QUANTUM COHERENCE IN NEUTRON SCATTERING ON PROTONS

2004 ◽  
Vol 18 (07n08) ◽  
pp. 247-268 ◽  
Author(s):  
ERIK B. KARLSSON
Keyword(s):  
Quantum Coherence ◽  
Time Window ◽  
Condensed Matter ◽  
Time Windows ◽  
Phase Relations ◽  
Time Intervals ◽  
Quantum Objects ◽  
Quantum Coherence Effects ◽  
Short Time ◽  
Small Clusters

Several recent observations of closely spaced hydrogen nuclei in condensed matter systems — water, polymers and metal hydrides — indicate that they cannot be considered as independent quantum objects when observed over very short time intervals. According to these measurements, pairs (or possibly small clusters) of protons or deuterons seem to be quantum correlated, and are able to preserve their mutual quantum phase relations over times of the order of femtoseconds even in strongly perturbing liquid or solid environments. These new experimental results are mainly based on scattering of neutrons with time windows in the atto- and femtosecond range, but there also exists corroborating evidence from other spectroscopies. When the width of the observational time window is increased above a characteristic value, the quantum coherence effects are seen to disappear. This time limit marks the onset of decoherence as the mutual phase relations between the particles are gradually lost. Possible reasons for short-time entanglement of nuclei in condensed matter systems, as well as mechanisms for its decoherence will be discussed.

Parameters of Spike Trains Observed in a Short Time Window

2008 ◽  
Vol 20 (5) ◽  
pp. 1325-1343 ◽  
Author(s):  
Zbyněk Pawlas ◽  
Lev B. Klebanov ◽  
Martin Prokop ◽  
Petr Lansky
Keyword(s):  
Coefficient Of Variation ◽  
Point Processes ◽  
Interspike Interval ◽  
Time Window ◽  
Time Windows ◽  
Time Interval ◽  
Short Time Interval ◽  
Target Neuron ◽  
Short Time Window ◽  
Short Time

We study the estimation of statistical moments of interspike intervals based on observation of spike counts in many independent short time windows. This scenario corresponds to the situation in which a target neuron occurs. It receives information from many neurons and has to respond within a short time interval. The precision of the estimation procedures is examined. As the model for neuronal activity, two examples of stationary point processes are considered: renewal process and doubly stochastic Poisson process. Both moment and maximum likelihood estimators are investigated. Not only the mean but also the coefficient of variation is estimated. In accordance with our expectations, numerical studies confirm that the estimation of mean interspike interval is more reliable than the estimation of coefficient of variation. The error of estimation increases with increasing mean interspike interval, which is equivalent to decreasing the size of window (less events are observed in a window) and with decreasing the number of neurons (lower number of windows).

scholarly journals Earthquake risk over time windows: Do people bias risk to the end of an interval?

2021 ◽  
Author(s):  
◽  
Justin Velluppillai
Keyword(s):  
New Zealand ◽  
Perceived Risk ◽  
Time Window ◽  
Time Windows ◽  
Earthquake Risk ◽  
Time Intervals ◽  
Tectonic Plates ◽  
Perceptions Of Risk ◽  
Bias Risk ◽  
Perceive Risk

<p>Located on the edge of two tectonic plates, New Zealand has numerous fault lines and seismic risk across the whole country. The way this risk is communicated affects whether people prepare effectively or at all. Research has shown that perceptions of risk are affected by slight changes in wording, and that probabilities commonly reported by experts and media are often interpreted subjectively based on context. In the context of volcanoes, research has found that given a certain probability of a volcano in a specific time window, people perceive risk as higher in later time intervals within that window. The present study examines this pattern with regard to earthquakes and aftershocks in the New Zealand context. Participants in both Wellington (N = 102) and Christchurch (N = 98) were presented an expert statement of earthquake risk within a given time window in Wellington and aftershock risk in Christchurch, and asked to rate their perception of risk in specific intervals across the time window. For a Wellington earthquake, participants perceived risk as incrementally higher toward the end of the 50 year time window whereas for a Christchurch aftershock, risk perception increased slightly for the first three intervals of the 12 month time window. Likelihood of preparing was constant over the time windows, with Wellington citizens rating themselves more likely than Christchurch citizens to prepare for either an earthquake or aftershock, irrespective of current level of preparedness. These findings suggest that people view earthquakes as more likely later toward the end of a given time window and that they view aftershocks very differently to scientific predictions.</p>

scholarly journals Earthquake risk over time windows: Do people bias risk to the end of an interval?

