text:
\section{Introduction}
Concurrent multiagent planning is a branch of multiagent planning in which several agents {\em collaborate} to solve a given problem. Collaboration takes the form of {\em concurrent} or {\em joint} actions that are executed together by multiple agents. Concurrent multiagent planning is challenging for several reasons: the number of concurrent actions is worst-case exponential in the number of agents, and restrictions are needed to ensure that concurrent actions are well-formed. Usually, these restrictions take the form of {\em concurrency constraints}~\cite{BoutilierB01,Cr13b}, which model both the case for which two actions {\em must} occur in parallel, and for which they {\em cannot} occur in parallel.

In spite of recent progress in multiagent planning, there are relatively few multiagent planners that can reliably handle concurrency. CMAP~\cite{Borrajo13}, MAPlan~\cite{StolbaFK16} and MH-FMAP~\cite{TorrenoOS14} can all produce concurrent plans, but are not designed to handle more complex concurrency constraints. \citeauthor{CrosbyJR14}~(\citeyear{CrosbyJR14}) associate concurrency constraints with the {\em objects} of a multiagent planning problem and transform the problem into a sequential, single-agent problem that can be solved using a classical planner. \citeauthor{ShekharB18}~(\citeyear{ShekharB18}) adapt this approach using {\em collaborative actions}, i.e.~single actions that involve the minimum number of agents necessary to perform a given task. \citeauthor{BrafmanZoran14}~(\citeyear{BrafmanZoran14}) extend the distributed forward-search planner MAFS \cite{NissimB14} to support concurrency constraints while preserving privacy. \citeauthor{MaliahBS17}~(\citeyear{MaliahBS17}) propose MAFBS, which extends MAFS to use forward and backward messages.

In this paper we describe a planner that can handle arbitrary concurrency constraints, as originally proposed by~\citeauthor{BoutilierB01}~(\citeyear{BoutilierB01}) and later extended by \citeauthor{kovacs2012multi}~(\citeyear{kovacs2012multi}). Our approach is similar to previous approaches in that we transform a multiagent planning problem into a single-agent problem with much fewer actions, avoiding the exponential blowup associated with concurrent actions. The concurrency constraints of \citeauthor{BoutilierB01} are significantly more expressive than those of~\citeauthor{Cr13b}~(\citeyear{Cr13b}), enabling us to solve multiagent problems with more complex interactions (e.g.~effects that depend on the concurrent actions of other agents). We show that our planner is sound and complete, and perform experiments in several concurrent multiagent planning domains to evaluate its performance.

The remainder of this paper is structured as follows. We first introduce the planning formalisms that we need to describe our planner. Next, we describe the compilation from multiagent planning to single-agent planning. We then present the results of experiments in several domains that require concurrency. Finally, we relate our planner to existing work in the literature, and conclude with a discussion.

\section{Background}

In this section we describe the planning formalisms that we use: classical planning and concurrent multiagent planning.

\subsection{Classical Planning}

We consider the fragment of classical planning with conditional effects and negative conditions and goals. Given a fluent set $F$, a {\em literal} $l$ is a valuation of a fluent in $F$, where $l=f$ denotes that $l$ assigns true to $f\in F$, and $l=\neg f$ that $l$ assigns false to $f$. A literal set $L$ is {\em well-defined} if it does not
assign conflicting values to any fluent $f$, i.e.~does not
contain both $f$ and $\neg f$. Let $\mathcal{L}(F)$ be the set of well-defined literal sets on $F$, i.e.~the set of all partial assignments of values to fluents. Given a literal set $L\in\mathcal{L}(F)$, let $\neg L=\{\neg l:l\in L\}$ be the {\em complement} of $L$. We also define the {\em projection} $L_{|X}$ of a literal set $L$ onto a subset of fluents $X\subseteq F$.

A {\em state} $s\in\mathcal{L}(F)$ is a well-defined literal set such that $|s|=|F|$, i.e.~a total assignment of values to fluents. Explicitly including negative literals $\neg f$ in states simplifies subsequent definitions, but we often abuse notation by defining a state $s$ only in terms of the fluents that are true in $s$, as is common in classical planning.

A classical planning problem is a tuple $\Pi=\left\langle F,A,I,G \right\rangle$, where $F$ is a set of fluents, $A$ a set of actions, $I\in\mathcal{L}(F)$ an initial state, and $G\in\mathcal{L}(F)$ a goal condition (usually satisfied by multiple states). Each action $a \in A$ has a precondition $\pre(a)\in\mathcal{L}(F)$ and a set of conditional effects $\cond(a)$. Each conditional effect $C\rhd E\in\cond(a)$ has two literal sets $C\in\mathcal{L}(F)$ (the condition) and $E\in\mathcal{L}(F)$ (the effect).

An action $a\in A$ is applicable in state $s$ if and only if $\pre(a)\subseteq s$, and the resulting (triggered) {\em effect} is given by
\[
\eff(s,a)=\bigcup_{C\rhd E\in\cond(a),C\subseteq s} E,
\]
i.e.~effects whose conditions hold in $s$. We assume that $\eff(s,a)$ is a well-defined literal set in $\mathcal{L}(F)$ for each state-action pair $(s,a)$. The result of applying $a$ in $s$ is a new state $\theta(s,a)=(s\setminus \neg\eff(s,a))\cup\eff(s,a)$. It is straightforward to show that if $s$ and $\eff(s,a)$ are in $\mathcal{L}(F)$, then so is $\theta(s,a)$.

A {\em plan} for planning problem $\Pi$ is an action sequence $\pi=\langle a_1, \ldots, a_n\rangle$ that induces a state sequence $\langle s_0, s_1, \ldots, s_n\rangle$ such that $s_0=I$ and, for each $i$ such that $1\leq i\leq n$, action $a_i$ is applicable in $s_{i-1}$ and generates the successor state $s_i=\theta(s_{i-1},a_i)$. Plan $\pi$ {\em solves} $\Pi$ if and only if $G\subseteq s_n$, i.e.~if the goal condition holds after applying $\pi$ in $I$.

\subsection{Concurrent Multiagent Planning}

The standard definition of multiagent planning problems (MAPs) is due to~\citeauthor{BrafmanD08}~(\citeyear{BrafmanD08}). Formally, a MAP is a tuple $\Pi = \langle N,F,\lbrace A^i\rbrace_{i\in N},I,G \rangle$, where $N = \left\lbrace1,\ldots,n\right\rbrace$ is a set of agents and $A^1,\ldots,A^n$ are disjoint sets of atomic actions of each agent. The fluent set $F$, initial state $I$ and goal condition $G$ are defined as for classical (single-agent) planning. The definition and semantics of a plan $\pi$ are also identical to those for classical planning, except that $\pi$ is a sequence of {\em joint actions}, which we proceed to define.

Let $A=A^1\cup\cdots\cup A^n$ be the set of all atomic actions. A joint action $a=\{a^1,\ldots, a^k\}\subseteq A$ is a subset of atomic actions such that $|A^i\cap a|\leq 1$ for each $i\in N$, i.e.~each agent contributes at most one action to $a$, implying $k\leq n$. The precondition and effect of $a$ are defined as the union of the preconditions and effects of the constituent atomic actions:
\[
\pre(a)=\bigcup_{j=1}^k \pre(a^j), \;\;\;\; \eff(s,a)=\bigcup_{j=1}^k \eff(s,a^j).
\]
A joint action $a$ is {\em well-defined} if $\pre(a)$ and $\eff(s,a)$ are well-defined literal sets in $\mathcal{L}(F)$ for each state $s$.

In general, nothing prevents two atomic actions $a^1$ and $a^2$ of different agents from having conflicting preconditions or effects. Hence any joint action that includes both $a^1$ and $a^2$ is not well-defined. Moreover, some atomic actions may only be applicable together. For example, in the \textsc{BoxPushing} domain~\cite{BrafmanZoran14}, some boxes are too heavy to push for a single agent, and a joint action is only applicable if enough agents push a box concurrently.

To ensure that joint actions are applicable and well-defined, researchers usually impose {\em concurrency constraints} on joint actions, which can be either positive or negative:
\begin{itemize}
	\item A \textit{positive concurrency constraint} states that a subset of atomic actions \textit{must} be performed concurrently.
	\item A \textit{negative concurrency constraint} states that a subset of atomic actions \textit{cannot} be performed concurrently.
\end{itemize}

In PDDL 2.1~\cite{Fox:PDDL21:JAIR2003}, two actions $a^1$ and $a^2$ cannot be applied concurrently if $a^1$ has an effect on a fluent $f$ and $a^2$ has a precondition or effect on $f$. This concurrency constraint requires no prior knowledge apart from the action definitions, but can only model negative concurrency.

\citeauthor{Cr13b}~(\citeyear{Cr13b}) defines concurrency constraints in the form of {\em object affordances}, i.e.~integer intervals that determine how many agents can interact with an object concurrently. An object with affordance $[1,1]$ can only be manipulated by one agent, while $[2,10]$ requires manipulation by at least two and at most ten agents. This approach assumes that joint actions are well-defined whenever object affordances are satisfied. \citeauthor{CrosbyJR14}~(\citeyear{CrosbyJR14}) extend the approach to affordances on {\em object sets}.

\citeauthor{BoutilierB01}~(\citeyear{BoutilierB01}) proposed an alternative definition of concurrency constraints, later extended by \citeauthor{kovacs2012multi}~(\citeyear{kovacs2012multi}). The idea is to extend the preconditions of actions with {\em other actions} in addition to fluents. If an atomic action $a^1$ has precondition $a^2$, then $a^2$ must be applied concurrently with $a^1$, while a precondition $\neg a^2$ implies that $a^2$ cannot be applied concurrently with $a^1$. A joint action is only applicable if the concurrency constraints (i.e.~preconditions) of {\em all} constituent atomic actions hold, and applicable joint actions are assumed to be well-defined.

