Unfortunately, not all of you know me, as our site has continued to grow increasingly and my job was taking over most of my life. For those who don't know me- I joined Microbeta back in 2004 and joined x64bit when it was born. I've done lots of graphic design, helped make the portal and skin on microbeta and designed the logos/news logos on x64bit.
I am going on a mission for my church for 2 years. I leave on November 1st, so this is the last day I have here in California. I'm going to Little Rock, Arkansas and the states surrounding it. I'm very excited for this mission but I will miss x64bit, as it is the only forum i regularly visit. I cannot use computers on the mission except for exclusively writing email to family. I'm sure it's going to be really interesting when I come back..just imagining the pages of "view new posts" will be, lol. Probably will get a couple thousand emails in my email accounts too. And Vista will be long RTM'd (or will it? ). I'm pretty sure that when I come back, I'll be hopelessly outdated on technology, so the first place I will visit when I come back will be x64bit.net.
Thanks for all of you for your friendship and helpfulness. Thank you Dave for giving me many opportunities on this site.
Take care mate hope to see you around again!
Good luck on your missions trip!! Look forward to getting to know you when you come back.
What is your mission, or is it a secret mission?
Ok, to all please keep this serious, because it very much is! a small joke is ok, but leave it there please.
I wish you all the best, and take care. Good luck, hold on and don't forget! There is only one power for which everyone, even demons, are afraid of!
All the best and we'll see you back with stories as soon as possible I hope.
By man, I did not talk to you much but i wish you the best of luck.
I believe he meant he was starting a mission or Church, at least thats what I understand as its religious.
God bless you and good luck on your mission, hope you come back as soon as possible !


text:
Why has Line of Duty disappeared from Netflix?
The crime drama is no longer available on the streaming site
By Grace Henry
Monday, 6th April 2020 at 1:17 pm
Fans went into meltdown this weekend after realising that British crime drama Line of Duty had been taken off Netflix.
Up until this weekend, series one to four were available to watch on the streaming site, so why are they no longer available?
It seems the BBC drama has been removed due to Netflix terminating all licensing arrangements it has in place with Kew Media Distribution, which is responsible for Line of Duty.
Line of Duty, BBC Pictures
It's believed Netflix has the rights to stream a number of Kew Media Distribution titles in various territories and made administrator FTI Consulting a financial offer to maintain these licences.
Netflix insisted that the majority of its payment to FTI be passed on to the producers of the titles in question, which, in the case of Line Of Duty, would have been ITV Studios-owned World Productions.
FTI is understood to have rejected these terms, leading to a break down in talks.
The police drama is a BBC original and was one of the British broadcaster's biggest shows of 2019. Following its removal from Netflix, many viewers took to Twitter to express their disappointment.
You have got to be kidding me, line of duty has just been taking down off of Netflix I only got onto season 2 episode 3 ???? bugger.
— Ryan Ward (@RyanWard09) April 4, 2020
Some had been in the middle of watching the tense crime drama, including voice actor Andrea Libman.
Noooooooooooo I was in the middle of Line of Duty on @Netflix and now I'll never knowwwwww!!!!
— ???????????????????????? ???????????????????????? (@AndreaLibman) April 6, 2020
According to Deadline, Netflix is encouraging affected producers to get in touch directly to discuss the Kew Media Distribution issue. It has even set up a dedicated email address to handle inquiries, which is: kmd_claims@netflix.com.
Get Netflix and on demand news and recommendations direct to your inbox
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Sign up to get alerts on Netflix and on demand services and receive TV and entertainment email newsletters from our award-winning editorial team. You can unsubscribe at any time. For more information about how we hold your personal data, please see our privacy policy.
Although fans won't be able to watch the series on Netflix, all five series are available to watch on BBC iPlayer.
Kew Media Distribution was part of Kew Media Group, which went bust in February after months of financial mismanagement.
A spokesman said: "The joint administrators, alongside their legal counsel, are in the process of considering all the distribution and licensing arrangements in place including between various producers and Kew.
"Any claims to termination or otherwise will be dealt with in accordance with the terms of the relevant distribution agreements. We appreciate the continued patience of producers and customers, as we continue to work through a complex situation with over 1,000 titles."
If you're looking for alternatives of what to watch on Netflix, check out our best Netflix TV series round-up.
John Krasinski's Some Good News brings together original Hamilton cast
ITV extends #ITVKidsCreate Project owing to "huge" response
All about Line of Duty
Jaws actress Lee Fierro dies of coronavirus complications age 91
The first episode of Game of Thrones' making-of series is available online now

redpajama_set_name: RedPajamaCommonCrawl


These days, my day job (Santa Fe Metal Design–santafemetal.com) keeps me very busy. Lately, customers are requesting zinc counter tops. Zinc is the hot metal right now. For those that do not know, I do lots of work with copper, as in copper lighting, copper counter tops, copper range hoods, and all kinds of copper architectural work.
Zinc is another food-safe surface. It is a beautiful, silvery metal that with time, creates natural and darkening highlights and low lights. It is highly reactive (like copper) to acidic elements like lemons and tomato juice. That's not a bad thing, in my opinion. It adds to its character! It's a soft metal, too, so little scratches will only add to its luster.
Here is my Facebook page. It has images of various work that I have done (just the tip of the proverbial iceberg). "Like" my page if it motivates you.


text:
Q: incompatible types : java.lang.Object cannot be converted to T Here is my code:
package datastructures;

import java.util.Iterator;

public class Stack<T>{
    private class Node<T>{
        T data;
        Node next;
    }
    private int size;
    private Node head;
    private Node newNode(T data){
        Node new_node = new Node();
        new_node.data = data;
        new_node.next = null;
        return new_node;
    }
    public Stack(){
        size = 0;
        head = null;
    }

    public T pop() {
        if(head == null)
            return null;
        T ret_val = head.data; //Error here
        head = head.next;
        this.size--;
        return ret_val;
    }
}

I get the error in the pop method, here is the error:
Error: incompatible types : java.lang.Object cannot be converted to T

I don't understand this error, I'm not using Object anywhere in my code.

A: When you declare Node as
private class Node<T>

you're declaring a generic type with another T as the T in the enclosing type. You're hiding T. So, in short, it's as if you were declaring it as
private class Node<E>

Just use
private class Node

A: Node's data field doesn't know it's type.
Try giving the type when you initialize head.
private Node<T> head;
private Node<T> newNode(T data){
    Node new_node = new Node<T>();
    new_node.data = data;
    new_node.next = null;
    return new_node;
}

Now, new_node knows that the data field is of type T.

A: You are declaring Node inner type with its own type parameter, so T of Node shadows T of Stack, and they are essentially different types.
Remove declaration of type parameter <T> in your Node type (that's probably what you mean for it)

A: (For Java 8) You can use a typed variable with the diamond operator:
private Node newNode(T data) {
    Node<T> new_node = new Node<>();
    new_node.data = data;
    new_node.next = null;
    return new_node;
}

Otherwise the generic is initalized as Object and therefore the error is shown.

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}

In the last 20 years, inverse probability weighted estimators (IPW) have attracted considerable attention in the statistical community.
Among other things, they are used for estimating a population mean $E(Y)$  from an incomplete data
set under the missing at random (MAR) assumption. MAR establishes
that the variable of interest $Y$ and the response indicator $A$ are conditionally independent given an always observed vector $\mathbf X$ of covariates. See References \cite{robinsetal94,robinsetal95,littlean04,kangshafer07}.
In the causal framework IPW estimators are used
for estimating the average effect of a binary treatment on a scalar
outcome under the assumption of no unmeasured confounders  in observational studies. See References \cite{rosenbaum98,hiranoetal03,lunceforddavidian04,crumpetal09,yaoetal10,hill13}.

In the missing data setting, the propensity score is defined as the response probability given the vector of covariates $\mathbf X$ whereas,
 in the causal context, the propensity score is the conditional probability of treatment assignment given a set of measured baseline covariates. IPW estimators are essentially weighted means of observed responses in which the weights are determined as the inverse of the estimated propensity scores. The aim of these weights is to compensate for the missing responses.

One of the conditions required to derive the asymptotic properties of  IPW estimators is the strict positivity condition (also known as the positiviy assumption).
It states, in the missing data setting, that the propensity score is bounded away from zero and,
in the causal context, that the propensity score is bounded away from zero and one (see References \cite{robinsetal95,kangshafer07,lunceforddavidian04,crumpetal09,hill13}).
 Besides theoretical issues, the violation of the strict positivity condition causes the estimates to be very unstable and to have a large variability.
See also References \cite{littlerubin87,colehernan08,leeetal11}.

Most users of IPW procedures assume models for the propensity score that are usually incompatible with
the positivity condition. For instance, this is the case of the generalized linear models (GLM), when some of the covariates are continuous.
Despite this incompatibility, GLM are the most popular models considered in the field.

