text:
\section{Introduction}
	Warm Dense matter is both found in astrophysical systems \cite{Ehrenreich_2020,REDMER2011798,Nettelmann_2008}, ranging from planetary interiors to solar cores \cite{Benuzzi_Mounaix_2014,Guillot:1999aa,Nguyen_2004,Stevenson_2009,Chabrier_2000,10.2307/2892796}, and generated in dynamic high energy density experiments \cite{Falk_2018,doi:10.1063/5.0007476,PhysRevLett.90.175002}, \emph{e.g.} using Xray free-electron lasers (XFELs) \cite{doi:10.1063/5.0048150} or in inertial confinement fusion (ICF) shots \cite{Hinkel_2013}. One method of both generation \cite{doi:10.1063/5.0026595, PhysRevE.100.043204} and  characterization\cite{Hayes:2020un, doi:10.1063/1.4928104} of WDM is with high energy ion beams. High energy ($k$eV-$M$eV) particles (projectiles), often protons or $\alpha$-particles, are shot into a target, heating electrons and potentially generating WDM.\cite{PhysRevE.92.063101} For ICF, ion stopping is critical to topics such as $\alpha$-particle self heating \cite{doi:10.1063/1.5030337}, ion-driven fast ignition \cite{Fern_ndez_2009}, or heavy-ion fusion \cite{RevModPhys.86.317,HOFMANN20181,doi:10.1063/1.874031}. Measuring the loss of energy of the ion beam as it passes through a finite thickness target allows for the calculation of the target's electronic stopping power (ESP). Combining this ion-beam measurement with laser generation of WDM allows one to measure the ESP of WDM directly. \cite{PhysRevLett.114.215002, PhysRevLett.115.205001, PPR:PPR393148}  For velocities near the electron velocity, $v_{e}$ the ESP becomes sensitive to the density and temperature ($\rho$ \& T) of the system, allowing for a powerful diagnostic \cite{PhysRevLett.115.205001}. However, inference of $ \rho$ and/or T directly from energy loss measurements requires a predictive ESP model \cite{Cayzac:2017un}.  Models are often in agreement for classical plasmas or  high-velocity projectiles $v_{p}>>v_{e}$, where the electron-projectile interaction is weak. However, there is significant disagreement for velocities near the so-called `Bragg Peak', \emph{ i.e.} $v_{p}\approx v_{e}$ where the interaction is strongest. \cite{PhysRevE.92.053109,Cayzac:2017un}

	Modeling of WDM, and ESP of WDM,  is complicated by the combination of non-negligible electronic coulomb interaction, degeneracy and thermal excitation. That is, the (partially degenerate) plasma coupling parameter, $\Gamma={E_{C}\over{T + E_F}}$, the electron degeneracy, $\Theta={T\over{E_{F}}}$, and their inverses are non-negligible.  $E_C \sim r_{WS}$ is the Coulomb energy, $r_{WS}$ is the Wigner Seitz radius, $E_F=1/2(3\pi^2\rho_e)^{2/3}$ is the Fermi energy, and $\rho_e$ is the electron number density.   With the exception of identifying the $\rho$ and $T$ of our carbon systems, we utilize Hartree atomic units throughout the article, \emph{i.e.} $\hbar$, $m_e$, $e$, $k_B$, and $4\pi\epsilon_0 = 1$, including all figures, parameters, and equations. Figure \ref{plasma_param} shows these parameters, $\theta$ red dotted contour, $\Gamma$ white dashed contour, for the carbon ``free" electron contribution, based on the Thomas Fermi model for the partial ionization \cite{doi:10.1063/1.871806}, $Z^*$ black solid contour.

For warm dense carbon, and other mid-to-high atomic number ($Z$) materials, the changing $Z^*$ with $\rho$ \& T adds additional complication. $Z^*$ is a poorly defined quantity for atoms in dense phases. Though they can be useful in roughly determining the state of a system, plasma parameters such as $v_e$, $\Gamma$, and $\Theta$ are only strictly valid in nearly-homogeneous electron gas, where a single $E_F$ is a meaningful quantity. The nearly-homogeneous 'free' electron density, depends on the poorly defined $Z^*$.

	The pure plasma picture is overly simplistic for a material such as carbon where inner shell (K-shell), outer shell (L-shell) and different angular momenta (s, p) electrons can contribute to material response to varying degrees. While energetic separation between K and L shell may be large enough to allow for clear distinction, in dense systems the intra-shell ionization of the L-shell is model and definition dependent. \emph{Ab initio} methods, such as Density Functional Theory (DFT), do not depend on a model for defining a sharp separation between ``core" and ``free"  electrons, at least for the outer shells.

\begin{figure}[t]
	\begin{center}
	\includegraphics[width=1.0\columnwidth] {plasma_param.pdf}
	\caption{(Color online) Electron kinetic energy, $K_e$, for Carbon based on Thomas Fermi approximation. Colorbar shown on right. Solid contours show the integer ionization states,  white-dashed and red-dotted contours show the electron degeneracy $\Theta$ and electron coupling $\Gamma$ across 4 orders of magnitude. White stars indicate the 3  density \& temperatures explicitly considered in this article. }
	\label{plasma_param}
	\end{center}
\end{figure}

	Due to their similar velocities, high energy ($k$eV-$M$eV) projectile ions interact primarily with the electrons, rather than bulk ions. The average force against the projectile motion is known as the stopping power of that bulk material for the type of projectile. The interaction is greatest when the electron and projectile velocities are close, \emph{i.e.} when $v_p \approx v_e$. Here we have defined
\begin{eqnarray}
\label{ve}
	v_{e}\equiv\sqrt{3T+ v^2_{F}}\:,
\end{eqnarray}
	where $v_{F}$ is the Fermi velocity. This definition accounts for both thermal excitation and degeneracy. The colormap shown in Fig. \ref{plasma_param} shows the proton projectile energy, $E_p$ in eV, where $v_p \approx v_e$, \emph{ i.e.} the estimated `Bragg Peak' position for a proton projectile.

	 Analytical models typically coalesce in the  limit of high projectile velocities, assuming they use the same plasma conditions, \emph{i.e.} $Z^*$. However, for lower projectile velocities near the Bragg peak, the energy and wavevector dependence of the materials response functions becomes important and these models disagree. This is also the regime where the ESP becomes sensitive to the temperature of the WDM target and shows non-trivial density dependence \cite{White:2018aa}. Thus it is the ideal regime for plasma diagnostic measurements. Often, the plasma conditions of WDM target can only be estimated from hydrodynamic calculations.\cite{Duffy_2006,PPR:PPR393148} One needs an accurate atomistic and \emph{i.e. ab initio}, model such as time-dependent density functional theory (TD-DFT), in order to validate these more approximate methods, or to provide an alternative method for comparison to experimental measured stopping.\cite{PhysRevLett.121.145001,White:2018aa}

	For ambient conditions, direct simulation of ESP through TD-DFT, using periodic boundary conditions, has arisen as a high accuracy method for calculation of ESP \cite{PhysRevB.91.014306,Sand:2019wi}. However, for WDM its application is limited due to a combination of finite simulation-box effects and increased computational cost due to high temperatures. This lead us to the development of orbital-free TD-DFT methods\cite{PhysRevLett.121.145001,White:2018aa}, which proved accurate at high velocities but are insufficient for velocities near the Bragg Peak \cite{White:2018aa}. Fortunately, the development of stochastic DFT / TD-DFT (sDFT / TD-sDFT) provide a feasible alternative to orbital-free TD-DFT or analytical models, while retaining the full accuracy of Kohn-Sham (KS) DFT \cite{Cytter:2018aa,doi:10.1002/wcms.1412}. However this comes at the cost of finite precision due to stochastic error. The sDFT calculation of initial electron density scales as $V/T$ where $V$ is the system size and $T$ is the temperature, while the proceeding TD-sDFT calculation only scales as $V$\cite{Cytter:2018aa}.  The precision of TD-sDFT scales as $1/\sqrt{N_\chi}$ where $N_\chi$ is the number of `stochastic vectors', $\chi$. Achieving desirable stochastic error often requires large numbers of orbitals to converge \cite{Chen_2019,Chen_2019_2,Li_2019,Neuhauser_2014}. This is improved by generalizing our recently developed mixed stochastic-deterministic algorithm \cite{PhysRevLett.125.055002}, mDFT, to the novel TD-mDFT for time-dependent systems. We demonstrate this new method on simulations of warm dense carbon. A subset of these simulations were recently validated by experiment energy-loss measurements.\cite{PPR:PPR393148}

	In this article we will 1) briefly discuss the traditional models for ESP, 2) discuss the use of TD-DFT to calculate ESP at an atomistic level, 3) briefly review stochastic TD-DFT, 4) describe our mixed stochastic-deterministic TD-DFT method, and 5) present results for the stopping power of carbon at 0.5, 3.5, and 10 g/cc and a temperature of 10 eV, with comparison to dielectric models with bound-electron corrections and orbital-free TD-OF-DFT. Finally, we will attempt to combine atomistic calculation of electron relaxation times and $Z^*$, from electrical conductivity calculations or electronic density of states to improve agreement between the model and atomistic TD-DFT.

\section{Theory}
\subsection{Stopping Power Models}
	Electrons and ions both contribute to the stopping of projectiles. However, due to the mass disparity between carbon nuclei and electrons,  $\approx$ 22k $\times$, the interaction between the carbon nuclei and the projectile is small near or above the Bragg peak ($v_p \approx v_e$), and can be neglected. Thus, we only discuss the electronic contribution to the stopping power, ESP. We will simply refer to the stopping power $S_{p}$ in the remainder of the article. For a homogeneous electron liquid / gas (HEG), and within the linear response (Born approximation) regime, the $S_p$ is related to the energy loss function, $ \mathrm{Im}[{-1 \over \varepsilon (G,\omega)} ]$, the projectile charge $Z_p$ and velocity $v_p$:

\begin{eqnarray}
S_{p}&=-dE/dx \\ \nonumber
&={ 2 Z_{p}^2 \over \pi v^2_{p} } \int^\infty {dG\over G} \int ^{G\cdot v_{p}} d\omega \: \omega \: \mathrm{Im}[{-1 \over \varepsilon (G,\omega)} ]\:,
\end{eqnarray}
 where $G$ is the electron wavevector and $\omega$ is the angular frequency   \cite{refId0}.

For plasmas of arbitrary degeneracy, the finite temperature Born random phase approximation (BRPA) quantum dielectric can be used \cite{PhysRevA.29.1471},  often referred to as the Maynard-Deutsch (MD) method \cite{refId0}.  The Mermin dielectric approximation can be used to approximate the effects of electron scattering by other electrons or ions.\cite{PhysRevE.76.016405, barriga-carrasco_2008, PhysRevE.94.063203} Electron correlation can also be included through local field corrections, for example using the STLS \cite{PhysRev.176.589, barriga-carrasco_2008} or quantum Monte-Carlo method \cite{doi:10.1063/5.0007476}.

