Short-Term Irradiance Forecasting on the Basis of Spatially Distributed Measurements

2019 ◽  
pp. 604-611
Author(s):  
Antonino Laudani ◽  
Gabriele Maria Lozito ◽  
Valentina Lucaferri ◽  
Martina Radicioni
Keyword(s):  
Short Term ◽  
Spatially Distributed

scholarly journals Diurnal fluxes of CO2 and N2O from cattle-impacted soil and implications for emission estimates

2010 ◽  
Vol 56 (No. 10) ◽  
pp. 451-457 ◽  
Author(s):  
M. Šimek ◽  
P. Brůček ◽  
J. Hynšt
Keyword(s):  
Spatial Heterogeneity ◽  
Time Course ◽  
Diurnal Changes ◽  
Short Term ◽  
Gas Emissions ◽  
Gas Fluxes ◽  
Spatially Distributed ◽  
The Relationship ◽  
Overwintering Area

Short-term diurnal changes in emissions of CO<sub>2</sub> and N<sub>2</sub>O were determined in a cattle overwintering area during three specific periods of the year. Production of both N<sub>2</sub>O and CO<sub>2</sub>, as determined with gas chambers buried in soil and spatially distributed changed rapidly, and the general course of fluxes of the two gases was different. CO<sub>2 </sub>emissions were basically controlled by temperature, and most gas chambers showed the same trends in CO<sub>2</sub> flux, indicating low spatial heterogeneity. In contrast, N<sub>2</sub>O emissions were much more spatially heterogeneous and each chamber had its own time course of emission; therefore, the relationship between flux and temperature was more complicated for N<sub>2</sub>O than CO<sub>2</sub>. For estimating gas emissions over long periods, we strongly recommend the use of frequent emission measurements during periods of high gas fluxes. &nbsp;

Stochastic behavior of atrial and ventricular intrinsic cardiac neurons

2006 ◽  
Vol 101 (2) ◽  
pp. 413-419 ◽  
Author(s):  
M. Waldmann ◽  
G. W. Thompson ◽  
G. C. Kember ◽  
J. L. Ardell ◽  
J. A. Armour
Keyword(s):  
Nervous System ◽  
Correlation Coefficient ◽  
Cross Correlation ◽  
Mechanical Deformation ◽  
Short Term ◽  
Stochastic Behavior ◽  
Sensory Stimuli ◽  
Spatially Distributed ◽  
Tightly Coupled ◽  
Anesthetized Dogs

To quantify the concurrent transduction capabilities of spatially distributed intrinsic cardiac neurons, the activities generated by atrial vs. ventricular intrinsic cardiac neurons were recorded simultaneously in 12 anesthetized dogs at baseline and during alterations in the cardiac milieu. Few (3%) identified atrial and ventricular neurons (2 of 72 characterized neurons) responded solely to regional mechanical deformation, doing so in a tightly coupled fashion (cross-correlation coefficient r = 0.63). The remaining (97%) atrial and ventricular neurons transduced multimodal stimuli to display stochastic behavior. Specifically, ventricular chemosensory inputs modified these populations such that they generated no short-term coherence among their activities (cross-correlation coefficient r = 0.21 ± 0.07). Regional ventricular ischemia activated most atrial and ventricular neurons in a noncoupled fashion. Nicotinic activation of atrial neurons enhanced ventricular neuronal activity. Acute decentralization of the intrinsic cardiac nervous system obtunded its neuron responsiveness to cardiac sensory stimuli. Most atrial and ventricular intrinsic cardiac neurons generate concurrent stochastic activity that is predicated primarily upon their cardiac chemotransduction. As a consequence, they display relative independent short-term (beat-to-beat) control over regional cardiac indexes. Over longer time scales, their functional interdependence is manifest as the result of interganglionic interconnections and descending inputs.

scholarly journals Influence of short-term transfers on nitrogen fluxes, budgets and indirect N<sub>2</sub>O emissions in rural landscapes

