When acquiring technologies that have a general and wide use within the organization one must consider how the enterprise will adopt it. Particularly as enterprises continue to expand geographically the success of such a project is governed by this adoption. The DocTrix DMS is the best choice in the market place because it is simple to use and implement whilst not sacrificing functionality.
The use of the web-based search tool eliminates the need to install software on multiple client machines across the network.
Most document management solutions are over engineered for the typical user.Although this "super-user" functionality exists, the great majority of the enterprise users require a simpler interaction. The DSD web client is similar to most search engines found on the Internet, which users adopt quickly.
The document management system is built on industry standard, open architecture that allows the business to integrate existing applications.
With all the information centralized in one location, regardless of the format management and protection of the information is simplified.
The DocTrix document management system self-contained appliance. It is configured as RAID 0, 1 or 5 enhancing the information protection offered.


Welkin College School is situated in the cozy town of Coombs, British Columbia right beside a tranquil body of flowing water called French Creek. Coombs is an ideal location for a school because it is safe, quiet, clean and friendly.
Coombs is a small town which has 1,300 residents on Vancouver island in British Columbia. The old country market is is famous for the the goat family which are living on the roof of the market. Thousands of people come to the small town of Coombs every year to watch the goat family. The Butterfly World which is made in indoor rainforest area and the weekend festival which is held on the British Columbia Day is popular among tourists as well.
Our student residence is located in the town of Parksville. Parksville is a mid-sized Vancouver Island town that offers picturesque beaches, parks, restaurants and shops. Visit parksville.ca for more information.
Vancouver for occasional excursions. Nanaimo is just big enough to give students their big city fix but not so big as to present typical big city dangers. Nanaimo also offers an airport with connecting flights to Vancouver making international travel nice and easy.
Mid Vancouver Island in general is an ideal location for a boarding school in that it is renowned for its majestic natural setting, fresh ocean air and absence of the distractions of city life. The "Island", as it is commonly known, is revered by all Canadians for its relaxed, unhurried lifestyle, friendly inhabitants and splendid scenery. What better place to spend one's formative years?


Cannibal Pops are hand painted baby arms on a stick. This variation comes with added coloring to make the item look more like a sweet. However this Item is not edible. Perfect accessory for Clown costumes, butcher scenes and Halloween displays.


text:
Journal / Author Name Estimator
What about confidentiality? Is it safe to send my abstract to Jane?
How does Jane work?
How often is the data behind Jane updated?
Which journals are included in Jane?
Which authors are included in Jane?
Which papers are included in Jane?
What is the Article Influence score?
I would like to include Jane in my application. Is it possible to access Jane programmatically?
Return to Jane
The information sent to the Jane server is not stored. It is kept in memory for as long as needed to calculate the scores and formulate the response page, and then it is discarded from memory. The server itself is protected using standard protection measures. However, we understand that there is still the possibility that someone could intercept the transmission, and of course you do not know whether you can trust us. We therefore included an option in Jane to 'scramble' your input (see the button below the input box). Scrambling simply entails putting all the words in alphabetical order, and this is done by your browser (i.e. no information is sent for the scrambling). We admit that putting the words in alphabetical order does not completely disguise your input, but it does make it extremely hard to read, and it has no effect on the performance of Jane.
Jane first searches for the 50 articles that are most similar to your input*. For each of these articles, a similarity score between that article and your input is calculated. The similarity scores of all the articles belonging to a certain journal or author are summed to calculate the confidence score for that journal or author. The results are ranked by confidence score. For more information, you can read our paper.
* For the computer geeks: we use the open source search engine Lucene. Queries using keywords are parsed with the QueryParser class, titles and abstracts are parsed using the MoreLikeThis parser class.
We are currently updating the data once every month.
Basically, all journals included in PubMed are included in Jane. However, in order to show only active journals, we do not show journals for which no entry was found in PubMed in the last year.
All authors that have published one or more articles in the last 10 years that have been included in PubMed, are included in Jane.
All records in PubMed have been included that:
contained an abstract,
were published in the last 10 years,
did not belong to one of these categories: comment, editorial, news, historical article, congresses, biography, newspaper ar-ticle, practice guideline, interview, bibliography, legal cases, lectures, consensus development conference, addresses, clini-cal conference, patient education handout, directory, technical report, festschrift, retraction of publication, retracted publica-tion, duplicate publication, scientific integrity review, pub-lished erratum, periodical index, dictionary, legislation or government publication.
The Article Influence (AI) measures how often articles in the journal are cited within the first five years after its publication. These citations are weighted based the influence of the journals from which citations are received: being cited in an article in Science can boost a journal's AI more than being cited in an article in an obscure journal. For more detailed information, see the eigenfactor.org website.
Yes, there is an API that is freely available. A brief description can be found here.
If you still have unanswered questions, or would like to make a remark regarding Jane, you can always send an e-mail to .
Copyright 2007, The Biosemantics Group. Research funded by NBIC. Created and maintained by
Martijn Schuemie.
Hosting provided by the Observational Health Data Science and Informatics

redpajama_set_name: RedPajamaCommonCrawl


text:
Social Work Schools
In Stockton, California
There is only one social work school in Stockton for faculty to choose from. The trends in Stockton's social work academic community can be evaluated by looking at the statistics and graphs below, which includes social work training at the social work bachelors degree level.
Stockton Vs. California Social Work Employment
Stockton 1,020
Of the 59,550 social workers employed in California, 1,020 work in Stockton. Between 2006 and 2010, the number of social workers in Stockton has grown by 67%. Overall employment in Stockton has decreased.
Stockton Vs. California Social Work employment growth
610 530 920 930 1,020
Grey: California
Dark Yellow: Stockton
Social Work VS. All professions salaries in Stockton
Light Blue: Social Work in Stockton
Dark Yellow: All Professions in Stockton
Salaries for social workers in Stockton have increased. In 2010 Stockton social workers earned an average yearly salary of $56,947. Four years earlier in 2006, the average salary for social workers in Stockton was $49,610 per year. This growth is faster than the salary trend for all careers in Stockton.
Salary percentiles for Social workers in Stockton
Social workers make a median salary of $56,440 per year in Stockton. The difference in pay between the social workers in the lowest pay bracket and those in the highest pay bracket is approximately 145%. Those in the lowest 10% of the pay bracket earn less than $33,360 per year, while those in the highest 10% of the pay bracket earn more than $81,603 per year.
Average Salaries for Social workers and related professions in Stockton
Human Services $41,885
Social Work $56,947
Public Health $68,210
Stockton social workers have earnings that are lower than the salaries earned by others in related fields, on average.
Social Work schools in Stockton
Many of Stockton's social workers are graduates of the only accredited social work school in the city.
Social Work Programs Offered In Stockton
Stockton's social work school offers one social work degree and certificate program.
Student Completed Social Work Degrees In Stockton
In 2010 approximately 2 students graduated from social work courses.
Social Work Faculty Salaries in Stockton, California
The number of social work faculty, growth in the field of social work academia and social work faculty salaries in Stockton, is all data we are currently in the process of collecting. Please enter your information in the form below if you are involved in teaching social work courses to students at the bachelors degree in social work level. This will help us build a valuable free database resource for the benefit of current and future faculty in the field of social work in Stockton. All information you submit will be anonymous. Once you have submitted your information, you will be able to view a summary of some of the data we have collected so far.
California Schools
California Social Work Schools
Human Resources Schools in Stockton
Human Services Schools in Stockton
Social Work Schools in Turlock, CA
Social Work Schools in Hayward, CA
Social Work Schools in San Jose, CA


text:
using System.Threading.Tasks;

namespace SFA.DAS.CommitmentsV2.Domain.Interfaces
{
    public interface IFilterOutAcademicYearRollOverDataLocks
    {
        Task Filter(long apprenticeshipId);
    }
}

redpajama_set_name: RedPajamaGithub


text:
Q: How to combine short term and long term time series? I need to combine two traffic data series which are long term ( with 15 S / 30 S/1 minute interval) and short term( with 2 S/ 3 s) for my research purpose. These two types of data series reflect different traffic dynamics.
What would be the best way to do it/ what would be the most suitable technique?

