text:
History of Ceiling Fan
From Superfan, India's first super energy efficient ceiling fan
September 18, 2019 Superfan Comments 12 comments
Ceiling fans are the primary cooling device in tropical countries of the Asian subcontinent. In India alone, there are 400+ million ceiling fans running for an average of 12 hours daily. Such an electric appliance has a long history dating back to the 19th century.
Manual Ceiling Fans
In the early days of about 4000 BC, people used hand fans made up of palm leaves to represent royalty and comfort. These hand fans evolved into a palm leafed manual ceiling fan in the 500 BC named Punkah. Punkahs originated in India and were extensively used by the rich and royal people until the 17th century. The era of manual fans in the country ended with Punkahs. From the 17th to 19th century, ceiling fans were developing at a very fast rate in countries like the USA.
Electric Ceiling Fans
Indian Electric Works manufactured the first ceiling fans in India by 1930. Crompton Greaves, a joint venture between Greaves Cotton of India and Crompton Parkinson of England, had been manufacturing ceiling fans since 1937. Crompton-Greaves, Ltd of India invented ceiling fans with highly efficient cast aluminium housings in 1957, and Encon Industries was the first to import them to the United States in 1973.
Energy Efficient Ceiling Fans
In the late 80s and early 90s as India faced an energy crisis and hence the government regulated the electric appliances by star rating which gave birth to energy-efficient and star rated ceiling fans. The rating was done by a government organization, BEE (Bureau of Energy Efficiency) from 2006.
Super Energy Efficient Ceiling Fans
The industry was stagnant for a long time, until 2012. A new era of super energy efficient ceiling fans began in India with the launch of Superfan. It revolutionized the ceiling fan industry with its advanced technology. Superfan is the first fan that is developed in India, which runs at 35W. Just like Punkahs, which were indigenous and revolutionary, Superfans are also designed, developed and manufactured in India.
Ceiling fans are an integral part of comfy life and this electrical appliance has a very long history. The following infographic will tell more about it.
Edit 27th March 2021
Superfan is designed and developed in India which is on par globally. The development of these power saving ceiling fans marked a new ceiling fan era [1].
[1] N. C. Lenin, S. Padmanaban, M. S. Bhaskar, M. Mitolo and E. Hossain, "Ceiling Fan Drives–Past, Present and Future," in IEEE Access, vol. 9, pp. 44888-44904, 2021, doi: 10.1109/ACCESS.2021.3052899.
[2] www.fantique.com
35W, 5 star rated, 5 Star rated ceiling fan, BEE 5 Star Rated, BLDC ceiling fans, BLDC technology, Ceiling fan Manufacturers in Coimbatore, Ceiling fans online, energy efficiency, energy saving, evolution of fan, Fan, first ceiling fan, history of fans, Most energy efficient ceiling fans in India, palm leaves fan, Power saving, punkah, Super Energy Efficient Ceiling Fans
Misconceptions About Ceiling Fans
Superfan – Fan Re 'invented'
12 thoughts on "History of Ceiling Fan"
Ankit Patel says:
Thanks for sharing this valuable information about history of ceiling fan.
Superfan says:
Thank you. Keep reading 🙂
Shanmugapriya Subramanian says:
It' s too interesting about life line of Ceiling Fan. Thank you
Ganesh S says:
Very Useful information about the fan.
Dharani devi Manickam says:
We all see ceiling fans as an appliance in our home. It is our mandatory appliance required in every day of life, but we have never thought about the history behind the ceiling fan. Good to read this now. Thank you Superfan for recording a new era in this history.
Thank you. Keep reading!
nexa electrical supplies says:
thanks for providing useful information on ceiling fans
Zennat Sana says:
This is really a awesome review. Thanks for sharing this.
Greenhome101 says:
The ceiling fan makes the house designing attractive . i like your blog hope to see more blogs about home decor. feel thankful to see the designs of fans.
OM PRAKASH says:
Thank you ?
Leave a Reply to Shanmugapriya Subramanian Cancel reply


text:
Q: Interpolar cadenas C#6 Estoy interpolando cadenas y al final de la línea de código al poner el ; me da un mensaje (Empty statement is redundant), pero igual compila el código, muestro imagen.

El código es el siguiente:
static void FillTheseValues(out int a, out string b, out bool c)
{
    a = 9;
    b = "Enjoy your string.";
    c = true;
}
Console.WriteLine("***** Fun with Methods *****");
int i;
string str;
bool b;
FillTheseValues(out i, out str, out b);
Console.WriteLine($"Int is: {i}");
Console.WriteLine($"String is: {str}");

Por cierto, estoy usando Visual Studio 2015, tengo instalado ReSharper.

A: Bueno , puede que sea el ReSharper que esté dando esos warnings inexplicables
trate de reiniciar esto
Herramientas -> Opciones
-> ReSharper ->Suspender y Resume otra vez (sin necesidad de cerrar la ventana)
(Si no funciona reinicie Visual Studio)

o tratar de darle formato así , (su equivalente )
/*Console.WriteLine($"Int is: {i}");*/
 Console.WriteLine(string.Format("Int is {0}", i));
/*Console.WriteLine($"String is: {str}");*/
  Console.WriteLine(string.Format("String is {0}", str));

A: para evitar ese warning deberías inicializar la variable str por lo menos a cadena vacía.
string str = string.Empty;

redpajama_set_name: RedPajamaStackExchange


The ability to serve children and families effectively is a crucial skill that must be developed with extensive training. This is a key competency that greatly affects graduate and doctoral counseling students' future employment and effectiveness as professionals in the field. It is imperative that students gain hands-on supervised clinical experiences to complement what they are learning in their coursework.
The on-site CARE Center is able to offer a wide variety of hands-on experiences for graduate and doctoral counseling students. Practicum and intern counselors-in-training are able to gain supervised clinical experiences in their specialty areas as mental health, marriage and family counseling/therapy, and school counseling.
Graduate and doctoral counseling students receive supervised hands-on training to actual clients. This experience strengthens our students' resume and portfolio and gives them an edge when applying for internships because they have direct experience.
Additionally, Barry University recognizes that true change, growth, and leadership come when our internal resources are leveraged for maximum impact. As a result, the CARE Center seeks grants that support the University's mission to serve under resourced and underrepresented populations.
Graduate and doctoral counseling students are to participate in these grant funded programs through the Family CARE Center. For example, College Reach-Out Program (CROP) offers services that focus on enhancing resources for students and families, while also addressing factors outside of school that impact a student's ability to pursue a college education.


text:
\section{Introduction}
Accurate and reliable brain tumour segmentation from neuroimaging scans is a critical step towards improving disease diagnosis and proper treatment planning that increases the survival chance of patients. Brain tumours are highly heterogeneous in terms of shape, size and location, which makes their segmentation challenging. In addition, brain tumours can be highly infiltrating  and it may be difficult to distinguish healthy brain tissue from the tumour. Manual segmentation of brain tumours in MR images is a laborious task that is both time-consuming and subject to rater variability. Therefore, reliable automatic segmentation of brain tumours has attracted considerable attention over the past two decades. Most recent automatic segmentation methods build on convolutional neural networks (CNNs) trained on manually annotated dataset of a large cohort of patients. The Brain Tumour Segmentation (BraTS) challenge  public dataset has become the benchmark in this area \cite{Menze2015, BraTS, Bakas, Bakas2017,Bakas20171, Bakas20172} since 2012.

The BraTS challenge organizers provide magnetic resonance imaging (MRI) scans in four modalities: T1-weighted (T1), T1-weighted post-contrast (T1c), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR) MRIs, with  corresponding manual segmentation. The participants are required to produce segmentation masks of three glioma sub-regions that contain enhancing tumour (ET), tumour core (TC) and whole tumour (WT). The BraTS2020  dataset consists of 369 and 125 cases for training and validation respectively.

Since the revolution of deep learning and more specifically CNN, the most successful models for BraTS are based on CNN \cite{Ghaffari2020}. The top ranked models submitted to recent BraTs challenges were DeepMedic, that is based on multi-scale processing \cite{Kamnitsas2016}, cascaded Fully  Convolutional  Networks, based on hierarchical binary segmentation \cite{Wang2018} and U-net based models \cite{Isensee2018},\cite{Crimi2019}.

U-net, which was first introduced in 2015 \cite{Ronneberger2015}, is a CNN architecture consisting of an encoder and a decoder. Due to its straightforward architecture as well as its high segmentation accuracy, U-net or its variants such as V-net \cite{Milletari2016a} are used in most state-of-the-art medical image segmentation tasks \cite{Isensee}, \cite{Li2018}, \cite{Chen2018a} and it has been argued that that 'a well-trained U-net is hard to beat' \cite{Isensee2018}. Hence, while using U-net as the backbone of their network, most BraTS18 and BraTS19 participants focussed on enhancing their model performance by optimizing the preprocessing step, training procedure, co training using local datasets, or applying ensemble learning. Most of the top-ranked models in recent BraTS challenges were inspired by U-net \cite{Ghaffari2020} including all top ranked models of BraTS19. As an example, the top-ranked model in BraTS 2019 was a two stage Cascaded U-net in which the first stage U-net predicts a coarse segmentation map and the second stage U-net provides a more accurate segmentation \cite{Crimi2019}. Zhao et al\cite{Crimi2019} applied a bag of tricks including data sampling, random patch-size training, semi-supervised learning, self-ensembling, result fusion, and warming-up learning rate to enhance their U-net performance. McKinley et al \cite{Crimi2019} modified their 3rd place entry to the BRaTS18: 'DeepSCAN' model, (a shallow U-net-style network with densely connected blocks of dilated convolutions and label-uncertainty loss) by adding a lightweight local attention mechanism and were ranked 3rd again in BraTS19.\\
In this work a modified 3D version of the U-net is utilized, and in order to enhance the model accuracy, pre- and post- processing steps as well as training procedure optimization are applied, and ensembling and data augmentation are utilized during inference time. We report preliminary results on the validation dataset of BraTS20 dataset. The results are computed online using the CBICA Image Processing Portal (\url{https://ipp.cbica.upenn.edu}). The rest of this paper is organized as follows: Methodology including the dataset preprocessing, network architecture, and post-processing are explained in section \ref{methodology}. Experiments and results are provided in section \ref{experimentsAndResults}, followed by a discussion and conclusion in section \ref{conclusion}.

