text:
\section{Introduction}

With development of wireless sensor networks (WSN) and Internet of Things (IoTs), more and more sensors will be deployed in networks to monitor  the status of environment, devices and living creatures, such as temperature, speed and heart rate. Then, the status updates are sent to a sink node for some applications such as smart city, industrial control and human \textcolor[rgb]{0,0,0}{health} detection. Some traditional metrics, e.g., delay \cite{new1,new2} and throughput\cite{new3,new4}, are widely adopted to evaluate the system performance {of wireless networks}. However, for some real-time applications {including} \textcolor[rgb]{0,0,0}{smart} drive, keeping the collected status {data} ``fresh" becomes a vital concern, and the traditional metrics cannot reflect such kind of requirements. To capture the ``freshness" of the status update, {a new metric referred to as} age of information (AoI) has drawn great attentions  {recently}. AoI, which was first defined in \cite{first_aoi}, denotes the amount of time that elapsed since the moment the freshest received update was generated. {The AoI oriented  network design is very different from traditional throughput and delay oriented network designs}. For a network aiming to maximize the throughput, the status updates may be generated and transmitted with a very high frequency, which may cause networks congestion and high delay among delivered status updates. For a network aiming to minimize the delay, the status updates may be generated and transmitted with a very low frequency, which may cause the last received status update becomes stale.  {\textcolor[rgb]{0,0,0}{However}, to keep information fresh  (with minimal AoI), the status updates should not  be generated at neither a high frequency nor a low frequency.}

 {So far, the system AoI performance has been studied in some existing works for various wireless systems}. For instance, in \cite{first_aoi} and \cite{aoi3}, the AoI was analyzed for a single-source single-server network and a multiple-source $M/M/1$ system, respectively. In \cite{aoi},  {it was pointed out} that increasing the number of servers at the sink node can reduce the average AoI but may cause waste of network resources. In \cite{AoI1}, an optimal transmission was designed to minimize long-term average AoI, which showed that zero time waiting policy does not always minimize the average AoI.

 {In the mentioned works \textcolor[rgb]{0,0,0}{above}, the sensors were supposed to be with fixed power supply. However, in practice,} it is with a tremendous economical drawback to charge the sensors with cables  {or batteries} due to expensive cost of installing and maintaining conventional battery recharging operation manually, especially in the hard-to-reach areas. As the sensors are usually ultra-low power, it is more efficient to use wireless power to charge them \cite{EH}. Compared with natural energy source, such as wind, solar, geothermal and hydropower, employing a dedicate power station to transfer energy is more {controllable and} reliable \cite{SWIPT1,SWIPT2,SWIPT3}. In particular, the sensor  {is equipped with a small} energy harvesting (EH) circuit to convert the received radio frequency (RF) signals  into direct current (DC) power. {That is, the wireless powered} sensors need to scavenge energy from the power station at first, and then,  {may use the harvested energy to} transmit status updates to the sink node.

\textcolor[rgb]{0,0,0}{So far, a few works have started  investigat\textcolor[rgb]{0,0,0}{ing} AoI in EH-enabled networks. In \cite{AoI2},  the update submission policy was optimized with a fixed update rate. In \cite{aoieh}, optimal online status update policies for an EH-enabled sensor were proposed with various battery sizes. In \cite{aoieh2}, the average AoI was analyzed for wireless powered networks in low SNR region.  {However, in these works, }, the energy arrivals were  {described as} to occur as a point process.  {But in practice, with wireless power transfer}, the sensor has to accumulate energy over a period of time to charge itself.   {Thus, the point process model is not suitable.} Moreover, in existing works, the AoI  was regarded to be linearly  {increased} with time  between the moments of two received updates, which cannot reflect the thirst for the fresh update that becomes more and more urgent as time elapsing  in many applications.}

 {To fill this gap, in this paper, we consider a scenario where a sink node collects status updates from a sensor charged by a wireless power station.} A waiting time is inserted before transmitting next status update such that the sensor could scavenge enough energy to transmit \textcolor[rgb]{0,0,0}{it}. To capture the thirst for the fresh update becoming more and more urgent as time elapsing,  {we adopt the \emph{urgency-aware AoI (U-AoI)} which exponentially increases with time  between the moments of two received updates to characterize the user experienced AoI  performance of the system. To minimize the system average U-AoI}, we formulate an optimization problem under the constraint of the energy causality. As the considered problem is non-convex  and with no known solution,  {we develop a two-layer algorithm to solve it,  where the outer loop is designed based on Dinklebach's method, and in the inner loop, a semi-closed-form expression  for the optimal waiting time policy is derived based on Karush-Kuhn-Tucker (KKT) optimality conditions.} Numerical results shows that the proposed transmission policy outperforms the zero time waiting policy and the equal time waiting policy in terms of long-term average U-AoI, especially when the networks are not congested. It is also observed that the sensor should  transmit  next update without waiting when the network is congested while should  wait a moment before transmitting next update when the network is not congested, in order to achieve the lower U-AoI. Besides, it is also shown that the system U-AoI first decreases and then keep unchanged with the increments of the  charging power level and the EH outage probability.

This paper is organized as follows. In Section II, the system model is introduced and the considered problem is formulated. In Section III, a status update policy is given. Some simulation results are presented in Section IV and this paper concludes in Section V.

\section{System Model and Problem Formulation}\label{systemmodel}

\subsection{System Model}

\begin{figure}[t]
\includegraphics[width=0.5\textwidth]{sys.eps}
 \caption{System model.}
 \label{Sys}
\end{figure}

\begin{figure}[t]
\includegraphics[width=0.5\textwidth]{aoi3.eps}
 \caption{Evolution of the AoI $\Delta\left(t \right)$ at the sink node in terms of $t$.}
 \label{AoI}
\end{figure}

Consider an EH-enabled networks system as shown in Figure \ref{Sys}, where a power station provides energy to a sensor via wireless power transfer  and the sensor is in charge of continuously monitoring a sensing object, collecting status updates and transmitting the collected data to a sink node. For the sensor, it is energy-limited, so it may require a few seconds to scavenge energy from transmit signals by the power station  to fulfill the sensing and transmission task.

Let  ${P_{T}}$ denote the transmit power of the power station. The power carried in the received RF signals at the sensor can be given by
$$P_{R}\left( t \right) = \frac{{{{\left| {h_1\left( t \right)} \right|}^2}}}{{{d_1^\alpha }}}{P_{T}},$$
where $ {h_1\left( t \right)}$ denotes the channel coefficients of the links between the power station and the sensor at time $t$, $d_1$ denotes the distance, and $\alpha$ denotes the pass loss factor.

The non-linear EH model  \cite{Nonlinear1,Nonlinear2,Nonlinear3} is adopted to describe the harvested RF energy. Then, at time $t$, the output DC power (i.e., the harvested power) at the sensor is
 {\begin{flalign}
\Phi \left( {{P_R}\left( t \right)} \right) = \frac{{Me^{\left( {ab} \right)} - Me^{\left( { - a\left( {{P_R}\left( t \right) - b} \right)} \right)}}}{{e^{\left( {ab} \right)}\left( {1 + e^{ \left( { - a\left( {{P_R}\left( t \right) - b} \right)} \right)}} \right)}},\nonumber
\end{flalign}
where $M $ is a constant denoting the maximum output DC power, indicating the saturation limitation of the EH circuits. $a $ and $b $ are constants representing some properties of the EH system, e.g., the resistance, the capacitance and the circuit sensitivity.}

For the sink node, it keeps receiving the statues of the sensing object  with the received updates transmitted by the sensor.  For clarity, we use $i$ to represent index of the update with $i\in\left\{1,2,...\right\}$.

Suppose that the sensor generates and transmits the update\footnote{The package of update $i$ includes its generation time $S_i$. When it is delivered, a ACK including the delivered time $D_i$ will be fed back to the sensor from the sink node.} $i$ at time $S_i$ and the sink node receives the update $i$ at time $D_i$. Then, the transmission time for the update $i$ is $Y_i=D_i-S_i$, with $Y_i\ge0$.  {Let $C$ be the data size of each update}. Due to the channel uncertainty, the energy for transmitting one unit data may vary  versus time. Denote the transmit time as $T_c$, the bandwidth as $B$ and the transmit power  as $P_c$. Then we have that $$C = {T_c}B\log \left( {1 + \frac{{{{\left| {{h_2}\left( {{S_i}} \right)} \right|}^2}{P_c}}}{{d_2^\alpha B{n_0}}}} \right),$$
where $n_0$ denotes the noise spectral density, $d_2$ denotes the distance between the sensor and the sink node, and $ {h_2\left( {S_i} \right)}$ denotes the channel coefficient at time $S_i$. Then, at time ${S_i}$ the energy required for transmitting an update is $${\cal E}_i =P_cT_c= \frac{{\left( {{2^{\frac{C}{{{T_c}B}}}} - 1} \right)d_2^\alpha {T_c}B{n_0}}}{{{{\left| {{h_2}\left( {{S_i}} \right)} \right|}^2}}}.$$

\subsection{\textcolor[rgb]{0,0,0}{Average AoI and Average U-AoI}}

 {Due to \textcolor[rgb]{0,0,0}{variation} of network conditions, the transmission times $\left\{Y_1,Y_2,...,Y_i,...\right\}$ may vary from one update to another, which are treated as  $\emph{i.i.d}$ random variables.} When the update $i$ is received by the sink node, the sensor is notified to collect and transmit a new status update.   {As EH is employed, a period time of $Z_i$ may be  required to perform EH and detect and collect the sensing data}. That is, the amount of time to scavenge energy for transmitting the update $\left(i+1\right)$ is $\left(Y_i+Z_i\right)$. During this period, if the sensor has sufficient  energy, it may send the update to the sink node; otherwise, it  {has to accumulate energy}  at first.

\begin{figure}[t]
\includegraphics[width=0.5\textwidth]{aoi6.eps}
 \caption{Evolution of the U-AoI $ g\left( \Delta\right)$ in terms of $t$}
 \label{AoI2}
\end{figure}

The time-stamped status updates should be as ``fresh" as possible at the sink node. At time $t$,  the generation time of the freshest update received at the sink node is
\begin{flalign}
U\left( t \right) = \max \left\{ {{S_i}\left| {{D_i} \le t} \right.} \right\},\label{freshest}
\end{flalign}
where,  {without loss of generality,} it is assumed that $S_0=0$.