In this work we present a slight modification to the GLM originally postulated for the propensity score that is compatible with the strict positivity condition. To explore plausible applications of the new model, we  revise a real example in the causal context. We consider the data collected by  Tager et. al \cite{tageretal79, tageretal83}, who
investigate the effects of cigarette smoking on children's pulmonary function.
They study the effect of parental smoking on the pulmonary function of their children as well as the effect of direct smoking by the children themselves. Besides, in his book,
Rosner \cite{rosner99} performs another analysis of the mentioned data. The author considers the forced expiratory volume (FEV) of a group of children as a response variable as well as their height, age, sex and a binary variable indicating whether or not they smoke. Kahn \cite{kahn05} studies this data using linear regression. In this work we study the effect of smoking in the forced expiratory volume of children by estimating the average treatment effect. This is an example of an observational study in which, under the assumption of no unmeasured confounders, the average treatment effect can be estimated using IPW procedures. The validity of no unmeasured confounders is beyond the scope of this work, and thus, the estimates here presented for the ATE are included only to illustrate the use of the new model for the propensity score. Since the proportion of smokers among the younger children is very small, the corresponding propensity scores take on values near zero and therefore the classical IPW estimators may have a large variance. In situations such as this, we expect that the models we propose in this work will provide more stable estimators than the classical IPW procedures.

The paper is  structured as follows: in
Section 2 we review methods based on propensity scores for estimating a population mean from incomplete data and we present our model for the propensity score in that missing data context. In Section 3 we review methods for estimating average treatment effects that use weighting
by the inverse of the probability of treatment and present our model in the causality context. In Section 4 we describe the computational method we used to estimate the parameters indexing the new model. In Section 5 we study the performance of our model through its impact in IPW estimators by means of a Monte Carlo study. As an illustration, in Section 6, we apply these methods to estimate the effect of smoking in children.

\section{Missing Data }\label{sec:missing}
Assume that we are interested in estimating the mean value of a scalar response $Y$, based on a sample
composed by an always observed vector $\textbf{X} \in  \mathbb{R}^p $ of covariates while the response of interest  is missing by happenstance for some subjects.
We will assume that data are missing at random (MAR) \cite{rubin76}. This means that the  missing mechanism is not related to the response of interest and  it is only
related to $\textbf{X}$, the observed vector of covariates. Let $A$ be a binary variable indicating whether $Y$ is observed or not,
namely, $A=1$ if $Y$ is observed and $A=0$ if $Y$ is missing.
MAR establishes that
\begin{equation}
 \label{MAR}
 P(A=1\mid  \boldsymbol X,Y)=P(A=1\mid \boldsymbol X)=\pi(X).
\end{equation}
$\pi(\boldsymbol X)$ is known in the literature as the propensity score \cite{rosenbaumrubin83}.
Up to integrability conditions, under the MAR assumption, $\mu=E(Y)$  can be represented in terms of the distribution of the
observed data as $E(Y)=E\left\{AY/\pi(\textbf{X})\right\}$. This representation invites us to estimate $\mu$ by
\begin{equation}
 \label{ipw_missing}
 \hat\mu(\hat \pi) =\mathbb P_n\left\{\frac{AY}{\hat \pi(\textbf{X})}\right\},
\end{equation}
where $\hat\pi(\textbf{X})$ is a consistent estimator of $\pi(\textbf{X})$, and $\mathbb P_n $ is the empirical mean operator, i.e.
$\mathbb P_n V=n^{-1}\sum_{i=1}^n V_i$ \cite{horvitzthompson52}.
Observed responses corresponding to  low values of the estimated propensity score are highly weighted since they should compensate for the high missing rate associated to such a level of covariates.  For more details see \cite{robinsrotnitzky92,robinsetal94}.
Different ways of estimating $\pi(\textbf{X})$ will lead to different estimators for $\mu$; parametric and nonparametric estimators of the propensity score have been considered.
Robins, Rotnitzky and Zhao \cite{robinsetal94} presented a class of  estimators for $\mu$, which containes $ \hat\mu(\hat \pi)$. These estimators are asymptotically normal when $\pi$ follows a parametric model.
 Little and An \cite{littlean04} proposed to estimate the propensity by fitting a
spline to the logistic regression of the missing-data indicator $A$ on $\boldsymbol X$.
Kang and Shafer \cite{kangshafer07} presented $\hat\mu(  \hat \pi)$,  where $\pi$ follows a linear logistic regression model. More generally, parametric models are often assumed for the propensity score and, in particular,  generalized linear models (GLM) play a prominent role  among them.  These models postulate that
\begin{equation}
  \label{para}
  \pi(\textbf{X})=\phi(\boldsymbol{\beta}_0^T \textbf{X}),
 \end{equation}
where  $\phi$ is a strictly increasing cumulative distribution function and $\boldsymbol{\beta}_0 \in  \mathbb{R}^p$. Taking $\phi(u) = 1/\{1 +exp(-u)\} $
results in the linear logistic  model, which is one of the most popular choices in the literature.

Most of the asymptotic results established for
 $\hat \mu(\hat{\pi})$, defined in (\ref{ipw_missing}),
 require the strict positivity condition, which establishes that $P\left\{\pi(\textbf{X})\geq \varepsilon\right\}=1$, for some $\varepsilon >0$ (see References \cite{robinsetal95,kangshafer07}).
  In practice, when this condition does not hold, values of $\hat \pi (\boldsymbol X_i)$ close to zero may arise causing $\hat \mu(\hat \pi)$ to be a very unstable estimator, having  a large variability ( see also Reference \cite{littlerubin87}).

Unfortunately,  generalized linear models
prevent the validity of the strict positivity condition, except when $\boldsymbol{\beta}_0^T \textbf{X}$ is bounded from below.
Thus, strict positivity  is typically violated as far as continuous  covariates are included in the vector $\boldsymbol X$.

In this work, we attempt to conciliate between the strict positivity condition and the most popular parametric models used for the propensity score.
To do so, we slightly perturb the original parametric model postulated by the practitioner by incorporating an explicit lower bound for the propensity score. Namely, model (\ref{para})  is replaced by
$$\pi(\textbf{X})=(1-\varepsilon_0)\phi(\boldsymbol{\beta}_0^T \textbf{X})+\varepsilon_0,$$ where $\varepsilon_0 \in (0,1)$  and $\boldsymbol{\beta}_0 \in  \mathbb{R}^p$. This model contemplates the validity of the strict positivity condition and thus we  call it \textit{strictly positive propensity score} (SPPS) model for the missing mechanism.
Let $\boldsymbol\theta=(\varepsilon, \boldsymbol{\beta}^T)^T$, with $\varepsilon \in (0,1)$  and $\boldsymbol{\beta} \in  \mathbb{R}^p$. Define
\begin{equation}
 \label{pitheta}
 \pi(\mathbf{X}, \boldsymbol\theta)=(1-\varepsilon)\phi(\boldsymbol{\beta}^T \mathbf{X})+\varepsilon.
\end{equation}
Assume that for some $\boldsymbol\theta_0 \in  \mathbb{R}^{p+1}$, $\pi(\textbf{X})=\pi(\textbf{X},\boldsymbol\theta_0)$.
We propose to estimate the mean of $Y$ by
\begin{equation}
 \label{ipw_missinga}
\hat\mu_{P}=\mathbb P_n\left\{\frac{AY}{ \pi(\textbf{X},\hat{ \boldsymbol{\theta}}_n)}\right\},
\end{equation}
where $\hat{\boldsymbol{\theta}}_n$ is
the maximum likelihood estimator of  $\boldsymbol{\theta}_0$ under model (\ref{pitheta}).

In order to identify the parameter $\boldsymbol\theta_0$  from the distribution of  $(\textbf{X}, A)$, the following assumptions are required:
\label{supuestosmissing}
\begin{enumerate}
\item[\textbf{A.1}]  The function $\phi$ is injective.
\item[ \textbf{A.2}]  The distribution of the observed covariates is not concentrated at a hyperplane.
\item[\textbf{A.3}]  For all $\boldsymbol{\beta} \in \mathbb{R}^{p}$ such that $ A\,|\,\mathbf X \sim A\,|\, \boldsymbol{\beta}^T\mathbf X$, the support of $\boldsymbol{\beta}^T\mathbf X$ is unbounded from below:
\begin{equation*}
\inf \left\{z \in \mathbb R: P(\vert\boldsymbol{\beta}^T\mathbf X -z\vert <r)>0, \hbox{for all $r>0$}\right\}=-\infty.
\end{equation*}
\end{enumerate}

While the first two assumptions are the classic requirements to obtain the identifiability of the parameter
given in a GLM model as (\ref{para}), the last assumption guarantees the identifiability of the parameters involved in the model introduced in this work.\\
Assume that  \textbf{A.1}, \textbf{A.2} and \textbf{A.3} hold. Under
regularity conditions, it can be proved that
$\hat{\boldsymbol\theta}_n$  is a consistent estimator of $\boldsymbol \theta_0$,
and therefore $\hat \mu_P\,$ is a
consistent estimator of $\mu$.

