text:
\section{Introduction}
\IEEEPARstart{F}{orest} fires are highly devastating, leading to massive loss of wildlife, vegetation, wildlife habitats, etc. Forest fires cause the menace of extinction of plants and animals, disturbing the biodiversity and the ecosystem. Unfortunately, the annual global destruction due to forest fire aggregates to several million hectares of forest lands. The detection and mitigation of nascent forest fires are imperative to curtail the damage. Forest survey of India issues alerts of forest fires based on real-time detection of forest fires by Moderate Resolution Imaging Spectro-radiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) data. The number of forest fire detected in India during the 2019-2020 fire season records $22447$ and $124473$ alerts using MODIS and VIIRS sensors \cite{moef}. In India, total forest area affected by fire is estimated as $1.1094$ Mha, with a $95\%$ confidence level for the $2019-2020$ \cite{fsi}. The forest fire in the Bandipur Tiger Reserve and National Park in 2019 destroyed nearly $40,000$ acres of forestland. The traditional methods emphasize preventing forest fire by improving the resilience of the forests against fire hazards with several forest management policies. The traditional forest fire suppression methods constitute firefighting techniques like directly beating out the fire with hand tools, fuel separation in advance to active fire, etc. \cite{ndma}. The existing forest fire monitoring, detection, and mitigation techniques engaging human firefighters are dangerous and unreliable.

UAV systems have been used for surveillance, monitoring, detection, and mitigation of forest fires inorder to reduce human interaction in unsafe circumstances \cite{yuan2015survey}. The single UAV systems are inadequate for large and multiple wildfire suppression measures. Thus team-based multi-UAV systems are promising for multi-task missions and increased area coverage. Multi-task missions like forest firefighting include search, detection, verification, monitoring, and neutralization of the targets \cite{akhloufi2020unmanned}. Multi-UAV performing multi-task missions need coordination strategies to manage communication and task allocation. The centralized, distributed, and decentralized control systems with global and local communication have been used in different fire assistance systems \cite{yuan2015survey}, \cite{akhloufi2020unmanned}. In the case of forest firefighting, the unknown targets are located in an extensive search area. In a practical scenario, forest fire spread can be uneven and time-varying due to the direction and speed of the wind, vegetation type, other environmental conditions, land cover type, etc. The large search area, together with uncertain and challenging conditions, makes it difficult for the UAVs to manage centralized or distributed communication. A decentralized control with local communication between UAVs is preferred for achieving scalability and robustness in forest fire search missions \cite{senanayake2016search}.

In existing literature, fire monitoring problem is the most addressed research direction in firefighting. The satisfactory tracking of perimeter depends on the fire models used to estimate the fire front and fire behavior. The simplest models use an elliptical fire profile with a constant rate of increase for the semi-minor and semi-major axis. The rate of increase of fire depends on the available fuel energy, intensity of the fire, direction, and speed of wind \cite{byram1959combustion}, \cite{anderson1982modelling}. Multi-UAV systems are effective for monitoring the dynamic fire perimeter to provide real-time data to firefighters \cite{casbeer2005}. Recently formation control based approaches \cite{ghamry2016cooperative}, \cite{yu2021fault}, re-configurable approaches \cite{casbeer2006cooperative}, \cite{ghamry2016fault}, auction-based approaches \cite{sujit2007fire}, potential field-based algorithms \cite{Pham}, and meta-heuristic based approaches \cite{Ruiz2022real} have been used to monitor the fire front given the location. The existing monitoring methods require a priori information on the number and location of forest fire for planning the search and coverage path. The UAVs for the mision are launched closer to the fire location, assuming that all vehicles are within a specific range of each other and the base station for uninterrupted communication.

The search task is one of crucial tasks in forest firefighting, considering the time required to locate the spreading fire in a large unknown area. Different deterministic and stochastic strategies have been proposed for multi-UAV search and destroy missions \cite{george2011search}. Stochastic search methods are efficient for missions involving large unknown search areas with limited or no communication, and Markovian search strategies are more efficient for dynamic targets \cite{flenner2012levy}. The optimal animal foraging behavior is well modeled by random walks, in which the predator optimizes the search for prey or food. Brownian and levy flight are the major random walk strategies found in searching behavior of different classes of animals including marine predators \cite{viswanathan2000levy}, \cite{bartumeus2003helical}. A multi-level search strategy inspired from the foraging behavior of the marine predator, Oxyrrhis Marina is used for a multi-UAV mission to search the forest fire locations \cite{OMS}. A combination of Levy, Brownian and directionally driven Brownian search is used to detect the fire locations. A two-step search and monitoring procedure for forest fire with a group of UAVs has been addressed in \cite{SARKAR2021}. The grid-based sandclock pattern search developed has a shorter path for scan within the grid cell. The monitoring strategy discovers the boundary points of the fire for the fire area estimation considering an arbitrary fire spread model and safe hovering time for the UAVs above the fire zone.

The monitoring and mitigation of forest fire with multiple UAVs is first addressed in \cite{Kumar2011}. An artificial potential field-based control law coordinates the motion of UAVs to track and quench the fire, avoiding the collision. The UAVs distributed uniformly over the fire boundary extinguish a circular area of the fire directly beneath the UAVs. The efficiency of fire extinguishing balls for firefighting is experimentally verified in \cite{aydin2019use} and results indicate that fire extinguishing balls might be effective in extinguishing short grass fires if a swarm of UAVs drop at an optimal location in optimal numbers. A conceptual framework for UAV assisted fire mitigation is explained in \cite{ausonio2021drone}. The system involves a platform for the UAVs to manage the replacement of batteries and payloads. The linear meters of fire front that can be extinguished is calculated based on the payload capacity of the UAV, capacity of the platform, time for reaching the fire front, and critical flow rate. In \cite{OMS}, UAVs use the Dynamic Formation Control (DFC) to mitigate the fire by spraying water covering an equal non-overlapping fire area between all UAVs acting on the same fire. The cumulative area quenched by all the UAVs should be less than the active fire area to maintain a non-overlapping area. In \cite{OMS}, UAVs mitigate overlapped areas when the area shrinks below a value and the overlapped area needs to be accounted as a buffer time in total mission time. Even though the methods above cover search, detection, and mitigation of forest fire, the distributed multi-UAV methods have longer detection and mitigation time as each UAV acts independently. The cooperative swarm-based search and mitigation of forest fires results in faster detection and mitigation as the members of the swarm cooperate and act together during the mission.

In this paper, a Multi-Swarm Cooperative Information-driven search and Divide and Conquer mitigation control (MSCIDC) is presented for search and mitigation of forest fire. Multi-swarm missions are efficient in quenching the spreading fires as there is a high likelihood of multiple UAV detecting the same target. Multi-swarm cooperative information-driven search is used in the target search phase to drive the swarm in the direction of maximum sensor information. The search phase uses cooperative information-driven exploration and exploitation depending on the sensor information of members in the swarm. The cooperative nature between swarm members helps it to move toward the maximum temperature gradient sensed by the swarm. The swarm member detecting the fire locally attracts other members in that group to reach the fire front faster. The swarm members reaching the fire front start quenching fire using divide and conquer mitigation control. The quenching members are assigned to non-overlapping sectors of fire, and the sector division assures a non-overlapped quenching even with a shrinking fire area. The cooperative swarms with global regulative repulsion and merging, balance target detection and quench time reduction. The MSCIDC is assessed for a forest fire scenario in the pine forest, and the performance is compared with existing multi-UAV search methods. The performance of MSCIDC is also analyzed for varying numbers of swarms with the same number of UAVs. The Monte-Carlo simulations are carried out to verify the results. The results of MSCIDC are superior to the best case of multi-UAV missions with a $60\%$ reduction in total mission time and $65\%$ reduction in burnt area of the forest.

The rest of this article is organized as follows. In Section II, problem definition and different models of subsystems are presented. Section III explains the information-driven multi-swarm search and subsequently divide and conquer mitigation control. Numerical simulation results are given in Section V to demonstrate the performance of the proposed method over the multi-UAV methods. Section VI concludes the paper.