\subsubsection{ {Traditional AoI}}

To measure the level of dissatisfaction for data (i.e., the updates) staleness,  traditional AoI is defined as
\begin{flalign}
\Delta \left( t \right) = t - U\left( t \right),\label{AoI_defination}
\end{flalign}
which denotes the amount of time that elapsed since the moment the freshest received update was generated.  With (\ref{AoI_defination}), AoI increases linearly with $t$ between the moments of two received updates and is with a downward jump when an update is received.

As illustrated in Figure \ref{AoI}, the integral of AoI over the time period of $\left[{D_0},{D_n}\right]$ is enclosed by the curve of $\Delta(t)$ and $t$-axis.  Thus, the average AoI of the system over $\left[{D_0},{D_n}\right]$ can be given by
\begin{flalign}
A\left( n \right) = \frac{{\int_{{D_0}}^{{D_n}} {\Delta \left( t \right)} dt}}{{{D_n} - {D_0}}}.
\end{flalign}
The numerator of  $A\left( n \right)$ can be calculated by  summing the ares of the  triangles and the trapezoids. Then,
$$\int_{{D_0}}^{{D_n}} {\Delta \left( t \right)} dt = \sum\nolimits_{i = 0}^{n-1} {{Q_i}}.$$
Further, the area of triangle or trapezoid $i$ between ${D_{i}}$ and ${D_{i+1}}$ can be calculated by   $$Q_i=\frac{1}{2}{\left( {{Z_i} + 2{Y_i} + {Y_{i + 1}}} \right)\left( {{Z_i} + {Y_{i + 1}}} \right)},~{\rm for}~i=0,1,...,n-1,$$
with $Y_0=0$. As a result, the expression of $A\left( n \right)$ can be given by
\begin{flalign}
A\left( n \right)  = \frac{{\sum\limits_{i = 0}^{n - 1} {\left( {{Z_i} + 2{Y_i} + {Y_{i + 1}}} \right)\left( {{Z_i} + {Y_{i + 1}}} \right)} }}{{2\left( {{D_n} - {D_0}} \right)}}.\nonumber
\end{flalign}
Actually, $A\left( n \right)$ reflects that the increment of data staleness keeps constant as time elapses, as the slopes of the lines associated with $\Delta(t)$ are constant.  {Thus, such a AoI model is a linear AoI model, which} is able to describe the AoI experience of some applications. But, for some real-time applications, such as online advertisement placement and online Web ranking, the linear AoI model may not be so efficient any more. Because in these applications, the dissatisfaction for date staleness  {(or the user's experience the thirst of the data freshness)} may grow more and more  quickly as time elapses.

\subsubsection{ {U-AoI}}

Thus, similar to \cite{AoI1}, we adopt an exponential function $g\left(\Delta \right):\left[ {0,\infty } \right) \to \left[ {0,\infty } \right)$ to denote the dissatisfaction for date staleness, which is given by
\begin{flalign}
 g\left(\Delta \right)=e^{a\Delta}-1,
 \end{flalign}
where $a\ge0$ is a pre-given constant to characterize the desire for data refreshing. The term ``-1" is to make  $g\left(\Delta \right)$ begin with zero. $g\left(\Delta \right)$ characterizes that the dissatisfaction for date staleness increases exponentially\footnote{Note that there exist other types of penalty functions such as $g\left(\Delta \right)=\ln\left({a\Delta+1}\right)$ which represents the logistic growth of dissatisfaction for date staleness over time.} as time elapses, which  means that with time elapsing, the thirst for the fresh update becomes more and more urgent. Therefore, we call $g(\Delta)$  as the \emph{U-AoI} in the sequel.

Similar to the traditional linear AoI, the average U-AoI  over $\left[{D_0},{D_n}\right]$ can be given by
\begin{flalign}
&{ A}_U\left( n \right) = \frac{{\int_{D_0}^{D_n} {g\left( \Delta  \right)} dt}}{D_n-{D_0}} = \frac{{\sum\nolimits_{i = 0}^{n-1} {\int_{{D_{i}}}^{{D_{i+1}}} {g\left( \Delta  \right)} dt} }}{D_n-{D_0}}. \nonumber
\end{flalign}

As mentioned previously, $Y_{i+1}=D_{i+1}-S_{i+1}$ and $S_{i+1}=D_{i}+Z_{i}$, we have that ${D_{i+1}} = {D_{i }} + {Z_{i }} + {Y_{i+1}}.$ Then, by defining the following auxiliary function for $t\in\left[D_{i},D_{i+1}\right]$, it is obtained that
\begin{flalign}
\beta_i &= \int_{{D_{i}}}^{{D_{i+1}}} {g\left( \Delta  \right)} dt \label{beta}\\ &= \int_{{D_{i }}}^{ {D_{i }} + {Z_{i }} + {Y_{i+1}}} {g\left( \Delta  \right)} dt.\nonumber
\end{flalign}
\textcolor[rgb]{0,0,0}{When $t\in\left[D_{i},D_{i+1}\right]$, the freshest update received at the sink node is the update $i$. Following (\ref{freshest}) and (\ref{AoI_defination}), we have
\begin{flalign}
{\Delta  = t - \underbrace {\left( {{D_i} - {Y_i}} \right)}_{{S_i}}}\label{beta:A}
\end{flalign}
By submitting (\ref{beta:A}) into (\ref{beta}), $\beta_i$ can be further expressed as
\begin{flalign}
\beta \left( {{Y_{i}},{Z_{i}},{Y_{i+1}}} \right) & = \int_{{Y_i}}^{{Y_i} + {Z_i} + {Y_{i + 1}}} {g\left( \tau  \right)} d\tau \label{beta:B}\\ &= \frac{1}{a}\left( {{e^{a\left( {{Y_i} + {Z_i} + {Y_{i + 1}}} \right)}} - {e^{a{Y_i}}}} \right) - {Z_i} - {Y_{i + 1}}.\nonumber
\end{flalign}}

As $D_n-{D_0} = \sum\nolimits_{i = 0}^{n-1} {\left( {{Y_{i+1}} + {Z_i}} \right)}  $, the long-term average U-AoI also can be calculated by
\begin{flalign}
{\mathbb E}\left[ {{A_U}\left( n \right)} \right] = \mathop {\lim }\limits_{n \to \infty } \frac{{{\mathbb E}\left[ {\sum\nolimits_{i = 1}^{n - 1} \beta  \left( {{Y_i},{Z_i},{Y_{i + 1}}} \right)} \right]}}{{{\mathbb E}\left[ {\sum\nolimits_{i = 0}^{n - 1} {\left( {{Y_{i + 1}} + {Z_i}} \right)} } \right]}}.
\end{flalign}

 \subsection{Problem Formulation}
For the considered wireless powered sensor network, our objective is to minimize the average U-AoI of the system under the constraint of the energy causality. To this end, an optimization problem aiming to find the optimal online schedule policy $\left\{ {{Z_0},{Z_1},{Z_2},...} \right\}$ is formulated by

\textcolor[rgb]{0,0,0}{\begin{subequations}
\begin{align}
\mathop {\min }\limits_{\left\{ {{Z_0},{Z_1},{Z_2},...} \right\}} &\mathop {\lim \sup }\limits_{n \to \infty } \frac{{ {\mathbb E}\left[ {\sum\nolimits_{i = 0}^{n - 1} \beta  \left( {{Y_i},{Z_i},{Y_{i + 1}}} \right)} \right]}}{{ {\mathbb E}\left[ {\sum\nolimits_{i = 0}^{n - 1} {\left( {{Y_{i + 1}} + {Z_i}} \right)} } \right]}}\label{obj}\\
{\rm s.t.}~&{\rm{Prob}}\left\{  {\cal E}_j\le\mu\right\} \ge 1-\rho  ,\label{cons:1}\\
&Z_i\in\left[0,T\right],~\forall i,j \in \left\{1,2,...\right\}\label{cons:2},
\end{align}\label{p1}
\end{subequations}
with
\begin{flalign}
\mu ={\mathop {\lim \inf }\limits_{n \to \infty } \frac{1}{n}{\mathbb E}\left[ {\sum\nolimits_{i = 0}^{n - 1} {{\psi _i}} } \right] }\label{cons:1A}
\end{flalign}
and$${\psi _i} = \int_{{S_i}}^{{S_i} + {Y_i} + {Z_i}} {\Phi \left( {P\left( t \right)} \right)dt}.$$}

\textcolor[rgb]{0,0,0}{The symbols $\sup$ in (\ref{obj}) and $\inf$ in (\ref{cons:1A}) are due to the fact $\left\{Y_1,Y_2,...\right\}$  {being} random variables and the result of the functions of $\left\{Y_1,Y_2,...\right\}$ {being different}  even with the given distribution. In (\ref{cons:1}), as it is hard to guarantee the harvested energy always being over a random number ${\cal E}_j$ in both mathematics and engineering, we use $\rho$ to denote the EH  outage probability such that the long-term average received energy is greater than the required energy with a probability no less than $1-\rho$. In (\ref{cons:2}), $T$ is the maximum waiting time.}

\section{Solution Approach}
Problem (\ref{p1}) is a non-convex problem due to the infinite number of probability constraints (\ref{cons:1})  and the objective function. In order to efficiently solve it, we first deal with (\ref{cons:1}) as follows.