2011 ◽  
Vol 8 (4) ◽  
pp. 7593-7622 ◽  
Author(s):  
S. Duretz ◽  
J.-L. Drouet ◽  
P. Durand ◽  
P. Cellier
Keyword(s):  
Landscape Scale ◽  
Soil Conditions ◽  
N2o Emissions ◽  
Spatial Interactions ◽  
Short Term ◽  
Rural Landscapes ◽  
Environmental Threats ◽  
Spatially Distributed ◽  
Simulation Results ◽  
Oligotrophic Ecosystems

Abstract. Spatial interactions at short-term may lead to large inputs of reactive nitrogen (Nr) to oligotrophic ecosystems and induce environmental threats such as additional N2O emissions and global warming. The paper presents a new methodology to estimate Nr fluxes, especially additional N2O emissions, at the landscape scale by taking into account spatial interactions between landscape elements. We used the NitroScape model which integrates processes of Nr transformation and short-term transfer in a dynamic and spatially distributed way to simulate Nr fluxes and budgets at the landscape scale. Four configurations of NitroScape were implemented by taking into account or not the atmospheric, hydrological or both pathways of Nr transfer. We simulated Nr fluxes, especially direct and indirect N2O emissions, within a test landscape including pig farms, croplands and unmanaged ecosystems. Simulation results showed the ability of NitroScape to simulate patterns of Nr losses and recapture for each landscape element and the whole landscape. They made it possible to quantify the contribution of both atmospheric and hydrological transfers in Nr fluxes and budgets. Indirect N2O emissions were estimated at almost 25 % of the total N2O emissions. They varied within the landscape according to land use, meteorological and soil conditions as well as topography. This first attempt has proved that the NitroScape model is a useful tool to estimate the effect of spatial interactions on Nr fluxes and budgets as well as indirect N2O emissions within landscapes. Our approach needs to be further tested by applying NitroScape to several spatial distributions of ecosystems within the landscape and to real and larger landscapes.

scholarly journals Forecasting Spatially-Distributed Urban Traffic Volumes via Multi-Target LSTM-Based Neural Network Regressor

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2233
Author(s):  
Alessandro Crivellari ◽  
Euro Beinat
Keyword(s):  
Neural Network ◽  
Traffic Control ◽  
Urban Areas ◽  
Short Term Memory ◽  
Urban Traffic ◽  
Entry And Exit ◽  
Short Term ◽  
Spatially Distributed ◽  
Traffic Volumes ◽  
The City

Monitoring the distribution of vehicles across the city is of great importance for urban traffic control. In particular, information on the number of vehicles entering and leaving a city, or moving between urban areas, gives a valuable estimate on potential bottlenecks and congestions. The possibility of predicting such flows in advance is even more beneficial, allowing for timely traffic management strategies and targeted congestion warnings. Our work is inserted in the context of short-term forecasting, aiming to predict rapid changes and sudden variations in the traffic volume, beyond the general trend. Moreover, it concurrently targets multiple locations in the city, providing an instant prediction outcome comprising the future distribution of vehicles across several urban locations. Specifically, we propose a multi-target deep learning regressor for simultaneous predictions of traffic volumes, in multiple entry and exit points among city neighborhoods. The experiment focuses on an hourly forecasting of the amount of vehicles accessing and moving between New York City neighborhoods through the Metropolitan Transportation Authority (MTA) bridges and tunnels. By leveraging a single training process for all location points, and an instant one-step volume inference for every location at each time update, our sequential modeling approach is able to grasp rapid variations in the time series and process the collective information of all entry and exit points, whose distinct predicted values are outputted at once. The multi-target model, based on long short-term memory (LSTM) recurrent neural network layers, was tested on a real-world dataset, achieving an average prediction error of 7% and demonstrating its feasibility for short-term spatially-distributed urban traffic forecasting.

scholarly journals Modeling impacts of water transfers on alleviation of phytoplankton aggregation in Lake Taihu

2014 ◽  
Vol 17 (1) ◽  
pp. 149-162 ◽  
Author(s):  
Jiacong Huang ◽  
Junfeng Gao ◽  
Yinjun Zhang ◽  
Yan Xu
Keyword(s):  
Chlorophyll A ◽  
Algal Blooms ◽  
Lake Taihu ◽  
Water Transfer ◽  
Short Term ◽  
Phytoplankton Distribution ◽  
Spatially Distributed ◽  
Transfer Strategies ◽  
Chlorophyll A Distribution

