text:
\section{Conclusion}
\label{sec:conclusions}
A support ticket escalation can be a costly experience for software organizations. Our research described in this paper attempts to identify indicators of when a particular support ticket may be escalated using three sentiment analysis tools: NLTK, SentiStrength, and Watson NLU. Using these tools we analyzed a very large support ticket database with assistance from our industrial partner, IBM, and found that there are notable differences between the sentiment in the escalated support tickets and the sentiment in the non-es\-ca\-la\-ted support tickets. These preliminary results provide us with confidence in our belief that we will be able to develop a model, based on sentiment analysis, which provides the ability for support analysts to focus on support tickets that have the potential to enter the escalation process.
\section{Data and Processing}
\label{sec:data}
The repository data used in this research consisted of numerous support tickets from IBM.  Each support ticket is an artifact that records a series of emails between a customer and support analysts for a particular case. The workload in processing the support tickets involved splitting each plain-text file into individual emails and then manually labelling each email as either from support to customer (support) or customer to support (customer). The annotation process revealed that some support tickets contained no emails, so these support tickets were subsequently removed. Additionally, some entries within a particular support ticket were not an email and were subsequently removed from the dataset. Details on this dataset, its size and its pre-processing are provided in what follows.
\subsection{The Data: Support Tickets}
A support ticket is an artifact that records a series of emails between a customer and the various entities of the company's support team regarding  this particular customer's issue. A support ticket exists for the entire duration of a particular customer's issue, which can last several days, weeks, months, or even longer. The emails contained within the support ticket are either sent from a customer to support (customer) or from support to a customer (support). Together, these two types of emails form the entirety of the interactions that will be investigated by this research. A shortcoming of these support tickets is that they do not record or transcribe phone calls, thus leaving a notable information gap when customers prefer over-the-phone support.

At the time of this research, support tickets in IBM's system were stored as plain-text files. This introduced two problems that concerned this research: 1) how to separate emails from each other; and 2) how to classify who sent a particular email, the customer or support. The first problem was solved with a Python parsing script that took advantage of notable patterns in appended emails, which allowed the plain-text of each support ticket to be separated into distinct emails. However, the second problem was not as easily solved due to inconsistencies across support teams as to how emails were documented and stored in the support ticket. To solve this issue, our research team manually annotated the support tickets to classify each of them as being customer or support.
\subsection{Manual Annotation}
\label{ssec:manual_annotation}
The goal of manually annotating each email was to classify each email as either from a customer or from support. However, to further complicate the process of annotating these emails, some system messages attached to the support tickets looked similar to an email, so the annotation process also involved removing these system messages from the dataset. After the manual annotation process was completed, there were 655 support tickets containing a total of 10172 emails.  Once all of the pre-pro\-cess\-ing was complete, the emails could now be analyzed using the sentiment analysis tools.
\subsection{Dividing the Data}
\label{ssec:dividing_data}
The output of the manual annotation in Section \ref{ssec:manual_annotation} included 10172 emails in 655 support tickets; however, the data had to be divided into subsets for the purpose of this research.
The first division of the data occurs when each email is classified as either a customer email or a support email, as described in Section \ref{ssec:manual_annotation}. This initial division allows the sentiment values that were being tracked during a support ticket to be mutually exclusive as to who is represented: a customer or support.

The second division occurs when the customer and support datasets were further divided into "escalated" and "non-es\-ca\-la\-ted" subsets, thus creating a total of four subsets. In summary, the emails were divided into the following datasets:

\begin{enumerate}
\item customer escalated,
\item customer non-es\-ca\-la\-ted,
\item support escalated, and
\item support non-es\-ca\-la\-ted,
\end{enumerate}
At this point, each email\footnote{Please note that while our aim is to study an entire support ticket entity for the purpose of this paper we used individual emails as the unit of analysis. Section \ref{sec:future_work} discusses this limitation in further detail.}, from each subset, was individually fed into the sentiment analysis APIs, described in Section \ref{ssec:sent_tools}, and the results from each API were then cached for further analysis.
\section{Discussion}
\label{ssec:discussion}
This Section provides a discussion and further analysis of the data from Section \ref{ssec:sent_emo_data_results}.

\subsection{NLTK}
The NLTK results in Table \ref{tab:nltk_avg} show considerable differences between the escalated and non-es\-ca\-la\-ted datasets. The biggest difference can been seen by looking at the average of the customer compound sentiment value for escalated and non-es\-ca\-la\-ted datasets. In particular, the customer compound sentiment value for non-es\-ca\-la\-ted is 39\% higher than escalated, which is a significant difference. Furthermore, the customer positive sentiment for non-es\-ca\-la\-ted is 19\% higher than escalated. Interestingly, the customer negative sentiment value is actually lower for escalated than non-es\-ca\-la\-ted, while the customer neutral sentiment is only slightly higher. The overall averages for support (compound, positive, neutral, and negative) datasets share similar statistical trends, albeit with slightly less magnitude. Moreover, the difference testing in Table \ref{tab:nltk_norm_diff} shows, with high confidence (\(p < 0.001\)), that the escalated and non-es\-ca\-la\-ted datasets are indeed different.  The exceptions include the customer negative sentiment (\(p = 0.036273\)), support compound sentiment (\(p = 0.001383\)), and support negative sentiment (\(p = 0.176728\)) datasets. However, two of aforementioned exceptions maintain \(p < 0.05\), thus our confidence remains high. These statistically significant differences provide supporting evidence to help answer our RQ by showing that the escalated and non-es\-ca\-la\-ted datasets are identifiably distinct.

