Pesticides in Doormat and Floor Dust from Homes Close to Treated Fields: Spatio-Temporal Variance and Determinants of Occurrence and Concentrations

2021 ◽  
Author(s):  
Daniel Martins Figueiredo ◽  
Rosalie Nijssen ◽  
Esmeralda Krop ◽  
Daan Buijtenhuijs ◽  
Yvonne Gooijer ◽  
...  
Keyword(s):  
Temporal Variance ◽  
Floor Dust ◽  
Spatio Temporal

Assessing spatio-temporal variability and biases of climate models

2020 ◽  
Author(s):  
Nedjeljka Žagar
Keyword(s):  
Temporal Variability ◽  
Prediction Models ◽  
Climate Model ◽  
Weather Prediction ◽  
Ocean Model ◽  
Spatial And Temporal Variability ◽  
Dynamical Regime ◽  
Temporal Variance ◽  
Slab Ocean ◽  
Spatio Temporal

<div>Atmospheric spatial and temporal variability are closely related with the former being relatively well observed compared to the latter. The former is also regularly assessed in the validation of numerical weather prediction models while the latter is more difficult to estimate. Likewise, thermodynamical fields and circulation are closely coupled calling for an approach that considers them simultaneously.  </div> <div>In this contribution, spatio-temporal variability spectra of the four major reanalysis datasets are discussed and applied for the validation of a climate model prototype.  A relationship between deficiencies in simulated variability and model biases is derived. The underlying method includes dynamical regime decomposition thereby providing a better understanding of the role of tropical variability in global circulation. </div> <div>Results of numerical simulations are validated by a 20th century reanalysis. A climate model was forced either with the prescribed SST or with a slab ocean model that updates SST in each forecast step.  Scale-dependent validation shows that missing temporal variance in the model relative to verifying reanalysis increases as the spatial scale reduces that appears associated with an increasing lack of spatial variance at smaller scales. Similar to variability, bias is strongly scale dependent; the larger the scale, the greater the bias. Biases present in the SST-forced simulation increase in the simulation using the slab ocean. The comparison of biases computed as a systematic difference between the model and reanalysis and between the SST-forced model and slab-ocean model (a perfect-model scenario) suggests that improving the atmospheric model increases the variance in the model on synoptic and subsynoptic scales but large biases associated with a poor SST remain at planetary scales.</div> <p> </p>

Impacts of Holding Area Policies on Shared Autonomous Vehicle Operations

2021 ◽  
pp. 036119812110286
Author(s):  
Richard Twumasi-Boakye ◽  
Xiaolin Cai ◽  
Chetan Joshi ◽  
James Fishelson ◽  
Andrea Broaddus
Keyword(s):  
Service Providers ◽  
Spatial Clustering ◽  
Autonomous Vehicle ◽  
Waiting Times ◽  
Service Performance ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Temporal Variance ◽  
Spatio Temporal ◽  
Improved Performance

Shared mobility has an important role in supporting existing transportation options in cities. However, when not deployed carefully, shared services may have operational inefficiencies such as low occupancies and increased deadheading. One reason is the spatio-temporal variance in the distribution of urban trip demand, which may lead to an unbalanced fleet displaced in cities thus unable to serve requested trips. Strategically siting holding areas (depots for dispatching and relocating fleets) could help improve fleet performance. Therefore, this paper considers shared autonomous vehicle (SAV) fleet operations by modeling the impacts of different holding area policies on service performance. Modeling and comparing multiple holding area policies for tactically deploying SAVs is novel, and the insights from this paper can inform service providers on how to site holding areas for improved performance. We develop a model of SAV fleet with pooling in the City of Toronto, with 27,951 total SAV trip requests across a 16-h period. We then integrate four holding area policies estimated using different spatial clustering methods, centralized positioning, and existing taxi stands. Findings indicate that using agglomerative clustering results in superior SAV fleet performances (average passenger waiting times reduced by about 20% compared with the worst performing policy), with increased served demand and reduced deadheading. A single holding area at a high trip density location yields efficient service performance at lower fleets but struggles to serve sparse demand (producing worst results); this method may suffice for operating SAV services within a small geofence with high trip densities.