A: Try the MIDAS (mixed data sampling) approach. It is intended for situations such as yours.
Software implementation is available in R (package midasr), Matlab (users guide) and perhaps other statistical packages.

redpajama_set_name: RedPajamaStackExchange


Ariana Grande, Pharrell and Maroon 5 have been announce to headline 'A Very Grammy Christmas Special' which will air on Dec. 5 on CBS. Country legend Tim McGraw will also be performing as part of the special.
The special will be taped on Nov. 18 at the Shrine Auditorium in Los Angeles, and will feature artists performing both hits and Christmas records to ring in both the holiday and Grammy Awards seasons.
The National Academy of Recording Arts and Sciences will be announcing the nominations for the 57th Annual Grammy Awards on the morning of Dec. 5, and will then announce the nominees for Album of the Year during the one-hour concert broadcast.
The 2015 Grammy Awards will air on Feb. 8. The Academy announced in August that it had received over 13,000 submissions for nomination consideration during its first round of entries.


text:
\section{Introduction}
\footnotesize{Solar granules are bright patches in the solar photosphere surrounded by dark channels called intergranular lanes.
The granules are a manifestation of overshooting hot upflows from the convectively unstable subsurface layers into the stable photosphere \citep{Stix2004}.
There, the excess pressure above the upflows accelerates the gas sideways.
Radiative cooling, along with an associated increase in density, makes the material both darker and less buoyant, and the material finally starts to flow down, where it collides with horizontally flowing gas from a neighbouring granule. A dark intergranular feature is formed at the location of the downflow. \\
 In the past 2 decades, three-dimensional numerical simulations have progressed enough to reproduce many properties of the solar atmosphere, providing physical insights into, and leading to a better understanding of, solar convection (e.g., \citealt{Nordlund2009}).
Simulated granulation (\citealt{Stein1998}) shows a typical pattern with slower upflowing gas, and with faster downflows, e.g., \citet{Hurlburt1984}.
However, so far observational results do not support downward motions faster than upflows.
\citet{Hirzberger2002} investigated the velocity field of the photosphere and reported almost the same magnitude of 1.5 km/s for both, upflows and downflows.
\citet{Kostik2007} used a bisector analysis to extract the height structure of the velocity field from the asymmetric shape of
an absorption line and also found that both upward and downward motions have approximately the same amplitude at heights from 0 km to 500 km.
By avoiding image degradation due to atmospheric seeing and with its precisely known PSF, the \textit{Hinode} spacecraft \citep{Kosugi2007} is well suited to precisely derive the velocity field.
Using the spectrum observed by the Solar Optical Telescope (SOT) \citep{Tsuneta2008} onboard \textit{Hinode}, \citet{Yu2011b} found that the velocity field ranged from $-$2.0 km/s (upward) to less than +2.0 km/s (downward) and the peak of their distribution coincided with upflows.
Using a Milne-Eddington inversion code, \citet{Jin2009} found a velocity field dominated even more strongly by blueshifts with a range from $-$3.3 km/s to +2.0 km/s and a peak at -1.0 km/s.
Finally, using a bisector analysis, \citet{Oba2017} investigated the average convective velocity field of upflows and downflows separately.
They found 1.5 times stronger upward than downward motions at photospheric heights from 40 to 160 km. \\
 One possible cause of this discrepancy between the numerical and observational results is the imaging performance of the employed telescopes.
\citet{Danilovic2008} reported the significance of this effect based on a careful study in which they applied the point-spread function (PSF) of the instrumental configuration of the \textit{Hinode}/SP (the spectropolarimeter on \textit{Hinode}) on 3D magneto-hydrodynamic (MHD) simulations (cf. \citealt{Wedemeyer2008}, who derived the \textit{Hinode}/SOT PSF empirically).
They blurred the continuum intensity map obtained by running the SPINOR spectral synthesis code \citep{Frutiger2000} on a MHD simulation snapshot produced by the MURaM code \citep{Vogler2005}.
The degradation of the data reduced the RMS intensity contrast from 14.4\% to 7.5\%.
The latter value is nearly the same as its observational counterpart of 7.0\%.
This difference in contrast of a factor of 2 suggests that the ratio between the velocities in the upflows and downflows may also be significantly affected by the PSF of the \textit{Hinode}/SP.
Therefore, a removal of the degradation owing to the PSF could help restore the velocities to values closer to the solar ones.
Testing this hypothesis is the main aim of this study. \\
 We attempted to properly capture a better approximation of the convective velocity signal through a spatial deconvolution process.
Recently, some deconvolution analyses have been performed on the \textit{Hinode}/SP data.
This instrument is well-suited for such an image reconstruction technique because its PSF is well-known and almost time-independent (except for focus changes).
Early work was done by \citet{Mathew2009} and \citet{Wedemeyer2009}, who investigated the intensity contrast in the photosphere.
A self-consistent approach was developed by \citet{vanNoort2012} and \citet{vanNoort2013}, who extended the SPINOR code to iteratively invert a set of profiles while also taking account of the influence of the PSF.
The resulting technique was applied to investigate active regions (cf. \citealt{Riethmuller2013}; \citealt{Tiwari2013}; \citealt{Buehler2015}, etc.) and the quiet Sun \citep{Danilovic2016}.
To precisely derive the photospheric physical parameters in active regions using the SIR inversion code (\citealt{RuizCobo1992}), \citet{Ruizcobo2013} and \citet{Quintero2015} deconvolved spectroscopic scans with the known PSF of \textit{Hinode}/SP.
They countered the enhancement of noise, typical for deconvolution techniques, by suppressing the noise through a principal component analysis (PCA).
To these established approaches (\citealt{vanNoort2012} and \citealt{Ruizcobo2013}), we add a simple alternate deconvolution method combining noise suppression with deconvolution and investigate how its application affects the up- and downflows due to the photospheric convection. \\
 We carry out our analysis in two main steps. First we evaluate our deconvolution technique.
To determine how well the deconvolution recaptures the original image, we use synthesized maps created by numerical radiation MHD simulations that were degraded by the \textit{Hinode} PSF.
In a second step, the vertical velocity in solar granulation is determined more reliably than in previous analyses.
This observational result is obtained by applying the deconvolution technique to a time series of spatial scans carried out by the \textit{Hinode}/SP. \\
 This paper is structured as follows.
In Section 2, we describe the observations, numerical simulations, and our approach to deriving the convective motion.
In Section 3, we evaluate the deconvolution technique and observational results.
In Section 4, we discuss how the photospheric convection changes through the deconvolution.
Finally, in Section 5, we summarize our findings. \\

\section{Methodology}

\subsection{Observations}
The observations analyzed in this work were taken by the Spectropolarimeter ($SP$) \citep{Lites2013b}
on the SOT on board the \textit{Hinode} spacecraft \citep{Kosugi2007,Tsuneta2008,Suematsu2008,Shimizu2008} on August 25, 2009, between 08:01 and 09:59 UT. The \textit{Hinode}/SP observed a quiet region at solar disk center, recording the Stokes $I$, $Q$, $U$, and $V$ profiles of the Fe~{\sc i} 630.15~nm and 630.25~nm spectral lines. It used a spectral sampling of 21.5~m\AA, and a pixel size of approximately 0.$^{\prime \prime}$16, providing a spatial resolution of 0.$^{\prime \prime}$32. The integration time was 1.6 s per slit position. The slit had a length of 60.$^{\prime \prime}$9 and was oriented in the solar N--S direction.
A small field-of-view (FOV) of $4.^{\prime \prime}5\times60.^{\prime \prime}9$ was scanned 117 times with 30 slit positions per scan (each slit step corresponded to 0.$^{\prime \prime}$15) and a cadence of approximately 62~s. Fig.~\ref{fig:1} shows the continuum intensity for the first 11 scans where the granulation pattern and its evolution can be clearly identified.
The SP data were calibrated with the standard routine SP\_PREP in the Solar SoftWare package (\citealt{Lites2013a}).
Finally, the velocity was calibrated as in \cite{Oba2017}. \\
 \textit{Hinode}/SP obtains spectral images by slit scanning. Consequently, each slit position along the E--W direction is recorded at a different time.
For the precise derivation of the convective velocity field, we wanted to create a two-dimensional spectral map where all spatial locations corresponded to the same solar time because the deconvolution process needs the information outside the observed slit position.
Such a uni-temporal image is also needed to remove the 5-min oscillations, which are larger scale phenomena producing fluctuations in both horizontal directions at the solar surface.
Accordingly, we performed a linear time interpolation using the information from consecutive raster scans to produce a pseudo-instantaneous spatial image (see more details in Fig.\ref{fig:20} and its caption).
Since the time interval of linear interpolation (roughly 1 min in this case) is shorter than the typical 6-min granulation lifetime \citep{Hirzberger1999} and the 5 min period of p-modes, this interpolation should not unduly influence our results in unwanted ways.