 {Suppose the channel  between the sensor and the sink node follows Rayleigh distribution. Then,} the channel coefficient ${{{\left| {{h_2}} \left( {{S_j}} \right)\right|}^2}}$ follows the exponential distribution and its probability density function (PDF) can be expressed by
\begin{flalign}
{f_{{{\left| {{h_2}} \left( {{S_j}} \right)\right|}^2}}}\left( x \right) = \lambda {e^{ - \lambda x}}\label{Ray}
\end{flalign}
where $\lambda$ is the exponential distribution parameter. Then, following (\ref{Ray}), (\ref{cons:1}) can be rewritten as
\textcolor[rgb]{0,0,0}{\begin{flalign}
&{\rm{Prob}}\left\{ {{{\left| {{h_2}\left( {{S_j}} \right)} \right|}^2} \ge \frac{{\left( {{2^{\frac{C}{{{T_c}B}}}} - 1} \right)d_2^\alpha {T_c}B{n_0}}}{\mu}} \right\} \nonumber\\
&= 1 - \int_0^{\frac{{\left( {{2^{\frac{C}{{{T_c}B}}}} - 1} \right)d_2^\alpha {T_c}B{n_0}}}{\mu}} {{f_{{{\left| {{h_2\left( {{S_j}} \right)}} \right|}^2}}}\left( x \right)dx}\nonumber \\
& = \exp \left\{ { - \frac{{\lambda {\left( {{2^{\frac{C}{{{T_c}B}}}} - 1} \right)d_2^\alpha {T_c}B{n_0}} }}{\mu}} \right\} \ge 1-\rho. \label{cons:2A}
\end{flalign}}

 {By doing so, (\ref{cons:1})  with infinite number of constraints is equivalently transformed to be as  (\ref{cons:2A}) which is with only one constraint.}

Further, with the non-linear EH model, the output DC power of the sensor is limited by the maximum output DC power. That is, if the received signals are with relatively high power level, output DC power of the sensor  {will be} constant, i.e., the maximum output DC power. Therefore, it is assumed that the sensor is close to the power station and always works in the saturation state of the EH circuit. Then, ${\psi _i}$ can be given by
\begin{flalign}
{\psi _i} = \left({Y_i} + {Z_i}\right)M\label{auxiliary:1}.
\end{flalign}
By substituting (\ref{auxiliary:1}) into (\ref{cons:2A}), we have  that
\begin{flalign}
\mathop {\lim \inf }\limits_{n \to \infty } \frac{1}{n}{\mathbb E}\left[ {\left( {{Y_i} + {Z_i}} \right)} \right] \ge  \omega \label{cons:2B}
\end{flalign}
where $$\omega  =- \frac{{\lambda \left( {{2^{\frac{C}{{{T_c}B}}}} - 1} \right)d_2^\alpha {T_c}B{n_0}}}{{\ln \left( {1-\rho } \right)M}}.$$

 {
As illustrated in Figure \ref{AoI2}, $\beta_i$ is only relevant to ${Y_{i}}$, ${Z_{i}}$ and ${Y_{i+1}}$, which is consistent with (\ref{beta:B}).
Thus, we have the following remark.}
\newtheorem{Rem}{Remark}
\begin{Rem}
 {The optimal online transmission policy $Z^\star_i$ is only related to $Y_i$ not related to $\left\{Y_1,Y_2,...,Y_{i-1}\right\}$ and/or $\left\{Z_1,Z_2,...,Z_{i-1}\right\}$.\label{Remark:1}}
\end{Rem}
Intuitively, Remark \ref{Remark:1} indicates that the waiting time depends on the last transmission time and the distribution of the transmission time. The similar result can be found in \cite{AoI1} and \cite{AoI2} which shows that there exists an optimal \emph{stationary deterministic policy} with $Z_i={\cal Z}\left(Y_i\right)$.

As $Y_i$ and $Y_{i+1}$ are $i.i.d$, we have
\begin{flalign}
\mathop {\lim }\limits_{n \to \infty } \frac{1}{{n}}{\mathbb E}\left[ {\sum\nolimits_{i = 1}^{n - 1} \beta  \left( {{Y_i},{Z_i},{Y_{i + 1}}} \right)} \right] = {\mathbb E}\left[ {\beta \left( {Y,Z,{{Y'}}} \right)} \right]\label{numerator}
\end{flalign}
and
\begin{flalign}
\mathop {\lim }\limits_{n \to \infty } \frac{1}{{n}}{\mathbb E}\left[ {\sum\nolimits_{i = 0}^{n - 1} {\left( {{Y_{i + 1}} + {Z_i}} \right)} } \right] = {\mathbb E}\left[ {{Y'} + Z} \right].\label{denominator}
\end{flalign}

By submitting (\ref{numerator}) and (\ref{denominator}) into Problem (\ref{p1}), solving Problem (\ref{p1})  {is equivalently transformed into} the following problem.
\begin{subequations}
\begin{align}
&\mathop {\min }\limits_{ {\cal Z}\left(\cdot\right)} \frac{{{\mathbb E}\left[ {\beta \left( {Y,Z,{{{{Y'}}}}} \right)} \right]}}{{{\mathbb E}\left[ {Y + Z} \right]}}\label{p2:1}\\
&{\rm s.t.}~{\mathbb E}\left[ {{Y} + {Z}} \right] \ge  \omega\label{p2:cons:1}\\
&~~~~~Z\in\left[0,T\right]\label{p2:cons:2}
\end{align}\label{p2}
\end{subequations}

Problem (\ref{p2}) is an optimization problem with fractional objective function, which is still non-convex. Nevertheless, it is observed that the numerator of the objective function, i.e., ${{\mathbb E}\left[{\beta \left( {Y,Z,{{{{Y'}}}}} \right)}\right]}$, is convex in $Z$, and the denominator function, i.e., ${{\mathbb E}\left[Y'+Z\right]}$, is an affine function. Thus, fractional programming can be employed to handle Problem (\ref{p2}), and the main idea is summarized in the following Lemma 1.

\newtheorem{Lem}{Lemma}
\begin{Lem}
The optimal solution to Problem (\ref{p2}) can be achieved if and only if $\gamma^\star$ being the unique zero of the auxiliary function $F\left(\gamma\right)$ where
\begin{flalign}
&F\left( \gamma  \right) \buildrel \Delta \over = {{\mathbb E}\left[{\beta \left( {Y,Z,{{{{Y'}}}}} \right)}\right]} - \gamma  {{\mathbb E}\left[Y'+Z\right]}.\label{new.opt}
\end{flalign}\label{fractional_programming}
\end{Lem}
\begin{IEEEproof}
The proof of Lemma 1 is similar to that in \cite{FP,new5}, which is omitted here.
\end{IEEEproof}

Lemma 1 indicates that Problem (\ref{p2}) can be transformed into an equivalent problem where the original fractional objective function (\ref{p2:1}) is replaced by a function in subtractive form, i.e., (\ref{new.opt}). Thus, instead of solving Problem (\ref{p2}), we can solve the following auxiliary Problem (\ref{p4}) which has the same optimal solution to Problem (\ref{p2}).
\begin{flalign}
&\mathop {\min }\limits_{Z,\gamma}~ F\left( \gamma  \right)\label{p4} \\
&{\rm s.t.}~(\rm \ref{p2:cons:1}),(\rm \ref{p2:cons:2}).\nonumber
\end{flalign}

Problem (\ref{p4})  is still not jointly convex w.r.t. $Z$ and $\gamma$. But it is convex w.r.t. $Z$ (or $\gamma$) with a fixed $\gamma$ (or $Z$). Thus, we develop an algorithm as shown in Algorithm 1 to solve Problem (\ref{p4}), where the outer loop  is designed based on Dinkelbach's method. In particular, in the $q$-th iteration, with the given $\gamma$, i.e., $\gamma\left(q\right)$, by solving the following Problem (\ref{p5}),
\begin{flalign}
&\mathop {\min }\limits_{Z}~ F\left( \gamma\left(q\right)  \right)\label{p5} \\
&{\rm s.t.}~(\rm \ref{p2:cons:1}),(\rm \ref{p2:cons:2}),\label{p5:cons:1}\nonumber
\end{flalign}
the optimal ${Z}^\star\left(q\right)$ is derived, which can be calculated in terms of the following Theorem 1.

\newtheorem{The}{Theorem}
\begin{The}
The optimal solution to Problem (\ref{p5}) can be given by
\begin{equation}
Z^\star\left(q\right) = \left[ {\frac{1}{a}\ln \left( {\frac{{\gamma \left( q \right) + \eta  + 1}}{{{\mathbb E}\left[ {{e^{\left( {aY'} \right)}}} \right]{e^{\left( {ay} \right)}}}}} \right)} \right]_0^T.\label{Z}
\end{equation}
\end{The}

\begin{IEEEproof}
Since Problem (\ref{p5}) is convex, KKT conditions can be used  to optimally solve it. The Lagrangian of (\ref{p5}) can be given by (\ref{L}), where ${\eta}$ is the Lagrange multiplier associated with (\ref{p2:cons:1}) and ${f_Y}\left( y \right)$ is the PDF of $Y$. According to KKT conditions, the optimal $Z^\star\left(q\right)$ satisfies
\begin{flalign}
&\frac{{\partial \Gamma \left( Z^\star \right)}}{{\partial Z}} = \label{Z:2}
\\&~~~~ \left( {\left( {{e^{a\left( {y + {\cal Z}^\star(y) + Y'} \right)}}} \right) - 1 - \gamma\left(q\right)   - {\eta}} \right){f_Y}\left( y \right) = 0.\nonumber
\end{flalign}
Following (\ref{Z:2}), the optimal $Z^\star\left(q\right)$ can be calculated as  (\ref{Z}).
\end {IEEEproof}

\begin{Rem}
\textcolor[rgb]{0,0,0}{It is seen from Theorem 1 that for a given distribution of the transmission time, the waiting time decreases with the increment of the last transmission time.}
\end{Rem}