\subsection{SentiStrength}
The analysis of the sentiment data provided by SentiStrength in Table \ref{tab:sentistrength_avg} shows a remarkable difference between the escalated and non-es\-ca\-la\-ted datasets. For the customer datasets, the overall sentiment for the non-es\-ca\-la\-ted is 149\% higher than escalated. Similarly, for the support datasets, the overall sentiment for the non-es\-ca\-la\-ted is 75\% higher than escalated. From an arithmetic mean point of view, these two sets are clearly distinct. Furthermore, the difference testing in Table \ref{tab:senti_norm_diff} shows, with considerable confidence (\(p < 0.05\)), that there are statistically significant differences between the escalated and non-es\-ca\-la\-ted datasets for the overall and positive values for both customer and support datasets. In fact, the customer positive, support overall, and support positive all maintain \(p < 0.00001\), while customer overall is slightly higher with \(p < 0.01\). However, the same conclusion cannot be applied to the negative sentiment datasets for both customer (\(p = 0.051594\)) and support (\(p = 0.251714\)).

Nonetheless, the results from SentiStrength corroborate our findings from the NLTK results. As such, our confidence in answering our RQ has increased, as we have shown via two independent tools that there are strong statistical indications that the escalated and non-es\-ca\-la\-ted datasets are different.

\subsection{Watson NLU}
Analysis of the average Watson NLU emotions and sentiment in Table \ref{tab:wat_avg} show some interesting details. First, the customer disgust for non-es\-ca\-la\-ted is 25\% less than escalated, while the customer joy is 10\% more. Second, the overall customer sentiment is 61\% higher for non-es\-ca\-la\-ted versus escalated. This exceptionally large difference for sentiment between escalated and non-es\-ca\-la\-ted is a very telling indication that there are considerable differences between the two datasets. Third, the support sadness is 12\% lower for non-es\-ca\-la\-ted versus the escalated. Finally, the support sentiment is 41\% greater for non-es\-ca\-la\-ted. These statistics provide additional evidence in our continuing confidence to answer our RQ. One can speculate that not only can we identify differences between the escalated and non-escalated datasets, but we can observe that customers who have a support ticket escalated display more disgust, less joy, and a lower overall sentiment than those that are not escalated and support analysts dealing with escalated support tickets display more sadness and a lower overall sentiment. The details of why these particular emotions are more prominent are potential areas for future research, but further speculation can also be left to the reader's imagination. In addition, the Mann-Whitney tests in Table \ref{tab:e_norm_diff} reveal that five of the twelve subsets have a significant difference between them, with p-values less than 0.05. The customer subset has three emotions, anger, disgust, and sentiment, each of which have significant differences. The support subset has two emotions, sadness and sentiment, that have significant differences. These results highlight the importance of using difference testing in addition to standard arithmetic mean, as it can reveal otherwise undiscoverable information in research such as this.

\subsection{Answering Our RQ}
Based on the results of the three independent sentiment analysis tools, we have shown significant statistical differences between the escalated and non-es\-ca\-la\-ted datasets. Thus, we can confidently provide a concrete answer to our RQ: the sentiment in an escalated support ticket is indeed significantly different than the sentiment in a non-es\-ca\-la\-ted support ticket. We can then turn our focus to the \textit{implications} of exactly how we can use this research to further advance our understanding of predicting if, and when, a support ticket may be escalated.

Previous work by Blaz and Becker\cite{7832903} developed a tool that classifies the polarity of sentiment in IT support tickets. However, this tool is not yet perfect, as it continues to misclassify neutral sentiment as negative sentiment. Thus, the maturity of using sentiment analysis tools in the support ticket domain is an immature field and certainly merits future work. Our research provides additional novel contributions in two directions. First, we provide positive results of how off-the-shelf sentiment analysis tools \textit{can} be used in the software engineering domain. Second, we have shown that there exists the \textit{potential} in using  sentiment analysis to predict the escalation of a support ticket. Our preliminary research simply demonstrates the motivation and justification for additional research into methods that use sentiment analysis to differentiate and predict support tickets at high risk of escalation.

