text:
\section*{\sffamily Introduction}
\vspace{-0.5em}
Fine particles with a mass median aerodynamic diameter of less than 2.5$\mu m$ (PM\textsubscript{2.5}) that can penetrate deeply into the lung and damage the human respiratory system have been identified as a major public health concern \cite{xing8,Kioumourtzoglou2016,T2014}. The World Health Organization (2016) attributes an estimated 4.2 million deaths to exposure to ambient PM\textsubscript{2.5} in air pollution \cite{Organization2016}. Low-income populations are at higher risk of death from being exposed to PM\textsubscript{2.5} \cite{Jbaily2022}. Hence, understanding the exposure of different population groups to PM\textsubscript{2.5} air pollutants is imperative to inform policy interventions not only to reduce the overall exposure but also to provide all people with the same degree of protection from environmental risks \cite{Caplin2019}.

Recognizing the significance of PM\textsubscript{2.5} emission exposure, researchers have devoted significant efforts to measuring disparities in the exposure of PM\textsubscript{2.5} emissions among social-demographic groups at the local, regional \cite{E2021}, and national levels \cite{W2022,Browning210}. Traditional understanding of disparate exposure, however, is largely based on empirical models and air quality data detected by environmental sensors that estimate the concentrations of air pollutants in different areas of a city \cite{Bell2012}. The exposure to air pollutants is often approximated by the concentrations sampled at individual residences and is sensitive to the resolution of demographic data. For example, a prior study assigned the emissions of PM\textsubscript{2.5} from industrial facilities to nearby census block groups and then incorporated this pollutant into calculations of the burden of these emissions on racial groups and by poverty status \cite{Mikati2018} in the adjacent census block. As emissions of PM\textsubscript{2.5} are highly associated with land use and locations of industrial facilities, estimating the exposure based on the emissions from nearby facilities is a simplified and proper way to examine the disparities. To obtain a finer-grained resolution of PM\textsubscript{2.5} concentrations, a number of empirical models have been developed to calculate the spatial gradients of the concentrations. The InMAP air quality model is a commonly adopted model used to estimate concentrations of air pollutants resulting from anthropogenic emissions \cite{Tessum2017}. Then emissions from the source facilities are allocated to individual grid cells within a city using spatial surrogates \cite{W2019}. More recently, advanced technologies such as street-view vehicles equipped with mobile platforms have been employed to measure outdoor air pollutants in neighborhoods \cite{Apte2017}. Researchers could describe such outdoor concentration as "exposure" for block residents \cite{E2021}. These methods from prior studies allow us to estimate fine-scale resolutions of PM\textsubscript{2.5} concentrations at individual residences. However, exposure to PM\textsubscript{2.5} emissions is also influenced by dynamic human activities—work and life activities—that occur mostly outside the home, creating variations in the exposure to ambient emissions \cite{Fan2021,fan2021neural}.

The significance of human dynamics in environmental justice problems has become a matter of concern. An increased number of recent studies have attempted to process human-generated data and capture dynamic mobility patterns of populations \cite{Deng2021,Fan2022}. In particular, the ubiquity of smartphones and their embedded technology for location collection offers unique opportunities for research and practices \cite{Maas2019}. Mobile phone data that captures the timestamped locations of anonymized devices capture the trajectories of individuals, population density at places \cite{Deville2014}, and visit patterns of populations \cite{Blondel2015}. One of the most popular metrics that prior research has developed and been commonly adopted to characterize the patterns of human mobility is the radius of gyration \cite{Song2010}, interpreted as the characteristic distance traveled by an individual in a given time window. This metric has been successfully integrated into statistical models to predict individual mobility \cite{Gonzalez2008} and crowd movements \cite{Yan2017a}. The results from existing literature \cite{Widhalm2015} show the suitability of this metric in describing the mobility scales of individuals. By integrating radius of gyration with social and physical contexts in a city, some studies \cite{Pan2013} investigated visit patterns extracted from population mobility, such as individual lifestyles based on the sequences of visited places \cite{DiClemente2018}. These studies overlay mobile phone data onto the points of interest data that document attributes of places, such as names, brands, and sectors \cite{Zeng2017,yao2018}. Specifically, this contextual information enables the capture of frequently visited places of individuals and the length of stay at the places to uncover the daily life needs and work activities of city dwellers \cite{Ren2014}. In short, the gyration radius and contextual information in analytics of mobile phone data have promoted a variety of applications, including the examination of disparities in disaster evacuation by race and income \cite{Deng2021}, quantification of income segregation during social interactions \cite{Moro2021}, and characterization of urban structures with social and demographic inequalities \cite{fan2021neural}. These studies \cite{Gao2020,Alexander2015,barbosa2018human}, however, have not considered the intersection between human dynamics and the environment to understand how mobility influences the exposure of the populations to PM\textsubscript{2.5} air pollutants.

In this study, using a large collection of fine-scale mobility data generated by mobile phones, we address how mobility behaviors extend the exposure of populations to PM\textsubscript{2.5} emissions. This study integrates human dynamics into exposure measurement. We introduce a time-weighted and location-associated exposure metric to examine the time people spend at urban places and the emissions released by nearby industrial facilities. As a testbed, we selected Harris County in the Houston metropolitan area, the third most populous US county, whose population has a wide range of household incomes \cite{Bureau}. As the Energy Capital of the World, Harris County is a diverse and vibrant metro area that is home to a concentration of the oil and gas industries, chemical and allied product manufacture, and petroleum and related industrial processes. A consequence of prosperity in the oil and gas industry is a steep increase in the emissions of air pollutants, which may increase health risks for city dwellers from routine exposure to these pollutants. Hence, Harris County could serve as a suitable and representative study area to measure and compare the exposure of urban dwellers to air pollutants. The implementation of the metrics in Harris County reveals the disparities in mobility-based exposure across urban income groups. Furthermore, we identify residential and mobility features associated with the disparate exposure, which provide important insights related to the drivers of the disparity and could inform targeted policymaking for not only reducing the overall PM\textsubscript{2.5} exposure but also providing all populations with the same level of protection from the PM\textsubscript{2.5} risks in an equitable manner.

\section*{\sffamily Methods}
\vspace{-0.5em}
\subsection*{\sffamily Estimate PM\textsubscript{2.5} Concentration}
\vspace{-0.7em}
We used the 2017 air emissions inventory data released by the United States Environmental Protection Agency (USEPA) in 2021 to estimate the presence of air pollutants in Harris County. The National Emissions Inventory (NEI) data \cite{emissions2017} includes a facility-level inventory that summarizes the emissions of a variety of air pollutants, and locations and sectors of specific facilities which discharge air pollutants in industrial processes. This study specifically focused on air pollutants for PM\textsubscript{2.5}, one of the main causes of respiratory diseases. To estimate the concentration levels of air pollutants for PM\textsubscript{2.5} across the urban areas, we established a grid map dividing the Houston metro area of the city into 4280 1-km x 1-km (approximate) grid cells. Air pollutants emitted from the facilities within a grid cell account for the total concentration of PM\textsubscript{2.5} in the grid cell. Based on detailed geographical information of the facilities emitting air pollutants for PM\textsubscript{2.5}, we calculated the PM\textsubscript{2.5} concentration by summing up the emissions from all facilities within the grid cell.

\subsection*{\sffamily Measure Experienced Exposure}
\vspace{-0.7em}
The experienced exposure to PM\textsubscript{2.5} is estimated from the mobility activities of the populations. Here, we capture the mobility activities from anonymized and aggregated location data of mobile phone devices. The data set is provided by Spectus Inc., a location data intelligence company which collects anonymous, privacy-compliant location data of mobile devices using their software development kit (SDK) technology in mobile applications and ironclad privacy framework \cite{Aleta2020a}. The Spectus techniques process data only from mobile devices whose owners have opted in to share their location and require all application partners to disclose their relationship with Spectus, directly or by category, in the privacy policy. With this commitment to privacy, the data set contains location data for roughly 15MM daily active users in the United States. Through Spectus' Data for Good program \cite{Spectus}, Spectus provides mobility insights for academic research and humanitarian initiatives. All data analyzed in this study are aggregated to preserve privacy. Each entry in the data table comprises anonymized device ID, coordinates, start time, and dwell time of the stop for the device, as well as the points of interest associated with the stop. In order to further preserve privacy, Spectus also obfuscates home areas by estimating the census block group (CBG) where the mobile devices stayed during nighttime hours. The representativeness of this data has been demonstrated by prior studies \cite{Aleta2020a,Nande2021}.

We upscaled each stop of the mobile devices to a grid cell in Harris County in Texas. The trajectories of the mobile devices are then converted into a set of grid cells capturing the dwell time in each grid cell. The plot in the top right corner of \textbf{Fig. \ref{fig:fig1}} shows the mobility dynamics of the population captured by our method and data set. The spatial resolution of the mobility activities of the population becomes consistent with the spatial resolution we estimated for PM\textsubscript{2.5} concentration. Such spatial consistency allowed us to superimpose one data layer over the other to measure mobility-based exposure of populations to PM\textsubscript{2.5}. We define a time-weighted PM\textsubscript{2.5} concentration as the exposure of a mobile device. Specifically, the experienced exposure to PM\textsubscript{2.5} is associated with the PM\textsubscript{2.5} concentration in a grid cell and the device's dwell time in the cell. Hence, the time-weighted PM\textsubscript{2.5} concentration e, which an individual $i$ is exposed to, is given by:

\begin{equation}
    e_i=\sum_{\alpha}^{n}{\tau_{i\alpha}\cdot\psi_\alpha}
\end{equation}

where $\tau_{i\alpha}$ denotes the time the individual $i$ spent at the grid cell $\alpha$, $\psi_\alpha$ denotes the PM\textsubscript{2.5} concentration at the grid cell $\alpha$, and $n$ is the total number of grid cells visited by that individual. As such, the measurement of experienced exposure to PM\textsubscript{2.5} concentration for a neighborhood (same as CBG in this study) can be obtained by averaging the exposure of all individuals whose homes are in the same neighborhood. Here, we define, $\kappa_\beta$, the average time-weighted exposure of people from a CBG $\beta$ as:

\begin{equation}
    \kappa_\beta=\frac{1}{N_\beta}\sum_{i}^{N_\beta}\sum_{\alpha}^{n}{\tau_{i\alpha}^{\left(\beta\right)}\cdot\psi_\alpha}
\end{equation}

where $N_\beta$ denotes the number of individual devices from CBG $\beta$. This measurement serves as the strategy to upscale the experienced exposure to PM\textsubscript{2.5} from individuals to neighborhoods, which reveals the extent of experienced exposure to air pollutants for a group of people with a similar social and economic status.