Note that $\eta$ is still unknown in (\ref{Z}). Thus, we employ a bisection method to find the optimal $\eta^\star$ in the inner loop  of Algorithm 1 where $\varepsilon$ denotes the small positive tolerance, $\iota$ is the lower bound of $\eta$ which can be set as $0$, and  $u$ is the upper bound of $\eta$ which can be set as a relatively large positive number. {Once the optimal $\gamma ^ \star$ and $\eta^\star$ are obtained, the corresponding $Z^\star$ is also derived which is the final \textcolor[rgb]{0,0,0}{numerical} solution to Problem (\ref{p2}).}

\begin{figure*}
\begin{flalign}
&{\mathcal L}\left(Z\right)={\mathbb E}\left[ {\frac{1}{a}\left( {{e^{a\left( {Y + Z + Y'} \right)}} - {e^{aY}}} \right) - Z - Y' - \gamma\left(q\right) \left( {Y' + Z} \right)} \right] + {\mathbb E}\left[ {{\eta}\left( {\omega  - Y - Z} \right)} \right] \label{L}  \\
&=\int_0^\infty  {\left( {{\mathbb E}\left[ {\frac{1}{a}\left( {{e^{a\left( {y +{\cal Z}(y) + Y'} \right)}} - {e^{aY}}} \right)} \right] - {\cal Z}(y) - {\mathbb E}\left[ {Y'} \right] - \gamma \left( q \right)\left( {{\mathbb E}\left[ {Y'} \right] + {\cal Z}(y)} \right) + {\eta}\left( {\omega  - y - {\cal Z}(y)} \right)} \right){f_Y}\left( y \right)dy} \nonumber\\
& \buildrel \Delta \over = \int_0^\infty  {\Gamma \left( Z \right){f_Y}\left( y \right)dy}  \nonumber
\end{flalign}
\hrule
\end{figure*}

\begin{algorithm}[t]\label{FP_GEE}
\caption{ Iterative solution approach based on Dinkelbach's Algorithm for solving Problem (\ref{p5})}
 \While{$F\left({\gamma\left(q\right)}\right)<\varepsilon$}{{\bf Initialize} ${\gamma\left(0\right)}$ with $F\left({\gamma\left(0\right)}\right)\ge0$\;
 Set $q=0$\;
 {\bf Initialize} $\iota \le{\eta}\le u$\;
\While{$u-\iota\ge\varepsilon$}{
 Update $\eta$=$\left(\iota+u\right)/2$\;
 Solving problem (\ref{p5}) by  calculating $Z\left(q\right)$ in terms of  (\ref{Z}) \;
 \eIf{ (\ref{p2:cons:1}) is not satisfied } {Update $\iota=\eta$\;}{Update $u=\eta$\;}
 }
 Update $$F\left( {\lambda \left( q \right)} \right)= {\mathbb E}\left[{{\beta \left( {Y,Z\left(q\right),{{{{Y'}}}}} \right)} }\right] - \gamma {\mathbb E}\left[Y'+Z\left(q\right)\right];$$\
Update
\begin{flalign}
\gamma \left( {q + 1} \right) = \frac{{\mathbb E}\left[{{\beta \left( {Y,Z\left(q\right),{{{{Y'}}}}} \right)} }\right]}{ {\mathbb E}\left[Y'+Z\left(q\right)\right]}\nonumber;
\end{flalign}\
 $q=q+1$\;}
 {\bf Return} $Z\left(q\right)$.
\end{algorithm}

\section{Simulation Results}\label{simulation}

This section represents some simulation results to show the validity and efficiency of the proposed \textcolor[rgb]{0,0,0}{solution approach} and present some interesting insights. First, we compare the proposed transmission policy with  two benchmark policies, i.e., zero time waiting policy and equal time waiting policy. Then, we show impact of the EH circuit saturation level  and EH outage probability on the system average U-AoI.

The simulation network scenario is shown in Figure \ref{Sys} and the simulation parameters are set according to \cite{Sim}. The distance between the sensor and the sink node is set as $2$m. The Rayleigh channel parameter $\lambda$ is set as $3$ and the pass loss factor $\alpha$ is set as $2$. The block length $T_c$ is set as $10^{-3}$s, the noise power spectral density is $-70$ dBm/Hz and the bandwidth $B$ is $10$MHz. The size of each update $C$ is set as $8$bits. For the non-linear EH model, we set $M$ as $20$mW which corresponds to the maximum output DC power, i.e., the charging power of the sensor. The EH outage probability $\rho$ is set as $1\%$.

 {We define an event $\left\{Y\le\kappa\right\}$ with a probability $\theta$, i.e. $\theta=\rm Prob\left\{Y\le\kappa\right\}$, \textcolor[rgb]{0,0,0}{which is used to characterize the congestion degree of the wireless networks}. Note that for a given $\theta$, the network gets more congested as $\kappa$ increasing. While for a given $\kappa$, the network gets less congested as $\theta$ increasing.}

\begin{figure}[t]
\begin{center}
\centerline{\includegraphics[ width=0.5\textwidth]{prob7.eps}}
\caption{Average U-AoI v.s. waiting time with $\rm Prob\left\{Y\le0.1\right\}=0.7$.}
\label{prob7}
\end{center}
\end{figure}

\begin{figure}[t]
\begin{center}
\centerline{\includegraphics[ width=0.5\textwidth]{prob3.eps}}
\caption{Average U-AoI v.s. waiting time with $\rm Prob\left\{Y\le0.1\right\}=0.3$.}
\label{prob3}
\end{center}
\end{figure}

Figure \ref{prob7} compares the proposed transmission policy with  {two benchmark policies when $M$ and $T$ are both set as $\inf$ so that the zero waiting policy can be always feasible, and $\theta=0.7$, i.e., $\rm Prob\left\{Y\le0.1\right\}=0.7$}. It is seen that the system average U-AoI first increases and then decreases with the  waiting time when the equal time waiting policy is adopted. The zero time waiting policy and the proposed policy do not change with the  waiting time. It is also observed that the proposed transmission policy is superior to the other two transmission policies in terms of the system average U-AoI. In Figure \ref{prob3}, we compare the mentioned three transmission policy when $M$ and $T$ are both set as $\inf$, and $\rm Prob\left\{Y\le0.1\right\}=0.3$. Different from Figure \ref{prob7}, it is seen that in this case, the system average U-AoI increases with the waiting time when the equal time waiting policy is adopted. Besides, the zero time waiting policy and the  equal time waiting policy are very close to the proposed policy.

With Figure \ref{prob7} and Figure \ref{prob3}, we note that even without EH constraint and maximum waiting time constraints, zero time waiting policy may not be optimal, especially when the network is not congested (e.g., $\rm Prob\left\{Y\le0.1\right\}=0.7$). It means that in this case, the sensor should be a little $lazy$. For instance, the transmission times of four consecutive updates are $\left\{0,0,0,2\right\}$. If zero time waiting policy is adopt, the average U-AoI  is $\frac{1}{{3}}\left({{{e^2} + e - 5}}\right)\approx {\rm{1.70}}$ when $a$ is set as $1$. However, if we insert a waiting time $0.9$ before transmitting the third update, the average U-AoI is $\frac{1}{{4}}\left({{{e^2} + 2e - 7}}\right)\approx {\rm{1.45}}.$ The reason is that transmitting the third update without waiting will waste the potential benefit of zero transmission time. Meanwhile, when the network is congested (e.g., $\rm Prob\left\{Y\le0.1\right\}=0.3$), zero time waiting policy is very close to the proposed transmission policy. In this case, the sensor should transmit updates without waiting. In fact, the proposed transmission policy adjusts the waiting time based on the transmission time (i.e., the network conditions), so that the sensor avoids high-frequent updates transmitting when the network is not congested and reduces waiting time when the network is congested. By doing so, the long-term system average U-AoI is therefore reduced.

\begin{figure}[t]
\begin{center}
\centerline{\includegraphics[ width=0.5\textwidth]{PROB_new.eps}}
\caption{Average U-AoI v.s. $\theta=\rm Prob\left\{Y\le\kappa\right\}$.}
\label{prob}
\end{center}
\end{figure}

\textcolor[rgb]{0,0,0}{Figure \ref{prob} compares the mentioned three policies versus $\theta$ where $\kappa$ is set as $0.1$, $0.3$, and $0.7$, respectively. It is seen that the proposed transmission policy outperforms the other two policies as it adjusts the waiting time based on the transmission time. Besides, the equal time waiting policy is better than the zero time waiting policy because zero time waiting policy is actually a special case of equal time waiting policy. It is also observed that when $\kappa=0.3$ and $\kappa=0.7$, the proposed transmission policy is very close to the other two policies. However, when $\kappa=0.1$, the difference is relatively obvious, especially with a relatively large $\theta$. The reason is that when $\kappa$ is relatively large, the network is relatively congested. In this case, the sensor tends to transmit the next updates without waiting.  While, when $\kappa$ is relatively small, the network is relatively not congested. In this case, the sensor tends to wait a moment before transmitting next updates. The insight drawn based on  Figure \ref{prob} is consistent with that associated with Figure \ref{prob7} and Figure \ref{prob3}.}

\begin{figure}[t]
\begin{center}
\centerline{\includegraphics[ width=0.5\textwidth]{AOIEH.eps}}
\caption{U-AoI v.s. EH circuit saturation  level.}
\label{AOIEH}
\end{center}
\end{figure}

\begin{figure}[t]
\begin{center}
\centerline{\includegraphics[ width=0.5\textwidth]{outage.eps}}
\caption{U-AoI v.s. outage probability.}
\label{outage}
\end{center}
\end{figure}

Figure \ref{AOIEH} shows the U-AoI versus the EH circuit saturation level, i.e., the maximum output DC power. It is seen that the average U-AoI first decreases and then keep unchanged with the EH circuit saturation. The reason is that when the output DC power is relatively low, the sensor need more time to charge itself for transmitting the next update. In this case, the waiting time may be larger than the optimal waiting time, and thus, the system average U-AoI is degraded. However, when the output DC power is relatively high, the required waiting time for charging can be lower than the optimal waiting time plus the last transmission time. Therefore, the system can always works with the minimal average U-AoI. Thus, for EH-enabled networks, to keep the information fresh, charging the sensor with high power level may cause the waste of energy.

\textcolor[rgb]{0,0,0}{The average U-AoI versus the outage probability is shown in Figure \ref{outage}. Similar to Figure \ref{AOIEH}, the average U-AoI first decreases and then keep unchanged with the outage probability. This is because the low outage probability requires high average EH power, which makes the energy causality constraint relatively hard to be satisfied.}

\section{Conclusion}\label{conclusion}
{This paper studied the AoI in EH-enabled networks where a sensor first scavenges energy from a power station and then, transmits its status update to a sink node. U-AoI was defined to capture thirst for the fresh update becoming more and more urgent as time elapsing. Then, an optimization problem was formulated to minimize the long-term average U-AoI  under constraint of energy causality. To solve the considered non-convex problem, we developed a two-layer  algorithm. Numerical results shows that the proposed transmission policy is superior to the zero time waiting policy and the equal time waiting policy in terms of U-AoI, especially when the networks are not congested. It is also found that sensor tends to transmit the next update without waiting when the network is congested while tends to wait a moment before transmitting the next update when the network is not congested. Besides, U-AoI first decreases and then keep unchanged with the charging power level and the EH outage probability.}

\begin{appendices}

\end{appendices}

\section*{Acknowledgment}
\addcontentsline{toc}{section}{Acknowledgment}
This work was supported in part by the National Key R\&D Program of China (No. 2016YFE0200900), in part by the General Program of the National Natural Science Foundation of China (NSFC) (No. 61671051), and also in part by the major projects of Beijing Municipal Science and Technology Commission  (No. Z181100003218010).