In addition to the Born dielectric approaches, the Brown-Preston-Singleton \cite{BROWN2005237, doi:10.1063/1.2840134} and Li-Petrasso  \cite{doi:10.1063/1.5114637} models are commonly used. These  and other kinetic theories combine Boltzmann and Lenard-Balescu kinetic equations, with corrections for quantum degeneracy, plasmon excitations, etc \cite{doi:10.1063/1.5095419}. These give analytical expressions, thus efficient enough to be used in-line with hydrodynamics codes \cite{doi:10.1063/1.5119144}, while the MD approach requires non-negligible numerical integration.  Casas and Barriga-Carrrasco incorporated single-atom electronic structure calculations of the mean-excitation energy with Bethe's formula, corrected for the asymptotic limits of the BPRA \cite{refId0, doi:10.1063/1.338141}, to calculate the stopping power of bound electrons \cite{PhysRevE.88.033102, barriga-carrasco_casas_2013}. They then combine this with the BRPA method for the free electrons to extend the calculation from cold gas to plasmas. This method does account for both bound and free electrons, but requires a  $Z^*$ and potentially partial populations of the bound electron orbitals for $T>0$.

All these models require $Z^*$ in any partially ionized plasmas. We note that the ``local density approximation" has been applied to the calculation of stopping power using an electron density determined from an average atom calculation.\cite{doi:10.1063/1.3420276} This involves averaging the density-dependent BRPA HEG stopping over the calculated range of electron densities.\cite{doi:10.1063/1.3420276} However, this neglects any differences the between responses of an inhomogeneous electron density and a HEG. This approximation is suspect when integrating the response over wavelengths which exceed inter-atomic spacing, or when considering bound electrons.

\subsection{Deterministic DFT / TD-DFT and Atomistic Electronic Stopping Power}

	In principle, \emph{ab initio} density functional calculations are advantageous in the fact that one does not need to separate and use different models, for different electrons. In practice, for KS TD-DFT of carbon systems,  the K-shell electrons are typically considered frozen in the calculation and treated by a non-local pseudo-potential. Thus, they do not explicitly contribute to the TD response. For the systems here, at $T=10$ eV, the ionization of the K-shell electrons is minimal, and we can include their contribution from a cold-gas model. We choose to use the bound-electron model employed by Casas and Barriga-Carrrasco (CBC method) \cite{barriga-carrasco_casas_2013}. The atomic orbital parameters were determined using the atomic DFT code, with PBE functional \cite{Perdew:1996aa}, written by Hartwigsen for fitting HGH/GTH pseudo-potentials\cite{PhysRevB.54.1703,Hartwigsen:1998aa}. The K-shell contribution is more important at higher velocities and/or higher densities. The use of this model exclusively for the low energy fully-occupied K-shell electrons, with TD-DFT for all other electrons, is a significant improvement compared to combining this CBC model with another model for the free-electrons response and another model for determining $Z^*$.

\begin{figure}[h]
	\begin{center}
	\includegraphics[width=1.0\columnwidth] {fig2.pdf}
	\caption{(Color online) Atomistic stopping power simulation, 10 g/cc Carbon at 10 eV, proton projectile with $v_p=2.5$, near Bragg Peak. a) the instantaneous stopping power, $S_{p}(t)$ dark green solid line, and it's running time average, $S_p$ light green dashed line, Eq. \ref{ourSp}. X axis is the time dependent position of projectile. b) a colormap of a 2D slice of the initial electron density, taken at the position of the projectile. Position of atoms is shown as circles with sized defined by distance from the 2D slice. c-e) similar 2D slices, but the difference between the instantaneous and initial density is plotted. The projectile is shown as a green triangle. The colormap for the density difference is shown on the right, and is set to emphasize the small differences near zero. The times are 3.2, 6.4, and 8.6 atomic units. This simulation uses TD-mDFT with $N_{\psi} = 512 $, $N_{\chi} = 64 $ for 256 carbon atoms.}
	\label{snapshot}
	\end{center}
\end{figure}

	The ``real time" (RT)-TD-DFT approach has been utilized extensively to calculate the stopping power for condensed phase materials\cite{Caro:2017uw,PhysRevLett.116.043201,Fu_2019,PhysRevLett.108.225504}, single layer graphene\cite{Zhao_2014}, DNA \cite{doi:10.1021/jacs.8b12148}, etc. It has been applied in only a limited number of cases to WDM, due to the computational cost of traditional algorithms.  In RT-TD-DFT, the KS states are propagated via coupled (through the total time-dependent electron density) single-particle TD Schr{\"o}dinger equations. That is,

\begin{eqnarray}
{i\partial\psi_b(t)\over \partial t}&={\hat H_{KS}(t) \psi_{b}(t)}
\\
\hat H_{KS}(t)& =  {-(\vec\nabla+i\vec{A}_{ext})^2\over2} +\int d{\vec R}^{\,3} \frac{\rho({\vec R'})}{\vert {\vec R}- {\vec R'} \vert}
\\\nonumber &\:\:\:+ {\hat V}_{xc}(\rho) +  {\hat V}_{ext} (t)
\\
\label{Mermin}
\textrm{and } \rho(t)&=\sum_b F_D(\varepsilon_b,\mu, T) \vert \psi_{b} (t)  \vert^2 \textrm{,}
\end{eqnarray}
where $\psi_b$'s are Kohn-Sham states, initially eigenvectors of $H_{KS}(0)$ with eigenvalue $\varepsilon_b$,  $\rho$ is the electron density, $F_D$ is the Fermi-Dirac function, $\mu$ is the electron chemical potential, ${\hat V}_{xc}$ is the adiabatic exchange correlation potential, and ${\vec A}_{ext}$ / ${\hat V}_{ext}$ are any other interacting vector / scalar potential, including the potentials due to the bulk nuclei and projectile. Temperature is accounted for through the Mermin formulation \cite{Mermin:1965aa} applied to the initial conditions, Eq. \ref{Mermin}, where $\varepsilon_b$ is the eigenvalue of $H_{KS}(0)$. In order to calculate electronic stopping power our external potential is :

\begin{eqnarray}
\hat{V}_{ext}(t)=&\hat{V}_{ext}(0) \\\nonumber
&+ \theta(t) {\mathcal F^{-1}} [e^{i \{\vec{R}_{0}+{\vec{v}_{p}t \}\cdot \vec{G}}} V_{p}(\vec{G})] \textrm{,}
\end{eqnarray}
where $V_{p}(G)$ is a local all-electron pseudopotential for the projectile nuclei and $\mathcal F^{-1}$ is the inverse Fourier transform. That is, we ``drop" the projectile nuclei into the simulation at the initial time and position, $\vec{R}_0$, propagate it with a constant velocity, $v_p$, while keeping the background ions frozen. This is only a minor approximation, due to the finite simulation time and the velocity difference between the projectile and the bulk nuclei. Practically this allows for the removal of the net-zero contribution to the electronic stopping power from the periodic ground-state density and bulk nuclei.

We calculate the TD force on the ion and subtract the background force due to the density at t=0,

\begin{eqnarray}
\label{ourSp}
S_{p} (v_p) &=-\langle dE_{KS}/dx (t) \rangle = \int_{t_{min}}^{t_{max}} {dt S_p(v_p, t)\over t_{max}-t_{min}}  \\\nonumber
S_p(v_p, t)&=-\frac{\vec{v}_p}{\vert\vec{v}_p\vert} \cdot \int\: d\vec{G}\:i\vec{G}[\rho^*(\vec{G},t)-\rho^*(\vec{G},0)] \times\\
&\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:e^{i \{\vec{R}_{0}+{\vec{v}_{p}t \}\cdot \vec{G}}} V_{p}(\vec{G})\;.
\end{eqnarray}
Here the force is calculated by the Hellman-Feynman theorem. For periodic boundary conditions and a frozen bulk ion background, the ground-state force: $ -\int\: d\vec{G}\: i{\vec{G}}\rho^*(\vec{G},0) e^{i \{\vec{R}_{0}+{\vec{v}_{p}t \}\cdot \vec{G}}} V_{p}(\vec{G})$, will lead to a net-zero contribution for long time averages, but will produce a highly oscillatory background to the TD force, $ -\int\: d\vec{G}\: i{\vec{G}}\rho^*(\vec{G},t) e^{i \{\vec{R}_{0}+{\vec{v}_{p}t \}\cdot \vec{G}}}$, which makes it difficult to track convergence of the average, unless initial and final times are chosen carefully. We simply subtract this background when calculating $S_p$.

We discard the beginning portion of the trajectory to eliminate any transient contribution due to the projectile ``drop in". For high velocities, $v_p>v_e$ increasingly larger simulation cells are required to converge the simulation, particularly in the direction of the projectile motion, due to plasmon excitation of increasing wavelength. Thus we use large and elongated (4:1:1 aspect ratios) simulations cells to converge this and any other finite size effects. We systematically test this convergence on representative trajectories.

 In Fig. \ref{snapshot} we show a representative simulation from a single carbon snapshot at 10g/cc and 10 eV with a proton projectile with a velocity of 2.5. Fig \ref{snapshot}-a) shows the calculated instantaneous and running  time-averaged stopping power on the projectile. We have removed the initial $1/8^{\mathrm{th}}$ of the simulation cell from the average. Fig. \ref{snapshot}-b) shows a 2D slice of the initial electron density, taken at the projectile position, with colormap. Dark colors indicate higher electron density. Nuclei positions are shown as dots with size depending on perpendicular distance from the slice. As a passing note, we see transient clusters and diatomics of carbon in the image, based on high electron density seen between nuclei. Fig. \ref{snapshot}-c-e) display the difference in the time dependent and initial density at $t=$ 3.2, 6.4, and 8.6. We see that the stopping power predictably increases when entering the regions of higher electron density near atoms/clusters. The nonlinear colormap is selected to amplify the appearance of small fluctuations.  The projectile position is shown as a green triangle. We see the electron wake, predicted in HEG, is significantly distorted by the background atoms \cite{doi:10.1063/1.3600533,PhysRevA.58.357}.

\subsection{Stochastic TD-DFT}

 \begin{figure}[t]
	\begin{center}
	\includegraphics[width=1.0\columnwidth] {fig_stats.pdf}
	\caption{(Color online) Statistics demonstrating convergence of TD-mDFT / TD-sDFT stopping power calculation with respect to the shown number of stochastic vectors, $N_\chi$ and deterministic KS orbitals, $N_\psi$. Calculations with non-zero $N$ for both types are mDFT calculations. a) the difference in proton stopping power, for 10 g/cc 10 eV carbon with $v_p=4.0$, between mDFT/sDFT and dDFT, averaged over 5 repeated calculations for the same ionic configuration, smaller red number and bars. sDFT calculation uses $N_\psi=756$ for 64 atoms. Error bars are $\pm$ one standard deviation over the calculations also shown as $\pm$ blue numbers. b) a representative stopping power trajectory for one calculation but for different  $N_\psi$, and  $N_\chi$. Instantaneous stopping power is scaled by the fully deterministic average. A small cell of 64 atoms is used in order to facilitate converged fully deterministic calculations. The projectile is seen passing through the periodic boundary of the box multiple times. The first two passes are removed from the stopping power time averaging. c) Similar to a), but for $v_p = 2.0$, d) similar to a), but for 3.5 g/cc. For 3.5 g/cc the dDFT references uses $N_\psi=512$ for 32 atoms.}
\label{stats}
\end{center}
\end{figure}

For high temperature systems the number of KS states, $N_\psi$ required to converge the density is $\propto VT^{3/2}$. For the initial self-consistent field calculation this leads to a cost $\propto V^3T^{3}$, due to orthogonalization of the states \cite{doi:10.1063/5.0016538}. For the TD propagation the cost is $\propto V^2T^{3/2}$, due to the nearly linear dependence on $N_\psi$. However, this is still limiting for calculations that need both large system sizes and high temperatures.