\subsection{Synthesis of Stokes profiles}
 Before using the deconvolution technique developed in this work on \textit{Hinode}/SP observations we wanted to first test it on synthetic observations to verify its possibilities and limitations.
For this purpose, we needed to synthesize the Stokes profiles, solving the radiative transfer equation starting from a given atmosphere. This was performed using the numerical code SPINOR (\citealt{Frutiger2000,Frutiger2000b}) that works under the assumption of local thermodynamic equilibrium, which is adequate for the Fe~{\sc i} lines at 630~nm. \\
 The input atmospheres used by SPINOR were a series of snapshots taken from a rediative MHD simulation generated by the  MURaM code \citep{Vogler2005}.
The chosen setup was the same as that used in \cite{Riethmuller2014}.
The calculation ran for 1 hour of solar time and we stored a snapshot containing all the physical parameters (needed for the line computation) every 30 seconds, thus producing 120 snapshots in all.
Each snapshot had a size of 6 $\times$ 6~Mm with a spatial sampling in the horizontal directions of 10.42~km.
The vertical extent of the simulated domain was 1.4~Mm and the grid size was constant at 14~km. The average height where the continuum optical depth ($\tau_{500}$) was unity was reached 0.5~Mm below the top boundary of the computational box.
The simulation was performed without any initial magnetic flux, and therefore, we focused only on the pure hydrodynamics in the photosphere. \\
 Figure \ref{fig:21} shows an example of the spatial distribution of the intensity signals at the continuum wavelength $\lambda=630.1$~nm for a selected snapshot.
 In addition, we can see that the contrast is large (with the RMS being 16.8\%), as we had not yet applied any type of spatial degradation to these images.

\subsection{Spatial deconvolution}\label{bozomath}

In this section, we explain the basis of the deconvolution technique. The observed image can be expressed as
\begin{equation}
I\left( x,y\right) =\int \int ^{\infty }_{-\infty }I_{0}\left( \xi ,\eta \right) PSF\left( x,y:\xi ,\eta \right) d\xi d\eta + \epsilon \ (x, y),
\label{eq:deconv}
\end{equation}
where $I\left( x,y\right)$ is the observed intensity, $I_{0}\left( \xi ,\eta \right)$ is the ideal image, $PSF\left( x,y:\xi ,\eta \right)$ is the point-spread function, and $\epsilon$ is random noise (the equation is often abbreviated in the form $I=I_{0} \ast PSF+\epsilon$, where asterisk means convolution ).
This equation signifies that the observed intensity at a given pixel results from the contribution by the surrounding pixels in the ideal image.
The goal of deconvolution is to obtain the intensity at that pixel in the ideal image $I_{0}$, using a given $PSF$ and the observed intensity $I\left( x,y\right)$. \\

\subsubsection{Richardson-Lucy algorithm with regularization}\label{bozomath}
 Several types of spatial deconvolution techniques have been proposed (see for instance \citealt{Jansson1997} for an overview).
In this study, we applied the Richardson-Lucy (RL) algorithm as a deconvolution method (\citealt{Richardson1972}; \citealt{Lucy1974}), which was also used in the previous studies involving deconvolution of \textit{Hinode} data (\citealt{Ruizcobo2013}; \citealt{Quintero2015}).
This algorithm is an iterative scheme used to increase the probability of being Poisson distributed.
We assumed the following image formation model:
\begin{equation}
i(X)=o \ast h,
\label{eq:eq1}
\end{equation}
where $i$ is the image, the variable $X$ is two dimensional and represents $(x, y)$, $o$ is the object, and $h$ is the $PSF$.
Under the influence of Poisson noise, we have to deal with $i=\varphi (o \ast h)$, $\varphi$ being a Poisson distribution.
The probability of our measurements falling within a Poisson distribution can be expressed as
\begin{equation}
p\left(  i | o\right) =\prod _{X}\left( \frac {\left[ h \ast o\left( X\right) \right] ^{i\left( X\right) }e^{-\left( h \ast o \right) \left( X\right) }}{i\left( X\right) !}\right),
\label{eq:dop}
\end{equation}
where ! is the factorial.
Our goal was to find the $o(X)$ that maximized the probability of $p\left(  i | o\right)$ to most likely produce the observed value of $i(X)$. To search for the solution, we used the following iterative algorithm,
\begin{equation}
o_{k+1}\left( X\right) =\left\{ \left[ \frac {i\left( X\right) }{\left( o_{k}\ast h\right) \left( X\right) }\right] \ast h\left( -X\right) \right\} o_{k}\left( X\right).
\label{eq:rl0}
\end{equation}
Equation (4) describes an iteration step of the RL algorithm that retrieves the real image through an iterative scheme. However, in the case of an observation with noise, the RL algorithm does not always converge to a suitable solution because of the amplification of the noise after several iterations.
\citet{Ruizcobo2013} and \citet{Quintero2015} reduced the noise on the profiles  by the PCA (\citealt{Loeve1955}) before deconvolution. This involved creating a base of eigenvectors that included the spectral information of the original data set. \\
 We followed a different approach on treating the noise: we added a regularization term, so-called Tikhonov-Miller regularization, to reach a suitable solution \citep{dey2004}.
This method searches for a spatially-smoothed solution in the ideal image by allowing non-maximum probability in finding the answer, by limiting the enhancement of fluctuations at the highest spatial frequency.
Instead of the original Richardson-Lucy algorithm given in Equation (\ref{eq:rl0}), we obtained a regularized form of the RL algorithm,
\begin{equation}
o_{k+1}\left( X\right) =\left\{ \left[ \frac {i\left( X\right) }{\left( o_{k}\ast h\right) \left( X\right) }\right] \ast h\left( -X\right) \right\} \frac {o_{k}\left( X\right) }{1+2\lambda _{TM}\Delta o_{k}\left( X\right) },
\label{eq:rl}
\end{equation}
where $\lambda _{TM}$ is the regularization parameter, and $\Delta$ is the Laplacian operator. The observed image $o$ and the PSF $h$ are known parameters, whereas the regularization parameter $\lambda_{TM}$ can be chosen freely. It is important to set an appropriate value of $\lambda_{TM}$ because the success of the regularization depends on the amount of the noise to be suppressed; the larger the value of the parameter $\lambda_{TM}$, the greater the likelihood of a spatially-smoothed image with a large departure from the observed image; however, for a smaller value, the algorithm regards any observed signal (including the noise component) as a true one, so that it increases noise considerably, which is not desirable for a noisy image. The selection of the best parameter value is explained in Section 3.1.\\
 In this study we refrained from correcting for spectral degradation caused by the dispersion in the grating because the deformation of spectral profiles was considered to be negligible. Using a tunable laser, \cite{Lites2013b} measured the SP-spectral-response profile prior to launch and reported the difference of intensity before and after the convolution. They found that the line core showed the largest deviation of about 3\% in the absorption line. Our bisector analysis (see Section 2.4) did not make use of the line core, but only the intensity range from 0.45 to 0.70, so that the spectral degradation likely had little effect on the deduced velocity fields.

\subsubsection{PSF}\label{bozomath}
Our spatial deconvolution code, introduced in the previous section, requires a spatial PSF (see Eq.\ref{eq:rl}).
We used the instrument-PSF data previously obtained from the \textit{Hinode} pupil presented in \citet{Suematsu2008} (for more details, see \citealt{Danilovic2008}).
They modeled the optical path of the telescope and transfer optics to the entrance spectrograph slit, based on the commercial optical design software ZEMAX.
Their computation included blurring effects due to the telescope aperture, central obscuration by the secondary mirror and its attachment spider, spatial sampling of the CCD detector, and defocus.
The PSF function, sampled at 0.$^{\prime \prime}$16 ($\approx$100 km), which is the same pixel sampling as the dataset of the \textit{Hinode}/SP, is plotted in Fig.\ref{fig:2}.
Since the scanned area is narrow, we need to limit the extent of the PSF.
To this end, we calculated the total energy contained within a set of squares of different areas, each corresponding to a square number of detector pixels, 1$\times$1, 2$\times$2, 3$\times$3, etc.  The results are plotted in Fig.\ref{fig:3}.
In raw data (i.e., without any deconvolution), any given pixel contains only 13\% of the observed intensity from the corresponding location, while the remaining 87\% originates from outside.
The total energy contained in a given area increases rapidly as the area increases (Fig.\ref{fig:3}).
We found that a square of 13$\times$13 pixels contained 96\% of the intensity. Assuming that the remaining 4\% does not significantly contribute to the observed information, we use only the part of the PSF contained within these 13$\times$13 pixels. To allow for a proper deconvolution, we normalized the PSF covering a square of 13$\times$13 pixels to have a total value of 100\%.