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Dr. Alsayed is a hematologist oncologist in the Pensacola area. He is affiliated with Baptist Hospital and 2 more hospitals.
Dr. Alsayed saw more anemia patients than 85% of similar doctors in the Pensacola area in 2014–2018. Learn more.
Dr. Alsayed treated magnetic resonance imaging (MRI) patients in 2014–2018, but we couldn't determine from our database whether he was was a top doctor for magnetic resonance imaging (MRI) in the Pensacola area during 2014–2018. Learn more.
Dr. Alsayed treated more creatinine test patients than 75% of similar doctors in the Pensacola area for 2014–2018. Learn more.
Dr. Alsayed treated more CT scan patients than 85% of similar doctors in the Pensacola area for 2014–2018. Learn more.
Dr. Alsayed treated breast cancer risk assessment patients in 2014–2018, but we couldn't determine from our database whether he was was a top doctor for breast cancer risk assessment in the Pensacola area during 2014–2018. Learn more.
Dr. Alsayed treated more assessment of disease risk patients than 75% of similar doctors in the Pensacola area for 2014–2018. Learn more.
Dr. Alsayed treated vitamin supplement patients in 2014–2018, but we couldn't determine from our database whether he was was a top doctor for vitamin supplement in the Pensacola area during 2014–2018. Learn more.
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Dr. Alsayed treated ferritin test patients in 2014–2018, but we couldn't determine from our database whether he was was a top doctor for ferritin test in the Pensacola area during 2014–2018. Learn more.
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Dr. Alsayed saw more lung cancer patients than 75% of similar doctors in the Pensacola area in 2014–2018. Learn more.
Dr. Alsayed saw sepsis patients in 2014–2018, but we couldn't determine whether he was a top doctor for the condition in the Pensacola area. Learn more.
Dr. Alsayed saw more blood cancer patients than 75% of similar doctors in the Pensacola area in 2014–2018. Learn more.
Dr. Alsayed saw malnutrition patients in 2014–2018, but we couldn't determine whether he was a top doctor for the condition in the Pensacola area. Learn more.
Dr. Alsayed saw lung condition patients in 2014–2018, but we couldn't determine whether he was a top doctor for the condition in the Pensacola area. Learn more.
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Dr. Alsayed saw lymphoma patients in 2014–2018, but we couldn't determine whether he was a top doctor for the condition in the Pensacola area. Learn more.
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Dr. Alsayed saw acute myeloid leukemia (AML) patients in 2014–2018, but we couldn't determine whether he was a top doctor for the condition in the Pensacola area. Learn more.
Dr. Alsayed saw colon cancer patients in 2014–2018, but we couldn't determine whether he was a top doctor for the condition in the Pensacola area. Learn more.
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Dr. Alsayed sees patients and can be reached at the following location.
Dr. Alsayed is able to admit and treat patients at the following hospitals.


text:
Q: Using Enterprise Workflow via API to enable/disable features in Banno Is anyone using Enterprise Workflow via API to enable and disable features in Banno (i.e. external transfers, ect.). If so, I'm interested in how you're doing it.
Information gathering

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text:
Яков Дорі (, ім'я при народженні Яків Достровський, , Одеса, Херсонська губернія, Російська імперія — 1973) — ізраїльський військовик українського походження, перший начальник штабу в Збройних силах Ізраїлю (IDF).

Життєпис
Народився в Одесі. Після погромів 1905 року сім'я Достровських емігрувала до Палестини, яка в той час входила в Османську імперію.

Після закінчення школи в Хайфі, під час Першої світової війни, Яков записався у Єврейський легіон британської армії.

У 1926 році повернувся до Палестини і приєднався до Аґани, де отримав підпільне прізвисько «Дан».

У 1939 році Дорі був призначений начальником штабу Хагані й обіймав цю посаду до 1946 року. З 1946 року по 1947 рік Дорі очолював делегацію із закупівлі озброєнь у США.

Після утворення Збройних сил Ізраїлю Дорі став першим начальником Генерального штабу. В цей час у нього спостерігалися серйозні проблеми зі здоров'ям, що заважало йому керувати військовими діями під час війни за Незалежність 1948. З цієї причини під час війни все навантаження перейшло на його заступника — Іґаеля Ядіна.

Політична і військова кар'єра Дорі завершилася 9 листопада 1949 року. Після відставки з армії Дорі був головою наукової ради при канцелярії прем'єр-міністра, а в 1951 році став президентом хайфського Техніона. Цю посаду Дорі обіймав до 1965 року.

Син Якова, Єрахміель Дорі був командувачем інженерних військ Армії Оборони Ізраїлю в 1969–1972.

Посилання
 Яаков Дорі біографія (іврит)
 Ukrainian Jews, Bible Discovered

Примітки

Українські євреї
Українські емігранти до Ізраїлю

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Charles T. Hayden was a risk-taker.
After initial business success in 1850s, he decided to leave his comfortable home in Missouri and head west to the dangerous, hot, and sparsely populated state of Arizona. He landed in Tucson and started a successful freighting company that brought supplies to rugged men in mining camps and military posts.
Who formed the world of nature (which provides the raw material for physical sciences)? Who formed the universe of human interactions (which is the raw material of politics, economics, sociology, and history)? Who is the source of all harmony, form, and narrative pattern (which is the raw material for art)? Who is the source of the human mind (which is the raw material for philosophy and psychology? And who, moment by moment, maintains the connection between our minds and the world beyond our minds?" God did, and God does.
Fantastic look at the history of the place of Tempe, Jim! As a creative, I can connect with the spirit and inspiration for entrepreneurship. Thanks for presenting it in a new light.


What are Main Structure of Honeycomb Girder Gantry Crane?
Honeycomb girder gantry crane is mainly composed of crane moving system, gantry mounting frame, operation cab, main longitudinal beam, walkway, lifting trolley and etc.
Crane moving system is usually include two driving wheels, two driven wheels and four wheel boxes. Inside driving wheels there is a driving traveling wheel and a driven traveling wheel. Outside the wheel boxes there are installed a reducer with a braking motor. While the wheel boxes are working, braking motor will drive the reducer to work, after that, crane moving system will travel along the track. If the honeycomb girder gantry crane stop, braking flange on the braking motor will reset to make sure it can be locked immediately.
Gantry mounting frame of honeycomb girder gantry crane is composed of main beam and rigid outrigger and flexible outrigger and trimming joist. Outrigger and main beam are connected with bolts, which is easily installed and firmly fixed. On the flexible outrigger there is welded a row of climbing ladder to arrange circuit line. Operators can go in to the driver's cab through escalator, or they can also go upside to get to the walkway.
Driver's cab of honeycomb girder gantry crane includes cab body and electrical control box. Outside of the driver's cab is welded with steel plate. Supported by fashioned iron, the driver;s cab is installed in the middle of inclined outrigger and straight outrigger. In front and at both sides of the cab there is glass window with open view, operators can see all the working situation over the window. All the control system of the honeycomb girder gantry crane is mounted inside the driver's cab the crane operator can control all the actions of the gantry crane when he is in the cab.
Main longitudinal beam of the honeycomb girder gantry crane is in double amplitude triangle shape. Each segments of main beam is connected by steel pins. Two main longitudinal beams are a steel structure through three connection frames and they are fixed on the gantry mounting beam by bolts. On the top of main longitudinal beam there are steel tracks to support the traveling trolley.
In order to maintain and repair the honeycomb girder gantry crane, walkway will be designed on one side of main longitudinal beam. Both sides of walkway is arranged guarding rail and operators can get the the walkway trough climbing ladder. There is also a platform set on rigid outrigger to repair electric hoist.
Lifting trolley of honeycomb girder gantry crane is mainly composed of trolley frame, winch, pulley block and movable block.
Trolley frame is load-bearing and traveling mechanism of lifting trolley, which is welded by steel plate. Both sides of trolley frame are arranged a wheel box, each wheel box is installed with a driving and driven wheel. Outside of the wheel box is mounted a reducer and a braking motor, whose working principle is similar with crane moving system.
Winch mechanism is arranged on the top of lifting trolley to lift and lower load. Every winch has pulley block and movable block. Pulley block is fixed on trolley frame and movable block is hanging under the trolley by steel wire rope and the end of steel wire rope is twined and locked on the rope roll or pulley block. When the winch is working, steel wire rope will pull the movable block to lift the load up and down, thus loaded goods will be lifted and lowered.
Electrical hoist is used for lifting small goods, joist steel track is connected with the lower main longitudinal beam. Electrical hoist can move fast along the track.
pre:What are Quick-wear Parts of Cranes?


The Prince Albert Tourism Association has announced that Avis Rent a Car has appointed the Prince Albert Tourism Office as a booking office for their hire car services.
According to Gadija Jappie, the manager of Avis' Garden Route office, this is probably the first formal relationship between a major international car hire company and a tourism office in South Africa.
For bookings or more information visit the Tourism Office or give them a call at (023) 5411 366.