Stochastic density functional theory has emerged over the last decade as a linear scaling method for full KS DFT \cite{Cytter:2018aa,doi:10.1002/wcms.1412}. The method is based on the Hutchison stochastic estimation of the trace of a matrix\cite{doi:10.1080/03610919008812866}, $i.e.$ the density matrix .
Stochastic vectors, $\chi(\vec{R})\equiv {e^{i\phi(\vec{R})}\over\sqrt{d\vec{R}^3}}$, are generated, where $\phi(\vec{R})$ is an  independent random phase, uniformly sampled between $0$ and $2\pi$, for each real-space grid-point. The electron density can be calculated as :
\begin{eqnarray}
\label{Mermin_sdft}
 \rho(t)&= N^{-1}_\chi \sum_\beta^\infty  \chi^*_\beta(t) F_{D}(\hat{H}_{KS}(t=0) ,\mu, T)  \chi_{\beta}(t)  \textrm{.}
\end{eqnarray}
For a finite number of stochastic vectors, there will be stochastic fluctuations in the density. Moreover, the Fermi-Dirac function must now be approximated as a polynomial \cite{Cytter:2018aa}. The cost of this polynomial expansion is $\propto1/T$, while the stochastic precision is increased at a rate of $1/\sqrt{N_\chi}$, as is typical of Monte Carlo methods.  The time dependence of the stochastic vectors is the same as the deterministic KS states, \emph{ i.e.} ${i\partial \chi_\beta(t) \over \partial t}={\hat H}_{KS}(t) \chi_{\beta}(t)$ .  This is a trivial application of the equivalence between Schr{\"o}dinger and Heisenberg pictures. In principle, for systems of sufficient size and temperature, the time-dependent density can be more rapidly converged using this stochastic vectors than the traditional deterministic orbitals. This approach has been applied to the calculation of optical response in clusters \cite{doi:10.1063/1.4905568} , but not, to our knowledge, for WDM or for stopping power. Properties which depend on the integration over the simulation volume and energy domains, \emph{i.e.}, total energies, pressure, etc., can benefit from cancelation of stochastic error over the simulation volume, leading to sublinear scaling of sDFT \cite{Baer:2013aa, doi:10.1063/1.4905568}. However, local quantities such as the electron density or force on a particular atom do not \cite{Chen_2019,Chen_2019_2,Neuhauser_2014}. For stopping power the force on the projectile is calculated as it passes through the simulation volume, thus we expect some cancelation of error, but to a lesser degree than the total energy.

\subsection{Mixed Stochastic Deterministic TD-DFT}

Recently we developed a mixed-stochastic-deterministic approach for KS-DFT (mDFT) and showed that, for a given precision, the algorithm can lead to significant decrease in the computational time \cite{PhysRevLett.125.055002}. Alternatively, for the same computational time, it can improve the precision and accuracy compared to a purely stochastic calculation. For simulation of ion diffusion coefficients, also in WD carbon, we showed that we can achieve excellent accuracy for significant cost reductions. This is done by ``deflation" of the matrix which is traced stochastically \cite{doi:10.1137/16M1066361, PhysRevLett.125.055002}. That is, we replace the stochastic vectors $\chi$ with the subspace stochastic vectors $\tilde\chi$.
\begin{eqnarray}
\label{mDFT_ortho}
\tilde \chi_{\beta}=  \chi_{\beta}  - \sum_b^{N_b} \psi_b \int d\vec{R}\: \psi^*_b(\vec{R}) \chi_\beta(\vec{R})\textrm{,}
\end{eqnarray}
which are orthogonal to a pre-determined set of KS orbitals (eigenvectors of $\hat{H}_{KS}$).

 The SCF calculation in the Mermin Kohn-Sham method lends itself particularly well to this approach \cite{Mermin:1965aa}. For WDM temperatures the lower energy KS states, where the density of states is lower, but have high occupancy, are calculated deterministically, while the  higher energy orbitals, which have a higher density of states, but have lower individual occupancy, are treated implicitly through the stochastic vectors. The density is given by:
\begin{eqnarray}
\label{mDFT_den}
 \rho(t)&=\sum_b^{N_\psi} F_D(\varepsilon_b,\mu, T) \vert \psi_{b} (t)  \vert^2 + \\\nonumber &N^{-1}_\chi \sum_\beta^{N_\chi}  \tilde\chi_\beta^*(t) F_{D}(\hat{H}_{KS}(t=0) ,\mu, T)  \tilde\chi_{\beta}(t) \\\nonumber
 &=  \sum_i^{N_\psi + N_\chi} W_i  \vert \phi^2_i\vert  \textrm{,}
\end{eqnarray}
 where $\phi$ can be either $\psi$ or $\sqrt{F_{D}(\hat{H}_{KS}(t=0) ,\mu, T)}  \tilde\chi$ with $W_i = F_D(\varepsilon_i,\mu, T)$ or $N^{-1}_\chi$ respectivly. General finite temperature expectation values, including for time-dependent operators, can similarly be expressed as:
 \begin{eqnarray}
\label{mDFT_op}
 \langle \hat{O } \rangle &=  \sum_i W_i   \phi^*_i \hat{O} \phi_i    \textrm{.}
\end{eqnarray}

In Figure \ref{stats}-a we compare the $S_{p}$ calculated from traditional deterministic DFT (dDFT), and various sDFT and mDFT configurations ($N_\psi$ and  $N_\chi$) for a single snapshot of  64 carbon atoms at 10 g/cc carbon at 10 eV, with a proton velocity of 4.0. We take 5 identical trajectories to collect rough statistics on the stochastic sDFT/mDFT error. The percent relative difference between the dDFT $S_{p}$ and the average $S_p$ for each configuration is shown as a bar and red text. The $\pm$  percent relative standard deviation is shown as error bars and blue text.  Fig. \ref{stats}-b shows, for a single representative trajectory, a plot of the $S_{p}(t)$ (Eq. \ref{ourSp}), for some of the configurations, normalized to the TD-dDFT time average $S_{p}$ (Eq. \ref{ourSp}) for the trajectory. The TD-dDFT calculation uses $N_\psi=756$.  Fig. \ref{stats}-c is similar to Fig. \ref{stats}-a, but with a velocity of 2.0. Fig. \ref{stats}-d is similar to Fig. \ref{stats}-a, but for 32 atoms at  a density of 3.5 g/cc, $N_\psi=$ 512 for dDFT,  and different configurations. We see that by increasing either $N_\chi$ or $N_\psi$ we can narrow the deviation to less than 1\%, while using significantly less total orbitals/vectors. For perspective, the standard deviation of the TD-dDFT taken over 5 trajectories with different ionic backgrounds is 6.9\% for the case in Figure a).

\section{Stopping of Carbon}
\begin{figure}[t]
	\begin{center}
	\includegraphics[width=0.9\columnwidth] {fig1.pdf}
	\caption{(Color online) a-c) The projectile velocity, $v_p$, dependent stopping power of warm dense carbon at 10 eV and 0.5, 3.5, and 10 g/cc respectively. We compare the TD-mDFT (solid markers)  calculation's, $N_\psi : N_\chi = 16:64, 512:64,$ and $512:64$ respectively, with TD-OF-DFT (open markers),  Born random phase approximation (BRPA, solid line). The BRPA and TD-mDFT are based on 4 electrons per carbon (4EPC), with Casas and Barriga-Carrrasco (CBC) bound electron estimation for the K-shell electrons both added to the results and plotted separately (light dashed lines). The TD-OF-DFT is an all-electron approach. For reference we also plot the CBC result where all 6 electrons are bound, \emph{ i.e.} the result for cold gas. d) we again plot the TD-mDFT results, but without the K-shell contribution and scaled by the relevant plasma parameters,  the Fermi energy, $E_F$,  and the electron velocity $v_e$, again using 4EPC. This gives $E_F=$ 0.29, 1.06 and 2.13 and 0.5, 3.5, and 10 g/cc respectively }
	\label{fig1}
	\end{center}
\end{figure}
\subsection{TD-mDFT results}
	We have simulated the stopping power of carbon in three WDM conditions, 0.5, 3.5, and 10 g/cc, all at $T=10$ eV, using simulation cells of 256, 128 and 256 carbon atoms,  an energy cutoff of 38, 55 and 38, LDA , PBE \cite{Perdew:1996aa} and PBE exchange correlation potentials, respectively. Exchange correlation functionals are implemented through the LibXC package \cite{LEHTOLA20181}. The time step is dependent on energy cutoff for low velocities and projectile velocity at high velocities, ranging from $dt=0.01$ to $0.003$. We utilize the unitary short-iterative Lancoz method to propagate the orbitals/vectors \cite{SCHNEIDER201695}. These parameters are determined by manually checking convergence with respect to sample trajectories, for multiple velocities, at each condition. We employ 4 electrons per carbon (4EPC) HGH non-local pseudo-potentials \cite{PhysRevB.54.1703,Hartwigsen:1998aa}.  These points are indicated as white stars in Fig. \ref{plasma_param} and are representative of a partially ionized blowoff gas, an isochoricaly-heated solid, and shocked compressed dense plasma, respectively. The results are shown in Figure \ref{fig1}. The low density, high proton velocity cases (0.5 g/cc $v_p$ = 4-5) was recently compared to experimental measurements and demonstrated superior agreement to standard models.\cite{PPR:PPR393148} Here, we compare to the BRPA result with 4EPC, and the TD-OF-DFT result. We have added the 1s core electron contribution, estimated by the CBC method (also plotted), to the BRPA and TD-mDFT results. The TD-OF-DFT utilizes a 6 electron local pseudo-potential, thus no core contribution is added.  Additionally, we plot the CBC result assuming all electrons are bound (i.e. for $T\approx 0$).

The BRPA agreement with the TD-mDFT is better for the two denser systems, while the CBC result agrees reasonably well for the low density case, but overestimates in the dense systems. The TD-OF-DFT provides statistically quantitative agreement in the velocities above the Bragg peak, but either overestimates, or underestimates the result below the Bragg peak, depending on the conditions.  This is consistent with our previous OF/KS comparison.  To compare the valance contribution across the densities, we plot the scaled TD-mDFT stopping power (with no K-shell addition) for scaled velocities. This valence stopping power, $S^{val}_{p}$ is scaled by the Fermi energy, $E_F$ and the square of the projectile charge, $Z$, while the velocity is scaled by $v_e$ (Eq. \ref{ve} ). We used 4EPC for calculating $v_e$ and $E_F$, considering the tightly-bound 1s electrons as separated from the plasma. We see that the position of the Bragg peak is consistent with this basic plasma parameter, across densities, however the magnitude of the 0.5 g/cc stopping breaks the monotonic trend one would expect simply based on BRPA. This would indicate a breakdown of the 4EPC plasma assumption.