2021 ◽  
Author(s):  
◽  
Justin Velluppillai
Keyword(s):  
New Zealand ◽  
Perceived Risk ◽  
Time Window ◽  
Time Windows ◽  
Earthquake Risk ◽  
Time Intervals ◽  
Tectonic Plates ◽  
Perceptions Of Risk ◽  
Bias Risk ◽  
Perceive Risk

<p>Located on the edge of two tectonic plates, New Zealand has numerous fault lines and seismic risk across the whole country. The way this risk is communicated affects whether people prepare effectively or at all. Research has shown that perceptions of risk are affected by slight changes in wording, and that probabilities commonly reported by experts and media are often interpreted subjectively based on context. In the context of volcanoes, research has found that given a certain probability of a volcano in a specific time window, people perceive risk as higher in later time intervals within that window. The present study examines this pattern with regard to earthquakes and aftershocks in the New Zealand context. Participants in both Wellington (N = 102) and Christchurch (N = 98) were presented an expert statement of earthquake risk within a given time window in Wellington and aftershock risk in Christchurch, and asked to rate their perception of risk in specific intervals across the time window. For a Wellington earthquake, participants perceived risk as incrementally higher toward the end of the 50 year time window whereas for a Christchurch aftershock, risk perception increased slightly for the first three intervals of the 12 month time window. Likelihood of preparing was constant over the time windows, with Wellington citizens rating themselves more likely than Christchurch citizens to prepare for either an earthquake or aftershock, irrespective of current level of preparedness. These findings suggest that people view earthquakes as more likely later toward the end of a given time window and that they view aftershocks very differently to scientific predictions.</p>

Frequency estimates of seismic traces

Geophysics ◽  
2003 ◽  
Vol 68 (1) ◽  
pp. 370-380 ◽  
Author(s):  
H. H. Hardy ◽  
Richard A. Beier ◽  
Jonathan D. Gaston
Keyword(s):  
Seismic Data ◽  
Time Window ◽  
Time Windows ◽  
Average Frequency ◽  
Frequency Method ◽  
Zero Crossings ◽  
Time Frequency ◽  
Shale Volume ◽  
True Frequency ◽  
Short Time

Local estimates of amplitude, frequency, and phase have been used in the past to characterize seismic data. In particular, these attributes have sometimes been successfully related to well attributes at the reservoir scale (net pay thickness, sand fraction, etc.). This paper introduces a method called SINFIT for computing local amplitude, frequency, and phase estimates of seismic traces over short‐time windows. The SINFIT method uses a sine‐curve fitting approach. The method is shown to give more accurate and robust frequency estimates than four other common methods on a set of test traces where the true frequency components are known. The four methods compared with SINFIT are instantaneous frequency, zero‐crossings, short‐time Fourier analysis, and a more recent time‐frequency method called AOK. In a field case with fluvial sands, an average frequency over a 30‐ms time window of seismic data correlates with estimated shale volume from well logs. The SINFIT method gives an average frequency attribute that more strongly correlates with shale volume than corresponding attributes from any of the other four methods.

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 A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

2021 ◽  
Author(s):  
Hongguang Wu ◽  
Yuelin Gao ◽  
Wanting Wang ◽  
Ziyu Zhang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Ant Colony ◽  
Routing Problem ◽  
Multiple Strategies ◽  
Hard Time ◽  
Mutation Operation

AbstractIn this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method; the second is the introduction of adaptive parameters; and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.

scholarly journals The last-mile vehicle routing problem with delivery options

OR Spectrum ◽  
2021 ◽  
Author(s):  
Christian Tilk ◽  
Katharina Olkis ◽  
Stefan Irnich
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Resource Constraints ◽  
Time Window ◽  
Time Windows ◽  
Service Level ◽  
Optimal Solutions ◽  
Routing Problem ◽  
Common Location ◽  
Delivery Options

AbstractThe ongoing rise in e-commerce comes along with an increasing number of first-time delivery failures due to the absence of the customer at the delivery location. Failed deliveries result in rework which in turn has a large impact on the carriers’ delivery cost. In the classical vehicle routing problem (VRP) with time windows, each customer request has only one location and one time window describing where and when shipments need to be delivered. In contrast, we introduce and analyze the vehicle routing problem with delivery options (VRPDO), in which some requests can be shipped to alternative locations with possibly different time windows. Furthermore, customers may prefer some delivery options. The carrier must then select, for each request, one delivery option such that the carriers’ overall cost is minimized and a given service level regarding customer preferences is achieved. Moreover, when delivery options share a common location, e.g., a locker, capacities must be respected when assigning shipments. To solve the VRPDO exactly, we present a new branch-price-and-cut algorithm. The associated pricing subproblem is a shortest-path problem with resource constraints that we solve with a bidirectional labeling algorithm on an auxiliary network. We focus on the comparison of two alternative modeling approaches for the auxiliary network and present optimal solutions for instances with up to 100 delivery options. Moreover, we provide 17 new optimal solutions for the benchmark set for the VRP with roaming delivery locations.

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