text:
\section{Introduction}
With new computing and communication technologies coming up, the communication network's rapid development provides the possibility for advanced vehicular services and applications \cite{wang2021intelligent}, such as autonomous driving and content delivery. In this context, the Internet of Vehicles (IoV), as a combination of the Internet of Things (IoT) and Vehicular networks, has gained considerable attention from industry and academia. Some works in \cite{hamer2020fedboost,9448062,2021Reputation} have shown that compared with data centralized in traditional machine learning (ML), Federated Deep Learning (FDL) solves privacy concerns and reduces the cost of data transmission by distributing the training work to clients. Clients keep their data locally and send the model parameters to a central server for model aggregation\cite{li2020review}. In this way, FDL provides a parallel scheme to learn the global model cooperatively and protect data privacy \cite{hao2019towards}.

As for the FDL application in the IoV, it demands the efficiency of information exchanging about vehicle statuses and road conditions while protecting data privacy \cite{zhou2020evolutionary}. Scholars have made several communication efficiency improvements and achieved sound performance in IoV scenarios. For instance, \cite{2021FedVCP} proposed a Federated-Learning-Based Cooperative Positioning Scheme (FedVCP) to fully utilize the potential of collaborative edge computing while protecting data privacy in the IoV system. \cite{2021A} proposed a novel
Federated Learning-based framework for Plate Recognition (FedLPR) to identify traffic signs and participants in low latency and consumption while keeping pace with preserving data privacy. \cite{zhang2019deep} proposed a two-timescale federated Deep Reinforcement Learning(DRL)-based algorithm to help obtain robust models in the IoV scenarios.

\cite{fu2020privacy} integrated homomorphic cryptography into parking space searching and booking for communication security. \cite{wang2019privacy} designed a data collection and pre-processing scheme based on deep learning, reducing the amount of data uploaded to the cloud and effectively protecting the data privacy in the IoV system. Blockchain technology has been applied to IoV for data privacy in recent years as a newly emerging technology. For instance,
\cite{shrestha2020new} created a local Blockchain for real-world event message exchange among vehicles within the boundary of the country, and \cite{lin2020blockchain} proposed a DRL and blockchain-empowered Spatial Crowd-sourcing System (DB-SCS). \cite{lu2020blockchain} developed a hybrid blockchain architecture that consists of the permission blockchain and the local Directed Acyclic Graph to relieve transmission load and address privacy concerns of data providers.

Based on blockchain technology and FDL, The authors in \cite{warnat2021swarm} introduced Swarm Learning (SL) and achieved outstanding performance in the medical field. The SL framework dispenses with a dedicated server, shares the parameters via the Swarm network, and builds the models independently on private data at the individual sites. However, all the above works don't consider vehicles' mobility, unreliable communication connection, and a highly dynamic driving environment. These factors will bring some new challenges for FDL and blockchain applications in the IoV system, including overcoming the model's ineffective and communication inefficiency caused by clients (such as malicious and redundant sharing parameters) and ensuring clients' privacy security from the third party in the process of data transmission \cite{CHEN2021}.

This paper addresses these issues by proposing a Swarm-Federated Deep Learning framework in the IoV system (IoV-SFDL) with a credibility weights prediction algorithm. The contributions of this paper can be summarized as follows:

(1) An IoV-SFDL framework is proposed to ensure data privacy with blockchain technology while minimizing the total cost of edge-to-global communication overhead, consisting of a central server, RSU-based edge servers, and vehicles.

(2) A credibility weights prediction algorithm is proposed to improve the model convergence efficiency in the global model aggregation process.

(3) Various experiments in the IoV system are implemented to verify the performance of the IoV-SFDL framework and credibility weights prediction algorithm on the Next Generation Simulation (NGSIM) dataset.

The rest of this paper is organized as follows: The system architecture is present in Section 2. The proposed credibility weights prediction algorithm is detailed in Section 3.  Section 4 provides the NGSIM trajectory dataset, framework performance evaluation, and analysis. Section 5 concludes this paper.

\section{System Architecture}

The overview of the proposed IoV-SFDL framework is shown in Figure \ref{fig:overview}.  The problem formation, data transmission and framewrok detail of the proposed IoV-SFDL will be introduced in this section.

\begin{table}[t]
\centering
	\caption{SUMMARY OF MAIN NOTATIONS}
	\label{table1}
\begin{tabular}{l|l}
\hline
\scriptsize $F(w)$ & \scriptsize The loss function of global model in cloud server    \\ \hline
\scriptsize $F_{SL}(w)$ & \scriptsize The loss function of the aggregated model in SL group   \\ \hline
\scriptsize$f_{i}(w)$ &\scriptsize The loss function of the model in the $i^{th}$ vehicle  \\ \hline
\scriptsize$W$& \scriptsize Aggregated model of central server    \\ \hline
\scriptsize$W_{SL}$& \scriptsize Aggregated model of SL group    \\ \hline
\scriptsize$L_m(i)$ & \scriptsize Local model parameters of the $i^{th}$ vehicle    \\ \hline
\scriptsize$R_m(i)$ & \scriptsize Merged model parameters of the $i^{th}$ vehicle  \\ \hline
\scriptsize$C_{b}(i)$ & \scriptsize The robustness value of the $i^{th}$ SL group  \\ \hline
\scriptsize$C_{e}(i)$ & \scriptsize The credibility value of the $i^{th}$ SL group  \\ \hline
\scriptsize$\lambda_{i}$ & \scriptsize The model weights of the $i^{th}$ RSU node \\ \hline
\scriptsize$D_{i}$ & \scriptsize
The dataset obtained by onboard sensors of the $i^{th}$ vehicle \\ \hline
\scriptsize$e_{i}$ & \scriptsize The driving environment of the $i^{th}$ vehicle \\ \hline
\scriptsize$w^{i}$ & \scriptsize Onboard model of the $i^{th}$ vehicle \\ \hline
\scriptsize$\gamma^{i}$ & \scriptsize The model weights of the $i^{th}$ vehicle \\ \hline

\end{tabular}
\label{tab:notation}
\end{table}

\subsection{Problem Formation}
This paper assumes that a highway with $N$ vehicles facing different traffic conditions. The main notations used throughout the paper are listed in Table \ref{tab:notation}.
Because vehicular GPS, radars, cameras, and other onboard sensors can obtain real-time vehicle trajectory, we believe that each vehicle can be modeled as a triple $G=(V, P, E)$.

(1) $V=\left\{v_{i} \mid i \in\{1, 2, \ldots\}\right\}$ represents all vehicles, where $v_{i}$ represents vehicle $i$ on the road. $N=|V|$ represents the number of vehicles on the road.

(2) $P=\left\{p_{i} \mid v_{i} \in V\right\}$ represents the private information of the vehicle, including the speed, acceleration, orientation, and position.

(3) $E=\left\{e_{i} \mid v_{i} \in V \right\}$ is a matrix, which is used to store the road environment information around the vehicle, that is, information of the vehicle from four directions in the visual distance.

The information $e_{i}$ of vehicle $v_{i}$ in road environment matrix is defined as:

\begin{equation}
\begin{aligned}
\mathrm{e}_{i} &=\left[\begin{array}{cc}
    I_{r} & I_{l} \\
    I_{p} & I_{f}
\end{array}\right]\\
\mathrm{I}\left(v_{i}, v_{j}\right) &= \begin{cases}0 & d\left(v_{i}, v_{j}\right)>R \\
1-\frac{\left(d\left(v_{i}, v_{j}\right)\right)^{2}}{R^{2}} & d\left(v_{i}, v_{j}\right) \leq R\end{cases},
\end{aligned}
\end{equation}
where $d\left(v_{i}, v_{j}\right)$ denotes the Euclidean Distance between $v_{i}$ and $v_{j}$, $R$ represents the visual range of vehicles.

\begin{figure*}[t]
\centering
\includegraphics[width=\textwidth]{work_flow.png}
\caption{The workflow in the proposed IoV-SFDL framework}
\label{fig:workflow}
\end{figure*}