A water transfer project has been ongoing since 2002 to alleviate severe phytoplankton aggregation in Lake Taihu. This study aimed to quantify the effectiveness of the water transfer project on alleviation of phytoplankton aggregation in Lake Taihu on a short-term scale. In this study, a spatially distributed hydrodynamic-phytoplankton model was used to predict the short-term (3–4 days) changes in phytoplankton distribution (represented by chlorophyll a) in Lake Taihu. Four simulations with different water transfer strategies were carried out based on this model. During the water transfer period, phytoplankton aggregation was alleviated in some areas, suggesting that the water transfer project has the potential to alleviate algal blooms on a short-term scale. However, the effectiveness of the water transfer project on alleviating severe algal blooms was strongly affected by other environmental factors (e.g. wind conditions, chlorophyll a distribution, and the amount and quality of the transfer water). This study demonstrates the success of the hydrodynamic-phytoplankton model in evaluating the contribution of the water transfer project to alleviation of phytoplankton aggregation. These evaluation results could assist managers in decision-making before conducting a water transfer plan in Lake Taihu.

scholarly journals Impact of Spatial Distribution Information of Rainfall in Runoff Simulation Using Deep-Learning Methods

2021 ◽  
Author(s):  
Yang Wang ◽  
Hassan A. Karimi
Keyword(s):  
Spatial Distribution ◽  
Deep Learning ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Learning Models ◽  
Short Term ◽  
Runoff Simulation ◽  
Look Back ◽  
Spatially Distributed

Abstract. Rainfall-runoff modelling is of great importance for flood forecast and water management. Hydrological modelling is the traditional and commonly used approach for rainfall-runoff modelling. In recent years, with the development of artificial intelligence technology, deep learning models, such as the long short-term memory (LSTM) model, are increasingly applied to rainfall-runoff modelling. However, current works do not consider the effect of rainfall spatial distribution information on the results, and the same look-back window is applied to all the inputs. Focusing on two catchments from the CAMELS dataset, this study first analyzed and compared the effects of basin mean rainfall and spatially distributed rainfall data on the LSTM models under different look-back windows (7, 15, 30, 180, 365 days). Then the LSTM+1D CNN model was proposed to simulate the situation of short-term look-back windows (3, 10 days) for rainfall combined with the long-term look-back windows (30, 180, 365 days) for other input features. The models were evaluated using the Nash Sutcliffe efficiency coefficient, root mean square error, and error of peak discharge. The results demonstrate the great potential of deep learning models for rainfall runoff simulation. Adding the spatial distribution information of rainfall can improve the simulation results of the LSTM models, and this improvement is more evident under the condition of short look-back windows. The results of the proposed LSTM+1D CNN are comparable to those of the LSTM model driven by basin mean rainfall data and slightly worse than those of spatially distributed rainfall data for corresponding look-back windows. The proposed LSTM+1D CNN provides new insights for runoff simulation by combining short-term spatial distributed rainfall data with long-term runoff data, especially for catchments where long-term rainfall records are absent.

Conceptual short-term memory (CSTM) supports core claims of Christiansen and Chater

2016 ◽  
Vol 39 ◽  
Author(s):  
Mary C. Potter
Keyword(s):  
Working Memory ◽  
Rapid Serial Visual Presentation ◽  
Language Comprehension ◽  
Short Term Memory ◽  
Visual Presentation ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Use Of Knowledge

AbstractRapid serial visual presentation (RSVP) of words or pictured scenes provides evidence for a large-capacity conceptual short-term memory (CSTM) that momentarily provides rich associated material from long-term memory, permitting rapid chunking (Potter 1993; 2009; 2012). In perception of scenes as well as language comprehension, we make use of knowledge that briefly exceeds the supposed limits of working memory.