\section{Methodology}\label{methodology}
A top level diagram of the proposed method is illustrated in Fig.~\ref{model}. The proposed model is composed of four different modules. In this section, each of these modules is discussed in more detail.

\begin{figure}
    \input{diagrams/model}
    \caption{Top-level schematic of the proposed model} \label{model}
\end{figure}

\subsection{Dataset Preprocessing}
The BraTS20 dataset consists of 369 multi-institutional pre-operative multimodal MRI scans. This dataset already has been put through various pre- processing steps by the organizers, so that all images are skull-stripped, have isotropic resolution, and are co-registered MR volumes \cite{Menze2015}. MR scans often contain intensity non-uniformities due to magnetic field inhomogeneity, which can impact the segmentation results.  To compensate for this, a bias field correction algorithm was applied using N4ITK \cite{Tustison2010}.  All modalities were  then  normalized  by  subtracting  the  mean  from  each  of  their  voxels  and dividing by the standard deviation of the intensities within the brain region of that image, so that each modality has zero mean and unit variance. In order to address the small size of the dataset, data augmentation was performed using random rotation (-6 and 6 degree),  scaling (0.9..1.1) and mirroring.

\subsection{Network Architecture}
Inspired by other work \cite{Zhou2018}, we propose a modified 3D version of the well known U-net, as shown in Fig.~\ref{Proposed_network}. The main differences between this network and a generic U-net are threefold:  (i) a heavy encoder, light decoder structure using residual blocks \cite{He2016} (ii) employment of dense blocks instead of skip connections \cite{Zhou2018} and (iii) utilization of self-ensembling in the decoder part of the network.

\begin{figure}[htp]
    \includegraphics[scale=0.50]{diagrams/nnArchitectureNew.png}
    \centering
    \caption{Schematic visualization of the network architecture \cite{Ghaffari2020b}} \label{Proposed_network}
\end{figure}

\begin{figure}[htb!]
    \includegraphics[scale=0.35]{diagrams/ResidualBlock.png}
    \centering
    \caption{Schematic illustration of the residual block used in the proposed Network \cite{Ghaffari2020b} } \label{Residual_Blocks}
\end{figure}

\begin{enumerate}[(i)]
\item The residual block as shown in Fig.~\ref{Residual_Blocks} consists of two $3\times3\times3$ convolutions and Group Normalization \cite{Wu2020} with group size of 8 and Rectified Linear Unit (ReLU) activation, followed by additive identity skip connection. Residual blocks learn a non-linear residual that is added to the input and provide a deeper architecture to improve the gradient flow.
\item Dense convolutional blocks merge the feature maps at each level of the network by integrating the output of all previous convolutional blocks in the same level with the upsampled output of the corresponding lower-level dense block. This method provides the network decoder with more semantic information from the encoder feature maps. The transferred information helps the optimizer in optimizing the network more effectively \cite{Zhou2018}. \par
\item Segmentation blocks were also employed for self-ensembling and deep supervision purposes \cite{Isensee2018} within the decoder of the network. These segmentation blocks make predictions at each scale of the U-net by reducing the number of feature maps at each level of the decoder to the number of feature maps at the final output layer of the network. Combining these segmentation maps helps the network converge faster by transferring more information from the earlier levels of the decoder.
\end{enumerate}

The network was trained on both a multi-task framework as well as a cascaded binary segmentation task   to perform sequential segmentation of the tumour components,  and experiments were conducted to evaluate the performance of each model as well as an ensemble of both models. In the multi-task framework, the network output has three 3D channels of size 128\textsuperscript{3}, each of which corresponds to one of the three mutually inclusive tumour subregions and the network is trained to predict all tumour subregions at the same time. This multi-task learning regularizes the network by providing additional information related to the learning task. For the cascaded model, three cascaded networks were trained separately, one  for each of the three tumour subregions. Each of these networks has a single 3D output of size 128\textsuperscript{3} corresponding to one of the tumour subregions and at the time of inference, the input of each stage of the model is limited to patches containing the tumour region extracted by the previous cascaded stage. This means that the predicted tumour core is forced to lie inside the whole tumour, and the enhanced tumour core also  inside the tumour core region.  \par

\subsection{Training Procedure}
In the proposed method, all the four modalities were fed into the network at the same time to benefit from the information present in all.  Due to memory limitations of the Graphical Processing Unit (GPU) used, the network was trained with a batch size of 1.  To extract patches, each image was cropped so that the manually segmented tumour was located in the middle of each patch. Overlapping patches were extracted if the size of tumour was bigger than 128 voxels in any dimension. This strategy of extracting appropriate patches addresses the class imbalance issue to some extent, and also reduces the training time significantly.
To further address the class imbalance problem, Dice Score was used as the loss function \cite{Shen2018b};  for the multi-task framework it was modified to take into account  the mean value of the Dice Scores of all output segmentation maps. Dice score (DS) and  multi-class Dice loss (MDS) function are expressed as:

\[
DS=2\times\frac{Y_{true} \times Y_{pred}}{Y_{true} + Y_{pred} + \epsilon} \;\;\;\;\;\;\;\;\;\;\; MDL = \frac{-1}{n}\sum_{1}^{n}DS
\]

where $Y_{true}$ and $Y_{pred}$ are the reference segmentation map (gold standard) and the predicted segmentation map for each of the output channels respectively. $n$ is the number of channels (3 for the multi-task model), and $\epsilon$ is a small value used to avoid division by zero (we set $\epsilon$ = 0.00001)\par
The network was trained using the Keras framework with TensorFlow backend, on an Nvidia Tesla Volta V100 GPU.  The GPU memory allowed  training of the network with batches of size 1 while allowing 16 filters in the highest level of the Dense U-net. The network was trained using Adam Optimizer with an initial learning rate of 5e-4, and reducing by a factor of 0.5 if the validation accuracy was not improving within the last 10 epochs. Dropout with a rate of 0.3 and also L2 norm regularization with a weight of 1e-5 were used for  regularization purposes. The network was programmed to be trained for a maximum of 300 epochs or until the validation accuracy did not improve in the last 50 epochs.

\subsection{Tumour localization}
To increase the prediction accuracy, a cascaded method was used in which the whole tumour region was localized using low-resolution images. To do so, the images were resized to 128\textsuperscript{3} voxels and then the model was applied to obtain an estimate of the tumour location. The prediction map was then resized to its original size and patches of size 128\textsuperscript{3} voxels were extracted such that the whole tumour is in the middle of that patch. If the predicted whole tumour is larger than 128\textsuperscript{3} voxels, more overlapping patches were extracted to cover the whole tumour region. The extracted patches were then used to predict the three tumour subregions. Finally, the segmentation maps were reconstructed by zero-padding, considering the location of the extracted patches. This method of patch extraction reduces both the test time and the rate of false positives prediction.

\subsection{Post-processing}
To further improve the accuracy of brain tumour segmentation, a post-processing method was applied. The post-processing may be summarized as follows:
\begin{enumerate}
\item  It was ensured that the hierarchy of the tumour subregions was respected. This means that the enhancing tumour subregion is inside the tumour core, and the tumour core is inside the whole tumour. To achieve this, any enhancing tumour voxels appearing outside the tumour core were removed, as well as any tumour core appearing outside the whole tumour.
\item Connected components of any subregion smaller than 10 voxels were removed.
\item Connected components of whole tumour that were primarily uncertain ( did not include any tumour core or enhancing tumour voxels, usually in cerebellum region, and/or having higher intensities in only FLAIR or T2 modalities). Such simple post-processing worked well in discarding false positive predictions possibly due to inherent system noise. This heuristics was verified by examining validation subject having such connected components as whole tumour.
\item  Considering the fact that low grade gliomas (LGG) patients may have no enhancing tumour sub-region and also inspired by other work \cite{Isensee2018}, all enhancing tumour regions with less than 50 voxels were replaced by necrosis.
\end{enumerate}
After these steps, the remaining tumour subregions were fused to reconstruct the final segmentation map.

\section{Experiments and Results}\label{experimentsAndResults}
All scans in  the BraTS20 training dataset were used for training the proposed network in two different frameworks separately: cascaded binary segmentation task as well as the multi-task framework. Validation set results were obtained by using  each of these models separately as well as their ensemble. Ensembling was performed by averaging the Sigmoid probabilities of both model predictions. Thresholding and post-processing were then applied to the final prediction map. A threshold of 0.5 was chosen for whole tumour and tumour core subregion while for Enhancing tumour subregion the threshold was reduced to 0.4. This choice of threshold for enhancing tumour subregion was done based on the performance of the validation dataset and it enhanced the prediction accuracy for the subjects with under-segmentated prediction. However, it did not resolve the issue for some of the LGG cases as a result of which the Hausdorff distance is not as expected. So, in our ensemble experiment the enhancing core threshould was gradually decreased to 0.3, 0.2, and 0.1 if no enhancing tumour was not detected. This also resolved the under-segmentation issue for some of those LGG cases and enhanced the Hausdorff distance of enhancing tumour subregion (Table \ref{results}). Test time augmentation was applied for enhancing the prediction accuracy in all of the experiments. This was done by flipping the input image axes, and averaging the outputs of the resulting flipped segmentation probability maps. The results are listed in Table \ref{results}. All reported values were computed by the online evaluation platform. We also trained models using 5 fold cross-validation and a qualitative example generated using the trained model is depicted in Fig.~\ref{resultsVisulization}. Images include three sample cases from the training dataset, along with the manual annotations and  the model prediction. According to reported results, the multi-task model and the cascaded model have almost the same performance, while their ensemble resulted in a more accurate and robust model.

\begin{table}
    \input{tables/results}
\end{table}

\begin{figure}
    \centering
    \includegraphics[width=\linewidth]{diagrams/Results.png}
    \caption{Qualitative results for three patients from BraTS20 training dataset. From left to right: Flair, T1, T1c, T2,  manual segmentation overlaid on T2, and model prediction.}\label{resultsVisulization}
\end{figure}

\section{Conclusion}\label{conclusion}
In this paper a 3D densely connected U-net network was trained on the BraTS20 training dataset using two different approaches: a multi-task framework and a three-stage cascaded framework. The networks were trained using 3D patches of size 128\textsuperscript{3}. To address the class imbalance issue, instead of random patch selection, patches were selected by cropping images so that the manually segmented tumour was located in the middle of each patch. Brain tumour segmentation was performed in two stages: first a coarse prediction was performed on low resolution images to localize the tumour, and then fine segmentation was performed by extracting patches in the tumour region, applying the model to those patches, and finally stitching patches together. The model predictions were then filtered through a post-processing step in which connected components analysis was used to reduce the false positive predictions.
The performance of the multi-task model, the cascaded model and also an ensemble of both models was evaluated using the BraTS20 validation dataset, and results were calculated on the online CBICA Image Processing Portal. The reported average Dice Score for the ensemble model were 0.90, 0.82, and 0.78 for whole tumour, tumour core and enhancing tumour respectively. We suffered from some hardware limitations while training our models, which we believe has  impacted the model performance. For example, the maximum duration time for the cloud GPU we were using was 48 hours after which the training was terminated, and also due to GPU memory limitation we could only train with a batch size of one. We believe that with access to more powerful hardware,  the model performance would be further enhanced.