As a side effect of the latter approach, we can also add concurrency constraints to the conditional effects of atomic actions. We illustrate this idea using the \textsc{TableMover} domain~\cite{BoutilierB01}, in which two agents move blocks between rooms using two alternative strategies:
\begin{enumerate}
	\item Pick up blocks and carry them using their arms.
	\item Put blocks on a table, carry the table together to another room, and tip the table to make the blocks fall down.
\end{enumerate}

\begin{figure}
	\begin{footnotesize}
		\begin{lstlisting}[escapechar=ä]
(:action lift-side
 ä\textbf{:agent ?a - agent}ä
 :parameters (?s - side)
 :precondition
   (and (at-side ?a ?s)
          (down ?s) (handempty ?a)
          ä\textbf{(forall} \textbf{(?a2 - agent ?s2 - side)}ä
            ä\textbf{(not (lower-side ?a2 ?s2)))}ä)
 :effect
   (and (not (down ?s)) (lifting ?a ?s)
          (up ?s) (not (handempty ?a ?s))
          (forall
            (?b - block ?r - room ?s2 - side)
            (when
              (and (inroom Table ?r)
                     (on-table ?b) (down ?s2)
                     ä\textbf{(forall (?a2 - agent)}ä
                       ä\textbf{(not (lift-side ?a2 ?s2)))}ä)
              (and (on-floor ?b) (inroom ?b ?r)
                     (not (on-table ?b)))))))
		\end{lstlisting}
	\end{footnotesize}
	\caption{Definition of the \textsc{TableMover} action $\mathsf{lift\mh side}$ using the notation of Kovacs (concurrency constraints in bold).}
	\label{fig:tablemover_liftside}
\end{figure}

Figure~\ref{fig:tablemover_liftside} shows the definition of the $\mathsf{lift\mh side}$ action in the notation of~\citeauthor{kovacs2012multi}~(\citeyear{kovacs2012multi}), which is used by agent $\mathsf{?a}$ to lift side $\mathsf{?s}$ of the table. The precondition is that the side must be down (i.e.~on the floor) and the agent cannot be holding anything. Moreover, the precondition also states that no other agent $\mathsf{?a2}$ can lower side $\mathsf{?s2}$ at the same time. When the action is applied, $\mathsf{?s}$ is no longer down but up, and $\mathsf{?a}$ is busy lifting $\mathsf{?s}$. The action also has a conditional effect (represented by the $\mathsf{when}$ clause): if some side $\mathsf{?s2}$ is not lifted by any agent $\mathsf{?a2}$, then all blocks on the table fall to the floor. This conditional effect is what makes it possible to tip the table in order to implement the second strategy above.

Note that the action $\mathsf{lift\mh side}$ is defined using $\mathsf{forall}$ quantifiers. In practice, such quantifiers are compiled away, such that the resulting actions have quantifier-free preconditions and conditional effects, as in our definition of actions.

Below we extend the notation for classical planning to incorporate the concurrency constraints of \citeauthor{BoutilierB01}~(\citeyear{BoutilierB01}). The idea is to view $A$, the full set of atomic actions, as a set of fluents that can be true or false. We can now use a set of literals on $A$ to model a joint action $a=\{a^1,\ldots, a^k\}$: the fluents in $A$ corresponding to actions $a^1,\ldots, a^k$ are true, while all other fluents in $A$ are false. Let $L(a)$ denote the literal set on $A$ that encodes $a$.

The next step is to define an extended fluent set $F\cup A$, as well as a set $\mathcal{L}(F\cup A)$ of well-defined literal sets on $F\cup A$. We can now encode a state $s$ and a joint action $a$ as an extended state $s\cup L(a)$, i.e.~a literal set in $\mathcal{L}(F\cup A)$.

To include concurrency constraints in the precondition and conditional effects of an action $a^j$, we simply define the precondition $\pre(a^j)\in\mathcal{L}(F\cup A)$ and condition $C\in\mathcal{L}(F\cup A)$ of each conditional effect $C\rhd E\in\cond(a^j)$ as well-defined literal sets on extended fluents. Each effect $E\in\mathcal{L}(F)$ is defined exclusively on fluents as before.

We can now define the semantics of a joint action $a=\{a^1,\ldots, a^k\}$. Concretely, $a$ satisfies the concurrency constraints if and only if the projected precondition $\pre(a^j)_{|A}$ holds in $L(a)$ for each $j$, $1\leq j\leq k$. If $a$ is applicable, its precondition and effect in a state $s$ are the union of the preconditions and effects of the constituent atomic actions:
\begin{align*}
\pre(a)&=\bigcup_{j=1}^k\pre(a^j)_{|F},\\
\eff(s,a)&=\bigcup_{j=1}^k\eff(s\cup L(a), a^j), \;\; \forall s.
\end{align*}
Note that the effects of atomic actions are conditional on the extended state $s\cup L(a)$.
As before, we assume that $\eff(s,a)$ is a well-defined literal set in $\mathcal{L}(F)$ for each pair of a state $s$ and an applicable joint action $a$.

We use a small example to illustrate the notation. Consider a MAP with two agents and action sets $A^1=\{a^1,a^2\}$ and $A^2=\{a^3,a^4\}$. The full set of actions is $A=A^1\cup A^2=\{a^1,a^2,a^3,a^4\}$. Assume that $a^1$ and $a^3$ are defined as
\begin{align*}
\pre(a^1) &= \{\neg a^4\}, & \pre(a^3) &= \emptyset,\\
\cond(a^1) &= \{\{\neg a^3\}\rhd\{f\}\}, & \cond(a^3) &= \{\emptyset\rhd\{g\}\}.
\end{align*}
The joint action $a=\{a^1,a^4\}$ is not applicable since the precondition $\neg a^4$ of $a^1$ does not hold in the extended state $L(a)=\{a^1,\neg a^2,\neg a^3,a^4\}$. The joint action $a'=\{a^1,a^3\}$ is applicable and results in the effect $\eff(s,a')=\{g\}$ in any state $s$. The joint action $a''=\{a^1\}$ is also applicable and results in the effect $\eff(s,a'')=\{f\}$ in any state $s$, since the condition $\neg a^3$ in the conditional effect $\{\neg a^3\}\rhd\{f\}$ of $a^1$ holds in the extended state $L(a'')=\{a^1,\neg a^2,\neg a^3,\neg a^4\}$.

\section{Compilations for MAPs}
\label{sec:compilation}

In this section we describe an approach to solving a MAP $\Pi= \langle N,F,\lbrace A^i\rbrace_{i\in N},I,G \rangle$. The idea is to model each joint action $a=\{a^1,\ldots,a^k\}$ using multiple atomic actions: one set of actions for {\em selecting} $a^1,\ldots,a^k$, one set of actions for {\em applying} $a^1,\ldots,a^k$, and one set of actions for {\em resetting} $a^1,\ldots,a^k$. The result is a classical planning problem $\Pi'=\langle F',A',I',G'\rangle$ such that the size of the action set $A'$ is {\em linear} in $|A|$, the number of atomic actions of agents.

Simulating a joint action $a$ using a sequence of atomic actions $\langle a^1,\ldots,a^k\rangle$ is problematic for the following reason: when applying an atomic action $a^i$, we may not yet know which atomic actions will be applied by other agents. Since those other actions may be part of the precondition and conditional effects of $a^i$, it becomes difficult to ensure that the concurrency constraints of $a^i$ are correctly enforced.

Our approach is to divide the simulation of a joint action $a$ into three phases: selection, application, and reset. In the selection phase, we use an auxiliary fluent $\mathsf{active\mh}a^i$ to model that the atomic action $a^i$ has been selected. In the application phase, since the selection of atomic actions is known, we can substitute each action $a^i$ in preconditions and conditional effects with the auxiliary fluent $\mathsf{active\mh}a^i$. In the reset phase, various auxiliary fluents are reset. Note that each agent can apply at most one atomic action per time step, and agents collaborate to form joint actions whose constituent atomic actions are compatible and/or inapplicable on their own.

\subsection{Fluents}
We describe the fluents in PDDL format, i.e.~each fluent is instantiated by assigning objects to predicates.

The set of fluents $F'\supseteq F$ includes all original fluents in $F$, plus the following auxiliary fluents:
\begin{itemize}
	\item Fluents $\mathsf{free}$, $\mathsf{select}$, $\mathsf{apply}$ and $\mathsf{reset}$ modeling the phase.
	\item For each agent $i$, fluents $\mathsf{free\mh agent}(i)$, $\mathsf{busy\mh agent}(i)$ and $\mathsf{done\mh agent}(i)$ that model the agent state: free to select an action, selected an action, and applied the action.
	\item For each action $a^i\in A^i$ in the action set of agent $i$, a fluent $\mathsf{active\mh}a^i$ which models that $a^i$ has been selected. We use $F_{act}$ to denote the subset of fluents of this type.
\end{itemize}
By simple inspection, the total number of fluents in $F'$ is given by $|F'|=|F| + 4 + 3n + \sum_{i\in N}\left|A^i\right|=O(|F|+|A|)$.

 The initial state $I'$ of the compilation $\Pi'$ is given by
\[
I'=I\cup\{\mathsf{free}\}\cup\{\mathsf{free\mh agent}(i):i\in N\},
\]
i.e.~the initial state on fluents in $F$ is $I$, we are not simulating any joint action, and all agents are free to select actions. The goal condition is given by $G'=G\cup\{\mathsf{free}\}$, i.e.~the goal condition $G$ has to hold at the end of a joint action simulation.

\subsection{Actions}

For a literal set $L\in\mathcal{L}(F\cup A)$, let $L_{|A}/F_{act}$ denote the projection of $L$ onto $A$, followed by a substitution of the actions in $A$ with the corresponding fluents in $F_{act}$. Note that both $L_{|F}$ and $L_{|A}/F_{act}$ are literal sets on fluents in $F'$, i.e.~the dependence on actions in $A$ is removed.

The first four actions in the set $A'$ allow us to switch between simulation phases, and are defined as follows:
\begin{table}[h]
\noindent
\begin{tabular}{ll}
	$\mathsf{select\mh phase}$: & $\mr{pre}=\{\mathsf{free}\}$,\\[2pt]
	& $\mr{cond}=\{\emptyset\rhd\{\neg\mathsf{free},\mathsf{select}\}\}$.\\[2pt]
	$\mathsf{apply\mh phase}$: & $\mr{pre}=\{\mathsf{select}\}$,\\[2pt]
	& $\mr{cond}=\{\emptyset\rhd\{\neg\mathsf{select},\mathsf{apply}\}\}$.\\[2pt]
	$\mathsf{reset\mh phase}$: & $\mr{pre}=\{\mathsf{apply}\}$,\\[2pt]
	& $\mr{cond}=\{\emptyset\rhd\{\neg\mathsf{apply},\mathsf{reset}\}\}$.\\[2pt]
	$\mathsf{finish}$: & $\mr{pre}=\{\mathsf{reset},\mathsf{free\mh agent}(i): i\in N\}$,\\[2pt]
	& $\mr{cond}=\{\emptyset\rhd\{\neg\mathsf{reset},\mathsf{free}\}\}$.
\end{tabular}
\end{table}

For each action $a^i\in A^i$ in the action set of agent $i$, we define three new actions in $A'$: $\mathsf{select\mh}a^i$, $\mathsf{do\mh}a^i$ and $\mathsf{end\mh}a^i$. These actions represent the three steps that an agent must perform during the simulation of a joint action.