\subsection{Derivation of the convective velocity field}
 To derive the convective velocity field, we processed the deconvolved spectrum in two steps. First, bisector analysis provided the velocity field at multiple heights \citep{Stebbins1987, Dravins1981}, and then a subsonic filter \citep{Title1989} removed the 5-min oscillations to obtain the convective velocities.
Through these steps, we obtained convective velocity fields at six bisector levels (intensity level of $I/I_{c}$=0.70, 0.65, 0.60, 0.55, 0.50, 0.45) using the Fe~{\sc i} 630.15~nm spectral line (the details are described in \citealt{Oba2017}).
We avoided sampling other intensity levels (more than 0.70 or less than 0.45), to make sure that our results are not adversely affected by pixels lost in intergranular lanes owing to their lower continuum and higher line core intensity, e.g., if a value of $I/I_{c}$ of 0.90 is chosen, then the bisector at this $I/I_{c}$ value cannot be determined in all the pixels at which the continuum intensity drops below  this value. This happens regularly in the intergranular lanes. The range of $I/I_{c}$ values are chosen such that the bisectors at these $I/I_{c}$ values can be determined in almost all spatial pixels.
In a simple 1D model one can formally assign a geometrical height to an intensity level, i.e. to a bisector level.
Based on the mean temperature stratification, those six bisector levels correspond to geometrical heights of 49, 62, 77, 92, 112, and 135 km, respectively.
We stress, however, that as the solar atmosphere is highly structured, the actual height at which radiation is emitted changes strongly from one horizontal position to another. Such changes in height are often considerably larger than the whole height range formally covered by the bisectors (as given above). Therefore, these heights should be used with extreme caution.

\section{Results}
\subsection{Evaluation of the deconvolution technique using the synthesized image}
To estimate how accurately our deconvolution technique removed the degradation by the instrument PSF and restored the observed images, we used images synthesized from a numerical simulation as test images.
The advantage of this approach is that the ideal image (the simulated image) is known, and has a similar morphology and intensity contrast as the Sun \citep{Danilovic2008}.
Recall that the forward calculation of the SPINOR code provided spectral data covering the wavelength range from 630.1 to 630.3 nm based on the atmospheric parameters simulated by the MURaM code (one snapshot of the continuum intensity is shown in Fig.\ref{fig:21}).
This simulated continuum intensity map was binned to correspond to a spatial sampling of 100 km for a direct comparison with the \textit{Hinode}/SP observations.
Hereafter we call it \textit{the answer-image}.
\textit{The answer-image} obtained from the snapshot displayed in Fig.\ref{fig:21} is shown in Fig.\ref{fig:5} (a).
\textit{The answer-image} is free of noise.
To mimic the observed intensity map of the \textit{Hinode}/SP, we then convolved \textit{the answer-image} with the instrument PSF and introduced Gaussian random noise.
We call the resulting, degraded image the \textit{imitated-image}.
An example is shown in Fig.\ref{fig:5} (b).
The variance of the introduced Gaussian random noise was calculated from the observed continuum intensity, and is 0.012.
The retrieved image through the RL algorithm without any regularization after 50 iterations is shown in Fig.\ref{fig:5} (c).
The scatter plot of this image vs. the answer image is drawn in Fig.\ref{fig:5} (e) with a standard deviation of 0.0406.
An image retrieved by the extended RL algorithm with regularization ($\lambda_{TM}=0.0016$ is adopted because this is the optimum value as explained later) is shown in Fig.\ref{fig:5} (d).
A scatter plot of the image deconvolved including regularization of the deconvolved image versus the intensities of \textit{the answer-image} is shown in Fig.\ref{fig:5} (f) and displays a standard deviation of 0.0371.
These standard deviation are much smaller than those between \textit{the answer-image} and \textit{the imitated-image}, 0.145.\\
 In most tested cases, the RL algorithm without regularization retrieved satisfactory images after an appropriate number of iterations.
However, sometimes the RL algorithm did not converge to a suitable solution.
Fig.\ref{fig:23} shows the standard deviation between \textit{the answer-image} and the deconvolved image as a function of iteration number; the solid curve shows the result of the original RL algorithm, and the dashed curve shows that of the RL algorithm with regularization using a $\lambda_{TM}$ of 0.0016.
In the RL algorithm without regularization, the minimum standard deviation (the best output) occurred at the 29th iteration.
For additional iterations, the standard deviation increases again, i.e. noise is enhanced.
Since the optimal iteration number is a priori unknown, it is easy to continue iterating too long and hence enhance noise unduly.
One example of this is seen in Fig.\ref{fig:5} (c) with 50 iterations.
The RL algorithm with regularization is better behaved in this respect. Although, the standard deviation increased slightly with increasing number of iterations beyond the optimal iteration number (36th iteration in this case), this method still attained high accuracy without excessive noise enhancement long after the optimal iteration number. Whereas both methods, original RL algorithm and RL algorithm with regularization, attain almost the same accuracy at their optimal iteration number, the latter algorithm can provide a stable output regardless of the iteration number. \\
 As mentioned previously, although users need to assign a value to the regularization parameter $\lambda_{TM}$, it is difficult to find the appropriate one.
Thus, we checked the accuracy of the various deconvolutions, adopting different values of $\lambda_{TM}$ to determine which parameter value provides the most reliable image.
To visualize the correlations between \textit{the answer-image} and retrieved image, we used various log$_{10} \lambda_{TM}$ from $-2$ to $-5$ with a spacing of 0.1.
In Fig.\ref{fig:22}, we plotted the corresponding standard deviations between \textit{the answer-image} and the image retrieved by the regularized RL algorithm and found that the minimum value (i.e., the best correlation between their images) is located around 0.0016 (equivalently $-2.8$ on the logarithmic scale).
Therefore, this value of $\lambda_{TM}$ was adopted because it provided the best estimation of \textit{the answer-image}.
Besides, Figs.\ref{fig:23} and \ref{fig:22} reveal that the best results obtained by the two versions of the RL algorithm are nearly of the same quality, although the algorithm in its regularized form leads to slightly lower standard deviations. The main shortcoming of the RL algorithm with regularization is illustrated by Fig.\ref{fig:22}. The appropriate value of $\lambda_{TM}$ to use for a particular type of data set is not {\it a priori} known and simulations cannot always be used to fix it, as we have done here.

\subsection{Observational results}
\subsubsection{Spatial distribution of granulation}
 Figure \ref{fig:7} shows the snapshot of the spatial distribution of the continuum intensity, as well as the convective velocity and the 5-min oscillations at a bisector level of 0.70.
The top panels are derived from the observations before deconvolution, and the bottom panels correspond to the spatially deconvolved results.
The deconvolution process enhances the continuum intensity contrast, from 7.2\% before applying the technique to 13.0\% after, which is close to the 14.4\% contrast found by \citet{Danilovic2008} from MURaM undegraded simulations.
The remaining difference is partly due to the binning to the instrumental pixel scale, with the rest possibly due to a small defocus of the SP (see \citealt{Danilovic2008}).
While we do not find notable differences in the shapes of the granules, we found the presence of small-scale spatial variations inside them that were very poorly visible before the deconvolution process.
One example can be found around [1.0,1.0]~Mm:
in the top panel (before deconvolution), the granules is nearly monotonously bright; in the bottom panel (after deconvolution), the darkening of the inner part of the granule can be seen clearly.
A very weak indication of this darkening is already present in the original image, but is not nearly as clear as in the deconvolved one. \\
 The deconvolution process also improves the contrast of the convective velocity along with a much smaller enhancement of the 5-minutes oscillation.
The RMS of 0.61 km/s in the originally observed convective velocity field increased to 1.12 km/s after the deconvolution.
Importantly, the downflows display a larger increase than the upflows.
It can be seen immediately that the area occupied by downflows is larger in the deconvolved observation (bottom panel) than in the original observation (top panel).
More quantitatively, we defined the area fraction of downflows as the ratio between the downflowing portion of the image area to the total area, and found that this value increased from 0.34 before the deconvolution to 0.48.
To obtained these area fractions we discriminate between up- and downflows beyond a threshold of $\mp$0.18 km/s, respectively, and this value is the estimated error of the wavelength, using the spatially-time averaged profile of the Fe~{\sc i} 630.15~nm line to match the well-calibrated quiet-sun line profile of \citealt{Allende1998} (see more details in \citealt{Oba2017}).
This increase in the area fraction is because the weak upflows at the edge of each granule transformed into downflows after deconvolution, and the narrow intergranular channels sandwiched between granules appeared to increase in width.
One example can be detected at around [0.3, 5.5]~Mm, where the deconvolved observation shows a clear downflow in a spatial location with zero LOS velocities in the original observation. \\
 Figure \ref{fig:14} displays scatter plots of the deconvolved versus original intensity (top panel) and LOS velocity (bottom panel).
The slopes of the scatter plot for the continuum intensity and convective velocity field are 1.78 and 2.14, respectively, implying that the continuum intensity contrast and the convective velocity field are nearly/more than doubled by the deconvolution, respectively.
In the case of the continuum intensity, the scatter is uniform for the whole range of intensities, whereas for the LOS velocities we found larger deviations for stronger downflows.

