  
Time Intervals ◽  
Short Time

scholarly journals Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1213
Author(s):  
Ahmed Aljanad ◽  
Nadia M. L. Tan ◽  
Vassilios G. Agelidis ◽  
Hussain Shareef
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Solar Irradiance ◽  
Particle Swarm ◽  
Time Interval ◽  
Time Intervals ◽  
Swarm Optimization ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

scholarly journals Optimal Perturbations of an Oceanic Vortex Lens

Fluids ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. 63 ◽  
Author(s):  
Thomas Meunier ◽  
Claire Ménesguen ◽  
Xavier Carton ◽  
Sylvie Le Gentil ◽  
Richard Schopp
Keyword(s):  
Normal Mode ◽  
Growth Rates ◽  
Time Interval ◽  
Time Intervals ◽  
Horizontal Structure ◽  
Azimuthal Wave ◽  
Wave Numbers ◽  
Non Linear ◽  
Long Time ◽  
Short Time

The stability properties of a vortex lens are studied in the quasi geostrophic (QG) framework using the generalized stability theory. Optimal perturbations are obtained using a tangent linear QG model and its adjoint. Their fine-scale spatial structures are studied in details. Growth rates of optimal perturbations are shown to be extremely sensitive to the time interval of optimization: The most unstable perturbations are found for time intervals of about 3 days, while the growth rates continuously decrease towards the most unstable normal mode, which is reached after about 170 days. The horizontal structure of the optimal perturbations consists of an intense counter-shear spiralling. It is also extremely sensitive to time interval: for short time intervals, the optimal perturbations are made of a broad spectrum of high azimuthal wave numbers. As the time interval increases, only low azimuthal wave numbers are found. The vertical structures of optimal perturbations exhibit strong layering associated with high vertical wave numbers whatever the time interval. However, the latter parameter plays an important role in the width of the vertical spectrum of the perturbation: short time interval perturbations have a narrow vertical spectrum while long time interval perturbations show a broad range of vertical scales. Optimal perturbations were set as initial perturbations of the vortex lens in a fully non linear QG model. It appears that for short time intervals, the perturbations decay after an initial transient growth, while for longer time intervals, the optimal perturbation keeps on growing, quickly leading to a non-linear regime or exciting lower azimuthal modes, consistent with normal mode instability. Very long time intervals simply behave like the most unstable normal mode. The possible impact of optimal perturbations on layering is also discussed.

An Automatic Paperless Velocity Measuring and Recording System

2014 ◽  
Vol 889-890 ◽  
pp. 745-748
Author(s):  
Jian Sheng Cao ◽  
Wan Jun Zhang ◽  
Xin Hua Zeng
Keyword(s):  
Flow Velocity ◽  
Optical Pulse ◽  
Electric Pulse ◽  
Automatic Monitoring ◽  
Time Intervals ◽  
Stream Flows ◽  
Fluid Flow Velocity ◽  
Photoelectric Sensor ◽  
Velocity Measuring ◽  
Short Time

Automatic monitoring of hydrologic properties such as water velocity at short-time intervals is critical for understanding watershed eco-hydrological processes. This can also be used to study the laws of stream flows and interactions ecological process. The advent of modern electronic technology (and the near-perfection of especially sensor and data collection technologies), has made it possible to use automatic monitoring systems to continuously measure hydrologic properties at short-time intervals. This paper introduces one such paperless flow velocity measuring/recoding system. The system uses a photoelectric sensor that is mainly comprised of photoelectric velocity sensor and pulse recorder. The system uses propellers (with reflective panels and photoemission cells) to transform flow velocities into optical pulse signals. It also uses photosensitive tubes to transform optical pulse signals into electric pulse signals. The electric pulse counts (generated in unit time) are recorded via pulse recorders. This therefore accomplishes automatic monitoring and continuous recording of fluid flow velocity.