In particular, it should be noted that each of the metrics utilized in this research are document-level assessments that calculate values based on the entire plain-text sent to the API. For the purpose of this research, each "document" is an email that is processed. Therefore, after being processed by the sentiment analysis tools each email is assigned a particular set of metrics. However, it is important to note that the overall goal of our research is to analyze a particular support ticket and not individual emails. Therefore, some sort of averaging mechanism is required to summarize each support ticket, since any single support ticket could have contained multiple emails. Therefore, further research is required in order to provide analysis at a support ticket level. In addition, this preliminary research was performed in order to assess whether sentiment analysis tools could be used in order to predict the escalation of a particular support ticket. Thus, further research is required in order to develop a model that will enable support analysts to predict, in real-time, as to when a particular support ticket may be escalated. We discuss these two topics in further detail in the next section.
\section{Sentimental \& Emotional Data Results}
\label{ssec:sent_emo_data_results}
To address our RQ, we analyzed the customer escalated, customer non-es\-ca\-la\-ted, support escalated, and support non-es\-ca\-la\-ted datasets using three sentiment analysis tools: NLTK, Sentistrength, and Watson NLU. After compiling the data from the tools, we first computed the average of each tool's output for all datasets. In addition, the difference between each average was calculated. Respectively, the averages and differences of the datasets can be found for NLTK, SentiStrength, and Watson NLU in Tables \ref{tab:nltk_avg}, \ref{tab:sentistrength_avg}, and \ref{tab:wat_avg}. Further discussion of these results shall appear in Section \ref{ssec:discussion}.

\begin{table}
\caption{NLTK - Averages}
\label{tab:nltk_avg}
\begin{tabular}{l l l l}
    \hline
    \textbf{Sentiment} & \textbf{Escalated}
    & \textbf{Non-Escalated} & \textbf{\%-age Change} \\
    \hline\hline
        \multicolumn{4}{c}{Customer Analysis}
    \\\hline\hline
        Compound & 0.278301 & 0.387733 & 39\%
    \\\hline
        Positive & 0.089594 & 0.106865 & 19\%
    \\\hline
        Neutral & 0.871785 & 0.858120 & -2\%
    \\\hline
        Negative & 0.032771 & 0.035022 & 7\%
    \\\hline\hline
        \multicolumn{4}{c}{Support Analyst Analysis}
    \\\hline\hline
        Compound & 0.457937 & 0.544874 & 19\%
    \\\hline
        Positive & 0.102066 & 0.114807 & 12\%
    \\\hline
        Neutral & 0.872724 & 0.857422 & -2\%
    \\\hline
        Negative & 0.024706 & 0.026055 & 5\%
    \\\hline
\end{tabular}
\end{table}

\begin{table}
\caption{SentiStrength - Averages}
\label{tab:sentistrength_avg}
\begin{tabular}{l l l l}
    \hline
    \textbf{Sentiment} & \textbf{Escalated}
    & \textbf{Non-Escalated} & \textbf{\%-age Change} \\
    \hline\hline
        \multicolumn{4}{c}{Customer Analysis}
    \\\hline\hline
        Overall & 0.080222 & 0.200000 & 149\%
    \\\hline
        Positive & 1.964517 & 2.230769 & 14\%
    \\\hline
        Negative & -1.619253 & -1.683077 & 4\%
    \\\hline\hline
        \multicolumn{4}{c}{Support Analyst Analysis}
    \\\hline\hline
        Overall & 0.237968 & 0.414802 & 74\%
    \\\hline
        Positive & 2.242132 & 2.435456 & 9\%
    \\\hline
        Negative & -1.647794 & -1.614458 & -2\%
    \\\hline
\end{tabular}
\end{table}

\begin{table}
\caption{Watson NLU - Averages}
\label{tab:wat_avg}
\begin{tabular}{l l l l}
    \hline
    \textbf{Emotion} & \textbf{Escalated} & \textbf{Non-Escalated}
    & \textbf{\%-age Change} \\
    \hline\hline
        \multicolumn{4}{c}{Customer Analysis}
    \\\hline\hline
        Anger & 0.074828 & 0.070261 & -6\%
    \\\hline
        Disgust & 0.042419 & 0.031909 & -25\%
    \\\hline
        Fear & 0.060669 & 0.057919 & -5\%
    \\\hline
        Joy & 0.166685 & 0.184073 &10\%
    \\\hline
        Sadness & 0.208549 & 0.206040 & -1\%
    \\\hline
        Sentiment & 0.120708 & 0.194327 & 61\%
    \\\hline\hline
        \multicolumn{4}{c}{Support Analyst Analysis}
    \\\hline\hline
        Anger & 0.063902 & 0.059661 & -7\%
    \\\hline
        Disgust & 0.034842 & 0.032092 & -8\%
    \\\hline
        Fear & 0.055817 & 0.054628 & -2\%
    \\\hline
        Joy & 0.187015 & 0.199418 & 7\%
    \\\hline
        Sadness & 0.185572 & 0.163391 & -12\%
    \\\hline
        Sentiment & 0.258169 & 0.364316 & 41\%
    \\\hline
\end{tabular}
\end{table}

\subsection{Normality Testing}
\label{ssec:normality}
In choosing which difference test to use, the normality of the data contributes to that decision. Normal data allow for stronger conclusions to be drawn using more powerful tests \cite{D'Agostino:1986:GT:19293}. The results of applying the D'Agostino-Pearson\cite{d1973tests} normality test to our data can be found in Tables \ref{tab:nltk_norm_diff}, \ref{tab:senti_norm_diff}, and \ref{tab:e_norm_diff} for each of the sentiment analysis tools. The results of the D'Agostino-Pearson normality test indicate our data were mostly non-normal, as the resulting p-values were considerably less than 0.05.