\subsubsection{Magnitude of convective velocity field}
The histogram of the convective velocity field in Fig.\ref{fig:24} shows quantitatively how deconvolution changes the upflows and downflows by different amounts.
These velocities were computed using bisector analysis (see Section 2.4 for more details) for the pixels in the center of the FOV along the scanning direction and around the central part of the slit, thus avoiding uncertainly deconvolved pixels close to the edges, as these need information outside the FOV.
The magnitude of the convective velocity field at all the heights is enhanced in the deconvolved observation, especially for the downward motions.
Before the deconvolution, at a bisector level of 0.70 (depicted by the black line in Fig.\ref{fig:24}), the distribution stretches from -2.0 (upward) to +1.5 (downward) km/s.
Consequently, the blue-shifted signal is typically stronger than the red-shifted signal.
After the deconvolution, downflows reach approximately the same magnitude as upflows with the amplitude ranging from -3.0 to +3.0 km/s.
Similarly, the average speeds of upflows and downflows are found to be -0.64 km/s and +0.49 km/s before deconvolution, but -1.14 km/s and +1.20 km/s after that. \\
 The velocity histograms change their shape significantly as the height decreases.
In the highest layer (blue line), the deconvolution process broadens the velocity distribution without significantly shifting the peak.
In the lowest layer (black line), the deconvolution not only broadens the velocity distribution, but by changing the shape of the distribution, also shifts the peak.
The first-moment of the convective velocity distribution (i.e., averaged over all pixels) is a rough indicator of how well up- and downflowing mass flux balance, although the density of gas also contributes and the difference in height of formation in granules and intergranular lanes affects the results as well, as shown in Fig.\ref{fig:25}.
Clearly, after the deconvolution, the average flow speeds lie closer to 0 km/s (the values fluctuate between +0.05 and -0.05 km/s), whereas prior to deconvolution, the average flow speed exceeded -0.2 km/s at the some bisector levels.
At lower intensity levels (geometrically higher layers), this shift by the deconvolution process decreases (i.e., the average flow speeds before and after deconvolution is similar).

\subsubsection{Comparison with MHD simulations}
 For a direct comparison to the numerical simulations, we applied bisector analysis to the spectral profiles synthesized with the SPINOR code in the atmosphere computed by the MURaM code.
We take this approach instead of comparing directly with the velocities in the simulated cubes, because the Doppler velocity fields obtained via bisector analysis are unavoidably smeared by the different atmospheric contributions to the line profile along the LOS and thus are smaller than the actual atmospheric velocity fields.
We started with the original resolution simulations and computed the line profiles and bisectors.
Then we spatially degraded them using the \textit{Hinode}/SP PSF.
Histograms are displayed in Fig.\ref{fig:13}.
It shows the same quantities as Fig.\ref{fig:24}, but now for velocities derived from the simulated histograms, both at original resolution and after convolution with the \textit{Hinode}/SP PSF.
The RMS of the convective velocity field at a bisector level of 0.70, dropped from 1.10 km/s before the spatial convolution to 0.62 km/s after it, which is almost the same amplitude as the observation.
The first moment of the velocity also shows the same trend as the observations.
It became less negative i.e. tended more towards a red-shift through the spatial convolution.
Thus its value goes from -0.11 km/s to -0.33 km/s.
Note that these values are more blue-shifted than the observation.
The area fraction of downflows decreases from 0.46 before the spatial convolution to 0.27 after that; averaged speeds of upflows and downflows are -1.06 km/s and +0.98 km/s before convolution, getting reduced to -0.75 km/s and +0.51 km/s after that.
These up- and downflows are distinguished by the same threshold ($\mp$0.18 km/s) as the observational one (see Section. 3.2.1).}

\section{Discussions}
 The continuum contrast of \textit{Hinode}/SP is 7.2\% for the original observation and 13.0~\% after the deconvolution.
We considered whether these values are realistic by comparing them with the results obtained using hydrodynamic simulations. The contrast of the synthesized image in the original pixel sampling ($\approx$10 km), the summed sampling ($\approx$100 km), and the summed sampling after the convolution with the instrument's PSF shows 16.8\%, 16.1\%, and 8.2\%, respectively. The third one is similar to the one observed by \textit{Hinode}/SP, i.e., 7.2\%, although the \textit{Hinode} value  is somewhat lower.
According to \citet{Danilovic2008} this difference may be due to a small defocus in the \textit{Hinode}/SP, as the best focus is generally determined using the filtergraph.
The contrast value of 13.0\% in the deconvolved observation is lower than 16.1\% in the original simulation.
This suggests that the image is not over-restored, so that the deconvolution provided a realistic image in terms of the contrast.
Moreover, these values are consistent with the previous work of \citet{Danilovic2008}, who found RMS contrasts of 14.4\%, 7.5\%, and 7.0\% in the original pixel sampling ($\approx$10 km), the summed sampling  ($\approx$100 km) with spatial convolution, and the \textit{Hinode}/SP observation used in their work, respectively.
Their contrast values are slightly lower than ours because their simulation included magnetic flux, which hinders the granulation and, hence, reduces the intensity contrast (\citealt{Biermann1941, Riethmuller2014}).
The contrast of 13.0\% found in our work is also consistent with the value of 12.8\% estimated by the observational approach of \citet{Mathew2009}, who deconvolved \textit{Hinode} images using a PSF of the \textit{Hinode}/SOT derived using images during the Mercury transit. \\
 Before the deconvolution, the results displayed stronger convective upflows and weaker downflows.
However, after the deconvolution, these amplitudes of up- and downflows became comparable, resulting from a preferential enhancement of the downflows.
This enhancement can be explained by considering the difference in emergent intensity in granules and intergranular lanes.
Convective blue- and red-shifted signals within a sub-arcsec spatial scale are unavoidably mixed because of the imperfect imaging performance of a telescope.
Thus, they partially cancel each other, leading to spatially degraded signals.
The higher intensities of granules compared to intergranular lanes, implied that more light from the brighter granules is scattered into the darker intergranular lanes than the other way around.
Since radiation from granules is blue-shifted, it reduces the redshift of the radiation from intergranular lanes.
This effect is larger than the decrease in the blueshift of radiation coming from granules, due to the smaller amount of straylight from intergranules, so that finally the downflows are decreased more strongly than the upflows.
After deconvolution, the two velocities are better separated from each other again, so that downflows are more strongly enhanced than upflows.
The preferential enhancement in downflows by the deconvolution leads to some weak upflows (mainly at the boundaries of granules) turning into downflows after deconvolution. This either decreases the areas of particular granules somewhat, or brings to light, usually narrow downflow lanes that are not visible in the original data. These effects increase the area covered by downflows at the cost of that covered by upflows. \\
 Before the deconvolution, we found stronger upflows and weaker downflows, in agreement with previous works (\citealt{Yu2011b, Jin2009, Oba2017}).
After deconvolution, there was still an asymmetry between up- and downflows, but now with downflows being on average faster (although still covering a somewhat smaller area).
The LOS flow averaged over the full FOV speed now lay within $\pm$0.05 km/s at all bisector levels, compared with an offset of up to -0.2 km/s in the original data.
We found that the effect of the deconvolution on the data is similar in magnitude (but of opposite sign) as the changes introduced by convolving the data from the the MHD simulations.
In Fig.\ref{fig:24} for the observation and Fig.\ref{fig:13} for the numerical simulation, small differences, e.g., in the exact shape of the velocity histograms, are present between simulations and observations, but these are minor compared with the otherwise surprisingly good agreement.
The consistency between the observations and the numerical simulation are a good indication that \textit{Hinode} resolves much of the granular structure.
It also implies that the MURaM simulations provide a good description of solar granulation. \\
 We found that the magnitude of convective fields decreases with increasing height (see Fig.\ref{fig:24}), which is consistent with overshooting convection.
The photosphere is convectively stable, so that buoyancy acts against the material's movement, and thus weakens the convective motions \citep{Stix2004}.
We also detected that the effect of the deconvolution process on the convective motions is height-dependent;
at the higher layers, the changes through the deconvolution process are less prominent.
We believe that the reason behind this behavior is the photospheric temperature stratification, in which, towards higher layers, the intensity contrast in the granulation becomes smaller and eventually reverses, i.e.,  \textit{reversed granulation} is seen.
In this layer, dark blue-shifted granules are surrounded by bright red-shifted intergranular lanes \citep{Kostik2009}.
The transition from the normal granulation to the reversed one occurs at roughly 130 to 140~km according to the numerical simulations of \citet{Wedemeyer2004} and \citet{Cheung2007} and at roughly 140~km in the observational work of \citet{RuizCobo1996}.
The lowest bisector level investigated the present work samples an average height (135 km) roughly corresponding to the height of this transition.
At around this geometrical height, the first-moments before and after the deconvolution process show no significant difference, which is consistent with this temperature distribution because the intensity from granules and intergranular lanes should be almost identical.