\bibliographystyle{splncs04}

\section{Conclusion}\label{conclusion}
In this paper a 3D densely connected U-net network was trained on the BraTS20 training dataset using two different approaches: a multi-task framework and a three-stage cascaded framework. The networks were trained using 3D patches of size 128\textsuperscript{3}. To address the class imbalance issue, instead of random patch selection, patches were selected by cropping images so that the manually segmented tumour was located in the middle of each patch. Brain tumour segmentation was performed in two stages: first a coarse prediction was performed on low resolution images to localize the tumour, and then fine segmentation was performed by extracting patches in the tumour region, applying the model to those patches, and finally stitching patches together. The model predictions were then filtered through a post-processing step in which connected components analysis was used to reduce the false positive predictions.
The performance of the multi-task model, the cascaded model and also an ensemble of both models was evaluated using the BraTS20 validation dataset, and results were calculated on the online CBICA Image Processing Portal. The reported average Dice Score for the ensemble model were 0.90, 0.82, and 0.78 for whole tumour, tumour core and enhancing tumour respectively. We suffered from some hardware limitations while training our models, which we believe has  impacted the model performance. For example, the maximum duration time for the cloud GPU we were using was 48 hours after which the training was terminated, and also due to GPU memory limitation we could only train with a batch size of one. We believe that with access to more powerful hardware,  the model performance would be further enhanced.

\section{Introduction}
Accurate and reliable brain tumour segmentation from neuroimaging scans is a critical step towards improving disease diagnosis and proper treatment planning that increases the survival chance of patients. Brain tumours are highly heterogeneous in terms of shape, size and location, which makes their segmentation challenging. In addition, brain tumours can be highly infiltrating  and it may be difficult to distinguish healthy brain tissue from the tumour. Manual segmentation of brain tumours in MR images is a laborious task that is both time-consuming and subject to rater variability. Therefore, reliable automatic segmentation of brain tumours has attracted considerable attention over the past two decades. Most recent automatic segmentation methods build on convolutional neural networks (CNNs) trained on manually annotated dataset of a large cohort of patients. The Brain Tumour Segmentation (BraTS) challenge  public dataset has become the benchmark in this area \cite{Menze2015, BraTS, Bakas, Bakas2017,Bakas20171, Bakas20172} since 2012.

The BraTS challenge organizers provide magnetic resonance imaging (MRI) scans in four modalities: T1-weighted (T1), T1-weighted post-contrast (T1c), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR) MRIs, with  corresponding manual segmentation. The participants are required to produce segmentation masks of three glioma sub-regions that contain enhancing tumour (ET), tumour core (TC) and whole tumour (WT). The BraTS2020  dataset consists of 369 and 125 cases for training and validation respectively.

Since the revolution of deep learning and more specifically CNN, the most successful models for BraTS are based on CNN \cite{Ghaffari2020}. The top ranked models submitted to recent BraTs challenges were DeepMedic, that is based on multi-scale processing \cite{Kamnitsas2016}, cascaded Fully  Convolutional  Networks, based on hierarchical binary segmentation \cite{Wang2018} and U-net based models \cite{Isensee2018},\cite{Crimi2019}.

U-net, which was first introduced in 2015 \cite{Ronneberger2015}, is a CNN architecture consisting of an encoder and a decoder. Due to its straightforward architecture as well as its high segmentation accuracy, U-net or its variants such as V-net \cite{Milletari2016a} are used in most state-of-the-art medical image segmentation tasks \cite{Isensee}, \cite{Li2018}, \cite{Chen2018a} and it has been argued that that 'a well-trained U-net is hard to beat' \cite{Isensee2018}. Hence, while using U-net as the backbone of their network, most BraTS18 and BraTS19 participants focussed on enhancing their model performance by optimizing the preprocessing step, training procedure, co training using local datasets, or applying ensemble learning. Most of the top-ranked models in recent BraTS challenges were inspired by U-net \cite{Ghaffari2020} including all top ranked models of BraTS19. As an example, the top-ranked model in BraTS 2019 was a two stage Cascaded U-net in which the first stage U-net predicts a coarse segmentation map and the second stage U-net provides a more accurate segmentation \cite{Crimi2019}. Zhao et al\cite{Crimi2019} applied a bag of tricks including data sampling, random patch-size training, semi-supervised learning, self-ensembling, result fusion, and warming-up learning rate to enhance their U-net performance. McKinley et al \cite{Crimi2019} modified their 3rd place entry to the BRaTS18: 'DeepSCAN' model, (a shallow U-net-style network with densely connected blocks of dilated convolutions and label-uncertainty loss) by adding a lightweight local attention mechanism and were ranked 3rd again in BraTS19.\\
In this work a modified 3D version of the U-net is utilized, and in order to enhance the model accuracy, pre- and post- processing steps as well as training procedure optimization are applied, and ensembling and data augmentation are utilized during inference time. We report preliminary results on the validation dataset of BraTS20 dataset. The results are computed online using the CBICA Image Processing Portal (\url{https://ipp.cbica.upenn.edu}). The rest of this paper is organized as follows: Methodology including the dataset preprocessing, network architecture, and post-processing are explained in section \ref{methodology}. Experiments and results are provided in section \ref{experimentsAndResults}, followed by a discussion and conclusion in section \ref{conclusion}.
\section{Methodology}\label{methodology}
A top level diagram of the proposed method is illustrated in Fig.~\ref{model}. The proposed model is composed of four different modules. In this section, each of these modules is discussed in more detail.

\begin{figure}
    \input{diagrams/model}
    \caption{Top-level schematic of the proposed model} \label{model}
\end{figure}

\subsection{Dataset Preprocessing}
The BraTS20 dataset consists of 369 multi-institutional pre-operative multimodal MRI scans. This dataset already has been put through various pre- processing steps by the organizers, so that all images are skull-stripped, have isotropic resolution, and are co-registered MR volumes \cite{Menze2015}. MR scans often contain intensity non-uniformities due to magnetic field inhomogeneity, which can impact the segmentation results.  To compensate for this, a bias field correction algorithm was applied using N4ITK \cite{Tustison2010}.  All modalities were  then  normalized  by  subtracting  the  mean  from  each  of  their  voxels  and dividing by the standard deviation of the intensities within the brain region of that image, so that each modality has zero mean and unit variance. In order to address the small size of the dataset, data augmentation was performed using random rotation (-6 and 6 degree),  scaling (0.9..1.1) and mirroring.

\subsection{Network Architecture}
Inspired by other work \cite{Zhou2018}, we propose a modified 3D version of the well known U-net, as shown in Fig.~\ref{Proposed_network}. The main differences between this network and a generic U-net are threefold:  (i) a heavy encoder, light decoder structure using residual blocks \cite{He2016} (ii) employment of dense blocks instead of skip connections \cite{Zhou2018} and (iii) utilization of self-ensembling in the decoder part of the network.

\begin{figure}[htp]
    \includegraphics[scale=0.50]{diagrams/nnArchitectureNew.png}
    \centering
    \caption{Schematic visualization of the network architecture \cite{Ghaffari2020b}} \label{Proposed_network}
\end{figure}

\begin{figure}[htb!]
    \includegraphics[scale=0.35]{diagrams/ResidualBlock.png}
    \centering
    \caption{Schematic illustration of the residual block used in the proposed Network \cite{Ghaffari2020b} } \label{Residual_Blocks}
\end{figure}

\begin{enumerate}[(i)]
\item The residual block as shown in Fig.~\ref{Residual_Blocks} consists of two $3\times3\times3$ convolutions and Group Normalization \cite{Wu2020} with group size of 8 and Rectified Linear Unit (ReLU) activation, followed by additive identity skip connection. Residual blocks learn a non-linear residual that is added to the input and provide a deeper architecture to improve the gradient flow.
\item Dense convolutional blocks merge the feature maps at each level of the network by integrating the output of all previous convolutional blocks in the same level with the upsampled output of the corresponding lower-level dense block. This method provides the network decoder with more semantic information from the encoder feature maps. The transferred information helps the optimizer in optimizing the network more effectively \cite{Zhou2018}. \par
\item Segmentation blocks were also employed for self-ensembling and deep supervision purposes \cite{Isensee2018} within the decoder of the network. These segmentation blocks make predictions at each scale of the U-net by reducing the number of feature maps at each level of the decoder to the number of feature maps at the final output layer of the network. Combining these segmentation maps helps the network converge faster by transferring more information from the earlier levels of the decoder.
\end{enumerate}

The network was trained on both a multi-task framework as well as a cascaded binary segmentation task   to perform sequential segmentation of the tumour components,  and experiments were conducted to evaluate the performance of each model as well as an ensemble of both models. In the multi-task framework, the network output has three 3D channels of size 128\textsuperscript{3}, each of which corresponds to one of the three mutually inclusive tumour subregions and the network is trained to predict all tumour subregions at the same time. This multi-task learning regularizes the network by providing additional information related to the learning task. For the cascaded model, three cascaded networks were trained separately, one  for each of the three tumour subregions. Each of these networks has a single 3D output of size 128\textsuperscript{3} corresponding to one of the tumour subregions and at the time of inference, the input of each stage of the model is limited to patches containing the tumour region extracted by the previous cascaded stage. This means that the predicted tumour core is forced to lie inside the whole tumour, and the enhanced tumour core also  inside the tumour core region.  \par