The action $\mathsf{select\mh}a^i$ causes $i$ to select action $a^i$ during the selection phase, and is defined as follows:
\begin{align*}
\mr{pre}&=\{\mathsf{select, free\mh agent}(i)\} \cup \pre(a^i)_{|F},\\
\mr{cond}&=\{\emptyset\rhd\{\mathsf{busy\mh agent}(i)\mathsf{, \neg free\mh agent}(i)\mathsf{, active\mh}a^i\}\}.
\end{align*}
The precondition ensures that we are in the selection phase, that $i$ is free to select an action, and that the precondition of $a^i$ holds on fluents in $F$. The effect prevents $i$ from selecting another action, and marks $a^i$ as selected.

The action $\mathsf{do\mh}a^i$ applies the effect of $a^i$ in the application phase, and is defined as follows:
\begin{align*}
\mr{pre} &= \{\mathsf{apply, busy\mh agent}(i)\mathsf{, active\mh}a^i\}\cup\pre(a^i)_{|A}/F_{act},\\
\mr{cond}&=\{\emptyset\rhd\{\mathsf{done\mh agent}(i)\mathsf{, \neg busy\mh agent}(i)\}\}\\
&\,\cup\{C_{|F}\cup C_{|A}/F_{act} \rhd E : C \rhd E \in \mathsf{cond}(a^i) \}.
\end{align*}
The precondition ensures that we are in the application phase, that $a^i$ was previously selected, and that all concurrency constraints in the precondition of $a^i$ hold. The effect is to apply all conditional effects of $a^i$, where each condition $C_{|F}\cup C_{|A}/F_{act}$ is generated from $C$ by substituting each action $a^j\in A$ with $\mathsf{active\mh}a^j$. Agent $i$ is also marked as done to prevent $a^i$ from being applied a second time.

The action $\mathsf{end\mh}a^i$ resets auxiliary fluents to their original value, and is defined as follows:
\begin{align*}
\mr{pre}&=\{\mathsf{reset, done\mh agent}(i)\mathsf{, active\mh}a^i\},\\
\mr{cond}&=\{\emptyset\rhd\{\mathsf{free\mh agent}(i)\mathsf{, \neg done\mh agent}(i), \mathsf{\neg active\mh}a^i\}\}.
\end{align*}
The precondition ensures that we are in the reset phase and that $a^i$ was previously selected and applied (due to $\mathsf{done\mh agent}(i)$). The effect is to make agent $i$ free to select actions again, and to mark $a^i$ as no longer selected.

Again, by inspection we can see that the total number of actions in $A'$ is given by $|A'|=4 + 3\sum_i|A^i|=O(|A|)$.

\subsection{Properties}

\begin{figure}
	\centering
	\includegraphics[width=\columnwidth]{tablemover_layout_example.pdf
	\caption{Initial state of a simple \textsc{TableMover} instance.}
	\label{fig:tablemover_example_fig}
\end{figure}

Figure~\ref{fig:tablemover_example_fig} shows an example instance of \textsc{TableMover} in which the goal is for agents $a_1$ and $a_2$ to move block $b_1$ from room $r_1$ to room $r_2$. An example of concurrent plan that solves this instance is defined as follows:

\begin{lstlisting}[numbers=left, numbersep=2pt, xleftmargin=1.1em, basicstyle=\fontsize{8}{8}\ttfamily]
(to-table a1 r1 s2)(pickup-floor a2 b1 r1)
(putdown-table a2 b1 r1)
(to-table a2 r1 s1)
(lift-side a1 s2)(lift-side a2 s1)
(move-table a1 r1 r2 s2)(move-table a2 r1 r2 s1)
(lower-side a1 s2)
\end{lstlisting}
In this plan, agent $a_2$ first puts the block on the table, and then $a_1$ and $a_2$ concurrently lift each side of the table and move the table to room $r_2$. Finally, $a_1$ lowers its side of the table, causing the table to tip and the block to fall to the floor.

The following sequence of classical actions in $A'$ can be used to simulate the first joint action of the concurrent plan:
\begin{lstlisting}[numbers=left, numbersep=2pt, xleftmargin=1.1em, basicstyle=\fontsize{8}{8}\ttfamily]
(select-phase )
(select-to-table a1 r1 s2)
(select-pickup-floor a2 b1 r1)
(apply-phase )
(do-pickup-floor a2 b1 r1)
(do-to-table a1 r1 s2)
(reset-phase )
(end-to-table a1 r1 s2)
(end-pickup-floor a2 b1 r1)
(finish )
\end{lstlisting}

\noindent
We show that the compilation is both sound and complete.

\begin{theorem}[Soundness] A classical plan $\pi'$ that solves $\Pi'$ can be transformed into a concurrent plan $\pi$ that solves $\Pi$.
\end{theorem}

\begin{proof}
When fluent $\mathsf{free}$ is true, the only applicable action is $\mathsf{select\mh phase}$. The only way to make $\mathsf{free}$ true again is to cycle through the three phases and end with the $\mathsf{finish}$ action.

During the selection phase, a subset of actions $a^1,\ldots,a^k$ are selected, causing the corresponding agents to be busy. Because of the precondition $\mathsf{free\mh agent}(i)$ of the $\mathsf{finish}$ action, each selected action $a^i$ has to be applied in the application phase, and reset in the reset phase. The resulting simulated joint action is given by $a=\{a^1,\ldots,a^k\}$.

The precondition of $a$ holds since the precondition of each $a^i$ on fluents in $F$ is checked in the selection phase, during which no fluents in $F$ change values. The concurrency constraints of $a^i$ are checked in the application phase when all actions have already been selected. This also ensures that the conditional effects of $a^i$ are correctly applied. Finally, auxiliary fluents are cleaned in the reset phase. Hence the joint action $a$ satisfies all concurrency constraints and is correctly simulated by the corresponding action subsequence of $\pi'$.

Let $\pi$ be the concurrent plan composed of the sequence of joint actions simulated by the plan $\pi'$. Since $\pi'$ solves $\Pi'$, the goal condition $G$ holds at the end of $\pi'$, implying that $G$ also holds at the end of $\pi$. This implies that $\pi$ solves $\Pi$.
\end{proof}

\begin{theorem}[Completeness] A concurrent plan $\pi$ that solves $\Pi$ corresponds to a classical plan $\pi'$ that solves $\Pi'$.
\end{theorem}

\begin{proof}
Let $a=\{a^1,\ldots,a^k\}$ be a joint action of the concurrent plan $\pi$. We can use a sequence of actions in $A'$ to simulate $a$ by selecting, applying and resetting each action among $a^1,\ldots,a^k$. Since $a$ is part of $\pi$, its precondition and concurrency constraints have to hold, implying that the precondition and concurrency constraints of each atomic action hold. Hence the action sequence is applicable and results in the same effect as $a$. By concatenating such action sequences for each joint action of $\pi$, we obtain a plan $\pi'$. Since $\pi$ solves $\Pi$, the goal condition $G$ holds at the end of $\pi$, implying that $G$ holds at the end of $\pi'$. This implies that $\pi'$ solves $\Pi'$.
\end{proof}

\subsection{Extensions}
The basic compilation checks concurrency constraints in the application phase. Here we describe an extension that checks negative concurrency constraints in the selection phase, allowing a classical planner to identify inadmissible joint actions as early as possible, reducing the branching factor.

Assume that action $a^i$ has a negative concurrency constraint $\neg a^j$. As before, we can simulate this constraint using the fluent $\neg \mathsf{active\mh}a^j$. However, $a^j$ may be selected {\em after} $a^i$ in the selection phase, in which case $\neg \mathsf{active\mh}a^j$ holds when selecting $a^i$. To prevent inadmissible joint actions from being selected, we introduce additional fluents in the set $F'$:

\begin{itemize}
	\item For each action $a^i\in A^i$ in the action set of agent $i$, a fluent $\mathsf{req\mh neg\mh}a^i$ which indicates that $a^i$ cannot be selected.
\end{itemize}
We now redefine the action $\mathsf{select\mh}a^i$ as follows:
\begin{align*}
\mr{pre}&=\{\mathsf{select, free\mh agent}(i),\neg\mathsf{req\mh neg\mh}a^i\} \cup \pre(a^i)_{|F}\\ &\cup \; \{ \neg\mathsf{active\mh}a^j : \neg a^j \in \pre(a^i)\},\\
\mr{cond}&=\{\emptyset\rhd\{\mathsf{busy\mh agent}(i)\mathsf{, \neg free\mh agent}(i)\mathsf{, active\mh}a^i\}\}\\
&\cup\;\{\emptyset\rhd\{\mathsf{req\mh neg\mh}a^j : \neg a^j \in \pre(a^i)\}\}.
\end{align*}
To select $a^i$, $\mathsf{req\mh neg\mh}a^i$ has to be false. For each negative concurrency constraint $\neg a^j$ of $a^i$, action $\mathsf{select\mh}a^i$ adds fluent $\mathsf{req\mh neg\mh}a^j$, preventing $a^j$ from being selected after $a^i$.

With this extension, we only need to check {\em positive} concurrency constraints in the application phase. We also redefine $\mathsf{end\mh}a^i$ such that fluents of type $\mathsf{req\mh neg\mh}a^i$ are reset in the cleanup phase, using the opposite effect of $\mathsf{select\mh}a^i$. The initial state and goal condition do not change since the new fluents are always false while no joint action is simulated.

The second extension is to impose a bound $C$ on the number of atomic actions in the selection phase, resulting in a classical planning problem $\Pi_C'=\langle F_C',A_C',I_C',G_C' \rangle$. The fluent set $F'_C\supseteq F'$ extends $F'$ with fluents $\mathsf{count}(j)$, $0\leq j\leq C$. We add counter parameters to select and reset actions so that they can respectively increment and decrement the value of the counter. Crucially, no select action is applicable when $j=C$, preventing us from selecting more than $C$ actions. The benefit is to reduce the branching factor by restricting joint actions to have at most $C$ atomic actions.

We leave the following proposition without proof:

\begin{prop}
	The compilation $\Pi_C'$ that includes both proposed extensions is sound.
\end{prop}
Note that the compilation $\Pi_C'$ is not complete. For instance, consider a concurrent multiagent plan that contains a joint action involving 4 atomic actions. If $C < 4$, then the concurrent multiagent plan cannot be converted into an equivalent classical plan without exceeding the bound $C$.

\section{Experimental Results}
\label{sec:results}
We tested our compilations in four concurrent domains: \textsc{TableMover}, \textsc{Maze}, \textsc{Workshop} and \textsc{BoxPushing}\footnote{The code of the compilation and the domains are available at https://github.com/aig-upf/universal-pddl-parser-multiagent.}.