\begin{figure}
  \centering
  \includegraphics[width=17cm]{Figures/Figure_1.png}
  \caption{\textbf{Mobility dynamics and income segregation.} (Synthetic user trace displayed in visualization to preserve privacy for demonstrative purposes.) The schematic illustration shows the development of the metrics and implementation of analyses in this study. We associated the median household income of a neighborhood with the residents living in the corresponding neighborhood. Based on their associated income, residents are then classified into four groups of equal size. The module on mobility dynamics proves the fact that people move along a trajectory and spend time visiting places of interest. The dwell time that a person spends in a place of interest varies across the places on the trajectory. With the associated income groups of visitors, the module of income segregation indicates that people who visited the same place may be from different income groups. This concept motivates the development of our metrics for emission exposure. Finally, the study reveals relevant factors for understanding the mechanisms of mobility patterns and emission exposure. }
  \label{fig:fig1}
\end{figure}

\subsection*{\sffamily Measure Income Segregations}
\vspace{-0.7em}
The disparities in air pollutant exposure are the result of segregated visits from different population groups to emission places. A population group can be denoted, for instance, as those in the first decile of an income group. The variable of interest in this study is the median household income of a population group. We obtained this economic variable for CBGs in Harris County, Texas, from the 2014–2018 five-year American Community Survey (ACS) conducted by the US Census Bureau \cite{USCensusBureau2019}. We assigned all individual devices with the median household income based on their estimated home CBGs and categorized them into four income groups of equal population size. Throughout the paper, we use Q1 to Q4 to label the income groups from the lowest to the highest, respectively.

The plot on the top left corner of \textbf{Fig. \ref{fig:fig1}} illustrates the segregated visit patterns of people to different places. We quantified income segregation at places based on data from an existing study \cite{Moro2021}, first defining a metric, $\theta_{q\alpha}$, which indicates the proportion of average time an individual from income quantile $q$ spent at a grid cell $\alpha$. The metric is formulated as follows:

\begin{equation}
    \theta_{q\alpha}=\frac{\frac{1}{N_{q\alpha}}\sum_{i}^{N_{q\alpha}}\tau_{iq}^{\left(\alpha\right)}}{\sum_{q=1}^{4}\left[\frac{1}{N_{q\alpha}}\sum_{i}^{N_{q\alpha}}\tau_{iq}^{\left(\alpha\right)}\right]}
\end{equation}

where $\tau_{iq}^{\left(\alpha\right)}$ denotes the time an individual $i$ from income quantile $q$ spent at a grid cell $\alpha$, and $N_{q\alpha}$ denotes the total number of individuals from income quantile $q$ visiting the grid cell $\alpha$. This metric quantifies the visit and dwelling patterns of people from different income groups at specific places (grid cells). Then the income segregation within a grid cell is measured by the deviation from a balanced mixing of income groups based on the average amount of time they spent. The measure, $S_\alpha$, is defined as:

\begin{equation}
    S_\alpha=\frac{2}{3}\sum_{q}\left|\theta_{q\alpha}-\frac{1}{4}\right|
\end{equation}

when people from different income quantiles visited the grid cell $\alpha$ with equality of time, $\theta_{q\alpha}=1/4$ for all income quantiles and $S_\alpha=0$. When people's visit time is highly unequal across different income quantiles at the grid cell $\alpha$, $\theta_{q\alpha}$ could be extremely close to 0 or 1, and $S_\alpha$ would be close to 1. Hence, the measure $S_\alpha$ is bounded between 0 and 1, and a smaller value of $S_\alpha$ represents a more equal visit pattern at the grid cell $\alpha$. This metric enables the quantification of income segregation at places and provides a rationale for the unequal air pollutant exposure of people from different income quantiles at places.

\subsection*{\sffamily Characterize Mobility Activities}
\vspace{-0.7em}
The disparities in experienced exposure to air pollutants among income population groups are the outcome of the interactions between mobility activities of humans and the places of the emission sources. Understanding such interactions is critical for the mitigation of the extent and disparities of experienced exposure. Two dimensions in the mobility activities are associated with the emission places: the sectors of the emission places where people spent the most of their time; and the scale of mobility, indicating the extent to which the scale of people's activities have overlaps with places of emission sources. Hence, we propose appropriate metrics to characterize the mobility activities in both dimensions.

The first metric is defined to measure the extent to which the time spent at a sector of facilities by people differs across different income quantiles. Here, a sector of facilities means the type of facilities, which is regulated by the Multi-Sector General Permit. The US Environmental Protection Agency delineates 12 sectors for emission facilities among 29 industrial sectors. We used the deflection of time, $\delta_{sector}$, spent at an emission sector by individuals from income quantile $q$ to characterize the patterns of mobility activities. The metric is computed as follows:

\begin{equation}
    \delta_{sector}=\frac{1}{N}\sum_{\gamma\in\left\{sector\right\}}\delta_{q\gamma}
\end{equation}

where $N$ represents the total number of people visiting the emission sectors. $\delta_{q\gamma}$  measured the deflection of time spent at facility $\gamma$ by individuals from income quantile $q$:

\begin{equation}
    \delta_{q\gamma}=\frac{\frac{1}{N_{q\gamma}}\sum_{i}^{N_{q\gamma}}\tau_{iq}^\gamma-e}{e}
\end{equation}

\begin{equation}
    e=\frac{1}{N_\gamma}\frac{1}{N}\sum_{q=1}^{4}\sum_{\gamma}^{N_\gamma}\sum_{i}^{N_{q\gamma}}\tau_{iq}^\gamma
\end{equation}

where $\tau_{iq}^\gamma$ represents the time an individual $i$ from income quantile $q$ spent at the facilities in a sector $\gamma$, $N_{q\gamma}$ represents the total number of people from income quantile $q$ visited facilities in sector $\gamma$, and $N_\gamma$ represents the total number of documented emission sectors.

The second metric we propose to characterize mobility activities is the mobility scales of people from their home grid cells to all other visited grid cells. Specifically, we adopted the idea of the radius of gyration for the mobility activities of each individual. The mobility radius of an individual $i$ from a neighborhood $\beta$ is calculated based on the distances $d_{i\alpha}$ from the home grid cell to the visited grid cells $\alpha$. The equation is shown as follows:

\begin{equation}
    m_i^2=\frac{1}{N_\alpha}\sum_{\alpha}^{N_\alpha}d_{i\alpha}^2
\end{equation}

where $N_\alpha$ is the total number of grid cells an individual $i$ has visited. Then, the scale of mobility for the people from a specific neighborhood can be calculated as:

\begin{equation}
    m_\beta=\frac{1}{N_\beta}\sum_{i}^{N_\beta}m_i
\end{equation}

where $N_\beta$ is the total number of individuals living in the neighborhood $\beta$.

To examine the overlap between the scales of mobility activities and the places of emission facilities, we also measured the average radial distance from a neighborhood $\beta$ to emission facilities.

\begin{equation}
    M_\beta^2=\frac{1}{N_\alpha}\sum_{\alpha}^{N_\alpha}d_{\beta\alpha}^2
\end{equation}

where, $d_{i\alpha}$ represents the distance from neighborhood $\beta$ to the grid cell $\alpha$. This metric will shed light on how close the neighborhood is located to emission facilities.

\section*{\sffamily Results}
\vspace{-0.5em}
\subsection*{\sffamily Patterns of PM\textsubscript{2.5} Concentrations and Segregation}
\vspace{-0.7em}
Air pollutant-emitting facilities are unevenly distributed throughout the urban areas of Harris County; their emissions vary significantly, leading to uneven concentrations of PM\textsubscript{2.5} in different areas of the county. \textbf{Fig. \ref{fig:fig2}A} maps the concentrations of PM\textsubscript{2.5} emissions in Harris County by aggregating the emissions from all facilities in each grid cell. The county has many areas with low concentrations of PM\textsubscript{2.5}, while high-intensity PM\textsubscript{2.5} is concentrated in a few areas, such as the eastern part of the county. Such a spatial pattern of pollutant concentrations indicates a heavy-tailed probability distribution function, as shown in \textbf{Fig. \ref{fig:fig2}B}. Of the grid cells in Harris County, 1\% of the grid cells emit more than 100 tons/year PM\textsubscript{2.5}. The concentrations of PM\textsubscript{2.5} in the rest of the grid cells range from 0 to 10 tons/year. This highly unequal distribution of quantities and locations of PM\textsubscript{2.5} concentrations suggests the potential of unequal exposure of populations to PM\textsubscript{2.5} pollution based on their activities in different areas of the county.

Due to dynamic human activities, exposure to emissions extends beyond the location of residence. People from different income groups may exhibit diverse activity patterns in different urban areas. We employed a segregation metric for each grid cell to measure the degree of segregation of the visited places in terms of the activity patterns of different income groups. The segregation metric captures the time spent in each grid cell by different income groups and the extent to which the time spent varies across different income groups. \textbf{Fig. \ref{fig:fig2}C} shows the results of measuring the experienced income segregation of grid cells in Harris County. The majority of urban areas in Harris County are income segregated. That is, the amount of time residents from different income groups spent in these grid cells is significantly unequal. Income segregation is pronounced, especially in grid cells at the center of the county. The extremely segregated grid cells are dispersed across the county and surrounded by moderately and mildly segregated grid cells. Those extremely segregated grid cells may only be visited by people from a single income group, such as high-expense places expenses or places where only a particular income group population would likely work.