\subsection{Training Procedure}
In the proposed method, all the four modalities were fed into the network at the same time to benefit from the information present in all.  Due to memory limitations of the Graphical Processing Unit (GPU) used, the network was trained with a batch size of 1.  To extract patches, each image was cropped so that the manually segmented tumour was located in the middle of each patch. Overlapping patches were extracted if the size of tumour was bigger than 128 voxels in any dimension. This strategy of extracting appropriate patches addresses the class imbalance issue to some extent, and also reduces the training time significantly.
To further address the class imbalance problem, Dice Score was used as the loss function \cite{Shen2018b};  for the multi-task framework it was modified to take into account  the mean value of the Dice Scores of all output segmentation maps. Dice score (DS) and  multi-class Dice loss (MDS) function are expressed as:

\[
DS=2\times\frac{Y_{true} \times Y_{pred}}{Y_{true} + Y_{pred} + \epsilon} \;\;\;\;\;\;\;\;\;\;\; MDL = \frac{-1}{n}\sum_{1}^{n}DS
\]

where $Y_{true}$ and $Y_{pred}$ are the reference segmentation map (gold standard) and the predicted segmentation map for each of the output channels respectively. $n$ is the number of channels (3 for the multi-task model), and $\epsilon$ is a small value used to avoid division by zero (we set $\epsilon$ = 0.00001)\par
The network was trained using the Keras framework with TensorFlow backend, on an Nvidia Tesla Volta V100 GPU.  The GPU memory allowed  training of the network with batches of size 1 while allowing 16 filters in the highest level of the Dense U-net. The network was trained using Adam Optimizer with an initial learning rate of 5e-4, and reducing by a factor of 0.5 if the validation accuracy was not improving within the last 10 epochs. Dropout with a rate of 0.3 and also L2 norm regularization with a weight of 1e-5 were used for  regularization purposes. The network was programmed to be trained for a maximum of 300 epochs or until the validation accuracy did not improve in the last 50 epochs.

\subsection{Tumour localization}
To increase the prediction accuracy, a cascaded method was used in which the whole tumour region was localized using low-resolution images. To do so, the images were resized to 128\textsuperscript{3} voxels and then the model was applied to obtain an estimate of the tumour location. The prediction map was then resized to its original size and patches of size 128\textsuperscript{3} voxels were extracted such that the whole tumour is in the middle of that patch. If the predicted whole tumour is larger than 128\textsuperscript{3} voxels, more overlapping patches were extracted to cover the whole tumour region. The extracted patches were then used to predict the three tumour subregions. Finally, the segmentation maps were reconstructed by zero-padding, considering the location of the extracted patches. This method of patch extraction reduces both the test time and the rate of false positives prediction.

\subsection{Post-processing}
To further improve the accuracy of brain tumour segmentation, a post-processing method was applied. The post-processing may be summarized as follows:
\begin{enumerate}
\item  It was ensured that the hierarchy of the tumour subregions was respected. This means that the enhancing tumour subregion is inside the tumour core, and the tumour core is inside the whole tumour. To achieve this, any enhancing tumour voxels appearing outside the tumour core were removed, as well as any tumour core appearing outside the whole tumour.
\item Connected components of any subregion smaller than 10 voxels were removed.
\item Connected components of whole tumour that were primarily uncertain ( did not include any tumour core or enhancing tumour voxels, usually in cerebellum region, and/or having higher intensities in only FLAIR or T2 modalities). Such simple post-processing worked well in discarding false positive predictions possibly due to inherent system noise. This heuristics was verified by examining validation subject having such connected components as whole tumour.
\item  Considering the fact that low grade gliomas (LGG) patients may have no enhancing tumour sub-region and also inspired by other work \cite{Isensee2018}, all enhancing tumour regions with less than 50 voxels were replaced by necrosis.
\end{enumerate}
After these steps, the remaining tumour subregions were fused to reconstruct the final segmentation map.
\section{Experiments and Results}\label{experimentsAndResults}
All scans in  the BraTS20 training dataset were used for training the proposed network in two different frameworks separately: cascaded binary segmentation task as well as the multi-task framework. Validation set results were obtained by using  each of these models separately as well as their ensemble. Ensembling was performed by averaging the Sigmoid probabilities of both model predictions. Thresholding and post-processing were then applied to the final prediction map. A threshold of 0.5 was chosen for whole tumour and tumour core subregion while for Enhancing tumour subregion the threshold was reduced to 0.4. This choice of threshold for enhancing tumour subregion was done based on the performance of the validation dataset and it enhanced the prediction accuracy for the subjects with under-segmentated prediction. However, it did not resolve the issue for some of the LGG cases as a result of which the Hausdorff distance is not as expected. So, in our ensemble experiment the enhancing core threshould was gradually decreased to 0.3, 0.2, and 0.1 if no enhancing tumour was not detected. This also resolved the under-segmentation issue for some of those LGG cases and enhanced the Hausdorff distance of enhancing tumour subregion (Table \ref{results}). Test time augmentation was applied for enhancing the prediction accuracy in all of the experiments. This was done by flipping the input image axes, and averaging the outputs of the resulting flipped segmentation probability maps. The results are listed in Table \ref{results}. All reported values were computed by the online evaluation platform. We also trained models using 5 fold cross-validation and a qualitative example generated using the trained model is depicted in Fig.~\ref{resultsVisulization}. Images include three sample cases from the training dataset, along with the manual annotations and  the model prediction. According to reported results, the multi-task model and the cascaded model have almost the same performance, while their ensemble resulted in a more accurate and robust model.

\begin{table}
    \input{tables/results}
\end{table}

\begin{figure}
    \centering
    \includegraphics[width=\linewidth]{diagrams/Results.png}
    \caption{Qualitative results for three patients from BraTS20 training dataset. From left to right: Flair, T1, T1c, T2,  manual segmentation overlaid on T2, and model prediction.}\label{resultsVisulization}
\end{figure}

redpajama_set_name: RedPajamaArXiv


text:
"Pâinea veșnică" (în ) este o povestire științifico-fantastică a scriitorului Aleksandr Beleaev. Lucrarea lui Beleaev are o tematică parțial medicală, ea este dedicată perspectivelor dezvoltării biotehnologiei. A apărut prima dată în 1928 în colecția Борьба в эфире (Luptă aeriană) alături de romanul care dă numele colecției și de alte două povestiri "Ни жизнь, ни смерть" și "Над бездной".

Prezentare

Povestea are loc toamna târziu într-un sătuc german de pescari de pe o insulă din Marea Nordului. Profesorul biochimist Breuer, care a părăsit Universitatea din Berlin pentru a-și continua cercetările, trăiește lângă acest sătuc de pescari, sperând ca noua sa descoperire să-i facă pe oameni fericiți. Pe baza culturii celor mai "simple" organisme, care cresc fără a avea nevoie de îngrijire, Breuer a creat un aluat gustos - pâinea veșnică.

Treptat alimentul se răspândește de la săteanul căruia i-a încredințat "pâinea" pentru supraveghere, ajungând pe toată insula și apoi la niște negustori care încep comercializarea produsului. Cu venirea verii, experimentul scapă de sub control, aluatul crescând necontrolat datorită căldurii și amenință să ocupe uscatul și apa mării. Breuer este nevoit să creeze o ciupercă minusculă care să oprească creșterea alimentului și să îl distrugă.

Povestirea este inspirată de basmul din 1815 Terciul dulce - Der süße Brei de Frații Grimm și de romanul din 1904 Hrana zeilor de H. G. Wells.

Traduceri în limba română
 "Pâinea veșnică" (1928), traducere de M. Cardaș și V. Bîrlădeanu. Publicată în Aleksandr Beleaev - Steaua KEȚ, Opere alese II, Editura Tineretului, București, 1963.

Note

Legături externe
  Pâinea veșnică la fantlab.ru
  Pâinea veșnică text integral

Vezi și
 "Cheia laxiană" de Robert Sheckley - o povestire asemănătoare
 Război cu salamandrele (1936) de Karel Čapek - un roman asemănător

Povestiri din 1928
Povestiri de Aleksandr Beleaev
Povestiri științifico-fantastice
Povestiri rusești
Povestiri sovietice

redpajama_set_name: RedPajamaWikipedia


Weapon Hardpoint: You can equip 1 cannon, torpedo, or missile upgrade.
After you reveal a red Tallon Roll (talon roll left or talon roll right), if you have 2 or fewer stress tokens, treat that maneuver as white.
After you gain a stress token, if there is an enemy ship in your front at range 0-1, you may remove that stress token.
After you fully execute a speed 2-4 maneuver, you may perform a boost action.
While you boost, you may use the [1 left turn] or [1 right turn] template instead.
While you defend or perform an attack, you may spend 1 energy or 1 non-recurring energy from your equipped upgrade to reroll up to 1 of your dice for each other friendly ship at range 0-1.
After a ship at range 1-2 is dealt a damage card, you may acquire a lock on that ship.
After you fully execute a blue maneuver, you may choose a friendly ship at range 0-1. If you do, that ship removes 1 stress token.
While you perform a primary attack, if the defender is not in your bullseye, roll 1 fewer attack die.
After you perform a slam action, lose 1 energy. Then you may gain 1 ion token to remove 1 disarm token.
If your energy is inactive, you cannot perform the slam action.
How do you grade the relative strength of Joph Seastriker?


the progressive care practices and programs we provide.
We also offer daycare/preschool services in some locations. Through our service offerings, we strive to meet the healthcare and housing needs of senior adults, at whatever level of care they need.