In each domain, we used three variants of our compilations: unbounded joint action size, and joint action size bounded by $C=2$ and $C=4$. In all variants, we used the extension that identifies negative concurrency constraints in the selection phase. The resulting classical planning problems were solved using Fast Downward~\cite{Helmert06} in the LAMA setting~\cite{richter:lama:JAIR2010}.
All experiments ran on Intel Xeon E5-2673 v4 @ 2.3GHz processors, with a time limit of 30 minutes and a memory limit of 8 GB.

The \textsc{Maze} domain \cite{crosby2014multiagent} consists of a grid of interconnected locations. Each agent in the maze must move from an initial location to a target location. The connection between two adjacent locations can be either a door, a bridge or a boat. A door can only be used by one agent at once, a bridge is destroyed upon first use, and a boat can only be used by two or more agents in the same direction.

The \textsc{Workshop} domain is a new domain in which the objective is to perform inventory in a high-security storage facility. It has the following characteristics:
\begin{itemize}
	\item To open a door, one agent has to press a switch while another agent simultaneously turns a key.
	\item To do inventory on a pallet, one agent has to use a forklift to lift the pallet while another agent examines it (for security reasons, labels are located underneath pallets).
	\item There are also actions for picking up a key, entering or exiting a forklift, moving an agent, and driving a forklift.
\end{itemize}

The \textsc{BoxPushing} domain \cite{BrafmanZoran14} consists in a grid of interconnected cells. Agents must push boxes from one cell to another. Boxes have different sizes and require different numbers of agents to push (1, 2 or 3).

We use two algorithms for comparison: \citeauthor{CrosbyJR14}~(\citeyear{CrosbyJR14}) and \citeauthor{ShekharB18}~(\citeyear{ShekharB18}), which we refer to as CJR and SB respectively. Both algorithms define concurrency constraints in the form of affordances on sets of objects. {For example, the affordance on the object set $\{\text{location}, \text{boat}\}$ in $\textsc{Maze}$ is defined as $\left[2, \infty\right]$ in CJR, representing that at least two agents have to row the boat between the same two locations at once. SB define the same affordance as $\left[2, 2\right]$, only allowing the minimum number of agents necessary to row a boat (i.e.~$2$).}

CJR do not separate atomic action selection from atomic action application. This is a problem since one of the atomic actions can delete the precondition of other atomic actions, thus canceling the formation of the joint action. For example, in the \textsc{Maze} domain, the action for crossing a bridge requires that the bridge exists, and destroys the bridge as an effect. Therefore, as this approach does not separate the selection from the application, this action can be done just by one agent at a time (and not by infinite agents as the problem states). The same occurs in the \textsc{BoxPushing} domain. Instances where a medium or a large box must be moved cannot be solved with this approach because the first agent to ``push'' the box will move it. Thus, the box location precondition for the other agent(s) does not hold, so the box is not moved in the end. On the other hand, SB extend CJR with mechanisms to avoid this problem, deferring effects until after all agents have applied their atomic actions.

Moreover, concurrency constraints in the form of object affordances are not as expressive as those of~\citeauthor{kovacs2012multi}~(\citeyear{kovacs2012multi}):
\begin{itemize}
	\item Actions cannot appear in conditional effects, making it impossible to model \textsc{TableMover} instances (SB present results from a simplified version without blocks).
	\item To define concurrency constraints, actions need at least one shared object, which is not the case in \textsc{Workshop}.
\end{itemize}

In experiments, we used Fast Downward in the LAMA setting to solve the instances produced by CJR and SB.

\begin{table*}[]
\centering
\resizebox{\textwidth}{!}{%

\begin{tabular}{lrrrrrrrrrrrrrrrrrrrrrrrrr}
\toprule
Domain              &$N$ & \multicolumn{5}{c}{Coverage}                                                                                & & \multicolumn{5}{c}{Time (s.)}                                                                                & & \multicolumn{5}{c}{Makespan}                                                                                & & \multicolumn{5}{c}{\# Grounded actions ($\times\num{e3}$)}           \\ \cmidrule{3-7} \cmidrule{9-13} \cmidrule{15-19} \cmidrule{21-25}
                    &    & \multicolumn{1}{c}{2} & \multicolumn{1}{c}{4} & \multicolumn{1}{c}{$\infty$} & \multicolumn{1}{c}{CJR }  & \multicolumn{1}{c}{SB}   & & \multicolumn{1}{c}{2} & \multicolumn{1}{c}{4} & \multicolumn{1}{c}{$\infty$} & \multicolumn{1}{c}{CJR } & \multicolumn{1}{c}{SB} & & \multicolumn{1}{c}{2} & \multicolumn{1}{c}{4} & \multicolumn{1}{c}{$\infty$} & \multicolumn{1}{c}{CJR } & \multicolumn{1}{c}{SB}  & & \multicolumn{1}{c}{2} & \multicolumn{1}{c}{4} & \multicolumn{1}{c}{$\infty$} & \multicolumn{1}{c}{CJR } & \multicolumn{1}{c}{SB} \\
\midrule
\textsc{Maze}       & 20 & \textbf{13}           & 8                     & 6                            & 11                        & 9                        & & 361.5                 & 444.2                 & \textbf{145.6}               & 195.1                    & 216.1                  & & 47.2                  & 22.0                  & \textbf{11.7}                & 77.3                     & 67.7                    & & 41.7                  & 69.3                  & \textbf{27.9}                & 156.8                    & 108.2                       \\
$a=10$              & 10 & \textbf{8}            & 6                     & 5                            & 7                         & 6                        & & 250.2                 & 575.6                 & \textbf{170.4}               & 228.4                    & 323.1                  & & 48.3                  & 25.0                  & \textbf{12.2}                & 79.6                     & 69.8                    & & 39.9                  & 67.4                  & \textbf{26.1}                & 119.3                    & 102.1                       \\
$a=15$              & 10 & \textbf{5}            & 2                     & 1                            & 4                         & 3                        & & \textbf{539.5}        & -                     & -                            & -                        & -                      & & \textbf{45.4}         & -                     & -                            & -                        & -                       & & 43.9                  & 71.8                  & \textbf{30.0}                & 194.3                    & 115.1                      \\
\midrule
\textsc{BoxPushing} & 20 & 9                     & 15                    & 16                           & -                         & \textbf{18}              & & \textbf{5.2}          & 36.4                  & 143.3                        & -                        & 305.8                  & & \textbf{11.2}         & 11.3                  & 12.9                         & -                        & 20.5                    & & 3.5                   & 5.7                   & 2.5                          & -                        & \textbf{2.0}                               \\
$a=2$               & 10 & 9                     & 9                     & 9                            & -                         & \textbf{10}              & & \textbf{5.2}          & 7.6                   & 6.0                          & -                        & 158.9                  & & \textbf{11.2}         & 11.9                  & 11.3                         & -                        & 18.4                    & & 1.8                   & 3.2                   & \textbf{1.1}                 & -                        & 1.2                               \\
$a=4$               & 10 & 0                     & 6                     & 7                            & -                         & \textbf{8}               & & -                     & \textbf{79.7}         & 319.9                        & -                        & 489.5                  & & -                     & \textbf{10.5}         & 15                           & -                        & 23.1                    & & 5.2                   & 8.2                   & 3.8                          & -                        & \textbf{2.9}                               \\
\midrule
\textsc{TableMover} & 24 & \textbf{15}           & 12                    & \textbf{15}                  & -                         & -                        & & \textbf{263.4}        & 336.7                 & 341.1                        & -                        & -                      & & \textbf{58.7}         & 59.0                  & 61.5                         & -                        & -                       & & 7.4                   & 13.1                  & \textbf{4.6}                 & -                        & -                           \\
$a=2$               & 12 & 10                    & 10                    & \textbf{11}                  & -                         & -                        & & \textbf{103.9}        & 226.6                 & 214.7                        & -                        & -                      & & 63.5                  & \textbf{62.0}         & 64.5                         & -                        & -                       & & 3.4                   & 6.1                   & \textbf{2.0}                 & -                        & -                           \\
$a=4$               & 12 & \textbf{5}            & 2                     & 4                            & -                         & -                        & & \textbf{582.4}        & -                     & -                            & -                        & -                      & & \textbf{49.0}         & -                     & -                            & -                        & -                       & & 11.5                  & 20.1                  & \textbf{7.2}                 & -                        & -                           \\
\midrule
\textsc{Workshop}   & 20 & \textbf{15}           & 13                    & 13                           & -                         & -                        & & 134.3                 & 301.4                 & \textbf{52.5}                & -                        & -                      & & 35.7                  & 37.0                  & \textbf{32.5}                & -                        & -                       & & 18.0                  & 31.0                  & \textbf{11.5}                & -                        & -                              \\
$a=4$               & 10 & \textbf{8}            & \textbf{8}            & \textbf{8}                   & -                         & -                        & & 42.8                  & 263.3                 & \textbf{37.1}                & -                        & -                      & & \textbf{37.3}         & 43.9                  & \textbf{37.3}                & -                        & -                       & & 7.7                   & 13.6                  & \textbf{4.8}                 & -                        & -                              \\
$a=8$               & 10 & \textbf{7}            & 5                     & 5                            & -                         & -                        & & 238.8                 & 362.3                 & \textbf{77.1}                & -                        & -                      & & 33.9                  & 26.0                  & \textbf{24.8}                & -                        & -                       & & 28.2                  & 48.3                  & \textbf{18.1}                & -                        & -                              \\
\bottomrule
\end{tabular}%
}
\caption{Summary of results; see text for details. $a$ is the number of agents, $N$ is number of instances; time and length are averages for all planners that solved at least 5 instances. The number of grounded actions is an average over all instances.}
\label{tab:req_conc_results}
\end{table*}

Table~\ref{tab:req_conc_results} shows the results for the four domains. To provide an idea of how each planner behaves as a function of the number of agents, the table shows for each domain the same metrics for different numbers of agents.

In terms of coverage (i.e. number of solved instances), the compilation variant bounded to 2 performs the best (52, 61.9\%). The unbounded compilation ($\infty$) and the variant bounded to 4 have similar coverage: 50 (59.5\%) and 48 (57.1\%) respectively. The performance of the variant bounded to 2 is not very good in \textsc{BoxPushing} for instances involving four agents because all of them require a large box to be pushed (i.e. three agents are required). Finally, SB and CJR are the approaches with the worst coverage: 27 (32.1\%) and 11 (13.1\%) respectively. The main reason is that they cannot solve \textsc{TableMover} and \textsc{Workshop} instances; CJR cannot solve \textsc{BoxPushing} instances either.