Revisiting the Role of Caves and Rockshelters in the Hunter-Gatherer Taskscape of the Archaic Midsouth

2015 ◽  
Vol 80 (2) ◽  
pp. 332-352 ◽  
Author(s):  
Lara Homsey-Messer
Keyword(s):  
Symbolic Meaning ◽  
Hunter Gatherers ◽  
Middle Holocene ◽  
Temporal Variance ◽  
Middle Archaic ◽  
Spatio Temporal ◽  
And Function ◽  
Over Time

This paper evaluates previous models of cave and rockshelter use in the American Midsouth from the Early to the Middle Archaic periods. Four sites are compared in order to identify variability in activities, seasonality, occupation intensity, and function. Focus is placed on using the often overlooked feature assemblages to discern these activities. Data suggest that the changing use of many caves and rockshelters is not one of longer term occupation as base camps, as has been previously argued, but rather as specialized field camps dedicated to the processing of mast resources. This shift takes place as Middle Holocene warming prompted hunter-gatherers to adopt a more logistical mobility strategy in order to take advantage of the spatio-temporal variance associated with increased mast availability. It is further argued that these sites were likely locations of women's activities and that foraging in the Midsouth involved groups of women engaged in daily tasks centered around mast, tasks that over time imbued caves and rockshelter s with symbolic meaning such that they came to function simultaneously as both processing camps and as persistent places of ritual significance in the hunter-gatherer taskscape.

Impact of crop management and environment on the spatio-temporal variance of potato yield at regional scale

2021 ◽  
Vol 270 ◽  
pp. 108213
Author(s):  
Jonathan J. Ojeda ◽  
Ehsan Eyshi Rezaei ◽  
Bahareh Kamali ◽  
John McPhee ◽  
Holger Meinke ◽  
...  
Keyword(s):  
Regional Scale ◽  
Crop Management ◽  
Potato Yield ◽  
Temporal Variance ◽  
Spatio Temporal

scholarly journals The Response of Soil pH and Exchangeable Al to Alum and Lime Amendments

Agriculture ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 547
Author(s):  
Christopher Jorelle Gillespie ◽  
João Arthur Antonangelo ◽  
Hailin Zhang
Keyword(s):  
Great Plains ◽  
Soil Ph ◽  
Wheat Yield ◽  
Yield Potential ◽  
Temporal Variance ◽  
Winter Wheat Yield ◽  
Cubic Polynomial ◽  
Spatio Temporal ◽  
Study Sites ◽  
The Impact

Intensive cultivation and unprecedented utilization of ammoniacal fertilizer has accelerated soil acidification in the southern Great Plains and many other parts of the world. During a two-year study that evaluated the impact of soil pH and aluminum (Al) toxicity on winter wheat yield potential, we observed a variance in the edaphic responses of the two study sites (Stillwater and Chickasha) to two soil amendments, Alum [Al2(SO4)3] and lime [Ca(OH)2]. We found that AlKCl values at Stillwater were 223% and 150% higher than Chickasha during Year 1 and Year 2, respectively, with similar soil pH. Additionally, Alsat values at Stillwater were 30.6% and 24.9% higher than Chickasha during Year 1 and Year 2, respectively. Surprisingly, when treated as a bivariate of Alsat, soil buffer indices differed in graphical structure. While Chickasha was identified with a cubic polynomial (p < 0.0001), Stillwater was characterized by linear regression (p < 0.0001). We have reason to believe that this divergence in edaphic response might be attributed to the organically bound Al, dissolved organic carbon (DOC), spatio-temporal variance, and adsorption reactions regulated by the solubility of Al(OH)+2 species in acidic soils.

Monitoring Spatio-temporal Variance of an Extreme Heat Event Using Multiple-source Remote Sensing Data

2018 ◽  
Vol 28 (5) ◽  
pp. 744-757 ◽  
Author(s):  
Shanyou Zhu ◽  
Yi Liu ◽  
Junwei Hua ◽  
Guixin Zhang ◽  
Yang Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Extreme Heat ◽  
Multiple Source ◽  
Sensing Data ◽  
Extreme Heat Event ◽  
Temporal Variance ◽  
Heat Event ◽  
Spatio Temporal

scholarly journals A Framework of Spatio-Temporal Fusion Algorithm Selection for Landsat NDVI Time Series Construction

2020 ◽  
Vol 9 (11) ◽  
pp. 665
Author(s):  
Yangnan Guo ◽  
Cangjiao Wang ◽  
Shaogang Lei ◽  
Junzhe Yang ◽  
Yibo Zhao
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Linear Relationship ◽  
Vegetation Index ◽  
Input Image ◽  
Fusion Algorithm ◽  
Algorithm Selection ◽  
Temporal Variance ◽  
Ndvi Time Series ◽  
Spatio Temporal