\subsection{Electrical Conductivity, relaxation times, and Mermin Dielectric}
\begin{figure}[t]
	\begin{center}
	\includegraphics[width=0.75\columnwidth] {fig3.pdf}
	\caption{(Color online) a) The current density, scaled by the electric field pulse strength and electron density vs the time (multiplied by the $E_F$). b) The conductivities, \emph{i.e.} Fourier transforms of the top panel, multiplied by $E_F$. Multiple Drude Fits are also plotted (Eq. \ref{Drude}). c) The density of states (DOS, thin dark lines) and occupied DOS(thick light lines) for the three densities. Both densities of states are scaled by the number of electrons in the system and multiplied by the $E_F$, the energy axis is scaled by $E_F$. The DOS of a non-interacting free electron gas (depends only on $\rho_e$ and $E_F$) is plotted for reference. $E_F=$ 0.29, 1.06 and 2.13 for 0.5, 3.5, and 10 g/cc respectively.}
	\label{fig3}
	\end{center}
\end{figure}

	To understand the physical differences between the low and high density cases, which extend beyond the degeneracy and coupling effects of the plasma, we can look to the conductivity and density of states. Previous attempts to calculate stopping power based on fitting dielectric functions to experimental or theoretical, in the optical $G \to 0$ limit, has been reported previously \cite{Ashley_1991, Heredia_Avalos_2007}. In this limit the dielectric and conductivity are straightforwardly related to one another, but the conductivity is easily calculated from RT-TD-DFT, including TD-mDFT, by subjecting the system to an instantaneous in time, uniform in space, electric field pulse. That is:
\begin{eqnarray}
\label{current}
A_{x, ext}(t)&= \Theta(t-t_0) E_0 \\
\sigma(\omega)&= \int dt e^{-i\omega t} J_{x}(t)/E_0 \\
J_{x}(t)&= Re\big\{\sum_i \phi^*(t) [\hat{H}_{KS}(t),R_x] \phi(t) \big\} \:,
\end{eqnarray}
where $E_0$ is the electric field strength, $\Theta$ is the Heaviside function, $\sigma$ is the conductivity, $\vec{J}$ is the current density, and  $[.,.]$ is the commutator. In Figure \ref{fig3}-a we plot the current, scaled by the field strength and electron density, $\rho_e$. We scale the time by $E_F$. $\rho_e$ and $E_F$ are again calculated assuming 4EPC.   We calculated the current by TD-mDFT using $N_\psi=256$, $N_\chi=64$, $E_0=0.5$, and the same atomic cells as was used for the stopping power calculations. We tested the field strength as low as $E_0=0.005$ to insure the field is within the linear response regime. In Fig. \ref{fig3}-b we plot the corresponding conductivity scaled by the density and multiplied by the Fermi energy vs the angular frequency, $\omega$, scaled by the Fermi energy. Error bars are due to sampling over both stochastic vectors and ion snapshots.

One of many possible choices to determine a $Z^*$ for carbon under these conditions, is to fit the low frequency real conductivity to a Drude model:
\begin{eqnarray}
\label{Drude}
\sigma (\omega) = {n_a Z^* \tau \over 1 + (\omega \tau)^2}
\end{eqnarray}
where $n_a$ is the atomic number density. Despite scaling the current and conductivities, the two systems show the dramatic difference between the hot ionized gas and compressed plasma systems. In the latter, the conductivity is well-fit, globally, by a Drude model, indicating that all 2s and 2p electrons behave similar to nearly-free electrons, $i.e.$ $Z^*\approx 4$ and $\tau \approx 2/E_F$, see light dashed line.  In the prior, higher frequency oscillations appear at $\omega \approx 0.3$, $0.35$, and $0.62$. These roughly correspond to $2p\to$ continuum, $2s\to2p$ (facilitated by partial ionization), and $2s\to$ continuum, though broadened and shifted by interaction between ions. The $2s\to2p$ peak is significantly sharper than the excitations to continuum. Due to these excitations the conductivity is not well-fit, globally, by a Drude model, see dash-dot line. However, we can fit the low frequencies, $\omega < 0.25 $, to a Drude with $Z^*\approx 1.75$ and $\tau \approx 2/E_F$, see dotted line.

 Additionally, we calculate the density of states (DOS) and Occupied DOS (ODOS), for one snapshot.

\begin{eqnarray}
\label{DOS}
DOS(E)&\approx 2 \int dt e^{i 2 \pi [E t-i {\gamma \over 2}] t} Tr \{e^{-i \hat{H}_{KS} t}\}  \\ \nonumber
&=2\:\:Tr \{\tilde\delta(E-\hat{H}_{KS})\}\\
ODOS(E)&=Tr \{ F_{D}(H_{KS}(t=0)) \tilde\delta(E-\hat{H}_{KS})\}
\end{eqnarray}
\begin{figure}[t]
	\begin{center}
	\includegraphics[width=0.9\columnwidth] {fig4.pdf}
	\caption{(Color online) Projectile velocity dependent stopping power. The TD-mDFT results are repeated from Fig. \ref{fig1}, Mermin dielectric plus CBC, solid line, assuming $Z^*=4.0$ and $1.75$ for 10g/cc (a) and 0.5 g/cc (b) respectively and $\tau=2.0/E_F$. BRPA plus CBC, dashed line, assuming  $Z^*=4.0$, $Z_{2s}=0$, and $Z_{2p}=0$ for 10 g/cc and $Z^*=1.45$, $Z_{2s}=0.94$, and $Z_{2p}=1.62$ for 0.5 g/cc. }
	\label{fig4}
	\end{center}
\end{figure}
where $\tilde\delta$ is a sharp Lorentzian approximation to a delta function with width $\gamma$. The DOS and ODOS for all three densities is plotted in Fig. \ref{fig3}-c. For reference the continuum of states for a non-interacting free electron gas, $DOS(E)=\frac{3}{2}\frac{\rho_e}{E_F}(\frac{E}{E_F})^{1/2}$, is shown as a dotted line.  For the 0.5 g/cc case the 2s and 2p peaks are distinct from the continuum band, with only small shift downward from their single atom, $T=0$, energies, shown as dashed vertical lines. While for the 3.5 and 10 g/cc cases the continuum has dropped and mixed with the atomic states.  Qualitatively this makes it clear why the BRPA disagrees with the KS-DFT significantly for the 0.5 g/cc, but works reasonably well for the solid density and dense plasma cases.

	We can attempt to improve the stopping power models by using the Drude model parameters, $Z^*$ and $\tau$,  extracted from our conductivity calculations.  We can include the scattering time, $\tau$, into the Mermin formulation \cite{PhysRevE.76.016405} for the dielectric function:
\begin{eqnarray}
\label{MerminSP}
\varepsilon_M(G,\omega) = 1+ \frac{(\omega +  \frac{i}{\tau})[\varepsilon(G, \omega +  \frac{i}{\tau})-1]}{\omega + \frac{i}{\tau} [\varepsilon(G, \omega +  \frac{i}{\tau})-1]/[\varepsilon(G,0)]-1]},
\end{eqnarray}
 where $\varepsilon$ is the RPA response function, and use the $Z^*$ to adjust the Fermi Energy.

 For the 10g/cc case, $Z^*$ from the conductivity fit is still 4EPC, so the only effect is the inclusion of scattering time. In  Fig. \ref{fig4}-a, we see that the inclusion of the finite scattering time increases the stopping for low velocities, in better agreement with TD-mDFT, but lowers it for higher velocities, where the agreement with BRPA was already quantitative.  An overall improvement is not clear. For the 0.5 g/cc case, $Z^*$ should be reduced due to the 2s and 2p electrons which remaining bound. Reducing $Z^*$ then requires us to include a larger contribution from the CBC method for the bound electrons. Fitting the conductivity does not provide a value for the population of each bound state, only the total $Z^*$. We can approximate $Z_{2s}$ and $Z_{2p}$ by integrating over the two peaks in the $ODOS(E)$, \emph{i.e.} from $-3\:E_F$ to $-1.6\: E_F$ and from $-1.6\:E_F$ to $0$, respectively. This yields a $Z_{2s}=0.94$, $Z_{2p}=1.61$, and $Z^*=1.45$. We assume that the deeply bound 1s orbitals (K-shell) are still fully populated.

 This lower $Z^*$, compared to the Drude fit, is due to partial ionization of the 2s and 2p bands, which can have intra-band transitions that contribute to low frequency $\sigma$, and hence a higher $Z^*$ predicted for the Drude fit. These transitions are not allowed for single atoms. However, they are allowed for disordered condensed phases. Inconsistency between the $ODOS$ and $\sigma$ derived $Z^*$, even from the same electronic structure, but with different definitions, points to the limitation of the concept.  Thus to compare to KS results, we calculate two CGC plus dielectric curves, one with the $Z$'s from the $ODOS$ and using BRPA dielectric (infinite $\tau$), and one with Drude $Z^*$ and the Mermin RPA. In the latter case the ionization is assumed to come fully from the highest energy occupied orbital (the 2p) in the CGC calculation. We see that in both cases the agreement is improved for high velocities, but neither predicts the decreased magnitude near and below the Bragg peak seen in TD-mDFT.
 \section{Conclusion}
 	We have presented a new approach for \emph{ab initio} time-dependent Kohn-Sham density functional theory, based on mixed deterministic and stochastic methods. This method obtains the efficiency required to perform stopping power calculations at many eV temperatures, with the large box sizes required to overcome finite size effects. We can utilize these high fidelity atomistic results to interrogate more easily calculated stopping power models. Here we have focused on using the BRPA and Mermin dielectric functions for the nearly free electron contribution with the bound electron contribution from the CGC method. Unfortunately,  quantitative agreement is not significantly improved by including parameters taken directly from the \emph{ab initio} method. This could indicate that improvements to the models are needed, such as improved dielectric functions \cite{barriga-carrasco_2010, PhysRevE.97.023202} or bound state energies derived from finite temperature average atom simulations with density effects included \cite{GILL201733}. It could indicate a fundamental limitation of the separation between bound and free electrons in the calculation of stopping power of condensed phases, or limitations of the BRPA and Mermin dielectric functions for dissorederd plasmas. Additional studies will certainly be required.

	For now, this motivates continued use of TD-DFT to validate or invalidate models in different parameter regimes. Tuning of tight-binding models is another potential route to achieve efficiency and accuracy \cite{Race_2013, Mason_2012} .  Alternatively, one could seek to improve the TD-OF-DFT results through novel kinetic energy functionals. We note that, other than TD-OF-DFT, all calculations presented here have used the same method to calculate the K-shell contribution to the stopping power. Future work will involve calculation of the 1s stopping using TD-KS-DFT and an all-electron pseudo-potentials.

\section{Acknowledgment}

This work was supported by the U.S. Department of Energy through the Los Alamos National Laboratory (LANL). Research presented in this article was supported by the Laboratory Directed Research and Development program of LANL, under project number 20210233ER, and Science Campaign 4. This research used computing resources provided by the LANL Institutional Computing and Advanced Scientific Computing programs. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218CNA000001). This work was supported by the Department of Energy National Nuclear Security Administration Award Number DE-NA0003856 through Laboratory for Laser Energetics, at University of Rochester.