\section{Problem Definition and Modelling}
The primary goal of forest firefighting is to prevent biodiversity loss by detecting and mitigating fires faster. The forest fire spreads faster based on the available biomass fuel, wind, and terrain conditions. The fire regions are often difficult to access from the ground when the forest fire spreads. UAV missions are more reliable for for detecting and neutralizing multiple fire points located in vast inaccessible forest regions. Multiple UAVs should act simultaneously and continuously for efficient mitigation of the fast-spreading forest fires. A typical forest fire scenario in a pine forest is shown in Fig. \ref{swarmsearchschematic}. Multiple swarms of UAVs are employed for the detection and mitigation of multiple unknown fire locations. The swarm marked in black is searching the area, and the other swarm marked in red is quenching a fire area by flying along the fire front. The cooperative behavior of members of the swarm leads to faster detection and mitigation of targets.\\
\begin{figure}[htbp]
	\centerline{\includegraphics[width=75mm]{forestfirefighting.png}}
	\caption{A forest firefighting scenario where multiple swarms of UAVs perform search and mitigation of forestfire.}
	\label{swarmsearchschematic}
	\vspace{-5.5mm}
\end{figure}
Let the area to search be $\Omega\subset {\rm I\!R}^2$, and the boundary of the search area is marked in black color, as shown in Fig. \ref{swarmsearchschematic}. Here, multiple swarms of UAVs search the area for multiple forest fire locations. Let $n_s$ be the number of swarms, and the search area is known to all the swarms. Let $S =s_1,s_2....s_{n_s}$ be the set of multiple swarms of UAVs deployed in the search area to detect and mitigate fire locations. Let swarm $s_i\in S$ has $n_{s_i}$ number of UAVs where $i=1,2,.....,n_s$. Let $n_f$ be the number of unknown fire locations within the search area. The fire profile is approximated as a circle or ellipse in this work. The coordinates of the $j^\text{th}$ fire profile are denoted as $p_{f_j}\in {\rm I\!R}^2$ with fire center locations $C_{f_j}\in {\rm I\!R}^2$ where $j=1,2,.....,n_f$. Let the major and minor axis lengths of $j^\text{th}$ fire be $a_j(t)$ and $b_j(t)$, respectively. The location and spread area of fire points are unknown to the searching agents. The fire spread rate depends on the affected area's topography, fuel content, terrain, wind speed, etc. It is necessary to detect the fire at a nascent stage to reduce the quenching time and destruction of biodiversity. The objective of the multi-swarm mission is to detect the maximum number of fire locations and quench the fire in the shortest duration possible. The objective is mathematically expressed using different models of associated subsystems as given in the following subsections.
\subsection{Fire Spread Model}
The circular or elliptical approximation is the most commonly used fire spread model. The fire size grows depending on the rate of fire spread, and the dynamic spread of the fire can be expressed as a function of fireline intensity, $I_l$ \cite{hansen2012},\cite{byram1959combustion}. The fireline intensity is defined as the heat release rate per unit length of the fire front, and $I_l$ in (\si{kW/ m}) is given by
\begin{equation}
	I_l = \alpha*(L_f)^\beta
\end{equation}
where $L_f$ is the flame length in \si{m}, $\alpha$ and $\beta$ are coefficients that depends on the available fuel content. The fire spread rate ($R$) in \si{(m/s)} is given by
\begin{equation}
	R=\frac{I_l}{H_cF_m}
\end{equation}
where $H_c$ is the heat of combustion in \si{kJ/ kg} and $F_m$ is the mass of the available fuel per unit area in \si{kg/m^2}. The fire spread rate is assumed to be constant and is calculated assuming a uniform fuel availability, terrain, and wind conditions in the search area. For a typical pine forest scenario, the values $\alpha=$ 259.833, $\beta=$ 2.174, $L_f=$ 4 \si{m}, $H_c=$ 18600 \si{kJ/ kg} and $F_m=$ 4 \si{kg/m^2} are considered \cite{hansen2012},\cite{penney2019calculation}.
\subsection{Swarm Model}
A swarm is a set of UAVs flying together cooperatively, keeping the swarm intact. The swarm members are free to move independently within the swarm boundary. UAVs in each swarm have an attractive force to the center of the swarm to keep the swarm intact. The schematic diagram of a typical swarm with four members is shown in Fig. \ref{swarmpic}.
\begin{figure}[htbp]
	\centerline{\includegraphics[width=65mm]{swarmpic.png}}
	\caption{Illustration of the swarm model showing sensing boundaries.}
	\label{swarmpic}
\end{figure}
A circular swarm boundary with a constant swarm radius, $r_{s_i}$ is assumed for all the swarms. The swarm center, $C_{s_i}\in {\rm I\!R}^2$ is defined as the mean of the positions of all UAVs in the $s_i^\text{th}$ swarm.
\begin{equation}
	C_{s_i}=\frac{1}{n_{s_i}}\sum_{k=1}^{n_{s_i}} p_{s_i}^{\,k}
\end{equation}
where $p_{s_i}^{\,k}=\begin{bmatrix}
	x_{s_i}^{\,k}&y_{s_i}^{\,k}
\end{bmatrix}$ is the position of $k^\text{th}$ swarm member of the $s_i^\text{th}$ swarm. Each member of a swarm satisfies the condition given below.
\begin{equation}
	\norm{p_{s_i}^{\,k}-{C_{s_i}}}<r_{s_i}
\end{equation}
\subsection{UAV Kinematic Model}
Let $v_{s_i}^{\,k}\in {\rm I\!R}^2$ and $v_{rs_i}^{\,k}\in {\rm I\!R}^2$ be the velocity and reference velocity vector of $k^\text{th}$ swarm member of the $s_i^\text{th}$ swarm at a time, $t$. All UAVs are homogeneous and belong to multi-rotor category. The mathematical model governing the motion of UAV is given by a first-order system.
\begin{flalign}
	\nonumber
	&\dot{{p}_{s_i}^{\,k}}(t)=v_{s_i}^{\,k}(t)\\
	&\dot{{v}_{s_i}^{\,k}}(t)=-\lambda_{s_i}^{\,k}v_{s_i}^{\,k}(t)+\lambda_{s_i}^{\,k}v_{rs_i}^{\,k}(t)
\end{flalign}
where $\lambda_{s_i}^{\,k}$ is the pole of first-order approximation of the mathematical model of the UAV. The reference velocity inputs for the position to velocity reference feedback are given by
\begin{flalign}
	&v_{rs_i}^{\,k}(t)=\frac{V_0(p_{rs_i}^{\,k}(t)-p_{s_i}^{\,k}(t))}{\tau+\|e_{s_i}^{\,k}(t)\|_2}+\dot{{p}_{rs_i}^{\,k}}\\
	&e_{s_i}^{\,k}(t)=p_{rs_i}^{\,k}(t)-p_{s_i}^{\,k}(t)
\end{flalign}
where $p_{rs_i}^{\,k}(t)$ and $\dot{{p}_{rs_i}^{\,k}}$ are the position and velocity of reference trajectory. $V_0 > 0$ is the cruise speed in \si{m/s}, $\tau>0$ is a small positive quantity, and $e_{s_i}^{\,k}(t)$ is the tracking error vector.
\subsection{Fire Detection Model}
A temperature detector and a thermal imaging sensor are mounted in each UAV for the detection of fire locations. The temperature detector measures the approximate temperature and rate of change of temperature.. The thermal imaging sensor gives the accurate fire profile and location. Fig. \ref{swarmpic} also shows the probability distribution as a function of the distance between UAV and fire location. Let $T_{s_i}^{\,k}$ be the temperature sensed by $k^\text{th}$ UAV, and $R_{sen}$ be the sensing radius within which fire detection is possible. The potential fire area identified by the UAV is confirmed using a Gaussian probability model.
\begin{equation}
	P_j^{\,k} = exp(-\frac{d_{jk}^2}{2\sigma^2})
	\label{probconf}
\end{equation}
where $P_j^{\,k}$ is the probability of detection of the $j^\text{th}$ fire front by the $k^\text{th}$ UAV, $d_{jk}=\norm{p_{s_i}^{\,k}-p_{f_j}}$ is the distance between $k^\text{th}$ UAV and $j^\text{th}$ fire front, and $\sigma$ is the standard deviation of the sensor range. The fire location is detected if the probability of detection exceeds the detection threshold ($\gamma$). After the detection of fire, the swarm members can sense the fire profile data and start quenching the fire, which follows the quench model.
\subsection{Quench Model}
The critical flow rate ($CF$) is the water flow rate required to extinguish the fire with infinite time. UAVs can quench the fire area by spraying water at a higher rate than $CF$. The critical flow rate is proportional to the flame length, and $CF$ in \si{kg/m^{2}s} is given by
\begin{equation}
	CF=c*L_f^{\nu}
\end{equation}
where $c$ and $\nu$ are constants.

\begin{figure}[htbp]
\vspace{-2.5mm}
	\begin{minipage}{.5\linewidth}
		\centering
		\subfloat[]{\label{QT:a}\includegraphics[trim=5.5 1 1 1,clip,width=4.8cm]{quenchvsareawithqtmax.eps}}
	\end{minipage}%
	\begin{minipage}{.5\linewidth}
		\centering
		\subfloat[]{\label{QT:b}\includegraphics[trim=3.5 1 4 1,clip,width=4.8cm]{slopevsnuav.eps}}
	\end{minipage}
	\caption{Plots showing (a) Variation of quench time with fire area for different number of UAVs. (b)Variation of slope for different number of UAVs.}
	\label{quenchvsarea}
\end{figure}
The quench time is defined as the time taken to quench the fire completely after the detection of fire by any number of UAVs. The quench time $Q_t^j$ for the $j^\text{th}$ fire depends on the quench area rate ($r_q$), fire area to be extinguished ($A_f^j$), number of UAVs detecting the fire ($N_{qu}^j$), and the time of joining of UAVs acting on the fire ($T_m^j$).
\begin{equation}
	Q_t^j=f(r_q,A_f^j,N_{qu}^j,T_m^j)
\end{equation}
Let the UAVs spray water at a flow rate $W_r$ in \si{kg/s}, then the quench area rate ($r_q$) in (\si{m^{2}/s}) is given by
\begin{gather}
	r_q=\frac{W_r}{CF}
\end{gather}
The quenched area $A_q^j(t)$ at time $t$ \si{s} from the start of mission is given by
\begin{gather}
	A_q^j(t)=\sum_{m=1}^{N_{qu}^j} r_q u(t-T_m^j)
	\end{gather}
The fire area $A_f^j$ (\si{m^{2}}) could be quenched in $Q_t^j$ (\si{s}) assuming same time of joining for $N_{qu}^j$ UAVs.
\begin{gather}
	Q_t^j=\frac{A_f^j}{N_{qu}^jr_q}
\end{gather}

Fig. \ref{QT:a} shows the variation of quench time with fire area for different numbers of UAVs with the same joining time. If multiple UAVs act on the same target, then the reduction in fire area multiplies with the number of UAVs, considerably reducing the quench time. The slope variation of the $Q_t$ vs $A_f$ graph with different number of UAVs is shown in Fig. \ref{QT:b}. The slope reduces with an increase in UAVs, but the reduction is minimal after a point. This explains the importance of multi-swarm missions with regulated merging and repulsion of swarms for the dynamic target scenarios.
\subsection{Performance Model}
The goal behind forest firefighting is to reduce the mission time, minimizing the destruction of biodiversity. The total time required to mitigate all the fire locations in the search area is defined as the mission time. This includes the detection time and quench time for all fire locations. The search objective of the mission is to maximize the number of targets detected in a given time, or equivalently, minimize the detection time. The mitigation objective is to minimize the quench time of detected targets. The active fire area increases with the increase in detection time. The minimization of detection time results in the smallest fire area to be quenched by the UAVs. Thus, the search objective can alternatively be represented as the minimization of area of the detected fire locations. Increasing the number of swarms acting on the same fire reduces quench time and achieves the mitigation objective, but the effect diminishes after a certain point. Furthermore, more swarms quenching the same fire reduce the detectability of undetected fires, increasing the detection time of undetected fire locations. The fire will spread, resulting in crown fires and a longer quench time. It is vital to balance the number of searching and quenching swarms for a successful mission. The search objective is split into minimization of the area of detected and undetected targets to achieve this goal. The multi-objective performance model can be represented using a weighted sum model as
\begin{gather}
	\nonumber
	\text{min}~ w_1\sum_{j=1}^{n_{df}} A_f^j+w_2\sum_{j=n_{df}+1}^{n_f} A_f^j+w_3 \sum_{j=1}^{n_{df}} Q_t^j\\
	\text{s.t} Q_t^j<Q_{tmax}
	\label{perfobj}
\end{gather}

where $n_{df}$ is the number of fire locations detected, $w_1$, $w_2$ and $w_3$ are the weights of the objective functions. The choice of weights determines the condition for repulsion and merging of swarms and helps maintain the balance between searching and quenching swarms. The MSCIDC algorithm is formulated to achieve the search and mitigation objectives of multi-swarm forest firefighting.
\section{Multi-Swarm Cooperative Information-driven search and Divide and Conquer mitigation control}
The Multi-Swarm Cooperative Information-driven search and Divide and Conquer mitigation control (MSCIDC) uses swarms of UAVs to search and mitigate forest fire. The schematic diagram of the proposed method is shown in Fig. \ref{blockdia}. In the proposed method, swarms use multi-swarm cooperative information-driven search during the search phase of the mission. The search method combines cooperative information-driven exploration and exploitation for the faster detection of targets.
\begin{figure}[htbp]
	\centering
	{\includegraphics[ width=8.7cm]{blocknew.png}}%
	\caption{Schematic diagram of the proposed MSCIDC}
	\label{blockdia}
\end{figure}The cooperation between the members of the same swarm based on sensor information is termed local intra-swarm cooperation, which helps the members of the swarm search in the direction of maximum information and helps detect the same fire location by the non-detector members of the swarm. After detecting the fire location, swarm members fly in divide and conquer mitigation control to mitigate the fire by spraying water. The cooperation between swarms is global inter-swarm cooperation, which helps in the merging and repulsion between swarms during the mitigation of detected fires. The global inter-swarm cooperation leads to faster mitigation of detected fires and faster detection of other undetected fire locations.
\subsection{ Multi-Swarm Cooperative Information-driven Search}
Multi-swarm cooperative information-driven search is a multi-stage search process depending upon the temperature value sensed by the temperature detector. The two-stages of MSCIDC are cooperative information-driven exploration and exploitation. The balance between exploration and exploitation is a crucial factor in any stochastic search. The temperature threshold value helps in establishing the balance in the proposed method. The search process will switch from cooperative information-driven exploration to exploitation if the temperature sensed is higher than a threshold value.