text:
The Role of the University in the Promotion of Human Rights in the Southern Provinces
by admin | Jul 12, 2012 | Articles | 0 comments
A point that must never be forgotten, especially among the ruling elites and those with important administrative positions at the national and provincial levels and in the private and public sectors, is that as a society we have a penchant for self-delusion. Often, we would turn away from the truth. Also, we have been almost completely eradicated from our roots. It?s often said that we have successfully and wisely blended the universal with the local and adapted foreign ideas to the local context. But is this really true? Have we been able to carefully discern the strengths and weaknesses of Western civilization and prudently appropriate the features that are most suitable for our society? To what extent do we really understand ?democracy??and not to say of human rights?
Universities often portray themselves as a model of democratic governance and as accountable and transparent institutions. But have university presidents become too powerful? Have university councils become too dictatorial? Hasn?t the patron-client system flourished in university councils? A recent spate of maleficent acts by university presidents and councils, ranging from Srinakarinwirote to Ramkhamheng Universities, Thammasat to Chulalongkorn Universities, have thrown the picture of universities as a bastion of democracy into serious doubt.
What is the social function of the university? Is it to produce capable graduates for the job market and for life in the capitalist world? If the university is conceived in this way, then it is not radically different from a driving or computer school.
Thai universities are modeled after Western universities. Many if not most of their top-level administrators proclaimed to be Buddhist, but have they ever heard of the University of Nalanda or Takshila University? Oxford and Cambridge are reputedly the two best universities in the UK. But are we aware that William Blake once condemned these two universities for producing graduates who went on to exploit and oppress the peoples in the British colonies? Nehru was a leading figure in the Indian independence movement. However, when he became prime minister he governed India like an English colonist. He was a byproduct of the English education system; Harrow School and the University of Cambridge had brainwashed Nehru to see himself as superior to his fellow countrymen. To what extent have our Oxford-educated prime ministers been better than Nehru? For example, the Pramoj brothers and Abhisit Vejjajiva were Oxford graduates. Anand Panyarachun graduated from Cambridge. Was he a better prime minister than the rest of the British-educated ones? Isn?t Tanin Kraivixien, a law graduate from England, one of the worst prime ministers in Thai history thus far?
This doesn?t mean that all English universities are bad or have always been bad. Tony Judt recalled that during his time at Cambridge, professors there were truly knowledgeable in their areas of expertise and devoted their lives to cultivating the academic excellence of their students. They did not aspire for fame, wealth and political power. But then Judt conceded that today most professors at both Oxford and Cambridge no longer adhered to this selfless position.
The top university in the US is Harvard. Its motto is ?veritas?, which is Latin for truth. I have a Canadian friend who was a Harvard student during the Vietnam War. He didn?t see Harvard professors siding with the forces of veritas. Rather they gravitated toward serving (imperial) power in an ethically blind manner. A good example is Henry Kissinger who was a Harvard professor before he became National Security Advisor and Secretary of State. Kissinger presided over the destruction
of Vietnam, Cambodia, and Laos. Another notorious Harvard professor was the late Samuel Huntington who preached about the clash of civilizations between the West and the Rest and who fanned fear of the ?Muslim? world.
Students from universities throughout the US came out to protest against the Vietnam War. But campus culture at Harvard, Yale and Princeton was much less radical, if not outright reactionary. Can we really say that these universities stand for ?veritas?, justice and human rights?
My Canadian friend turned to Buddhism to overcome the hatred he had for his alma mater. After graduating from Harvard, my friend became professor at a university in Canada. He introduced the study of nonviolence (especially as taught by Gandhi) into the university?s curricula. He told me that every time he returned to Harvard he felt nauseated by its hypocrisy and ethical blindness.
This is no small matter. But how many Thais graduates from Western universities have actually stood steadfastly for truth and justice, sided with the most marginalized and vulnerable social groups, lived in harmony with nature, and dedicated their lives to the social good?
Only a few names come across my mind: Pridi Banomyong from France, Puey Ungphakorn and MC Sittiporn Kridakorn from England. The ruling elites had betrayed both Pridi and Puey, and they ultimately had to spend the rest of their lives in exile.
The next question is why is it very difficult if not impossible for Western-style universities to teach their students to be good, ethical, compassionate and willing to serve the marginalized and vulnerable in society?
Erasmus had already pondered on this question at the time when Protestantism broke with Catholicism. The former accused the latter of exerting absolute control over knowledge, thought, goodness and morality. Therefore, the people had no space for freedom. Protestants began to ask daring questions that Catholics couldn?t answer. In the end, there was a huge pile of questions without answers. This gave rise to the creation of numerous sects, which provided answers to (some of) these questions.
Erasmus stood with Catholicism or the status quo. Nevertheless, he contended that it must be open to the new ideas raised by Protestantism. Erasmus was a significant figure who advocated the reorganization or reform of university education. He saw the importance of creating a college system within the university structure. The university and its colleges would be responsible for providing knowledge to students. But the colleges would have an additional function. They would be responsible for fostering the culture and practices that were necessary for the cultivation of truth, beauty and goodness. For example, the practice of professors and students praying together at the college chapel should be promoted. And so on.
Put another way, the university would lead in the provision of worldly knowledge. But the colleges would provide guidance in terms of ethical and moral development. College professors who served this function were called ?moral tutors? or ?in loco parentis?, which literally means parents of students.
Students were often influenced by their professors. Professors thus could act as the kalyanamitta of students. As long as they still believed in God and His Commandments, both professors and students could flourish as good Christians. As for worldly knowledge, it became a separate sphere.
When the belief in God became increasingly challenged because it could not be scientifically proven, education in the college system simply became ceremonial; that is, it became devoid of spiritual or moral substance. Modern science since Galileo and Newton had turned its back to religious teachings. And with the advent of Cartesian Reason, thought became the judge of all things. For instance, the more one is able to think or the deeper one?s thought the more one is seen as dominant?the more one is seen as superior to other human beings and animals?and even to nature as a whole.
This mainstream way of thinking cannot talk about the good. G.E. Moore of the University of Cambridge had clearly stated that the good cannot be defined, proven and concretely taught. Religion has also become less effective in teaching about the good. Moreover, mainstream thinking became embedded with capitalism and consumerism. Therefore, in mainstream culture people are inclined to be more power-seeking and sociopathic. As such, the excluded and marginalized in society became more vulnerable and oppressed. Nature is increasingly destroyed. Although Marxism is different from capitalism it is still not conducive to santi pracha dhamma.
Mainstream knowledge is also compartmentalized. For instance, medical science is only about the body that is delinked from the mind. The mind is disregarded because it is not a concrete organ. Medical science has also been attached to the interest and profit of transnational corporations. This fact must be fully exposed and critiqued. Another example is that the study of geography is detached from the issue of Earth rights as enshrined in the Earth Charter. We must respect the Earth. We must be humble in our relationship with Mother Earth.
The history that is being taught in the classroom is highly nationalistic. It often legitimates the violence and oppression of the powerful. Often, it provides hagiographies of great men, leading to for instance the uncritical adulation of King Naraesuan. People?s history as popularized by the late Howard Zinn is not taught. Thai history also tends to depict our neighbors and ethnic minorities in the kingdom in a horrendous light. Where are equality and human rights in all this?
I will not go into the details, but it seems that every discipline produces compartmentalized knowledge. Mainstream economics promotes greed. Mainstream political science nurtures hatred. Mainstream science promotes delusion. We must try to understand this in order to understand the role of the university in society.
There are however good signs here and there. We have more alternative sources of education. For instance, Schumacher had trailblazed the study of Buddhist economics. Venerable P.A. Payutto further developed it, and now even mainstream economists like Amartya Sen and Joseph Stiglitz have picked up on it. Stiglitz has warned that runaway capitalism will end up destroying the world. Today, the concept of Gross National Happiness is gaining currency. At the beginning of this year, an international conference on ?The Economics of Happiness? was even held in the US. Glenn Paige has pioneered the study of a nonviolent political science in his book Political Science without Killing. The Mind and Life Institute under the patronage of H.H. the Dalai Lama is also experimenting with Buddhist science. And so on.
So we are back to the question ?what is the role of the university in the promotion of human rights in the southern provinces??
To answer this question, we must first find out whether or not university presidents and especially university councils clearly understand the role of the university. Do they really know how to transform their rhetorical allegiance to truth, beauty and goodness into concrete results? Are they morally courageous and willing to open the university to be a place for the exchange of views and dialogues between academics and the most vulnerable groups in society?
An environment must be created to enable professors and students to serve as one another?s kalyanamitta. They must have mutual respect for and be willing to learn from one another. They must see each other as equals. If this spirit is successfully cultivated, it will be an important foundation on which selflessness and humility can be built.
This spirit can then be spread beyond the university. As our kalyanamitta, individuals from other walks of life, cultures, religions, etc. can broaden our horizon. We must be willing and ready to learn from them with an open mind and heart. From this point, we will be able to develop human rights concretely, not only theoretically.
If we understand that local wisdom is not necessarily inferior to academic knowledge, then this will be an important groundwork for democratic governance, which is not reducible to holding periodic elections and having a government led by the party winning the majority of votes.
Real democracy means absolute power of the people. Economic inequality in society must be drastically lessened.
Basic human rights entail the respect for one another; that is, mutual coexistence, the majority respecting the rights of minorities, unity in diversity, etc.
In the country?s southern provinces, most people are of Malay ethnicity. Most are also Muslims. We must pay special respect to them?to their culture, religion, and language. If we openly admit that the Thai state and ruling elites have long exploited and oppressed these people, there is still a possibility they will come to understand and trust us. This will not be easy. It will require a lot of patience and perseverance. We must first admit that we were in the wrong.
Excerpted from a speech delivered at Thaksin University, Songkhla campus, on 29 June 2012.
Sulak Sivaraksa
credit: photo, from http://www.peace.mahidol.ac.th/th/index.php and http://www.muslim2world.com/index.php?topic=396