\section{Average Treatment effect}\label{sec:ate}
Causal Inference is a second field where inverse probability weighted procedures play a crucial role.
Consider, for instance,  a dichotomous treatment
variable $T$, where $T=1$ represents an active treatment  and $T=0$
means that a  control is assigned. The potential outcomes framework,  introduced by Neyman \cite{neyman23} and Rubin \cite{rubin74}, is used to  quantify the effect of the treatment on some response of interest, whenever this difference is different from zero.
To do so, two potential outcomes (or counterfactual variables)
$Y^{(0)}$ and $Y^{(1)}$  are defined to  represent the   outcome variable of interest that would be seen if an individual   were to receive the treatment and the control, respectively.
 We are interested in estimating the average treatment effect (ATE),
defined as the difference between the mean values of the potential outcomes:
$\tau= E(Y^{(1)})-E(Y^{(0)})$.
$E(Y^{(1)})$ (respectively $E(Y^{(0)})$) represents the hypothetical mean of the response for the population of individuals where all of them  are assumed to  receive treatment (respectively control). So, the difference between these means may be considered a resultant of the treatment, meaning that it  has a  \textit{causal effect} on the response of interest, whenever this difference is different from zero.

The potential outcomes  $Y^{(1)}$ and  $Y^{(0)}$  constitute an artificial
contraption that allows us to conceptualize
what we mean by \textit{causality}.
Only one of these variables is observed in each individual and  it is  related to the observed response through the consistency assumption which
establishes (see Reference \cite{colefrangakis09})  that, if an individual follows the treatment ($T=1$), then the potential outcome $Y^{(1)}$ is precisely his observed outcome. It also establishes the same thing for the control case.
Therefore, under the consistency assumption,
the observed outcome $Y$ is related to the counterfactual variables through the  identity $Y=TY^{(1)}+(1-T)Y^{(0)}$.
Thus, estimating the average treatment  effect is a missing data problem, since one of the counterfactuals ($Y^{(1)}$ or  $Y^{(0)}$)  is missing for each  individual.
To identify $\tau$  we assume  that a vector $\boldsymbol X$ with \textit{all  possible counfounders}  is observed at each subject. This means  that   potential outcomes  $Y^{(1)}$ and $Y^{(0)}$ are conditionally independent of the
treatment exposure $T$ given $\boldsymbol X$:
\begin{equation}
\label{NUC}
 (Y^{(0)}, Y^{(1)}) \coprod T \vert \boldsymbol X.
\end{equation}
This assumption is known in the literature as strongly ignorable treatment
assignment or no unmeasured confounders (see Reference \cite{rosenbaumrubin83}).
Assuming (\ref{NUC}),   we have that
\begin{equation}
\label{identification}
E\left(Y^{(1)}\right)=E\left\{\frac{TY}{\pi(\boldsymbol X)}\right\} \;,\quad E\left(Y^{(0)}\right)=E\left\{\frac{(1-T)Y}{1-\pi(\boldsymbol X)}\right\},
\end{equation}
where now  the propensity score $\pi(\boldsymbol X)$ is defined by  $\pi(\boldsymbol X)=P(T=1\mid \boldsymbol X)$.
These representations of $E\left(Y^{(1)}\right)$ and $E\left(Y^{(0)}\right)$ immediately suggest the estimator
for the ATE  proposed by Rosenbaum \cite{rosenbaum98}, given by
\begin{equation}
 \label{est.gen}
 \hat \tau(\hat \pi)= \mathbb P_n \left\{\frac{TY}{\hat \pi(\textbf{X})}\right\} -\mathbb P_n \left\{\frac{ (1-T)Y}{1-\hat \pi(\textbf{X})}\right\},
\end{equation}
where  $\hat \pi(\textbf{X})$ is a consistent estimator of $\pi(\textbf{X})$.
Lunceford and Davidian \cite{lunceforddavidian04}, following the general framework of \cite{robinsetal94}
and  the  theory of M-estimation (see Reference \cite{stefanskiboos02}),  presented large-sample  theoretical properties of this estimator when $\pi$ follows a linear logistic model.
 Yao, Sun and Wang \cite{yaoetal10} postulate a generalized linear model for the propensity score, as in (\ref{para}),  and  estimate $\pi(\boldsymbol X)$  with
 $ \phi\left(\hat{\boldsymbol{\beta}}_n^T \boldsymbol X\right)$, where $\hat {\boldsymbol{\beta}}_n$ denotes the MLE under model (\ref{para}).
Non parametric versions of $(\ref{est.gen})$ are presented and analysed by Hirano, Imbens  and Ridder  \cite{hiranoetal03}, who propose splines in order to estimate $\pi(\textbf{X})$. Their estimator achieves the semiparametric efficiency bound
established by Hahn \cite{hahn98}.
Both $\hat \pi(\boldsymbol X)^{-1}$ and $\{1-\hat \pi(\boldsymbol X)\}^{-1}$ are now involved in the estimator presented in (\ref{est.gen}). Thus,
 in this scenario, the strict positivity assumption is restated in terms of a lower and  an upper bound for the propensity score, which is now assumed to be bounded away from zero and one,  in the sense that $P\left\{\varepsilon \leq \pi(\boldsymbol X)\leq 1-\delta\right\}=1$, for some $\varepsilon$ and $\delta$ greater than zero. This assumption, also  known as (existence of)  \textit{overlap} in the covariate distributions, is usually assumed to derive the asymptotic properties of $\hat \tau(\hat \pi)$, for the different estimators $\hat{\pi}$ of $\pi$ considered in the literature.  See References \cite{robinsetal95, hiranoetal03,lunceforddavidian04,crumpetal09}. Besides, the lack of overlap  leads to an erratic behavior of $\hat \tau(\hat\pi)$, making the  precise estimation  of  $\tau$ difficult.
 To deal with this issue,  some authors propose trimmed   versions   of  (\ref{est.gen}) (see References \cite{colehernan08,crumpetal09,leeetal11,hill13}).
As already mentioned in Section \ref{sec:missing}, generalized linear models for the propensity score are typically incompatible with the strict positivity assumption.
Thus, using the same ideas developed in the missing data setting,
for $\boldsymbol\theta=(\varepsilon, \delta, \boldsymbol{\beta}^T)^T \in \boldsymbol\theta \subseteq \mathbb{R}^{p+2}$,  where $\varepsilon, \delta \in (0,1)$ with $\varepsilon +\delta <1$ and $\boldsymbol{\beta} \in \mathbb{R}^p,$  we propose the following model
\begin{equation}
 \label{propate}
 \pi(\textbf{X}, \boldsymbol\theta)= (1-\delta-\varepsilon)\phi(\boldsymbol{\beta}^T \textbf{X})+\varepsilon
\end{equation}
and assume that
$\pi(\textbf{X})=\pi(\textbf{X},\boldsymbol\theta_0)$,  for some $\boldsymbol\theta_0\in \boldsymbol\theta.$  Model (\ref{propate})  intends to preserve as much as possible from the original family postulated for the propensity ($\phi(\boldsymbol{\beta}^T \textbf{X})$); the proposed modification contemplates the strict positivity condition by the inclusion of $\varepsilon$ and $\delta$.   Model (\ref{propate})  will be called
strictly positive propensity score (SPPS) for treatment assignment,  or simply SPPS model if the context is clear. So, the estimator for the ATE that we consider under model (\ref{propate}) for the propensity score  is given by
\begin{equation}
\label{nuestroate}
 \hat \tau_{P}= \mathbb P_n \left\{\frac{TY}{ \pi(\textbf{X}, \hat{\boldsymbol\theta}_n)}\right\}-\mathbb P_n \left\{ \frac{(1-T)Y}{1- \pi(\textbf{X},  \hat{\boldsymbol\theta}_n)}\right\},
\end{equation}
where $ \hat{\boldsymbol\theta}_n$ is the maximum likelihood estimator of  $\boldsymbol\theta_0$ under model (\ref{propate}).\\