The target search missions require initial identification and subsequent detection of targets. If the atmospheric temperature reading from the temperature detector exceeds a threshold, then a potential fire area is identified. Cooperative information-driven exploration is performed until the identification of a possible fire location. UAV swarm moves to the next stage of the search process once a potential target is identified. The swarm uses a cooperative information-driven exploitation in this stage and switches back to the cooperative information-driven exploration if the temperature falls below the threshold value.

The working of cooperative information-driven search is shown in Fig. \ref{IDLschematic}. The swarms are desired to move in the direction of the maximum temperature gradient sensed by UAVs in a swarm. The movement of swarms to the desired location depends on the cooperative action of all swarm members. The cooperative behavior of swarm members attributes to the shift of the swarm center. Communication between swarm members is essential to maintain the swarm structure and share the information sensed about the fire location to ensure the cooperative mission.
\begin{figure}[htbp]
    \vspace{-3.5mm}
	\centerline{\includegraphics[width=7.8cm]{CIDSschematic.png}}
	\caption{Working of cooperative information-driven search.}
	\label{IDLschematic}
    \vspace{-1.5mm}
\end{figure}

 The swarm members broadcast their positions, temperature, rate of temperature, the probability of detection, and the fire location specifications sensed by each member within the swarm. Thus $k^\text{th}$ swarm member of the $s_i^\text{th}$ swarm will have data $ \hat{Y}_{s_i}^{\,k}(t)=(p_{s_i}^{\,k},T_{s_i}^{\,k},\frac{dT_{s_i}^{\,k}}{dt},P_j^{\,k})$ from the sensors for the corresponding position of UAV. If the $P_j^{\,k}>\gamma$, then $j^\text{th}$ fire is detected and the UAV have the information $\hat{Y}_{s_i}^{\,k}(t)=(p_{s_i}^{\,k},T_{s_i}^{\,k},\frac{dT_{s_i}^{\,k}}{dt},P_j^{\,k},C_{f_j},a_j,b_j,h_j^{\,k})$ where $h_j^{\,k}$ is the heading direction towards the $j^\text{th}$ fire from the $k^\text{th}$ UAV. Each swarm member broadcasts the sensed information to all the members in the swarm. The information structure available for the $s_i^\text{th}$ swarm is $\hat{Y}_{s_i}(t)=\hat{Y}_{s_i}^1\cup \hat{Y}_{s_i}^2....\cup \hat{Y}_{s_i}^{n_{s_i}}$. The information structure is used by the swarm to push the swarm center towards the desired direction. The swarm member with maximum information, $k^\ast$ is defined as the member having the highest temperature gradient.
\begin{equation}
	k^\ast=\argmax\limits_{(k\in s_i)}\frac{d}{dt}(T_{s_i}^{\,k})
\end{equation}
The maximum information direction, $\phi^{k^\ast}_{s_i}$ is defined as the velocity direction of swarm member having maximum information and $\hat{Y}^{k^\ast}(t)$ is used to drive the swarm in maximum information direction. In Fig. \ref{IDLschematic}, UAV $2$ has a velocity direction towards the fire and has the highest temperature gradient, hence $k^\ast=2$. The range of waypoints generated for swarm members in the search area is shown as a shaded region. The heading angle range of swarm members depends on the range of waypoints. Therefore, the search space of swarm member is constrained to heading angle range. The search angle $\Psi^{\,k}_{s_i}$ for the $k^\text{th}$ swarm member to generate a waypoint in the maximum information direction is drawn from a uniform distribution from $\phi^{k^\ast}_{s_i}-\phi_0$ to $\phi^{k^\ast}_{s_i}+\phi_0$.
\begin{gather}
	\Psi^{\,k}_{s_i}\sim U({\phi^{k^\ast}_{s_i}-\phi_0, \phi^{k^\ast}_{s_i}+\phi_0})
 \end{gather}
The angle $\phi_0$ is calculated using
\begin{gather}
	\phi_0=\frac{K_\phi}{1+exp(-K_eT_s)}

\end{gather}
where $T_s$ is the maximum temperature sensed by the swarm, $K_\phi$ and $K_e$ are constants. The new waypoint for $k^\text{th}$ swarm member, $p_{rs_i}^{\,k}(t)$ is calculated as
\begin{gather}
	p_{rs_i}^{\,k}(t)=p_{s_i}^{k^\ast}(t)+L_s*l^{\,k}_{s_i}
	\begin{bmatrix}
		cos(\Psi^{\,k}_{s_i})&sin(\Psi^{\,k}_{s_i})
	\end{bmatrix}
	\label{stochsearch}
\end{gather}
where $p_{s_i}^{k^\ast}(t)$ is the position of swarm member with maximum information, and the second term in Eq.\ref{stochsearch} corresponds to the movement contributed by the stochastic search process. $L_s$ is the search step length, $l^{\,k}_{s_i}$ is individual lengths drawn from the probability distribution functions. The waypoints can be generated using different probability distribution functions. The appropriate selection of these functions results in effective exploration and exploitation of the search area. The swarm members are navigated towards the generated point, and only after reaching the current waypoint, a new waypoint is generated.
\subsubsection{Cooperative Information-driven Exploration}
The cooperative information-driven exploration is performed as long as the maximum temperature sensed by the swarm, $T_s$ is less than the threshold value.
\begin{gather}
	T_s<\xi
\end{gather}
where $\xi$ is the detection threshold of temperature. In the case of information-driven exploration, levy distribution is used to calculate the stochastic search factor. The search step length $L_s$ in Eq.\ref{stochsearch}
 is the levy step length, and $l^{\,k}_{s_i}$ is the individual lengths of the levy flight legs, which are drawn from the Levy distribution. The cooperative information-driven exploration uses higher step lengths, which helps in the exploration of the search area. When $\phi_0=\pi$, the search becomes a conventional levy search. The heading angle and randomized leg lengths compose to form the cooperative information-driven exploration trajectory.
\subsubsection{Cooperative Information-driven Exploitation}
The cooperative information-driven exploitation is used for exploiting the search area once the UAV swarm reaches a possible target location. The possible target location means a location with a temperature greater than the detection threshold.
For cooperative information-driven exploitation, Brownian step length is used for better exploitation of the region, and $l^{\,k}_{s_i}$ in Eq.\ref{stochsearch} is drawn from normal distribution $N(0,1)$.
 The step length used in this exploitative search is considerably shorter compared to the explorative search, $s_b<<<s_l$. The shorter step length aids in the efficient exploitation of the search area to reach the precise target location.
\subsubsection{Local attraction}
The attractive force acting within the swarm is termed as Local attraction, and it is the reason for the preservation of swarm structure in the search and detect phase. The local attraction comes into play either when a swarm member moves outside the swarm boundary or when a swarm member detects a fire. If any UAV in a swarm tends to move outside the swarm boundary, the attractive force of the swarm center pushes the UAV inside. A fire location is detected if the probability of detection determined using Eq. (\ref{probconf}) exceeds the detection threshold ($\gamma$). The swarm member with maximum information detects the fire and attracts other swarm members towards it. The attraction towards the fire-detected member helps the other swarm members to reach the fire front faster. After target detection, Divide and conquer mitigation control is employed for target mitigation.

\subsection{Divide and Conquer Mitigation Control}
Dynamic targets like forest fires require uninterrupted attention to stop the spread and for accelerated mitigation. The swarms should start quenching the fire along the fire front to prevent spreading after detecting the fire location. The mitigation control is designed to cover an equal non-overlapping area by UAVs detecting fire. The members of the swarm should cover an equal non-overlapping area even when the area is shrinking to avoid the buffer time regarding the overlapped area.
\begin{figure}[htbp]
\vspace{-2.5mm}
	\centerline{\includegraphics[width=60mm]{formation.png}}
	\caption{Divide and conquer mitigation sectors for 8 UAVs for an elliptical fire front.}
	\label{formation}

\end{figure}
Each UAV in the swarm is restricted to quench an elliptical or circular sector of the equal fire area. Fig. \ref{formation} shows the initial alignment of 8 UAVs according to the proposed mitigation control. The UAVs move in to and fro motion in the allotted sector along the firefront.

Let $N_{qu}^j$ be the total number of UAVs in the $N_{qs}^j$ swarms quenching the $j^\text{th}$ fire. The angular position and reference angular position of the $m^\text{th}$ UAV at time $t$ be $\theta_{s_i}^{\,m}(t)$ and $\theta_{rs_i}^{\,m}(t)$ where $m = 1, 2,..., N_{qu}^j$. The total fire area is divided into $N_{qu}^j$ equal area sectors. The area of $m^\text{th}$ sector is
\begin{multline}
	A_{m*m+1}(t)=\frac{a_jb_j}{2}\bigg[\taninv\bigg(\frac{a_j\tan\Gamma_{m+1}(t)}{b_j}\bigg)\\-\taninv\bigg(\frac{a_j\tan\Gamma_{m}(t)}{b_j}\bigg)\bigg]
	\label{nonoverlappingarea}
\end{multline}
The sector angle, $\Gamma_m$ will be same for all sectors in the case of circular profile but different for the elliptical profile. The sector angle of the first sector, $\Gamma_1=0$, and $\Gamma_m$ for $m\ge2$ is calculated to get equal-area sectors. The control law for the $m^\text{th}$ UAV is given by
\begin{gather}
	\nonumber
	\dot{\theta_{s_i}^{\,m}}(t)=\omega(t)+K_m(\theta_{s_i}^{\,m}(t)-\theta_{rs_i}^{\,m}(t))\\
	\dot{\theta_{rs_i}^{\,m}}(t)=\mu(t)\omega(t)
\end{gather}
where $\omega(t)$ is the nominal angular velocity of the UAVs, and the control law asymptotically tracks the reference angular displacement for gain values, $K_m<0$. The factor $\mu(t)$ controls the direction of motion of the UAV along the firefront.
\begin{gather}
	\mu(t)=
	\begin{cases}\label{F2}
		1& \text{if $\theta_{rs_i}^{\,m}(t)-\Gamma_{m-1} <\delta_\theta$ and}\\\quad& \text{sgn$(\dot{\theta_{rs_i}^{\,m}}(t))=-1$ }\\
		-1& \text{if $\Gamma_{m}-\theta_{rs_i}^{\,m}(t)<\delta_\theta$ and}\\\quad& \text{sgn$(\dot{\theta_{rs_i}^{\,m}}(t))=1$}
	\end{cases}
\end{gather}
The waypoint for the $m^\text{th}$ UAV mitigating $j^\text{th}$ fire location is generated as follows
\begin{gather}
	p_{rs_i}^{\,m}(t)=C_{f_j}+\begin{bmatrix}
		a_j(t)\cos\theta_{s_i}^{\,m}(t)&b_j(t)\sin\theta_{s_i}^{\,m}(t)
	\end{bmatrix}^T
\end{gather}
\begin{algorithm}[t]
	\caption{MSCIDC Algorithm}\label{alg:one}
		Initialize with $n_s$ swarms with $n_{s_i}$ swarm members\;
		Initialize $N_{qu}^j=0,N_{qs}^j=0$ for $j=1\text{ to }n_{f}$\;
		\For{$i=1\text{ to }n_s$}
		{Each $i^\text{th}$ swarm has information $\hat{Y}_{s_i}(t)$\;
		\For{$j=1\text{ to }n_{f}$}{
		\For{$k=1\text{ to }n_{s_i}$}{
		\eIf{$P_j^{\,k}<\gamma$}
		{\eIf{$T_s<\xi$}
		{Cooperative information-driven exploration\;}
		{Cooperative information-driven exploitation\;}}
		{$k^\text{th}$ swarm member of $s_i^\text{th}$ swarm detects $j^\text{th}$ fire\;
		$N_{qu}^j=N_{qu}^j+1$\;
		Local Attraction of swarm members\;
		\For{$m=1\text{ to }N_{qu}^j$}
		{Move to the initial alignment position of $N_{qu}^j$ member\;
		Quenches $N_{qu}^j$ sector\;
		\eIf{$(A_f^j>\delta_A \text{ or }F_r<\delta_f)\text{ and }N_{qs}^j<\delta_s$}
		{$N_{qs}^j=N_{qs}^j+1$\;
		Global regulative merging}
		{\If{$k\notin s_i \text{ and }\gamma_0<P_j^{\,k}< \gamma$}{Global regulative repulsion}}
		}
		}
		}
		}
		}
\end{algorithm}
\subsubsection{Global Regulative Merging and Repulsion}
The regulative merging of swarms helps in reducing the quench time, when multiple swarms detect the same fire location. The regulative merging of swarms happens either if the fire area detected is higher than a threshold, $\delta_A$ or if the number of remaining active fires, $F_r$, is substantially lesser. The total number of swarms allowed to merge, $N_{qs}^j$ is limited to $\delta_s$ to minimize the detection time of undetected targets. The condition for regulative merging can be represented as
\begin{equation}
	(A_f^j>\delta_A \text{ or }F_r<\delta_f)\text{ and }N_{qs}^j<\delta_s
	\label{mergecond}
\end{equation}
The threshold values of area $(\delta_A)$, remaining fires $(\delta_f)$ and number of swarms under mitigation $(\delta_s)$ are chosen to achieve the performance objectives of forest firefighting in Eq.\ref{perfobj}. This minimizes the detection time and quench time of detected fire based on the total number of swarms and its capacity. It is assumed that the approximate number of fire locations is known towards the end of the mission due to the effective coverage of the search area. The merged swarms split after completely quenching the fire and split swarms search for other potential fire areas.