\section{Summary}
 To correct the instrumental imaging performance, we applied a deconvolution technique based on the Richardson-Lucy algorithm to \textit{Hinode}/SP data.
We incorporated a regularization term into the algorithm, which minimizes the risk of enhancing the noise component.
Using a synthesized image, we confirmed that the RL algorithm in a regularized form restricted the noise enhancement.
The deconvolution of the data led to a major change in the magnitude of the LOS velocity obtained via bisector analysis, in particular at higher intensities in the spectral line.
The resulting convective velocity amplitudes are almost the same for up- and downflows (ranging from approximately -3.0 km/s to 3.0 km/s at bisector level 0.70).
Whereas the original data showed a global upflow (i.e., net LOS velocity averaged over all pixels) of up to -0.2 km/s, after the deconvolution this value dropped to below $\pm$0.05 km/s.
This indicates that the downflows were preferentially increased by the deconvolution, while the upflows were only moderately enhanced.
The upflow obtained from the original data is consistent with the earlier investigations based on \textit{Hinode} data (\citealt{Yu2011b}, \citealt{Jin2009}, and \citealt{Oba2017}).
We found the observed LOS velocities before and after deconvolution matched well with those derived from the numerical simulations, obtained by applying bisector analysis to the synthesized spectral profiles after and before convolution with the \textit{Hinode} PSF, respectively.
This agreement with MHD simulations is very heartening and suggests that the best current MHD simulations provide a relatively good description of solar granulation.
Some remaining disagreement in the RMS contrast and velocity distributions may be due to a residual defocus in the \textit{Hinode} data.
Observations at higher spatial resolution (e.g. with the Sunrise balloon-borne solar observatory; \citealt{Solanki2010, Solanki2017, Barthol2011}) and covering more spectral lines will provide a more sensitive test. \\
 The deconvolution also changed the spatial distribution of the velocity field, such that the area occupied by upflowing material at the granular edges was trimmed off by downflows pertaining to the intergranular lanes.
By revealing more downflow lanes, a deconvolution technique such as ours will provide new insights into magneto-convection by allowing to see the immediate surroundings of small flux tubes, preferentially located in intergranular lanes (\citealt{Title1987, Grossmann-Doerth1988, Solanki1989}).
This should enable the magnetic features to be better probed, e.g., as done by \citet{Dominguez2003} and \citet{Buehler2015} using complementary approaches.
Possible future applications of deconvolution include catching the detailed process of convective collapse coinciding with a downflow within the magnetic features \citep{Parker1978}, as observed by \citet{Nagata2008} and \citet{Requerey2014}, and described using MHD simulations by \citet{Danilovic2010}, or the magneto-acoustic waves generated intermittently in flux tubes (e.g., \citealt{Stangalini2014}, \citealt{Jess2016}), which are excited by the surrounding convective motions \citep{Kato2016}.

\acknowledgments
\textit{Hinode} is a Japanese mission developed and launched by ISAS/JAXA, collaborating with NAOJ as a domestic partner, NASA and STFC (UK) as international partners. Scientific operation of the \textit{Hinode} mission is conducted by the \textit{Hinode} science team organized at ISAS/JAXA. This team mainly consists of scientists from institutes in the partner countries. Support for the post-launch operation is provided by JAXA and NAOJ (Japan), STFC (U.K.), NASA, ESA, and NSC (Norway). We are grateful to the \textit{Hinode} team for performing observations on August 25, 2009, which were well suited to this analysis.
The present study was supported by the Advanced Research Course Program of SOKENDAI and by JSPS KAKENHI Grant Number JP16J07106.
This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 695075) and has been supported by the BK21 plus program through the National Research Foundation (NRF) funded by the Ministry of Education of Korea.

\bibliographystyle{apj}

redpajama_set_name: RedPajamaArXiv


text:
Nanotouch® Unveils New Brand Identity
With 10 Million Touchpoints in Use, Firm Unveils New Name, Corporate Design, Website
FOREST, Va., Sept. 30, 2021 – Nanotouch® Materials, the leading provider of green, self-cleaning surfaces, today unveils a reimagined brand identity that includes a new name, corporate design, website, and overall look and feel for its market-leading line of products.
Nanotouch Materials, founded in 2012, manufactures toxin-free, self-cleaning surfaces that are used in healthcare, hospitality, corporate, sports, education, commercial cleaning, and facility management industries across the globe – the firm just eclipsed 10 million touchpoints in use. The name Nanotouch replaces NanoSeptic as part of the company rebranding.
"Our new name better communicates who we are and why thousands of safety-conscious facility managers have trusted our surfaces to foster a cleaner environment," said Dennis Hackemeyer, co-founder of Nanotouch.
Coupled with the new product name is a new look and feel for Nanotouch Self-Cleaning Surfaces, which range from door handle wraps and sleeves to elevator button covers and portable mats. The designs, complete with a new Nanotouch logo, feature a patterned surface and sleek, clean palette meant not only to blend in with corporate and business aesthetics, but also to reflect a facility manager's commitment to "healthy cleanliness" by using products that are free of harsh chemicals and heavy metals.
"The technology behind our surfaces hasn't changed, but we believe our new brand engenders more understanding how our surfaces create hygienic spaces in public buildings," said Mark Sisson, the other co-founder of Nanotouch Materials, adding "Enabling safer surfaces to exist exactly where and when they are needed –– and without toxins –– is the future of clean."
In addition to the rebranding, Nanotouch has launched a revamped website that enhances and streamlines the e-commerce experience for customers. The website also adds industry-specific enhancements like managing multiple facility addresses for facility managers.
"Numerous national surveys report that employees and tenants demand proof of what cleaning protocols are in place. Along with our new designs, our new website provides information for property and facility managers to provide that proof, said Hackemeyer."
Nanotouch Self-Cleaning Surfaces work 24/7 to oxidize all organic contaminants using nanotechnology and the power of light. Customers include 26 leading pharmaceutical companies, the Top-10 commercial real estate service providers, each American professional sports league, 250 educational campuses, elite four- and five-star hotels, major financial institutions, and all levels of government.
Contact: Faun Finley
NanoTouch Materials
Faun@nanoseptic.com
About Nanotouch® Materials
Founded in 2012, Nanotouch Materials is a leader in self-cleaning surfaces, utilizing nanocrystal technology to transform dirty, high-traffic touchpoints into continuously self-cleaning surfaces. Based in Forest, Virginia, the company manufactures a line of self-cleaning products, Nanotouch Self-Cleaning Surfaces, which are used throughout the world, serving all industries where hygiene is a priority. Nanotouch Materials has received numerous awards including the 2016 and 2017 ISSA Innovation Award, 2021 Vision Award from FacilitiesNet.com, and a $2 million research and development grant from the Commonwealth of Virginia. There are over 10 million Nanotouch Materials touchpoints in use worldwide, in facilities ranging from Fortune 200 companies to healthcare facilities, medical schools, pharmaceutical companies, hotels, sports and event venues, airports, and all levels of government. For more information, visit www.nanotouch.com
See Post
Nanotouch® Materials Named 2021 Vision Award Winner 5 minutes
Nanotouch® Materials Adds to Leadership Team
Nanotouch® Materials in Growth Mode 5 minutes