Lunceford and Davidian \cite{lunceforddavidian04} propose a modified IPW estimator for $\tau$, in which the weights in $\hat\tau(\hat\pi)$ are redefined in such a way that they not do not take on extremely large values.
The authors  show that this modification gives rise to a more stable procedure. We apply the same modification to the weights computed assuming the SPPS model and obtain the following estimator for the ATE, which combines the proposal of Lunceford and Davidian \cite{lunceforddavidian04} and the one proposed in this work.\\
\begin{eqnarray}
\nonumber
 \hat \tau_{LDP}=\left ( \mathbb P_n \left[\dfrac{T}{{\pi}(\textbf{X},\hat{\boldsymbol\theta}_n)}\left\{ 1- \dfrac{C_1}{{\pi}(\textbf{X}, \hat{\boldsymbol\theta}_n)}\right\} \right]\right)^{-1}   \mathbb P_n \left[\dfrac{TY}{ \pi(\textbf{X}, \hat{\boldsymbol\theta}_n)}\left\{ 1- \dfrac{C_1}{{\pi}(\textbf{X},\hat{\boldsymbol\theta}_n)}\right\} \right]
\end{eqnarray}
\begin{eqnarray}
\label{estilucen}
    - \left ( \mathbb P_n \left[\dfrac{1-T}{1- {\pi}(\textbf{X},\hat{\boldsymbol\theta}_n)}\left\{ 1- \dfrac{C_0}{1-{\pi}(\textbf{X},\hat{\boldsymbol\theta}_n)}\right\}  \right]\right)^{-1}   \mathbb P_n \left[\dfrac{(1-T) Y}{1- \pi(\textbf{X},\hat{\boldsymbol\theta}_n)}\left\{ 1- \dfrac{C_0}{1-{\pi}(\textbf{X},\hat{\boldsymbol\theta}_n)}\right\} \right]
\end{eqnarray} \normalsize
where
$$ C_1= \dfrac{ \mathbb P_n \left\{ \dfrac{T-{\pi}(\textbf{X},\hat{\boldsymbol\theta}_n)}{{\pi}(\textbf{X},\hat{ \boldsymbol\theta}_n)} \right\}
}{ \mathbb P_n \left[ \left \{\dfrac{T-{\pi}(\textbf{X},\hat{\boldsymbol\theta}_n)}{{\pi}(\textbf{X},\hat{\boldsymbol\theta}_n)}\right\}^2 \right]},\,\,\,\; C_0= - \dfrac{ \mathbb P_n \left\{ \dfrac{T-{\pi}(\textbf{X},\hat{\boldsymbol\theta}_n)}{1-{\pi}(\textbf{X},\hat{ \boldsymbol\theta}_n)} \right\}}{ \mathbb P_n \left[ \left \{\dfrac{T-{\pi}(\textbf{X},\hat{\boldsymbol\theta}_n)}{1-{\pi}(\textbf{X},\hat{\boldsymbol\theta}_n)}\right\}^2 \right]},$$
\normalsize
and $ \hat{\boldsymbol\theta}_n$ is the maximum likelihood estimator of  $\boldsymbol\theta_0$ under model (\ref{propate}).

In this setting, in order to identify the parameter $\boldsymbol\theta_0$ and in addition to \textbf A.1, \textbf A.2 and \textbf A.3 we need the following assumption.
\begin{enumerate}
\item[\textbf A.4]  For all $\boldsymbol{\beta} \in \mathbb{R}^{p}$ such that $ A\,|\,X \sim A\,|\, \boldsymbol{\beta}^T \mathbf X,$ the support of $\boldsymbol{\beta}^T\mathbf X$ is unbounded from above:
\begin{equation*}
\sup \left\{z \in \mathbb R: P(\vert\boldsymbol{\beta}^T\mathbf X -z\vert <r)>0, \hbox{for all $r>0$}\right\}=+\infty.
\end{equation*}
\end{enumerate}
Under regularity conditions and assumptions \textbf{A.1}, \textbf{A.2},  \textbf{A.3} and \textbf{A.4} , it can be proved that $\boldsymbol\theta _0$ is identifiable and consistently estimated by   $\hat{\boldsymbol\theta}_n$, and so, $\hat \tau_P$ is a
consistent estimator of $\tau$.\\
\section{Computational Method for Fitting the SPPS Model}
In this section we describe the computational method used for the maximum likelihood estimation of the $p+1$ parameters in the SPPS model for the missing mechanism and the $p+2$ parameters in the SPPS model for treatment assignment.
We describe in detail the method we propose for estimation in the treatment assignment case, giving in between brackets the necessary modifications for the missing data setting.
Given $(\mathbf X_1,T_1)\dots (\mathbf X_n,T_n)$ a random sample from model (\ref{propate}) described in  Section \ref{sec:ate}, we estimate $\varepsilon,\delta$ and $\boldsymbol{\beta}$ iteratively with the following procedure: consider that $T_i|\mathbf X_i$ follows a Bernoulli distribution with $P(T_i=1 |\mathbf X_i)=\pi(\textbf{X}_i, \boldsymbol\theta_0)$  where $\pi$ is given by equation (\ref{propate}). Estimate iteratively $\boldsymbol\beta$ assuming $\varepsilon$ and $\delta$ are known, $\varepsilon$ assuming $\boldsymbol\beta$ and $\delta$ are known, and $\delta$ assuming $\boldsymbol\beta$ and $\varepsilon$ are known.\\
\begin{enumerate}
\item[Step 1] Substitute $0$ for $\varepsilon$ and $\delta$ in (\ref{propate}) and compute an initial estimate of $\boldsymbol{\beta}$ by maximum likelihood. Call this estimate $\hat{\boldsymbol{\beta}}_0$.
\item[Step 2] Compute the fitted values of the model considered in Step 1, $\hat{\pi_i}= \phi\left( \hat{\boldsymbol{\beta}}_0^T \mathbf X_i\right)$.
\item[Step 3] Compute initial estimates of $\varepsilon$ and $\delta$ by $\hat\varepsilon_1=min\left(\hat\pi_i\right)$ and $\hat\delta_1=1-max\left(\hat\pi_i\right)$. (In the missing data case set  $\hat\delta_1=0$).
\item[Step 4] Substitute $\hat\varepsilon_1$ for $\varepsilon$ and $\hat\delta_1$ for $\delta$ in (\ref{propate}). Then recompute an estimate of $\boldsymbol{\beta}$ by maximum likelihood. Call this estimate $\hat{\boldsymbol{\beta}_1}$.
\item[Step 5] Substitute $\hat{\boldsymbol\beta}_1$ for $\boldsymbol\beta$ and $\hat\delta_1$ for $\delta$ in (\ref{propate}) and estimate $\varepsilon$ by maximum likelihood. This estimate is the new $\hat{\varepsilon_1}$.
\item[Step 6] Substitute $\hat{\boldsymbol\beta}_1$ for $\boldsymbol\beta$ and $\hat\varepsilon_1$ for $\varepsilon$ in (\ref{propate}) and estimate $\delta$ by maximum likelihood. This estimate is the new $\hat{\delta_1}$. (Skip this step in the missing data case).
\item[Step 7] Repeat steps 4 to 6 until convergence (4 and 5 in the missing data case). Return $\hat{\boldsymbol\pi}=(\hat\pi_1,\dots,\hat\pi_n)$.
\end{enumerate}
This iterative method converges rapidly in the case of the missing data setting. In the causality setting it also converges rapidly if the real values of $\varepsilon$ and $\delta$ are such that $\varepsilon+\delta $ is not too large. If this is not the case, the modified link function in the GLM in Step 4 can be very flat, growing very slowly from $\hat\varepsilon_1$ to $1-\hat\delta_1$, which are very similar. When this happens the GLM method in R usually does not converge, except for very large samples. To solve this problem, in the causality setting, we change Step 3  by the following

\begin{enumerate}
\item[Step 3 '] Compute initial estimators of $\varepsilon$ and $\delta$ by $\hat\varepsilon_1=min\left(\hat\pi_i\right)$ and $\hat\delta_1=1-max\left(\hat\pi_i\right)$. If $\hat\varepsilon_1+\hat\delta_1>0.6$, return $\hat{\boldsymbol\pi}=(\hat\pi_1,\dots,\hat\pi_n)$, otherwise proceed to step 4.
\end{enumerate}
We remark that Step 3' implies that, if  after fitting a regular GLM we have that the range of the fitted values is not very large, namely smaller than $0.4$, then we keep the regular GLM; there is no need for the SPPS model. \\
The code that defines the necessary functions for computing the ATE and IPW estimators is available under request.