\section{References}
\bibliographystyle{iopart-num}

redpajama_set_name: RedPajamaArXiv


text:
Q: Add row in to a table if ID does not exist in other version of the same table I searched the solution in google but I can't find (or I don't understand) the right solution.
I have two tables, they are the same, but in the newest version, some rows have been loose.
So I would like to be able to compare this two tables and inject the missed rows by comparing the ID. For example:
If in the table v1 we have the row with the ID 12 and not in the table of the v2 then we add this row in the v2 table.
I wrote an php script but I can't go further, I don't get it how I can compare this values at the same time, maybe I'm using a bad approach, here is my php code:
<?php
    function_db_connect();
    $req_src = "SELECT * FROM `table_v1` ";

    $req_ex_src=mysql_query($req_src) or die("Erreur: ".mysql_error());

    $i =0;

    while($row = mysql_fetch_array($req_ex_src)) {
        $i = $i +1;
        $rowID[$i] = $row['ID'];
    }

    mysqli_close();

    $n = count($rowID);

    for ($j=0;$j<=$n;$j++){
       ??????????????????????
    }

I get all the ID in an array and my idea was to compare this values with the v2 table in the for loop.
That's all I have done, I'm a little confused... Do you think there's another method better than this?

A: You can use a simple mysql query to do this no need to use php unless there are other factors needing to be taken into account
To do this use insert ignore:
INSERT IGNORE INTO table_v2 (SELECT * FROM table_v1)

this will select all the rows in table_v1, and try to insert them into table_v2, if a row's keys already exist that row is ignored and the query continues on.

redpajama_set_name: RedPajamaStackExchange


"The student worked on homework that covered and reviewed arithmetic sequences, finding the nth term given the difference and the first term, the arithmetic sum given the number of terms, the first sum and the difference. He also reviewed the geometric sequences, finding the nth term given the rate and the first term, and finding the summation of n number of terms. He seemed to have no trouble setting up the problems and solving them. "
"We reviewed logarithmic and logistic functions. We discussed their general form and how to derive their functions from x and y coordinates. After going over the material, we went through the student's homework. I tried to use each problem as a way to teach her the material, which was effective. "
"We went through review problems from Chapter 6 to prepare for The student's upcoming exam, discussing angle between two vectors, polar and rectangular equations, and parameterization of equations."
"On the whole, it went well. We worked through the preparatory material in the student's book pertinent to inequalities and absolute values and inequalities combined. We still have a small amount to do using quadratics in inequalities. "
"Tonight, the student needed to create a review for her (9-week) test on Thursday. She had already created much of the review, but needed a check of the work and solutions. We reviewed all of the material covered by the review and made sure that her work was accurate. We also made sure that she knew how to use her calculator, mostly when using DMS (degrees-minutes-seconds). I gave her some pointers in how to create a systems of equations problem. The best way is to begin with the solution and then create the equations. I also gave her suggestions for a matrix multiplication problem; she will create this problem and then add it to her review as the "bonus." Our next meeting will be in one week. Good job!"


We are a brand new purpose designed yoga studio in the heart of the Essex countryside near to Ongar and Chelmsford, Essex.
We offer weekly classes, as well as workshops and courses in yoga and mindfulness.
We are very excited about growing our yoga community with you, and it is our aim to share our knowledge and experience in a rich variety of yoga across all ages and stages of life. We have the best teachers, and we all love yoga. We are always happy to talk about the types of yoga we teach and our own experiences of how great it is, come along and meet us to find out more.
Opening Times: Open for classes. See schedule. Online booking only.
Location/Directions Info: The Stock Yard is a converted barn in the middle of the Ongar wilds. Our studio in just off the A414. When you're on Bassetts Lane you'll come to a farm house on your left, pass a tight bend and our building is directly after not he left. Our door is on the front facing the road.


text:
Mayoral Kids - Celebrity Fashion
Mayoral Kids Clothes from Spain SHOP
Mayoral Kids Clothes has been designing children's fashion for over 70 years… Since 1941, Mayoral has been "making friends" – the brand's slogan is a fundamental part of Mayoral's philosophy, paving the way for a company that bases its success on quality and fashion at its best.
Shop Mayoral Kids - Celebrity Style
Mayoral Girls Ivory Queen T-Shirt & Gold Polkadot Tutu Skirt
MAYORAL Boys Beige Blazer & Girls Floral Dress
MAYORAL Boys Blue Sailboat Sweater & Girls Stripe Dress
MAYORAL Baby Boy Navy Blue Jersey Blazer & Khaki Shorts
MAYORAL Baby Girls Blue Pinafore Dress
MAYORAL Yellow & Ivory Floral Dress
MAYORAL Girls Ice-Cream Print Cotton Dress
MAYORAL NEWBORN Baby Boy Light Blue Vest, Shorts & Striped Sweater
MAYORAL Baby Boys Yellow Shoes Print T-Shirt & Denim Shorts
The Mayoral story began as a modest company named Dominguez Toledo dedicated to the production and commercialization of socks and shoes. Today the company under the name Mayoral Moda Infantil has grown to become a large group with international recognition, representing Spain's style of fashion around the world.
The Mayoral strategy is to promote its designs and brand with personality and its own style, providing our customers with a well-balanced product that is fashionable, price conscious and of the highest quality.
Today, Mayoral is an important producer of fashion that has the capability to offer customers all types of items, coordinated and ready to dress children worldwide.
To ensure that the principles that guide the progress of the company are focused on ethics and social responsibility, the company has been operated by the same family since its origin.
Mayoral - Designer Children's Clothing at Great Prices
At Mayoral, we know that friends are one of the most valuable things in the world, which is why our slogan, "making friends", is and always has been a fundamental part of our philosophy. We base our success on our exceptional effort, excellence, and fashion at its finest. Our objective is to create high-quality garments at the best possible price and to deliver them on time. We produce more than 21 million garments a year, or, how we like to think about it, 21 million smiles.
Mayoral Kids, the leading brand for children's wear in Spain and one of the most important groups specialized in children's fashion in Europe, designs, produces, markets and distributes children's fashion in over 50 countries, through a sales network of: 11 branches, 180 sales agents, 140 Mayoral stores and more than 7,500 clients worldwide.
Watch Mayoral Fashion Show at Pitti Bimbo Kids Fashion Week
MAYORAL KIDS

redpajama_set_name: RedPajamaCommonCrawl


Looking for glass shower doors in Owings Mills, MD?
Welcome to Shower Door Experts, a custom shower door company that serves Owings Mills, MD and surrounding metro areas! Whether you're a builder, contractor or homeowner, if you're looking for a custom glass shower door in Owings Mills, shower door replacements, hardware and/or bathroom remodeling we're your Shower Door Experts!


text:
What is it Like to Play Alien: Isolation on Oculus Rift?
Prepare for a xenomorph kiss through the forehead.
Published June 17, 2014, 11:15 a.m. about Alien: Isolation
by Christopher Buffa
Good fortune smiled upon us last week in Los Angeles, as we played Sega's Alien: Isolation twice, with the popular Oculus Rift strapped to our heads during the second demo. This marked the first time we experienced both the survival horror game and the new virtual reality device; the one Facebook paid $2 billion to acquire. Saying there's a huge difference between the HD flat screen and VR goggles is a big understatement, but don't worry if you can't afford an Oculus Rift. The game will scare the hell out of you regardless.
The biggest difference between an HDTV and the Rift is depth of field and the higher level of immersion. While hiding in a storage locker, you have the usual range of movement viewing the game via television, where manipulating the analog sticks lets you look up, down, left, right and at various angles.
With the Oculus Rift, however, you can physically lean in to move closer to the locker door and stare out the slits. You also have the ability to look behind you without touching the controller. It's an undeniably cool experience.
That said, we spent most of our time in lockers getting acclimated to using the Oculus Rift, one of the few safe places in Alien: Isolation; doing this with a drooling xenomorph in pursuit is a ticket to a quick death.
Paring the Rift with an Xbox One controller does present the player with a slight learning curve because you have to use both in tandem. With a standard TV, changing direction results in the weapon or motion tracker remaining on-screen at all times. Turning your head left or right with the Oculus Rift (as you would in the real world) leaves the aforementioned item out of view. Fail to adapt and you won't see which direction the Alien comes from, as noted on the motion tracker.
As for the Rift itself, while the device comes with adjustable straps, we had trouble getting comfortable with our own glasses on; not to mention worried sick we'd scratch the lenses. In addition, there's a ghosting effect that we noticed as soon as the game began, but this had more to do with training our eyes. More importantly, despite initial complaints of the screen being a lower resolution than expected, this wound up enhancing Alien: Isolation because of the game's art direction that focuses on the 1970's interpretation of science fiction, similar to the original Alien film. We're in the future, but it's appropriately crude and dirty.
Which play style did we prefer? We'll take the easy road and say both. Oculus Rift is a promising device, no question, but there may come a point where the headset's weight provides slight discomfort, ultimately resulting in us gently removing it to enjoy the game the old-fashioned way. For now this remains to be seen, and we thoroughly enjoyed the VR-produced adrenaline rush.
In regard to Alien: Isolation, it's the scariest game since Dead Space and its bloody sequel. Outlast on PC and PS4 made us scream, but we eventually figured out enemy placements and developer tricks. With this game, it's impossible to predict where the Alien will appear from, and objects like a flamethrower with limited fuel, flares and Molotov cocktails only provide brief moments of safety. You can't run and the flashlight is almost useless. Instead, you spend the majority of time holed up in lockers and crouched behind equipment freaking out at the slightest sound. Cliché, but it's a game of inches.
Hardcore players in search of a challenge, of course, will attempt to make things even tougher by completing levels without using the motion tracker, which is one of the in-game objectives. Not impossible to achieve, but it helps to know the environment.
After dying several times, we thought we'd get used to seeing the Alien, but the opposite was true. The creature moves so fast and is so big we immediately thought back to the most terrifying scenes from the film, particularly the one where Dallas wanders into the Alien while exploring the Nostromo air ducts; the seemingly abandoned Sevastopol Station bears a welcome resemblance to the doomed ship, right down to the air lock doors and dark industrial hallways. One moment we took an inner jaw to the head, and the next, the monster drove its razor-sharp tail through our abdomen, quickly removed it and then did something nasty to hero Amanda Ripley's skull. At that point, there's literally nothing you can do but watch the gruesome scene unfold, and through Oculus Rift, you have an up close and uncomfortably personal scene to digest.
We left Alien: Isolation confident that Sega and Creative Assembly's game plays wonderfully on both flat screens and the Oculus Rift. There's no firm release date for the latter, but the game arrives October 7th for multiple platforms. Alien fan or no, you're in for a hair-raising treat.