Back propagation of the loss function is essential for ML training to adjust weights and parameters. The loss function in Deep Neural Network (DNN) models quantifies the modeling effect of the algorithm on the training data. The goal of training is to find the optimal parameters to minimize the loss function. The loss function in machine learning can be roughly divided into classification loss and regression loss.
The general loss functions for classification include logarithmic loss, focus loss, Kullback-Leibler(KL) divergence, exponential loss, hinge loss, etc.
This paper uses the Mean Square Error (MSE) as the loss function of the vehicle onboard model. The expression of the loss function $f_{i}(w)$ of the vehicle $v_i$ is as follows:
\begin{equation}
f_{i}(w)=\frac{\sum_{n=1}^{N}\left|{y}_{predict}^{n}-y_{observe}^{n}\right|^2}{N},
\end{equation}
where $y_{predict}^{n}$ and $y_{observe}^{n}$ represent the model prediction and observation of the sample batch $n$, and $N$ is the number of batches.
Different functions are available for parameter merging as a configuration of the Swarm Learning, such as average, weighted average, minimum, maximum, or median functions. In this paper, the weighted average is defined as :
\begin{equation}
R_{m}(i)=\frac{\sum_{k=1}^{i}\left(\gamma^{k} \times L_{m}(k)\right)}{i \times \sum_{k=1}^{i} \gamma^{k}},
\end{equation}
in which $R_{m}(i)$ is merged parameters of the $i^{th}$ vehicle. $L_{m}(k)$ is model parameters from the $k^{th}$ vehicle, $\gamma$ is the model weight. In this way, the loss function of an SL group in this paper is defined as:
\begin{equation}
F_{SL}(w)=\frac{1}{\left|SL\right|} \sum_{i \in SL} [f_{i}(w)+f_{i-1}(w)],
\end{equation}
where $|SL|$ means the number of vehicles in an SL group.

Redundant or invalid experience data decrease the efficient implementation of the model aggregation process. According to the model's effectiveness, the weights could be flexibly adjusted by the proposed credibility weights prediction algorithm. Making the model with better results have a higher weight in model aggregation helps the model converge faster and achieve higher accuracy in FDL. To describe the problem of model aggregation in IoV-SFDL, we introduce ${\lambda_i = \lambda_1, \lambda_2, \dots, \lambda_{|SL|}}$ as the weights in global model aggregation. $|SL|$ represents the number of SL groups on the road.
The objective function of global learning optimization is defined as:
\begin{equation}
F(w)=\frac{1}{\left|SL\right|} \sum_{i \in SL} \lambda_{i}F_{{SL}_i}(w).
\end{equation}

As mentioned earlier, we use the minimization of the loss of the global model for the current training task as the objective function:
\begin{equation}
min\,F(w)=min\,[\frac{1}{\left|SL\right|} \sum_{i \in SL} \lambda_{i}F_{{SL}_i}(w)].
\end{equation}

\subsection{Swarm-Federated Deep Learning Framework}
The IoV-SFDL framework in the IoV system contains three layers: A central server in the cloud layer, RSU-based edge nodes in the edge layer, Vehicle clients in the end layer. The three-layer structure constitutes the whole IoV-SFDL architecture, and the operation is divided into three steps as a whole. First of all, vehicles in the end layer collect trajectory information and train a time-sensitive local model. According to the SL process, vehicles with the same training task will form a blockchain network, with local model parameters transferred and aggregated. Then, the RSUs receive the model parameters from the SL group and predict model weights according to the credibility weights prediction algorithm. At last, the parameters and weights are transmitted to the central server for a global model aggregation.

The model's aggregation process can be divided into local SL and global FDL processes. Each vehicle aggregates the model passed from the previous vehicle with its onboard model in the local swarm learning process. In this process, the distributed feature of SL is combined with the point-to-point network based on blockchain. Compared with the general operation of federated deep learning, it provides a higher level of data security, central coordinator elimination, and machine learning model attacks protection. According to the proposed credibility weights prediction algorithm, each RSU predicts the model's weight in the global FDL process and sends the model parameters to the central server. In this process, the global FDL realizes the global model aggregation through a central server, which aggregates the value of local model parameters according to their weights to generate a global model. Compared with the general operation of FDL, the IoV-SFDL provides less edge-to-global communication overhead and a more effective training process.

Above all, the data transmission scheme in the framework consists of five steps: (1) Group forming and model updating; (2) Vehicle group-based Swarm Learning; (3) Credible weights prediction and comparison; (4) Model parameters and weights uploading; (5) Global Model Aggregation. After several iterations of these steps, the final model with good performance is obtained.

\section{Credibility Weights Prediction Scheme}
The key to guaranteeing model security and training efficiency in the IoV system is detecting ineffective models and scheduling less of them in each communication round. This work proposes a credibility weights prediction algorithm in the IoV-SFDL framework to prevent unreliable updates and minus noise from models with poor effectiveness. With the credibility weights prediction algorithm, the RSU node will evaluate the model's effectiveness and robustness of the received model and global model. The model's credibility will be predicted as the weight for the global model aggregation process in each communication round. The total workflow is shown in Figure \ref{fig:workflow}. The algorithm can help the central server in FDL distinguish effective models from ineffective ones, thereby preventing malicious or incorrect updates of the global aggregate process in FDL.

\subsection{Credibility Weights Prediction Algorithm}
The Credible value $C_{i}$ of model $i$ in this paper can be divided as $C_{b}$ and $C_{e}$, where $C_{b}$ and $C_{e}$ represent the robustness value and effectiveness value of model from an SL group.
To represent the node's robustness, we consider the logarithms' formulation, a single increasing function.
In ML, we believe that the more datasets in the training process, the better robustness of the model will be. Therefore, in global model aggregation, the model trained on more datasets can be given higher weight. Thus, the robustness value of the model with n vehicles in SL is given by the following formula:
\begin{equation}
C_{b}=\log _{k} n.
\end{equation}
In this formula, we believe that $n$ is the number of vehicles in the current SL group, $k$ is the max group size among all SL groups. The specific experimental parameters will be classified in the next section.

To represent the model's effectiveness, we use observation $B$ made by the RSU node to illustrate the effective prediction $P(e_i)$ of a model.
The effectiveness of the model, $P\left(e_i \mid B\right)$ is calculated and predicted by the RSU node based on the observation, which can be derived as:
\begin{equation}
P\left(e_i \mid B\right)=\frac{P\left(e_i\right) P\left(B \mid e_{i}\right)}{\sum_{j=1}^{n} P\left(e_{j}\right) P\left(B \mid e_{j}\right)},
\end{equation}
To facilitate the expression and update of effectiveness value, we use beta distribution to represent aggregate weights from the effectiveness of an RSU node, defined as:
\begin{equation}
\begin{aligned}
f(x ; p, q)=\frac{x^{p-1}(1-x)^{q-1}}{\int_{0}^{1} u^{p-1}(1-u)^{p-1} d u}\\\\
=\frac{\Gamma(p+q)}{\Gamma(p) \Gamma(q)} x^{p-1}(1-x)^{q-1}\\\\
=\frac{1}{B(p, q)} x^{p-1}(1-x)^{q-1}
\end{aligned},
\end{equation}
where $ p>0 \text { and } q>0 $ and p and q respectively represent the number of times that the receiving model is better than the global model and the number of times that the global model is better than the receiving model, the $\Gamma(x)$ is the Gamma function, which could be written as:

\begin{equation}
\Gamma(x)=\int_{0}^{+\infty} t^{x-1} e^{-t} \mathrm{~d} t(x>0),
\end{equation}

For example, we assume the $a_{i}$ and $b_{i}$ represent the positive and negative behaviors currently, while p and q represent the initial positive and negative behaviors.
To update the credibility, it is equivalent to updating the two parameters p and q, as:
\begin{equation}
\left\{\begin{array}{l}
p^{\text {new }}=a_{i}+p \\
q^{\text {new }}=b_{i}+q
\end{array}\right.,
\end{equation}

When a node is initialized without prior knowledge, the credibility of a node can be expressed as a uniform distribution on $(0,1)$, defined as:

\begin{equation}
P(x)=\operatorname{uni}(0,1)=\operatorname{Beta}(1,1).
\end{equation}
By comparing the effectiveness of the receiving model and the global model previous time, the RSU can evaluate the effectiveness of the receiving model, that is, convert into positive behavior and negative behavior. The evaluation process of the models is detailed in the next section. Suppose that in $n$ rounds of communication, the SL group has sent the model to RSU $(p+q)$ times. By comparing the effectiveness of the models, these interactions are described as $p$ times positive behavior and $q$ times negative behavior. With this information, the RSU can predict the model's effectiveness from SL group in the next event. The validity probability of the model is defined as:
\begin{equation}
C_{e}=E[Beta(p,q)]=\frac{p}{p+q}.
\end{equation}