\section{Monte Carlo Study}
In this section we report the results of a Monte Carlo study we performed in order to assess the advantages of considering the proposed model over the traditional approach.
In practice, several covariates will be available for modeling the propensity score. In order to investigate performance in a realistic setting, Lunceford and Dadivian \cite{lunceforddavidian04}, carried out simulations involving continuous and discrete variables, some of them associated with both treatment exposure and potential response and others associated with potential responses but not treatment exposure. We generate variables as they did in one of their proposed scenarios. We consider covariates $\textbf{X}=(X_1,X_2,X_3,V_1,V_2,V_3),\,$ a binary variable $T\,$ and an outcome $Y\,$ such that the variable $\,T\,$ follows a Bernoulli distribution with
\begin{equation*}
\pi(\textbf{X})=\pi(\textbf{X},\boldsymbol\theta_0)= \varepsilon_0 + \left(1 - \delta_0 - \varepsilon_0  \right) \left\{1 + exp(\boldsymbol{\beta}_0^T \textbf{X} ) \right\},^{-1}
 \end{equation*}
where  $\boldsymbol\theta_0=(\varepsilon_0,\delta_0,\boldsymbol{\beta_0})$ and $\boldsymbol{\beta_0}=(0,0.6,-0.6,0.6,0,0,0)$.\\
  We remark that, when $\varepsilon_0=\delta_0=0$ this SPPS model reduces to the usual linear logistic model.
Different settings of $\varepsilon_0$ and $\delta_0$ will be chosen. The outcome is generated by
  $$ Y=\nu_0 + \nu_1 X_1+\nu_2 X_2+ \nu_3 X_3 + \nu_4 T +  \xi_1 V_1 + \xi_2 V_2 + \xi_3 V_3 + Z,$$
  where $Z$ is a normal standard variable independent of $\mathbf X$ and $T$, $\left( \nu_0, \nu_1,\nu_2,\nu_3,\nu_4 \right)= \left( 0, -1, 1, -1, 2 \right)^T$ and $\xi=(-1, 1, 1)
.$
  The joint distribution of $\mathbf X$ is specified by taking $X_3$ with Bernoulli(0.2) distribution, and then generating $V_3$
as Bernoulli with $P(V_3=1 \vert X_3)= 0.75X_3 + 0.25(1-X_3).$ Conditional on $X_3,$ the vector $\left( X_1,V_1,X_2,V_2\right)$ is generated as multivariate normal $\mathcal N(\rho_{X_3},\Sigma_{X_{3}}),$ where $ \rho_0=\left(1,1,-1,-1 \right),\,\rho_1=\left(-1,-1,1,1 \right),$ and
$$ \Sigma_{0}=\Sigma_1
= A = \left(\begin{array}{cccc}
                          1&0.5& -0.5 & -0.5 \\
                          0.5&1& -0.5 & -0.5 \\
                          -0.5 & -0.5 & 1&0.5 \\
                          -0.5 & -0.5 & 0.5&1
            \end{array}
     \right)$$

We generate $Nrep=1000$ samples of size $n=1000$ following the described model, under which the real value of the average treatment effect is $\tau=2$. For each sample we compute four estimators, namely $\hat\tau_O$, $\hat\tau_P$, $\hat\tau_{LD}$ and $\hat\tau_{PLD}$. $\hat\tau_P$ and $\hat\tau_{PLD}$ are defined in (\ref{nuestroate}  ) and (\ref{estilucen}) respectively; both of them use the SPPS model (\ref{propate}) introduced in this work for the propensity score.  On the other hand, $\hat\tau_O$ is the  original IPW estimator, defined by modeling the propensity score with the linear logistic model. That is to say, $\hat\tau_O=\hat \tau( \hat \pi)$ given in (\ref{est.gen})  assuming that  $\pi(\textbf{X})= \phi(\boldsymbol{\beta}_1^T \textbf{X})$, with $\phi(u) = 1/(1 +exp(-u)) $ for some $\boldsymbol\beta_1 \in  \mathbb{R}^7.$
Finally, $\hat \tau_{LD}$ denotes the estimator proposed in Lunceford and Davidian (2004). This estimator is defined as in equation (\ref{est.gen}) but using a linear logistic regression model for the propensity score.
For each estimator $\hat\tau$, we compute an empirical mean square error with the following formula
\begin{equation*}
{MSE}(\hat\tau) =\frac{1}{Nrep} \sum_{i=1}^{Nrep} \left(\hat\tau-\tau\right)^2.
\end{equation*}

The results of the Monte Carlo study are reported in Table \ref{mse}. In these simulations we can see that, when the true model is the linear logistic model, both our proposed estimators give a smaller MSE than the corresponding classical ones.
On the other hand, if the samples are generated following the SPPS model with $\epsilon_0$ and $\delta_0$ both greater than zero, then $\hat\tau_{PLD}$ remains very small, as compared to the other proposals, followed sometimes by $\hat\tau_{P}$ and sometimes by $\hat\tau_{LD}$. We remark that in almost all the situations considered our proposed estimators give better results than the corresponding classical ones. In the remaining situations, namely when samples are generated with large values of $\epsilon_0$ and $\delta_0$, the mean square errors are similar and all the estimators give good results.  When $\epsilon_0+\delta_0$ is greater than $0.6$, all four estimators are equal or almost equal. \\

\section{Example: Children's FEV Data}
This data set contains measurements of the forced expiratory volume  (FEV) of 654 children and teenagers aged 3 to 19 years, together with their height, age, sex and and a binary variable indicating whether or not they smoke. It is basically the data set considered in \cite{rosner99}, which has been included in the R package covreg \cite{niuhoff14}
with slight modifications.
In order to estimate the average smoking effect in the FEV in this population, we consider only children aged 9 or older since there are not any smokers among the younger children in this data set.

We wish to emphasize that this example is included to show the use of the proposed model in a causal inference context. The confiability of  results  depend on the no unmeasured counfounders assumption, and  the discussion on its validity given the observed covariates is beyond the scope of this work. This example is therefore considered only as an illustration of the application of the proposed methods.

All four estimates yield a negative average treatment effect but differ in the size of this effect.
As a means to determine the significance of these differences, we computed $95\%$ normal confidence intervals were we estimated the standard error based on 1000 bootstrap samples.
 To obtain the bootstrap samples, we first break the data set in two, smokers (65 subjects) and non-smokers (374 subjects). Then we resample separately 65 observations from the smokers group and 374 observations from the non smokers group. Let $\hat\tau_i$ be the estimator of the ATE based on the $ith$ sample and $\overline{\hat{\tau}}$ the mean of $\hat\tau_1,\dots \hat\tau_{Nboot}$. The bootstrap estimator of the standard error of each estimator $\hat\tau$ is computed by
\begin{equation}
\hat{SE}=\sqrt{\frac{1}{Nboot-1}\sum_ {i=1}^{Nboot} \left(\hat\tau_i-\overline{\hat{\tau}}\right)^2}
\end{equation}
and the $95\%$ bootstrap confidence interval based on $\hat\tau$ is computed as \begin{equation}
\left[ \hat\tau-1.96\ \ \hat{SE} ,\ \  \hat\tau+1.96\ \ \hat{SE} \right]
\end{equation}

As reported in Table \ref{tablesmoke}, the obtained intervals show that the  ATE is not significant. The conclusion is the same for all the estimators considered.

 In Table \ref{tablesmoke} we report the estimates, together with their corresponding standard errors and confidence interval for the ATE.
 We remark that the standard error of our proposed estimator $\hat{\tau}_P$ is slightly smaller than the standard errors of the classical ones.

 \bigskip

\textbf{Acknowledgement}
This research was partly supported by grants 20020150200110BA and 20020130100279BA from Universidad de Buenos Aires.

redpajama_set_name: RedPajamaArXiv


Why are daily water requirements important to know, and not necessarily flow per hour?
Unlike conventional electric pumps which pump at a constant rate, once switched on, flow rates for solar pumps are variable over the course of the day. Therefore solar direct pumps start pumping slowly in the morning, with peak flow rate over midday, then dropping off in the afternoon.
Why is the borehole depth, and pump depth, of minor importance in solar submersible pump selection and sizing?
Why are Solar Water Pumps not specified in Horsepower, Watts or Kilowatts?
Although solar pump motors and controllers have nominal power ratings, the power at which solar pumps operate are variable. This depends on the solar array size, and time of day ( sun radiation intensity varies over course of the day), and factors such as temperature, pump geometry and dynamic head of the application.
Are solar pumps cheaper than Diesel pumps?
Although the initial capital cost of diesel pumps may be lower than solar, the operating and maintenace cost for diesel accummulate quickly so that the break-even point for solar can be reached after a fairly short time, after which further savings accumulate, making solar a far cheaper option over the solar pump's life time.


Samancor Chrome is a progressive, innovative company, striving to be an employer of choice. We are committed to providing growth and development opportunities at our two mines, four smelters and our corporate head office.
When looking for Graduate Jobs in Mining, Oil & Gas, be prepared to start your search from the bottom. You will be required to start as an apprentice or sometimes as a trainee. Here are some of the qualities that Graduate Jobs in Mining, Oil & Gas are looking for.
The ability to work as an individual when the nature of the work requires it, or as a part of the team.
Willingness to abide and ensure the safety of the project.
The ability to work in rotating rosters.
Welders and boilermakers are responsible for repairing and building metal by incorporating different methods or using welding machineries. These metals will be later used for construction, as well as manufacturing equipment. It is also preferable to be an expert in different welding professions, such as construction welding or repair welding.
Civil engineers are in charge of the planning, designing, constructing and maintaining both the physical and natural structure of the environment. This may include transport system, mine, sewerage and water systems.
Concreters Concreters deal with placing, mixing, spreading, compacting, finishing and curing the concrete in different structures using wide varieties of tools, machinery and other equipment.
In South Africa, there are different types of plant operator. There are plant operators that usually function with other labourers, and there are plant operators who are responsible for assisting in the function of the machineries and other equipment.