\begin{table}[t]
\caption{NLTK - Normality \& Difference Testing}
\label{tab:nltk_norm_diff}
\begin{tabular}{l l l l}
    \hline
        \multirow{2}{*}{\textbf{Sentiment}}
        & \multicolumn{2}{|c|}{\nlcell{c}{\textbf{Pearson P-Value}}}
        & \multirow{2}{*}{\nlcell{c}{\textbf{Mann-Whitney}\\ \textbf{2-Tailed P-Value}}}
    \\\cline{2-3}
        & \multicolumn{1}{|c|}{\textbf{Escalated}} & \multicolumn{1}{|c|}{\textbf{Non-Escalated}} &
    \\\hline\hline
        \multicolumn{4}{c}{Customer Analysis}
    \\\hline\hline
        Compound & 0.000000 & 0.000000 & 0.000012 \quad \(< 0.05\)
    \\\hline
        Positive & 0.000000 & 0.000000 & 0.000002 \quad \(< 0.05\)
    \\\hline
        Neutral & 0.000000 & 0.000000 & 0.000147 \quad \(< 0.05\)
    \\\hline
        Negative & 0.000000 & 0.000000 & 0.036273 \quad \(< 0.05\)
    \\\hline\hline
        \multicolumn{4}{c}{Support Analyst Analysis}
    \\\hline\hline
        Compound & 0.000000 & 0.000000 & 0.001383 \quad \(< 0.05\)
    \\\hline
        Positive & 0.000000 & 0.000000 & 0.000004 \quad \(< 0.05\)
    \\\hline
        Neutral & 0.000000 & 0.000000 & 0.000015 \quad \(< 0.05\)
    \\\hline
        Negative & 0.000000 & 0.000000 & 0.176728
    \\\hline
\end{tabular}
\end{table}

\begin{table}[t]
\caption{SentiStrength - Normality \& Difference Testing}
\label{tab:senti_norm_diff}
\begin{tabular}{l l l l}
    \hline
        \multirow{2}{*}{\textbf{Sentiment}}
        & \multicolumn{2}{|c|}{\nlcell{c}{\textbf{Pearson P-Value}}}
        & \multirow{2}{*}{\nlcell{c}{\textbf{Mann-Whitney}\\ \textbf{2-Tailed P-Value}}}
    \\\cline{2-3}
        & \multicolumn{1}{|c|}{\textbf{Escalated}} & \multicolumn{1}{|c|}{\textbf{Non-Escalated}} &
    \\\hline\hline
        \multicolumn{4}{c}{Customer Analysis}
    \\\hline\hline
        Overall & 0.000000 & 0.000000 & 0.008909 \quad \(< 0.05\)
    \\\hline
        Positive & 0.000000 & 0.003232 & 0.000001 \quad \(< 0.05\)

    \\\hline
        Negative & 0.000000 & 0.000000 & 0.051594
    \\\hline\hline
        \multicolumn{4}{c}{Support Analyst Analysis}
    \\\hline\hline
        Overall & 0.000000 & 0.000000 & 0.000004 \quad \(< 0.05\)
    \\\hline
        Positive & 0.000000 & 0.000010 & 0.000005 \quad \(< 0.05\)
    \\\hline
        Negative & 0.000000 & 0.000000 & 0.251714
    \\\hline
\end{tabular}
\end{table}

\begin{table}[t]
\caption{Watson NLU - Normality \& Difference Testing}
\label{tab:e_norm_diff}
\begin{tabular}{l l l l}
    \hline
        \multirow{2}{*}{\textbf{Emotion}}
        & \multicolumn{2}{|c|}{\nlcell{c}{\textbf{Pearson P-Value}}}
        & \multirow{2}{*}{\nlcell{c}{\textbf{Mann-Whitney}\\ \textbf{2-Tailed P-Value}}}
    \\\cline{2-3}
        & \multicolumn{1}{|c|}{\textbf{Escalated}} & \multicolumn{1}{|c|}{\textbf{Non-Escalated}} &
    \\\hline\hline
        \multicolumn{4}{c}{Customer Analysis}
    \\\hline\hline
        Anger & 0.000000 & 0.000000 & 0.017498 \quad \(< 0.05\)
    \\\hline
        Disgust & 0.000000 & 0.000000 & 0.002044 \quad \(< 0.05\)
    \\\hline
        Fear & 0.000000 & 0.000000 & 0.117554
    \\\hline
        Joy & 0.000000 & 0.000000 & 0.828245
    \\\hline
        Sadness & 0.000000 & 0.000000 & 0.646006
    \\\hline
        Sentiment & 0.000000 & 0.000982 & 0.006840 \quad \(< 0.05\)
    \\\hline\hline
        \multicolumn{4}{c}{Support Analyst Analysis}
    \\\hline\hline
        Anger & 0.000000 & 0.000000 & 0.391951
    \\\hline
        Disgust & 0.000000 & 0.000000 & 0.540574
    \\\hline
        Fear & 0.000000 & 0.000000 & 0.434205
    \\\hline
        Joy & 0.000000 & 0.000000 & 0.856163
    \\\hline
        Sadness & 0.000000 & 0.000000 & 0.003692 \quad \(< 0.05\)
    \\\hline
        Sentiment & 0.000000 & 0.000000 & 0.000000 \quad \(< 0.05\)
    \\\hline
\end{tabular}
\end{table}