text:
HomeNewsDangote oil refinery in Nigeria, Africa's biggest oil refinery
Dangote oil refinery in Nigeria, Africa's biggest oil refinery
After seven years of construction, the Dangote oil refinery, a 650,000 barrels day (BPD) integrated refinery and petrochemical located in the Lekki Free Zone near Lagos, Nigeria was completed in 2023, becoming Africa's biggest oil refinery and the world's biggest single-train facility.
The US $12bn project was announced in 2013 by the Dangote group, however, construction did not begin until 2016. The Refinery was originally meant to be built in the oil-producing state of Ondo but due to a lack of political will from the State Government, the project was moved to Lagos state.
The Dangote oil refinery will process a variety of light and medium grades of crude to produce Euro-V quality clean fuels including gasoline and diesel as well as jet fuel and polypropylene.
The processing facilities include a crude distillation unit (CDU) and associated facilities, a mild hydrocracking (MHC) unit, a residual fluid catalytic cracking (RFCC) unit, a naphtha hydrotreater, and a gasoline hydrodesulfurization (HDS) unit as well as alkylation units.
The refinery complex will also house sulfur recovery and hydrogen generation facilities and a polypropylene unit. Comprising two steam methane reformer (SMR) units, the hydrogen generation facility will generate 200,000Nm³/h of hydrogen and steam to produce sulfur-free fuels.
The other processing units at the refinery include the STRATCO® alkylation unit, the MECS® sulphuric acid regeneration (SAR) unit, the MECS® DynaWave® sulfur recovery unit, and the BELCO® EDV® fluid catalytic cracking unit. The refinery is designed to produce up to 50 million liters of gasoline and 15 million liters of diesel a day.
The refinery is expected to produce 10.4 million tonnes (Mt) of gasoline, 4.6 Mt of diesel, and 4 Mt of jet fuel a year. It will also produce 0.69Mt of polypropylene, 0.24Mt of propane, 32,000t of Sulphur, and 0.5Mt of carbon black feed yearly. Furthermore, the refinery will generate electricity of up to 12,000MegaWatts.
A group of local and international banks led by Standard Chartered Bank provided a US $3.3bn syndicated loan facility for the project. Furthermore, the United States Trade and Development Agency provided a US $0.997M training grant for the human resource development for the refinery operation.
Nigerian Businessman Aliko Dangote unveiled plans for the new refinery. He announced that he had secured about US $3.3bn financing for the project.
Construction began with excavation and infrastructure preparation works. planned completion was scheduled for late 2018.
Major structural construction began, and Aliko Dangote estimated that the refinery would be mechanically complete in late 2019 and commissioned in early 2020.
Mammoet to provide heavy lifting and transport solutions for the new Dangote oil refinery in Nigeria
Mammoet, the global leader in engineered heavy lifting and transport, will be transporting, lifting, and installing all over-dimensional cargo for the refinery.
During a signing ceremony in Lagos on Tuesday, January 16, the Dangote Group of Companies and Mammoet signed the contract for this ambitious project. The new multi-billion-dollar refinery is being built by the Dangote Group of Companies in resource-rich Nigeria.
Nigeria's Dangote Refinery to welcome the world's largest Atmospheric Tower
The world's largest atmospheric tower has been built for Nigeria's Dangote Refinery. The huge atmospheric tower was built by Sinopec, a frontline energy and chemical company in China.
According to Sinopec, it will be transported to Nigeria and be installed at the world's biggest single-train facility, Nigeria's Dangote Refinery.
The completion date for Africa's largest oil refinery shifted
The completion date for Dangote crude oil refinery- Africa's largest oil refinery has been shifted to the end of 2020 due to problems importing steel and other equipment.
Group Executive Director, of Dangote Group, Mr. Devakumar Edwin announced the reports and said that the refining operation which was expected to begin at the facility in early 2020 has been shifted, implying that it may start by early 2021. Mr. Devakumar added that the firm could start using the refinery's tank farms as a depot to warm up operations despite the delays at the congested Apapa and Tin Can Island ports.
"We will be able to complete the refinery project by the end of next year – mechanical completion. We are also in talks with the Nigerian National Petroleum Corporation (NNPC), two other international oil companies, and two large oil traders, who were all interested in supplying crude and buying products," said Edwin.
In October, pilling works were completed. According to Group Executive Director (GED), Strategy, Capital Projects, and Portfolio Development for Dangote, Mr. Edwin Devakumar, the pilling work was 148,000 piles, making it a construction project with the largest number of piles in the world. He added that the civil works and underground works have been completed while the superstructure work and some of the building works are coming up.
AFRICA's largest oil refinery is built with 4 ELKON concrete plants by Dangote
In November, concrete machinery company Elkon announced that Dangote Group had purchased 4 units of ELKOMIX – 135 Stationary Concrete Batching Plant for the giant project.
ELKOMIX – 135 Stationary Batching Plant which is equipped with 4500/3000 l. Twin-shaft Mixer with its high-efficient mixing system provides up to 120-135 m3 of concrete per hour. The configuration has T type aggregate weighing system, T type aggregate weighing system provides faster aggregate weighing time with 2 separate aggregates weigh hoppers.
Construction of Dangote oil refinery Africa's largest nears completion
Dangote oil Refinery, which is expected to be Africa's largest oil refinery and the world's biggest single-train facility upon completion, is set to be operational early next year. This was reported by Devakumar Edwin who is the Dangote Industries Executive Director.
Mr. Edwin said that they had initially planned to complete the mechanical works, which would mean the completion of the entire project, by the end of this year and to commission the plant in January next year but there has been a shift of 2 months maximum which he says "it is still within the time frame of completion".
Also Read: Construction works on Ajaokuta-Kaduna-Kano (AKK) Gas Pipeline project in Nigeria to begin this month
"We have laid out plans to catch up with the delay period and we are trying to put it into action. If it works out well, we will still be able to maintain our original completion dates and if it doesn't, the maximum delay period will be 2 months," explained the Dangote Group Executive Director.
Dennis Ayemba
Country/ Features Editor, Kenya
Aza Aondoakaa Sylvanus Jan 27, 2022 At 5:19 pm
There is no gain saying that Nigeria and Nigerians are blessed to have Aliko Dangote.
Like the proverbial light in the room that cannot be hidden in the dark,he has endured,persisted and is surely being notice by the global business community thereby putting Nigeria on the map of viable economic development part way.
We are proud of you and your entire winning team.
Nigerians can't wait to be part of the commissioning of this giangantic project with so much help it will bring in terms of human capital development and to address the long problems of refined motor spirit brought about by outdated refineries.
Long live Aliko Dangote,
Long live Dangote group,
Long live federal republic of Nigeria.
George Nov 29, 2021 At 4:17 pm
am really amazed with this plans and I pray its works .Nigeria we are blessed
James_Dipo Nov 24, 2021 At 6:31 am
May the Almighty God bless Dangote in good health and long life and prosperity. Well done for a good start. Nigeria will now see the back of all her enemies.
Meenah Sep 13, 2021 At 1:18 pm
May this work in sha Allah
May dangote unconscious attempt for a better Nigeria be blessed …
Thanks for your persistence
Ibrahim Abubakar Ahmad May 26, 2021 At 11:22 pm
God bless Nigeria
Shior solex Apr 25, 2021 At 4:54 pm
I really appreciate him for such a nice job.
Greater height to attain.
Onoja Sunday Apr 16, 2021 At 12:41 am
Is there vacancy in your company?

redpajama_set_name: RedPajamaCommonCrawl


Ultra Omega Burn Review - Users Reviews and Experience EXPOSED!
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text:
import {
  Directive,
  ElementRef,
  OnInit,
  Input,
  Renderer2,
  HostBinding,
} from '@angular/core';

@Directive({
  selector: '[sprkFieldError]',
})
export class SprkFieldErrorDirective implements OnInit {
  /**
   * @ignore
   */
  constructor(public ref: ElementRef, private renderer: Renderer2) {}

  /**
   * The value supplied will be assigned
   * to the `data-id` attribute on the
   * element. This is intended to be
   * used as a selector for automated
   * tools. This value should be unique
   * per page.
   */
  @HostBinding('attr.data-id')
  @Input()
  idString: string;

  /**
   * The value supplied will be assigned to the
   * `data-analytics` attribute on the element.
   * Intended for an outside
   * library to capture data.
   */
  @HostBinding('attr.data-analytics')
  @Input()
  analyticsString: string;

  ngOnInit(): void {
    this.renderer.addClass(this.ref.nativeElement, 'sprk-b-ErrorContainer');
  }
}

redpajama_set_name: RedPajamaGithub


text:
Q: Html css dropdown submenu i am trying to design this search box filter using html & css. I tried many times but it is not look same as xd design. please help me to design this using html css.
search box dropdown filter - xd file
design i got

.wrapper{
    margin-right: auto;
  }

  .wrapper .search box{
    width: 400px;
    background: #fff;
    border-radius: 30px;
    height: 40px;
    display: flex;
    padding: 10px;
    box-shadow: 0 8px 6px -10px #b3c6ff;
  }

  .wrapper .search box .dropdown{
    width: 150px;
    border-right: 2px solid #dde2f1;
    color: #9fa3b1;
    position: relative;
    cursor: pointer;

  }

  .wrapper .search box .dropdown .default option{
    padding: 5px 25px;
    font-size: 10px;
    user-select: none;
  }

  .wrapper .search box .dropdown ul{
    position: absolute;
    top: 30px;
    padding-right: 20px;
    border-radius: 25px;
    padding: 10px;
    display: none;
    box-shadow: 8px 8px 6px -10px #b3ffbd;
    font-style: none;
    color: #000;
    width: 200px;
    height: 300px;
    z-index: 10;
  }

  #menu{
    background: #fff;
  }

  #moveul{
    background: #fff;
    border-radius: 25px;
    width: 220px;
  }

  .wrapper .search_box .dropdown ul ul {
    top: 0;
    left: 150px;
    margin: 0 0 0 20px;
    border-radius: 25px;
  }

  .wrapper .search_box .dropdown ul a {
    padding: 10px;
    width: 130px;
    display: block;
    white-space: nowrap;
    float: none;
    text-transform: uppercase;
    font-size: 10px;
  }

  .wrapper .search_box .dropdown ul.active{
    display: block;
  }

  .wrapper .search_box .dropdown ul li{
    padding-bottom: 10px;
    user-select: none;
    list-style: none;
  }

  .wrapper .search_box .dropdown ul li:last-child{
    box-shadow: none;
  }

  .wrapper .search_box .dropdown ul li a{
   color: #000;
  }

  .wrapper .search_box .dropdown ul li ul{
    border-radius: 5px;
    font-style: none;
    color: #000;
    padding-left: 20px;
  }

  .wrapper .search_box .dropdown ul li ul li a{
    align-items: center;
    width: 170px;
    height: 20px;
    padding: 20px;
    border: 1px solid rgb(10, 10, 10);
    border-radius: 25px;
  }

  .wrapper .search_box .dropdown ul li:last-child{
    padding-bottom: 0;
  }

  .wrapper .search_box .dropdown ul li:hover{
    color: #6f768d;
  }

  .wrapper .search_box .dropdown:before{
    content: "";
    position: absolute;
    top: 10px;
    right: 5px;
    border: 8px solid;
    border-color: #5078ef transparent transparent transparent;
  }

  .wrapper .search_box .search_field{
    width: 350px;
    height: 100%;
    position: relative;
  }

  .wrapper .search_box .search_field .input{
    width: 100%;
    height: 100%;
    border: 0px;
    font-size: 12px;
    padding-left: 20px;
    padding-right: 50px;
    color: #6f768d;
    outline: none;
  }

  .wrapper .search_box .search_field .fas{
    position: absolute;
    top: 5px;
    right: 10px;
    font-size: 15px;
    color: #5078ef;
    cursor: pointer;
  }

          <option value="" class="default_option">All Categories
          </option>

Graphic Design

Graphic Design

Example

Example

App Development

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 <input type="text" class="input" placeholder="Search Your Items Here">

i am trying to design this search box filter using html & css. I tried many times but it is not look same as xd design. please help me to design this using html css.