Ultrastructural Changes of Canine Kidneys During 24-Hour Perfusion on a LI-400 Preservation System

1971 ◽  
Vol 29 ◽  
pp. 316-317
Author(s):  
M. O. Magnusson ◽  
D. G. Osborne ◽  
T. Shimoji ◽  
W. S. Kiser ◽  
W. A. Hawk
Keyword(s):  
Electron Microscopy ◽  
Electrolyte Solution ◽  
Ultrastructural Changes ◽  
Midline Incision ◽  
Short Term ◽  
Pentobarbital Anesthesia ◽  
Pulsatile Perfusion

Short term experimental and clinical preservation of kidneys is presently best accomplished by hypothermic continuous pulsatile perfusion with cryoprecipitated and millipore filtered plasma. This study was undertaken to observe ultrastructural changes occurring during 24-hour preservation using the above mentioned method.A kidney was removed through a midline incision from healthy mongrel dogs under pentobarbital anesthesia. The kidneys were flushed immediately after removal with chilled electrolyte solution and placed on a LI-400 preservation system and perfused at 8-10°C. Serial kidney biopsies were obtained at 0-½-1-2-4-8-16 and 24 hours of preservation. All biopsies were prepared for electron microscopy. At the end of the preservation period the kidneys were autografted.

Polychlorinated biphenyl induced hepatocyte alterations

1987 ◽  
Vol 45 ◽  
pp. 716-717
Author(s):  
D.N. Collins ◽  
J.N. Turner ◽  
K.O. Brosch ◽  
R.F. Seegal
Keyword(s):  
Lipid Droplets ◽  
Wistar Rats ◽  
Homovanillic Acid ◽  
Polychlorinated Biphenyl ◽  
Corn Oil ◽  
Environmental Pollutants ◽  
Short Term ◽  
Pcb Congeners ◽  
Urinary Levels ◽  
The Brain

Polychlorinated biphenyls (PCBs) are a ubiquitous class of environmental pollutants with toxic and hepatocellular effects, including accumulation of fat, proliferated smooth endoplasmic recticulum (SER), and concentric membrane arrays (CMAs) (1-3). The CMAs appear to be a membrane storage and degeneration organelle composed of a large number of concentric membrane layers usually surrounding one or more lipid droplets often with internalized membrane fragments (3). The present study documents liver alteration after a short term single dose exposure to PCBs with high chlorine content, and correlates them with reported animal weights and central nervous system (CNS) measures. In the brain PCB congeners were concentrated in particular regions (4) while catecholamine concentrations were decreased (4-6). Urinary levels of homovanillic acid a dopamine metabolite were evaluated (7).Wistar rats were gavaged with corn oil (6 controls), or with a 1:1 mixture of Aroclor 1254 and 1260 in corn oil at 500 or 1000 mg total PCB/kg (6 at each level).

Acetaminophen: Macula densa hypersecretory activity by induced hepatotoxicity

1987 ◽  
Vol 45 ◽  
pp. 934-935
Author(s):  
S.S. Poolsawat ◽  
C.A. Huerta ◽  
S.TY. Lae ◽  
G.A. Miranda
Keyword(s):  
Cell Proliferation ◽  
Liver Function ◽  
Chronic Inflammation ◽  
Foam Cell ◽  
Term Effect ◽  
Short Term ◽  
Drug Induced ◽  
Experimental Induction ◽  
Tissue Alterations ◽  
Toxic Responses

Introduction. Experimental induction of altered histology by chemical toxins is of particular importance if its outcome resembles histopathological phenomena. Hepatotoxic drugs and chemicals are agents that can be converted by the liver into various metabolites which consequently evoke toxic responses. Very often, these drugs are intentionally administered to resolve an illness unrelated to liver function. Because of hepatic detoxification, the resulting metabolites are suggested to be integrated into the macromolecular processes of liver function and cause an array of cellular and tissue alterations, such as increased cytoplasmic lysis, centrilobular and localized necroses, chronic inflammation and “foam cell” proliferation of the hepatic sinusoids (1-4).Most experimentally drug-induced toxicity studies have concentrated primarily on the hepatic response, frequently overlooking other physiological phenomena which are directly related to liver function. Categorically, many studies have been short-term effect investigations which seldom have followed up the complications to other tissues and organs when the liver has failed to function normally.

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