The distribution for the time-spent income segregations in grid cells is plotted in \textbf{Fig. \ref{fig:fig2}D}. Most of the segregated grid cells tend to have 0 to 0.5 deflection from equality of visit time among income groups. Extremely segregated areas, such as the grid cells with income segregation greater than 0.6, are observed and account for 1\% to 2\% among the grid cells in the county. By juxtaposing the time-spent income segregation and PM\textsubscript{2.5} emission maps, we find that areas with high PM\textsubscript{2.5} concentrations are overlapping with the areas with significant time-spent income segregation. This result confirms that the interactions between mobility activities and emission distributions

\begin{figure}
  \centering
  \includegraphics[width=17cm]{Figures/Figure_2.png}
  \caption{\textbf{Distribution of air pollutant (PM\textsubscript{2.5}) emissions and experienced income segregation.} {\sffamily \textbf{A.}} Geographical distribution of air pollutant emissions by grid cell across Harris County. The magnitude of emissions is in the logarithmic scale and is shown in a color bar. The size of the ball represents the magnitude of the emissions. {\sffamily \textbf{B.}} Distribution of the total emission intensity (ton). PDF is probability density function; and CCDF is complementary cumulative density function. Both axes are in log10 scale. {\sffamily \textbf{C.}} Mapping of the income segregation in grid cells in Harris County, Texas. The values of the metric range from 0 to 1, distinguished by color. The higher value of income segregation in a grid cell indicates a highly unequal amount of time that people from different income groups spent in the grid cell. {\sffamily \textbf{D.}} Probability mass function (PMF) for income segregation in grid cells in Harris County showing the distribution of the extent of segregation across the county. Note that grid cells with no visits are excluded from the distribution plot.}
  \label{fig:fig2}
\end{figure}

\subsection*{\sffamily Disparities in Mobility-based Exposure}
\vspace{-0.7em}
Integrating the mobility activities of people at places into the exposure metric, the results reveal the patterns of disparate exposure to PM\textsubscript{2.5} among urban populations. \textbf{Fig. \ref{fig:fig3}B}. shows a heavy-tailed distribution of the mobility-based exposure to PM\textsubscript{2.5} for the neighborhoods (CBGs) in Harris County. This result indicates that, although a large number of neighborhoods are exposed to pollutants with a light concentration of PM\textsubscript{2.5}, considering mobility activities, a few CBGs suffer severely from the exposure to high concentrations of PM\textsubscript{2.5}. Such distinct disparities of exposure unveil an important environmental injustice issue related to how different income groups are placed in this distribution.

We mapped both the mean household income of CBGs (\textbf{Fig. \ref{fig:fig3}A}.) and exposure of CBGs to PM\textsubscript{2.5} (\textbf{Fig. \ref{fig:fig3}C}.) to examine how the income groups and the air pollutant exposure are spatially related. On one hand, according to \textbf{Fig. \ref{fig:fig3}A}., we find that the high-income populations are concentrated in the southwest of the county, while low-income populations are located from the center to the east and north of the county. On the other hand, the map of mobility-based exposure at the CBG level in \textbf{Fig. \ref{fig:fig3}C}. shows that PM\textsubscript{2.5} exposure is pervasive throughout the urban populations. All neighborhoods are more or less exposed to emissions of PM\textsubscript{2.5} due to population mobility activities. In particular, residents of the west and south of the county are less exposed to PM\textsubscript{2.5} based on their mobility activities, compared to residents of the north and east of the county. This qualitative spatial understanding of the interactions between income groups and mobility-based air pollutant exposure implies their association underneath the overall exposure disparities.

To future examine the significance of this finding, based on the classification of income groups we defined previously, \textbf{Fig. \ref{fig:fig3}D}. shows how different the mobility-based exposure is across urban income groups. Low-income population groups (Q1 and Q2 groups) have a significantly higher exposure to PM\textsubscript{2.5} based on their mobility activities, compared with the exposure of high-income population groups (Q3 and Q4). This result indicates a disproportionate exposure of low-income populations to air pollutants (PM\textsubscript{2.5}) in the city. This inequality of mobility-based exposure to the emissions is greater than exposure determined based on traditional approaches.

\begin{figure}
  \centering
  \includegraphics[width=17cm]{Figures/Figure_3.png}
  \caption{\textbf{Disparities of mobility-based exposure of PM\textsubscript{2.5} due to mobility.} {\sffamily \textbf{A.}} Mean household income of census block groups (CBG) in Harris County, Texas. The polygon and income data are obtained from US Census survey data. {\sffamily \textbf{B.}} Probability density function (PDF) and complementary cumulative density function (CCDF) of the experienced exposure to PM\textsubscript{2.5}. The value of exposure is calculated by the sum of concentrations (ton) at visited grid cells. Both x and y axes are in the log10 scale. {\sffamily \textbf{C.}} Mobility-based exposure to PM\textsubscript{2.5} at CBG level. This CBG-level exposure is the average of the individual mobility-based exposure from a CBG, using equation (2). Since the exposure varies significantly, we transformed the values into a log10 scale on the map. {\sffamily \textbf{D.}} Exposure distribution of four income groups in Harris County. The error bar shows 95\% confidence intervals around the mean value on each bar. Q1 is the lowest-income group; Q4, the highest-income group. The colors representing income groups are consistent with the colors in the subplot A. Significance tests are also conducted to examine the significance of the differences in mobility-based exposure among income groups. Note: $*** p \leq 0.01$; $** p \leq 0.05$; $* p \leq 0.1$. The detailed results for the significance tests are provided in the Supplementary Information Table 1.}
  \label{fig:fig3}
\end{figure}

\subsection*{\sffamily Disparities Relative to Mobility Patterns}
\vspace{-0.7em}
The spatial and quantitative understanding of disparate exposure among income groups reveals an important phenomenon in which the concentrations of PM\textsubscript{2.5} disproportionally impact low-income populations due to their mobility activities as well as to the location of pollutant-emitting facilities. This mobility-based measure of exposure relies on specific places people visit, the concentration of PM\textsubscript{2.5} at these places, and the time people spent at these places. To deepen the understanding of the mechanisms of disparate mobility-based exposure, we investigate the patterns of mobility activities of different income population groups and how these patterns are associated with the emissions of air pollutants. In this section, we mainly examine two relationships: (1) the relationship between the sectors of the emissions and the mobility activities of people in areas where these sectors are located to understand the impacts of visit patterns on air pollutant exposure; and (2) the relationship between scales of mobility and the distances from home neighborhoods to emission locations, to capture the spatial reach and overlaps of population activities and the emission sources.

The results in \textbf{Fig. \ref{fig:fig4}} reveal that the times people from different income groups spent in the concentration areas are similar, such as the time spent in the space research and technology sector and medical and hospitals sector. This phenomenon is dominant in most sectors; however, in a few sectors, the difference in the time people spent varies among different income groups, which shows an underlying mechanism for disparate mobility-based exposure to PM\textsubscript{2.5}. Specifically, the greater exposure of low-income populations is a result of the greater-than-average time they spent in these emission sectors, including the oil and gas extraction sector, petroleum bulk stations and terminals sector, and warehousing and storage sector. This observation indicates that low-income people may have a higher probability to work or interact with facilities such as mining, oil and gas extraction, compared with high-income populations. Such mobility patterns of the low-income people result in greater exposure of these sub-populations to PM\textsubscript{2.5} in urban areas.

\begin{figure}
  \centering
  \includegraphics[width=17cm]{Figures/Figure_4.png}
  \caption{\textbf{Relationship between emission sectors and mobility patterns of the populations.} The PM\textsubscript{2.5} concentrations and the time spent by people at places are aggregated by the sectors of the emission sources. $\delta$(\%), here, denotes the deflection of time spent at facilities in a specific sector by individuals from an income group. The ticks and numbers on the axis for the first four subplots are in the log10 scale. According to the average time spent by the individuals at all places in a county, the metric $\delta$ could show the negative and positive deflection of the time spent by the population groups in the sectors. The first four subplots from the left show the deflection of time for four income groups.  Q1 to Q4 represent four income groups based on their mean household incomes from low to high. The rightmost subplot describes the PM\textsubscript{2.5} emissions of each sector based on EPA data. A table of the sectors' codes and descriptions sectors on the right of the plot. A complete table showing the names of the sectors is provided in the Supplementary Information.}
  \label{fig:fig4}
\end{figure}

In addition to the exposure of people in the work environment, the association between the visit patterns of people for their life activities and the exposure to PM\textsubscript{2.5} is another important driver of exposure. The scale of an individual's mobility has been studied for decades and is considered a key measurement of mobility patterns \cite{Gonzalez2008}. In this study, we characterize the scale of mobility for people from the same neighborhood based on the radius of gyration, and we examined the varying relationships between the scales of mobility activities and the distances to emission facilities among income groups. Results depicted in \textbf{Fig. \ref{fig:fig5}}. indicate that the distances from the home neighborhoods to emission facilities are generally consistent across the four income groups; however, the scales of mobility for people from different income groups vary significantly.

We observed that the average radius of gyration for mobility activities of low-income populations (Q1 and Q2) is even 1 kilometer more than the average radius of gyration of high-income populations (Q3 and Q4). Hence, for low-income populations, the scales of their mobility activities are greater than the distances from their home neighborhoods to emission source facilities, while high-income people tend to have a smaller scale of mobility. A great scale of mobility that comes across a large spatial area would increase the possibilities of exposure to air pollutants. People with higher incomes move shorter distances for life activities, which leaves them less exposed to air pollutants, compared to people with lower incomes. This result raises important implications related to the catchment and accessibility of low-income populations for their life needs. We will discuss these implications in detail in the discussion section.

\begin{figure}
  \centering
  \includegraphics[width=17cm]{Figures/Figure_5.png}
  \caption{\textbf{Relationship between the scales of mobility activities and the distances to emission facilities.} Both metrics are estimated at the neighborhood (CBG) level. Each subplot represents the relationships for an income population group. Q1 through Q4 represent four income groups based on their mean household incomes from low to high. The red dots in the plots represent the neighborhoods in Harris County. The blue vertical and horizontal lines are the mean values of the distances $M_\beta$ and mobility scale $m_\beta$. The intersection of these two lines could show the difference between the distances to emission facilities and the scale of population mobility in the income group. The dark dot line of $y=x$ supports the evaluation of differences between the two metrics. The numbers on both the $x$ and $y$ axes in the subplots are in kilometers.}
  \label{fig:fig5}
\end{figure}

\section*{\sffamily Discussion and Concluding Remarks}
\vspace{-1em}
It is well known to researchers and practitioners that air pollutants like PM\textsubscript{2.5} have disparate impacts on the respiratory health of urban populations; however, little was known about the extent to which the interweaving of emission sources and mobility behaviors exposes low-income populations disproportionately to PM\textsubscript{2.5} emissions in cities. In this study, we integrate human dynamics into the measurement of air pollution exposure in urban communities. The approach we proposed here leverages a unique and large set of mobile phone data which documents the mobility activities of the populations to capture the time people spent at places surrounding emission facilities. The emission data provided by the US Environmental Protection Agency allows us to estimate the concentration of PM\textsubscript{2.5} at places and calculate the exposure of people to PM\textsubscript{2.5} due to their mobility activities. We quantify the relationships between mobility-based exposure and household incomes. Our results illustrate how detailed and extensive fine-scale mobility data related to human dynamics can add new insights into differences and disparities in air pollution exposures among urban income groups. The methods and findings of this study could inform policymaking to reduce disparities in urban air pollution exposure and the risks to respiratory health.