Regarding execution time, the unbounded compilation and the compilation bounded to 2 are the fastest. The higher the number of agents, the longer it takes to compute a plan.

In terms of makespan (i.e.~number of joint actions), our approach obtains the shortest plans. CJR and SB obtain longer plans because they only construct joint actions associated with specific concurrency constraints. Any atomic action that can be applied on its own thus becomes a joint action of size 1. In contrast, our approach can combine atomic actions arbitrarily and compress the solution while planning.

The main reason that SB works better in \textsc{BoxPushing} is due to the hardcoded representation of collaborative actions that involve a minimum number of agents. For example, to push a box that requires $b$ agents to move, SB defines collaborative actions that involve exactly $b$ agents, while in our case, a joint action involving more than $b$ agents will also satisfy the concurrency constraints. This results in a larger branching factor which in turn affects the performance.

Note however that such a minimalist representation of collaborative actions is not always complete. For example, we can define a \textsc{Maze} instance where three agents have to use a boat to cross a stream. If we only define collaborative actions that involve the minimum number of agents needed to row a boat (i.e.~2), such an instance becomes unsolvable since no sequence of 2 agents rowing the boat in different directions is capable of moving all three agents to the other side. In contrast, our approach can generate a joint action that allows all three agents to cross the stream concurrently.

We also performed a scalability experiments in the \textsc{Maze} domain. We compare our approach (the $\infty$ variant) to the naive approach of converting a MAP into a classical problem by creating a classical action for each combination of agents. The instances consisted of (1) a 3x3 grid, (2) a set of agents with the same initial and goal locations, and (3) a single path to the goal that consists of interleaved boats and bridges.

Table \ref{tab:scalability_maze} shows the number of grounded actions and solution time for varying numbers of agents. The naive approach cannot solve instances with 7 or more agents due to grounding, while our approach can solve instances with 100 agents.

\begin{table}[]
	\centering
	\resizebox{0.9\columnwidth}{!}{%
		\begin{tabular}{rrrrrr}
			\toprule
			\#Agents             & \multicolumn{2}{c}{\# Grounded actions}                 & & \multicolumn{2}{c}{Time (s.)}                            \\ \cmidrule{2-3} \cmidrule{5-6}
			\multicolumn{1}{c}{} & \multicolumn{1}{c}{Naive} & \multicolumn{1}{c}{$\infty$}& & \multicolumn{1}{c}{Naive} & \multicolumn{1}{c}{$\infty$} \\ \midrule
			2                    & 48                        & 100                         & & 0.089                     & 0.226                        \\
			4                    & 992                       & 260                         & & 0.494                     & 0.226                        \\
			6                    & 31248                     & 484                         & & 53.864                    & 0.354                        \\
			8                    & -                         & 772                         & & -                         & 0.535                        \\
			10                   & -                         & 1124                        & & -                         & 0.758                        \\
			50                   & -                         & 21604                       & & -                         & 41.979                       \\
			100                  & -                         & 83204                       & & -                         & 289.887                      \\ \bottomrule
		\end{tabular}%
	}
	\caption{Scalability of our approach ($\infty$) compared to the naive compilation in the \textsc{Maze} domain.}
	\label{tab:scalability_maze}
\end{table}

\section{Related Work}

Several other authors consider the problem of concurrent multiagent planning. \citeauthor{BoutilierB01}~(\citeyear{BoutilierB01}) describe a partial-order planning algorithm for solving MAPs with concurrent actions, based on their formulation of concurrency constraints, but do not present any experimental results. CMAP~\cite{Borrajo13} produces an initial sequential plan for solving a MAP, but performs a post-processing step to compress the sequential plan into a concurrent plan.

\citeauthor{JonssonR11}~(\citeyear{JonssonR11}) present a best-response approach for MAPs with concurrent actions, where each agent attempts to improve its own part of a concurrent plan while the actions of all other agents are fixed. However, their approach only serves to improve an existing concurrent plan, and is unable to compute an initial concurrent plan. FMAP~\cite{TorrenoOS14} is a partial-order planner that also allows agents to execute actions in parallel, but the authors do not present experimental results for MAP domains that require concurrency.

The planner of~\citeauthor{CrosbyJR14}~(\citeyear{CrosbyJR14}) is similar to ours in that it also converts MAPs into classical planning problems. The authors only present results from the \textsc{Maze} domain, and concurrency constraints are defined as affordances on object sets that appear as arguments of actions. As we have seen, these concurrency constraints are not as flexible as those of~\citeauthor{BoutilierB01}~(\citeyear{BoutilierB01}).

\citeauthor{BrafmanZoran14}~(\citeyear{BrafmanZoran14}) extended the MAFS multiagent distributed algorithm \cite{NissimB14} to support actions requiring concurrency while preserving privacy. Messages are exchanged between agents in order to inform each other about the expansion of relevant states. Consequently, agents explore the search space together while preserving privacy. As pointed out by \citeauthor{ShekharB18}~(\citeyear{ShekharB18}), it has two main problems: (1) it does not consider the issue of subsumed actions, and (2) it does not support concurrent actions that affect each others preconditions.

\citeauthor{MaliahBS17}~(\citeyear{MaliahBS17}) proposed MAFBS, which extended MAFS to use forward and backward messages. This approach reduced the number of required messages and resulted in an increase in the privacy of agents.

\citeauthor{ChouhanN16}~(\citeyear{ChouhanN16}, \citeyear{ChouhanN17}) proposed a PDDL-like language for specifying problems involving required concurrency, which is very similar to the one by \citeauthor{BoutilierB01}~(\citeyear{BoutilierB01}). Their planner does not make assumptions on the number of agents required to perform a joint action; rather, the number of agents is determined from the \emph{capability} of agents and the objects they are interacting with. For example, in a robot domain, the number of robots required to lift an specific object can depend on the weight of the object.

\citeauthor{ShekharB18}~(\citeyear{ShekharB18}) extended the planner of \citeauthor{CrosbyJR14}~(\citeyear{CrosbyJR14}). Thus, it is also based on compiling the multiagent problem into a classical problem. With respect to previous work, they added collaborative actions and removed all collaborative actions that are subsumed by others (i.e.~that do not involve a minimum number of agents). Besides, they showed that their approach can also be used in a distributed privacy preserving planner.

Compilations from multiagent to classical planning have also been considered by other authors. \citeauthor{muise-codmap15}~(\citeyear{muise-codmap15}) proposed a transformation to respect privacy among agents. The resulting classical planning problem was then solved using a centralized classical planner as in our approach. Besides, compilations to classical planning have also been used in temporal planning, obtaining state-of-the-art results in many of the International Planning Competition domains~\cite{conf/icaps/Jimenez15}.

\section{Conclusion}
This paper proposes a new compilation for concurrent multiagent planning problems. As far as we know, our algorithm is the first to handle concurrent conditional effects. In experiments we show that our approach is competitive with previous work, and that it can solve concurrent multiagent planning problems that are out of reach of previous approaches.

Since the number of atomic actions is exponentially smaller than the number of joint actions, a distributed action definition has the potential to scale to much larger instances, which we demonstrate in our experiments. It is not always easy to determine beforehand how many joint actions are needed; in \textsc{Maze}, we may need $k$ agents to cross a bridge together, requiring joint actions for $2,3,\ldots,k$ agents.

In future work, we would like to explore strategies for optimizing the makespan, improving scalability and introducing the notion of {\em capability} \cite{ChouhanN17}. We also want to automatically derive the bounds of our algorithm. Furthermore, privacy preserving is a central topic on multiagent planning; thus, this approach could be combined with suitable privacy-preserving mechanisms in the future.

\section{Acknowledgments}
This work has been supported by the Maria de Maeztu Units
of Excellence Programme (MDM-2015-0502). Anders Jonsson is partially supported by the grants TIN2015-67959 and PCIN-2017-082 of the Spanish Ministry of Science.