Those brave souls who have read the 3 part story of our trip through Mozambique, will know that we had to cut it short due to a propshaft bearing that collapsed under the punishment meted out by a certain rutted road, leaving us temporarily stranded at Mphingwe camp, just south of the Zambezi river. I can't think of a better spot to get stranded mind you – comfortable accommodation surrounded by lush forest, good meals, cold beer, not to mention good company and bird life to savour.
I woke up early and rather despondent this Sunday morning, not knowing how we were going to get ourselves and the vehicle back home. There seemed to be just 2 options – fix the car here and drive home or get home by other means and arrange to repatriate the car – neither would be simple but it would be a lot easier if we could just drive the car back ourselves.
Back at Mphingwe we enjoyed a last dinner with the full group, as those still mobile would be heading to Zimbabwe the next day. Our fate lay squarely in the hands of Joe, the resident mechanic looking after the sawmill, who would look at the Touareg in the morning and confirm our options. We had a date with him for 6.30 am at the sawmill.
We took the car to the sawmill just after 6 am for Joe to have a look and once we had it up on the ramp, his recommendation was simple and quick – get the part and he will fix it.
By lunchtime our arrangements were done and we could relax for the rest of the day. I took a walk along the Suni Trail which winds through the forest and had a magical time with the many butterflies, which posed for some beautiful photos in the soft dappled light of the forest.
Birds were not plentiful in the immediate vicinity of the camp but some nice specials occur including Crested Guineafowl (aka The Rockers – which will make sense if you've ever heard a group of them doing their call, which sounds like a rock band warming up, plus their wild hairstyle), Black-bellied Starling and Yellow-bellied Greenbul. The resident Emerald-spotted Wood-Doves and Tambourine Doves provide a constant soothing background sound with their calls right through the day.
The evening meal was memorable for a great sirloin steak and good conversation with Neithard and Kathrin, all of us in the same boat at that stage, in rough waters and no paddle in sight.
A day of waiting, while the vital spare part travels from Pretoria to Mphingwe near Caia in Mozambique via Jo'burg (by plane to) Beira (by Shapa to) Mphingwe all courtesy of Pat and her daughter Carrie, or Wonder Woman as I was now regarding her. Our hopes were that it would arrive the next morning and be fitted without a problem – what if it was the wrong part?
To pass the time George and I took a long birding walk along the Suni Trail, diverted to the track down to the sawmill and returned via the main dirt road back to the camp.
The rest of the day was relaxed with another good dinner to close out the day.
We took the Touareg down to the sawmill just after 6 am and left it with Joe who had assured us it would be a quick job. Well it didn't turn out that way as an "hour or so" job turned into a whole day affair as Joe and his men had to dis-assemble and re-assemble the propshaft bearings to get the centre carrier bearing in place, in the process having to fashion special tools to fit the VW components.
So we postponed our departure until the next day, hoping an early start would get us to Beit Bridge in the day.
We got through the day with much sitting around, reading and contemplating – the only birds that raised any interest were a pair of Crested Guineafowl making their way through the camp and a Bateleur and what looked like a Long-crested Eagle soaring high overhead.
With plenty of time on our hands, there was a chance to appreciate the smaller wildlife and the camp was full of interesting lizards, bugs, ants and others – the shower and toilet block was a magnet for them and I counted over 20 species of insects, moths and dragonflies during one "sitting". A couple of small black snakes had to be persuaded to leave the toilet including one which was curled up under the hollow seat!
We set off at our planned time of 5 am in near dark, gradually getting lighter as we progressed slowly along the potholed road to Inchope. The first 260 km took all of 4 and a half hours as I drove gingerly around and through the badly potholed sections.
From Inchope to the Zimbabwe border was a little better, but there was a constant stream of trucks to contend with and some hazardous overtaking manoeuvres were the only way to make some progress. It was a relief to get onto the Zim roads which are generally in a good condition, albeit narrow.
We proceeded through Mutare and Masvingo (Fort Victoria in the "old days") where we decided to carry on to the Lion and Elephant Motel another 2 hours away, as it was only 4.30 pm. With the light fading, I didn't see a 60 km/h sign along the way and was stopped by a cop who tried the by now familiar scare tactics – according to him we would have to appear in court the next morning and he was clearly looking for some "persuasion" to help us avoid this fate. I stood my ground refusing to play along and eventually he wrote a R200 fine which I paid and we proceeded on our way. I was only too glad to get away from that spot, having smelt alcohol on his breath.
The motel was a welcome sight some time later, after 14 hours of driving the 950 kms from Mphingwe.
Next morning we were up early and at the Beit Bridge border post where yet another pair of cops looked for some reward for letting us through, after a veiled threat of having to search the vehicle for drugs – "but the guy who does the search will only be here at 10 am" implying a 4 hour wait for us.
Once again we refused to play along but "rewarded" them with a couple of our remaining snack bars which they took in disgust and waved us on.
The rest of the trip home was uneventful other than another speeding ticket – think I was just too tired to spot the signs by then – and we got back to Pretoria around 1 pm, thankful that Joe had done a good job on the Touareg, which had made it without further problems.
Birds were plentiful and the Moz specials were there, although it proved quite difficult to get a good view at times. I was very pleased with the 30 lifers that I chalked up and with the general quality of birding overall.
The vast majority of people are obviously poor but friendly and we saw some back-breaking tasks being performed for probably very little compensation. I did not pick up any feeling of aggression from the "have-nots" in the way we often experience it in SA.
Bicycles are an important form of transport and we were amazed to see the loads that get transported – 3 or 4 heavy bags of charcoal and in one case cement get transported long distances in this way.
Strings of people along the roads near towns are a common sight as taxis are few and money tight.
Chicken is a popular item on menus and often the safe choice. Surprisingly fish was scarce, even at the coast. Our best meals were at Mphingwe where we had superb sirloin steaks and all meals were tasty. The local beers were good – Manica and 2M (Dosh M). Our other meals were self-prepared and simple – rusks early morning with coffee, snack bars in between and tuna, pork in tins, sardines, cheese etc on cracker bread and the like for lunch. Being able to boil water for coffee/tea wherever we stopped long enough was a real boon.
We expected hot and humid and we got exactly that – winter may be a better option as being in a constant bath of sweat is not enjoyable and can detract from the pleasure of the surroundings and bird life.
Occasional rain brought some respite from the oppressive heat, but not for long.
There is no question that having a guide with the expertise in birding and the knowledge and experience of Mozambique is invaluable and trying to do this trip without Etienne would have resulted in dipping on many of the special birds.
Personally, I would have liked to have spent more time photographing some of the birds, but this would only be possible if a specific photography trip was arranged, as it requires a very different approach – ie spending a lot more time in certain locations rather than trying to cover as much ground as possible.


The Mathews High School varsity football team dropped to 0-3 last week after being shut out by the visiting Lancaster Red Devils on Friday, 24-0.
Lancaster got on the scoreboard for the first time late in the first quarter. On the first play after Mathews turned the ball over on downs at the Red Devil 10-yard line, senior running back Christopher Dameron blew past the Blue Devil defense for a 90-yard touchdown run. A pair of second-quarter TD receptions by Benjamin Lee gave Lancaster an 18-0 edge going into halftime.
Mathews had its best chance to score with about two minutes left in the third quarter, when Blue Devil Noah Lewis blocked a Lancaster punt, giving Mathews the ball on the Red Devils' 25. Several plays later, a fumble near the goal line extinguished any hope of a Mathews score. Lancaster scored its fourth and final touchdown with 2:31 left to play, sealing the victory.
Our effort defensively was better than the previous week," head coach C.J. Kelly said, assessing Friday's loss. "We had flashes of not wanting to quit and wanting to get back into the game and we made Lancaster work for everything that they got in the game.
Because of the impending hurricane, Wednesday's junior varsity game against Northumberland and Friday's varsity contest against the Indians were cancelled.


text:
Mahinda and Vijayakala will never be arrested – Gammanpila
Former President Mahinda and Former State Minister Vijayakala Maheswaran will never be arrested by the current government, says MP Udaya Gammanpila.
Gammanpila stated that he will take action to issue a writ against the Attorney General to immediately arrest Vijayakala over her controversial statement but refused to comment on Chinese funding in support of the then President Mahinda Rajapaksa's 2015 re-election bid.
He is a close associate of Mahinda and member of the Joint Opposition but refused to comment about Chinese funding in support of the then President Mahinda Rajapaksa's 2015 re-election bid and did not attend the debate in parliament with his friends. Rajapaksas should have responded to the allegations directed at them. But by 1 pm there hadn't been anyone from the Joint Opposition in parliament on that day, alleging that as they couldn't either admit or deny accusations they skipped the debate.
MP Udaya Gammanpila says that this will never be resolved seeing as the current government isn't investigating on this matter and this has made taking legal action against it difficult.
Meanwhile, the Northern Province Chief Minister is trying to create a war in this country again, Gammanpila charged.
The launch of the book 'Nandikadal Dhakwa Para Kapuwe Kawuda?' (Who made the way to Nandikadal?) by retired Navy official Lieutenant Commander B. P. S. Hemachandra was held yesterday (25), and MP Gammanpila made these comments, attending the event.
By Karuna, LATEST UPDATES, SPECIAL NEWS, SRI LANKA NEWS, WORLD NEWS
Debate on Chinese funding – Ranil and Maithiri trying to cover up the corruption and save Mahinda, WHY?
"Govt. cannot deny Federalism to Tamil people" -Gajan Ponnambalam


text:
La Compagnie du chemin de fer du Centre est une société anonyme belge créée en 1853.