redpajama_set_name: RedPajamaCommonCrawl


text:
Tag Archives: Pepino Mosaic Virus
Plant Pathogens, Viruses and viroids
Pepino Mosaic Virus (PepMV)
April 7, 2015 John Chitambar
Pest Rating: B
In January 2015, Tongyan Tian, CDFA Plant Pathologist, detected Pepino mosaic virus(PepMV) in two official tomato samples collected from plants grown in a greenhouse in San Diego County. The virus has been previously reported by researchers who collected non-official samples also from greenhouse tomatoes in Camarillo, Ventura County, California (Ling et al., 2008). The current regulatory status of PepMV and its potential risk of infestation in California are reassessed here for the proposal of a permanent rating.
Background: Pepino mosaic virus was first described in 1980 when it was isolated from pepino (Solanum muricatum) plants that were collected in 1974 from the coastal region of Peru (Jones et al., 1980). Following its original detection, it was not until 1999 that this pathogen was discovered infecting tomato plants grown in many greenhouses in the Netherlands, and later in the early 2000 in the UK, France and Germany. Since then, it initially spread to several countries in Europe but was subsequently eradicated from Croatia, Czech Republic, Norway, and Slovakia (EPPO, 2014a). Soon it was reported worldwide as a significant disease of greenhouse tomatoes in North America, South America, Asia and Africa. In North America, PepMV was first reported in 2001 (French et al., 2001). In their study of a US isolate of PepMV, Maroon-Lango and other scientists first reported the detection of the virus in tomato samples obtained from California (Maroon-Lango et al., 2003).
Pepino mosaic virus belongs to the genus Potexvirus in the family Alphaflexiviridae and RNA viruses group. Two strains of the virus are currently recognized: the pepino strain and the tomato strain (CABI, 2014). Ling (2007) reported that almost all North America PepMV isolates belonged to the European tomato strain. This was confirmed by Ling et al. (2008) in their study of genetic diversity and distribution of PepMV genotypes in North America. These scientists found four major genotypes of PepMV in the North America and only the European (EU) genotype in California.
Hosts: Pepino mosaic virus was originally described on pepino (Solanum muricatum). The natural and experimental host range is limited primarily to Solanceous plants. In addition to pepino, major and main hosts of PepMV are Solanum lycopersicum (tomato), S. melongena (eggplant) and S. tuberosum (potato). Natural infections have not been observed in potato and eggplant crops (EPPO, 2014b). During surveys in Peru the pathogen was found to be naturally present in several wild Lycopersicum species (L. chilense, L. chmielewskii, L. parviflorum, L. peruvianum) (EPPO, 2014b). Ocimum basilicum (basil) is a minor host. A number of wild and weed plants are hosts including, Amaranthus graecizans (tumbleweed/pigweed), A. retroflexus (red-root amaranth), A. viridis (green amaranth), Calendula arvensis, Chenopodium murale, Chrysanthemum segetum, Convolvulus arvensis, C. humilis, Datura stramonium (jimsonweed), Lycopersicon pimpinellifolium (currant tomato), Malva neglecta, M. nicaeensis, M. parviflora, M. sylvestris, Nicotiana glauca (tree tobacco), N. rustica (wild tobacco), Plantago lagopus, P. major, Solanum nigrum (black nightshade), Sonchus asper, S. oleraceus (common sowthistle) and S. tenerrimus (CABI, 2014; EPPO, 2014a, 2014b).
Symptoms: Symptoms may depend on climate conditions and become more distinct under low light conditions. Because of the variable influence of climate, leaf symptoms may be weak and difficult to observe thereby enabling PepMV-infected plants to escape unnoticed. Initial symptoms on tomato plants include small yellow leaf spots. However later, older, mature leaves may exhibit mottling, while top leaves may show slight curling. Fruit may show orange mottling which may differ between trusses (cluster of small stems bearing flowers and fruit) in a single plant. In Peru, young leaves of infected pepino exhibited yellow mosaic, while in the Netherlands affected tomato plants showed yellow spots, mild interveinal chlorosis and sometimes minor malformation (enation) of leaves. In UK, affected plants showed leaf distortion, chlorosis and bubbling of leaf surfaces, stunted and distorted plants (EPPO, 2014b; Mumford & Jones, 2005).
Damage Potential: Tomato plants infected with PepMV do not always result in significant economic damage since fruit symptoms may be absent. On the other hand, fruit setting may be delayed and yield affected. The virus can cause significant crop losses if early infections are not eliminated (EPPO, 2014b). Greenhouse-grown plants are particularly affected by the virus.
Transmission: Pepino mosaic virus is a very contagious pathogen that is transmitted mainly through mechanical means including contaminated tools, hands, clothing, direct plant to plant contact, grafting, cuttings, and seeds. High concentrations of the virus can be present in plant materials, leaves, fruits and roots of infected plants. The virus can be present in the seed coat of immature and mature tomato seeds, but not within the seed (embryo) (Ling, 2008). Damage or death of roots may release the virus in soil and drainage/irrigation water. Experimentally the virus has been shown to be spread by contact with bumble bees (Bombus terrestris, Bombus spp.) used as pollinators of tomato plants (EPPO, 2014b; CABI, 2014).
Worldwide Distribution: Asia: Syria, Turkey; Africa: South Africa; Europe: Austria, Belgium, Bulgaria, Cyprus, Denmark, France, Germany, Greece, Hungary, Ireland, Italy, Lithuania, Netherlands, Poland, Spain, Switzerland, United Kingdom; North America: Canada, Mexico, USA; South America: Chile, Ecuador, Peru (EPPO, 2014a, CABI, 2014).
The recorded absence of Pepino mosaic virus from China, Taiwan, Madagascar, Morocco and Guatemala is considered unreliable (EPPO, 2014a; CABI, 2014).
In the USA it is present in Alabama, Arizona, California, Colorado, Florida, Maryland, Minnesota, Oklahoma, and Texas.
Official Control: Currently, Pepino mosaic virus is on the 'Harmful Organism List' for 45 countries: Albania, Austria, Belgium, Brazil, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Guatemala, Holy See (Vatican City State), Honduras, Hungary, India, Ireland, Italy, Japan, Republic of Korea, Latvia, Lithuania, Luxembourg, Malta, Monaco, Montenegro, Netherlands, New Zealand, Poland, Portugal, Romania, San Marino, Serbia, Slovakia, Slovenia, SPaid, Sweden, Switzerland, Taiwan, Turkey and United Kingdom (PCIT, 2014).
In California, PepMV is currently a Q-rated, quarantine pathogen.
California Distribution: Greenhouse environments within San Diego and Ventura Counties.
California Interceptions: There are no official records of PSbMV detected in incoming plant shipments to California.
The risk Pepino mosaic virus would pose to California is evaluated below.
1) Climate/Host Interaction: Evaluate if the pest would have suitable hosts and climate to establish in California. Score:
Risk is High (3) – PepMV is likely to establish wherever its hosts are cultivated or grow naturally in California. Tomato, the primary host, is a major crop that is widely cultivated in the State.
2) Known Pest Host Range: Evaluate the host range of the pest. Score:
Risk is Low (1) – The host range is limited primarily to Solanaceous plants of which tomato is the primary host that is cultivated over significant acreage throughout California. Eggplant and potato are also affected, although natural infection of both crops has not been reported.
3) Pest Dispersal Potential: Evaluate the natural and artificial dispersal potential of the pest. Score:
Risk is High (3) – Pepino mosaic virus is a very contagious pathogen that is artificially spread mainly through mechanical means including contaminated tools, hands, clothing, direct plant to plant contact, grafting, cuttings, and seeds. It is externally seed-borne, and is also spread through infected planting materials. Experimentally, it has been transmitted by contact with bumble bees. Since symptoms are not always readily recognized, there is the potential for this virus to spread rapidly and unnoticed within nursery greenhouse environments as well as to outdoor field environments.
4) Economic Impact: Evaluate the economic impact of the pest to California using the criteria below. Score:
Risk is High (3) – PepMV could lower tomato yield, value and marketability particularly when infected fruit are symptomatic. Due to its contagious nature soil and irrigation water may become contaminated when infected roots and plant residue in soil are damaged or break down, thereby causing changes in normal cultivation practices.
5) Environmental Impact: Evaluate the environmental impact of the pest on California using the criteria below.
Risk is Low (1) – Several Solanaceous weeds have been experimentally shown to be hosts of PepMV. Natural infestations of such hosts could serve as reservoirs for the pathogen – although this would need to be confirmed through further research. The virus could negatively affect home/urban gardening and cultivation of tomato and eggplant in particular.
Consequences of Introduction to California for Peas seed-borne mosaic virus
Add up the total score and include it here. (Score)
-Low = 5-8 points
-Medium = 9-12 points
-High = 13-15 points
Total points obtained on evaluation of consequences of introduction of PepMV to California = (11).
6) Post Entry Distribution and Survey Information: Evaluate the known distribution in California. Only official records identified by a taxonomic expert and supported by voucher specimens deposited in natural history collections should be considered. Pest incursions that have been eradicated, are under eradication, or have been delimited with no further detections should not be included. (Score)
-Not established (0) Pest never detected in California, or known only from incursions.
-Medium (-2) Pest is widespread in California but not fully established in the endangered area, or pest established in two contiguous suitable climate/host areas.
-High (-3) Pest has fully established in the endangered area, or pest is reported in more than two contiguous or non-contiguous suitable climate/host areas.
Evaluation is Low (-1).
7) The final score is the consequences of introduction score minus the post entry distribution and survey information score: (Score)
Final Score: Score of Consequences of Introduction – Score of Post Entry Distribution and Survey Information = 10
The presence and distribution of PepMV in California agricultural field production and environmental sites is not known. Such information would be obtained through periodic surveys and may affect the current proposed rating. Due to the nature of its transmission, it is possible for the virus to escape detection and spread to non-infected sites.
Based on the evidence provided above the proposed rating for Pepino mosaic virus is B.
CABI 2014. Pepino mosaic virus full datasheet. Crop Protection Compendium. http://www.cabi.org/cpc/datasheet/1695
EPPO, 2014a. Pepino mosaic virus (PEPMV0). New PQR database. Paris, France: European and Mediterranean Plant Protection Organization. http://newpqr.eppo.int
EPPO. 2014b. Pepino mosaic virus. European and Mediterranean Plant Protection Organization. http://www.eppo.int/QUARANTINE/Alert_List/viruses/PEPMV0.htm (Panel review date 2014-03).
Ferguson, G. 2001. Management of Pepino mosaic virus in greenhouse tomatoes – factsheet. Modified November 13, 2013. Ontario Ministry of Agriculture, Food and Rural Affairs. http://www.omafra.gov.on.ca/english/crops/facts/01-017.htm .
French, C. J., M. Bouthillier, M. Bernardy, G. Fergusen, M. Sabourin, R. C. Johnson, C. Masters, S. Godkins and R. Mumford. 2001. First report of Pepino mosaic virus in Canada and the United States. Plant Disease 85:1121.
Jones, R. A. C., R. Koenig, D. E. Lesemann. 1980. Pepino mosaic virus, a new potexvirus from pepino (Solanum muricatum). Annals of Applied Biology, 94(1):61-68
Mumford, R. A., and R. A. C. Jones. 2005. Description of Plant Viruses: Pepino mosaic virus. Association of Applied Biologists 411 (DPV 350 revised version). http://www.dpvweb.net/dpv/showdpv.php?dpvno=411 .
Ling, K. S., 2007. The population genetics of Pepino mosaic virus in North America greenhouse tomatoes. Phytopathology 97:S65.
Ling, K. S., 2008. Pepino mosaic virus on tomato seed: virus location and mechanical transmission. Plant Disease, 92:1701-1705.
PCIT. 2014. USDA Phytosanitary Certificate Issuance & Tracking System. https://pcit.aphis.usda.gov/PExD/faces/ReportHarmOrgs.jsp .
Dr. John J. Chitambar, Primary Plant Pathologist/Nematologist, California Department of Food and Agriculture, 3294 Meadowview Road, Sacramento, CA 95832. Phone: 916-262-1110, plant.health[@]cdfa.ca.gov.
Pepino Mosaic Virusplant pathogensviruses