The introduction of regulative repulsion of swarms reduces the mission time for the scenarios that don't satisfy the condition in Eq. \ref{mergecond}. If a swarm detects a fire under mitigation, the second swarm gets repelled by the first swarm. The repulsion happens when the probability of detection value of the second swarm is $\gamma_0<P_j^{\,k}< \gamma$. The repelled swarm uses cooperative information-driven exploration in the direction opposite to the maximum information direction. This repulsive action between swarms helps identify other unattended fire locations at an earlier stage. The dynamic swarm approach helps to maximize the target detection minimizing the detection and quench time. The algorithm of the proposed method is summarized in Algorithm \ref{alg:one}.

	\begin{figure*}[t]
	\vspace{-5.5mm}
		\centering
		\subfloat[ Simulation frame at time $1 \si{s}$] {\label{sim:a}
		\includegraphics[trim=8 5 0 5,clip,width=4.45cm]{idsf.eps}}%
		\subfloat[Simulation frame at time $112 \si{s}$]{\label{sim:b}
			\includegraphics[trim=8 5 0 5,clip,width=4.45cm]{detlocattraf.eps}}%
		\subfloat[Simulation frame at time $420 \si{s}$]{\label{sim:c}
			\includegraphics[trim=8 5 0 5,clip,width=4.45cm]{dncglobrepf.eps}}%
		\subfloat[Simulation frame at time $1581 \si{s}$]{\label{sim:d}
			\includegraphics[trim=8 5 0 5,clip,width=4.45cm]{globattraf.eps}}%
		\caption{Working of MSCIDC: (a) All swarms performing cooperative information-driven search task (b) Swarm, $s_1$ detects fire, $f_1$ and UAVs in $s_1$ have local attraction, (c) $s_1$ starts quenching $f_1$ using Divide and Conquer mitigation control and swarms $s_2$ and $s_7$ get repelled due to global regulative repulsion (d) Global regulative merging of $s_1$ and $s_3$ to mitigate $f_1$.}
		\label{mscidcsteps}
		\vspace{-2.5mm}
	\end{figure*}

\section{Numerical Simulation Results}
The proposed MSCIDC is analyzed for an area of size $10$ \si{km} x $10$ \si{km} considering 15 UAVs with varying swarm sizes for detection and mitigation of five fire locations. Two of the five fire locations have a circular profile, and the rest have an elliptical profile. The fire coordinates and initial lengths of semi-major and semi-minor axes are given in Table \ref{fireloc}. The members of swarm are flying with a constant speed of $20$ \si{m/s} during the search. During mitigation, the speed is varied according to the size of the fire area. The swarms initiate the search from different locations within the search area under consideration.
	\begin{table}[htbp]
	\caption{Fire Locations and Initial Size}
	\centering
	\begin{tabular}{|c|c|c|c|}\hline
		Sl.No & $C_{f_j}$ (\si{m}) & $a_j$ (\si{m}) & $b_j$ (\si{m})\\ \hline
		1 & (2000, 6000) & 300 & 250\\ \hline
		2 & (3000, 9000) & 150 & 100\\ \hline
		3 & (4000, 3000) & 200 & 200\\ \hline
		4 & (8000, 2000) & 100 & 100\\ \hline
		5 & (9000, 8000) & 50 & 50\\ \hline
	\end{tabular}
	\label{fireloc}
	\end{table}
The simulations are performed in MATLAB R2021a environment with an Intel Core-i$7$, $3.2-$GHz processor and $16-$GB memory. The Monte-Carlo analysis is performed for $100$ iterations to evaluate the average performance of the MSCIDC by varying the number of swarms for the same number of UAVs. The results are compared with the Oxyrrhis Marina-inspired Search (OMS) \cite{OMS}, and other random search methods with different probability distributions to analyze the efficiency of the proposed method. The results of the proposed method are compared with different multi-UAV search methods with Dynamic Formation Control (DFC) used in \cite{OMS} for fire mitigation.

The performance indices used to analyze the results are detection time, mission time, and Fire Expansion Ratio (FER). Detection time is the time required to detect all the targets, whereas mission time is the time taken to complete the mission, i.e., the total time taken to detect and mitigate all the fires. The FER is an index to evaluate the extent of burnt area. It is the ratio of the increase in fire area after the commencement of the mission to the initial fire area.

The working of the MSCIDC is explained for a scenario of 15 UAVs searching for the five fire locations given in Table \ref{fireloc}. The 15 UAVs are grouped into $7$ swarms with a swarm size of $\begin{bmatrix}
	3&2&2&2&2&2&2
\end{bmatrix}$. The different features in the proposed method can be analyzed from Fig. \ref{mscidcsteps}. The swarms are initiated randomly in the search area at the beginning of the mission. In Fig. \ref{sim:a}, the swarms are in the search phase of the mission to detect the fire location using cooperative information-driven search. When the swarms are far from the fire, the members use cooperative information-driven exploration to spot potential fire spots, and when the swarms get closer to the fire, they use cooperative information-driven exploitation. In Fig. \ref{sim:b}, swarm $s_1$ detects the fire location $1$ at $(2000, 6000)$ in the search area. The swarm member with a probability of detection greater than the detection threshold confirms the fire location and moves towards the waypoint above the fire front for mitigation. Subsequently, other swarm members are attracted towards fire location $1$ due to local attraction, leading to the detection of the same fire location. Fig. \ref{sim:c} shows the divide and conquer mitigation control of fire location 1 by the swarm members. Each member of the swarm mitigates the fire by spraying water in the assigned sector of the fire area. The swarms $s_2$ and $s_7$ moving closer to fire $1$ get repelled due to the global regulative repulsion in Fig. \ref{sim:c}. The swarms $s_2$ and $s_7$ repelled from fire $1$ detects fires $5$ and $3$ respectively. The global regulative repulsion helps in the faster detection of undetected fire locations. All the fire locations are detected due to search area coverage of the swarms towards the end of the mission, and the condition for global regulative merging is satisfied. In Fig. \ref{sim:d}, two swarms $s_1$ and $s_3$ merge to mitigate fire $1$ to reduce the quench time.

The proposed method is evaluated using Monte Carlo simulations for a different number of swarms with the same number of UAVs. The case with three swarms and five swarms have an equal number of UAVs in all swarms. The cases with $6$ and $7$ swarms have a swarm size of $\begin{bmatrix}
	3&3&3&2&2&2
\end{bmatrix} $ and $\begin{bmatrix}
	3&2&2&2&2&2&2
\end{bmatrix}$ respectively.

\begin{table}[h]
\caption{Performance of MSCIDC for different swarm numbers}
	\centering
 \begin{tabular}{|c|c|c|c|}
\hline
\begin{tabular}[c]{@{}c@{}}Number of\\ swarms\end{tabular} & \begin{tabular}[c]{@{}c@{}}Mean detection \\ time (min)\end{tabular} & \begin{tabular}[c]{@{}c@{}}Mean mission \\ time (min)\end{tabular} & Mean FER \\ \hline
3 & 41.82 & 107.01 & 1.029 \\\hline
5 & 31.03 & 81.19 & 0.642 \\\hline
6 & 27.59 & 83.27 & 0.550 \\\hline
7 & 24.13 & 71.56 & 0.477 \\ \hline
\end{tabular}
\label{varyingswarm}
\end{table}

 Table \ref{varyingswarm} gives the results for MSCIDC for different swarm numbers. The average detection time decreases with an increase in the number of swarms. More the number of swarms, more distributed the total UAVs as swarms. The regulative repulsion also helps in detecting unattended fire location reducing the mission time and improving the performance. The $80\%$ of total fire locations are detected in $15\%$ of the total mission duration for $7$ swarm case. Even though the average mission time decreases with an increase in number of swarms, the effect declines beyond a number. The FER also decreases with increase in number of swarms as fire locations are detected at an earlier stage. Thus the $7$ swarm case has the lowest mean detection time, mission time, and destroyed area.

\begin{figure}[htbp]
    \vspace{-5mm}
    \centering
	\subfloat[{} \label{sssq1}]
	{\includegraphics[trim=5 5 0 5,clip,width=4.5cm]{SsSq1.eps}}%
		\subfloat[{} \label{sssq2}]
	{\includegraphics[trim=5 5 0 5,clip,width=4.5cm]{SsSq2.eps}}%
	\caption{Number of swarms performing search ($S_s$) and mitigation ($S_q$) for (a) single run (b) Monte-Carlo simulation.}
	\label{SSSQ}
	 \vspace{-2.5mm}
\end{figure}
\begin{figure}[htbp]
    \vspace{-2.5mm}
	\centering
	\subfloat[{} \label{fffdfr1}]
	{\includegraphics[trim=5 5 0 3,clip,width=4.5cm]{fffdfr1.eps}}%
		\subfloat[{} \label{fffdfr2}]
	{\includegraphics[trim=5 5 0 3,clip,width=4.5cm]{fffdfr2.eps}}%
	\caption{Number of fire locations detected ($F_d$), fire locations under mitigation ($F_f$), and fire locations remaining ($F_r $) for (a) single run (b) Monte-Carlo simulation.}
	\label{fffdfr}
	 \vspace{-3.5mm}
\end{figure}
The number of swarms executing the search ($S_s$) and quenching ($S_q$) for a total mission duration in $7$ swarm case is shown in Fig. \ref{SSSQ}. The number of swarms performing search decreases as more fire locations are detected and increases eventually when fires get quenched. About $50\%$ of the total mission time, swarms perform the search operation.