\subsection{Difference Testing with Mann-Whitney}
To further address our RQ, difference testing was applied to all datasets comparing each escalated dataset to the corresponding non-es\-ca\-la\-ted dataset. Due to the non-nor\-mal\-ity of our datasets, shown in the Section \ref{ssec:normality}, the Mann-Whitney\cite{mann1947} difference test was chosen using 2-tailed p-value thresholds. The results of the difference testing revealed that there are statistically significant differences between the escalated and non-es\-ca\-la\-ted datasets across all three of the sentiment analysis tools used for this research.

\section{Future Work}
\label{sec:future_work}
Previous research has shown that leading sentiment analysis tools provide decent results in the software engineering domain; although, it has also shown that the results are not highly accurate\cite{7332508}. That being said, the sentiment analysis tools used in our research have provided us with sufficient evidence in order to confidently answer our RQ and have thus served their purpose. In order to provide further supporting evidence to our RQ we need to perform additional sentiment analysis using tools that were developed specifically for the software engineering domain, IT support tickets in particular, as described in other relevant works\cite{7832903}\cite{Gachechiladze:2017:ADC:3102962.3102966}\cite{benchmark-novielli}.

As previously mentioned, this paper focused on the sentiment of individual emails across the datasets. However, this paper does not examine the sentiment of a particular support ticket, which may contain multiple emails. Therefore, we propose an additional research question: Are the \textit{trends} in the sentiment of an escalated support ticket significantly different than the \textit{trends} in the sentiment of a non-es\-ca\-la\-ted support ticket? In order to answer this research question we must further develop a solution that will enable us to analyze \textit{how} the sentiment \textit{change} over the lifespan of a particular support ticket, which we shall call the sentimental tendency of a support ticket. The tendency value shall take into consideration the highs, lows, and trends of sentiment across all emails in a particular support ticket to attempt to capture a certain subset of sentimental situations. In conjunction, a similar research question can also be posed to study the trends of the various emotions over the lifespan of a particular support ticket.

Furthermore, the ultimate goal is to develop a model that can reliably predict if, and when, a support ticket may be escalated. Given the results described in this paper, we believe that a model \textit{can} be developed; however, at this time we do not necessarily know \textit{how} to develop such a model. That being said we propose an additional query to investigate \textit{how} the extracted sentiment and emotions from a support ticket can be utilized in assisting the prediction and reaction to potential escalations. This subsequent research will harness both sentiment analysis and machine learning tools to provide insight as to how such a model may be developed and used in industry standard tools in order to provide escalation prediction to industry support analysts.
\section{Introduction}
Companies selling software products often provide support for their products in the form of person-to-per\-son interaction, whether that be through email, live text chat, or over-the-phone conversations. When a customer contacts a company's support team a support ticket is opened that represents the issue brought forth by this particular customer. A support ticket exists to record all communication between the customer and the support team for information management and historical tracking purposes. Companies, looking to track their customers beyond individual support tickets, may maintain a customer database with various business metrics. These business metrics, such as products purchased, date of product purchases, and average amount purchased per year, are useful for calculating return on investments and the likelihood of product re-purchases. These types of business metrics, although fruitful for a company looking to be successful \cite{businessmetrics}, ignore the sentiment of customers both from a historical and real-time perspective.

In our research, we investigate the sentiment of customers through the conversations they have on their support tickets using leading sentiment analysis tools. A support analyst, provided with the sentiment of a customer, could adapt to a support situation based on the sentiment of the customer. Support ticket escalation is a process offered by some support organizations and occurs when a customer asks for additional help resolving their issue because they feel the current assistance they are receiving is inadequate in some way \cite{ecritsmont}. An escalation can cost a company an unnecessary amount of resources \cite{predsftwrescling}\cite{Sheng:2014:CLD:3056241.3056255}, and may involve a support analyst performing additional non-cus\-tomer-orien\-ted work. In this paper we present analysis that highlights the potential of using sentiment analysis in order to predict an escalation in support tickets. If we are able to reliably and accurately predict escalations then we can help reduce the cost of escalations. However, this research is focused on the \textit{potential}, and not the actual, possibility of predicting an escalation. The actual prediction will be the focus of subsequent research. As such, our research was guided by the following research question:
\begin{description}
\item[RQ:] \hfill \\
Is the sentiment in an escalated support ticket significantly different than the sentiment in a non-es\-ca\-la\-ted support ticket?
\end{description}
The RQ served as a preliminary inquiry to determine if there were statistically significant differences that would be indicative of the phenomenon we were looking for. A lack of any significant differences would have meant that no information could be gained or harnessed towards assisting support analysts. In order to assess our RQ, we focused our efforts on analyzing a proprietary large dataset containing actual support tickets from our large and industrial partner, IBM. The sentiment of the support tickets were extracted using the Watson Natural Language Understanding (NLU) API\footnote{https://www.ibm.com/watson/services/natural-language-understanding/}, SentiStrength\footnote{http://sentistrength.wlv.ac.uk/}, and the Natural Language Toolkit\footnote{https://www.nltk.org/} (NLTK). In addition, the Watson NLU API was used to extract the various emotional readings from the support tickets. The resulting sentimental and emotional datasets were then analyzed and we were able to identify significant concrete differences between the escalated and non-es\-ca\-la\-ted datasets.