Spatio-temporal fusion algorithms dramatically enhance the application of the Landsat time series. However, each spatio-temporal fusion algorithm has its pros and cons of heterogeneous land cover performance, the minimal number of input image pairs, and its efficiency. This study aimed to answer: (1) how to determine the adaptability of the spatio-temporal fusion algorithm for predicting images in prediction date and (2) whether the Landsat normalized difference vegetation index (NDVI) time series would benefit from the interpolation with images fused from multiple spatio-temporal fusion algorithms. Thus, we supposed a linear relationship existed between the fusion accuracy and spatial and temporal variance. Taking the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM) as basic algorithms, a framework was designed to screen a spatio-temporal fusion algorithm for the Landsat NDVI time series construction. The screening rule was designed by fitting the linear relationship between the spatial and temporal variance and fusion algorithm accuracy, and then the fitted relationship was combined with the graded accuracy selecting rule (R2) to select the fusion algorithm. The results indicated that the constructed Landsat NDVI time series by this paper proposed framework exhibited the highest overall accuracy (88.18%), and lowest omission (1.82%) and commission errors (10.00%) in land cover change detection compared with the moderate resolution imaging spectroradiometer (MODIS) NDVI time series and the NDVI time series constructed by a single STARFM or ESTARFM. Phenological stability analysis demonstrated that the Landsat NDVI time series established by multiple spatio-temporal algorithms could effectively avoid phenological fluctuations in the time series constructed by a single fusion algorithm. We believe that this framework can help improve the quality of the Landsat NDVI time series and fulfill the gap between near real-time environmental monitoring mandates and data-scarcity reality.

scholarly journals Salmon louse infestation in wild brown trout populations generates multi-modal mixture distributions

2020 ◽  
Vol 12 ◽  
pp. 447-456
Author(s):  
OH Diserud ◽  
R Hedger ◽  
B Finstad ◽  
D Hendrichsen ◽  
AJ Jensen ◽  
...  
Keyword(s):  
Habitat Use ◽  
Brown Trout ◽  
Negative Binomial ◽  
Louse Infestation ◽  
Marine Habitat ◽  
Salmon Louse ◽  
Sea Trout ◽  
Salmon Lice ◽  
Temporal Variance ◽  
Spatio Temporal

For successful evaluation of the overall effects of salmon louse infestation on brown trout population dynamics, it is crucial to have a realistic understanding of how lice infestation distributions are generated and how they should be interpreted. Here, we simulated the potential effects of spatio-temporal variance in lice larvae densities, temporal variance in sea trout marine migration timing and spatial variance in marine habitat use on lice infestation distributions. We show that, when sampling populations with individual variation in marine behaviour, e.g. from post-smolts to veteran migrants, we must expect multi-modal mixture lice infestation distributions. Applying standard statistical distributions, such as the Poisson, negative binomial or zero-inflated distributions, can be too simplistic and give biased results. Temporary increases in salmon lice load in a given area may have inconsistent effects among individuals of a population and may be critical for vulnerable groups such as post-smolts, dependent on timing. For many analyses, it will be necessary to resolve the contributions from groups of fish with different lice infestation expectations due to spatio-temporal differences in habitat use within the overall mixture distribution. Another consequence is that different data sources, obtained by different methods or sampled at different locations and periods, must be expected to give different lice infestation distributions, even when sampling the same population. We also discuss additional factors that may complicate the interpretation of salmon lice infestation distributions on sea trout, such as lice-induced mortality, and behavioural changes, such as premature return to less saline water for delousing.

Spatio temporal smoke detection using hybrid deep Q learning classifier for educating engineering students

2020 ◽  
pp. 002072092093107
Author(s):  
Princy Matlani ◽  
Manish Shrivastava
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Detection Method ◽  
The Novel ◽  
Smoke Detection ◽  
Q Learning ◽  
Smoke Particles ◽  
Learning Classifier ◽  
Temporal Variance ◽  
Spatio Temporal

In this paper, we propose a deep learning based smoke detection method which overcomes drawbacks of the conventional smoke detection method. Real-time smoke detection via machine-based identification method in the area of surveillance system has been of great advantage in recent era. An effective smoke detection strategy is necessary to avoid the hazard resulting from fire. The conventional smoke detection method lacks in accuracy, therefore, deep learning based smoke detection is adopted for the same purpose. However, a lot of video smoke detection approach involves minimum lighting and it can be required for the cameras to discover the existence of smoke particles in a scene. Eliminating such challenges, our proposed work introduces a novel concept like hybrid reinforcement deep Q learning classifier of smoke detection. This work takes the correct decision about the smoke particles via reinforcement deep Q learning and classifies the smoke particle with the deep convolutional neural network. The proposed real-time algorithm is aimed to provide proper education for the engineers to detect moving objects for the purpose of developing surveillance systems. This method is also helpful for the beginners who have keen interest in the field of deep learning to control fire. Here, to observe the temporal variance of fire smoke, spatial analysis is identified in the present frame and in the subsequence of the video, spatio-temporal analysis has been taken into account. Finally, smoke particles are classified with the novel reinforcement Q learning-based classifier and experimental results show a better performance regarding classification accuracy. So we can detect smoke successfully with the novel method. The main purpose of this work is to describe and formalize a machine learning -based smoke detection algorithm that can be used by students.

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