Central server can normalize all the weights and model parameters sent by the RSUs, and the weights of model from $i^{th}$ RSU is defined as:
\begin{equation}
\lambda_{i}=\frac{C_{i}}{\sum_{j=1}^{n} C_{j}}.
\end{equation}

\subsection{Effectiveness Comparison Module}

In the model aggregation process of FDL, the credibility comparison module at RSU uses the test dataset to evaluate and compare the model received from SL group and global model download from central server. Based on the value of the loss function derived from the test dataset, we define the model's credibility for comparing the effectiveness of different models in model aggregation.

\begin{equation}
    e_{k}^{t}=\frac{f(w^{t}, {x}_{i j}, y_{i j}) - f(W_{k}^{t}, {x}_{i j}, y_{i j})}{f({w}^{t}, {x}_{i j}, y_{i j})},
\end{equation}
where $\left({x}_{i j}, y_{i j}\right)$ is a data sampled from the current RSU node's test dataset. We use the difference between the loss function value of the SL model and global model to evaluate the quality of the received SL model parameters.

For a tagged RSU node, its contribution to the global model aggregation can be considered positive or negative. Precisely, if $e_{k}^{t} > 0$, we assume that the effectiveness comparison module will characterize RSU node $k$'s transmission as positive behavior and vice versa. Moreover, we can also use $e_{k}^{t}$ to represent the effect gap between the local model and the receiving model imposed by the RSU.

\subsection{Vehicle Group-based Swarm Learning}
In the process of local model aggregation, vehicle group-based swarm learning is considered. RSUs, as the participants in the local model aggregation process, are evenly distributed on both sides of the road. The SL framework cooperatively optimizes the model inside the vehicle through continuous iteration of the following three steps:

1) Each vehicle carries out the model training task by collecting the privacy information of local vehicles, such as geographical location, speed, direction angle, etc.

2) Vehicles with the same subtask and close to each other can form groups and aggregate models through blockchain in the SL process.

3) After model aggregation, the vehicle sends the model parameters to the RSU for the further procession.

Generally, the number of vehicles in different SL groups is different due to the rapid mobility of vehicles on the road and the uncertainty of driving purpose. By evaluating the credibility of the SL groups and their internal LSTM model effectiveness, the best weight in the global FDL aggregation process could be predicted. The whole training process of vehicle group-based SL is shown in Algorithm \ref{alg:swarm_learning}.

\begin{algorithm}[htb]
    \caption{Vehicle Group-based Swarm Learning. The $K$ vehicles are indexed by $k$; $D_{k}$ is local dataset of vehicle $k$, $\eta$ is the learning rate, $B$ is the local mini-batch size, $w_{t}^{k}$ is the onboard model of the vehicle, and $W_{SL}$ represented the aggregated model of SL group.}
    \label{alg:swarm_learning}
    \LinesNumbered
    \hspace*{0.02in} {\bf{Input:} $w_{t}^{k}$}\\
    \hspace*{0.02in} {\bf{Output:} $W_{SL}$}
    Initialize  $w_{0}^{k}$ \;
    \For{each round t = 1, 2, \dots}{
        Update model ${w}_{t}^{k}$ from RSU\;
        $b \leftarrow$ spilt $D_{k}$ into batches of size $\mathcal{B}$\;
        \For{each vehicle k = 1, 2, \dots } {
            $w_{t+1}^{k} \leftarrow  w_{t}^{k}-\eta \nabla \ell(w_{t}^{k}; b)$\;
            $w_{t+1}^{k} \leftarrow \frac{w_{t+1}^{k}+ w_{t+1}^{k-1}}{2}$\;
        }
    }
    $W_{SL} = w_{t+1}^{k}$\;
    \Return $W_{SL}$\;
\end{algorithm}

\subsection{Global Federated Deep Learning}
In the global model aggregation process, an FDL framework with credibility is considered. As shown in Figure \ref{fig:overview}, the FDL enables the local RSUs and central server to collaboratively optimize the global model by continuously iterating the following two steps:

1) Each RSU receives the model parameters, predicts the weight by the credibility weights prediction algorithm, and sends the parameters to the central server in the up-link.

2) The central server collects the model parameters from RSU, aggregates the global model according to their weights predicted from RSU, and broadcasts the new model to all the RSUs in the down-link. Along the training process, each iteration is generally referred to as a communication round.

After sufficient communication rounds involving parameters exchanges and training, we can obtain the best global FDL model without sacrificing the vehicle's privacy. The whole training process of FDL is shown in Algorithm \ref{alg:global_federated_Deep_Learning}.

\begin{algorithm}[htb]
    \caption{Global Federated Deep Learning. The K RSUs are indexed by k; $w^{k}_t$ is local model parameters at time t, $\lambda_{t}^{k}$ is model weights of $w^{k}_t$, $W_t$ is the global model parameters}
    \label{alg:global_federated_Deep_Learning}
        \hspace*{0.02in} {\bf{Input:} $w^{k}_t$, $\lambda^{k}_t$} \\
        \hspace*{0.02in} {\bf{Output:} $W$}
        Initialize $w^{k}_0$\;
        \For{each round t = 1, 2, \dots}{
            \For{each RSU k}{
                $\lambda_{t+1}^{k} \leftarrow WeightsPrediction({w}^{k}_{t})$\;
            }
            $W \leftarrow \sum_{k=1}^{K} \frac{1}{K} \lambda_{t+1}^{k} w_{t+1}^{k}$\;
        }
        \Return $W$
\end{algorithm}

\begin{figure*}[t]
\centering
\includegraphics[width=\textwidth]{Loss.png}
\caption{Loss comparison of the IoV-SFDL, the Federated-Avg Learning in \cite{li2020federated}, and the Communication-Efficient Learning in \cite{mcmahan2017communication} on different traffic densities. (a)High-density traffic: 10 vehicles in one group and 6 vehicles in the other group. (b)Medium-density traffic: 6 vehicles in one group and 4 vehicles in the other group. (c)Low-density traffic: 2 vehicles in one group and 3 vehicles in the other group.}
\label{fig:loss_compare}
\end{figure*}

\section{Performance Evaluation}

In this section, we introduce the simulation setups and then compare the performance of the proposed IoV-SFDL with the other two baseline frameworks.  All source code about the framework, dataset used, and proposed algorithm in this study are provided on GitHub (https://github.com/CoderTylor/IoVSFDL-Swarm-Federated-Deep-Learning-).

\subsection{Experiment Setup}
The hardware is 32GB RAM, i7-10700 CPU 2.90GHz.
The experimental platform is built based on the PyTorch 1.9 framework, a flexible deep learning tool widely used in academics.  The software adopts Python 3.7.0 in the Ubuntu18.04 system.
Inspired by the performance of Social LSTM \cite{alahi2016social}, an LSTM-centered model is trained as an onboard vehicle training task to predict the vehicle's trajectory in 5s. The complete model parameters are listed in Table \ref{tab:LSTM}. For all LSTM learning models, we used the greedy exploration with linearly annealed from 1.0 to 0.1 in the whole training process. Adam optimizer is used to learn all neural network parameters with a learning rate of $10^{-4}$.

\begin{table}[b]
\caption{LSTM Model Parameters}
\centering
\begin{tabular}{cc}
\hline
\footnotesize CELL                   & \footnotesize (Input, Output) \\ \hline
\footnotesize LSTM CELL              & \footnotesize (64,32)  \\\hline
\footnotesize Input Embedding Layer  & \footnotesize (In Feature=9, Out Feature=32) \\\hline
\footnotesize Social Tensor Conv1    & \footnotesize (32,16, Kernel size=(5,3), stride(2,1)) \\\hline
\footnotesize Social Tensor Conv2    & \footnotesize (16,8, Kernel size=(5,3), stride(2,1))  \\\hline
\footnotesize Social Tensor Embedded & \footnotesize (In Feature=32, Out Feature=32)        \\\hline
\footnotesize Output Layer           & \footnotesize (In Feature=32, Out Feature=5)          \\\hline
\footnotesize Dropout          & \footnotesize Dropout(p=0, inplace=False)         \\\hline
\footnotesize Activation Function          & \footnotesize ReLU         \\\hline
\footnotesize Time Length          & \footnotesize 10        \\\hline
\footnotesize Episode          & \footnotesize 50        \\\hline
\footnotesize Learning rate          & \footnotesize $10^{-4}$          \\\hline
\footnotesize Optimizer          & \footnotesize Adam          \\\hline
\end{tabular}
\label{tab:LSTM}
\end{table}

