scholarly journals Shifts in the spatio-temporal growth dynamics of shortleaf pine

2007 ◽  
Vol 14 (3) ◽  
pp. 207-227 ◽  
Author(s):  
Mevin B. Hooten ◽  
Christopher K. Wikle
IMA Fungus ◽  
2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Liselotte De Ligne ◽  
Guillermo Vidal-Diez de Ulzurrun ◽  
Jan M. Baetens ◽  
Jan Van den Bulcke ◽  
Joris Van Acker ◽  
...  

Ecosphere ◽  
2016 ◽  
Vol 7 (5) ◽  
Author(s):  
Karen A. Bjorndal ◽  
Milani Chaloupka ◽  
Vincent S. Saba ◽  
Carlos E. Diez ◽  
Robert P. van Dam ◽  
...  

First Monday ◽  
2008 ◽  
Author(s):  
Federico Iannacci

The article summarises and reviews a new book on the consequences of information growth. It underlines the importance of studying technological change by focusing on information growth dynamics rather than individual and collective agents. It also shows that new organisational arrangements falling under the network umbrella should be conceptualised as spatio-temporal instantiations of institutional arrangements reflecting the quest for cross-boundary transactions involving mostly the exchange, transfer and generation of messages and information.


Proceedings ◽  
2019 ◽  
Vol 46 (1) ◽  
pp. 17
Author(s):  
Garima Nautiyal ◽  
Sandeep Maithani ◽  
Ashutosh Bhardwaj ◽  
Archana Sharma

Relative Entropy (RE) is defined as the measure of the degree of randomness of any geographical variable (i.e., urban growth). It is an effective indicator to evaluate the patterns of urban growth, whether compact or dispersed. In the present study, RE has been used to evaluate the urban growth of Dehradun city. Dehradun, the capital of Uttarakhand, is situated in the foothills of the Himalayas and has undergone rapid urbanization. Landsat satellite data for the years 2000, 2010 and 2019 have been used in the study. Built-up cover outside municipal limits and within municipal limits was classified for the given time period. The road network and city center of the study area were also delineated using satellite data. RE was calculated for the periods 2000–2010 and 2010–2019 with respect to the road network and city center. High values of RE indicate higher levels of urban sprawl, whereas lower values indicate compactness. The urban growth pattern over a period of 19 years was examined with the help of RE.


2021 ◽  
Author(s):  
Omkar Hegde ◽  
Ritika Chatterjee ◽  
Abdur Rasheed ◽  
Dipshikha Chakravortty ◽  
Saptarshi Basu

Deposits of biofluid droplets on surfaces (such as respiratory droplets formed during an expiratory event fallen on surfaces) are composed of the water based salt protein solution that may also contain an infection (bacterial/viral). The final patterns of the deposit formed are dictated by the composition of the fluid and flow dynamics within the droplet. This work reports the spatio temporal, topological regulation of deposits of respiratory fluid droplets and control of motility of bacteria by tweaking flow inside droplets using non contact vapor mediated interactions. When evaporated on a glass surface, respiratory droplets form haphazard multiscale dendritic, cruciform shaped precipitates using vapor mediation as a tool to control these deposits at the level of nano, micro, millimeter scales. We morphologically control dendrite orientation, size and subsequently suppress cruciform-shaped crystals. The nucleation sites are controlled via preferential transfer of solutes in the droplets; thus, achieving control over crystal occurrence and growth dynamics. The active living matter like bacteria is also preferentially segregated with controlled motility without attenuation of its viability and pathogenesis. For the first time, we have experimentally presented a proof of concept to control the motion of live active matter like bacteria in a near nonintrusive manner. The methodology can have ramifications in biomedical applications like disease detection, controlling bacterial motility, and bacterial segregation.


Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2245
Author(s):  
Murtaza Rangwala ◽  
Jun Liu ◽  
Kulbir Singh Ahluwalia ◽  
Shayan Ghajar ◽  
Harnaik Singh Dhami ◽  
...  

Effective management of dairy farms requires an accurate prediction of pasture biomass. Generally, estimation of pasture biomass requires site-specific data, or often perfect world assumptions to model prediction systems when field measurements or other sensory inputs are unavailable. However, for small enterprises, regular measurements of site-specific data are often inconceivable. In this study, we approach the estimation of pasture biomass by predicting sward heights across the field. A convolution based sequential architecture is proposed for pasture height predictions using deep learning. We develop a process to create synthetic datasets that simulate the evolution of pasture growth over a period of 30 years. The deep learning based pasture prediction model (DeepPaSTL) is trained on this dataset while learning the spatiotemporal characteristics of pasture growth. The architecture purely learns from the trends in pasture growth through available spatial measurements and is agnostic to any site-specific data, or climatic conditions, such as temperature, precipitation, or soil condition. Our model performs within a 12% error margin even during the periods with the largest pasture growth dynamics. The study demonstrates the potential scalability of the architecture to predict any pasture size through a quantization approach during prediction. Results suggest that the DeepPaSTL model represents a useful tool for predicting pasture growth both for short and long horizon predictions, even with missing or irregular historical measurements.


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