The number of fire locations detected ($F_d$ ), under mitigation ($F_f$), and remaining to be quenched ($F_r$), is shown in Fig. \ref{fffdfr}. It is necessary to maintain searching swarms to identify the unattended fires until all the fire locations are detected. The introduction of global regulative repulsion achieved this feature and the Monte-Carlo simulation results shown in Fig. \ref{SSSQ}b and \ref{fffdfr}b verify the feature.

\begin{table}[htbp]
\caption{Performance comparison for different methods}
	\centering
 \begin{tabular}{|c|c|c|c|c|}
\hline
\begin{tabular}[c]{@{}c@{}}Method \\ of forest\\firefighting\end{tabular} & \begin{tabular}[c]{@{}c@{}}Mean detection \\ time (min)\end{tabular} & \begin{tabular}[c]{@{}c@{}}Mean mission \\ time (min)\end{tabular} & Mean FER \\ \hline
Uniform-DFC & 66.10 & 217.47 & 1.808 \\\hline
Normal-DFC & 56.63 & 192.39 & 1.518 \\\hline
Levy-DFC & 52.59 & 184.14 & 1.398 \\\hline
OMS-DFC & 52.63 & 180.11 & 1.319 \\ \hline
MSCIDC & 24.13 & 71.56 & 0.477 \\ \hline
\end{tabular}
\label{diffdistr}
\end{table}

Table \ref{diffdistr} summarizes the mean detection time, mission time, and FER for different forest firefighting methods. Among the multi-UAV methods, OMS-DFC is superior with the lowest mean mission time and FER. Uniform search with DFC performs worst with the highest mean detection time, mission time and FER. The comparison study based on the Monte-Carlo simulation shows that the MSCIDC performs better with a $65\%$ reduction in the burned area and a $60\%$ reduction in mission time compared to OMS-DFC method.

\begin{figure}[htbp]
 \vspace{-5.5mm}
    \centering
	\subfloat[{} \label{area:a}]
	{\includegraphics[trim=5 5 0 5,clip,width=4.5cm]{firearea1.eps}}%
		\subfloat[{} \label{area:b}]
	{\includegraphics[trim=5 5 0 5,clip,width=4.5cm]{firearea2.eps}}%
	\caption{Comparison on variation of fire area with time for a single run between (a) OMS-DFC (b) MSCIDC.}
	\label{fireareaoms7swarm}
	\vspace{-2.5mm}
\end{figure}

	\begin{figure*}[t]
	\vspace{-5.5mm}
		\centering
		\subfloat[{} \label{main:a}]
		{\includegraphics[trim=15 2 50 20,clip,width=5.5cm]{detecttime.eps}}%
		\subfloat[{} \label{main:b}] {
			\includegraphics[trim=15 2 50 20,clip,width=5.5cm]{missiontime1.eps}}%
		\subfloat[{}   \label{main:c}]{
			\includegraphics[trim=15 2 50 20,clip,width=5.5cm]{fer.eps}}%
		\caption{Box plot showing (a) detection time, (b) mission time and (c) fire expansion ratio for different methods}
		\label{comparison}
		\vspace{-5.5mm}
	\end{figure*}
The variation in the fire area of all fire locations versus time for a single run with OMS-DFC and MSCIDC is shown in Fig. \ref{fireareaoms7swarm}. The fire area increases until the swarms detect and start quenching the fire. For a single run shown in Fig. \ref{fireareaoms7swarm}, the burnt area accounts for $0.9295$ \si{km^2} with a FER of $1.07$ and $0.6654$ \si{km^2} with a FER of $0.48$ for OMS and MSCIDC, respectively. The fire spread area increases in the OMS method due to a higher detection time than the proposed method. The Fig. \ref{fireareaoms7swarm} also shows a faster mitigation as all swarm members detect the same fire location. The global regulative merging also accounts for reduced quench time.

Fig. \ref{comparison} shows the box plot of detection time, mission time, and FER. The box plots depict that the spread of the interquartile range of the proposed method is smaller than all the other existing methods. This indicates that existing multi-UAV methods have greater variability for performance indices compared to proposed method. The MSCIDC has lower search and mitigation time even though the UAVs are distributed and free to search in multi-UAV methods. The cooperative information sharing within the swarm help to detect target faster, and the swarm behavior leads to the detection of the same target by swarm members reducing the mitigation time compared to the multi-UAV cases. Even though there are outliers in plots of the proposed method, the outlier values are smaller than the $75^\text{th}$ percentile of the OMS method in all plots. The box plots of MSCIDC show a higher agreement to the median value of performance indices compared to multi-UAV methods.

\section{Conclusions}
This paper presents multi-swarm cooperative information-driven search and divide and conquer mitigation control for forest firefighting. The swarm members communicate the information sensed and cooperate in searching faster and efficiently. The members of the swarms are free to operate within the swarm boundary, and information sensed is used to decide the search direction and detect the fire locations. The local attraction between the swarm members maintains the swarm structure and helps swarm members reach the fire front faster. The divide and conquer mitigation control ensures that the areas quenched by UAVs are non-overlapped for effective and faster mitigation. The proposed method has no communication between the swarms during target search, and global regulative repulsion between swarms detecting the same targets benefits the detection of other unattended targets. The quenching time for higher capacity fires is effectively reduced with the global regulative merging of swarms. The Monte-Carlo analysis shows that the total time to detect all the five targets in the search area is less than $35\%$ of mission time. The proposed method is more efficient, with a reduction of more than $55\%$ in detection time and 60$\%$ mission time than the OMS method. The proposed method limits destruction of forest land area by $65\%$ compared to the OMS method. The results establish that the MSCIDC is more competent for diminishing the destruction of forest land by faster detection and mitigation of the fire location. The future work focuses on the allocation of firefighting tasks considering resource availability and target capacity.

\bibliographystyle{IEEEtran}
\balance


The Memorial library is a special library with emphasis on Indo-British History and Arts relating to 18th & 19th century. The library has 10,674 books/periodicals. There are many rare books. The library mainly caters to the need of research scholars and is headed by Ms.Chaitali Rajbanshi. You can e-mail your requirements if needed.


In what I see as a signal of the importance of augmented reality (AR) to the company, Apple has put one of its most highly experienced executives, Frank Casanova, in command of AR product marketing.
Apple's senior director of worldwide product marketing, Casanova led iPhone marketing for the original 2007 launch.
He was also one of those who evangelized Apple's pioneering QuickTime multimedia standard – given that QuickTime arguably helped Apple transform itself into a streaming media powerhouse, what lessons can he bring to Apple's AR teams?
I imagine he may see AR adoption as a little like the explosion of 3G services. In the early days, when the US was still coming to terms with cellphones, Japan was already embracing fully digital experiences.
The rest is history: The digital transformation of everyday life, work, leisure and every industry is accelerating. Here are some implications from the history of QuickTime – a standard you'll now find inside almost every multimedia device on the planet — that may become relevant to Apple's work with AR.
Emerging from a backroom research project at Apple's advanced technology research group, QuickTime became a fundamental element across the Apple ecosystem.
"Everything Apple does is based on QuickTime", Casanova said in 2003. "It is core technology that's built into OS X. It is the file format for Final Cut, iMovie, iPhoto, iDVD – they all rely heavily on it to manage a lot of their work.
QuickTime also became an element of the MPEG-4 and 3GPP standards, which is why you find it inside almost every multimedia device.
In other words, QuickTime became part of the entire creation, distribution and consumption ecosystem of multimedia. It makes sense for Apple to move in a similar direction with its AR technologies, which have already spawned a trans-industry standard for AR objects, called USDZ.
QuickTime enabled Apple to develop its own suite of unique software solutions and services.
The aforementioned iMovie, for example, was a radical departure for an industry whose dominant computing platform (Windows) was at that time legendarily appalling at handling video assets. Apple's shrewd decision to make iMovie a free tool inside every Mac and iPhone underlined its platform advantages and helped new talent find itself.
Naturally, this end-to-end standards support – baked into the platform at a fundamental level –- also enabled Apple to introduce various media services, such as TV shows and movies via iTunes.
That's something that continues to scale: Just look at Apple's forthcoming movie and TV show streaming services as an example of this. I see no reason ARKit won't evolve in similar directions.
Apple's has always stood at the intersection between technology and the liberal arts. Within this mission, it has aimed to provide platform support for creative developers to build creative tools, as evidenced by the history of Apple/Adobe and the dizzying range of applications you can use on Mac, iPad, and iPhone.
Apple is doing the same thing with ARKit. it has very quickly built a user base of almost a billion compatible devices along with a set of off-the-shelf APIs (Application Programming Interfaces) developers can use to create new experiences.
When you have a huge audience and an available toolkit, you empower creative people to do creative things.
Apple has always worked in partnership with others since its inception.
When QuickTime became a multimedia standard in 2003, Casanova pointed out that establishing it as such required "dozens (of companies) working together".
When it comes to AR, Apple is already working with industry partners, including Adobe and Valve. We may see more of this — Apple's recent move to open up AirPlay 2 to third-party televisions reflects an even deeper insight into partnerships — it simply depends what makes sense.
At what point does mass market hardware availability of AR access devices translate into a mass market opportunity for cutting-edge AR experiences?
We can already indulge ourselves in ARKit so long as we have a compatible iPhone or iPad. What else makes sense? QuickTime was shipped in the box with hundreds of third-party digital cameras and became a component on DVDs. What about ARKit on Apple TV? Or Apple glasses?
Weaving together platforms, partnerships, technologies, consumers and developers around a brand-new media-based experience and productivity platform is a complex task. No wonder Apple's put one of its most experienced chaps in charge.


>>parsimonious assumption. Put the 'P' back in ESP?
I share the spirit of your scepticism, but widespread experience does seem to indicate that telepathy occurs now and again, often between people thousands of miles apart, which would seem to make it ES.
After uploading, however, the question of whether 'thought transference', as it used to be known, is possible between biological brains will surely become moot.
The only nag is whether such apparent ESP's, patchy as they are, indicate some 'dimension,' some nooshpere, which would be ontologically enriching if only we could find the key to exploring/mapping it.
If we vacate the receiving brain, we may never know.