First, one core finding is that emissions of PM\textsubscript{2.5} disproportionately impact low-income population groups during their mobility activities. We also observe a huge gap in the levels of exposure between medium-low (Q2) and medium-high (Q3) income groups, while the differences between Q1 and Q2 and the differences between Q3 and Q4 are not so conspicuous. This finding is statistically significant, rendering it improbable that they are attributable to concentration estimation or mobility data biases. This observation highlights that mobility activities of populations have a pronounced impact on the exposure to PM\textsubscript{2.5} between high- and low-income population groups. Hence, in addition to measuring and mitigating the exposure to air pollution based on places of residence \cite{Jbaily2022}, another important disparity, which is in the mobility-based exposure, should be recognized in developing policies for exposure reduction. This study serves as the first attempt to integrate human dynamics into the exposure metric to advance the understanding of exposure disparities among income populations. Based on the findings, we would suggest that more specifically targeted PM\textsubscript{2.5} reduction policies and strategies considering human mobility dynamics are necessary not only to reduce the overall air pollution levels but also to provide all people with a similar degree of protection from environmental and atmospheric hazards. For example, policies can limit urban development around polluting facilities to reduce human activity and mobility surrounding those facilities. The findings could also inform public health policies targeted at reducing respiratory diseases associated with air pollution.

Second, the problem of disparate exposures to environmental pollution has received growing attention from environmental agencies in the United States. Insights and knowledge that can dive into the drivers of the identified disproportionate impacts of air pollution, however, have been underexplored. Through the investigation of the dynamic human mobility activities, we find the low-income population activities increases exposure to PM\textsubscript{2.5}, including work locations and their mobility scales. In particular, low-income populations tend to work in industries with more PM\textsubscript{2.5} emissions, compared to the PM\textsubscript{2.5} emissions in the working environment of high-income populations. The work environment exposes low-income people more to air pollutants. Also, neighborhoods where the low-income people live may lack sufficient facilities for their life needs, resulting in a long travel distance and a large travel scale in daily mobility. These findings have notable implications for developing effective strategies to reduce both the overall extent and inequality of PM\textsubscript{2.5} exposure among the urban populations. For example, emission reduction policies for the industries where low-income people often work could contribute to reducing the exposure of people during work. In addition, eliminating food deserts in low-income areas by promoting the construction of grocery stores across the city to reduce the mobility scales of the low-income people for life needs could also be helpful for reducing the disproportional impacts of air pollution.

This study, as a first attempt to integrate human dynamics into environmental justice issues, also comes with some limitations. First, the data available for estimating the PM\textsubscript{2.5} emissions are limited to every three years from 2011 to 2017, released by US EPA. The concentrations and spatial gradients of the air pollutant may differ at depending upon time and location due to changes in atmospheric dynamics. Further research may consider the dynamics of the emissions and atmosphere in the model to improve the measure of disparities present in cities. Second, this study focuses on the exposure disparities of PM\textsubscript{2.5} emissions at a local scale. Pollutant control policies tend to be regional because of the transmission of the air pollutants through the atmosphere. Hence, it is important to consider a larger scale including both urban and rural areas in the analyses to explore the regional disparities in PM\textsubscript{2.5} exposure. Third, although the examination of exposure disparities shown in this study is based on human mobility data and facility emission data from Harris County (Houston), Texas, potential variations might exist in different cities due to the development of the cities and the focuses of the industrial sectors. Experiments can be conducted in other cities to support the generality of our findings from this study.

\section*{\sffamily Acknowledgement}
\vspace{-0.5em}
This material is based in part upon work supported by the National Science Foundation under Grant CMMI-1846069 (CAREER), the Texas A\&M University X-Grant 699, and the Microsoft Azure AI for Public Health Grant. The authors also would like to acknowledge the data support from Spectus. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation, Texas A\&M University, Microsoft Azure or Spectus Inc.

\section*{\sffamily Competing interests}
\vspace{-0.5em}
The authors declare that there are no competing interests.

\section*{\sffamily Data availability}
\vspace{-0.5em}
All data were collected through a CCPA- and GDPR-compliant framework and utilized for research purposes. The data that support the findings of this study are available from Spectus Inc., but restrictions apply to the availability of these data, which were used under license for the current study. The data can be accessed upon request submitted on Spectus.ai. Other data we use in this study are all publicly available.

\section*{\sffamily Code availability}
\vspace{-0.5em}
The code that supports the findings of this study is available from the corresponding author upon request.

\renewcommand{\refname}{\large \sffamily References}
\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