The simulation defines three traffic flow densities: high-traffic flow density, medium-traffic flow density, and low-traffic flow density. The number of total vehicles ranges from 5 to 16, and the size of the SL group ranges from 2 to 10. The NGSIM trajectory dataset is used to verify the effectiveness of the IoV-SFDL framework. The dataset is collected for the next-generation traffic simulation by Federal Highway Administration. It use videos to capture real-world traffic information, including vehicle velocity, position, acceleration, lane, etc. As high-resolution real-world vehicle trajectory data, NGSIM is widely used to explore the characteristics of the trajectory-prediction process and calibrate and validate the trajectory-prediction models. Detailed descriptions of the dataset can be found in \cite{coifman2017critical}.

To the best of our knowledge, there is no research investigating the SL application under the IoV system, so two other baseline FDL frameworks are added in the simulations for  better gauging the performance of the proposed IoV-SFDL framework, which are:

(1) Federated-Average Learning (Federated-Avg): Based on the Federated Average deep learning algorithm proposed in \cite{li2020federated}, all vehicles on the road participating in FDL. After training the model locally, the average aggregation of model parameters is carried out in the central server.

(2) Communication-Efficient Federated Learning (Communication-Efficient): Based on the Communication-Efficient federated deep learning algorithm proposed in \cite{mcmahan2017communication}, we add the LSTM network for trajectory prediction in their code and set the parameters $Frac$ as 0.8 for client selection.

\subsection{Indicator}
Three indicators are chosen to comprehensively evaluate the performance of the proposed IoV-SFDL framework, such as model Loss, trajectory Prediction Accuracy, and Prediction Error.
The descent speed of the Loss function is the first indicator to evaluate the converging rate. The Loss of the LSTM model is defined as:
\begin{equation}
{\rm Loss}=\small \frac{\sum_{i=1}^{N}\left|(x_{p r e d i c t}^{i},y_{p r e d i c t}^{i})-(x_{observe}^{i},y_{observe}^{i})\right|^{2}}{N}
\end{equation}
where $(x_{predict},y_{predict})$ is the vehicle trajectory predicted by LSTM, and $(x_{observe},y_{observe})$  is the actual trajectory of the vehicle in the dataset.

Then, the Prediction Error is used to evaluate the average Euclidean distance between predict trajectory and actual trajectory in the NGSIM dataset under different traffic densities in meters. The Prediction Error is defined as:
\begin{equation}
\begin{aligned}
\rm \tiny Pre& \rm diction \ Error=\\\\&\small \frac{\sum_{i=1}^{N}\sqrt{(x_{predict}^{i}-x_{observe}^{i})^{2}+(y_{predict}^{i}-y_{observe}^{i})^{2}}}{N}.
\end{aligned}
\end{equation}

At last, the Predict Accuracy is evaluated to estimate the model performance.
For the trajectory after 5 seconds, we compare the distance difference of each path point as the basis for whether the prediction is true or false. To be precise, we define positive prediction $P_{T}$, negative prediction $P_{F}$ and prediction accuracy as:
\begin{equation}
\text {P}= \begin{cases}\text { $P_{F}$ } & {\rm Prediction \ Error}>10 m \\ \text { $P_{T}$ } & {\rm Prediction \ Error}\leq 10 m\end{cases}.
\end{equation}
Based on equation (18), $N(P=P_{T})$ and $N(P=P_{F})$ is defined as the number of positive and negative prediction, and the Prediction Accuracy is defined as:

\begin{equation}
{\rm Prediction \ Accuracy}= \small \frac{N(P=P_{T})}{N(P=P_{T})+ N(P=P_{F})}.
\end{equation}

\begin{figure*}[]
\centering
\includegraphics[width=\textwidth]{Prediction_errors.png}
\caption{Prediction Error comparison of the IoV-SFDL, the Federated-Avg Learning in \cite{li2020federated}, and the Communication-Efficient Learning in \cite{mcmahan2017communication} on different traffic densities. (a)High-density traffic: 10 vehicles in one group and 6 vehicles in the other group. (b)Medium-density traffic: 6 vehicles in one group and 4 vehicles in the other group. (c)Low-density traffic: 2 vehicles in one group and 3 vehicles in the other group.}
\label{fig:pred_err_compare}
\end{figure*}

\begin{figure*}[]
\centering
\includegraphics[width=\textwidth]{Prediction_Accuracy.png}
\caption{Prediction Accuracy Comparison of the IoV-SFDL, the Federated-Avg Learning in \cite{li2020federated}, and the Communication-Efficient Learning in \cite{mcmahan2017communication} on different traffic densities. (a)High-density traffic: 10 vehicles in one group and 6 vehicles in the other group. (b)Medium-density traffic: 6 vehicles in one group and 4 vehicles in the other group. (c)Low-density traffic: 2 vehicles in one group and 3 vehicles in the other group. }
\label{fig:pred_acc_compare}
\end{figure*}

\subsection{Performance of the IoV-SFDL Framework}

In Figure \ref{fig:loss_compare}, we can see that the loss function change is almost the same in the first ten episodes among IoV-SFDL and the other two benchmark frameworks. As shown in Figure \ref{fig:loss_compare}(b) and \ref{fig:loss_compare}(c), the IoV-SFDL performs better on the same computing cost than the others. Although Figure \ref{fig:loss_compare}(a) shows that the change speed of the loss function of the three frameworks is almost the same under the condition of high-density traffic flow, the loss function of IoV-SFDL is lower than the other two benchmark frames after 20 iterations. This is because, with the increase of traffic flow, the proposed credibility weight prediction algorithm can help the cloud server identify the redundant model update, And give this update a minor weight to ensure the convergence speed of the model.

Figure \ref{fig:pred_err_compare} shows the Prediction Error between predict trajectory and actual trajectory under different traffic densities in meters. With the same experimental parameters settings of the three frameworks, the Prediction Error decrease with the iteration number increase, indicating that the LSTM learning agent has learned to predict trajectory more precisely. As shown in Figure \ref{fig:pred_err_compare}(a), \ref{fig:pred_err_compare}(b), and \ref{fig:pred_err_compare}(c), the more vehicle clients participating in the FDL, the redundant data will affect the model prediction error of the two baseline frameworks. Federated-Avg Learning is the most vulnerable framework facing redundant training data. In contrast, the IoV-SFDL can converge with less Prediction Error quickly under all traffic densities given.

Figure \ref{fig:pred_acc_compare} shows the model's Prediction Accuracy comparison for trajectory under the IoV-SFDL framework with the other two baseline frameworks. We use equation 18 to classify the positive and negative values of the predicted points so that the LSTM becomes a binary network. Figure \ref{fig:pred_acc_compare}(c) illustrates that the Prediction Accuracy difference between the IoV-SFDL framework and baselines in Prediction Accuracy is not evident under low-density conditions. However, as shown in Figure \ref{fig:pred_acc_compare}(a) and \ref{fig:pred_acc_compare}(b), with more vehicles clients participating in global model aggregation, the IoV-SFDL can achieve higher model accuracy with the same computing cost.

\begin{figure}[t]
\centering
\includegraphics[width=7cm]{Communication_links.png}
\caption{Communication Links of Different Frameworks}
\label{fig:comm_link}
\end{figure}
Figure \ref{fig:comm_link} shows the communication links establishment difference between IoV-SFDL and baseline frameworks. Unlike Fed-Avg Learning and Communication-Efficient Learning, each client participating in global model aggregation has to establish a two-way link with the central server. The IoV-SFDL only needs to establish a blockchain connection between clients in the SL group. Each group merely requires one two-way communication link with the central server to realize global model aggregation. Under the conditions of 5, 10, 16 vehicles (Low-density, Medium-density, and High-density) on the road, compared with the other two baseline frameworks, the IoV-SFDL needs the least communication links to complete the global model aggregation.