text:
Assume nothing, verify everything
in Business, Opinion
Why zero trust is the way forward
Organizations worldwide continue to grapple with cyber security challenges as the pace of digital transformation, fast evolving business models, remote work and increasingly complex partner ecosystems unleash new opportunities for cyber attacks.
Traditional cyber security approaches relying on security 'at the perimeter' were adequate in a world where data and its users resided within specific, well-defined locations. With physical boundaries disappearing — and with increasingly sophisticated cyber criminals using ransomware and other destructive malware to target organizations — conventional cyber security approaches are being rendered obsolete, ultimately driving the need for modern solutions to protect critical assets and information.
More and more businesses are wisely turning to a zero trust mindset to restructure their cyber defenses.
A zero trust approach puts user identity, access management and data at the heart of cyber security. It is an evolutionary cyber security approach and model that has been developing in response to the ever-expanding threat landscape.
Zero trust is not a technology solution but a model and approach that requires a mindset shift based on three key principles: Assume nothing, check everything and limit access. Zero trust relies on an identity-aware, context-driven and datacentric approach to cyber security strategy and operations. With user identity and data value as its key component, zero trust enables secure access to data and resources via strong identity management, modern software defined networks, continuous monitoring and advanced analytics.
No one either inside or outside the enterprise network is automatically trusted — every user must prove their identity to gain access. Within the zero trust framework, even with a valid username and password credentials, users are denied access to the system if their device has not been validated or the required trust level is not met.
Zero trust is different from previous approaches to IT security. Today's hyperconnected world has broken down traditional perimeters — enabling the fluid movement of data beyond organizational boundaries as multiple parties and devices access business data and systems from anywhere in the world. Add to this dynamic environment 5G technology, edge computing and hundreds of millions of emerging IoT devices and it becomes clear that conventional security approaches are fast becoming outdated and increasingly inadequate.
Businesses are waking up to a new reality of threats
While many businesses may not realize just how exposed they are to today's cyber threats, an increasing number are showing a new sense of urgency in adopting a zero trust model.
By 2025, damages resulting from global cybercrimes are expected to reach close to US$1 trillion annually. Primary drivers prompting more businesses to wisely pursue the zero trust model for enhanced security include ongoing digital transformation that is revolutionizing business models and workforces, the proliferation of cloud-based services, and today's increasingly complex supply chain networks.
Also accelerating adoption is geopolitical instability — including the ongoing conflict between Russia and Ukraine. As the geopolitical landscape continues to evolve, and tensions increase, organizations may be required to implement stronger, yet more flexible access controls should the need arise where a quick disassociation is required. Such instability could also further exacerbate supply chain disruptions, with organizations being required to change suppliers at short notice. All of this points towards a more flexible and adaptive model such as zero trust. And also, not to be underestimated is the impact of Section 3 of the 12 May 2021 US executive order requiring the federal government and associated agencies to adopt a zero trust architecture.
As the pursuit of the zero trust framework gains momentum, it is crucial that CISOs and CIOs work towards implementing organization-wide zero trust architectures that align with their operating priorities, risk management needs and technology capabilities.
In the race to better understand and manage today's ongoing cyber threats, zero trust puts businesses in a predictive and proactive mode, providing timely contextbased analysis, insights and automated responses to potential attacks. With a zero trust approach, companies build an end-to-end cyber security approach that is 'perimeter-less' — providing protection for every aspect of the ecosystem, including assets, workloads and other resources.
A key requirement for zero trust is that the enterprise consistently collects, inspects and analyzes traffic across its entire ecosystem, ensuring maximum real-time visibility into both the data that users can access and the potential for malicious activity.
In addition to zero trust, businesses are looking to bolster security using the Secure Access Service Edge (SASE) approach, minimizing complexity among remote users by replacing data centers with internet-first network security, rather than relying on traditional technologies such as Multi-Protocol Label Switching (MPLS). Similar to zero trust, SASE aims to maximize cyber security via a layered and unified system of security measures and a software defined network on traditional network infrastructure. Zero trust and SASE can work together to significantly enhance an organization's cyber security posture and more businesses are taking this approach.
Zero trust is a journey that continues to evolve
Make no mistake — organizations can no longer simply trust user identities, devices, workloads, networks or data. Zero trust is about knowing where your data is and controlling access to it via strong identity management, advanced analytics and a device inventory. KPMG's zero trust model anchored in NIST 800–207 (Zero Trust architecture — 5 pillars) provides an enterprise-wide focus on each client organization's unique cyber security capabilities and needs related to architecture, risk awareness and management, governance and compliance. Having it implemented helps organizations to be better positioned to detect unusual behavior and prevent communication with unauthorized apps, servers and accounts.
Courtesy KPMG Click here for PDF
Tags: CybersecurityKPMGZero Trust
Reshoring U.S. Supply Chain Will be Limited by Costs

redpajama_set_name: RedPajamaCommonCrawl


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The Moldy Peaches är en amerikansk musikgrupp bildad i New York. Gruppen består av sångarna Adam Green och Kimya Dawson, basisten Steve Mertens, trummisen Strictly Beats och gitarristerna Jack Dishel och Toby Goodshank.

De två frontfigurerna Adam Green och Kimya Dawson har numera inlett varsin solokarriär. Även gitarristen Jack Dishel har inlett en solokarriär och går numera under artistnamnet Only Son. The Moldy Peaches är enligt Dawson dock inte nerlagt utan bara inaktivt.

Diskografi
Studioalbum

Singlar

Låten Anyone Else But You är med i filmen Juno.
Samlingsalbum

Referenser

Externa länkar

 The Moldy Peaches officiell webbplats

Moldy Peaches, The

redpajama_set_name: RedPajamaWikipedia


This week The Ultimate Feminist Etsy Guide brings you Fatima's Custom Designs!
Founded by the lovely Fatima Williams. Fatima's Custom Designs is a beautiful Etsy shop that is an ode to black beauty. Fatima makes sure that no woman is overlooked.
I asked Fatima three questions to find out more about the ethos and inspiration of Fatima's Custom Designs.
"Feminism means to me, Inspiring all women from different walks of life. We can stand together as ONE and take over the world, and really show the guys how to really do things lol. Just like what James Brown said, "this is a man's world but it wouldn't be nothing without a woman or a girl".
How does FatimasCustomDesigns look to empower women and children?
I looked around further in Fatima's shop, and not only does she have wonderful accessories and clothing for women, but for babies too! It was such a pleasure having Fatima on The Ultimate Feminist Etsy Guide.
As soon as I saw this M E L A N I N top! I fell in love. I'm a fan of friends, not a fan of their lack of POC representation, but this top screams "fierce" and I loooove! Also the Queendom top, because how cute is it?!
You can check out the rest of The Ultimate Feminist Etsy Guide here!
Loving this series so much! A great collection of items from Fatima and love what she stands for!
This series is so awesome. I love Fatima's answers to your questions – she sounds like a badass!


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Home / 2009 / September / 04 / UBC Pharmaceutical Sciences Building
UBC Pharmaceutical Sciences Building
Sep 4, 2009 | For more information, contact
The new $133.3-million UBC Pharmaceutical Sciences Building, scheduled to open in 2012, will significantly expand the UBC Faculty of Pharmaceutical Sciences' capacity for learning and discovery. View the B.C. Government's announcement here.
The new building space will allow the professional pharmacy degree program to expand 47 per cent in enrolment. An additional 72 students will be able to start the program by 2011 for a total of up to 224 new pharmacists by 2015. The faculty's Masters, Doctoral and Pharm.D. graduate programs will also expand during the same period and the building will provide space for 896 professional practice degree students.
The 22,871-square-metre building will be built to Leadership in Energy and Environmental Design (LEED) gold certification and feature five acres of floor space that will house research and teaching spaces, as well as the Centre for Drug Research and Development (CDRD). The Faculty and CDRD's proximity will enable students to participate in pharmaceutical research and learn in an environment of innovation and discovery.
Download illustrations of the building here.
About the Faculty of Pharmaceutical Sciences
The Faculty of Pharmaceutical Sciences, established in 1946, is internationally recognized for excellence and innovation in education. Researchers are among the world's leading minds in the pharmaceutical sciences, engaging in multidisciplinary and collaborative projects that contribute to improving the health of British Columbians and the world.
UBC's Bachelor of Science in Pharmacy program currently produces more than 600 pharmacists each year for community and hospital pharmacies throughout B.C. The UBC Doctor of Pharmacy program, a two-year post-graduate program, prepares advanced clinical pharmacy specialists to join patient-care teams throughout B.C. and beyond.
In 1981, the Faculty created Canada's first Community Pharmacy Residency Program, which enables graduates to spend a year of community-based pharmaceutical care in such areas as palliative care, diabetes, asthma and other chronic diseases. Past residents are now medication management pharmacists in pain clinics, pharmacy instructors at colleges and universities and as specialists in geriatric pharmacy with B.C. health authorities.
Faculty highlights:
More than 4,000 alumni
Nearly 40 full-time academic faculty and more than 100 clinical instructors
First in Canada last year for external research funding
Undergraduate enrolment is one of the highest among Canada's 10 pharmacy faculties
UBC's Doctor of Pharmacy program was Canada's first to produce more than 100 graduates
Recent research highlights:
Amphotericin B: A new oral formulation of a drug developed by Prof. Kishor Wasan could positively impact the more than 1.5 million people worldwide affected by visceral leishmaniasis (VL), a parasitic disease typically transmitted by sand flies in areas with poor sanitation conditions.
Amphotericin B is the first drug licensed under UBC's Global Access Initiative, which works with industry to provide the drug at subsidized costs to developing countries. An estimated 60,000 people die from VL each year.
Collaboration for Outcomes Research and Collaboration (CORE): This multidisciplinary collaboration of epidemiologists, health economists, clinical pharmacists and health promotion and services researchers addresses the need for clinical and economic research and education in pharmaceutical outcomes.
Led by Profs. Carlo Marra and Larry Lynd, CORE researchers have completed clinical and policy relevant research in rheumatology, respiratory and cardiovascular disease, infectious diseases, diabetes, diagnostic testing and screening to achieve the overall mission of improving health-related outcomes associated with drug therapy.
About the Centre for Drug Research and Development
The Centre for Drug Research and Development (CDRD) provides drug-development expertise and facilities to enable researchers from B.C.'s research universities and associated institutions to bridge the commercialization gap between academic discoveries in the life sciences and the development of new medicines.
Since its launch in 2007, CDRD's unique academic-industry model has attracted over $70 million in funding from government and industry partners, which has enabled the creation of research facilities equipped with state-of-the-art instrumentation operated by highly qualified personnel at based at UBC, SFU and the B.C. Cancer Agency.
Brian Lin
E-mail: brian.lin@ubc.ca


text:
https://apnews.com/Business%2520Wire/55757a0724b74ff2a9f74dbd9722533b
Wolverine Advanced Materials Recognized as '2019 Hoosier Gasket Supplier of the Year'
DEARBORN, Mich.--(BUSINESS WIRE)--Nov 5, 2019--
Wolverine Advanced Materials, an ITT Inc. (NYSE: ITT) Company, has been named '2019 Supplier of the Year' by Hoosier Gasket Corporation.
"Hoosier Gasket Corporation is in the midst of transformational changes and Wolverine Advanced Materials is helping to enable a stronger future for all of us," said Dan McDowell, Chief Commercial Officer of Hoosier Gasket Corporation. "It is an honor to recognize their contributions and dedication to our shared success, as they represent the best in our industry. Our supply relationship with Wolverine Advanced Materials dates back over a decade, and we are proud of the longstanding partnership and collaboration that this award represents."
Wolverine Advanced Materials is the only supplier partner to receive the prestigious Supplier of the Year recognition from Hoosier Gasket Corporation. The award, presented during a ceremony at Wolverine's headquarters in Dearborn, Michigan, is based on Wolverine's 100% on-time material delivery to Hoosier and technical collaboration between the two companies on a variety of sealing applications and projects.
"We are incredibly proud to be recognized by Hoosier Gasket Corporation as their Supplier of the Year," said Remi Riou, general manager of Wolverine Advanced Materials. "At Wolverine, our focus on customer centricity and delivery are two core aspects of who we are as a business and imperative to how we succeed. We are driven by a desire to meet and exceed the expectations of our customers and partners and this award is a unique recognition of that achievement."
Hoosier Gasket Corporation's use of Wolverine Advanced Materials' rubber-coated metal sealing material is rapidly expanding Hoosier's technology offerings to meet their customer's demands. Hoosier provides objective sealing solutions to customers worldwide, through excellence in engineering, technology, manufacturing and customer service. The company produce 220,000 gaskets per day offering over 4,000 part numbers in their U.S. and China operations.
About ITT
ITT is a diversified leading manufacturer of highly engineered critical components and customized technology solutions for the transportation, industrial, and oil and gas markets. Building on its heritage of innovation, ITT partners with its customers to deliver enduring solutions to the key industries that underpin our modern way of life. ITT is headquartered in White Plains, NY, with employees in more than 35 countries and sales in approximately 125 countries. The company generated 2018 revenues of $2.75 billion. For more information, visit www.itt.com.
About Wolverine Advanced Materials
Wolverine Advanced Materials, an entity of ITT Inc.'s Motion Technologies Division, is a leading developer and manufacturer of high-performance materials. Wolverine's core competency is in performance-critical, specialty elastomer-coated metals that offer damping and sealing solutions in the Automotive Brake NVH, and Sealing industries. Extensive industry knowledge and innovative materials have made Wolverine a desired and trusted global supplier for over 80 years. Wolverine employs approximately 700 people worldwide and has manufacturing operations in Virginia, Florida, Germany, and China, as well as sales & technical offices in Michigan, Germany, Japan, China, Brazil, and India.
CONTACT: Investors:
Emmanuel Caprais
emmanuel.caprais@itt.comMedia:
Laurent Lawrence
laurent.lawrence@itt.comCustomers and Sales:
Steven Sehmer
steven.sehmer@wamglobal.com
KEYWORD: MICHIGAN UNITED STATES NORTH AMERICA
INDUSTRY KEYWORD: OTHER MANUFACTURING AUTOMOTIVE GENERAL AUTOMOTIVE ENGINEERING TRANSPORT AUTOMOTIVE MANUFACTURING MANUFACTURING LOGISTICS/SUPPLY CHAIN MANAGEMENT SUPPLY CHAIN MANAGEMENT RETAIL
SOURCE: Wolverine Advanced Materials
PUB: 11/05/2019 06:45 AM/DISC: 11/05/2019 06:45 AM