The problem for members of Churchianity is that they have not been spiritually baptized. That is why they can't baptize others.
1st. The disciples had already been converted to Christ, and their faith had been confirmed by His resurrection. But here let me say that conversion to Christ is not to be confounded with a consecration to the great work of the world's conversion. In conversion the soul has to do directly and personally with Christ. It yields up its prejudices, its antagonisms, its self-righteousness, its unbelief, its selfishness; accepts Him, trusts Him, and supremely loves Him. All this the disciples had, more or less, distinctly done. But as yet they had received no definite commission, and no particular enduement of power to fulfil a commission.
2nd. But when Christ had dispelled their great bewilderment resulting from His crucifixion, and confirmed their faith by repeated interviews with them, He gave them their great commission to win all nations to Himself. But He admonished them to tarry at Jerusalem till they were endued with power from on high, which He said they should receive not many days hence. Now observe what they did. They assembled, the men and women, for prayer. They accepted the commission, and, doubtless, came to an understanding of the nature of the commission, and the necessity of the spiritual enduement which Christ had promised. As they continued day after day in prayer and conference they, no doubt, came to appreciate more and more the difficulties that would beset them, and to feel more and more their inadequacy to the task. A consideration of the circumstances and results leads to the conclusion that they, one and all, consecrated themselves, with all they had, to the conversion of the world as their life-work. They must have renounced utterly the idea of living to themselves in any form, and devoted themselves with all their powers to the work set before them. This consecration of themselves to the work, this selfrenunciation, this dying to all that the world could offer them, must, in the order of nature, have preceded their intelligent seeking of the promised enduement of power from on high. They then continued, with one accord, in prayer for the promised baptism of the Spirit, which baptism included all that was essential to their success. Observe, they had a work set before them. They had a promise of power to perform it. They were admonished to wait until the promise was fulfilled. How did they wait? Not in listlessness and inactivity; not in making preparations by study and otherwise to get along without it; not by going about their business, and offering an occasional prayer that the promise might be fulfilled; but they continued in prayer, and persisted in their suit till the answer came. They understood that it was to be a baptism of the Holy Ghost. They understood that it was to be received from Christ. They prayed in faith. They held on, with the firmest expectation, until the enduement came. Now, let these facts instruct us as to the conditions of receiving this enduement of power.
1. We, as Christians, have the same commission to fulfil. As truly as they did, we need an enduement of power from on high. Of course, the same injunction, to wait upon God till we receive it, is given to us.
2. We have the same promise that they had. Now, let us take substantially and in spirit the same course that they did. They were Christians, and had a measure of the Spirit to lead them in prayer and in consecration. So have we. Every Christian possesses a measure of the Spirit of Christ, enough of the Holy Spirit to lead us to true consecration and inspire us with the faith that is essential to our prevalence in prayer. Let us, then, not grieve or resist Him: but accept the commission, fully consecrate ourselves, with all we have, to the saving of souls as our great and our only life-work. Let us get on to the altar with all we have and are, and lie there and persist in prayer till we receive the enduement. Now, observe, conversion to Christ is not to be confounded with the acceptance of this commission to convert the world. The first is a personal transaction between the soul and Christ relating to its own salvation. The second is the soul's acceptance of the service in which Christ proposes to employ it. Christ does not require us to make brick without straw. To whom He gives the commission He also gives the admonition and the promise. If the commission is heartily accepted, if the promise is believed, if the admonition to wait upon the Lord till our strength is renewed be complied with, we shall receive the enduement.
3. It is of supreme importance that all Christians should understand that this commission to convert the world is given to them by Christ individually.
Everyone has the great responsibility devolved upon him or her to win as many souls as possible to Christ. This is the great privilege and the great duty of all the disciples of Christ. There are a great many departments in this work. But in every department we may and ought to possess this power, that, whether we preach, or pray, or write, or print, or trade, or travel, take care of children, or administer the government of the state, or whatever we do, our whole life and influence should be permeated with this power. Christ says: "If any man believe in Me, out of his belly shall flow rivers of living water" that is, a Christian influence, having in it the element of power to impress the truth of Christ upon the hearts of men, shall proceed from Him. The great want of the Church at present is, first, the realizing conviction that this commission to convert the world is given to each of Christ's disciples as his life-work. I fear I must say that the great mass of professing Christians seem never to have been impressed with this truth. The work of saving souls they leave to ministers.
The second great want is a realizing conviction of the necessity of this enduement of power upon every individual soul. Many professors of religion suppose it belongs especially and only to such as are called to preach the Gospel as a life-work. They fail to realize that all are called to preach the Gospel, that the whole life of every Christian is to be a proclamation of the glad tidings.
A third want is an earnest faith in the promise of this enduement. A vast many professors of religion, and even ministers, seem to doubt whether this promise is to the whole Church and to every Christian. Consequently, they have no faith to lay hold of it. If it does not belong to all, they don't know to whom it does belong. Of course they cannot lay hold of the promise by faith.
A fourth want is that persistence in waiting upon God for it that is enjoined in the Scriptures. They faint before they have prevailed, and, hence, the enduement is not received. Multitudes seem to satisfy themselves with a hope of eternal life for themselves. They never get ready to dismiss the question of their own salvation, leaving that, as settled, with Christ. They don't get ready to accept the great commission to work for the salvation of others, because their faith is so weak that they do not steadily leave the question of their own salvation in the hands of Christ; and even some ministers of the Gospel, I find, are in the same condition, and halting in the same way, unable to give themselves wholly to the work of saving others, because in a measure unsettled about their own salvation.
It is amazing to witness the extent to which the Church has practically lost sight of the necessity of this enduement of power. Much is said of our dependence upon the Holy Spirit by almost everybody; but how little is this dependence realized. Christians and even ministers go to work without it. I mourn to be obliged to say that the ranks of the ministry seem to be filling up with those who do not possess it. May the Lord have mercy upon us! Will this last remark be thought uncharitable? If so, let the report of the Home Missionary Society, for example, be heard upon this subject. Surely, something is wrong.
An average of five souls won to Christ by each missionary of that Society in a year's toil certainly indicates a most alarming weakness in the ministry. Have all or even a majority of these ministers been endued with the power which Christ promised? If not, why not? But, if they have, is this all that Christ intended by His promise? In a former article I have said that the reception of this enduement of power is instantaneous. I do not mean to assert that in every instance the recipient was aware of the precise time at which the power commenced to work mightily within him. It may have commenced like the dew and increased to a shower. I have alluded to the report of the Home Missionary Society. Not that I suppose that the brethren employed by that Society are exceptionally weak in faith and power as labourers for God. On the contrary, from my acquaintance with some of them, I regard them as among our most devoted and self-denying labourers in the cause of God. This fact illustrates the alarming weakness that pervades every branch of the Church, both clergy and laity. Are we not weak? Are we not criminally weak? It has been suggested that by writing thus I should offend the ministry and the Church. I cannot believe that the statement of so palpable a fact will be regarded as an offense. The fact is, there is something sadly defective in the education of the ministry and of the Church.
Even true Christians can get things wrong!
We all need to heed the Apostle Paul's teaching. Timothy was a young Apostle who had been called by the Leaders in the Body of Christ. Yet even he needed to be reminded by the Apostle Paul of what God expected of him in the Ministry.
Do you want to go to heaven when you die? Then quite clearly, God has offered you his only Son in order that you can make a thoughtful choice in this matter. He does not offer you a neutral path. You will have to make a choice, if not the narrow then the wide. You will have to bear fruit in your life, if not good, then bad.
Biblical Interpretation can be a danger!
There are many examples in the Holy Bible where God communicated DIRECTLY with his chosen one. Yes, he used a variety of methods, but he was using them to speak directly to the person concerned.
Sadly, today and in the past, others placed their understanding so as to block the person who God wants to use.
However, our Powerful Lord Jesus helps us to overcome them.
We have a wonderful, powerful God who is perfectly loving, good willing and benevolent. A God who can do anything, even by using a family like ours.
When in 1987, the Rev. Ken Newton of Wholeness Ministries International called me out to the front of a Birmingham Bible Institute class in 2 Pakenham Rd, Birmingham B15 2NE, and prophesied that my ministry would be far wider than I could ever imagine, I never believed it would be as wide as it is today.
This second letter was 5 years before the Holy Spirit helped me to start my first website The Christian Signpost.
As is plainly clear, Our Lord Jesus gifted Ken to help us as a family and me in my ministry.
The second letter mentions my calling to mobilise the Churches for Spiritual Warfare – the battle between the desires of the human Flesh and the desires of the Holy Spirit of God. So far, after 30 years, the Church Doors are firmly closed against me here in Birmingham, England, and elsewhere. My hope is that some may be opened soon.