Overall, the proposed IoV-SFDL framework is superior to other baseline frameworks in edge-to-global communication overhead and convergence times.

\section{Conclusion}
This paper proposed an IoV-SFDL framework for protecting data privacy by integrating the parallel SL into the FDL process.
With the help of the SL process, the reliability of shared data among FDL is guaranteed by blockchain verification.
Moreover, to achieve the high-level performance of the proposed framework in the IoV system, a credibility weights prediction algorithm was designed to speed up the convergence of model training in the IoV scenery. Simulation results showed that the IoV-SFDL framework has less edge-to-global communication overhead and higher computing efficiency than other state-of-art FDL frameworks in the IoV system.
Our future work will expand the scale of the experiment and show how our framework protects vehicle privacy data in the case of attacks.

\bibliographystyle{elsarticle-num}

redpajama_set_name: RedPajamaArXiv


text:
Beverage Tech
Food Ordering and Delivery
Baking Technology
Winery Tech
Food Service Management
Automation and Manufacturing
Poultry and Meat Processing
Specials Food Service Management Automation and Manufacturing Poultry and Meat Processing
Food and Beverage Tech Review Weekly Brief
Be first to read the latest tech news, Industry Leader's Insights, and CIO interviews of medium and large enterprises exclusively from Food and Beverage Tech Review
What Packaging Trends are Influencing the Food Industry?
Kevin Lobo, FB Tech Review | Monday, January 18, 2021
The food packaging industry is getting influenced by the emerging trends because of the consumers' demand for clean, convenient, and smart solutions.
FREMONT, CA: In the food industry, the packaging is as important as the contents inside. The food packaging market makes it easy for the product to extend its shelf-life and also allows to conduct long-distance transportation. It will help the hoteliers to select their packaging materials if they are aware of the new food packaging trends. Here are a few of the food-packaging trends to follow.
1. Emotional Engagement
For the consumers purchasing a packet of food it more than just food because they want to be a part of the story. According to researchers, human beings get more impacted by emotions instead of information while making decisions while making the brand. While advertisement in social media or any other social platform, emotional content is very much popular among the consumers as it generates positive sentiments towards the brand.
2. Solutions with Technology
One of the best ways to package products is by using smart packaging and technology can be used in the packaging procedure that will offer the consumers with more security, information, and convenience. The food packaging that is implanted with printed QR codes, NFC chips, or Smart Labels can be scanned by using a smartphone so that the customers can get more data about the products.
Smart packaging can also keep track of numerous other parameters like temperature, fermentation, and PH level so that the sellers can make sure about its quality, maintenance, and flavor matches the health standards.
3. Transparent and Clear Labeling
Human beings are aware of the product that they eat even when food packets have information about the ingredients. Therefore, the food companies must adapt transparent packaging and precise labeling to make a list of the contents present in the product.
4. Designs Inspired from Vintage Graphics
The designs inspired by vintage graphics have a nostalgic value because it instigates happy feelings among consumers as it reminds them of simple times. The term vintage can refer to a single era or a combination of periods, but these types of designs evoke emotions that appeal to the people who lived in those eras.
See Also :- Top Food and Beverages Solution Companies
Processing and Packaging 2023
Key Emerging Trends in the Food and Beverage Industry
Technologies Influencing the Beverage Sector
Benefits of Automation in Food Processing Industries
How Technology has Revolutionized Food Delivery
Steeped Coffee and Connect Roasters' Partnership Integrates Premium Coffee with Single-Serve Coffee Drinkers
Agriculture Management Technology and its Potential to Increase Food Safety
Copyright © 2023 www.fbtechreview.com All Rights Reserved | Subscribe | Privacy Policy | About us
https://www.fbtechreview.com/news/what-packaging-trends-are-influencing-the-food-industry-nwid-534.html

redpajama_set_name: RedPajamaCommonCrawl


OBJECTIVE: To compare the diagnostic accuracy and radiation doses of two low-dose protocols for coronary artery imaging with second-generation, dual-source CT in comparison with catheter angiography (CA). DESIGN, SETTING AND PATIENTS: Prospective, single-centre study conducted in a referral centre enrolling 100 patients with low-to-intermediate risk and suspicion of coronary artery disease. All patients underwent contrast-enhanced, 128-slice, dual-source CT coronary angiography and CA. Patients were randomly assigned to two different low-dose CT protocols (each 100 kV/320 mA): in group A (n=50), CT was performed using the prospectively electrocardiography (ECG)-gated step-and-shoot (SAS) mode; in group B (n=50), CT was performed using the prospectively ECG-gated high-pitch mode (pitch 3.4). The image quality and presence or absence of significant coronary stenosis in all coronary segments were evaluated by two blinded and independent observers. CA served as the standard of reference. RESULTS: Sixty-one significant stenoses were found in group A, and 69 in group B. There was no significant difference in age (group A, 62+/-8 yrs; group B, 63+/-8 yrs; p=0.72), body mass index (group A, 26.4+/-3.1 kg/m2; group B, 25.9+/-2.8 kg/m2; p=0.41) and heart rate (HR) (group A, 58+/-8 bpm; group B, 56+/-10 bpm; p=0.66) between the groups. Diagnostic image quality was obtained in 98.6% (651/660) of segments in group A and in 98.9% (642/649) in group B, with no significant differences between groups. Sensitivity, specificity and positive and negative predictive values were 94%, 91%, 85% and 97% per-patient in group A, and 93%, 94%, 89% and 97% per-patient in group B (no significant differences). The effective radiation dose in group B (0.9+/-0.1 mSv) was significantly (p<0.01) lower than that in group A (1.4+/-0.4 mSv). CONCLUSIONS: Both the high-pitch and the SAS mode for low-dose CT coronary angiography provide high accuracy for the assessment of significant coronary stenoses, while the high-pitch mode further significantly lowers the radiation dose.
Download PDF 'Low-dose, 128-slice, dual-source CT coronary angiography: accuracy and radiation dose of the high-pitch and the step-and-shoot mode'. Item availability may be restricted.


Happy New Year to all you lovely beauty mad people out there!
I personally don't like New Years eve so I decided to spend the final hours of 2012 doing what I plan to do through 2013, blogging!
Although at first glance today's blog may not appear to be about beauty please bear with me and you will see where I am going with this.
Quidco is a cashback website I found a couple of years ago which features hundreds of online retailers as well as other companies such as banks and car breakdown services. It also features many beauty and cosmetics brands and retailers (see this is where i'm going). What could be better than earning cashback while shopping for your beauty goodies?
All you need to do is sign up to Quidco (which costs just £5 a year and is deducted from the cashback you earn) and then make sure you check the website before you make any online purchases. If the retailer is with Quidco you simply click the link to be taken to the retailers website to complete your purchase.
Unfortunately some retailers do take quite a long time to pay cashback to your account. A transaction will usually 'track' on your Quidco account within a few days but then some retailers may not pay cashback to your account for over a year!
However Quidco have addressed this by introducing 'faster paying' retailers. One of my personal favourites of the faster payers is The Body Shop. Not only do they currently have a fantastic cashback rate of 18% (yes really) and other offers such as 40% off your order (again, yes really) but their cashback tracks AND pays into your account almost immediately.
If you would like to sign up to Quidco please use the following link http://www.quidco.com/user/847891/ as this will let Quidco know that I referred you.