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Tag Archive | Alphabetical Movie
in Alphabetical Movie Project
Alphabetical Movie – The Man Who Shot Liberty Valance
I'll be doing some spoilers about major plot points so if that bothers you, don't read on.
I'm not sure what The Man Who Shot Liberty Valance has to say about guns solving problems.
Jimmy Stewart is a hero because he killed a man who, based on all evidence, would have killed him first. It was never his goal to kill Liberty Valance but he was pushed to do so because he knew he couldn't run away.
It complicates things when we learn that Stewart did not, in fact, kill Valance. Given that fact, is the killing of Valance self-defense, murder, or something else?
Valance was shot to save someone's life. Unlike other Westerns where everyone is a crack shot, the reason Valance is dead is because his killer aimed at the easiest part of the body to hit. Makes sense. He only had one shot.
So where does that leave us? How do we feel about the whole situation? We know that Stewart is conflicted about it. He doesn't want to be a hero for killing a man. Yet, it is also true that he may be able to do more good for others if he accepts that he is a hero.
To me, Stewart is far more heroic than he believes because he went to face Valance with the knowledge that he was going to be shot to death. Had John Wayne failed to act, the outcome of the duel was unquestionable. Everyone knew that.
The old west that we see in classic films doesn't exist with the same standards of morality as our world does. Stand your ground laws notwithstanding.
Alphabetical Movie – Man on the Moon
I've been a fan of R.E.M. since college. They were a college band in the 80's. I was a college student in the 80's. It was a match that was simply meant to be.
They are one of the few bands I've seen in concert more than once.
What does this have to do with Man on the Moon, you ask?
Because R.E.M. should have an Oscar.
You see, the song "The Great Beyond," which played over the closing credits of Man on the Moon, was written by the band specifically for the film and was not even nominated for an Oscar.
I bitch about the Oscars a lot but no more than the best song category because it is typically filled with the most idiotic music and at least four out of five years it will recognize a song that nobody will ever listen to again. The year "The Great Beyond" was ignored by the R.E.M. hating academy, they gave an Oscar to a song from "Prince of Persia."
I presume the award was given because Disney hadn't released a film with music that year. They had to give it to some animated movie so why not that one, right?
So a song by R.E.M. that was not only a great song but also a perfect song to punctuate the film that had just preceded it was ignored and a song from a crappy animated film got the award instead.
in Alphabetical Movie Project, Religion
Alphabetical Movie – A Man For All Seasons
A Man For All Seasons is, I think, one of the movies being parodied in Monty Python and the Holy Grail. It is filled with English people doing historical English things that are super important if you are Royalty and completely unimportant if you are anyone else.
I enjoy English actors acting the shit out of stuff so watching Paul Scofield and Robert Shaw chomping on the saga of Thomas More entertains me to no end.
As someone who is non religious, the central conflict seems petty to me. Sure, I can respect Thomas More for standing on his principles. You have to stand for something.
What he was standing for, however, was the religious sovereignty of the Pope. My response, of course, is a great big "who cares?"
I understand that More had some deeply held beliefs and Henry VIII was crapping all over those beliefs by trying to get a divorce. I further understand that he must have thought he was carving out a really sweet plot of land in heaven by being the good defender of the Catholic faith.
It totally scored him a sainthood so good on him, I guess.
On the other hand, why did anyone care? He wasn't making a nuisance of himself. He wasn't speaking out against the crown. In fact, he was going out of his way to avoid speaking out against the crown. Yet Henry felt some sort of personal slight had been made and as a result, he called for a good old-fashioned trial by rigged jury.
Alphabetical Movie – Mamma Mia!
I'm just going to come right out and admit I love ABBA. It will save a lot of time later. I think I was purchasing cassettes (yes cassettes) of ABBA before any other band. Maybe I had an Andy Gibb record (yes record) before that.
So was I in the theatre opening weekend for Mamma Mia?
I'd rather not say.
Here's the thing that gets me about this film every time I watch it and yes, I've watched it more than once.
Holy shit is Pierce Brosnan a horrible singer. Like embarrassingly bad. I cringe every time he has to sing a solo in the film. I feel bad for him, I feel bad for his fellow performers, and I feel bad for every single person who is watching the film.
Brosnan is an actor I like. I thought he made a very good Bond. I liked The Thomas Crown Affair. I love The Matador.
So I don't hate Brosnan as an actor. He's probably a great guy.
But when someone asked him to play the male lead in a musical, didn't he answer with "have you heard me sing?"
And if that was his answer, why did someone respond "don't worry Pierce – it'll be fine?"
I have news for you, folks, IT IS NOT FINE.
Alphabetical Movie – The Maltese Falcon
Alfred Hitchcock always referred to the Maguffin as the thing in a story that everybody wants. What that thing is may not be particularly important. What is important is the lengths your characters will go to in order to get that thing.
The Maltese Falcon revolves around something everyone but the central character wants. He has no interest in the falcon at all. He just feels the need to figure out why someone killed his partner to get it. Sam Spade's motivation is honor rather than love or money and that, to some men at least, is considered romantic.
I think a lot of guys enjoy the idea that they are they martyrs of society. Could Sam Spade just forget about his partner and try to get himself a few bucks out of the Falcon? Sure he could.
But he's working towards a higher cause. When someone kills your partner you are supposed to do something about it. You're a man, dammit, and that's what men do!
Alphabetical Movie – Major League
When watching Major League, I can't help but think of a time when Charlie Sheen and Wesley Snipes weren't punch lines.
I'm not suggesting that this film represented the finest work of their careers. But they sure come off as likable guys, you know? It doesn't seem like they will just slip into obscurity or insanity. I think they both show a kind of charisma that suggests they are unlikely to burn out.
Snipes was Blade for goodness' sake! He and Sandra Bullock are the reason that Demolition Man isn't a complete disaster. Snipes kind of wills his audience to enjoy that movie.
By the time Sheen makes this movie, he's been in Wall Street and Platoon! For both of these guys, this dopey baseball film should be little more than a speed bump. Sheen ran over it twice because he appeared in Major League II. His career then migrated to Television, where he did very well until – well – we all know what happened.
Snipes, for his part, decided to skip paying taxes for a while. So he spent a little while in jail.
He's in Expendables 3 so things are looking up for him. Good news there. The Expendables franchise is not at all about featuring action stars past their prime in mediocre films. Should work out great.
Sheen's got another show on television so I'm pretty sure that he'll be able to spend another few years swimming in a pool of cocaine on his days off.
Alphabetical Movie – Magnum Force
The first thing I think about every time I hear the name of this movie is how you can conveniently turn it into a porno version without changing the name. Actually, you probably wouldn't have to change the plot all that much.
Harry Ballahan is a tough cop who doesn't play by the rules. He and his partner are upset because a gangster beats the rap in court so they double team the court reporter.
At the same time, a cop shows up and kills the gangster and everyone in the car. Then he goes home and his wife gives him a blow job.
Ballahan goes in to talk to his boss, Lieutenant Bangs, who is having sex with his secretary. They talk about the murder of the gangster while Bangs is having sex. Because he can multi task.
Then a couple of female cops have sex. Just because that sort of thing happens all the time during a murder investigation.
We switch to a gigantic orgy. For like fifteen minutes. Just tons of people having sex. They switch partners. They don't use condoms. It's just insane. And strangely boring.
Then the party blows up because it turns out all those people having sex were gangsters.
Ballahan is stumped. He has no idea who could be killing all of the gangsters in town. In fact, he delivers this exact line: "Damn! I have no idea who is killing all the gangsters in town!" He pounds his fist on the table and takes a swig of beer for emphasis.
So he orders a pizza. For some reason, it takes two women to deliver a pizza. And Ballahan doesn't have any cash for a tip…
He goes to a really nice house with a pool in the suburbs to ask some questions. While he's on his way, one of the killer cops breaks into a prostitutes room. She doesn't have any information for him so they just have sex. Her pimp breaks in and he shoots the guy while he is still having sex with the prostitute.
The sex scene lasts for another two minutes and the entire time the pimp is lying on the floor dying. He's still able to masturbate while watching them have sex. He ejaculates and then expires.
Ballahan arrives at the house with the pool and there is a beautiful naked woman swimming in the pool. He asks her if he can ask her a few questions. She unzips his fly and he says he has no time for that right now.
Literally. His line is "I have no time for that right now."
Turns out he does though, because they have sex in the hot tub.
Cut to Ballahan's partner, who is having sex with his wife. At least we assume it is his wife because she's the only other black person in the entire movie.
They have sex for a while and then he goes to the bathroom while she masturbates. As soon as she finishes, there is an explosion and she screams. What I mean is there is an explosion in the bathroom. Like from a bomb.
Cut to Ballahan comforting his partner's wife (his partner didn't have a name because he was black). She tells him that she's so lonely so they have sex. She feels a little better.
Then we cut to Lieutenant Bangs office. Harry tells Bangs that he knows who did it. Bang says really, who?
Again, that is the exact line. "I know who did it."
"Really? who?"
"It's you Bangs."
"You'll never catch me!"
"Like hell I won't."
Then Ballahan punches Bangs in the face. Actually it looks more like he punches Bangs in the shoulder. But Bangs falls down as if he was punched in the face.
Then the two lesbian cops show up wearing really short skirts, high heels, and shirts unbuttoned to their navels. He tells them to take Bangs away but they say they have a better idea.
The movie ends with Bangs handcuffed to a chair watching Ballahan having sex with the lesbian cops and complaining that he doesn't have a hand free to masturbate.
After the credits, there is a credit cookie where the two pizza delivery girls show up. Ballahan looks at the camera and says "A cop's work is never done" and we fade to black as the pizza girls start to take off their pants (but not their shoes).
See? Practically the same movie.
Young Notions
Tin Lizard Productions

redpajama_set_name: RedPajamaCommonCrawl


Fog Lake is the project of Canadian singer-songwriter and musician Aaron Powell. Two years after Dragonchaser, he announced the release of a new album. Captain is out on July 5th via Stack Your Roster. "California" is the first excerpt. Listen below.