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The Crown's Claire Foy 'receives £200,000 back payment' after gender pay gap scandal
Main character Foy was earning less per episode than her co-star Matt Smith
Claire Foy arrives at the "The Crown" FYC event at the Saban Media Center at the Television Academy in Los Angeles. Willy Sanjuan/ Invision/AP
British actor Claire Foy is set to receive thousands of pounds in back pay for her part in Netflix series The Crown after it was revealed she was paid less than her male co-star Matt Smith, according to reports.
Last month, producers apologised to Foy, 34, who stars as main character Queen Elizabeth II in the royal saga but was reportedly paid £10,000 (Dh50,400) an episode less than Smith, 35, who stars as her on-screen husband Prince Philip.
Foy, whose credits include The Promise and Wolf Hall, has since been given a £200,000 back payment to make up for the discrepancy, according to the Mail on Sunday.
"The difference in the wages between Matt and Claire in the 20 episodes of the show so far was backdated. It has all been amicably resolved," a source told the newspaper.
Producers had previously defended the decision, saying that Smith's bigger pay packet was down to his six-year stint as Dr Who, a hugely popular show internationally.
He told the Hollywood Reporter: "Claire is one of my best friends and I believe that we should be paid equally and fairly and there should be equality for all. I support her completely and I'm pleased that it was resolved and they made amends for it because that's what needed to happen."
Claire Foy, right, and Matt Smith in a scene from series two of The Crown. Robert Viglasky / Netflix via AP
The Crown is believed to be the most expensive television series ever made at a reported £9.42m per episode. However, writer Peter Morgan has said this figure has been exaggerated.
Foy and Smith will be replaced for series three by British actors Olivia Colman and Tobias Menzies respectively as the show moves away from the Queen's early reign.
At a recent panel event, Foy said the experience had led her to have "the most extraordinary revelation about myself and womankind" in answer to a question about the pay gap.
"You asking me that question is probably the first time in my career that anybody has ever asked me about being a woman in this industry, or just being a woman in general, and to me I find that amazing," she told reporters at the Hollywood event.
[ Netflix royal drama 'The Crown' apologises over pay discrepancy ]
[ 5 times actresses were paid less than their male co-stars ]
[ The Crown's Queen Elizabeth paid less than her prince ]


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Tennessee Overhill
Tennessee Vacation Guide
Hiking & Wildlife in the Smokies Safety Video
Things to See & Do (All)
Uniquely Overhill
Outdoors-Nature
Fishing-Guides
Native Blooms
Parks, Forests & Natural Sites
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Savannah Oaks Winery & Gift Shop
Savannah Oaks Winery sits on a farm that has been in the same family for generations. A cantilever barn, built by 40 men in one day during 1861, holds the tasting room and gift shop. Muscadine and Scuppernong Grapes are grown in their own vineyards. Reservations needed for special events.
Hours: Mon-Thu, 10:00-6:00; Fri-Sat, 10:00-7:00; Sun, 1:00-6:00
Savannah Oaks Winery
1817 Delano Road
Delano, TN 37325
https://www.facebook.com/pages/Savannah-Oaks-Winery/138273117401
Tagged: Delano, Farm, Gift Shop, Grape Stomp, Muscadine Grapes, Scuppernong Grapes, Tasting Room, Winery
Englewood Textile Museum
Recounts the history of the many textile mills in Englewood and the women who worked the mills. See Miss Ella's handmade trousseau and learn why she never wore it. Tour the restored "Little White House," birthplace of an early mill owner. Shop in the "Company Store," a different kind of gift shop. Admire the hand painted murals throughout the downtown. Educational programs and classes take place year round. All located in a town that grew out of three turn-of-the century mill villages. To recapture the "feel" of this historic southern town, be sure to drop by during "Englewood Celebrates," held in June.
No Admission
Hours: Mon-Sat, 10:00-5:00
101 South Niota Road
Englewood, TN 37329
Tagged: Company Store, East Tennessee, Educational Programs, Englewood, Gift Shop, heritage, Museum, Textile Museum, Textiles
Throughout much of the 18th century the Overhill Towns of Tanasi and Chota were important Cherokee centers – beloved towns where Cherokees gathered for important councils and religious ceremonies. The State of Tennessee takes its name from Tanasi. After the waters of Tellico Lake covered the sites of Chota and Tanasi in 1979, memorials were placed at the edge of the lake near the site of each town.
Information & Directions to the Chota / Tanasi Memorials
Hours: Daily, Sunrise to Sunset
576 Highway 360
Vonore, TN 37885
www.sequoyahmuseum.org
Tagged: American Indian, American Indian Memorial, Cherokee, Chota, Gift Shop, Oconostota, Sequoyah, Tanasi, Tellico Lake
Fort Loudon Tour 1-19
01/19/2020- 01/19/2020 (1:00 PM – 2:30 PM)
Tellico Blockhouse Tour 1-22
Get to Know Your State Park
"The Story Behind the Tree"
02/01/2020- 02/01/2020 (11:00 AM – 1:00 PM)
Athens Daddy/Daughter Dance
Athens Daddy/Daughter Dance 2-8
Valentines Extravaganza 2020
02/08/2020- 02/08/2020 (6:30 PM – 10:00 PM)
MACA Nashville Legacy
Cherokee Language Classes Refresher
MACA Missoula Children's Theater – The Emperor's New Clothes 3-6
Tellico Blockhouse Tour 1-22 (01/22/2020 – 01/22/2020)
Get to Know Your State Park (01/24/2020 – 01/24/2020)
"The Story Behind the Tree" (02/01/2020 – 02/01/2020)
Athens Daddy/Daughter Dance (02/07/2020 – 02/07/2020)
Athens Daddy/Daughter Dance 2-8 (02/08/2020 – 02/08/2020)
Valentines Extravaganza 2020 (02/08/2020 – 02/08/2020)
MACA Nashville Legacy (02/29/2020 – 02/29/2020)
Cherokee Language Classes Refresher (03/02/2020 – 03/02/2020)
MACA Missoula Children's Theater – The Emperor's New Clothes 3-6 (03/06/2020 – 03/06/2020)
Tennessee Overhill Heritage Association
Etowah, TN 37331
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Onze webshop verkoopt medicatie voor impotentie in Nederland en bezorgt wereldwijd https://zonder-voorschrift.com/ Viagra, Cialis en Levitra, wat allen wereldwijd bekende namen zijn.
Listed below are the address, nightly rates, star rating (if applicable) and amenities of Hampton Inn Yazoo City in Yazoo City. Also, included is a picture, a description of the hotel and its rooms. Book a reservation for Hampton Inn Yazoo City in Yazoo City Mississippi, today or in advance.
Find the hotel rates for Hampton Inn Yazoo City, Yazoo City, Mississippi.
Approximate Nightly Rates starting at $101.15.
The Hampton Inn In Yazoo City occupies a modern town with an amazing range of shopping dining and convenience. The diverse culture of Yazoo County has produced a range of notable residents including the late comedian Jerry Clower. authors the late Willie Morris and Zig Zigler. actress Stella Stevens and blues singer Gatemouth Moore and Jack Owens. Whatever your interest - history the arts shopping recreation or commerce - you*ll find what you*re looking for where the Delta meets the hills in Yazoo County. So come stay with us at the Hampton Inn Yazoo City - Mississippi*s haven of shopping and convenience.


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Q: Using mathematical induction to prove divisibility of a number and its binary reverse by 3. Prove using Mathematical induction that,
For any given number $X$ : $X$ is divisible by 3 if and only if $r(X)$ is divisible by 3.
where $r(X)$ is value of the reverse of $X$ treated as a binary string (with no leading zeroes)
e.g. if $X$ is 10 (decimal) i.e. 1010 (binary) then $r(X)$ is 0101 (binary) i.e. 5 (decimal)
e.g. if $X$ is 19 (decimal) i.e. 10011(binary) then $r(X)$ is 11001 (binary) i.e. 25 (decimal)
[$Note:$ Binary string of $X$ cannot have leading zeroes. So all the zeroes, if any, are truncated before reversing the binary string.]
I tried doing it(inducting on the bit length of binary of $X$) but in the case when $X_k$
 $mod$ 3 = $1$ and $X_{k+1}$ = $X_k1$, I got stuck because $r(X_{k+1})$ = $1r(X_k)$ = $2^k$ + $r(X_k)$ and since $X_{k+1}$ = $2X_k$ + $1$ $\implies X_{k+1}$ $mod$ 3 = $0$ $i.e.$ I need to prove that $r(X_{k+1})$ is also divisible by 3 which I can't(I have tried taking two cases for k being even and odd but that didn't work).
Please help!

A: If you are familiar with the divisibility test for $11$ in base $10$, then we can build on that.
Let $X$ have binary expansion $\sum_{k=1}^n b_k2^k.$  Since $2\equiv -1 \pmod{3},$, we have
$$X \equiv \sum_{k=1}^n b_k(-1)^k \pmod{3}.$$
So a test for divisibility by $3$ is to find the alternating sum of the digits.  If that alternating sum is $0 \pmod{3}$, then so is the alternating sum of the digits of the reverse, eh?
It doesn't use induction, unless you use induction to prove the divisibility test, so maybe this isn't what you're looking for.

A: I suggest proving something slightly stronger—namely, that $r(X_k) \equiv X_k \pmod 3$ when $k$ is odd, while $r(X_k) \equiv -X_k \pmod 3$ when $k$ is even. (This is closely related to the divisibility test described in B. Goddard's answer.) This includes your desired equivalence as a special case, and I think the inductive step will be more evident to you.
(By the way, nothing changes in this problem even if we assume that integers can have leading zeros.)

A: Let the binary representation of your number be $ab$ and its reverse is $ba$
Then these two numbers in decimal are $a+2b$ and $b+2a$ which are congruent to $a-b$ and $b-a$, $\mod (3)$ respectively.
For the case of $abc$ and $cba$, we get $a+2b+4c$ and $c+2b+4a$ which are congruent to $a-b+c$ and $c-b+a$
 $ \mod (3)$, respectively.
Since powers of $2$ are congruent $\pm 1$, $ \mod (3)$ the decimal values of a  binary number and its revers are either identical or opposite $\mod (3)$.
Thus if one is a multiple of $3$, the other is also a multiple of $3$


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Britain, Italy, Japan Combine Forces to Build Fighter Jets
FILE - A F-2 fighter of Japan Air Self-Defense Force is seen at Tsuiki base, Fukuoka prefecture, southern Japan in March 2010.
Britain, Italy and Japan are combining their resources to create a new generation of fighter jets.
"We are announcing the Global Combat Air Program (GCAP) – an ambitious endeavor to develop a next-generation fighter aircraft by 2035," the three nations said in a joint statement.
Other countries, including the United States, may also join the collaborative jet-building effort.
"We have begun important collaboration through a series of discussions on autonomous systems capabilities, which could complement Japan's next fighter program among other platforms," the United States and Japan said in a joint statement.
The announcement of the development of the fighter jet comes as Russia, China and North Korea have become increasingly aggressive.
Australia, US to Reinforce Defense Ties With Japan
South Korea Scrambles Jets After Chinese, Russian Warplanes Approach
NATO to Discuss Beefing Up Defenses Across Europe
US Air Force Chief of Staff Tells VOA 'We've Got to Change'


We've already noted seven Halloween videos to help get you into a festive mood. If you're looking for even more horror, check out "Kept Man", a short film created by online video vet Wilson Cleveland.
Cleveland stars in "Kept Man" as a man who meets a new boyfriend, played by Hartley Sawyer. Throughout the nine-minute film, Sawyer's character slowly becomes unhinged, which leads up to a violent denouement.
"Kept Man" may seem like a romantic drama at first, though its dark tone foreshadows its bloody conclusion from the start. "Most people will (and should) see it's a twisty horror thriller," Cleveland told Tubefilter.