Abstract: Though deep neural networks have shown great success in the large data domain, they generally perform poorly on few-shot learning tasks, where a model has to quickly generalize after seeing very few examples from each class. The general belief is that gradient-based optimization in high capacity models requires many iterative steps over many examples to perform well. Here, we propose an LSTM-based meta-learner model to learn the exact optimization algorithm used to train another learner neural network in the few-shot regime. The parametrization of our model allows it to learn appropriate parameter updates specifically for the scenario where a set amount of updates will be made, while also learning a general initialization of the learner network that allows for quick convergence of training. We demonstrate that this meta-learning model is competitive with deep metric-learning techniques for few-shot learning.


text:
Q: Dojo layout, position absolute I'm want use the dojo library for my Alfresco page, so I decide to use dojo layouts. I decide to wrap all page in dojo layout. I use 'region: top' for my header part of page, but there is a problem with viewing popups because dojo top layout has style "position: absolute" and Alfresco popups have too.
I can set style "position: static" for header part and will be ok, but I think it non my way.
<@templateBody>

    <@region id="header" scope="global"/>
    <@region id="title" scope="page"/>

    <@region id="column" scope="page"/>

</@>

A: I haven't delved into Alfresco much to be honest, but can you not set the z-index properties of your dojo header and alfresco popups so that they overlay correctly?
Or am I missing the point?!

redpajama_set_name: RedPajamaStackExchange


The Department of Administration is responsible for many of the functions for the city, including all accounting, personnel, information systems, purchasing, utility billing, telecommunications, facilities management, and administrative functions of the city.
It is involved in the planning and coordination of City Council Agenda and provides staff support to the Mayor, City Council members and other City departments.
The Administration Department also assists citizens by resolving complaints and coordinating community activity, tracks legislation, provides public information and intergovernmental support and services and is the leader in customer service efforts for the City of Monroe.


text:
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Transmissive dynamic plasma steering method for radiant electromagnetic...
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text:
Calculate Mortgages
How to Calculate Mortgage Prorations
by Mark Kennan
Prorating the interest is common when you assume a mortgage from a seller.
When it comes to mortgage interest, chances are you'll have to prorate the cost for the first month of the mortgage. Mortgage prorations commonly occur when you're buying a home and assuming the seller's mortgage. Since home sales don't always fall nicely on the mortgage payment due date, you split the mortgage interest expense with the seller for the month of the sale. Prorating correctly ensures that both the buyer and the seller pay their fair share.
Divide the cost by the number of days in the cost period to find the cost per day. For example, say you are prorating the $600 of interest for the month and you fall in a month with 30 days. Divide $600 by 30 to get a cost of $20 per day.
Count the number of days that the cost applies to each party. For example, say you're buying a home and assuming the seller's mortgage on the 20th day of the month. Since you'll be in the home from the 20th through the 30th, you're responsible for the interest for the last 11 days, and the seller is responsible for the first 19 days.
Multiply the number of days that each party is responsible for by the cost per day to calculate the mortgage interest proration. In this example, multiply 11 by $20 to find that you owe $220 of interest for the month. Then, multiply 19 by $20 to find the seller owes $380 of interest for the month.
You are also likely to prorate other costs besides your mortgage, such as homeowner's association fee dues and property taxes.
Real Estate School: Sample Closing Statement
Cliffs Notes: Real Estate Exams: Figuring Mortgage Payments and Proration
Mark Kennan is a writer based in the Kansas City area, specializing in personal finance and business topics. He has been writing since 2009 and has been published by "Quicken," "TurboTax," and "The Motley Fool."
Thinkstock Images/Comstock/Getty Images
How to Figure 6.25% per Annum Interest on a Mortgage
How to Prorate Real Estate Taxes
If the Seller Pays Closing Costs, Are These Tax-Deductible?
How Is Interest Calculated for a Mortgage?
Explanation of a Mortgage Interest Payment
How to Calculate Unrealized Gain
How to Calculate Interest on a Promissory Note
How to Calculate Daily Mortgage Interest
How to Calculate Per Diem on a Mortgage
What Does Prorated Mean?
How to Compute Prorated Property Taxes
The Best Time of the Month to Close on a Mortgage
How to Calculate a New Mortgage
What Does Prorate Mean in Real Estate Terms?
How to Figure the Amount of Interest on a Mortgage Loan
How to Convert Annual Interest Rate to Daily Interest Rate


Look at big enough list of profiles sluts in Baltimore.
Hot girl, waiting non-serious relationship with the continuation. Do it, call me and come to me, if you want I'll get dressed, as you wish!
Choose yourself the one you need from the list of profiles skank in Baltimore.


text:
Neighbour alert sparks dramatic blaze rescue
A PENSIONER was dramatically rescued from a house fire after a neighbour spotted the flames on his way home from work. Firefighters dragged the man, thought to be in his early 70s, from the burning home in Marigold Street, Deeplish, after next-door neighbour Ijaz Rasool sounded the alarm at about 2.45am on Wednesday.
Damon Wilkinson
A PENSIONER was dramatically rescued from a house fire after a neighbour spotted the flames on his way home from work.
Firefighters dragged the man, thought to be in his early 70s, from the burning home in Marigold Street, Deeplish, after next-door neighbour Ijaz Rasool sounded the alarm at about 2.45am on Wednesday.
The father-of-three, a chef at Roti Indian restaurant in Drake Street, called 999 after he saw smoke pouring from the semi-detached house.
Firefighters kicked down the front door before rescuing the wheelchair-bound man from the kitchen.
He was given oxygen at the scene before being taken to Rochdale Infirmary suffering from smoke inhalation.
Mr Rasool, aged 34, said: "As I pulled up I heard the smoke alarm and saw the red flames flickering inside. There was fire and smoke everywhere.
"I tried to kick the door in two or three times but it was just too strong.
"I was sure the man was dead because there was just so much smoke.
"But thankfully the fire brigade got here in time and the main thing is he is OK."
Wife Fozia Ubaid added: "We were really shaken up by it. Luckily my husband happened to be coming home from work at that time.
"It is terrifying to think what might have happened if he wasn't there."
The fire is thought to have started in a settee in the living room.
An investigation into the cause of the blaze is being carried out.
Watch commander Kevin Tickle, from Rochdale fire station, said: "The fire was confined to the living room but the man was found in the kitchen.
"It appears he was unable to get out of the home.
"The man was quite distressed and obviously a bit shaken up, but he was conscious and talking when we rescued him.
"He was very, very lucky that his neighbour spotted the fire.
"We would always emphasise the importance of having smoke alarms fitted.


/ How to clean a carburetor on a lawn mower without removing it?
1 What Makes A Carburator to Stop Working?
2 Can You Clean A Mower Carburetor Without Removing It?
3.1 What Tools Do You Need?
How to clean a carburetor on a lawn mower without removing it is a common question if you are a lawn mower user. The carburetor is one of the key parts as long as the engine of a lawn mower is concerned. While this part is in a troublesome situation, the whole purpose of a lawn mower gets stuck.
So, lawn mower users have to keep a sharp eye to maintain a clean and functioning carburetor.
In case you're interested in performing the task without removing the carburetor itself, this article is written for you.
What Makes A Carburator to Stop Working?
Carburators are likely to be affected by various blockages if you keep it idle for a long time. Even during regular use on the field, this trouble can happen to lawn mower carburetors.
The types of dirt that take place within the carburetor are mainly dirt, gasoline deposits, oil deposits, varnishes, etc. Apart from these, there is a definite factor of lawn mower carburetor lifetime.
Can You Clean A Mower Carburetor Without Removing It?
The one word answer to the question is- Yes, you can.
But some conditions imply cleaning a lawn mower carburetor without removing it. If the carburetor is old enough not to produce a consistent service at a clean condition, you should rather remove and replace it with a new one.
In case your carburetor is not old enough, except for some gunk and dirt buildup, you can go through the next steps.
You need to have two kinds of tools in this case. One is the Aerosol spray cleaner for carburetors, and another is the lawn mower maintenance kit.
The aerosol spray is going to be our cleaning agent. And the kit box will contain all the essential tools like screwdrivers to open and close the air filter, carburetor cover, carburetor linkage, etc.
Once you have got both of these items in hand, let's proceed to the next steps.
First thing first, make sure that you are 100% certain about cleaning the carburetor up. We mean, if the mower engine stops working, the carburetor can be one of the suspects. Unless you make it 100% certain, your whole cleaning effort can go in vain.
Take a short burst of aerosol lubricant and apply on the carburetor. You can also use any cleaner that is specified for using or carburetors.
Now, try to start the mower properly. If it starts with no problem, then the source of the problem is something else than the carburetor. If it doesn't start yet, move to the next step.
At first, place, turn off the mower and let it cool down for a while.
Once done, open up the cover of the air filter with the help of a screwdriver. This will expose the carburetor to you.
Now, remove the carburetor exterior cover and the linkage as well. This will expose the insider parts of the carburetor itself.
Keeping it in that condition, the carburetor will dry up within a short time. Wait until then, and start the engine now.
As we've said before, you need to have a good quality carburetor cleaner aerosol spray. And this is the step where we are going to use it on the carburetor.
Place the tip of the spray into the center of the carburetor, and spray it onto the carburetor. Make sure that your mower engine and the carburetor is running while you're going through this step.
Make sure that the spray penetrates throughout the whole carburetor throat.
There will be oil and dirt deposit at the lower throttle plate of the carburetor. Remove that with care.
Now, spray the aerosol on the choke shaft which is located at the base of the carburetor throat. Make sure that the engine is shut off while you're applying this spray on the choke shaft.
We are pretending in today's concern that, we won't remove or replace the carburetor itself. But for optimal performance, you need to replace the carburetor cover and the linkage.
Right after replacing the old cover and linkage, put the air filter back on its place. Use a screw to tighten the air filter.
If you are still reading this article, it means you know how to clean a carburetor on a lawn mower without removing it. But there are safety tips that you might keep in mind.
First one is when the engine is on, and the carburetor is in action; don't touch the carburetor at all, not even with gloves. And the second advice is, don't start working on the carburetor open-up until it's freezing.
That being said, you're also advised to use the right tools to open the air filter and carburetor cover. Making sure that the connections are tight is one of the safety concerns that you need to follow.


