Dynamics at the demographic time scale

2012 ◽  
Author(s):  
Odo Diekmann ◽  
Hans Heesterbeek ◽  
Tom Britton
Keyword(s):  
Population Growth ◽  
Spatial Scale ◽  
Time Scale ◽  
Host Population ◽  
Temporal And Spatial ◽  
Two Parameters ◽  
Infective Agent

This chapter focuses on the influence of the demography on the persistence of an infective agent, and vice versa on the influence of the agent on host population growth and persistence. When new “fuel” is provided through the replacement of immune individuals by newborn susceptibles, the infective agent may strike again at a later time or even persist and become endemic. Whether we observe repeated outbreaks or relatively small fluctuations around a steady endemic level depends on the temporal and spatial scale that we, as investigators of the system, choose to monitor and/or to model. The chapter also suggests that two parameters, rather than just one, are needed to distinguish between a single outbreak (which may reoccur much later by reintroduction of the agent from the outside) and the endemic situation.

scholarly journals Localized fast flow disturbance observed in the plasma sheet and in the ionosphere

2005 ◽  
Vol 23 (2) ◽  
pp. 553-566 ◽  
Author(s):  
R. Nakamura ◽  
O. Amm ◽  
H. Laakso ◽  
N. C. Draper ◽  
M. Lester ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Current Sheet ◽  
Plasma Sheet ◽  
Time Scale ◽  
Ionospheric Disturbance ◽  
Flow Shear ◽  
Current Location ◽  
Field Aligned Current ◽  
Temporal And Spatial ◽  
Fast Flow

Abstract. An isolated plasma sheet flow burst took place at 22:02 UT, 1 September 2002, when the Cluster footpoint was located within the area covered by the Magnetometers-Ionospheric Radars-All-sky Cameras Large Experiment (MIRACLE). The event was associated with a clear but weak ionospheric disturbance and took place during a steady southward IMF interval, about 1h preceding a major substorm onset. Multipoint observations, both in space and from the ground, allow us to discuss the temporal and spatial scale of the disturbance both in the magnetosphere and ionosphere. Based on measurements from four Cluster spacecraft it is inferred that Cluster observed the dusk side part of a localized flow channel in the plasma sheet with a flow shear at the front, suggesting a field-aligned current out from the ionosphere. In the ionosphere the equivalent current pattern and possible field-aligned current location show a pattern similar to the auroral streamers previously obtained during an active period, except for its spatial scale and amplitude. It is inferred that the footpoint of Cluster was located in the region of an upward field-aligned current, consistent with the magnetospheric observations. The entire disturbance in the ionosphere lasted about 10min, consistent with the time scale of the current sheet disturbance in the magnetosphere. The plasma sheet bulk flow, on the other hand, had a time scale of about 2min, corresponding to the time scale of an equatorward excursion of the enhanced electrojet. These observations confirm that localized enhanced convection in the magnetosphere and associated changes in the current sheet structure produce a signature with consistent temporal and spatial scale at the conjugate ionosphere.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

scholarly journals Temporal and Spatial Nearest Neighbor Values Based Missing Data Imputation in Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1782
Author(s):  
Yulong Deng ◽  
Chong Han ◽  
Jian Guo ◽  
Lijuan Sun
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Missing Data ◽  
Nearest Neighbor ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Temporal And Spatial ◽  
Two Parameters

Data missing is a common problem in wireless sensor networks. Currently, to ensure the performance of data processing, making imputation for the missing data is the most common method before getting into sensor data analysis. In this paper, the temporal and spatial nearest neighbor values-based missing data imputation (TSNN), a new imputation based on the temporal and spatial nearest neighbor values has been presented. First, four nearest neighbor values have been defined from the perspective of space and time dimensions as well as the geometrical and data distances, which are the bases of the algorithm that help to exploit the correlations among sensor data on the nodes with the regression tool. Next, the algorithm has been elaborated as well as two parameters, the best number of neighbors and spatial–temporal coefficient. Finally, the algorithm has been tested on an indoor and an outdoor wireless sensor network, and the result shows that TSNN is able to improve the accuracy of imputation and increase the number of cases that can be imputed effectively.