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text:
Managing Fisheries in a Harsh New Environment
Pacific Standard Staff
On data gathering in the Timor Sea.
By Philip Jacobson
A worker sorts fish from the Timor Sea by size and type at a processing plant in Benoa Harbor, Bali. (Photo: Risal Pramana/The Nature Conservancy Indonesia Fisheries Conservation Program)
When researchers from The Nature Conservancy chose an Indonesian fishing grounds as the focus for a project in 2014, they thought it would be a relatively simple fishery. Word was, only five or 10 species were being pulled out of these waters in the Timor Sea, near Indonesia's maritime border with Australia. It seemed similar to a deep-slope fishery they had studied in Hawaii, and TNC thought it would be the perfect candidate to turn into a showcase of sustainable management.
The reality was far more complicated.
"Everyone is confused about fish identification," Elle Wibisono, a consultant on the project, wrote on her blog after the project was underway. "Government agencies have no idea, the fishermen themselves don't know, NGOs don't know. No one has a fucking clue."
Authorities need to know what species a fishery contains in order to design policies that prevent overfishing so the resource doesn't collapse. But, in the Timor Sea, fishers were catching any number of species, yet processing plants were labeling them as either snapper or tuna and shipping them to markets like Taiwan and the United States.
"Snapper just means, Not Tuna," Wibisono went on. "Tuna means tuna. Mackerel means tuna. Albacore means tuna. Anything vaguely snapper looking and reddish in colour is a fucking red snapper. Ruby snapper is a red snapper. Random snappers are red fucking snappers. Basically fish industries reduce 110+ species of fishes commonly caught into like … 6 fish categories. So if you look at the current data everything is probably tuna or red fucking snapper. And that, as you can imagine is not going to give [us] remotely accurate data about the fisheries condition in Indonesia."
Welcome to Indonesia, a sprawling archipelago of 17,000 islands that spans one-eighth of the world's circumference and constitutes one of the world's most complicated fisheries. The Southeast Asian country sports unparalleled marine biodiversity, with world-beating coral spreads, mangrove stands, and seagrass ecosystems. The creatures therein are similarly multifarious.
Yet the marine resources of Indonesia and other major fish producers are being exploited at a dangerous rate. From 1970 to 2010, the populations of fish used by humans fell by half. "The picture is now clearer than ever: humanity is collectively mismanaging the ocean to the brink of collapse," Marco Lambertini, director general of World Wildlife Fund International, wrote in 2015.
Climate change exacerbates the problem. Around the equator, where Indonesia lies, climate change is reducing fish productivity, making stocks more vulnerable to overfishing. More generally, warming oceans and rising acidity degrade the reefs that one-quarter of all sea dwellers call home. The World Wildlife Fund warns that, if present trends continue, all coral reefs could be gone by 2050.
"If you see many small fish in the catch — so many juveniles — you can be relatively sure that the fishery is not in good shape."
In Indonesia—unfortunately for the archipelago's vast natural wealth—governance is weak, corruption prevalent, and official data poor. For decades, the nation's fisheries were managed out of Jakarta, the capital, with a mind toward ramping up production to increase export revenue — how to get more boats with more nets and more fishermen catching more fish to send abroad. Sustaining fish stocks for the long term has traditionally been an afterthought.
That is starting to change. Last year, reformist Joko "Jokowi" Widodo was elected president. As one of his first orders of business, and in line with his promise to turn Indonesia into a "global maritime axis," Jokowi declared war on illegal, unregulated, and unreported (IUU) fishing and appointed maverick seafood and airline entrepreneur Susi Pudjiastuti as fisheries minister to carry it out. The popular Pudjiastuti is known for dropping out of high school and smoking a lot of cigarettes. She started out as a seafood distributor on Java's southern coast; later, the turboprop planes she had acquired to transport fresh fish to Jakarta became the foundation for Susi Air, now a successful airline.
After becoming minister, she became famous for rounding up illegal foreign fishing boats and literally blowing them out of the water (after evacuating their crews, of course). In her office, Pudjiastuti is said to keep three giant screens, one that serves as a cat-and-mouse board with the locations of naval vessels and suspicious ships she's tracking, one that functions as a dashboard for how she evaluates her efforts to resolve fisheries in the country — and one that displays her social media stats. The minister has 170,000 followers on Twitter.
In the face of vested interests, Pudjiastuti has moved boldly to reduce overfishing. She has imposed size-catch limits for certain species, cut fuel subsidies for fishing boats, and banned trawling, an all-too-effective fishing method in which a massive net is dragged through the sea, ensnaring everything in its path. Yet the task before Pudjiastuti is so Herculean, the problems on her plate so entrenched, that, even with the recent reforms, Indonesia's fisheries still hang on the precipice between sustainability and collapse.
The single greatest limitation might be a dearth of reliable data. To properly manage a fishery, authorities need to know both the state of the stock and how many fishers are hitting it. Then they can put two and two together and design policies to prevent there from being too many fishers for too few fish. "That's one of the main reasons we're interested in species composition," says Peter Mous, a researcher at TNC, the non-governmental organization working on the Timor Sea project. "For us, it's not really about discovery and finding new species; it's to draw conclusions about the exploitation status in this fishery and later on to help fishery managers make decisions on how to manage it."
An image from The Nature Conservancy's species identification guide shows how similar different species of fish can look. (Photo: The Nature Conservancy)
That's why TNC's discovery of widespread misidentification sounded alarm bells. Different fish have different biological profiles: They grow differently, they die differently, and how they grow and die basically determines how much you can take from a certain population in a given year. "That's called stock assessment, and you need to assess stock before you develop a fishery—and as it develops—so you can say how the fishery should grow," says Daniel Pauly, a French marine biologist who to his present regret helped introduce trawling to Indonesia in the 1970s.
In the Timor Sea, Mous' team quickly realized they would have to start from square one. Among the head-scratchers they encountered were two snappers that look almost identical, except one grows to be twice as large as the other. Between them, though, no distinction was being made, putting the bigger one in danger of being harvested at only half its full size.
"If you see many small fish in the catch — so many juveniles — you can be relatively sure that the fishery is not in good shape," Mous says. "People are fishing them so hard that very few of them make it to large size. You simply will not have enough parent stock anymore. And it is also inefficient — you are taking these fish before they have reached growth potential. Of course, you can only draw these conclusions if you know for sure that you've got the species right."
That's not as easy as it sounds. In line with the project, TNC produced an identification guide that includes a section on telling similar species apart. Often the difference is minute — a yellow stripe here, an eye shape there. Further muddying things is the lack of an accepted Indonesian nomenclature for fish names outside the most common species, or any standards on labeling of seafood in the country. But straightening out the nomenclature is becoming more crucial, especially as the U.S. Food and Drug Administration becomes stricter in its naming conventions to prevent misbranding. Previously, a trader could export fish to the U.S. under whatever name they desired — cherry snapper, salmon trout. Now, if an Indonesian company wants to send, say, a box of snapper into the country, they must either call it just that or label it with the precise scientific and English names.
In 1988, Pauly and a colleague began to encode the software that would become FishBase, now the largest and most-accessed online database for fish. Previously, information about different species lay uncollated in the world, scattered across multitudes of journal articles, academic papers, newsletters, and more. FishBase aspired to put it all in one place: a comprehensive record of every fish known to science. In the pre-Internet days, that meant on CD-ROM; in 1996, Pauly launched the Web version. Today, the independent platform has tens of thousands of entries, with data on taxonomy, morphology, geographical distribution, behavior, and more.
Despite its ambitions, however, FishBase isn't perfect — a fact TNC researchers were forced to confront. FishBase draws from scientific papers, but those aren't always accurate. Neither is it always updated with the latest taxonomic revisions, Mous says, so, for lesser-known species like many of those in the Timor Sea, it often falls short.
"If you're a fish company and you're not thinking about sustainability, you're not thinking about the sustainability of your company."
Pauly knows it isn't foolproof. "FishBase was conceived as a database that would enable stock assessment exactly in regions like eastern Indonesia where they don't have local data, to enable people to have at least some similar fish that they can build models to resemble the situation and run different scenarios," he says. If a fishery has 10 more species than previously believed, Pauly says, best practice would require that it be developed slowly, with managers assessing how much can be caught based on feedback from the resource itself.
Usually, though, "fisheries in the entire world become managed only after they have become developed," Pauly says. "Maybe I'm going too far, but the job of development nowadays should not be to find new resources that we can massacre until the next time we find new resources. The job of development is to change the mode of interaction with the resource so it becomes sustainable."
That's what Mous' team is trying to do. Working with the private sector and local communities, TNC is equipping Timor Sea fishing boats with smart weighing and measuring systems to enable better data collection at the point of capture. Previously, vessels kept pen and paper records, with crew members shouting numbers to each other at sea and one of them jotting it down. Now, they catch a fish, put it in a box on an electronic scale, and press a button to measure the weight, which is entered in a database. The companies share the data with TNC and can also use it for their own purposes.
Mous' team is working with small-scale traders on nearby islands too. TNC trains them in species identification and pays them to keep more accurate records of the fish they buy. Expensive technology isn't feasible at this scale — the typical partner works with a small house on the beach and four or five iceboxes — so they still use pen and paper.
One of TNC's larger partners is PT Prima Indo Ikan. Its director, Lucas Papierniak, is a Hawaii native who used to hunt snapper and other fish with droplines in the deepwater fishery there. Then signs of overfishing began to emerge, and the U.S. government bought out the existing licenses to shut it down. (It was one of the George W. Bush administration's final acts, Papierniak says.)
At the same time, cheaper echo sounding and GPS technology had made deepwater fisheries in Indonesia more accessible. And the closure of the Hawaiian fishery meant more demand for snapper, said to be abundant in the Timor Sea. Papierniak decided to try his luck.
The Timor Sea fishery is still in relatively good shape, all things considered. But Papierniak knows how easily things can go south. Constant monsoons as a result of the El Niño weather phenomenon, made more frequent by climate change, made last year a tough one for Prima Indo. Storms often limited his boats to protected bays, eroding his take significantly.
This year, Papiernak hopes to bounce back. TNC's equipment and software are a welcome addition to his ships. "If you're a fish company and you're not thinking about sustainability, you're not thinking about the sustainability of your company," he says.
TNC's project has already started to yield some results. The NGO just got Norpac, one of the buyers of snappers and groupers from the Timor Sea, to agree on a minimum size for the five species that are deepest in trouble. TNC is now trying to get other companies to follow suit. It is also encouraging the Indonesian government to establish legal minimum sizes and establish standards for labeling and traceability.
Mous is optimistic about finding the right technical and regulatory approaches to improve the fishery's health. Increasingly, he says, markets demand sustainable fish, and the public is getting more aware on sustainability.
"The big question is about the political will to address problems with open access, and the political will to take sound, well-informed decisions that will affect fishing practices of the domestic fishing fleet," he says. "I think the stars will align eventually, but I have no idea whether that will happen in two months or two years from now."
The Conservation in the Age of Climate Change Project is an effort to explore how conservation organizations around the world are responding to rising seas, droughts, extreme weather events, and other threats posed by global warming.
Conservation In 2016
Pacific Standard publishes stories that matter, stories that, by virtue of their ideas and craft, are capable of creating a better and more just society. With a methodology that mixes reporting and narrative journalism with peer-reviewed research, we are fiercely committed to covering social and environmental justice.
A Potentially Dangerous New Model for Saving the Environment
The Case of the Disappearing Salmon
Why Scientists Are Banking on Drones for Tracking Coastal Climate Research
Saving the Cows — and Grasslands — of Rural Zimbabwe
Moving Taholah Village Before It's Swallowed by the Sea
'There Are Innumerable Ways to Make a Difference'