Fire Awareness training course that covers the basic safety and competence required for personnel working within a wind turbine generator or associated infrastructure.
On completion of training, participants will be able to demonstrate knowledge of the development and spread of fire. The delegates will also be able to demonstrate knowledge of the causes of fire, signs of fire and smoke travel in a wind turbine. They will also be able to demonstrate contingency planning for emergency, emergency evacuation in a fire and safe use of fire fighting equipment.


text:
Chapter 23. Nonepithelial Neoplasms of the Breast
Tina W. F. Yen; Jennifer D. Lorek; Zainab Basir
Yen TF, Lorek JD, Basir Z. Yen T.F., & Lorek J.D., & Basir Z Yen, Tina W. F., et al.Chapter 23. Nonepithelial Neoplasms of the Breast. In: Kuerer HM. Kuerer H.M.(Ed.),Ed. Henry M. Kuerer.eds. Kuerer's Breast Surgical Oncology. McGraw-Hill; Accessed January 18, 2021. https://accesssurgery.mhmedical.com/content.aspx?bookid=428&sectionid=39936149
Yen TF, Lorek JD, Basir Z. Yen T.F., & Lorek J.D., & Basir Z Yen, Tina W. F., et al. (2010). Chapter 23. nonepithelial neoplasms of the breast. Kuerer HM. Kuerer H.M.(Ed.),Ed. Henry M. Kuerer. Kuerer's Breast Surgical Oncology. McGraw-Hill. https://accesssurgery.mhmedical.com/content.aspx?bookid=428&sectionid=39936149
Yen TF, Lorek JD, Basir Z. Yen T.F., & Lorek J.D., & Basir Z Yen, Tina W. F., et al. "Chapter 23. Nonepithelial Neoplasms of the Breast." Kuerer's Breast Surgical Oncology Kuerer HM. Kuerer H.M.(Ed.),Ed. Henry M. Kuerer. McGraw-Hill, 2010, https://accesssurgery.mhmedical.com/content.aspx?bookid=428&sectionid=39936149.
Nonepithelial Neoplasms of the Breast: Introduction
Primary Breast Lymphoma
Primary Malignant Melanoma
Metastases to the Breast
The disease processes discussed in this chapter are uncommon tumors: phyllodes tumors, sarcomas, lymphomas, melanomas, and metastases to the breast. As a group, these tumors are rare and heterogeneously diverse. Given their small numbers, there have been few large patient series or trials, and no large prospective trials will likely be performed. The majority of the literature consists of single-institution, retrospective studies/case reports with small numbers, varied follow-up, and inherent biases. Furthermore, these studies are difficult to compare as the definition, management, and treatment of these tumors have changed over time. Therefore, there are no established guidelines for the treatment of these uncommon tumors; patients with these rare tumors should be managed in a multidisciplinary fashion.
Phyllodes tumors represent a spectrum of fibroepithelial neoplasms that have biological behavior that is diverse and unpredictable. They account for less than 1% of all breast neoplasms.1,2 These tumors have also been referred to as cystosarcoma phyllodes, phylloides tumors, and periductalstromal tumors. The term cystosarcoma phyllodes was first introduced in 1838 by Johannes Mueller; "sarcoma" for the fleshy nature of the tumor and "phyllodes" for its leaf-like architecture.3 Mueller emphasized the benign nature of this tumor. The first report of histologically malignant features in these tumors was by Lee and Pack in 1931.4 In 1960, Lomonaco proposed the name tumor phyllodes to avoid any implications of biological behavior.5 The World Health Organization proposed phyllodes tumor to emphasize the putative origin of these tumors from specialized periductal stroma and to avoid the designation of sarcoma with its deceptive implication of malignancy for a majority of these tumors.6 These tumors can arise de novo, and less frequently from preexisting fibroadenomas or from the malignant transformation of benign phyllodes tumors.2
Clinical Presentation
Phyllodes tumors occur over a wide age range from adolescents to the elderly, with the majority of tumors occurring in women in their 40s to 50s.1,2,7,8 These tumors can occur in young children and men. Women usually present with a palpable, firm-hard, discrete, mobile mass with an average size of 4 to 5 cm. Most tumors are unilateral and painless. Some women may give a history of a stable mass that grows rapidly. Larger tumors may cause stretching of the overlying skin and ulceration. All of these findings can be seen in both benign and malignant tumors. Compared with fibroadenomas, phyllodes tumors are seen more frequently in older patients and those with a history of rapid tumor growth and/or larger tumors. Palpable axillary lymphadenopathy can be identified in up to 20% of patients but is usually due to reactive changes as metastatic involvement is rare.9-11 In some instances, tumors may be multifocal, bilateral, or occur in ectopic breast tissue. In 1% to 2% of cases, an in situ or invasive breast carcinoma occurs within a phyllodes tumor.12 Also, patients can have ...


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The Erasmus MC Proteomics Center provides mass spectrometry based proteomics services. Expertise is focused on the characterization of protein complexes ('interactomics'), the analysis of protein post-translational modifications in relation to their biochemical roles and on the analysis of (sub)cellular proteomes and the dynamics thereof. The key technology in our lab is quantitative mass spectrometry (nanoLC-MS/MS) based proteomics (SILAC, TMT/iTRAQ, 18O, dimethylation and label free approaches). The Erasmus MC proteomics Center operates several mass spectrometers (Q Exactive (Thermo); LTQ-Orbitrap (Thermo) plus ETD; LTQ-Orbitrap (Thermo); LTQ linear ion trap (Thermo); Q-ToF Xevo (Waters); Triple Quad Xevo (Waters); Orbitrap Fusion Tribrid).
The Erasmus Center for Biomics provides applications and services using Next Generation Sequencing technology operating Illumina HiSeq2000, HiSeq2500 and MiSeq sequencing platforms. Our NGS pipelines cover genetic analyses such as genome & exome sequencing and mutation & variant analyses and epigenetic analyses including RNA sequencing & expression analyses, ChIP sequencing and Chromatin Conformation Capture sequencing & analyses.
Via the Erasmus MC applied Optical Imaging Centre, we have access to the latest microscopy technology and expertise, including 4Pi microscopy for high optical resolution, 2-photon microscopy, and several confocal microscopes, especially adapted for different aspects of FRAP, FLIP and FRET technology.
In our mouse transgenics facility, we use the latest techniques to generate mouse (conditional) knock-outs and knock-ins and derive (ES) cell lines and other cells from mice. Moreover, the experimental animal center (EDC) houses experimental animals following the latest standards.
The Applied Molecular Imaging Erasmus MC (AMIE) provides a platform for scientists to study complex biological systems in vivo (e.g. mice) using state-of-the-art imaging technology and molecular imaging assays. It is equipped with molecular and optical imaging, ultrasound and MRI, CT, SPECT and PET scanners.