redpajama_set_name: RedPajamaArXiv


text:
On the Eve of Elections, Côte d'Ivoire Tries to Move on From Its Violent Past
Written byTanja Schreiner
Posted 20 October 2015 18:31 GMT
In the whole city of Abidjan have been put up election posters. Image by Tanja Schreiner
More than 3,000 people were killed in the violent political crisis that followed the presidential elections in Côte d'Ivoire in 2010. On October 25, 2015, the Ivorian population will be asked to go to the polls again. One week before the elections, the country seems to be holding its breath.
Already several months before, Ivorians were constantly talking about the presidential elections at the end of October, which will be decisive to the further stabilization of Côte d'Ivoire. Will or will there not be violence again? This probably is the most important question dominating the lead-up to the vote.
During the last months, I have been speaking with Ivorian friends who survived the crisis in 2010 and 2011, people I just happened to meet in the streets, Ivorian politicians, and members of the Ivorian diaspora.
Some have insisted that there will be violence again this year but said that it would not reach the severity of what followed the last presidential elections, which escalated into a civil war claiming some 3,000 lives. Others, though, swore that everything is going to be peaceful, the most common argument being that there currently is no strong opposition.
The main opposition party of former President Laurent Gbagbo, who is awaiting trial at the International Criminal Court in The Hague for war crimes during the post electoral crisis, is currently split.
Furthermore, there have been actions taken to make sure that the elections will be hold peacefully. In June 2015, the mandate of the United Nations Operation in Côte d'Ivoire was extended for another year.
In the month before the two-week-long election campaign began on 9 October, the media, which played an important role in heating up the debate in 2010 and 2011, faced calls to treat all candidates equally.
The African Union just announced this month that it is going to deploy 40 short-term election observers, and the Civil Society Platform for the Observation of the Elections in Côte d'Ivoire, which already observed the voter registration process, just trained 755 election observers.
Présidentielle 2015 : La POECI forme ses observateurs http://t.co/yAKDYmbtZv #observationpvt #civelections #ci225 pic.twitter.com/B23kttYtfQ
— POECI (@civelections) octubre 13, 2015
Presidential elections 2015: POECI trains its observers
Still coping with trauma
However, people's fear of another outbreak of violence seems omnipresent. After the horrific events following the last elections, many people in Côte d'Ivoire still cope with trauma. Some still dream of the dead bodies lying in heaps on the streets. A friend recently told me that she was not able to come back to the economic capital of Abidjan for three years because of what she experienced there during the crisis. Some weeks ago, all these traumatizing memories were brought back to her mind when a group of armed people robbed the hospital in Abidjan she now works in.
Throughout the city of Abidjan, election posters have been hung. Image by Tanja Schreiner
When I asked her what she thought about the coming elections, she just replied, "Il y aura rien" ("There won't be anything"). A sentence I have heard very often in these last weeks, being pronounced almost like an oath.
However, it contradicts how people are behaving. Some already bought their tickets to Ghana, Togo or Europe months before. There are companies that even obliged their international staff to go back to their home country during the elections. Others bought food in bulk and recommend to have a certain amount of money at home — just in case.
Moreover, many people will certainly not be going to the polls. Only 300,000 new voters have signed up – from an estimated 2 to 3 million. "On October 25, I am going to cook pork and stay at home with my friends, celebrating the non-voting", one of my friends told me.
Messages of peace
But while some leave the country or boycott the elections, others commit themselves to promote peace. Women's organisations in the west of the country have designed traditional fabrics to call for peaceful elections. A photo exhibition at the University Felix Houphouët-Boigny in Abidjan aimed to raising awareness among students.
Sensibilisation élections paisibles ds étudiants de l'université FHB par une exposition photo #civpaix #CIVElections pic.twitter.com/lkryHhoMDO
— USAID CITI2 (@CITI_2) agosto 31, 2015
Creating awareness of peaceful elections through a photo exhibition from students at the University Felix Houphouët-Boigny
And the football association "Foot'Attitude", with the participation of football stars like Didier Drogba, launched a campaign called "Election C' Pas Gnaga" ("Elections are not war") to raise awareness for non-violent elections. In addition to this, messages of peace have been going up on social media in these last weeks. On YouTube, a group of Ivorian artists posted a video "Jeune, ma voix, mon avenir" ("Young, my voice, my future") to promote the youth's participation in peaceful elections.
On Twitter, people have been calling for peace, using the hashtags #ElectionCpasGnaga and #civpaix. Kamara Kollo Rachel, a blogger based in Abidjan, writes on Twitter:
Engagement retenue " pour les élections 2015, je choisi la paix,mon candidat d'accord mais le pays d'abord " #votenjaillement #CIVPAIX #KKRD
— KAMARA KOLO RACHELLE (@kolo12rach) October 5, 2015
For the 2015 elections, I choose peace. I am for my candidate, but I am for my country first.
Choisir la voix de paix, c'est faire le choix d'un lendemain meilleur. #CIVPaix #CIVElections @klahon225 pic.twitter.com/IfnrUASU4j
— Marius K. Kwadyo (@MariusKwadyo) september 30, 2015
Choosing the voice of peace is making the choice of a better tomorrow.
Of course Facebook, being widely used in Côte d'Ivoire, is a big part of where the conversation takes place. One of my friends, a university student from Abidjan, wrote on his wall (republished with permission):
chers amis jeunes, nous sommes proche des élections présidentielles. et le doute s'installe dans le cœur de tous ceux qui aime la côte d'ivoire. si nous aimons notre pays, si notre avenir nous tiens vraiment à cœur, ne nous livrons pas à des actes de violence, ne nous laissons pas manipuler par qui que se soit…
Dear young friends, we are not far from the presidential elections and the doubt is installing in the hearts of all those that love Côte d'Ivoire. If we love our country, if our future really is in our hearts, do not give yourself to acts of violence, do not let you be manipulated, whatever it may be….
Now it just remains to hope that all these messages of peace will guide Côte d'Ivoire to presidential elections without violence.
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redpajama_set_name: RedPajamaCommonCrawl


text:
Study: Southeast Asia must close yield gap to remain major rice bowl
At least 40% of global rice exports come from Southeast Asia, and worldwide demand is steadily growing. Recent research by a global team including Nebraska's Patricio Grassini and recently published in Nature: Food indicates there is potential for rice producers to increase yields on existing agricultural land with improved management.
At least 40% of global rice exports come from Southeast Asia, making the region a major rice bowl. The region helps feed other parts of the world, such as Africa and the Middle East.
Projections show that global rice demand is set to increase 30% by 2050. With the continuing rice trade and limited scope available for other main rice-producing countries like China and India to generate a rice surplus, Southeast Asia faces a challenge in stepping up to ensure adequate global rice supply.
But crop yields stagnate, land allotted for agriculture does not increase, and climate change remains a looming threat, raising concerns about the capacity of the region to remain a large net exporter.
In a recent study published in Nature Food, an international team of researchers, including those from the major rice-producing nations in Southeast Asia, estimated the difference between yield potential and average farmer yield across six countries — Cambodia, Indonesia, Myanmar, Philippines, Thailand and Vietnam.
The initiative was led by the University of Nebraska–Lincoln and the International Rice Research Institute in the Philippines and included researchers from Huazhong Agricultural University in China, the International Fertilizer Association in France, the Institute of Policy and Strategy for Agriculture and Rural Development in Vietnam, Field Crops Research Institute in Vietnam, Thailand's Rice Department, Thailand Rice Science Institute, Mawlamyine University in Myanmar, IRRI-Myanmar Office, the Department of Science and Technology-Philippine Atmospheric, Geophysical and Astronomical Services Administration, IRRI-Cambodia Office, the General Directorate of Agriculture in Cambodia and the Indonesian Agency for Agricultural Research and Development.
Results from the project are available via the Global Yield Gap Atlas, a collaboration between the University of Nebraska–Lincoln and Wageningen University designed to estimate the difference between actual and potential yields for major food crops worldwide.
"Over the past decades, through renewed efforts, countries in Southeast Asia were able to increase rice yields, and the region as a whole has continued to produce a large amount of rice that exceeded regional demand, allowing a rice surplus to be exported to other countries," said lead author Shen Yuan, a postdoctoral research associate at Huazhong Agricultural University. "The issue is whether the region will be able to retain its title as a major global rice supplier in the context of increasing global and regional rice demand, yield stagnation and limited room for cropland expansion."
Through a data-intensive approach, the researchers determined that the region has the potential to increase production on existing cropland and remain a major global rice supplier, but changes in production and management techniques will be key, and producers could stress natural resources in the process.
Researchers found that the average yield gap represents nearly half of the yield potential estimated for the region, but it is not the same for every country. Yield gaps are larger in Cambodia, Myanmar, the Philippines and Thailand but comparably smaller in Indonesia and Vietnam.
Patricio Grassini
"We used an approach that consists of a combination of crop modeling, spatial analysis and use of detailed databases on weather, soil and cropping system data," said Patricio Grassini, associate professor of agronomy and horticulture at Nebraska. "The regional extent of the study together with the level of detail in relation to spatial and temporal variation in yield gaps and specificity in terms of cropping systems is unique, providing a basis for prioritizing agricultural research and development and investments at regional, national and sub-national levels."
According to the study, the region needs to close the existing yield gap substantially to reduce the need for rice imports, allowing for an aggregated rice surplus of 54 million tons available for exports.
"Our analysis shows that Southeast Asia will not be able to produce a large rice surplus in the future without acceleration of current rates of yield gains," Grassini said. "Failure to increase yield on existing cropland areas will drastically reduce the rice exports to other regions and the capacity of many countries in the region to achieve or sustain rice self-sufficiency. It will also put additional pressure on land and water resources, risking further encroachment into natural ecosystems such as forests and wetlands."
Researchers suggest a number of interventions needed to close the gap, including improvement of crop management practices, such as the use of fertilizer and irrigation, nutrients, water and pest management, as well as mitigation of production risks in lowland rainfed environments.
"The challenge is how to increase yield while minimizing the negative environmental impact associated with intensive rice production," said IRRI Senor Scientist Alice Laborte. "For example, tailoring nutrient management to each environment will help increase yield and farmer profits while reducing nutrient losses. Likewise, integrated pest management is a knowledge-intensive but valuable approach if applied correctly and holistically to reduce yield losses to weeds, pests and diseases while minimizing excessive use of pesticides and associated risks to the environment and people.
"Closing the rice yield gaps requires the concerted effort of policymakers, researchers and extension services to facilitate farmers' access to technologies, information and markets. Continued investment in rice research is crucial."
The study received support from Closing Rice Yield Gaps in Asia with Reduced Environmental Footprint, funded by the Swiss Agency for Development and Cooperation. The project also received complementary funding from the Global Water for Food Institute, as well as the Bill and Melinda Gates Foundation through the CGIAR Excellence in Agronomy 2030 Incubation Phase.
Patricio Grassini, Associate Professor of Agronomy
Faculty profile: Patricio Grassini
Global Yield Gap Atlas
Agronomy and Horticulture


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MEDIEVAL ARROWS. 13.95 FROM BOTTOM OF POINT TO NOCK 31" BOTTOM ENGLISH WAR ARROW WITH BODKIN POINT NEXT UP MONGOL ARROW WITH WAR POINT 3RD UP STANDARD FIELD POINT TOP SHAFT WITH NO POINT SO YOU CAN PUT ON ANY OF THE MEDIEVAL POINTS 8.95 EA. MADE FROM EUROPEAN NORTHERN PINE.. VERY STRAIGHT GRAIN. TIED FEATHERS.
HERE IS A PICTURE OF ISHI THE LAST "WILD" INDIAN MAKING AN ARROW SHAFT.
Foam tipped arrows for archery tag.
We strongly suggest wearing a helmet with a face mask.
Insert .246" Dia Shafts 5/16"
These full blown magnums create more drag and will straighten out your arrows immediately.
Small feathers create less drag and increase your arrow speed 5 feet per second.
The extended quill allows the feather to be tied the shaft.
11/32" Diameter - 125 Gr.
11/32" Diameter - 190 Gr.
Will not skip or get lost under grass.
2 Blade Broadhead Point 160 Gr.
If you've ever been sick of your points unscrewing, this is the answer. - Hold your broadheads and field points in place with rubber grommets.
Glue-on - 1 3/16" x 2 7/16"
In the ancient world whistle points were used to signal the army to attack or on a bad day to "run away, run away".
Works like a pencil sharpener.
Great for hot gluing on arrow heads and nocks. This glue holds points and nocks on better than hot glue from a hardware store. It works better at all temperatures.
These are the best 3D points I have ever found. Unless an extreme angle they dig in. - Made of stainless steel, they have a little rubber grommet to prevent unscrewing.
Comes on and stays on until you shut it off if you can't find your arrow go back after dark to find it.
I recommend this inexpensive polycarbonate fletcher. It can do 3 fletch or 4 fletch. It can also use Bitzenburger clamps and adjusts for offset feathers.