text:
'CC Analysts Give Their Predictions for Syracuse Football vs. Louisville
(AP Photo/Nick Lisi)
Saturday, Nov 18, 2017 at 4:25 pm by Sports Editor
Our analysts give their keys and predictions as the Orange heads South to Louisville to take on Heisman Winner Lamar Jackson and the Cardinals.
Jonathon Hoppe
How Louisville Wins: Start Fast
The Cardinals need to assert their dominance over Syracuse. Coming off three straight loses, the Orange is very vulnerable. If Louisville comes out fast, they should handle this game with ease.
How Syracuse Wins: Show up on Defense
It can't get much worse than the second half of last week's game. Allowing 64 points to Wake Forest in the Carrier Dome? Really? Hopefully the Orange uses that as motivation for this matchup against the Cardinals. If they don't make any adjustments, things could get even more out of hand on Saturday.
Prediction: Louisville 42, Syracuse 28
At this point, it's pretty clear that SU isn't going to win a road game this season. The Cardinals are going to be too much for the Orange in Jackson's final game at Papa John's Stadium. If Eric Dungey doesn't play in this one, expect the score to be even more one-sided.
JD Raucci
How Louisville Wins: Get to the quarterback
Whether it's Dungey or Zack Mahoney under center, the recipe for success in beating Syracuse is to disrupt the quarterback. Dino Babers's offense is predicated on quick timing routes that set up bigger plays downfield. However, if Louisville can put consistent pressure on SU's signal caller, then a lot of these timing routes can be taken away simply because there will be too much going on for the QB to get the pass away. This Syracuse team gives up the 10th most sacks per game and Louisville has two players with at least six sacks on the season, so that may be the way Louisville pulls out the victory on Saturday.
How Syracuse Wins: Limit Lamar Jackson's Legs
Dino Babers said it himself this week: Lamar Jackson is "going to get his." Babers went as far as to compare the reigning Heisman Trophy winner to Michael Jordan. Like Jordan, there isn't a definitive way to prevent Jackson from making an impact, so if Syracuse wants any shot at winning this game, it's going to have to at least limit his production. Jackson averages over 400 yards of total offense per game and leads the Cardinals in both passing and rushing. SU has to try to take away one of those abilities to stay in this one. The most effective way to do this is by limiting Jackson's ability to take off out of the pocket. If Syracuse uses something along the lines of a QB spy and is able to set the edge effectively, it may just be able to keep Jackson in the pocket and force him to beat them with his arm. He's far more effective when he can out of the pocket and create plays, so limiting his scrambling ability may be the key to a victory.
Louisville 45-21
After giving up nearly 500 yards of total offense to the likes of John Wolford last week, why should we expect anything better this week against a superstar in Lamar Jackson? Jackson is the most electrifying player in the entire country and torched SU for over 600 yards last season. Now he'll be playing at home and is likely to have an absolute field day against an SU defense that is just too beat up to stop him. On the offensive end, the quarterback situation is still very up in the air and for that reason, I just don't see SU being able to pull out a win down in Kentucky.
Tim Leonard
How Louisville Wins: Step Up in the Secondary
Louisville has underachieved this year mainly because of its porous secondary. The Cardinals are outside the top 100 in the country in passing defense and allow roughly 255 yards per game through the air. Knowing that and SU's customary style of offense, the Orange is going to throw as much as possible regardless of who gets the starting nod under center. Most of Louisville's losses have come in the shootout fashion because it's secondary has been exposed. Syracuse has the firepower to do the same and will likely need to in order to keep pace with Jackson and the Louisville offense.
How Syracuse Wins: Make Lamar Jackson Uncomfortable
Words really can't describe just how awful the Syracuse defense was in the second half against Wake Forest. SU simply let Wake's quarterback John Wofford do whatever he pleased. It was by far the worst defensive performance of the year for Syracuse and it looked eerily similar to what Lamar Jackson did to the Orange in the Carrier Dome last year. The only way SU is going to afford a repeat performance is if it gets to Jackson early and often. Brian Ward's defense was only able to contain Wofford when they dialed up blitzes. I expect Syracuse to send the house on third down a lot and sell out to stop Jackson because that's really the only way you can make the reigning Heisman trophy winner uncomfortable.
This isn't going to be the shootout or the blowout everyone is expecting. The SU defense is going to answer the call in a way and go back to playing like it had been before last week. However, if Eric Dungey and Dontae Strickland don't go, Syracuse still won't find enough offense to keep pace with a more talented Louisville team on the road. This game's going to play out like the movie we've been watching all season. Syracuse will keep this thing close and have a slim chance late but in the end SU will fall a couple turnovers and close plays away from making a moral victory a real victory.
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The work Così fan tutte : opera buffa in due atti represents a distinct intellectual or artistic creation found in Brigham Young University. This resource is a combination of several types including: Work, Audio, Musical, Music.
"Live recording made at the Salzburg Festival; Karl Böhm's 80th birthday, 28 August, 1974."


The Department of Epidemiology and Biostatistics at Temple University College of Public Health seeks a talented, highly motivated, and self-driven researcher with a background in epidemiology, biostatistics, applied mathematics, or other related social/behavioral science to work with Dr. Brandie DePaoli Taylor and Dr. Abby Rudolph. The College of Public Health is fully accredited by the Council on Education in Public Health as a school of public health and enrolls nearly 5,000 students in 8 undergraduate and 28 graduate degree programs. The College has experienced remarkable growth and organizational expansion in the educational programs offered, research activities supported by NIH, NSF, and other major funders, and interdisciplinary collaborations. Researchers at the College are actively engaged in projects which focus on social, behavioral, and environmental inquiries, medical/health interventions, and basic science and are sponsored by NIH, NSF and other major funders. Temple is an urban, research-intensive R1 university located in Philadelphia, a city rich in history, culture and diversity.
The post-doc will primarily work on two projects and will be expected to produce peer-reviewed publications and presentations at scientific meetings.
The first project will involve conducting analyses that incorporate census track level data, sexual risk behaviors, substance use, socioeconomic status, and mental health indicators to measure syndemic risk clusters for sexually transmitted infections in youth and young adults. This National Center for Health Statistics (NCHS) approved project will utilize restricted access data from the National Health and Nutrition Examination Survey (NHANES) through the Philadelphia Federal Statistical Research Data Center. Various approaches including latent class analyses, regression trees and structural equation modeling will be implemented to determine gender and race/ethnic specific risk profiles for sexually transmitted infections.
The second project will involve conducting analyses to understand the independent and joint contribution of social network and neighborhood/spatial factors on voluntary drug treatment enrollment, use of harm reduction services, and other health-seeking behaviors among persons who use drugs and/or who are living with HIV/AIDS. Spatial analyses will involve defining activity spaces, computing road network distances (miles and minutes) using varying modes of transportation, location-allocation models, and joining individual-level data with neighborhood and census data sources. Network analyses will include egocentric, sociometric, and dyadic analyses.
This position will provide postdoctoral research training, funds for travel to conferences for research dissemination and professional development, and a competitive salary and benefits for one year, renewable for a second year upon satisfactory performance. The fellow will be expected to learn new research skills, develop independent avenues of research, participate in interdisciplinary team science, and publish research findings.
Strong applicants will demonstrate evidence of scholarly potential that will make them competitive for tenure-track appointments at a research university. Interested candidates should send a CV, cover letter summarizing relevant training and research experiences, two publications (or representative writing samples), and the names of 3 references to abby.rudolph@temple.edu and b.depaoli-taylor@temple.edu.


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IPL 2022: Kolkata Knight Riders rope in Bharat Arun as bowling coach for upcoming season
Bharat Arun served the Indian cricket team as the bowling coach from 2017 to 2021.
By Salman Anjum - 14 Jan, 2022
Indian Premier League (IPL) franchise Kolkata Knight Riders (KKR) on Friday (January 14) roped in Bharat Arun as bowling coach for the upcoming season.
Taking to Twitter, KKR confirmed the appointment, stating: "???????????????????????????????????????????????? We are delighted to introduce you to our new bowling coach! Welcome to the Knight Riders family, Bharat Arun."
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We are delighted to introduce you to our new bowling coach! Welcome to the Knight Riders family, Bharat Arun ????????#KKR #AmiKKR #IPL2022 #BharatArun pic.twitter.com/MpAXJMa67C
— KolkataKnightRiders (@KKRiders) January 14, 2022
Notably, Bharat Arun served the Indian cricket team as the bowling coach from 2017 to 2021. His contract ended after the recently held ICC Men's T20 World Cup in UAE and Oman.
Arun has often been credited for the fast bowling revolution which the national team has seen in the last few years.
Under his mentorship, the likes of Jasprit Bumrah, Mohammad Shami, Ishant Sharma, Umesh Yadav, Mohammed Siraj among others improved their bowling by many folds and helped India win Test series away from home.
IPL 15 IPL 2022 Bharat Arun KKR Kolkata Knight Riders IPL Indian Premier League
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