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text:
Stanislav Circassian populations: FC Dinamo is not a favorite of the group E of the Europa League
Head coach of «Dynamo» Stanislav Circassian populations warned against earlier forecasts struggle in the group stage football League of Europe. About this he told the journalist on the phone.
on the last working day of the week was held the draw for the group stage of the Europa League the season 2014/15. In rivals Dynamo went Dutch PSV, Panathinaikos and Portuguese «Estoril».
«in the history of the Dynamo has not yet been output in the group stage of the European Cup,» said the Circassian populations.- We learned their rivals, will collect data on them and read it, to prepare for competitions. It is clear that the Europa League is a tournament where you have to show maximum results. But at the same PSV and Panathinaikos experience much more than we do — these clubs participate in the Champions League. For this reason, before We will understand how strong our opponents, it is not necessary to make any predictions «»
Stanislav Circassian populations: the match against Estoril will be the determining factor for Dinamo in the Europa League
Monaco will host the draw for the group stage of Europa League football
Players Dinamo will play against PSV, Panathinaikos and Estoril group stage LES
Players of the Moscow Dynamo will play with the Greek Panathinaikos in the game of the Europa League
CEO CSKA: the draw for the Champions League for us was unsuccessful
Posted in News of football and tagged European, football by admin420. Bookmark the permalink.
« The RFU has enabled Amcare to register new players
Players Dinamo will play against PSV, Panathinaikos and Estoril group stage LES »


text:
Q: Oracle JDBC and Oracle CHAR data type I have a tricky issue with the Oracle JDBC driver's handling of CHAR data types. Let's take this simple table:
create table x (c char(4));
insert into x (c) values ('a');  -- inserts 'a   '

So when I insert something into CHAR(4), the string is always filled with whitespace. This is also done when I execute queries like this:
select * from x where c = 'a';    -- selects 1 record
select * from x where c = 'a ';   -- selects 1 record
select * from x where c = 'a   '; -- selects 1 record

Here, the constant 'a' is filled with whitespace as well. That's why the record is always returned. This holds true when these queries are executed using a JDBC PreparedStatement as well. Now the tricky thing is when I want to use a bind variable:
PreparedStatement stmt =
  conn.prepareStatement("select * from x where c = ?");
stmt.setString(1, "a");    // This won't return any records
stmt.setString(1, "a   "); // This will return a record
stmt.executeQuery();

This is a workaround:
PreparedStatement stmt =
  conn.prepareStatement("select * from x where trim(c) = trim(?)");
stmt.setString(1, "a");    // This will return a record
stmt.setString(1, "a   "); // This will return a record
stmt.executeQuery();

EDIT: Now these are the constraints:

*

*The above workaround is not desireable as it modifies both the contents of c and ?, AND it makes using indexes on c quite hard.

*Moving the column from CHAR to VARCHAR (which it should be, of course) is not possible

EDIT: The reasons for these constraints is because I ask this question from the point of view of the developer of jOOQ, a database abstraction library. So my requirements are to provide a very generic solution that doesn't break anything in jOOQ's client code. That is why I'm not really a big fan of the workaround. And that's why I don't have access to that CHAR column's declaration. But still, I want to be able to handle this case.
What would you do instead? What's a good practice for handling CHAR data types when I want to ignore trailing whitespace?

A: I don't see any reason to use CHAR datatype even if it is char(1) in Oracle. Can you change the datatype instead?

A: Gary's solution works well. Here's an alternative.
If you are using an Oracle JDBC driver, the call to prepareStatement() will actually return an OraclePreparedStatement, which has a setFixedCHAR() method that automatically pads your inputs with whitespace.
String sql = "select * from x where c = ?";
OraclePreparedStatement stmt = (OraclePreparedStatement) conn.prepareStatement(sql);
stmt.setFixedCHAR(1, "a");
...

Obviously, the cast is only safe if you are using the Oracle driver.
The only reason I would suggest that you use this over Gary's answer is that you can change your column sizes without having to modify your JDBC code. The driver pads the correct number of spaces without the developer needing to know/manage the column size.

A: If you want
stmt.setString(1, "a");    // This won't return any records

to return a record, try
conn.prepareStatement("select * from x where c = cast(? as char(4))")

A: I have nice fix for this. You have to add one property while getting connection from database.
NLS_LANG=american_america.AL32UTF8

or in Java connection you can use below code:
java.util.Properties info = new java.util.Properties();
info.put ("user", user);
info.put ("password",password);
info.put("fixedString","TRUE");
info.put("NLS_LANG","american_america.AL32UTF8");
info.put("SetBigStringTryClob","TRUE");
String url="jdbc:oracle:thin:@"+serverName;
log.debug("url="+url);
log.debug("info="+info);
Class.forName("oracle.jdbc.OracleDriver");
conn  = DriverManager.getConnection(url,info);

A: the other way is modify your sql as
select * from x where NVL(TRIM(c),' ') = NVL(TRIM('a'),' ')

A: Simply add RTRIM() to the column name(which is defimed) in the update query.


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How-To - Write a Ticket

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The Rubinius issue tracker is http://github.com/rubinius/rubinius/issues.

To be useful, tickets must be concise, focused, and actionable. If not, the
ticket will languish and become clutter. Accordingly, tickets must fall into
one (or more) of the following categories:

A precise command line history showing how to install and invoke the
program and showing the backtrace for an exception.

A short piece of code illustrating the problem and the command line to
invoke it.

A patch, including specs if they do not already exist, and showing
the spec runs before and after applying the patch.

If your issue doesn’t fit into one of the categories, it is not invalid. It is
simply not appropriate for a ticket.

If it is a feature, consider discussing it on the mailing list. Also, you
could take a crack at implementing it and demonstrate how your feature is
useful.

If it is a library or gem that is not working, take some time to dig in
and see if you can create a reproduction for an issue and post that as a
ticket.

General procedure for submitting a ticket

Double-check.

Do a full rebuild (‘rake clean; rake’) after a ‘git pull’ or fresh clone.

Read Troubleshooting
to see if something there resolves the issue.

Read Specs.

Give your ticket a specific, preferably short title.

Give your ticket appropriate tags.

Give enough detail about the issue.

The command line for invoking the program

The backtrace or result from the program versus expected result.

Your machine information. uname -a is usually good (if there are any
“unknown” fields in it, please elaborate on those.)

Additional instructions for tickets with patches

Can be just a set of specs.

Patches must be accompanied by specs unless the specs already exist.

Relevant part of spec output and the exact ‘bin/mspec’ invocation from the
existing or added spec before the fix.

The spec output and the exact ‘bin/mspec’ invocation  showing success
after the fix.

Additional description of your patch and how it fixes the problem. In
particular with new functionality please indicate if it was already
discussed on #rubinius or ruby-dev.

Unless for some reason impossible, please use ‘git-format-patch’ to create the
patchset. It is much easier to apply and it preserves the correct attribution.
Otherwise, a unified diff.

Example of submitting a patch

Suppose the following spec exists and is failing:

describe "Kernel.format" do
  it "is accessible as a module function" do
    Kernel.format("%s", "hello").should == "hello"
  end
end

Ticket Title:

“[PATCH] No method ‘format’ on Kernel (Module)”

Tags:

“patch core spec”

Ticket Message:

The method ‘format’ is not available as a module function of Kernel.

$ bin/mspec spec/ruby/core/kernel/format_spec.rb
Started
.E

1)
Kernel.format is accessible as a module function ERROR
No method 'format' on Kernel (Module):

The method ‘format’ already exists but has not been set as a module
function.  This patch does so.

After the patch is applied:

$ bin/mspec spec/ruby/core/kernel/format_spec.rb
Started
..

Finished in 0.016031 seconds

2 examples, 2 expectations, 0 failures, 0 errors

Attachment:

Finally, put your patch in a gist and add the link to the gist to your issue.
Below the patch is reproduced inline for completeness:

From c61cecce6442347ebbdf1ded7a5c0832c97582c1 Mon Sep 17 00:00:00 2001
From: Brian Ford <bford@engineyard.com>
Date: Sat, 19 Jan 2008 17:48:19 -0800
Subject: [PATCH] Set Kernel#format as a module function.

diff --git a/kernel/core/kernel.rb b/kernel/core/kernel.rb
index 2d2e508..f2a382e 100644
--- a/kernel/core/kernel.rb
+++ b/kernel/core/kernel.rb
@@ -150,6 +150,7 @@ module Kernel
   end
   alias_method :format, :sprintf
   module_function :sprintf
+  module_function :format
   module_function :abort

   def puts(*a)

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