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text:
Civilian Conservation Corps (CCC) var et amerikansk offentligt beskæftigelsesarbejdsprogram for arbejdsløse mænd med fokus på bevarelse af naturværdier, som fungerede fra 1933 til 1942. Som led i New Deal lovgivningen, der blev foreslået af præsident Franklin D. Roosevelt (FDR) var CCC først og fremmest udformet som nødhjælp mod den høje arbejdsløshed under Depressionen i 1930'erne og dernæst for at gennemføre en bred vifte af initiativer til at bevare naturværdier på nationale, statlige og kommunale arealer. Lovgivningen bag programmet blev forelagt den 73. Kongres den 21. marts 1933 og loven blev vedtaget den 31. marts 1933. CCC blev et af de mest populære New Deal programmer i offentligheden og fungerede i alle de daværende 48 amerikanske stater samt i de amerikanske territorier Hawaii, Alaska, Puerto Rico og Virgin Islands. Den separate Indian Division var en stor hjælp for de amerikanske indianere.

Korpsets medlemmer boede i lejre, bar uniform og var under halv-militær disciplin. Ved deres indtræden i korpset var 70 % af rekrutterne fejlernærede og dårligt klædt. Få havde mere end et års uddannelse på gymnasieniveau, få havde haft andet end småjobs. Disciplinen blev opretholdt med truslen om "afsked i vanære". Der er ingen rapporter om oprør eller strejker. "Dette er en træningspost, vi vil skabe moralsk og fysisk rustede folk, der kan slå 'den gamle depression'" pralede et nyhedsbrev fra en lejr i North Carolina.

De i alt 200.000 sorte, der var indrulleret, var fuldt ud raceadskilt efter 1935, men fik samme løn og indkvartering. Indenrigsminister Harold Ickes lagde pres på direktør Fechner for at få udpeget sorte til ledende stillinger som uddannelsesdirektører i de 143 raceadskilte lejre.

I starten var CCC begrænset til unge mænd i alderen 18 til 25 hvis fædre var på bistand. Gennemsnitsalderen ved indtræden var 18-19 år. Der var to undtagelser til aldersbegrænsningen: veteraner og indianere, som havde et særligt CCC program og egne lejre. I 1937 ændrede Kongressen aldersgrænserne til 17-28 og fjernede kravet om at de indrullerede skulle være på bistand.

Indianske Division
CCC drev en helt adskilt division for medlemmer af føderalt anerkendte indianske stammer, Indian Emergency Conservation Work, IECW, eller CCC-ID. Det satte indianske mænd fra reservaterne til at bygge veje, broer, skoler, klinikker, herberger og andre offentlige værker ved reservaterne. CCC var ofte den eneste kilde til betalt arbejde i fjerne reservater. Indrullerede skulle være mellem 18 og 35 år. I 1933 var f.eks. omkring halvdelen af familiefædrene i Sioux reservaterne i South Dakota beskæftiget af CCC-ID. Takket være bevillinger fra Public Works Administration (PWA), byggede den indianske division skoler og drev et udstrakt vejbygningsprogram i og omkring mange reservater. IECW adskilte sig fra andre CCC aktiviteter ved at det direkte uddannede folk til at blive tømrere, lastbilchuffører, radiooperatører, mekanikere, landmålere og teknikere. I alt 85.000 indianere blev rekrutteret. Det viste sig at få stor betydning for de 24.000 indianere, som gjorde tjeneste i militæret og de 40.000 som forlod reservaterne og fik jobs i byerne.

Opløsning
Selv om CCC formentlig var det mest populære New Deal program, blev det aldrig til et permanent agentur. I en Gallup undersøgelse fra 18. april 1936, spurgtes der: "Er De tilhænger af CCC lejre?"; 82 % af svarerne sagde ja, heraf 92 % af Demokraterne og 67 % af Republikanerne.

Den sidste forlængelse blev vedtaget af Kongressen i 1939. CCC programmet fortsatte med at blive indskrænket i takt med at Depressionen tog af og jobmulighederne forbedredes. Der var også færre mulige kandidater efter at værnepligten blev indført i 1940. Fra maj 1940, da krigen hærgede i Europa, begyndte programmet et skifte i retning af nationalt forsvar og beskyttelse af skovarealer. Efter angrebet på Pearl Harbor i december 1941 blev alle føderale programmer fokuseret på krigsindsatsen. De fleste CCC arbejder, bortset fra brandbekæmpelse i uopdyrkede områder, blev ændret til bygning af baser til hæren. CCC blev opløst et år tidligere end planlagt, da Kongressen fjernede bevillingerne, og arbejderne blev formelt afsluttet ved slutningen af finansåret den 30. juni 1942. Afslutningen af CCC programmet og nedlæggelse af lejrene involverede tiltag for at efterlade de ufærdige projekter i den bedst mulige skikkelse, afskedigelse af 1.800 ansatte, overdragelse af CCC ejendom til hæren og flåden eller andre regeringsagenturer samt udarbejdelse af afsluttende regnskaber. Likvideringen af CCC blev berordret af Kongressen den 2. juli 1942, og så godt som gennemført den 30. juni 1943.
Bevillinger til nedlæggelse af CCC fortsatte indtil 20. april 1948.

Nogle tidligere CCC lejre i god stand blev reaktiveret fra 1941 til 1947 som Civilian Public Service lejre, hvor militærnægtere gennemførte "arbejde af national betydning" som alternativ til militærtjeneste. Andre lejre blev anvendt til internering af japanere og tyske krigsfanger. Efter at CCC var blevet opløst organiserede de føderale agenturer, der havde ansvar for administration af offentlige landområder, egne brandværn i de tørre årstider, i store træk efter model fra CCC og skabte de samme muligheder for at få udendørs arbejdserfaringer for unge mennesker.

Korpsbevægelsen i dag

Det oprindelige CCC blev lukket i 1942, men det dannede model for statslige agenturer, som åbnede i 1970'erne. Vore dages korps er nationale, statslige og lokale programmer, som fortrinsvis beskæftiger unge og unge voksne i alderen 16-25 år i samfundstjeneste, oplæring og uddannelsesaktiviteter. Landets ca. 113 korps programmer fungerer i 41 stater og District of Columbia. I 2004 omfattede de over 23.000 unge mennesker. De var oprindelig kendt som National Association of Service and Conservation Corps (NASCC) og arbejder på at udvide korpsbevægelsen i hele USA. Korps netværket tog form i 1985, da landets første 24 korpsdirektører sameledes for at få en fælles repræsenation på nationalt niveau og en central informationsformidling om hvordan man etablerer og driver "best practice"-baserede korps. Tidlig støtte fra Ford, Hewlett og Mott Foundations var af afgørende betydning for skabelsen af sammenslutningen.

Referencer

Yderligere læsning
 American Youth Commission. Youth and the Future: The General Report of the American Youth Commission  American Council on Education, 1942
 Colen, Olen Jr. The African-American Experience in the Civilian Conservation Corps (1999)
 Gower, Calvin W. "The CCC Indian Division: Aid for Depressed Americans, 1933-1942," Minnesota History 43 (Spring 1972) 7-12
 Douglas Helms, "The Civilian Conservation Corps: Demonstrating the Value of Soil Conservation" in Journal of Soil and Water Conservation 40 (March-April 1985): 184-188.
 Hendrickson Jr.; Kenneth E. "Replenishing the Soil and the Soul of Texas: The Civilian Conservation Corps in the Lone Star State as an Example of State-Federal Work Relief during the Great Depression" The Historian, Vol. 65, 2003
 Kenneth Holland and Frank Ernest Hill. Youth in the CCC (1938) detailed description of all major activities
 Leighninger, Robert D., Jr. "Long-Range Public Investment : The Forgotten Legacy of the New Deal" (2007), providing a context for American public works programs, and detailing major agencies of the New Deal: CCC, PWA, CWA, WPA, and TVA.
 Otis, Alison T., William D. Honey, Thomas C. Hogg, and Kimberly K. Lakin The Forest Service and The Civilian Conservation Corps: 1933-42 United States Forest Service FS-395, August 1986
 Paige, John C. The Civilian Conservation Corps and the National Park Service, 1933-1942: An Administrative History  National Park Service, 1985
 Parman, Donald L. The Navajos and the New Deal (1969)
 Parman, Donald L. "The Indian and the CCC," Pacific Historical Review 40 (February 1971): pp 54+
 Salmond John A. The Civilian Conservation Corps 1933-1942: a New Deal case study. (1967), the only scholarly history of the entire CCC
 Salmond, John A. "The Civilian Conservation Corps and the Negro," The Journal of American History, Vol. 52, No. 1. (Jun., 1965), pp. 75-88. in JSTOR
 Sherraden, Michael W. "Military Participation in a Youth Employment Program: The Civilian Conservation Corps," Armed Forces & Society, vol. 7, no. 2, pp. 227-245, April 1981 pp 227-245;  available online from SAGE Publications
 Steely, James W. "Parks for Texas : Enduring Landscapes of the New Deal" (1999), detailing the interaction of local, state and federal agencies in organizing and guiding CCC work.
 Wilson, James; "Community, Civility, and Citizenship: Theatre and Indoctrination in the Civilian Conservation Corps of the 1930s" Theatre History Studies, Vol. 23, 2003 pp 77-92
 Hill, Edwin G. In the Shadow of the Mountain: The Spirit of the CCC. Pullman, Washington: Washington State University Press, 1990.
 Kiran Klaus Patel. Soldiers of Labor. Labor Service in Nazi Germany and New Deal America, 1933-1945, Cambridge University Press, New York 2005, .