Thrill seekers look no further. Go flyboarding in Porlamar, Nueva Esparta.
Porlamar, Nueva Esparta is a great place to go flyboarding. You can take a flyboarding lesson to learn this exciting watersport and you'll be soaring above the water in no time. From birthday celebrations to family holidays, Porlamar, Nueva Esparta is the perfect flyboarding destination.


text:
GIF me a break: Twitter describes Colts awful first half against Pats
There are plenty of words that people could use to describe the carnage. But, we probably couldn't publish most of them.
GIF me a break: Twitter describes Colts awful first half against Pats There are plenty of words that people could use to describe the carnage. But, we probably couldn't publish most of them. Check out this story on IndyStar.com: https://www.indystar.com/story/sports/nfl/colts/2018/10/04/twitter-reacts-colts-vs-patriots-first-half/1529232002/
Matthew VanTryon, Indianapolis Star Published 10:03 p.m. ET Oct. 4, 2018 | Updated 10:08 p.m. ET Oct. 4, 2018
Indianapolis Colts take on New England Patriots
Indianapolis Colts quarterback Andrew Luck (12) shows dejection after making an incomplete pass on a fourth down play with three minutes left in the fourth quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. The Colts lost to the Patriots, 24-38. Matt Kryger/IndyStar
The New England Patriots celebrate a touchdown by wide receiver Josh Gordon (10) in the fourth quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. The Colts lost to the Patriots, 24-38. Matt Kryger/IndyStar
Indianapolis Colts running back Jordan Wilkins (20) shows dejection on the bench after his fumble in the third quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
New England Patriots cornerback Stephon Gilmore (24) tackled Indianapolis Colts wide receiver Zach Pascal (14) who drops a pass from Andrew Luck (12) in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts quarterback Andrew Luck (12) watches as the New England Patriots score a touchdown in the fourth quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. The Colts lost to the Patriots, 24-38. Matt Kryger/IndyStar
Indianapolis Colts wide receiver Chester Rogers (80) fights New England Patriots defense on a punt return in the third quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
New England Patriots quarterback Tom Brady (12) delivers a touchdown pass to running back James White (28) in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
The Indianapolis Colts celebrate a touchdown by tight end Erik Swoope (86) in the fourth quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts tight end Erik Swoope (86) receives a touchdown pass in the fourth quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts quarterback Andrew Luck (12) celebrates a touchdown by Erik Swoope (86) in the fourth quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts quarterback Andrew Luck (12) passes off to running back Nyheim Hines (21) in the third quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts wide receiver Marcus Johnson (16) shows dejection after the Colts lost to the New England Patriots, 24-28, at Gillette Stadium in Foxborough, MA., on Thursday, Oct 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts wide receiver Ryan Grant (11) deflects New England Patriots defensive back Jonathan Jones (31) in the fourth quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. The Colts lost to the Patriots, 24-38. Matt Kryger/IndyStar
Indianapolis Colts offensive guard Quenton Nelson (56) shows exhaustion after the Colts lost to the New England Patriots, 24-28, at Gillette Stadium in Foxborough, MA., on Thursday, Oct 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts wide receiver Ryan Grant (11) stretches for the first down in the fourth quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. The Colts lost to the Patriots, 24-38. Matt Kryger/IndyStar
Indianapolis Colts tight end Eric Ebron (85) receives a touchdown pass from quarterback Andrew Luck (12) in the third quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts wide receiver Chester Rogers (80) and tight end Eric Ebron (85) celebrate Ebron's touchdown in the third quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
New England Patriots linebacker Elandon Roberts (52) is unable to strip the ball from Indianapolis Colts wide receiver Ryan Grant (11) in the third quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts safety Matthias Farley (41), defensive tackle Grover Stewart (90) and linebacker Zaire Franklin (44) surround New England Patriots wide receiver Julian Edelman (11) in the third quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts safety Matthias Farley (41) celebrates his interception in the third quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
New England Patriots defensive back Jonathan Jones (31) tackles Indianapolis Colts running back Nyheim Hines (21) as he receives the ball in the third quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts safety Matthias Farley (41) makes an interception in the third quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts defensive back Clayton Geathers (26) is shaken up by a hit from New England Patriots running back Sony Michel (26), sending Geathers to concussion protocol, in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts defensive back Clayton Geathers (26) walks to be checked for a concussion after a collision with New England Patriots running back Sony Michel (26) in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
New England Patriots defensive back Jonathan Jones (31) tackles Indianapolis Colts wide receiver Chester Rogers (80) in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts kicker Adam Vinatieri (4) kicks a 54-yard field goal in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts running back Jordan Wilkins (20) carries the ball in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts defensive end Jabaal Sheard (93) is seen in the second quarter of the game against the New England Patriots at Gillette Stadium in Foxborough, Mass., Thursday, Oct 4, 2018. Matt Kryger/IndyStar
New England Patriots linebacker Kyle Van Noy (53) tackles Indianapolis Colts wide receiver Chester Rogers (80) in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
New England Patriots running back Sony Michel (26) fights for yards as he is tackled by Indianapolis Colts safety Matthias Farley (41) and defensive back Malik Hooker (29) in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts head coach Frank Reich calls for a timeout with under two minutes to go in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts defensive end Jihad Ward (51) is seen in the second quarter of the game against the New England Patriots at Gillette Stadium in Foxborough, Mass., Thursday, Oct 4, 2018. Matt Kryger/IndyStar
New England Patriots quarterback Tom Brady (12) calls an offensive play in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
New England Patriots running back James White (28) receives a touchdown pass from quarterback Tom Brady (12) in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts defensive back Clayton Geathers (26) waits in concussion protocol in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Geathers was shaken up in a collision with New England Patriots running back Sony Michel (26). Matt Kryger/IndyStar
The New England Patriots cheerleaders perform before the game between New England and Indianapolis at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
After receiving a pass from Indianapolis Colts quarterback Andrew Luck (12), Chester Rogers (80) is tackled by New England Patriots defensive back Devin McCourty (32) and defensive back Patrick Chung (23), in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
New England Patriots defensive end Adrian Clayborn (94) pressures Indianapolis Colts quarterback Andrew Luck (12) who throws an interception to New England Patriots defensive back Patrick Chung (23) in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
New England Patriots defensive back Patrick Chung (23) intercepts a pass intended for Indianapolis Colts running back Nyheim Hines (21) in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts tight end Eric Ebron (85) grips the ball as he is brought down by New England Patriots defensive back Patrick Chung (23) and linebacker Elandon Roberts (52) in the first quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts quarterback Andrew Luck (12) passes against New England Patriots defense in the first quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts quarterback Andrew Luck (12) is sacked by New England Patriots defensive back Patrick Chung (23) and defensive end Trey Flowers (98) in the first quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
New England Patriots Jonathan Jones (31), Trey Flowers (98) and Patrick Chung (23) celebrate a sack against Indianapolis Colts quarterback Andrew Luck (12) by Flowers and Chung, in the first quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts head coach Frank Reich seen on the sidelines before the game against the New England Patriots at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts offensive tackle Anthony Castonzo (74) did not start in the game against the New England Patriots at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Castonzo has been out due to a hamstring injury. Matt Kryger/IndyStar
Indianapolis Colts quarterback Andrew Luck (12) sets up an offensive play against New England in the first quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts defensive end Jabaal Sheard (93) tackles New England Patriots running back James White (28) in the first quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
New England Patriots wide receiver Julian Edelman (11) receives a pass from quarterback Tom Brady (12) and fights for extra yards in the first quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts quarterback Andrew Luck (12) drops back to pass against New England defense in the first quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts defensive end Jabaal Sheard (93) tackles New England Patriots running back Sony Michel (26), who earned the first down in the first quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts quarterback Andrew Luck (12) fakes a hand off to Indianapolis Colts running back Jordan Wilkins (20) in the first quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts quarterback Andrew Luck (12) passes in the first quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts head coach Frank Reich is seen in the third quarter of the game at Gillette Stadium in Foxborough, Mass., Thursday, Oct 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts defensive end Margus Hunt (92) is seen in the second quarter of the game against the New England Patriots at Gillette Stadium in Foxborough, Mass., Thursday, Oct 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts kicker Adam Vinatieri (4) kicks and misses a field goal late in the first quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Members of the Indianapolis Colts defensive line are seen in the second quarter of the game against the New England Patriots at Gillette Stadium in Foxborough, Mass., Thursday, Oct 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts quarterback Andrew Luck (12) seen on the sidelines before the game against New England at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts running back Nyheim Hines (21) weaves around New England Patriots defenders in the second quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct 4, 2018. Matt Kryger/IndyStar
Indianapolis Colts quarterback Andrew Luck (12) is sacked by New England Patriots defensive back Patrick Chung (23) and defensive end Trey Flowers (98) in the first quarter at Gillette Stadium in Foxborough, Mass., Thursday, Oct. 4, 2018.(Photo: Matt Kryger/IndyStar)
The first half of the Colts and Pats was, well, what plenty of people expected it to be. Only maybe worse. Not only did the Colts head into the break trailing by 21, they also headed to the locker room with a slew of injuries -- on top of the ones they already had.
► Colts vs. Patriots live blog: Colts' injury woes worsen vs. Patriots
► No one understands why sack on Andrew Luck wasn't a penalty
There are plenty of words that people could use to describe the carnage. But, we probably couldn't publish most of them. So we asked you, dear readers, to describe the first half in a GIF. And you delivered.
Here are some of the best responses.
pic.twitter.com/g3ePjeyPyn
— Andrew's Head (@AndrewLucksHead) October 5, 2018
pic.twitter.com/UJUUExUNSZ
— Laura (Insert Halloween-ized Name Here) (@lbrgdl) October 5, 2018
pic.twitter.com/qqpMihK10N
— Mitch Huppert (@Mitch_Huppert) October 5, 2018
pic.twitter.com/KzAqQtXF8Y
— Big Indy (@IndyBiggs) October 5, 2018
pic.twitter.com/8WsVSUWdZO
— Brigette (@Brigette912) October 5, 2018
pic.twitter.com/D8RZCOWJaN
— Connor Miller (@ConnorMiller24) October 5, 2018
pic.twitter.com/7WeBngXUTN
— justin (@JGipson9) October 5, 2018
pic.twitter.com/9ycjAh9P1N
— Nate Riley 🇺🇸 (@nariley_nate) October 5, 2018
pic.twitter.com/GOF3EXpXJS
— Dallas Harvey (@DallyJ33) October 5, 2018
pic.twitter.com/39ZU3B2q91
— Stacey Andrews (@staceya8) October 5, 2018
pic.twitter.com/CaDki3TJ27
— Tristian (@maizebluefan1) October 5, 2018