text:
Q: Exec Procedure Mysql with EF net core I try to execute a stored procedure with the following function, but the table is not updated and it does not show me an error,could you help me
public static void UpdateShareDate(string Sharepath, string ShareDate, int TransactionID, int Clientid)
{
    try
    {
        using (var context = new AppDBContext())
        {
            MySqlConnector.MySqlParameter[] queryParams = new MySqlConnector.MySqlParameter[] {
                    new MySqlConnector.MySqlParameter("@p1", Sharepath),
                    new MySqlConnector.MySqlParameter("@p2", ShareDate),
                    new MySqlConnector.MySqlParameter("@p3", TransactionID),
                    new MySqlConnector.MySqlParameter("@p4", Clientid)
                };

            string store_pocedure = "call MediaFile_Update(@p1,@p2,@p3,@p4)";
    var result = context.DBSetMediaFiles.FromSqlRaw(store_pocedure, queryParams);

        }
    }
    catch (Exception ex)
    {

        throw;
    }
}

A: What I would do in this case to see what is happening in DbContext level, I will enable logging like below,

*

*add logging like below in your dbcontext,
public static readonly ILoggerFactory loggerFactory = new LoggerFactory(new[] {
new ConsoleLoggerProvider((_, __) => true, true)
});

*protected override void OnConfiguring(DbContextOptionsBuilder optionsBuilder)
{
optionsBuilder.UseLoggerFactory(loggerFactory)
.EnableSensitiveDataLogging()
.UseSqlServer(@"Connection string");
}

*Then you will be see to logs in console with each and every line.
P.S : Please install Microsoft.Extensions.Loggin.Console nuget package
Thanks

A: Thank you all very much for your answers, but I solved it as follows
var result = context.Database.ExecuteSqlRaw(store_pocedure, queryParams);

redpajama_set_name: RedPajamaStackExchange


text:
PeaceHealth Sacred Heart Medical Center at RiverBend nationally recognized with an 'A' for the fall 2019 Leapfrog Hospital Safety Grade
PeaceHealth Sacred Heart Medical Center at RiverBend was awarded an 'A' in fall 2019 Leapfrog Hospital Safety Grade, a national distinction recognizing the hospital's achievements protecting patients from harm and providing safer health care. The Leapfrog Group is an independent national watchdog organization driven by employers and other purchasers of health care committed to improving health care quality and safety for consumers and purchasers. The Safety Grade assigns an 'A', 'B', 'C', 'D' or 'F' grade to all general hospitals across the country based on their performance in preventing medical errors, injuries, accidents, infections and other harm to patients in their care.
"This grade is a direct reflection of the consistent hard work, focus and dedication that our caregivers, providers and leaders have put forward every day, in support of PeaceHealth's quality and safety efforts – and most importantly our patients," said Mary Kingston, PeaceHealth Oregon chief executive. "For the patients, families and communities we serve, we will use this achievement as inspiration as we continue to transform the way we provide care."
"'A' hospitals show us their leadership is protecting patients from preventable medical harm and error," said Leah Binder, president and CEO of The Leapfrog Group. "It takes genuine commitment at every level – from clinicians to administrators to the board of directors – and we congratulate the teams who have worked so hard to earn this A."
Developed under the guidance of a national expert panel, the Leapfrog Hospital Safety Grade uses 28 measures of publicly available hospital safety data to assign grades to more than 2,600 U.S. acute-care hospitals twice per year. The Hospital Safety Grade's methodology is peer-reviewed and fully transparent, and the results are free to the public.
PeaceHealth Sacred Heart Medical Center at RiverBend was awarded an 'A' grade today, when Leapfrog announced grades for the fall 2019 update. To see PeaceHealth Sacred Heart Medical Center at RiverBend's full grade details, learn how employers can help and access patient tips for staying safe in the hospital, visit hospitalsafetygrade.org.
About The Leapfrog Group
Founded in 2000 by large employers and other purchasers, The Leapfrog Group is a national nonprofit organization driving a movement for giant leaps forward in the quality and safety of American health care. The flagship Leapfrog Hospital Survey collects and transparently reports hospital performance, empowering purchasers to find the highest-value care and giving consumers the lifesaving information they need to make informed decisions. The Leapfrog Hospital Safety Grade, Leapfrog's other main initiative, assigns letter grades to hospitals based on their record of patient safety, helping consumers protect themselves and their families from errors, injuries, accidents and infections.
About PeaceHealth: PeaceHealth, based in Vancouver, Wash., is a not-for-profit Catholic health system offering care to communities in Washington, Oregon and Alaska. PeaceHealth has approximately 16,000 caregivers, a group practice with more than 1,200 providers and 10 medical centers serving both urban and rural communities throughout the Northwest. In 1890, the Sisters of St. Joseph of Peace founded what has become PeaceHealth. The Sisters shared expertise and transferred wisdom from one medical center to another, always finding the best way to serve the unmet need for healthcare in their communities. Today, PeaceHealth is the legacy of the founding Sisters and continues with a spirit of respect, stewardship, collaboration and social justice in fulfilling its Mission. Visit us online at peacehealth.org.


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20%||||| I ADORE THEE: INCLUDES FATIMA MEDAL: DEDICATED TO LUCIA SANTOS. AVES: Semi precious Kaynite, a soft blue with metallic like inclusions that give an inner light.
PATERS: Hand carved wooden beads with antique flower caps from Germany (1930's- 60's) To remind us of the children and their daily life in the fields where flowers grew and were probably picked!
ABOUT OUR MEDALS: All our medals are hand cast from antiques in sterling silver and solid bronze then antiqued to restore original patina. Center is cut work of Mary of Carmel holding a rosary and scapular, Lourdes medal is attached to crucifix.


text:
A Vision of Vygotsky
Mumbo-Jumbo Theory
The following excerpt is taken from chapter four, The Zone of Proximal Development.
A vivid example of the ZPD recently happened with Joan and a classroom of adult students in the context of international education. As a way of preparing for a masters' comprehensive final, Joan spent two hours of class time reviewing the ebb and flow of the various theoretical perspectives throughout this century (Figure 1.1). With the test rapidly approaching, the students were focused, drawing their own time lines in preparation for the test, and asking many questions. At the end of this session, the whiteboard in the classroom was completely filled with a long red vertical line that ran the full length of the board. Obviously, it was a time line; dates, people, and ideas were rapidly scrawled above and below the vertical line. In fact, a quick, cursory glance could lead one to believe that it was mumbo-jumbo, a mess of scribbles, scrawls, and scratches. However, a closer look by anyone preparing for a master's final would show that the mess contained the big ideas of thought running through this century. At the end of class, Joan recreated in her class journal the exact time line that the students generated (Figure 4.7).
FIGURE 4.7 Time Line of Big Ideas
On the time line, the students had written Vygotsky's life (1896 to 1934); close to his name, they wrote the phrases dialectical teaching and learning, sociocultural learning, and problem solving. Near his name, and below the time line, they wrote the lifespans of Dewey (1859 to 1952) and Piaget (1896 to 1980). Below Dewey's name, they scrawled experiential learning and whole-to-parts. Above Piaget's name, they included the term generative. The time line included the simultaneous and conflicting notions ofProgressivism and Scientific Management in the early years of the 1900s.
Above the time line, they wrote Taylor under Scientific Management with the words memorization, and parts-to-whole. Not far from Taylor's name, Skinner, Thorndike, and Behaviorism were followed by Black Box theory, stimulus-response, and quantify-measure. At the very top of the whiteboard, above Behaviorism, they wrote Transmission.
In the middle of the horizontal line, 1957 appeared, with Sputnik written above the line and with Chomsky written below. Both the years 1944 and 1966 were highlighted. Below the late 1970s and moving into the 1980s, the words cognitivism, interactivism, constructivism, constructionism (see Table 1. 1), co-constructionism, and social constructionism appeared, followed by arrows tocritical pedagogy, democratic pedagogy, and transformative education.
Critical pedagogy had two arrows beside it, one moving to the right, as an implicated future, and another arrow to the left, pointing directly up to Critical Theory of the 1940s. Another arrow from critical theory went down to sociocultural learning. From Vygotsky'sname, an arrow indicated the link to classical pedagogy.
However, the original questions asked at the beginning of this section were: What is the ZPD? And, who is the more capable peer? What does that time line on a whiteboard have to do with answering those two questions? The answers can be found in what happened next.
At the end of the review session on the time line, Joan grabbed a whiteboard eraser to clean the board. She rapidly drew the eraser across the entire length of the board, but the red marks stayed on the board. She did it again. Nothing. The red marks remained. Joan had obviously used a nonerasable marker for this lesson, not the erasable marker. The students began to chant, "Leave it there, leave it there," because this was the same room where they would soon take their test. However, this international education program leases this classroom from another school. The thought of ruining the whiteboards for a school with a limited budget sent tremors through, at least, Joan's spine.
"Who knows what to do?' Joan asked the class with panic rising in her voice.
"I know what to do," Chris, who teaches in Kuwait said, as she ran from the room. She soon returned with a bottle of alcohol and lots of wet paper towels. She began to wipe the whiteboard, which soon turned into a smeared bright hot pink, and not so white, board. Steve, who teaches in Turkey, watched what she was doing and went to help her. With continued perseverance and many more wet and then dry towels, the board eventually returned to an almost passable shade of white.
"Now, let's try to refocus our review and remember some of Vygotsky's ideas. Let's start with the ZPD," Joan said to the class at the end of this near-classroom-fiasco.
"Enough of this mumbo-jumbo theory," Steve suddenly blurted out. "Just tell us in real language what the ZPD is" More laughter and nervous energy filled the air.
"Okay, Steve," Joan began. "Chris is your more capable peer when it comes to cleaning permanent markers from the whiteboard. You had never learned this and were thoroughly enjoying the possibility that I might have ruined the whiteboard. Chris, as the more capable peer, knew that learners could solve problems beyond their actual developmental level if they receive guidance from a more advanced learner. Thus, Chris pulled us all through our actual developmental level in whiteboard cleaning. What you did in cooperation with Chris today, you can do alone tomorrow. In addition, if this should ever happen again, you could suddenly become the more capable peer in whiteboard cleaning for someone else. And, that is nomumbo-jumbo theory."
As Joan was speaking and finally getting the adult learners thinking about their comprehensive final again, the door of the classroom suddenly burst open, Harriet, from the Czech Republic, said, "Dr. Wink, I am sorry to bother you, but do you have anything to clean markers off a whiteboard? I just wrote all over Dr, Titone's whiteboard and can't get it off." A roar of laughter swept through the class again.
"Steve, you are now the more capable peer. Go help her," Joan said.
"And, no mumbo-jumbo theory either, Steve. Just pull her up through the zone to her next developmental level in whiteboard cleaning."
Prairie Pedogogy
WinkWorld Archive
Research/Writing
WinkLinks
Faculty Pages
Student Treasures