This paper is structured as follows: Section \ref{ssec:related} summarizes related work; Section \ref{sec:data} defines how the data was collected; Section \ref{ssec:sent_tools} describes the tools used in this research; Section \ref{ssec:sent_emo_data_results} contains the results of the sentiment analysis; Section \ref{ssec:discussion} discusses the results and meaning of the sentiment analysis; Section \ref{sec:future_work} highlights areas for future work;  Section \ref{sec:threats} provides the threats and limitations of the conclusions; and Section \ref{sec:conclusions} concludes the paper.

\section{Machine Learning}

To address RQ3, the emotion and tendency data was fed into ML models, the results of which are reported in this section. In addition to the ML work, however, three incremental datasets were built to identify if the models would perform better or worse given less --- but more specific --- data to learn and predict against.

\subsection{Incremental Datasets}

When using historical data with multiple time-based data points per unit of analysis, it is common to use all available data when building and testing a predictive model --- which for our analysis means using all emails up until the escalation occurred; however, that choice means predicting against data available right before the critical event, when it may be beneficial to ask how a model performs when it has been fed less than all available data. In our live system, waiting until the last possible moment to predict whether an item will escalate is too late, as it leaves support analysts no time to proactively work towards preventing the escalation.

If all emails of a support ticket are used to calculate the mean and Tendency, then the analysis is considered to have been applied to the \textit{100\% set}. If the first three quarters of all available emails are used in calculating Tendency, then the analysis applied is to the \textit{75\% set}. The first half of the emails is considered to be the \textit{50\% set}, and the first quarter is considered to be the \textit{25\% set}.

For this research, the ML models were always trained using the 100\% sets, and then tested using all four sets separately. The reason for testing using all four sets (separately) is to understand how the models would have performed at different stages of the incoming data on these support tickets, if these models would have been running in real time. Testing on the 100\% set is conceptually asking how the ML models perform against data right up until the escalation event. By testing on the other sets, the results reflect how the ML models perform when only given the first 25\%, 50\%, and 75\% of the data, which conceptually allows an understanding of model performance at different stages of live support tickets.

The reason for training on the 100\% sets is because training on the smaller sets implies that some information is known about the test data that simply cannot be known at test time if these models are used against live data, which would give the models an unfair advantage for this analysis. For example, it may seem intuitive to train on the 25\% set when testing with the 25\% set, however, when working with live data, it will not be known how close to an escalation each support ticket is (or even which of these support tickets will escalate at all).

\subsection{Machine Learning Results}

\section{Methodology}
.
\\- data collection
\\- manual annotation
\\- normality testing
\\- difference testing
\\- machine learning
\\- stages of machine learning

To address our research questions, difference testing was applied to the data comparing Escalation and non-Escalation situations, and machine learning algorithms were utilized to test the performance of the data towards predicting Escalations.

Due to the state of the data,

\section{Related Work}
\label{ssec:related}
Our previous work performed similar research on IBM support tickets by developing a machine learning classifier to help predict the risk of support ticket escalation\cite{8049142}. Our research was performed in conjunction with multiple stakeholders from IBM and involved an iterative design science methodology to characterize the support process and data to better manage the escalation process\cite{8049142}. We obtained considerable insight into the ability of predicting customer escalations so that to help simplify the job of support analysts industry-wide\cite{8049142}. In this paper, however, we sought to identify the potential value that the sentiment of customer-support analyst conversations, alone, has on our ability to make such predictions when the other information about the support tickets is not available.

Sentiment analysis is emerging as a popular technology, especially with such a breadth of research topics that are applicable to the sentiment analysis field. As such, there are already a wide variety of sentiment analysis tools available, many of which are open-sou\-rce. Watson NLU, NLTK, SentiStrength, Alchemy\footnote{Acquired by IBM and subsequently deprecated}, Stanford NLP\footnote{\url{https://github.com/stanfordnlp/CoreNLP}}, and EmoTxt\footnote{\url{https://github.com/collab-uniba/Emotion_and_Polarity_SO}} are examples of sentiment analysis tools currently available. The aforementioned list of tools should not be considered exhaustive, as there are a multitude of others that have been developed and even more that are actively being developed. That being said, there are two different varieties of sentiment analysis tools. The first type will classify the sentiment polarity of text; it identifies the text as either neutral, positive, or negative. The second type will analyze text for various emotions and report the findings on either an individual emotion, a range of emotions, or the sentiment of the text. The majority of sentiment analysis tools were trained using openly available content from the internet, including reviews, comments, and social media data, therefore applying sentiment analysis tools to the software engineering domain may not be as useful due to the use of technical jargon\cite{Jongeling:2017:NRU:3135854.3135907}. However, most of the research has been performed on software development artifacts, such as developer commit messages and Stack Overflow. In order to limit the scope of our research, we assumed that the technical jargon commonly seen in software development artifacts will be vastly different from customer support artifacts, as the customer support conversations involve a support analyst and a customer as opposed to interactions between developers. Jongeling et al\cite{7332508} also researched the applicability of a range of sentiment analysis tools in the software engineering domain and found that SentiStrength and NLTK showed the highest degree of correspondence and the highest agreement with each other. Thus, the results of the use of off-the-shelf sentiment analysis tools when applied in the software engineering domain is still actively being researched.