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Tokusatsu Super Heronies #2: Generalworks
There are some super heronies I can find to discuss, there are so many that haven't been discussed or scanned online. I don't know much about these series. I will be covering more as soon as I get info on them.
Rosetta: The Masked Angel (1998)
A tokusatsu drama that aired from July to September 1998 on TV Tokyo. It featured the adventures of a young girl named Asuka who follows in her father's footsteps as Rosetta to battle evil monsters called Duats.
Sennen Ookoku San Jyushi Vanny Knights (1999)
Three girls transformed into bunny-inspired warriors, there was pink, orange and navy. Their outfits were white but their eye visors and underskirts were different colors. The navy one had black tights under her white skirt. Watch it online on YouTube, while it lasts of course.
Its funny, I always saw pictures of this movie on Japanhero but thought it was one lone female. If it wasn't for the great uploader to put the video up, I would have never known.
Jikuu Keisatsu (Chrono Police Wecker) (2001)
3 episodes were made by the producers (Generalworks) of Rosetta: The Masked Angel and Vanny Knights. The original Jikuu Keisatsu Wecker was initially released on DVD on June 21, 2001. It was followed by Jikuu Keisatsu Wecker D-02, which aired from January to March in 2002. They just wore the white outfits you see above.
Note: Photo below was screen-captured by my site partner Mike Bastian.
Wecker Signa (2007)
The third installment, it is the only one where the girls are actually suited up with helmets. Their colors were pink, orange, and purple. A black one also joined them. They made toys of both their regular gorms and armored forms. The blue one was a guy. The girl in the light blue outfit doesn't transform.
Check out their official website (Japanese)
[Click for larger image]
Labels: Heronie, Tokusatsu
Tokusatsu Super Heronies #1: Zen Pictures
The following are all from Zen Pictures:
Ninjya Special Investigate Justy Wind (2007)
Four school girls became ninja warriors. Their colors were blue, red, white, and navy. This one from Zen Pictures.
Idol Squadron (2007)
Their colors were basic red, yellow and blue.
Special Unit Bionic Force (2007)
They all had symbols. Red, Blue, Pink and Green.
Meteor Rangers Cosmic Five (2007)
After a strange meteor falls to Earth, five girls sneak into the National Science Laboratory to get it. The meteor came with an alien villain and gave them armor. They combined into one warrior to save the day. Aqua, White, Red, Pink, and Yellow.
Fi-Buster (2008)
Don't know much about this one. They had the basic sentai colors of red, blue, green, pink and yellow.
Burst Ranger (2008)
This is kind of embarrassing. Their main power was concentrated on their breasts. There is a lot of tokusatsu like this, with scantly clad women---some I saw in a friend's old tape but didn't copy, this was a long time ago. I believe this one was not for children.
Idol Force (2009)
Three heronies, one female villain, all their helmets were the same. A team of Pink, Cyan and Yellow.
There are many other you can see from Zen Pictures
Posted by Lavender Ranger at 11:50 PM 3 comments:
Non-Red Ranger Leaders
UPDATED AUGUST 28, 2009 8:30PM EST
Soukichi Banba/Big One
I forgot him, well, I didn't forget him, I was just not sure if he took leadership when he joined the team. He was a big show off and memorable character.
Tsuruhime/NinjaWhite
The first female leader in Super Sentai ever. She was the first non-Red ranger leader in a long time (about in 15 years). She was a stronger leader than Ninja Red. The Red Ranger was still the focus in merchandise and promos. Obviously, people don't want a female leading a team in a boys line. A producer has said in an interview that it is because of stores wouldn't sell toys for boys with a female leader because they don't think boys will want it.
Kouichirou Endou/Mega Black
While some do not consider him the leader, others do consider him the team leader at the beginning. He was not the protagonist. He was more stable than Mega Red.
Yuuri/Time Pink
Yuuri is the second female leader and the first Pink Ranger leader. She was tough as nails and has a underlined romance with Time Red.
Doggy Kruger/Deka Master
Called 'boss' in Dekaranger, he was strict, stern but fun and furry. He also had feelings for Swan but wasn't up for admitting it.
Ran Uzaki/Geki Yellow
While many don't agree that she was the leader. But Master ShaFu did call her 'captain' of the Gekiranger. So some people think that means she was captain in charge of getting new members. But she did lead the team. The others always ask her for her approval or input. When the GekiRinTohja was formed on the show, she was in the center of the image with the five of them. I wouldn't consider Jan (Geki Red) the leader, he was surely the protagonist but not the leader. Many fans were angry when Ran didn't get enough episodes or focus, fighting for the heronie.
Tommy Oliver/White Ranger
Initially he was not meant to be the leader (realistic sounding rumors that the decision was made when the actors playing Jason, Trini, and Zack were fired), but all fans were happy at the time for him to be the leader. He is the only male human leader not to be a Red Ranger.
Note: Above photo was scanned by my site partner Mike Bastian.
Delphine/White Aquitar Ranger
The first female leader in Power Rangers and the second White Ranger leader. I think she was a good leader.
Jen/Pink Time Force Ranger
Jen has an emotional story, as she was a cop who was engaged and her fiance died and then she fell in love with her dead fiance's ancestor and then found out her fiance was not dead and broke up the engagement. She was the second female leader but the first female leader in a long series, as the Aquitar Rangers were only part of a mini-series.
Doggie Cruger/Shadow Ranger
Spelled differently than his Japanese counterpart and more reptilian, he was a bit childish and had anger issues. He was the first Power Ranger to ever be married, but was not the last. Jack was indeed the leader of the B-Squad and Charlie of the A-Squad but Doggie was in charge of them. He was indeed the last non-Red leader in Power Rangers history.
~ Lavender Ranger
Posted by Lavender Ranger at 2:16 PM 1 comment:
Labels: Aquitar Rangers, Dekaranger, Power Rangers, Super Sentai, Time Force
Important African-American Non-Ranger Characters in Power Rangers
I am all for diversity and I like it that the Power Rangers welcomed that in its minor characters as well. Here are some allies that were African-American and memorable characters that made impact in the plot.
Adelle (Aloma Wright)
We all know the juice bar was the Ranger's hangout and was owned by Ernie. But, the Juice Bar was also owned by two others. The former police Lt. Stone in Power Rangers Turbo and then in Power Rangers in Space, the place went under new management by Adelle and it was called the Surf Spot. Formerly the Youth Center, this place became just a restaurant. Adelle stood up for what she believe in and was one of the brave citizens of Angel Grove and said she was a Power Ranger in order to protect the identity of the true Rangers. Aloma Wright has been in many shows but is best known as Laverne on "Scrubs."
High Commander Renier
In Power Rangers Lost Galaxy, the highest ranking officer on the space station was not a man nor a white person, it was a proud Black Woman. She was stern but fair and gave people the benefit of the doubt. For example, Mike got trapped in the middle of a planet due to an earthquake, he was MIA but when he returned and was excused by her.
Colonel Mason Truman
In RPM, He is the highest authority in the post-apocalyptic city of Corinth. He had two sons in the military as well, Marcus and Scott. But Marcus died in the line of duty and Scott is the leader of the Power Rangers. He can be a bit stubborn at times but he really does care about his son and others, he just doesn't express it as often as Scott would prefer.
While not being an extreme major character, he was only in a few episodes, he is still worth mentioning. He gave Merrick of Wild Force a home and work and gave him moral advice.
Labels: Disney, Power Rangers, Saban
Kamen Riders to not appear on Kamen Rider Decade or the movies
A lot of the Kamen Riders appeared in Kamen Rider Decade, but I also noticed that quite a few did not appear in the show nor the movies. I am counting Kamen Rider Decade the series, Cho Kamen Rider Den-O & Decade NEO Generations: The Onigashima Battleship, and All Riders vs. Dai-Shocker. Now, there can also be a chance in the movie coming in December.
Another Agito did not appear in Kamen Rider Decade. Many of the Oni Riders from Kamen Rider Hibiki did not appear in any form, including Sabiki, Eiki, Danki, Gouki, Toki, Shoki and Banki.
Certainly Shuki didn't appear in Decade. It's weird because Kamen Rider Shuki's stunt actor Naoki Nagase was working for Decade but didn't reprise his role as her. He reprised all his other roles (Psyga, Glaive, Tiger, Ketaro, Punch Hopper and Sieg.) Transformed Kyosuke also did not appear in Kamen Rider Decade. Kyosuke briefly transformed into this form. In stageshows, he is called Kamen Rider Kyoki.
Kamen RIder Kabuki of the Hibiki movie was summoned by Diend but the other movie-exclusive Riders of Hibiki did not appear in any of the Decade properties. Touki, Kirameki, Habataki and Nishiki above.
Correct me if I am wrong, I am not sure about these. Neither am I sure about Gaoh or Arc. I am sure that Nega Den-O did not appear in any. Kamen Rider Yu-Ki did not appear either. Additionally, Riderman appeared in All Riders vs. Dai-Shocker but Tackle did not, but she was never an official Kamen Rider.
Related Topic: Kamen Riders to Physically Appear in Kamen Rider Decade
Labels: Agito, Hibiki, KR Decade
Power Rangers: Promotional Differences
I thought I'd point out differences between merchandise and the show, through the 17 years of Power Rangers, there has been some inaccuracies. We all know these type of inaccuracies happens because pre-production, the merchandise people receive one idea of how the character looks and it might be changed later.
The first such inaccuracy and most blatant one is the power coin emblems on the Ranger's chest. This was in the early Mighty Morphin Power Ranger toys, coloring books, comic books, bed sheets, costumes, and all other illustrations (even in the Power Ranger Magazine comics but not the regular Comic Book). This was rectified sort of with the movie, where the Rangers wore the emblems on their chests. The Tyrannosaurus emblem was not even like the actual coin from Zyuranger because the coin had a tail. The Ninja Coin emblems also look different in the movie than in the merchandise and the show. It is understandable why the Ninja Coins changed in look because of deadlines and pre-approval, etc. (just to say it shorter), but why the emblems on the Ranger's chest if everyone could had access to Zyuranger footage. Either Saban or Bandai America had decided to put those things on their chests early on.
I do not know if younger fans know this but the Green Ranger toy had a different emblem (when you took off the shield) that the regular Dragonzord coin symbol we know and love. I have personally thought (for the past 16 years) this image looks more like Titanus. On the box, the Green Ranger had the show's Dragonzord coin. The Green Auto-Morphin Ranger came with a sticker using this image above. In the Power Rangers Fan Club package, the show's Dragonzord coin image was used for a tattoo that said Tommy on it. The above symbol was rarely used in any merchandise but I vaguely remember it being used once in another product. Maybe this Titanus-looking symbol was exclusively used for Bandai America. Other differences included the Green Ranger having diamonds on his cuffs and the Pink Ranger's color shade varying from the promotional image above and the actual product and the shade in the show. Additionally, the lack of skirt, which many fans have added skirts to the Pink Rangers that had them missing.
This Titanus-like Green Ranger image continued on to current Adult T-Shirts.
Yet some other shirts are show-accurate, yet the Dragon coin symbol here looks a bit like a Marijuana leaf.
Back when from 1993 to 1995, the costumes had the emblems on the chest. But now that official Red Ranger and Pink Ranger costumes (adult and child sizes) came out two months ago, the emblem is gone. In all other current illustrations of the MMPR Rangers, mainly the Red Ranger has no coin on the chest. The Super Legends toys also have gotten rid of the emblem, semi-accurate, many fans complain that the Green Ranger doesn't have his shield straps, but we get what we can.
Rita Repulsa's action figure infamously was never released because of problems with her chest, but in illustrations, she was colored purple and pink. I remember having a cut-outs package, where you had to cut out the Rangers and Rita Replusa and make 'sets' with them. The purple-version Rita was with it. Part of the reason I believe in the season 2 premiere "The Mutiny," Baboo said that Rita would be 'purple' with envy. Maybe a coincidence or maybe a reference. Recently I found out there was a plushie Rita, Goldar and Putty Patroller. I knew about the Rita squeeze night light (she was purple there too), but it's interesting her dress was not brown like on the show.
When Lightspeed Rescue hit McDonalds in 2000, the Yellow Ranger magically gained a skirt. She didn't have one in the show and for the illustration above and the McDonalds toy, she had a skirt. The MMPR Yellow Ranger never gained a skirt in any of the illustrations or merchandise. Curious.
By the time MMPR ended, many thought these inaccuracies had ended. Well, it didn't. In 2001 for Time Force, there was some illustrations of the Quantum Ranger with a strange two-streamed arrow and he was a darker shade of red. This held true for the costume as well. In fact he had a little yellow thing on his head, this made me believe to have a connection to the Titanium Ranger, which had the Max Solarzord symbol on his helmet. This strange version of the Quantum Ranger was also used for the walkie-talkies.
Around 2006, merchandise with the three male Mighty Morphin Power Rangers came and they had capes. Many suspected this was from the rumored animated series but now it is just believe it is only for merchandise. It has been seen in t-shirts, umbrellas, sunglasses, etc. This picture above is from a Latin American trading card. The newest brand of merchandise to sport the three caped Rangers is an Adult T-Shirt and children shirt below.
Posted by Lavender Ranger at 10:06 PM 1 comment:
Labels: Bandai America, Color: Green, Color: Yellow, Merchandise, MMPR, Power Rangers, PRLR, Toys
Important African-American Non-Ranger Characters i...
Kamen Riders to not appear on Kamen Rider Decade o...
Zordon as a Christ Figure?
Kivara finally helps make a Female Kamen Rider
Girls in Uniform: Kamen Rider Edition


An unforgettable wedding is not only about making sure each detail is executed flawlessly…it's about celebrating your style and creating wonderful memories. Your wedding is an opportunity for you, and your groom, to make your joint statement of style. Don't put yourself in a box when making your plans; infuse your personality into it every step of the way.
There are no textbook rules so if you want to dance down the aisle to your favorite song, by all means do it.
I once did a wedding for a bride who danced down the aisle with her father to Pretty Woman… then there was the couple who loved Frank Sinatra…their entire pre-ceremony, processional and recessional music selections were Sinatra songs.
You dream of a colored aisle runner…great…go for it! I had a bride who chose a custom raspberry aisle runner with their names hand painted and beaded on it. It was absolutely gorgeous.
The only rule today is that you make your wedding as personal as possible.