STUDY OF THERMODYNAMIC PROPERTIES OF ZINC-BLENDE-TYPE SEMICONDUCTORS: TEMPERATURE AND PRESSURE DEPENDENCES

2011 ◽  
Vol 25 (12n13) ◽  
pp. 1041-1051 ◽  
Author(s):  
HO KHAC HIEU ◽  
VU VAN HUNG
Keyword(s):  
Nearest Neighbor ◽  
Thermodynamic Quantities ◽  
Atomic Displacements ◽  
Zinc Blende ◽  
The Mean ◽  
Temperature And Pressure ◽  
Analytical Expressions ◽  
Theoretical Results ◽  
Nearest Neighbor Distances ◽  
Statistical Moment Method

Using the statistical moment method (SMM), the temperature and pressure dependences of thermodynamic quantities of zinc-blende-type semiconductors have been investigated. The analytical expressions of the nearest-neighbor distances, the change of volumes and the mean-square atomic displacements (MSDs) have been derived. Numerical calculations have been performed for a series of zinc-blende-type semiconductors: GaAs , GaP , GaSb , InAs , InP and InSb . The agreement between our calculations and both earlier other theoretical results and experimental data is a support for our new theory in investigating the temperature and pressure dependences of thermodynamic quantities of semiconductors.

scholarly journals The Toxic Effect of Manganese on the Acetylcholinesterase Activity in Rat Brains

2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
Vahid Yousefi Babadi ◽  
Leila Sadeghi ◽  
Kobra Shirani ◽  
Ali Akbar Malekirad ◽  
Mohammad Rezaei
Keyword(s):  
Toxic Effect ◽  
Ache Activity ◽  
Time Course ◽  
Acetylcholinesterase Activity ◽  
Time Intervals ◽  
Chronic Dose ◽  
Essential Nutrient ◽  
Short Time ◽  
Chronic Doses ◽  
Dose Dependent

Manganese (Mn) is a naturally occurring element and an essential nutrient for humans and animals. However, exposure to high levels of Mn may cause neurotoxic effects. Accumulation of manganese damages central nervous system and causes Parkinson’s disease-like syndrome called manganism. Mn neurotoxicity has been suggested to involve an imbalance between the DAergic and cholinergic systems. The pathological mechanisms associated with Mn neurotoxicity are poorly understood, but several reports have established it is mediated by changing of AChE activity that resulted in oxidative stress. Therefore we focused the effect of Mn in AChE activity in the rat’s brain by MnCl2injection intraperitoneally and analyzed their brains after time intervals. This study used different acute doses in short time course and different chronic doses at different exposing time to investigate which of them (exposing dose or time) is more important in Mn toxic effect. Results showed toxic effect of Mn is highly dose dependent and AChE activity in presence of chronic dose in 8 weeks reaches acute dose in only 2 days.

scholarly journals Extracting Features from Time Series

2018 ◽  
pp. 85-100 ◽  
Author(s):  
Christian Herff ◽  
Dean J. Krusienski
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Clinical Data ◽  
Noise Filtering ◽  
Time Intervals ◽  
Time Points ◽  
Biomedical Systems ◽  
Patient Weight ◽  
Short Time

AbstractClinical data is often collected and processed as time series: a sequence of data indexed by successive time points. Such time series can be from sources that are sampled over short time intervals to represent continuous biophysical wave-(one word waveforms) forms such as the voltage measurements representing the electrocardiogram, to measurements that are sampled daily, weekly, yearly, etc. such as patient weight, blood triglyceride levels, etc. When analyzing clinical data or designing biomedical systems for measurements, interventions, or diagnostic aids, it is important to represent the information contained within such time series in a more compact or meaningful form (e.g., noise filtering), amenable to interpretation by a human or computer. This process is known as feature extraction. This chapter will discuss some fundamental techniques for extracting features from time series representing general forms of clinical data.

scholarly journals Nature of the stimulation of biogenesis of cholesterol in the liver by noradrenaline

1977 ◽  
Vol 162 (3) ◽  
pp. 493-499 ◽  
Author(s):  
R George ◽  
T Ramasarma
Keyword(s):  
Protein Synthesis ◽  
Adrenergic Receptors ◽  
De Novo ◽  
Direct Action ◽  
Time Intervals ◽  
Coa Reductase ◽  
Short Time ◽  
Stimulation Of

1. Administration of noradrenaline increased the incorporation of [1-14C]acetate into hepatic sterols and the activity of liver microsomal 3-hydroxy-3-methylglutaryl-CoA reductase. 2. The stimulation was observed at short time-intervals with a maximum at 4h and was progressive with increasing concentrations of noradrenaline. 3. Protein synthesis de novo was a necessary factor for the effect. 4. The stimulatory effect was not mediated through the adrenergic receptors, but appears to involve a direct action of the hormone within the hepatocyte.

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