Eksterne kilder

 Civilian Conservation Corps (CCC) Legacy A merged non-profit foundation of the former National Association of CCC Alumni (NACCCA) and the Camp Roosevelt CCC Legacy Foundation
 National Archives & Records Administration: Records of the Civilian Conservation Corps (CCC)
 James F Justin Civilian Conservation Corps Museum, Online CCC Biographies Stories Photographs and Documents
 Rosentreter, Roger L. "Roosevelt's Tree Army: Michigan's Civilian Conservation Corps", with photographs
 Life in the Civilian Conservation CorpsPrimary Source Adventure, a lesson plan hosted by CCC in Texas
 The Corps Network (formerly known as NASCC)
 Living New Deal Project, California
 CCC Legacy in Massachusetts
 Top 10 New Deal Programs
 WE CAN TAKE IT- grassroot call for action citizens group urging our policy makers to reactivate the CCC
 Youtube Video: "The Great Depression, Displaced Mountaineers in Shenandoah National Park, and the Civilian Conservation Corps (C.C.C.)"
 Film, The March of Time: Youth Jobs Program (CCC) During Great Depression
 Film: President Visits Foresters (CCC), Roosevelt 1933/08/14
 NARA film: A Nation-Wide System of Parks 1939
 NARA film: Alabama Highlands 1937  Alabama State Parks
 NARA film: Down Mobile Way 1935  Alabama State Parks
 NARA film: Cradle of the Father of Waters 1938  Minnesota State Parks, Lake Itasca State Park
 NARA: Great Smoky Mountains National Park 1936
 NARA film: Outdoors in the Garden State 1937  New Jersey State Parks

New Deal


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This is good because they have got loads of coverage but kind of bad because it is sad. The company that makes Polaroid camera's has revealed that they are stopping production because of poor sales in the digital age. The end of an era. I am sure we can all remember funny polaroid moments!
House Builders! Insurance companies have felt the need to come out and say that they may not insure new buildings on flood zones. I always thought it was stupid to build where flood water was meant to go but Golden Brown and the builders obviously knew better.


Toyota Travel Series: Best Hiking Spots in Florida!
Toyota Travel Series: Summer fun on the water!
Toyota Travel Series: Relax this Memorial Day at one of Florida's best beaches!
Toyota Travel Series: Cool down this weekend at one of Florida's best springs!


Discover free woodworking plans and projects for rabbit hutch instructions. Start your next project for rabbit hutch instructions with one of our many woodworking plans. Woodworking project plans available for immediate PDF download.
Rabbit Hutch, The Best In The World.


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Fishers Parks
Bigger Park and More Homes on the Southwest Side of Fishers
February 17, 2022 February 17, 2022 Bradley JonesLeave a reply
The new park at 96th and Allisonville off the of the White River could increase in size by about 25% based on a proposal by CRG Residential to the City of Fishers. CRG Residential is proposing a mixed-use development in the area and as part of their plans would give 25 acres to be added to the Fishers White River Park.
It is worth noting that this development will also bring 380 new multi-family units and 66 townhomes if the current proposal goes through. This along with the new developments in the 106th near Lantern Road and a few other areas are likely to impact the demographics for the schools on the west side, which include Fishers Elementary School, which is in need of an update.
The press release from the City of Fishers on the development follows:
FISHERS – CRG Residential announced today a $98 million mixed-use project at the northwest corner of 96th Street and Allisonville Road. The project will include multi-family, townhomes, and commercial retail space situated next to the recently announced 98-acre parkland to the west. The City of Fishers, in close partnership with CRG, also announced the reconstruction of the Michigan Left intersection into a roundabout.
Additionally, CRG Residential has committed to donate the land required for the roundabout and an additional 25 acres of non-floodplain land to increase the size of adjacent parkland, totaling 123 acres for the White River passive nature park.
"This project will enhance the southwest corridor of our community in multiple ways," said Fishers Mayor Scott Fadness. "The new park, and now the additional buffer zone donated by CRG, will serve as a key amenity for our city and region, providing expanded public access to one of our community's greatest natural assets—the White River. And as we bring the roundabout intersection online, the additional vehicular traffic will be able to better access the local businesses in the area as a gateway to our city."
The mixed-use development from Carmel-based CRG will include 380 multi-family units, 66 townhomes for sale and 35,000 square feet of new commercial retail space. It will also include infrastructure improvements to support the City's new park.
CRG has significant experience with similar public-private partnerships in Indiana, throughout the Midwest and eastern United States. CRG is finishing construction of the Nickel Plate Station in downtown Fishers as well as projects in Greenwood, Westfield, Evansville, Elkhart, Newport, Ky., West Des Moines, Iowa, and Traverse City, Mich., among others.
We are excited about the opportunity to invest in this important corridor of the city and to enhance its value to the community," said CRG President and Fishers resident Christopher Reid. "There are so many dynamic pieces to this development, particularly the park and greenspace along the White River which will provide access to the overall community. We look forward to working with city officials, Councilors, key partners and the community to develop a project that instills pride in the community."
At its Feb. 21 meeting, the Fishers City Council will consider project agreement approval after hearing from City of Fishers Economic Development officials and CRG leaders. Their presentation will include more project details and proposed bond financing, which includes $23 million in developer-guaranteed bonds over a term of 25 years for land and infrastructure.
HSE Board Work Session on the 2023 Referendum – Slide Info
Three Million Meals: A Look at the HSE Food Services Department


*	Move the spider so that it covers each line once.
*	See screenshot for order of moves (U).
*	Take the fly from the center when complete.
*	Give the fly to the frog; take the 2nd wheel segment (V).
*	Examine the spindle and place the 2 wheel segments on the broken wheel (W).
*	Examine the pipe and place the valve on it: turn the valve (X).
*	Take the HAMMER HANDLE (Y).
*	examine the workbench: place the HAMMER HANDLE on the hammerhead and take the HAMMER (Z).
*	examine the basket; take the LEATHER BELT (A).
*	examine the window shelf and place the LEATHER BELT on the ehisel handle: take the CHISEL (B).
*	examine the axe: take the BLOODY PRUNER (C).
*	place the CHISEL in the slit and use the HAMMER	on it: take the Axe (D).


Park Secrets: The Hawaiian Style Cafe is a must-stop on the way to Pu'ukohola Heiau Park. We came here twice during our 5 days stay at Waikoloa. They have awesome tacos/burritos fare and killer malt shakes!!! They were so good and hearty.
During the two visits, we tried their oreo, coffee, mocha and lava flow shakes. All were delicious, a tad bit sweet, but still very good. The hubby loved his shakes! And we tried their spicy burrito, carnitas taco, baja style fish taco and one of their special fish taco.
Food was delicious, definitely a place we will keep coming back if we lived there… or if they move over to nyc!!!


The best defense is a good offense. This old adage holds true in sports tactics as well as oral health care and prevention. As Oral Health month draws to a close, your dental professionals at Affinity Dental Clinic want to empower you with the education, solutions and care that will keep you smiling for years to come!
At Affinity Dental we are invested in your long-term wellness and thus, we offer preventative oral cancer exams, as well as periodontal treatments and solutions which enable you and your family to live your lives to the fullest.


text:
Jamie Storr was better than you think
By Quisp Feb 25, 2010, 10:00pm PST
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via www.hockeytalk.biz
As you probably know, Jamie Storr has been helping the Kings out in practice this week, in place of Jonathan Quick who is somewhere I forget. RudyKelly at Battle of California had some fun at Jamie's expense. I laughed. And then I remembered that Storr:
is 4th on the Kings in career wins, behind Vachon, Hrudey and Lessard.
is 2nd in career GAA (2.52) among goalies with more than 100 games, behind Felix Potvin. Quick is 3rd.
is 1st in career SV% (.910).
is 2nd in career shut-outs with 16. Vachon had 32.
is 1st in single season GAA (more than 15 games played): 1.90 in 2001-2002.
is 1st in single season SV%: .929 in 1997-1998. (holds three of the top four).
was named to the NHL All-Rookie Team twice (qualified as a rookie two years in a row)
won back to back gold medals for Team Canada at the WJCs, during which he was undefeated.


Atlanta rap fans know the drill when it comes to attending concerts.
A major hub for hip-hop, rap concerts in this city usually turn into a reunion of sorts on stage, with the main act bringing out as many surprise guests as they can possibly fit into their set. Even the rappers who lack stage presence manage to pull off memorable shows this way.
It would be easy to assume that rapper Future's concerts would fit into this category, but you'd be wrong.
Future showed that he has the stage presence to match the fact that he is one of the most prolific rappers to come out of this city during the second stop on his "Nobody Safe" tour, the first of two hometown shows at Lakewood Amphitheatre. Where rapper Gucci Mane ran through his hits and brought out several special guests at his 2016 Fox Theatre show, his first Atlanta concert following a stint in prison, Future decided to hog the spotlight.
And, ultimately, that was a great decision.
Fans who watched the rapper's Coachella set may have recognized the staging from his shows a few weeks ago. To open his set, Future appeared at the top of a giant staircase before descending to the center of the stage. "Future" and "Hndrxx" (an homage to the rapper's rock star idol Jimi Hendrix and the name of his latest album) signs were prominently displayed. Giant LED screens behind the staircase and zig-zagged above the stage displayed pre-recorded video throughout the night.
Donning a yellow jacket and matching skullcap with his signature shades and white pants, the rapper opened with "Draco," a song off his 2017 self-titled album. The melodic, braggadocios song encapsulates the rapper's style perfectly. Future swayed back and forth, holding onto the mic stand as the bass rumbled throughout the amphitheater.
"I see Atlanta is ready for that [expletive] turn up tonight," he said to deafening screams from the crowd.
The rapper mentioned that he could see various family member in the audience during the show and this, mixed with the crowd's enthusiasm, likely motivated him to give such a high-energy show.
Much of his set consisted of snippets of his best verses and hooks. From "Same [Expletive] Time" to "Karate Chop" to "Bugatti," the rapper revisited his old hits and fans still knew every word. When he returned from a wardrobe change, he alternated between performing songs from his "Future" and "Hndrxx" albums, both of which were released one week apart earlier this year. The two albums topped the Billboard 200 charts at no. 1 and 2 following their release.
Future shimmied across the stage to the mumble-heavy "I'm so Groovy" before launching into the pop-friendly "Coming Out Strong" sans The Weeknd. And, again, fans knew every word.
The rapper ended his set with the help of his dancers by performing his current hit "Mask Off" and dedicating the song to the city that shaped him.
While Future gave frequent shout-outs to Atlanta during his set, opening act Young Thug didn't quite show as much hometown love. It took Thug some time to warm up to the crowd, but by the time he got to hit songs such as "Pick Up the Phone," "Check" and " 2 B's (Danny Glover)," both he and the crowd's energy had increased. Still, he didn't have any special messages for hometown fans.
Future's DJ, Esco entertained the crowd as the rapper changed into a yellow hoodie that said "Legalize It," ripped black jeans with a black bag and, yes, more shades, during the middle of his set. The Atlanta artist performed his single "Too Much Sauce," along with Migos' "Bad and Boujee" and "T-Shirt" as a dancer wore a bobblehead in his likeness. He closed the short intermission by dancing to Atlanta rapper 21 Savage's "X" and encouraging the audience to rap along to the Future collaboration loud enough so that Future would return to the stage. Ultimately, Esco's portion served its purpose of keeping fans entertained until Future returned.
Miami rapper Zoey Dollaz also performed earlier in the show. Kodak Black was billed as an opening act, but his current incarceration hindered him from appearing during the show.