text:
Centre for Transport and Society
Research projects by theme
Technologies and travel
FUTURES - Barriers to information use
Full project title: FUTURES: Traveller information services - assessing barriers to their use
Sponsor: EPSRC (Sustainable Urban Environment Programme)
Lead investigator: Professor Glenn Lyons
Principal researcher: Sendy Farag
Project partners: University of Southampton and University of Leeds
Finish date: April 2009
Project briefing sheet: Download the briefing sheet document (PDF)
About FUTURES
FUTURES (Future Urban Technologies: Undertaking Research to Enhance Sustainability) is a major five-year research programme as part of the EPSRC's Sustainable Urban Environment (SUE) Programme. FUTURES runs from April 2004 until March 2009. The principal academic partners are the Transportation Research Group at the University of Southampton (lead partner), the Institute for Transport Studies at the University of Leeds and the Centre for Transport and Society (CTS) at the University of the West of England. The consortium also involves a number of stakeholder partners. FUTURES is comprised of a number of key elements or activities. There are seven major research activities for which CTS is responsible for three:
Traveller information services - assessing barriers to their use (this project)
Navigating the city - supporting the unfamiliar traveller
Teleworking - trends in and causes of location independent working
Statement of Need
Traveller information services in the UK have grown in quality and number, particularly since the transport white paper (DETR, 1998) which identified the provision of better information as a key element to delivering integrated transport. A number of channels of delivery for these services now exist from the long-standing paper-based maps and timetables and telephone enquiry services to the more recent on-street kiosks and websites. At a national level, the Government's Transport Direct initiative symbolises the maturity of the industry and what can be achieved. Transport Direct aims to provide, initially via the Internet, a multi-modal information service to plan, book and pay for journeys and to check for real-time updates. At a local level, many urban authorities now have well developed information services, particularly for public transport.
The aim of such services is to empower people to make more fully informed travel choices. This may include: bringing to their attention choices of which they had not previously been aware; improving their understanding of the relative merits of the choices available; and making it easier to plan and undertake journeys. Achieving this aim can benefit the individual and, where travel choices are influenced by information provision, it can also benefit the transport system by supporting and promoting greater use of public transport and, potentially, reducing car use or enabling more efficient car use in terms of the route and/or timing of travel.
However, travellers can only be empowered to make more fully informed travel choices if they make use of the information services provided. Herein lies a significant problem that the providers of information services face. Many people are not using their services. Market research for the UK's national public transport information traveline service (TNS Harris, 2000) found that half of the UK population do not use public transport information services. A recent survey of rural residents (Social Research Associates, 1999) found that just under three-fifths of people don't have timetables. In spite of receiving in excess of one million telephone enquiries a week, a substantial proportion of the national population are unaware of the National Rail Enquiry Service (TNS Harris, 2000 and Lyons, 2001). Recent research for Transport Direct (DfT, 2003) involved a national survey of 1200 travellers and found that one in three travellers never consult a telephone or Internet traveller information service (of these, 41% consider they 'don't need to' or 'just don't bother'). Other qualitative research has highlighted habitual behaviour as a reason for low information demand, particularly concerning mode choice (Kenyon and Lyons, 2003).
Previous EPSRC research by Lyons (2001) has identified a series of barriers to the use of information services:
Lack of inclination, motivation or time to seek information;
Lack of awareness of information availability;
Lack of the available means to access the information;
Lack of faith or trust in the information service provider;
Prohibitive cost of using the information service;
Poor usability of the information service; and
Failure of information content to meet users' needs.
The barriers themselves, as listed above, are recognised. However, what is important is to establish the relative importance or significance of these barriers and to understand properly the nature of the barriers in question. Only limited evidence is available in this respect since much research to date has focused on information use rather than non-use. The purpose of this research is to respond to the experiences of, and growing concerns amongst, information service providers, notably in urban areas, and to assess, understand and explore remedies to the barriers to information services use.
To identify situational and psychological factors that govern individuals' propensity to use (urban) travel information services;
To investigate the extent to which and why each of these factors is currently inhibiting higher levels of information services' use;
To identify whether and which non-users, if converted to users, could yield benefits (in terms of informing and influencing travel behaviour) for the individual, service provider and the urban environment;
To identify measures that have the potential to address and overcome barriers to information services' use;
To trial one or more (or a combination of) such measures in a case study city;
To produce guidelines and recommendations for information service providers.
Cartledge, J. (1996). What sort of information do passengers really require? Public Transport Information, Feb/March, 9-13.
DETR (1998). A New Deal for Transport: Better for Everyone. Transport White Paper, TSO, London.
DfT (2003). MR04: Phase 2 report: public consultation. Transport Direct Market Research Programme.
Kenyon, S. and Lyons, G. (2003). The Value of Integrated Multimodal Information and its Potential Contribution to Modal Change. Transportation Research Part F - Traffic Psychology and Behaviour, 6(1), 1-21.
Lyons, G. (2001). From Advanced Towards Effective Traveller Information Systems. In: Hensher, D. Ed. Travel Behaviour Research The Leading Edge, Chapter 47, 813-826, International Association for Travel Behaviour Research, Pergamon.
Social Research Associates (1999). Rural Public Transport Issues - Survey of Residents. Social Research Associates, November.
TNS Harris (2000). Potential Demand for National Integrated Transport Information and Local Transport Information Line Trip Generation. Summary Report, TNS Harris, March, Richmond.
Werner Brog (2003). Reducing Car Use? Just do it.. In Thomas, S. (Ed.) Sharing in Success - Good News in Passenger Transport, Chapter 8, 69-80, Cambridge, CICC, March.


text:
Home Base Stocks Base Oil Report: Trends
Base Oil Report: Trends
Ray Masson - February 1, 2021
© Unclesam
2021 Will Be Marked by Pandemic and Politics
Almost everyone was glad to see the back of 2020, and in anticipation of improving public health conditions and a better trading environment, the base oil industry was one of the many sectors that welcomed the New Year. Hopes are pinned on COVID-19 vaccines, which should allow more freedom of movement and interaction of players around the globe.
In addition to crude and feedstock prices hardening over the past few months, other factors have influenced pricing on all types of base oil.
The pandemic's effect on oil refiners has extended into 2021. Production of distillate transportation fuels has continued to be severely affected by lockdowns, and cutbacks to refinery runs have led to a shortage of vacuum gas oil feedstock.
In turn, this curbs the production of some grades of base oil. API Group I output is being affected to the greatest extent, further shortening an already tight market. With Group I grades moving short, pressure has mounted, and prices have risen dramatically over the past few months.
Group II markets are faring a little better. Most Group II production has dedicated feedstock reserves, which have helped to maintain required production levels for these grades. However, as with Group I grades, demand has receded, destroying any hopes of expansion in sales in almost all regions around the world.
Sources estimate that, through 2020 and during the first part of 2021, Group II demand fell by around 17% year-on-year globally, with East Asian regions being affected the least. Markets in Europe, the United States, the Middle East and South America have all felt the pinch when it comes to decreased usage of Group II products.
Group III markets have been hit in a very different way from the other base oil groups, with a shortage in prime markets caused by lack of availability. Some postulate that this is due to increasing demand for Group III base stocks. Others have pinned the cause of the tight market on refiners that have experienced production problems or have undergone major turnarounds amid forecasts of dwindling demand due to the coronavirus pandemic. Demand predictions and assumptions appear to have been miscalculated, causing a worldwide tightening of the Group III market.
Prices of course have risen accordingly, while suppliers and distributors in some regions have initiated allocation procedures to some customers.
Other than COVID-19, there have been some major events early this year that could have both direct and indirect effects on base oil markets.
U.S. politics appear to have changed along with the presidential administration, and the final stages of Brexit have been completed in Europe. Having officially left the European Union on Jan. 31, 2020, the United Kingdom had 11 months of transition to complete a trade agreement before finally parting from EU terms and treaties on Dec. 31.
This transition time was used in its entirety to reach a trade agreement between the parting governments. From a base oil and finished lubricant perspective, the deal basically extends the tariff-free system for imports and exports of both controlled and uncontrolled goods between the two markets.
Whilst the effects of the split are being felt predominantly by the U.K., there are a few changes to base oil trade in the region. For example, from Jan. 1, the existing EU Registration, Evaluation, Authorisation and Restriction of Chemicals regulations were adopted into U.K. legislation. EU REACH continues in Northern Ireland, where this part of the U.K., along with the Republic of Ireland (which is still part of the EU), will be considered as a single market—at least for the next four years.
A new chemical registration framework will come into place in the U.K. in April. Starting from Oct. 28, companies operating within the country, such as producers, blenders and traders, will have either two, four or six years to complete registration, depending on production volumes.
Base oils, additives and other associated chemicals all come under UK REACH authorizations, so the regulation will have implications for all involved in the lubricants industry.
Additionally, there are changes to immigration into the U.K., border controls have been amended, and the adopted trade and tariffs may be altered from existing models.
All of these aspects of Brexit are affecting base oil trade between the EU and the U.K., as well as the export of finished lubricants from U.K. manufacturers to customers based in EU countries.
Another aspect of the base oil trade that continues to suffer from the pandemic is the many new installations that were to be brought onstream last year, but which were mothballed until markets returned to some form of normality after the end of the pandemic.
This new production will continue to be held back, including plants in China (Hainan Handi's 800,000 metric tons-per-year Group II and III expansion and Hebei Feitian Petrochemical's 250,000 t/y expansion in Xinji), Russia (50,000 t/y of Group II and 240,000 t/y of Group III from Lukoil in Volgograd, along with 250,000 t/y of Group II from Rosneft in Novo-Kuibyhshev), and elsewhere. They will remain on the sidelines until market conditions improve.
Despite some temporary tightness, the market does not need further production of Group II or Group III base oils at this time. Present capacity outstrips demand by some 15%-18%, and catch-up may take some time—if demand ever reaches pre-pandemic levels again.
All is not gloom and doom, however. There are pockets of growth starting to emerge from the dark days of the pandemic. Working practices and trade may never be the same as in pre-COVID days, but a new normal will encompass the contributions of base oil production and supply, performing a crucial role on the global stage.
Ray Masson is director of Pumacrown Ltd., a trader and broker of petroleum products in London, U.K. Send him comments or topic suggestions at pumacrown@email.com.
Base Stocks Conventional Base Stocks Market Topics


"Around Bruges in 80 Beers" written by C. Pollard and S. McGinn - 3rd Ed.
Discover all the best places in Bruges to go when planning your trip to Belgium and one of the most beautiful places in Europe.
Let this book be your guide !
Want to know where to find the best beers in Bruges ?
Wether you are looking for grand surroundings or a locals' bar, trying to find elegant dining or a quick bite, or simply trying to take some home to impress your friends, "Around Bruges in 80 Beers" will guide you to the best places to enjoy the finest beers on Earth !


text:
Q: UICollectionView didSelectItem Not Called I use a simple Navigation Controller with a Collection View added on top of it using IB.  I declare the delegate and datasource for the collection view in the header, and wire it up in IB for the same.  The collection view populates with all the images, and will scroll, but didSelectItemAtIndexPath is never called.  What is going on?
Sorry, forgot to copy in the code before I clicked Submit.
- (void)viewDidLoad
{

    [super viewDidLoad];

     arryData = [[NSArray alloc] initWithObjects:@"AIMCON", @"Devotional Songs", @"Saved Devotionals", @"Resources", @"Podcasts", @"Website", @"Links",  nil];
    CALayer * l = [viewofimage layer];
    [l setMasksToBounds:YES];
    [l setCornerRadius:11];
    [l setBorderWidth:2.0];
    [l setBorderColor:[[UIColor blackColor] CGColor]];
    tableView.contentInset = UIEdgeInsetsMake(0, 0, 0, 0);

    if (UI_USER_INTERFACE_IDIOM() == UIUserInterfaceIdiomPad) {
        [self.collectionView registerClass:[CVCell class] forCellWithReuseIdentifier:@"cvCell"];

        self.collectionView.backgroundColor = [UIColor blackColor];
    }
    else {
        [self.collectionView registerClass:[CVCell class] forCellWithReuseIdentifier:@"cvCell"];

        self.collectionView.backgroundColor = [UIColor blackColor];