Hi! Finally , after all this time , I decided to return to Elizaveta .
I love this character too , I find myself in her .
So I hope you will like , this cosplay .
Hungary is a kickass, pervy, yet really pretty and brave girl. You cosplay her well!
You made me very happy, with your compliment.
*\\\\\\\* thank you so much!


text:
Q: Implement Carrierwave_direct in Wicked Wizard Rails 4.2 I have an app set up with a Wicked Wizard form to create a new post.
The last step allows users to upload pictures to their post.
For image processing I am using Carrierwave and Fog (because I'm hosting the site on heroku and I am using Amazon S3 for storage).
It's all working fine, but the upload to Amazon S3 is very slow.
Following Ryan Bates' Uploading to Amazon S3 railscast (http://railscasts.com/episodes/383-uploading-to-amazon-s3) I tried Carrierwave_Direct (https://github.com/dwilkie/carrierwave_direct) to speed up the painfully slow upload to Amazon S3.
When doing this, however, I get an error:
NoMethodError in PostSteps#show

Showing
/Users/petersonneveld/rails_projects/doamer/doamer2/app/views/property_steps/picture.html.erb where line #1 raised:

undefined method `direct_fog_url' for nil:NilClass

I really don't know what to do to solve this. Does anyone have advice? Thanks a bunch for your help!
In the last step of my wicked wizard form I have
# app/views/post_steps/picture.html.erb

<%= direct_upload_form_for @uploader, url: wizard_path do |f| %>

Let's post some pictures!

    <% if @post.errors.any? %>

<%= pluralize(@post.errors.count, "error") %> prohibited this post from being saved:

          <% @post.errors.full_messages.each do |message| %>

<%= message %>

          <% end %>

    <% end %>

        <%= f.label :image, "Upload image" %>

        <%= image_tag(@post.image_url(:thumb)).to_s if @post.image? %>
        <%= f.file_field :image %>

        <%= f.label :remote_image_url, "or paste image URL" %>
        <%= f.text_field :remote_image_url, class: "form-control" %>
        <%= f.hidden_field :image_cache %>

<%= f.check_box :remove_image %>
        Remove image

        <%= f.button "Finish", class: "btn btn-primary", data: {disable_with: " Saving post..."} %>

<% end %>

And in my controller
# app/controllers/post_steps_controller.rb

class PostStepsController < ApplicationController
include Wicked::Wizard
steps :description, :picture

def show
  @post = Post.find(params[:post_id])
  render_wizard
end

def update
  @post = Post.find(params[:post_id])
  @post.update_attributes(post_params)
  render_wizard @post
end

private

def post_params
  params.require(:post).permit(...)
end

def redirect_to_finish_wizard(options = nil)
  redirect_to @post, notice: "Thanks for submitting a post."
end
end

And my post controller create action
# app/controllers/post_controller.rb

def create
  @post = current_user.posts.build(post_params)
    if @post.save
      redirect_to post_steps_path(:id => "description", :post_id => @post.id)
    else
      render :new
    end
end

A: Found the answer: had to update the 'aws-sdk' gem!


This box is great if you enjoy the leaner cuts, or you want to start on a health kick. But just because they're lean, there's no need to compromise on mind-blowing taste.
Each lean cut is packed with flavour, from tender, flavoursome steaks to versatile chicken cuts. With each order, expect a selection of meat and poultry cuts with typically less than 5% fat. Think stir-fries, low-carb salads and lean roasts.


text:
Banobius tener är en mångfotingart som beskrevs av Chamberlin 1938. Banobius tener ingår i släktet Banobius och familjen stenkrypare. Inga underarter finns listade i Catalogue of Life.

Källor

Stenkrypare
tener

redpajama_set_name: RedPajamaWikipedia


The Incredibles: Rise of the Underminer is more for the kids than anything else. Adults will find the game begins to be a bit of a bore. For younger children who want to experience the Incredibles and play as a couple of the neatest super heroes who take on wave after wave of robotic baddies this is the perfect game that covers all platforms.
I repeat though, adults may want to stay away from this one. There is not enough substance here to merit more than 30 minutes of play time for an adult.
This game picks up more or less where the film and the last game left off. This time around you get to play as Mr. Incredible and Frozone. As with any super hero game there are big super hero sized crisis to take care of. This time around it's saving the world from the top villain, or in this case the bottom villain. The underground dwelling Meany, The Underminer.
What is interesting and actually kind of fun in this game is you're able to switch back and forth between characters at any time. Both of them having different abilities come in handy and at key moments of the game you must use one rather than the other to complete a task to advance further. Often it's not all that complex though, it just comes down to either muscle or ice really.
Frozone's main abilities are of course shooting streams of ice and of course freezing objects and enemies. Mr. Incredible is only able to do simple moves of punching and pounding and sometimes picking things up and throwing them. Both Heroes are able to upgrade not only the abilities they start with, but can upgrade to new abilities as well. This is all very basic mind you; it just makes attacks such as punches more powerful and some increases in health, nothing really major that would prompt you to want to push for upping in levels really.
Through out the game there are a lot of switches to be pulled to get onward in the game. Villains? Enemies? Well we have robots, lots and lots of robots, there seems to be no end of the waves of robots and turrets that our heroes must wade through for the final showdown.
The game shows it was not really made for any one over maybe 6 years old because it is so linear and lacks any real appeal across the demographics of age groups. I guess I was hoping to have more an experience like the film; there was something there for everyone and not just children.
If you as a gamer enjoy some challenges in your game play, do not stop here. The Incredibles Rise Of The Underminer is very linear and has no real challenge in it to speak of. It does have co op play so a pal can jump in and play the character that you're not playing, but what is the use really? Two people playing just makes it even less of a challenge than just playing it alone. You can switch with your fellow player so you don't have to get too bored stiff playing only one or the other character, so there is a tiny bone they throw you in the game.
There are a lot of check points in the game which seem almost pointless since it is pretty simple all the way through.
Controls for the game are pretty simplistic and do the job well. No multi button presses to have to memorize here. This is a good thing since I really don't like to have to learn a huge amount of button presses really.
Graphics are pretty good; everything has a pleasing amount of detail while not going over board on anything. The effects are pretty good with some nice explosions. There is not much of a variety to be had but again it does its job.
There is not a lot of game play to speak of here but there is good presentation, the locations such as destroyed city streets, Caves with lava, and an interesting Ice area and of course the Underminer's base of operations. Sound is pretty darn good and adds to the game and almost makes up for some of the limited parts.
In the end and all is said and done, The Incredibles: Rise Of The Underminer is not for adults. Adults will most likely pass this one by, but that being said it is a good game for kids who enjoyed the moves and want more and maybe even dream of being a superhero like Mr. Incredible or Frozone.


Stop at Bed and Breakfast Baia del Sole to discover the wonders of Marina Di Modica. The hotel offers a high standard of service and amenities to suit the individual needs of all travelers. Service-minded staff will welcome and guide you at the Bed and Breakfast Baia del Sole. Each guestroom is elegantly furnished and equipped with handy amenities. The hotel offers various recreational opportunities. Convenience and comfort makes Bed and Breakfast Baia del Sole the perfect choice for your stay in Marina Di Modica.
Hotel Baia del Sole cost depends on the season and prices can be much different basing on selected dates. The diagram below illustrates price changing dynamic for Baia del Sole hotel by months. For example, the lowest prices - on the average $61 - are in May, and by August hotel Baia del Sole cost grows on the average up to $104.


text:
Q: Getting [object Object] how can I print each value I am defining a variable called user. The session storage gets the value of that variable as [object Object], but if I try to get or print different properties I get the following
user.name = I get nothing
user[0] = I get [

I have tried different ways but I am not able to get the property of that object called name.
Help please!
sessionStorage.user = userSessionStorage;

I get [object Object]
I just need to know how I can get the values of the differents properties

A: There are a number of different reason why user.name returns undefined. Firstly, name property might not exist in your user object. You can do that by doing this:
Object.keys(user)

and see if the string name is in the output array.
Once you have done that, You can try looping through the properties of user like so:
Object.keys(user).forEach(e => console.log(e + ' = ' + user[e]));

A: From the session storage you will get a string, you need to make use of JSON.parse() before getting the value
var usr = JSON.parse(user);
console.log(usr)  // this will log the object from where you can access the properties.