Recent research by Novielli et al\cite{benchmark-novielli} have replicated the study by Jongeling et al\cite{7332508} using three sentiment analysis tools that were unavailable at the time of the original study. These three sentiment analysis tools, Senti4SD\footnote{\url{https://github.com/collab-uniba/Senti4SD}}, SentiStrength-SE\footnote{\url{http://laser.cs.uno.edu/Projects/Projects.html}}, and SentiCR\footnote{\url{https://github.com/senticr/SentiCR}} have been developed specifically for the software engineering domain. The results of this study indicate that customized tools do indeed provide more accurate results. Furthermore, Lin et al\cite{howfar} confirmed with a "negative results" paper warning researchers that the current set of off-the-shelf sentiment analysis tools are not yet mature enough to be effectively used in practical applications in the software engineering domain. However, as previously mentioned our goal is to determine the \textit{potential} usefulness of using sentiment analysis tools in predicting support ticket escalations. As such, we are simply making use of the current off-the-shelf sentiment analysis tools as a stepping stone to provide us with an appropriate level of confidence prior to delving deeper into our research field.

A plethora of sentiment analysis research has already been performed across various domains including the analysis of social media, such as Twitter\cite{Thelwall:2012:SSD:2336247.2336261}\cite{godbole-news-blogs} and other media posted on social networks\cite{Ribeiro2016}. Other studies have researched sentiment analysis on a variety of software engineering domains, including: commit comments in GitHub\cite{Guzman:2014:SAC:2597073.2597118}\cite{Pletea:2014:SES:2597073.2597117}\cite{Sinha:2016:ADS:2901739.2903501}, Gentoo Linux community\cite{6686063}, openSUSE development community\cite{rousinopoulos}, Apache issue reports\cite{Gachechiladze:2017:ADC:3102962.3102966}, GitHub and StackOverflow\cite{Novielli:2015:CSD:2804381.2804387}, artifacts from various sources of JIRA trouble tickets\cite{Ortu:2016:ESS:2901739.2903505}, and other various software development artifacts\cite{Guzman:2013:TEA:2491411.2494578}\cite{Murgia:2014:DFE:2597073.2597086}. There have even been studies analyzing the sentiment of IT support tickets\cite{7832903}; however, this research focused on an internal IT Support organization, whereby both the support analysts and customers were employed by the same organization. The results of the aforementioned IT support ticket research lead to the development of a new sentiment analysis tool that specifically targets IT support tickets with better accuracy than the leading sentiment analysis tools\cite{7832903}. Therefore, a task remains for future research which should involve leveraging this newly developed sentiment analysis tool to corroborate the results discussed in this paper. That being said, all of the related research applied sentiment analysis tools to the software engineering domain and subsequently achieved highly successful results. In summary, we can see that the sentiment analysis tools, while still a maturing and evolving field, can help us provide substantial new research to the software engineering domain.

\section{Threats to Validity}
\label{sec:threats}
The biggest threats to the validity of the conclusions drawn from the data are the various assumptions and simplifications that were made to be able to conduct the analysis with the data that could be gathered. At each place such an assumption or simplification was made, it was described. On the other hand, since the data were mined well after the fact, there is no chance that these data could have been biased by a desire to support any particular hypothesis. In addition, the sentiment analysis tools used were developed using the context of  various data from social media platforms as input and therefore the results against technical support tickets may not be applicable. However, given that the tools were systematically, consistently, and equally applied to all data, the results should be consistent across the data, even if a particular tool was unable to comprehend any technical jargon. Furthermore, we have compared the results of three unique tools to overrule the bias of any one tool. Finally, as with any other case study, the results are about the particular case and cannot be generalized to other situations. So, we encourage others to do similar data mining studies on other data.