Wong Fei Hung "once defeated a group of thirty men at the same time with a pole in hand", says the "Tigress", Madame Mok Gwai Laan, his last wife in the interview for Real Kung Fu magazine (available in Real Hung Kyun, the complete collection of all Hung Ga Kyun articles from Real Kung Fu).
We don't know much details – his official biography from Fat Saan museum talks about incident in 1868 in Hong Kong harbor, where Wong Fei Hung defeated a local bully and about a dozen of the bad guy's henchmen with a long pole.
So or so, his skill with single-ended pole were extraordinary. No wonder that many of the movies about Grand Master Wong Fei Hung feature various long pole fight sequences.
Check out one of the oldest – and excellent!- choreography of single-ended long pole fighting from a 1953 movie called "Wong Fei Hung's Challenge Match" also known as "How Wong Fei Hung Defeated Three Bullies with a Single Long Pole".
Apart form long pole fight you can see a chain whip, three-persons sparring set "Sabre vs. Two Spears"(Daan Dou Chaak Seung Ying Cheung), "Sabre vs. Spear" (Daan Dou Chaak Ying Cheung) and other fighting sequences.
This entry was posted in Hung Kyun Videos and tagged Kwan Tak-Hing, Long Pole, Wong Fei Hung on July 8, 2016 by Pavel Macek.


In the Europe, Russia & New Independant States area, Insmi hosts 4 UMI, 2 LIA and 2 GDRI (for a definition of UMI, LIA and GDRI, take a look at the CNRS International Cooperation tool-kit).
The Pauli CNRS Institute (UMI 2842) is located in Vienna and involved in mathematics and in interface with informatics, physics and biology, via mathematical modelling and numerical simulation.
The European Institute for Statistics, probability, Stochastic Operations Researchs and its Applications (Eurandom - UMI 3022) is located in Eindhoven (the Netherlands). It is devoted to research in stochastics (statistics, probability, and stochastic operation research) and interactions with other sciences.
The Fibonacci Laboratory (UMI 3483) is located at the Scuola Normale Superiore of Pisa (Italy) and devoted to Franco-Italian collaboration in all the spectrum of mathematics and to their interactions with theoretical physics and informatics.
The UMI Abraham de Moivre (UMI 2004) was created in 2018. It is located at the Imperial College London, and concerns all areas in mathematics, their applications and their interactions with other sciences.
The Ypatia Laboratory in Mathematical Sciences (LYSM) is a French-Italian associated laboratory. It was created in 2017 by an agreement between the Aix-Marseille University, the Ecole Centrale of Marseille, the CNRS and the Istituto Nazionale di Alta Matematica "F. Severi" (INdAM). It covers the whole spectrum of mathematics. Contact: Pierre Picco.
The international associated laboratory Control, PDEs and Scientific Computing (LIA CoPDESC) aims to establish new as well as to strengthen the already strong scientific relations of German, French and Italian researchers in the field of partial differential equations, with emphasis on four thematics, namely : control and optimization, nonlinear PDEs, calculus of variations, and numerical analysis/scientific computing. Contact: Fatiha Alabau.
The French-British-German network in Representation Theory aims at structuring and encouraging the scientific cooperation between British, French and German mathematicians specialised in the field of representation theory. Contact: Nicolas Jacon.
The International Research Network Mathematics with Central and Eastern Europe (ECO-Maths) aims at developing collaboration between French, Hungarian and Romanian mathematicians. It covers a large spectrum of fundamental and applied mathematics. Contact: Colin Guillarmou.


1st Owner Unit. Energy Saving Petrol-Electric Car. 5 Years Unlimited Mileage Warrant And 10 Year Battery Warranty By KOMOCO, Immaculate Condition. Please Call/SMS For Enquiry Viewing By Appointment.
Facelift Model With LED Headlights! 1 Owner Agent Unit With Full Serviced History! Very Good Condition! High ARF Of $161k! View To Appreciate! Please Call For Appointment To View!
Don't Miss This M5 Look BMW 535i! Over $20k Worth Of Accessories! High Specs With High OMV! Please Call For Appointment To View!
Don't Miss! Selling Close To Export Value! BMW X3 M-Sport With Panoramic Sunroof, In Mint Condition. Well Maintain And Service Unit. Clean And Spacious Interior. Guarantee Great Drive And Handling.
Agent Stuttgart Auto Unit! Nice Colour Combo Of White With Red Interior. Very Good Condition! Please Call For Appointment To View! Loan/Trade In Available!
sgCarMart is the number one car classifieds for parallel import cars, Toyota, Honda, Nissan, Mitsubishi & BMW. There are plenty of cars for sale, from vans, trucks, sports cars or stationwagons, SUV, MPV, hatchback, to COE cars & OPC cars. You can also buy from a car auction, look up car loans, financial services, low mileage cars, car brands, carpark rates & car insurance. Find First CarMart Motor, used car dealer, car dealer Singapore or First CarMart Motor Singapore. We have new car dealers comprising parallel importers, authorised car dealers, and used car dealers on our site. Find new car price lists, new car launches and new car promotions. Visit our partner sites for job openings for Singapore jobs, real estate properties for sale, mover, car performance parts, car discussion, forum discussion, commercial vehicle leasing & COE results.


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Bob Clem Bio
Robert Clem, called "the most prolific hitter in city league history" by Georgia newspapers in the late 1950s began impressing the coaches early in his career. The Atlanta native attended Bass High School where he lettered in football and baseball for four years. He made All-State in football and was named to the North/South All-Star baseball team where he earned MVP honors. His 1957 batting average of .610 still holds the top spot in the city Atlanta.
After a brief season on the football team at Georgia Tech, Bob joined the Seminoles. His impact was immediate, batting .461 as a sophomore in 1958. His talent at the plate was noticed on a national scale as he was named to the District 3 All-Star team. Clem also became the youngest player named to the 30-man All-America squad in 1959 that made him only the second All-American in Seminole history behind Dick Howser who received the honor in 1957 and '58. Before leaving Florida State, the 6'1″ second baseman broke the records for the most triples and doubles in a season and led his team in hits, total bases, stolen bases, runs batted in and batting average. He is still listed in the Seminole record book for the highest single-season batting average.
On May 24, 1960, Bob signed a contract with the Baltimore Orioles, but after just four seasons in Baltimore, Clem saw his career cut short. While playing centerfield, Clem hit the fence going after a fly ball and injured his arm. Though his time in the pros was not as long as he liked, Clem did have the opportunity to hit a three-run homer off a famous major league pitcher named Satchell Paige.


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It's penis payback time, for Kolo Toure!
Written by Hippy fish
The story you are trying to access may cause offense, may be in poor taste, or may contain subject matter of a graphic nature. This story was written as a satire or parody. It is entirely fictitious.
If you wish to back out now, please click here to go back to the home page.
John Terry catches sight of Toure's penis. Looks like it added value to his day.
The champions league quarter final clash between Liverpool and arsenal at Anfield last night proved to be eventful, entertaining, and also controversial.
Arsenal started thee match with a spring in their step, and their efforts paid off when they scored. Arsenal seemed to be playing well, but when Liverpool equalized, Arsenal's confidence was crushed.
It went down hill for arsenal from then on. And when a controversial penalty was awarded to Liverpool, things looked really bad for the gunners. Kolo Toure denied fouling the Liverpool player, saying it wasn't him... it was someone else... No one knew what he meant, so after the final whistle (4-2 to Liverpool) we stalked Kolo to get some answers.
"you see, it wasn't me, it was my penis!" says Kolo Toure. "It's out for revenge!"
Revenge, what could he mean?
"well, i have trouble bringing ladies home, and my penis gets very angry when i deprive him of such needs! And now, its penis payback time!" At this point, Kolo runs away, screaming like a little girl, shouting "Down, boy, down!"
We spoke to the senior management team at arsenal, who say Toure's penis has a life of it's own, and attempted to attack the Liverpool player's. This is how the penalty was given away.
Of course, we doubt this is actually true. Toure's penis is just yet another person Arsene Wenger is able to blame for the defeat.
With every passing week, Arsene Wenger is being pushed nearer and nearer towards the exit. This week, it's Toure's penis that's doing the pushing.
ArsenalArsene WengerChampions LeagueFootball (Soccer)LiverpoolPenis
Worlds Smallest Vagina - New Record Holder 08 April 2008
Sam Allardyce Found Stuck in Door Frame at St. James' Park 07 April 2008
'Lack of sponsorship affects our performance' admits Gerrard 07 April 2008
Cardiff City To Be Thrown Out Of English League 07 April 2008
Keegan Quits Newcastle 06 April 2008
Wenger defiant after United 'victory' 06 April 2008
A Cock and Ball Story 05 April 2008
Joe Cole's Shorts too short for UEFA 03 April 2008
Sport Headlines


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Q: Is there a more efficient method than while loops for something that requires conditional checking? I have a problem that involves me wrapping a while loop around a bit of code that I believe can be vectorized efficiently. However, at each step, my stopping condition relies on the value at that stage. Consider this example as a representational model of my problem:
Generate N(0,1) random variables using rnorm()until you sample a value greater than an arbitrary value, k.
EDIT: A caveat of my problem, discussed in the comments, is that I cannot know, a priori, a good approximation of how many samples to take before my stopping condition.
One approach:

*

*Using a while-loop, sample suitably sized normal random vectors (for instance, rnorm(50) to sample 50 standard normals at a time, or rnorm(1) if k is close to zero). Check this vector to see if any observations are greater than k.

*If yes, stop and return all preceding values. Otherwise, combine your vector from step 1 with a new vector you make by repeating step 1.
Another approach would be to specify a completely overkill number of random draws for that given k. This might mean if k=2, sample 1,000 normal random variables using rnorm(1000).
Leveraging the vectorization that R offers in the second case gives faster results than the loop version in cases where the overkill number is not too much larger than necessary, but in my problem, I don't have a good intuition for how many runs I need to do, so I'd need to be conservative.
The question follows: Is there a way to do a highly-vectorized procedure, like method 2, but using conditional checking like method 1? Is doing small vectorized operations like rnorm(50) the "fastest" way, when considering that the highly-vectorized method is element-per-element faster, but more wasteful?