Biopharmaceuticals represent the fastest growing sector of the pharmaceutical industry, mainly driven by a rapid expansion in the manufacture of recombinant protein-based drugs . The majority of the therapeutic proteins are glycoproteins (monoclonal antibodies, blood proteins, growth factors, vaccines, fusion proteins) and hence, glycosylation is the most prominent post-translational modifications occurring on these protein drugs.
Chinese hamster ovary (CHO) cells are the most commonly used mammalian hosts for industrial production of recombinant protein therapeutics  (e.g., recombinant human erythropoietin (rhEPO), a heavily glycosylated protein with three N-glycosylation sites expressed by a Chinese Hamster Ovary (CHO) cell line).
Mass spectrometry (MS), either direct infusion or coupled to chromatographic methods, are routinely used to elucidate complex glycan structures. SimGlycan software predicts and scores candidate glycans for each MS/MS scan by creating a list of candidate structures using the precursor m/z and then comparing the in-silico fragment ions of each candidate against the observed product ions .
To facilitate rapid identification of N-glycans of rhEPO using tandem MS data, we have created a database of glycans curated from published literature. The MS/MS database search can be performed only on the specific database instead of searching the global SimGlycan database, thereby increasing the confidence of the IDs.
Future Direction: The current database does not contain glycans with modifications such as phosphorylation, sulfation, and sialic acids with possible O-Acetylation. Typically, rhEPO contains large number of tetraantennary sialylated glycans wherein sialic acids (mostly N-acetylneuraminic acid, Neu5Ac) are attached to galactoses via α-2,3 linkages. O-Acetylated sialic acid and a small amount of N-glycolylneuraminic acid (Neu5Gc) modified glycans are also present on rhEPO. Other modifications on rhEPO glycans are (a) phosphorylation – mostly high mannose glycans at low, but not negligible levels, and mannose 6-phosphate; and (b) sulfation . We are creating a database containing curated glycan structures with various modifications that were verified using the following workflows : (1) hydrophilic interaction chromatography (HILIC) coupled to a Q Exactive Orbitrap mass spectrometer, and data dependent HCD fragmentation; and (2) High performance anion exchange (HPAE) chromatography coupled to a Q Exactive Orbitrap mass spectrometer, and data dependent HCD fragmentation.


text:
Into You Ariana Grande
Ed Sheeran Home
Ed Sheeran Says New Album Will Focus On His "Relationship Troubles"
The 'Lego House' singer gives fans an update on the themes in his second album.
Ed Sheeran has revealed his new album will focus more on the "relationship troubles" he has had over the past year.
The 'Small Bump' singer has been steadily working on his second studio record, the follow-up to 2011's '+' for the past year or so, and says it will be different in theme to his debut.
"This album has been a lot of relationship troubles I guess," Ed revealed in an interview with The Review this week. "The first album I was inspired by everything that around me.
"I was kind of growing up surrounded by a lot of very interesting people and had a lot of stories such as 'The A Team' and 'Small Bump' and stuff like that," the 'Lego House' star explained. "This record there has been more drama that has happened in my personal life, so I've written a lot about that."
Despite already having many of the songs for his new album already written, Ed says he won't be performing them anytime soon and has yet to record them all.
"I have written them, I just haven't worked out properly how to play them live," he added. "I'm getting there though, I'm getting there."
Check out a picture of Ed Sheeran at the MTV VMAs last month below (Credit: Getty):
The UK singer-songwriter supported Taylor Swift on her 'Red' tour for much of this year, with the North American leg drawing to a close last week.
Ed Sheeran has teased a potential release date for his new album in February 2014.
Latest Ed Sheeran News
See more Latest Ed Sheeran News
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Ed Sheeran Afterglow Lyrics Explained As He Returns To Music
Ed Sheeran Throws Support Behind Marcus Rashford's Free School Meals Campaign & Opens Breakfast Club
Ed Sheeran's Baby Name 'Lyra Antarctica' Revealed: 'His Dark Materials' & Romantic Getaway Inspiration
Ed Sheeran Announces Birth Of Baby Daughter With Wife Cherry Seaborn


text:
How does CanSino, China's One-Shot COVID-19 Vaccine Work?
Image Credits: The News International
It's not just Johnson & Johnson which has come up with a single dose vaccine for Covid-19, Pakistan has recently received a new batch of one-shot vaccine from China which is 65% effective.
CanSino Biologics first got their approval for Convidecia Vaccine in Hungry, followed by Mexico, China and now Pakistan.
The idea behind CanSinoBio's One-Shot vaccine is to shorten the vaccination cycle and effectively vaccinate mass population in a short period of time.
The trial shows 90.07% effective result of CanSino in preventing severe COVID-19 disease 28 days post-vaccination, and 95.47% in preventing severe COVID-19 disease 14 days post-vaccination.
The Phase III clinical trial results announced by the Pakistani authority showed an efficacy rate of 100% against severe COVID-19 disease and 74.8% at preventing all symptomatic COVID-19 disease in Pakistan.
CanSino vaccine works on the similar technology as J&J is using for its one-shot vaccine; it is known as viral vector vaccine technology. According to Centers for Disease Control and Prevention, Viral vector vaccines use a modified version of a different virus (the vector) to deliver important instructions to our cells. The benefit of viral vector vaccines, like all vaccines, is those vaccinated gain protection without ever having to risk the serious consequences of getting sick with COVID-19.
Going into the details, we learn that this smart medical technology tricks the body into believing that it has been harmed by Covid-19 virus and triggers the body to produce anti-bodies to fight off what it thinks is an infection. The 'Vector' which is a harmless virus first enters a cell in the body and produces a harmless piece of virus which causes Covid-19 (But it wouldn't); the cell only displays it to our immune system which recognizes it as a threat and activates our immunity cells.
The body then starts producing anti-bodies in response to an anomaly it witnessed. The end result teaches the body to now be prepared to fight off any similar kind of infection it comes across with. The working of this vaccine is entirely safe and easy on the body. Contrary to the belief by anti-vaxxers, it does not affect a person's DNA in any way. It is highly advised to educate yourself about all the vaccines through World Health Organization.
TikTok's Viral Weight Loss Dance is Neither Safe Nor Effective, Experts Warn
Health Subsidies And Insurances, For Increasing Americans' Access To Healthcare


THE DEMANDS OF POLAR ACTIVITIES HAVE ALREADY BEGUN TO EXCEED THE CAPACITY OF THE EXISTING TELECOMMUNICATIONS INFRASTRUCTURE.
Indeed, apart from more densely populated areas in Iceland, Scandinavia and western Russia, the telecommunications infrastructure for vast spans of the poles is either poor or utterly non-existent. It is time to overcome these limitations and meet the accelerating connectivity needs for personal, commercial, scientific, and governmental interests. However, efforts to do so must overcome a unique set of regional challenges.
First, polar settlement and population is sparse and separated by large distances, which implies a low subscriber volume and makes the economics of fixed line connectivity such as fiber and cable problematic. Secondly, the presence of mountainous terrain, scattered throughout the polar regions, significantly increases the costs of establishing and linking a fixed, ground-based telecommunications infrastructure. Installing fiber-optic backhaul is exorbitantly costly in such terrain. Microwave backhaul is equally problematic, as mountainous terrain complicates the line-of-sight towers require to establish the network.
Thirdly, the extreme cold, high winds, heavy snowfall, and permafrost of these regions impede both the installation and maintenance of infrastructure, as well as substantially increasing the risk of damage to equipment once installed. Lastly, with a considerable portion of the increase in polar activity linked to offshore interests, vessels and offshore platforms simply cannot be served by fixed-line infrastructure. These challenges are not undefeatable, but they do require a more innovative approach than those offered by traditional, fixed, and terrestrial infrastructure alone.
In the emerging domain of the Fourth Industrial Revolution, the need for telecommunications systems with a reliable and affordable capacity for global integration has become a central concern. In order to address issues of connectivity, satellite systems have become an established means of overcoming the limitations of terrestrial infrastructure.
Most satellites operate in geostationary orbit, which exists in a fixed position above the equator at an altitude of about 35,000 km. At the high latitudes of the poles, the curvature of the earth or local obstructions such as trees or rolling waves on the ocean will block visibility of these satellites.
The fidelity of the signal will also be affected by the atmosphere. At high latitudes, the signals from GEO must travel through more atmosphere than at low latitudes. With higher frequency systems, such as Ku and Ka-band, this will lead to greater signal loss through the effects of rain fade. Moderate overcast at high latitudes can render a GEO system inoperable.
These challenges also, of course, ignore the reality of the high cost of satellite connectivity, with data costs exceeding their terrestrial counterparts by 10 to 100 times!
The solution to these unique challenges rests with a combination of the strengths of traditional fixed infrastructure with the emergence of a new generation of satellite technology. This new generation of satellite provides a critical component for a comprehensive solution for communication and connectivity at the poles.
KEPLER OVERCOMES THE CHALLENGES OF RELIABLE POLAR SATELLITE CONNECTIVITY THROUGH ITS NETWORK OF NON-GEO SATELLITES, WHICH PROVIDE CONNECTIVITY IN REGIONS MISSED BY TRADITIONAL SATELLITES. OUR HIGH-CAPACITY STORE-AND-FORWARD DATA BACKHAUL SERVICE, ENABLED BY LOW-COST NANOSATELLITES, WILL PROVIDE MUCH-NEEDED CONNECTIVITY RELIEF FOR HIGH LATITUDES.
Customers will be able to shift their delay-tolerant data to an economical and reliable connectivity service, thereby reducing congestion on expensive real-time links and increasing available bandwidth for their activities. Together, these will increase available bandwidth and reduce the customers' overall cost per GB of connectivity.
Reliable and affordable applications for Kepler's store-and-forward services include: the ability to send and receive large quantities of scientific data, such as environmental logs or meteorological data; the connective power to support media vaults and transport high bandwidth media content (e.g. Netflix) to a local server; and the expansive capacity to create local drop boxes with the means to share large, bandwidth-intensive files without clogging real-time links. Support for media vaults and drop boxes extend connectivity and empower communities, cruise ship passengers, and mining or offshore platform personnel with the ability to stay connected.
With the growth of polar activity, the issue of affordable connectivity has become a significant challenge, and oftentimes a deterrent, to those activities being undertaken more broadly. Fortunately, a constellation of new satellite technology, existing infrastructure, and contemporary satellite systems have the promise of delivering highly innovative and adaptive solutions to support to growing telecommunication demands of personal, commercial, scientific, and governmental interests in the Arctic and on Antarctica. Kepler is leading the industry in making this solution available in the polar regions.
This article is based on a white paper published by Kepler Communications. To download a copy of the original white paper click here.