Histoire

Demande en concession
Une première demande de concession pour une ligne de chemin de fer  donna lieu à une concession, actée par la loi du , qui sera accordée par l'arrêté royal du  à une compagnie. Cependant, elle renonça à construire la ligne car elle n'avait pas réussi à obtenir la concession des lignes des charbonnages du Centre à Manage à Mons, ainsi que celle de Namur à Liège.

La loi du  réitère cette concession, dans les mêmes termes qu'en 1845.

Création de la compagnie
La  est créée par un acte du , confirmé par un arrêté royal du , publié au Moniteur le . Son objet est . Le vicomte Émile-Alexandre de l'Espine, apporte la concession, de ce chemin de fer, qui lui a été accordée le  et il apporte également une  passée avec les entrepreneurs, de Paris, Lamie Murray et Marsuzy de Aguirre.

Selon les conditions de la loi , ce chemin de fer doit avoir son point d'origine à Manage, gare située sur la ligne de Braine-le-Comte à Charleroi de l'État belge ainsi que sur la ligne de Mons à Manage de la Société des chemins de fer de Namur à Liège et de Mons à Manage avec leurs extensions. Se dirigeant vers Haussu, Morlanwelz et Binche, il doit se raccorder au Chemin de fer de Charleroi à Erquelinnes.

La Compagnie des chemins de fer du Centre sollicite, et obtient, l'autorisation de modifier ce point de départ en le situant à Écaussinnes au lieu de Manage. Écaussinnes étant également situé sur la ligne de Braine-le-Comte à Charleroi.

La concession de la ligne de chemin de fer reliant Baume (Haine-Saint-Pierre) à Marchienne-au-Pont fut accordée en 1859. Une société anonyme, filiale des Chemins de fer du Centre, la Compagnie du chemin de fer de Baume à Marchienne, fut fondée en 1863.

Le , elle passe un traité avec la Compagnie du chemin de fer de Baume à Marchienne, pour notamment prendre à sa charge l'exploitation du réseau de la Société du chemin de fer de Braine-le-Comte à Courtrai.

Le , elle conclut un traité avec la Compagnie des chemins de fer des bassins houillers du Hainaut pour la reprise de l'exploitation de la totalité de ses lignes.

Au sein de la Société générale d'exploitation

La Société générale d'exploitation de chemins de fer, créée en 1867, regroupe de nombreuses compagnies ferroviaires belges dont celle des Chemins de fer du Centre.

Traité du  entre la Compagnie du Centre et la Compagnie des chemins de fer des bassins houillers du Hainaut pour régler les obligations des sociétés entre elles à la suite de la livraison des lignes à l'État et des versements financiers annuels que l'État doit verser suivant cette règle : .

Rachat par l'État
Le , l'État belge, représenté par le ministre des travaux publics Alexandre Jamar, la Société anonyme des chemins de fer des bassins houillers du Hainaut, représentée par Simon Philippart, et la Société générale d'exploitation de chemins de fer, représentée par son vice-président Félix Gendebien, signent une convention pour remettre à l'État, au , des chemins de fer concédés et notamment dans le paragraphe 10 : , auquel il faut ajouter dans le cadre des chemins de fer particuliers : le point 2 .

En 1873, la société devient propriétaire par une fusion/achat de la Compagnie du chemin de fer de Baume à Marchienne.

Dissolution

Réseau
Au moment de la reprise par l'État belge, le réseau du chemin de fer du Centre comprend trois lignes.

Chemin de fer d'Écaussinnes à la Sambre
Cette ligne, inaugurée en 1857, de La Louvière à Binche et Erquelinnes, et en 1860, d'Écaussinnes à La Louvière, était la première de la Compagnie. Elle se connectait dès sa création aux Chemins de l'État belge à Écaussinnes tandis qu'à La Louvière, elle se connectait au réseau de la Société des chemins de fer de Namur à Liège et de Mons à Manage avec leurs extensions, dont les lignes desservant la Louvière furent nationalisées en 1858. À Erquelinnes, au bord de la Sambre, la Compagnie du Nord-Belge exploitait la ligne de Charleroi à la frontière de France.

Actuellement, dans la nomenclature de la SNCB, la section de  Mariemont à Erquelinnes est désignée ligne 108, la section d'Écaussinnes à  Saint-Vaast est désignée ligne 107 et la section de La Louvière et  Saint-Vaast à  Mariemont est rattachée à la ligne 112.

Chemin de fer de Baume à Marchienne-au-Pont
Cette ligne fut construite en 1865 et permettait de relier les usines et gisements de La Louvière au bassin de Charleroi. Elle était concurrencée par la ligne de La Louvière à Piéton (actuelle ligne 183) construite par la Société des chemins de fer de Namur à Liège et de Mons à Manage avec leurs extensions et reprise par l'État en 1858.

Après le rachat du Chemin de fer du Centre par l'État, ce dernier ferma la ligne 183, moins directe et comportant deux plans inclinés, au profit de la ligne du Chemin de fer du Centre, mieux construite mais comportant cependant des rampes difficiles.

Actuellement, cette ligne est désignée ligne 112 et le tracé originel entre Piéton et Marchienne-au-Pont a été abandonné au profit d'un tracé plus rectiligne.

Chemin de fer de Piéton à Leval
Cette ligne secondaire, inaugurée en 1868, devant desservir plusieurs charbonnages et industries se détache de l'actuelle ligne 108 à Leval et rejoint la ligne vers Marchienne-au-Pont à Piéton.

La SNCB désigna cette ligne sous le numéro 112A, tout comme la ligne de Piéton à Trazegnies et Roux, construite par les Bassins Houillers puis par l'État belge de 1863 à 1874. Depuis la fermeture d'une partie de la ligne entre Piéton et le Charbonnage d'Anderlues, elle est désignée .

Notes et références

Notes

Références

Voir aussi

Bibliographie

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Articles connexes

 Histoire des chemins de fer belges
 Transport ferroviaire en Belgique
 Liste des anciennes compagnies de chemin de fer en Belgique
 Ligne 107 (Infrabel)
 Ligne 108 (Infrabel)
 Ligne 112 (Infrabel)

Liens externes
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Ancienne compagnie de chemin de fer en Belgique