A: Here is an implementation of my earlier suggestion: use your first approach but increase the number of new samples between each iteration, e.g., instead of 50 new samples at each iteration, multiply that number by two between each iteration: 50, then 100, 200, 400, etc.
With your sample size following a divergent geometric series, you are guaranteed to exit in a "few" iterations.
sample.until.thresh <- function(FUN, exit.thresh,
                                sample.start = 50,
                                sample.growth = 2) {

   sample.size    <- sample.start
   all.values     <- list()
   num.iterations <- 0L

   repeat {
      num.iterations <- num.iterations + 1L
      sample.values  <- FUN(sample.size)
      all.values[[num.iterations]] <- sample.values

      above.thresh <- sample.values > exit.thresh
      if (any(above.thresh)) {
         first.above <- match(TRUE, above.thresh)
         all.values[[num.iterations]] <- sample.values[1:first.above]
         break
      }

      sample.size <- sample.size * sample.growth
   }

   all.values <- unlist(all.values)

   return(list(num.iterations = num.iterations,
               sample.size    = length(all.values),
               sample.values  = all.values))
}

set.seed(123456L)
res <- sample.until.thresh(rnorm, 5)
res$num.iterations
# [1] 16
res$sample.size
# [1] 2747703

redpajama_set_name: RedPajamaStackExchange


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template<typename Type>
struct DefaultSingletonTraits;

class CONTENT_EXPORT ZygoteHostImpl : public content::ZygoteHost {
 public:
  // Returns the singleton instance.
  static ZygoteHostImpl* GetInstance();

  void Init(const std::string& sandbox_cmd);

  // Tries to start a process of type indicated by process_type.
  // Returns its pid on success, otherwise
  // base::kNullProcessHandle;
  pid_t ForkRequest(const std::vector<std::string>& command_line,
                    const base::GlobalDescriptors::Mapping& mapping,
                    const std::string& process_type);
  void EnsureProcessTerminated(pid_t process);

  // Get the termination status (and, optionally, the exit code) of
  // the process. |exit_code| is set to the exit code of the child
  // process. (|exit_code| may be NULL.)
  base::TerminationStatus GetTerminationStatus(base::ProcessHandle handle,
                                               int* exit_code);

  // ZygoteHost implementation:
  virtual pid_t GetPid() const OVERRIDE;
  virtual pid_t GetSandboxHelperPid() const OVERRIDE;
  virtual int GetSandboxStatus() const OVERRIDE;
  virtual void AdjustRendererOOMScore(base::ProcessHandle process_handle,
                                      int score) OVERRIDE;
  virtual void AdjustLowMemoryMargin(int64 margin_mb) OVERRIDE;

 private:
  friend struct DefaultSingletonTraits<ZygoteHostImpl>;
  ZygoteHostImpl();
  virtual ~ZygoteHostImpl();

  ssize_t ReadReply(void* buf, size_t buflen);

  int control_fd_;  // the socket to the zygote
  // A lock protecting all communication with the zygote. This lock must be
  // acquired before sending a command and released after the result has been
  // received.
  base::Lock control_lock_;
  pid_t pid_;
  bool init_;
  bool using_suid_sandbox_;
  std::string sandbox_binary_;
  bool have_read_sandbox_status_word_;
  int sandbox_status_;
};

#endif  // CONTENT_BROWSER_ZYGOTE_HOST_ZYGOTE_HOST_IMPL_LINUX_H_

redpajama_set_name: RedPajamaGithub


text:
This is the guy who took away the likes
Adam Bandt defends Greens rhetoric, says young people are terrified
"People may quibble about the words that are used but I would urge people to have a listen to the point that Jordon was making."
Mr Bandt also said he did not regret pointing the finger at Mr Morrison over Coalition climate policy, maintaining that Australia's enthusiasm for coal-fired power was contributing to climate change and exacerbating bushfires. He called on Australia to play an international leadership role on emissions reduction.
Scientific research, including from the Bureau of Meteorology and CSIRO, has found climate change to be driving the increased fire danger and lengthened bushfire seasons in Australia.
Coalition MPs have accused the Greens of exploiting an ongoing disaster for political gain, insisting that the focus should be on emergency response while political and policy discussions could wait. In particular, the Greens have been criticised for accusing individuals of complicity in the fires.
Sonny Bill Williams rates NRL triumphs over All Blacks' World Cup wins


What is the size of Alaska?
Surrounded by the Pacific Ocean, the Arctic Ocean, Russia and Canada, Alaska is considered the least populated U.S. state. Based on history books, it was purchased by the United States from the Russian Empire sometime in March 1867. It goes by the nickname Land of the Midnight Sun. The largest city in the state is Anchorage, while its capital is Juneau. In terms of population, it ranks 47th in the United States, with a population of 686,293 as of 2008.
What is the size of Alaska? As the largest state in the U.S., the total area covered by Alaska is 1,717,854 square kilometers or 663,268 square miles. In terms of width, it measures 3,639 kilometers or 2,261 miles. It has a length of 2,285 kilometers or 1,420 miles. The highest point of the state is Mount McKinley, with a height of 6,193.7 meters or 20,320 feet. Its size is larger than the size of the next three biggest states combined, which are Montana, California and Texas. Furthermore, its size is also bigger than the combined size of the 22 smallest states in the country.
The median income of Alaska ranks fourth among the different states in the U.S., which is valued at $64,333. In terms of geography, the state is subdivided further into different regions. The first one is South Central Alaska, which contains most of the population of the state. This area includes progressive towns such as Wasilla, Palmer and Eagle River. Furthermore, it also includes Anchorage. Its economy thrives on tourism, transportation and petroleum industrial plants.
Also referred to as Southeast Alaska, the Alaska Panhandle houses the larger towns in the state. It is made up of extensive forests, various channels and numerous islands. Its economy is anchored on forestry, fishing as well as tourism. Bordered by Bering Sea and the Pacific Ocean, Southwest Alaska is sparsely populated. One of the possible reasons for this is the unconnected road system. The fishing industry plays a major role on its economy.
Characterized by shorelines, arctic tundra lands and large braided rivers, the Alaska Interior is another highly important region. People can find here the highest peak in North America, which is Mount McKinley. Meanwhile, the Alaskan Bush is the less crowded and remote region in the state. It is made up of small towns like Barrow, Kotzebue and Nome. In addition, it also covers a total 380 native villages. The Arctic National Wildlife Refuge can be found at the northeast corner of the state.


A limited liability company (société à responsabilité limitée - SARL) is a specific type of commercial company: it has both the characteristics of a capital company (liability of the partners limited to the amount of their contributions) and those of a partnership (non-transferable company shares).
In Luxembourg, the SARL is the most widely used form of company (around 2/3 of the companies in Luxembourg are SARLs).
An SARL can have between 2 and 100 partners. There is also a 'single member' SARL, (SARL "unipersonnelle") which is an exception to the traditional concept of company law, allowing a single partner to set up an SARL.
create a relatively simple business structure requiring reduced administrative formalities (compared with a public limited company (Société anonyme - SA).
An SARL may be incorporated any purpose, provided that it is legal. However, insurance, savings or investment companies cannot take the form of an SARL.
registration with the Trade and Companies Register (registre de commerce et des sociétés - RCS) for publication in the electronic compendium of companies and associations (Recueil électronique des sociétés et associations - RESA).
not dissolved on the death, suspension, bankruptcy or insolvency of one of the partners, unless otherwise stipulated in the articles of association.
contributions in industry are allowed, they do not contribute to the share capital but give rise to the allocation of shares which open the right to a share of the profits and net assets, in return for a contribution to losses (these shares are non-transferable and non-negotiable).
private offering of bonds is allowed (subject to the approval of the partners if the bonds are convertible into shares).
the transfer of shares must be recorded in a notarised deed or in a private deed.
The conditions regarding the capacity to be a partner in an SARL are the same as for any other contract.
exception: a single partner for a single-person limited liability company.
for the redress of damage arising from either the nullity of the company or deceitful or missing statements in the company deed.
The memorandum of association can limit the notion of business founder to the subscribers who own at least one third of the share capital in common. In this case, all other partners in the memorandum of association will be considered to be simple subscribers.
the rules for calling meetings can be defined in the articles of association.
The management of the company is ensured by one or more business managers, partner or not, appointed by the partners in either the articles of association or by any later act, for a limited or unlimited term. These managers can carry out any operation which is necessary or useful for the corporate purpose with the exception of those reserved for the partners as provided for by law or by the articles of association.
The day-to-day management as well as the representation of the business can be delegated to one or more business managers, directors and other agents, partner or not, acting alone or jointly.
the accounts must be drawn up according to the 'Lux Gaap' rules.
annual turnover excluding tax > EUR 620,000 -> monthly.
the causes of dissolution of SARLs extend to single-person SARLs.


We all need a clean water supply, shelter, energy, transport, protection from flooding and an infrastructure able to meet societal needs.
Civil engineers are at the forefront in providing sustainable, environmentally acceptable solutions to such problems and play increasingly important roles in tackling the challenges and effects of climate change.
Our degrees are integrated, giving you the maximum opportunity to progress as far as your potential will take you. It is possible to transfer from the BSc to the BEng and then from the BEng to the MEng.
The MEng leads to Chartered Engineer (CEng) status and the BEng and BSc to Incorporated Engineer (IEng) status – on completion of approved work experience and training.
The thing I have found most useful is the breadth of transferable skills that I learnt at the University of Plymouth.
Project Engineer with Babcock International Group, Plymouth.
The key piece of advice I would give is to gain as much experience as you can. It doesn't have to be a year in industry; a summer placement or a few weeks a year work experience will really make a difference when you start applying for graduate jobs.


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Zaha screamer up for Premier League Goal of the Month
After a busy night of club awards, Wilfried Zaha's sensational goal against Chelsea has been nominated for Premier League Goal of the Month - and you can help him win.
Zaha's thunderous effort saw him finish second in Palace's Goal of the Season award, and now he's up for league-wide acclaim.
To help Wilf win, get voting here! Voting closes at 18:00 BST on Friday, 31st July and the winner will be announced at 13:00 BST on Wednesday, 5th August. Stay tuned to cpfc.co.uk, the official Palace app and our social media channels to find out how Wilf does!
And, while we're at it, why not relive the strike below?
READ NEXT: Roundup: Every winner from last night's End of Season Awards
Crystal Palace vs Chelsea on 07 Jul 20