An initial study of the dynamic influences and interactions upon levels of sustainability at the global spatial scale

2021 ◽  
pp. 205301962110358
Author(s):  
Jason Phillips
Keyword(s):  
Population Growth ◽  
Greenhouse Gases ◽  
Spatial Scale ◽  
Influencing Factors ◽  
Initial Study ◽  
Global Environment ◽  
Human Relationship ◽  
Sustainable Society ◽  
Dynamic Impacts ◽  
Dominant Influence

This paper conducts an initial determination and evaluation of the fundamental dynamic influences and interactions upon indicated levels and nature of sustainability occurring, at the global spatial scale over the specified period of 2006–2016. This is achieved by the first full application of the Sustainability Dynamics Framework (SDF) to the results of the Sustainable Society Index (SSI). The results indicate that obtained S-values are potentially influenced primarily by a triumvirate of influencing factors – Population Growth, GDP and Greenhouse Gases. A cumulative analysis of indicator categories indicated that Environmental Wellbeing was the dominant influencing category upon obtained S-values for the period 2008–2014, and Anthropospheric Wellbeing was the dominant influence in 2006 and 2016. The analysis concludes that the triumvirate has potentially caused fundamental breaches and dynamic impacts and feedbacks upon the global environment-human relationship and system. Unless the triumvirate is managed and mitigated urgently, then there is a potential realistic risk of unsustainability occurring.

scholarly journals Characteristics of Climate Change and Extreme Weather from 1951 to 2011 in China

2018 ◽  
Vol 15 (11) ◽  
pp. 2540 ◽  
Author(s):  
Chunli Zhao ◽  
Jianguo Chen ◽  
Peng Du ◽  
Hongyong Yuan
Keyword(s):  
Climate Change ◽  
Spatial Scale ◽  
Spatial Scales ◽  
Seasonal Fluctuation ◽  
Extreme Weather ◽  
Temperature And Precipitation ◽  
Weather Variation ◽  
Variation Characteristics ◽  
Established Fact ◽  
Temporal And Spatial

It has been demonstrated that climate change is an established fact. A good comprehension of climate and extreme weather variation characteristics on a temporal and a spatial scale is important for adaptation and response. In this work, the characteristics of temperature, precipitation, and extreme weather distribution and variation is summarized for a period of 60 years and the seasonal fluctuation of temperature and precipitation is also analyzed. The results illustrate the reduction in daily and annual temperature divergence on both temporal and spatial scales. However, the gaps remain relatively significant. Furthermore, the disparity in daily and annual precipitation are found to be increasing on both temporal and spatial scales. The findings indicate that climate change, to a certain extent, narrowed the temperature gap while widening the precipitation gap on temporal and spatial scales in China.

The regulation of host population growth by parasitic species

Parasitology ◽  
1978 ◽  
Vol 76 (2) ◽  
pp. 119-157 ◽  
Author(s):  
R. M. Anderson
Keyword(s):  
Population Growth ◽  
Reproductive Potential ◽  
Parasite Burden ◽  
Host Population ◽  
Parasitic Species ◽  
Regulatory Influence ◽  
Host Mortality ◽  
Parasite Reproduction ◽  
Over Dispersion ◽  
Host Parasite

SummaryThe nature of parasitism at the population level is defined in terms of the parasite's influence on the natural intrinsic growth rate of its host population. It is suggested that the influence on this rate is related to the average parasite burden/host and hence to the statistical distribution of parasites within the host population.Theoretical models of host–parasite associations are used to assess the regulatory influence of parasitic species on host population growth. Model predictions suggest that three specific groups of population processes are of particular importance: over-dispersion of parasite numbers/host, density dependence in parasite mortality or reproduction and parasite-induced host mortality that increases faster than linearly with the parasite burden. Other population mechanisms are shown to have a destabilizing influence, namely: parasite-induced reduction in host reproductive potential, direct parasite reproduction within the host and time delays in the development of transmission stages of the parasite.These regulatory and destabilizing processes are shown to be commonly observed features of natural host-parasite associations. It is argued that interactions in the real world are characterized by a degree of tension between these regulatory and destabilizing forces and that population rate parameter values in parasite life-cycles are very far from being a haphazard selection of all numerically possible values. It is suggested that evolutionary pressures in observed associations will tend to counteract a strong destabilizing force by an equally strong regulatory influence. Empirical evidence is shown to support this suggestion in, for example, associations between larval digeneans and molluscan hosts (parasite-induced reduction in host reproductive potential counteracted by tight density-dependent constraints on parasite population growth), and interactions between protozoan parasites and mammalian hosts (direct parasite reproduction counteracted by a well-developed immunological response by the host).The type of laboratory and field data required to improve our understanding of the dynamical properties of host–parasite population associations is discussed and it is suggested that quantitative measurement of rates of parasite-induced host mortality, degrees of over-dispersion, transmission rates and reproductive and mortality rates of both host and parasite would provide an important first step. The value of laboratory work in this area is demonstrated by reference to studies which highlight the regulatory influence of parasitic species on host population growth.

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