redpajama_set_name: RedPajamaWikipedia


I was invited to the Bosch Master Gourmet Launch on Saturday where I got the first hand experiential viewing of the new Bosch MaxxiMUM Kitchen Machine and the new Bosch MaxoMixx Hand Blender.
These cool kitchen gadgets will be available at all leading departmental and electrical stores from 1 April 2014 onwards.
During this media session, we got to see hands on action of 5 recipes by Chef Stephan Zoisi from My Private Chef. It was super cool watching Chef Stephan make bacon ice cream with dry ice, felt like a MasterChef episode! The food were all very yummy too!
This blog post will also be shared and linked up at Bosch's facebook page from 4 April onwards as well as the other blogger's postings as well.
The new Bosch MaxxiMUM Kitchen Machine boosts a 1600W motor and a dough sensor that keeps speed consistant even with stiff dough consistencies. I also personally saw how the whisk move in 3 directions touching the sides of the bowl – now this is really cool! In addition, it also comes with a 1.75 litres Thermosafe blender.
Definitely for the passionate cook!
The MUMXL40G model version has the additional shredder with four different changeable discs to get the chopping, dicing and shredding you need. Well, if this is not enough, you can also purchase additional accessories such as meat mincer, pasta, multi-mixer and even grain mill attachments from Tangs Orchard or Bosch's online store.
The new Bosch MaxoMixx Hand Blender is great for the kitchen enthusiasts and in term of price barrier, this will definitely more affordable to many. MaxoMixx is set with 12 speed settings, 750W power and is versatile as a whisk or chopper with the change of detachable accessories. This will also be good for recipes with smaller amounts.
This is Chef Stephan and here are 2 of Chef's recipes and more photos of the event!
I personally was super thrilled by the bacon ice-cream making process. The smoothie recipe is also a keeper – healthy and yummy. The pasta was so delicious too!
Cut the fruits like banana and pineapple into smaller pieces. Place all the ingredients into the MaxxiMUM Blender attachment and blend until the crushed ice is incorporated.
1. Roast bacon in a pan till crips. Remove and drain on kitchen towel.
3. Whisk sugar and egg yolk until creamy, add in the bacon infused milk mixture. Sieve to remove the bacon bits.
4. Put the mixture back on the stove and cooked (while stirring constantly) until it reaches a custard stage. Let it cool and transfer into fridge.
5. Transfer into the mixing bowl and add dry ice while whisking the mixture. Stop when it reaches the ice cream texture.
6. Serve or store in fridge. At the event, the bacon ice cream was served with cheese souffle! yum!
The other yummy food that Chef Stephen introduced included Pasta Bolognese (with homemade pasta!), Cheese Souffle and Bombe Alaska.
Now now! Comes the part where we got to make our own Bombe Alaska after watching Chef Stephen demonstrate his creation.
We made the strawberry sauce using fresh strawberries and icing sugar. Whipped up our meringue and each of us got to plate and torch our own dessert! So fun!!
This was my creation and plating! Hehe, what do you think??
Photo Credit: Photos without watermark are kindly provided by BSH Home Appliances.


Ubiquiti firmware header. Hi all I' m trying to find the newest FAT firmware for an WA2620- AGN, but can' t find it anywhere on the net.
WA2620- AGN Wireless Access Point pdf manual download. Running the very latest firmware for that AC.
Solved my problem by updating the firmware of both the wx as ap. Com to get the FAT firmware and.
You can download all H3C product software ( including patches and Bootrom). A new firmware to download.
View and Download H3C WA2620- AGN quick start manual online. H3C WA2620- AGN WLAN Access Point Quick Start. We also run WA2620- AGN' s and I just. WLAN Access Point.
H3C WA2620- AGN WLAN Access Point Quick Start describes the basic steps necessary to install and. The WA2620 does download it' s image from the controller.
IPhone 4S with iOS 6 can' t connect. Wa2620 agn firmware download.
View and Download H3C WA2620- AGN installation manual online. H3C WA2620- AGN BootWare, Version 2.
WX3024 & WA2620- AGN Do Not Talk. Right now the newest I managed to find is 1108.
Default Password, Login and IP for your H3C WA2620- AGN router. User Manuals and How to Factory Reset your H3C WA2620- AGN router.
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text:
Q: How do I access tabs in an Excel workbook once saved? I have an associate who sent me an Excel file with tabs at the bottom to switch to different worksheets within the doc.  When I save the document, I can't see/don't know how to open the different tabs to work on the different worksheets.  I see them when I click on "quick look" in my email, but when I save it as an Excel doc and open in Excel, I can't find the tabs.

A: Go to "Excel Options", "Advanced", "Display options for this workbook", and ensure that "Show sheet tabs" is checked:

Alternatively, use Ctrl+PageUp and Ctrl+PageDown to navigate between worksheets.


Cozi Family Organizer is an award-winning productivity app that is already helping 10% of all US households with kids manage their busy lives. Cozi does for everyday family life what an organizing tray does for your desk drawer: tidies things up nice and neat and makes it easy to find what you need.
Specifically, Cozi helps families organize appointments, routines, schedules, activities and lists in one place. It's designed to be shared so you only need one account for the whole family. Best of all Cozi is free and can be accessed from any computer (Windows and Mac) or mobile device (Android and iOS). No matter where or how family members access Cozi – they'll always be looking at the same information. Cozi has a 4.5 star rating in iTunes and Google Play and is consistently named a top app for moms and families.


text:
British women need to be more like Americans for a 'she-economy boost
Despite the gloomy economic conditions, one in five women business owners (22%), say they plan to take on more staff in the next 12 months. Of those, 8% intend to hire one new employee, another 8% intend to hire two to five employees and 6% intend to hire more than six employees within the next 12 months.
With an estimated 620,000 majority women owned small business in the UK this would create more than 360,000 new jobs over the next 12 months, so many are urged to follow the lead, set by one of the UK's leading female entrepreneur Ultimo founder, Michelle Mone.
The Federation of Small Business says that if we had the same level of female entrepreneurship as the US, there would be an extra 600,000 women-owned businesses here, adding £42 billion to the UK economy. But while almost half of women in the UK say they would like to run their own business, only about 8% actually do. It is clear we need to do more to encourage these business savvy women and give the British economy a much needed helping hand.
The survey, by software company Intuit, also revealed a strong entrepreneurial appetite from women who are not yet running their own business. Almost half (49%) of all women surveyed were interested in setting up a business in the future, and 23% of prospective female business owners are planning to launch their new venture in the next year.
The top motivations for women to set up a business were flexible working hours (57%), being their own boss (48%) and working from home (45%). Looking ahead, 47% of female entrepreneurs think there is no ideal time in life to start up your own business – you can start it at anytime as long as you have a great business idea.
Samantha Mayne is an entrepreneur and small business owner in Teddington, who set up TriAdventure, an adventure based events company in 2009. said: "It's been a lot of hard work, but now the risk has paid off and the business is doing well. We have ambitious plans to grow the company in the next 3 to 5 years, and I hope to hire someone else in the next couple of years. There's no right or wrong time to start a business, as long as you've got a great business idea and the motivation to make it work, I'd say to go for it!"
Pernille Bruun-Jensen, managing director, Intuit UK, who commissioned the survey said: "If there was ever a time to bring the 'she-economy' to the UK, now is it. With so many women wanting to be their own boss and fit work around their busy lives, there is an army of would-be entrepreneurs out there who could help drive the UK economic recovery."


•	Three 'giant' paperclips, decorated with buttons hand covered with fabric featuring red and white toadstools, printed from my original hand drawings.
•	Perfect for using as bookmarks and keeping papers organised in style they are also a great way to turn paperwork – invites, vouchers etc. into a special gift. The set also makes a lovely gift in itself and comes in a sealed cellophane package.
•	The paperclips are 9cms long and the button decorations are 24mm in diameter.
•	The fabric covering the buttons is a top quality 50%cotton, 50% linen blend fabric.
•	The buttons have been securely glued to the paperclip, however there is a risk of them being dislodged if treated too roughly.
• A complimentary gift wrapping service is available with this item.


text:
THE HERDWICK NEWS
June 6, 2020 newspaper2020
UK Government Strike Four New Deals To Bring Millions Of PPE To COVID-19 Frontline Staff
Four more companies answer government's call to arms to manufacture PPE
Millions more visors, aprons and gowns to be delivered over the next few months
New contracts are part of the government's 'Make' initiative to harness UK production power
Millions more visors, aprons and gowns will be delivered to frontline health and social care workers treating coronavirus patients, manufactured in response to the UK government's call to arms.
As part of the government's Make initiative to use the production power of the UK's world-leading manufacturing sector.
Four new partnerships have been struck to rapidly produce and deliver PPE to frontline workers over the coming months.
The companies are:
Photocentric, who are using an innovative 3D printing method to produce visors in large quantities. They will be supplying over 7.6 million protective visors over the next 6 months, with plans to scale up to making 500,000 visors a day if required
Ramfoam will be manufacturing and delivering 3 million visors to the NHS per week. They are recruiting an additional 100 staff to ensure demand is met, scaling up to more than 160
Elite Plastics Ltd, who have adapted their production lines to produce large volumes of aprons. Starting this week, 20 million will be delivered to the NHS and social care sector per month
Macdonald and Taylor Healthcare Ltd, who have scaled up production of isolation gowns and scrubs, so far delivering 40,000 gowns a week for the last 2 months. They will now be providing an additional 50,000 gowns a week with government fabric for the next 6 months
Lord Paul Deighton, adviser to the Health Secretary on PPE, said: "The response from our manufacturing industry has been phenomenal and I can't thank them enough for their continued efforts.
"Every health and care worker must have access to appropriate PPE and through this national effort we will ensure they do.
"We have already struck deals with around 30 UK companies to manufacture PPE and over 100 suppliers to deliver items at scale and pace.
"We will continue to identify further opportunities to get more PPE to the frontline rapidly."
The government has delivered over 1.65 billion pieces of PPE to the frontline and deals have already been struck with around 30 companies, including Amazon, Royal Mail and Jaguar Land Rover, through the Make initiative to produce much-needed PPE.
We are also working with over 100 new suppliers to deliver at the scale and pace the UK requires.
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