A: Set the data as a JSON String in session Storage instead of JSON Object.
var userData = JSON.stringify(userSessionStorage);

sessionStorage.setItem("user", userData);

And parse it again to JSON Object from JSON String when you get back from the storage.
var getUser = sessionStorage.getItem("user");

var user = JSON.parse(getUser);


The Trilene Knot was developed by Berkley Fishing Tackle after extensive research and testing more than 40 years ago as a stronger alternative to the Clinch Knot, which had a tendency to slip and fail. Passing the tag end of the line twice through the eye of the swivel, snap, ring or lure eye, the Trilene Knot eliminates slippage and the double loop also makes the knot substantially more durable. The Trilene Knot usually tests at 95 percent or more of the unknotted line. It's a proven knot for use with monofilament and fluorocarbon, and with Nylon and Dacron braids, but it is not an acceptable choice for gel-spun braided lines.
The Trilene Knot can be used with lines of up to 80-pound-test, such as when adding a lure or hook to the end of a heavy saltwater shock leader, but because the line is passed through the hook eye twice, the eye must be large enough to accept two turns of the line. Hooks with small eyes may be limited to 20-pound-test line or less. With lines of 30-pound or less, five or six wraps are required for maximum knot strength, but heavier lines need only three wraps.
When drawing the knot tight, it's a good idea to pinch the small loops at the hook or lure eye between your thumb and forefinger so the loop won't slip behind the eye. This will assure that both loops are in correct alignment for the strongest potential knot strength. With lines of 40-pound-test or more, use pliers or a reel handle to hold the hook so you can pull hard to seat the knot.
Pass the tag end of the line through the hook or lure eye.
Pass the tag end through the hook or lure eye a second time.
Make five to six wraps around the line.
Pass the tag end through the small double loop formed at the hook or lure eye.
Lubricate with saliva and pull tight. Trim the tag end.


"The day-to-day lives of omnipotent women who have reached the highest level of ascendance."
I have to say that's it a good read and I hope that bondjamesbond has more up his sleeve.
Thanks for your interest in my stories. I have written a second entry in the same universe , taking into consideration a lot of feedback I received on discord about the amount of cruelty in my original one (now toned down in this one). I probably will be too busy to write stuff like this ever again, but I hope you enjoy what I've written and I will definitely keep reading the stuff the awesome authors here produce in the future (espeically Not The One, which is probably my favorite superpowered woman series that I've ever read ever).


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text:
Voldemārs Elmūts, né le , à Liepāja, en Lettonie et décédé le , à Boston, aux États-Unis, est un ancien joueur letton de basket-ball.

Biographie

Palmarès

Notes et références

Liens externes

Joueur international letton de basket-ball
Joueur de basket-ball aux Jeux olympiques d'été de 1936
Naissance en novembre 1910
Naissance à Liepāja
Décès en juillet 1966
Décès à Boston
Décès à 55 ans


Far from it. Deterrence rests on risk calculation and doubt. The costs of challenging the deterrent have to be just high enough to discourage risk taking. While Putin was surprised to learn that the West would respond forcefully to his intervention in Ukraine, he now has to take Western deterrence seriously.
Featuring Stephen Szabo via Judy Dempsey's Strategic Europe on January 31, 2019.


You can download Free TIFF Viewer 1.0 from our software library for free. Our built-in antivirus scanned this download and rated it as 100% safe. This free program is a product of Free Picture Solutions. The software lies within Photo & Graphics Tools, more precisely Viewers & Editors.
You may want to check out more software, such as Free PST Viewer, Free OST Viewer or Black Ice Tiff Viewer, which might be related to Free TIFF Viewer.


Our services do not stop at restoration!
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text:
module Temple
  module Filters
    # Inlines several static/dynamic into a single dynamic.
    #
    # @api public
    class DynamicInliner < Filter
      def on_multi(*exps)
        result = [:multi]
        curr = nil
        prev = []
        state = :looking

        exps.each do |exp|
          type, arg = exp

          case type
          when :newline
            if state == :looking
              # We haven't found any static/dynamic, so let's just add it
              result << exp
            else
              # We've found something, so let's make sure the generated
              # dynamic contains a newline by escaping a newline and
              # starting a new string:
              #
              # "Hello "\
              # "#{@world}"
              prev << exp
              curr[1] << "\"\\\n\""
            end
          when :dynamic, :static
            case state
            when :looking
              # Found a single static/dynamic. We don't want to turn this
              # into a dynamic yet.  Instead we store it, and if we find
              # another one, we add both then.
              state = :single
              prev = [exp]
              curr = [:dynamic, '"']
            when :single
              # Yes! We found another one. Add the current dynamic to the result.
              state = :several
              result << curr
            end
            curr[1] << (type == :static ? arg.inspect[1..-2] : "\#{#{arg}}")
          else
            if state != :looking
              # We need to add the closing quote.
              curr[1] << '"'
              # If we found a single exp last time, let's add it.
              result.concat(prev) if state == :single
            end
            # Compile the current exp
            result << compile(exp)
            # Now we're looking for more!
            state = :looking
          end
        end

        if state != :looking
          # We need to add the closing quote.
          curr[1] << '"'
          # If we found a single exp last time, let's add it.
          result.concat(prev) if state == :single
        end

        result.size == 2 ? result[1] : result
      end
    end
  end
end


text:
25 April 1997 Near-infrared two-wavelength shift-and-add
Daigo Tomono,	Tetsuo Nishimura
Daigo Tomono,1 Tetsuo Nishimura2
1Univ. of Tokyo (Japan)
2National Astronomical Observatory of Japan (Japan)
Proceedings Volume 3019, Solid State Sensor Arrays: Development and Applications; (1997) https://doi.org/10.1117/12.275176
Event: Electronic Imaging '97, 1997, San Jose, CA, United States
With the advent of ground-based 8-meter telescopes and large format detector arrays, the mid-IR (MIR) astronomy is expected to produce images with higher angular resolution and improved sensitivity than currently available. TO obtain diffraction limited images, it is necessary to compensate for atmospheric seeing. We proposed a scheme in which, by detecting the near-IR (NIR) speckle positions of a reference star, for which we have higher sensitivity than in the MIR, we superpose the MIR images taken simultaneously by shifting its position to cancel the effects of seeing. We call this two-wavelength shift-and-add (TWSAA) method. In this paper, we describe an observation carried out in the fall of 1995 to test the concept. A NIR camera was built with 256 by 256 InSb array detector. With a dichroic splitter, a pair of images of a single start at two wavelengths were formed on two halves of the array. We obtained 126 pairs of images each integrated for 0.1 seconds simultaneously at the K- and L-bands. With the TWSAA method using the K-band images as the reference, the peak signal to noise ratio of the L-band integrated image was enhanced by a factor of 5.7 compared to simple addition.
Daigo Tomono and Tetsuo Nishimura "Near-infrared two-wavelength shift-and-add", Proc. SPIE 3019, Solid State Sensor Arrays: Development and Applications, (25 April 1997); https://doi.org/10.1117/12.275176
Near infrared
Image stacking
Development of a new mid infrared instrument for the TAO...
NASA'a Next Generation Space Telescope visiting a time when galaxies...
Test observation results from the mid infrared imager MIRTOS for...
X shooter UV to K band intermediate resolution high...
MANIAC a new mid and near infrared array...
Optical Interferometry With The MMT
Optical Arrays For Future Astronomical Telescopes In Space
Daigo Tomono, Tetsuo Nishimura, "Near-infrared two-wavelength shift-and-add," Proc. SPIE 3019, Solid State Sensor Arrays: Development and Applications, (25 April 1997); https://doi.org/10.1117/12.275176


text:
Niclas Schultz, född omkring 1660, död 15 november 1716 i Stockholm, vare en svensk målarålderman och konterfejare.

Han var från 1687 gift med Margaretha Andersdotter Lorentz och far till Andreas Schultz. Han upptas i Stockholms målarämbetes rulla 1689 som en utländsk mästare med två gesäller och en lärling i sin tjänst. Till hans kända arbeten hör marmoreringsmålningen av flyglarna till silveraltaret och förgyllningen av den rikt skulpterade läktaren i Stockholms Storkyrka.

Tryckta källor
Svenskt konstnärslexikon del V sid 88, Allhems Förlag, Malmö.

Svenska konstnärer under 1600-talet
Män
Födda 1660
Avlidna 1716