    }

    // Do any additional setup after loading the view.
}

-(NSInteger)numberOfSectionsInCollectionView:(UICollectionView *)collectionView {
    return 1;
    NSLog(@"1");
}
-(NSInteger)collectionView:(UICollectionView *)collectionView numberOfItemsInSection:(NSInteger)section {
    return [arryData count];
    NSLog(@"2");
}

-(void)collectionView:(UICollectionView *)collectionView didSelectItemAtIndexPath:(NSIndexPath *)indexPath {
    NSLog(@"Selected");
    if (indexPath.row == 1) {
        DevoSongs *dvController = [[DevoSongs alloc] initWithNibName:@"DevoSongs" bundle:[NSBundle mainBundle]];

        [self.navigationController pushViewController:dvController animated:YES];
        [dvController release];
    }
    if (indexPath.row == 2) {
        ListOfDevos *dvController7 = [[ListOfDevos alloc] initWithNibName:@"ListOfDevos" bundle:[NSBundle mainBundle]];

        [self.navigationController pushViewController:dvController7 animated:YES];
        [dvController7 release];

    }
    if (indexPath.row == 3) {
        AIMProject *dvController7 = [[AIMProject alloc] initWithNibName:@"AIMProject" bundle:[NSBundle mainBundle]];

        [self.navigationController pushViewController:dvController7 animated:YES];
        [dvController7 release];

    }
    if (indexPath.row == 4) {
        Podcasts *dvController2 = [[Podcasts alloc] initWithNibName:@"Podcasts" bundle:[NSBundle mainBundle]];

        [self.navigationController pushViewController:dvController2 animated:YES];
        [dvController2 release];

    }
    if (indexPath.row == 5) {
        FamilyMinistry *dvController5 = [[FamilyMinistry alloc] initWithNibName:@"FamilyMinistry" bundle:[NSBundle mainBundle]];

        [self.navigationController pushViewController:dvController5 animated:YES];
        [dvController5 release];

    }
    if (indexPath.row == 6) {
        LinksViewController *dvController6 = [[LinksViewController alloc] initWithNibName:@"LinksViewController" bundle:[NSBundle mainBundle]];

        [self.navigationController pushViewController:dvController6 animated:YES];
        [dvController6 release];
    }

    if (indexPath.row == 0) {
        NSLog(@"TABLE");
        SECTable *dvController7 = [[SECTable alloc] initWithNibName:@"SECTable" bundle:[NSBundle mainBundle]];
        [self.navigationController pushViewController:dvController7 animated:YES];
        [dvController7 release];
    }

}
-(UICollectionViewCell *)collectionView:(UICollectionView *)collectionView cellForItemAtIndexPath:(NSIndexPath *)indexPath {

    static NSString *cellIdentifier = @"cvCell";

    CVCell *cell = (CVCell *)[collectionView dequeueReusableCellWithReuseIdentifier:cellIdentifier forIndexPath:indexPath];

    NSString *thearticleImage = [[arryData objectAtIndex:indexPath.row] stringByAppendingString:@".png"];
    [cell.theimage setImage:[UIImage imageNamed:thearticleImage]];
    //titleLabel2.text = entry.articleTitle;

    CALayer * l = [cell.theimage layer];
    [l setMasksToBounds:YES];
     [l setCornerRadius:11];
    [l setBorderWidth:2.0];
    [l setBorderColor:[[UIColor whiteColor] CGColor]];
    [cell.labels setText:[arryData objectAtIndex:indexPath.row]];

    return cell;
}

A: As discussed in the comments, the issue happened due to not setting delegate for the collectionView. Adding the line
self.collectionView.delegate = self

resolves the issue


text:
On 19th October 1469, John Fisher was born in Beverley, the eldest son of Robert and Agnes Fisher. Chancellor of Cambridge University, Bishop of Rochester, and chaplain to royalty. He was executed for treason 22.6.1535 on Tower Hill, for speaking out against Henry VIII's divorce, and refusing to acknowledge the heirs of Henry and Ann Boleyn as legitimate successors to the throne. A Catholic martyr, he was canonized as St John Fisher.
On 19th October 1781, Rev George Lambert reported in his diary on a very high tide which inundated many houses in Hull.
On 19th October 1826, a Huggate parish jury of 12 men, 2 affearers (assessors of fines) and the pinder, set penalties for anyone allowing cattle into public lanes at night at 2s6d per head, for the first offence, and 5s per head for every offence afterwards; for allowing pigs in the streets without a ring between May Day and Michaelmas 3d per head; for allowing geese in the streets between Old Mayday and Old Lammas, 1s; for allowing anyone to stay who does not have a certificate allowing them to settle, £1 19s 11d.
On 19th October 1890, John Connell, boatman, of Waxholme, in the Coastguard Service, took part in the rescue of crew from the Grimsby vessel Genesta when it ran aground. All were rescued, except the captain, who died of exposure. Connell went on to the vessel as it was breaking up to rescue a man too weak to help himself. Connell was awarded the Humane Society Silver Medal. The unmanned vessel broke free the following day and travelled to Withernsea.
On 19th October 1964, rail passengers took their last trips on the Hull to Withernsea and Hull to Hornsea rail lines, which closed as a result of the Beeching Report. Goods services to Withernsea continued to 30.4.1965, to Hedon 3.5.1965, and to Marfleet to 1972. photo shows part of Hornsea Rail Trail today.
Posted on April 24, 2018 May 15, 2018 Author heyhistorycalendarCategories death at sea, major gentry families, religion, revolt/conflict, This weekTags Beverley, crime, health, Hessle, Hornsea, Huggate, Lambert, Marfleet, Pease, religion, royalty, saint, shipping, St John Fisher, Swanland, tranport, Waxholme, weather, Withernsea


Up to this point, I've done most of my recording work on a single victim (myself) and have done it via a simple audio I/F into DAW software into my computer (which, because it is seriously overbuilt, seems to have enough oommph to handle the job). I want to start recording my band. To do this in a timely/more simple fashion, I think I should have a console to allow me to more easily set levels and do some minor pre-processing of audio signals before they get into the DAW and to provide headphone mixes. So my first question is whether I need a console or not. Can I just tweak levels and look at the levels from the computer console program that came with my audio I/F? Would a console actually add anything worthwhile to the recording experience?
Finally, would the addition of a console really add anything to my mixing experience? Right now I'm a bit leery about this because I'm relatively addicted to automation and tweaking channel and plugin envelopes and given that I don't the cash to buy a fully automated board, I'd hate to give that up.
really a multi channel interface may serve you better, for less money, and yield better sounding results. it seems most people prefer dedicated recording hardware, and if they need a board, it's usually for live purposes. anything that will have that many features times that many channels, with good sonics, will probably be very expensive.
if you tell us more info, we may be able to offer some good suggestions.
what's the line-up of the band? what kind of mics do you have already? how many channels do you need? portable? do you plan to do live sound with the same equipment? will you record the live stuff? do you have bucketloads of cash burdening you down?
The lineup for the band is two guitars, bass, and drums. Plus vocals, but I figured I'd track those separately anyway. The main taker of microphones is the drum kit and I figure I'd need at least 4 (kick, snare, OHx2) or 8 (kick, snare, hat, OHx2, tom x 3) channels there. I'd be working with at least 8 tracks (4 channels of drums as above, 3 channels for bass & guitars, and tracking vocals) and, perhaps, as many as 16 (8 channels for drums as above, 6 channels for guitars and bass - DI for reamping + amp for each, and the ubiquitous tracking vocals).
I'm not really interested in recording at a live performance (or recording live in general). To be honest, I'd be OK with just tracking drums and doing overdubs for everything else, but I figured it would help the drummer keep track of the song better and the recordings would have a better vibe with everyone playing together. And, I figure as long as people are playing, I might as well record it.
As for using the mixer live, I'm not really interested in doing that. I figure that my crappy little Mackie does a good enough job for the places that don't have their own PA system and I don't think I'd like to scuff up the new board that soon.
As for microphones, right now I have a selection of low-to-medium end mikes - 3 Shure SM57s, 2 SM58s, 2 Rode NT-1a's, 1 AKG D-112, 2 Sennheiser MD-421s, 2 EV PL37s, 2 Cascade FatHeads, a few old mikes (2 Shure SM61s and a pair of old Shure small-diaphram condenser mikes whose model numbers I forget at the moment), and a random assortment of "science project" mikes. Some of these are being used when we play live, some I've bought for recording, and some I've just scavenged from eBay because it seemed like they were going for a cheap price. At this point, I figure I have enough microphones to do a decent (for smaller values of decent) job of capturing the sound.
I'd also need to beef up my audio I/F to do this. Right now I've got 8 channels of inputs (four mike, four line-level but I also have three preamp/channel strips to feed those), but I'm going to have to buy more I/O channels if I want to record the band.
So that's about it. I have a couple of friends who I might want to record (vocals and acoustic guitar), too, but I figure if I have the facilities to record the band, I'd have the facilities to record them. No, I don't have a bucket of cash. What I do have I've collected over the years and, frankly, I'd probably buy whatever gear I do get used.
I hope that this has answered your questions. In any case, it's the middle one of mine that's always intrigued me - what's the difference between a live console and a studio console of the same price range/quality?
cool! sounds like a fun lineup.
if 16 inputs can get you by, and it's usually enough for a small band... then consider one of those 8 channel expander boxes that goes into the adat port, if you have one.i have used several of these boxes. what interface are you using? maybe there are some other digital ports you can utilize?
i really like getting the drums, bass and rhythm guitars, at least, together in the same room. for solos and vocals i find overdubs easier.
and add another couple channels for a pair of room mics. people talk about "glue" compressors and stuff, but a good pair of room mics really pulls the mix together in a way nothing else can.
the difference between the mixers is blurring more and more lately. back in the day mixers were usually configured differently, either inline, or split, dealing with pre's during tracking, but switching to line inputs which can double up for mixdown. typically recording mixers should be quieter than a live one, and, in more recent days, some have the firewire interface built in.
some firewire mixers may be faster to set up 16 channels etc. than a compact rack setup. i see what you mean.... those onyx have acceptable sound, and are super easy plug-and-play type devices, esp. on the mac.
now a little 2 RU setup, and i'm good to go.
You don't NEED a console, but in my experience, for tracking a band, it is much preferable. I mix ITB, but for tracking, I'd never do it without a console. Of course I HAVE a console, so that's easy for me to say.
As for the workflow, no one can really tell you. You have to try it for yourself. Maybe go to a studio that has one and book a tracking session there to see how it benefits the workflow. That will give you a good idea.
As for specs and features....well, specs are generally better on studio consoles, BUT the feature set is radically different. And for good reason. The most notable difference is monitoring. In-line vs. Split design, etc..
Personally, although a lot of people do it, there's no way I would use a live console for studio use. Just too limiting. Id rather work with just outboard gear. Good luck with your journey.
Are you aware of the difference between a control surface and a mixer (or console)?