\section{Sentiment Analysis Tooling}
\label{ssec:sent_tools}
For the purpose of this research, three different tools were used in order to perform sentiment analysis on the emails from the support tickets: NLTK, SentiStrength, and Watson NLU. Each of these tools provide sentiment analysis in a similar manner; however, each tool provides its own flavour with respect to the results of the sentiment analysis. This section shall describe each API and how it was used.
\subsection{NLTK API}
\label{ssec:nltkapi}
The NLTK is a popular open-source Python toolkit that also contains multiple sentiment analysis APIs. This research made use of the Sentiment Intensity Analyzer API, which for the scope of this paper shall be referred to as the NLTK API. The NLTK API returns four real values per input, with each value representing the following sentiment of the input text:
\begin{enumerate}
\item positive,
\item neutral,
\item negative, and
\item compound.
\end{enumerate}
The positive (1), neutral (2), and negative (3) values, when summed, will equal exactly 1. The compound (4) value is an aggregate value, formed from the positive, neutral, and negative values, representing the overall sentiment of the input. One can infer the following based on the compound value, \(c\):
\begin{enumerate}
\item \((c < 0) \iff\) negative sentiment,
\item \((c = 0) \iff\) neutral sentiment, and
\item \((c > 0) \iff\) positive sentiment.
\end{enumerate}
However, given the compound value is simply an aggregate of the positive, neutral, and negative values, one can determine the sentiment by first looking at the neutral score. If the neutral score is greater than 0.5, then the sentiment is considered to be neutral; however, if the neutral score is less than or equal to 0.5 then it is considered to be either positive or negative, whichever has the higher value.

The NLTK API was used to analyze every email that was annotated using the process described in Section \ref{ssec:manual_annotation} and the results were tabulated according to the divisions of data described in Section \ref{ssec:dividing_data}. A summary of the results can be found in Section \ref{ssec:sent_emo_data_results}.
\subsection{SentiStrength API}
\label{ssec:sentistrengthapi}
SentiStrength is another popular sentiment analysis tool that has been described and evaluated by a number of peer-reviewed academic articles. Furthermore, it has been used in a large number of research projects. The SentiStrength tool has a large number of features that merit further exploration; however, for the purposes of this research we used the trinary option provided by the SentiStrength API. The trinary option returns three integer values per input, namely:
\begin{enumerate}
\item positive,
\item negative, and
\item overall.
\end{enumerate}
The positive (1) value, \(p\), is assigned an integer value in the range \(1 \leq p \leq 5\), whereby \(p = 1\) indicates a positive sentiment and \(p = 5\) indicates an extremely positive sentiment. Similarly, the negative (2) value, \(n\), is assigned an integer value in the range \(-5 \leq n \leq -1\), whereby \(n = -1\) indicates a negative sentiment and \(n = -5\) indicates an extremely negative sentiment.

The overall (3) value follows an approach described by Thelwall\cite{Thelwall:2012:SSD:2336247.2336261} and is assigned: -1, if \(p + n < 0\); 0, if \(p + n = 0\); or 1, if \(p + n > 0\). A value of: -1 indicates an overall negative sentiment, 0 indicates an overall neutral sentiment, and 1 indicates an overall positive sentiment. However, one caveat is that if \(p + n = 0\) \textit{and} \(p \geq 4\), then the sentiment is considered to be undetermined and the particular input that produced these values is subsequently removed from dataset\cite{7332508}.

The aforementioned SentiStrength API was used for this research to analyze every email that was annotated using the process described in Section \ref{ssec:manual_annotation} and the results were tabulated according to the divisions of data described in Section \ref{ssec:dividing_data}. A summary of the results can be found in Section \ref{ssec:sent_emo_data_results}.
\subsection{Watson API}
\label{ssec:watsonapi}
For this research we utilized the Watson NLU API. This API offers nine distinct natural language features, two of which are used in this research: emotion and sentiment. To use the NLU API, plain-text is sent to the API with either the sentiment or emotion feature requested and the appropriate metrics are returned. The two NLU APIs utilized for this research each return unique and distinct metrics.

The NLU Emotion API returns the level of each of the five basic emotions\cite{ekman1972emotion}, sadness, joy, fear, disgust, and anger,  that are measured from the plain-text input. Each emotion is assessed a real value between 0 and 1; whereby 0 indicates the emotion is not being exhibited and 1 indicates a high level of confidence that the emotion is being exhibited.

The NLU Sentiment API returns the level of sentiment being expressed in the  plain-text input. "Sentiment" is a view, attitude, or emotion that is expressed by the words chosen in a sentence \cite{Liu10sentimentanalysis}. The NLU sentiment feature returns a real value between -1 and 1, where -1 indicates an overly negative sentiment and 1 indicates an overly positive sentiment.

As with SentiStrength and NLTK, we used the emotion and sentiment APIs from Watson NLU to analyze every email and the results were tabulated according to the divisions of data described in Section \ref{ssec:dividing_data}. A summary of the results can be found in Section \ref{ssec:sent_emo_data_results}.

redpajama_set_name: RedPajamaArXiv


text:
Sampa kan avse:

SAMPA –  ett skriftsätt för att med 7-bitars ASCII-tecken återge ett språk fonetiskt

Geografi

Brasilien

 São Paulo, delstatshuvudstad,

Burkina Faso

 Sampa (ort), Centre-Est,

Ghana
 Sampa, Ghana – huvudort för distriktet Jaman North

Robotskapade Brasilienförgreningar
Robotskapade Burkina Fasoförgreningar

redpajama_set_name: RedPajamaWikipedia


text:
Nebraska doomed to find a room at Penn St.
NEIL RUDEL,For the Pocono Record
The Nebraska football team plans to make Altoona its home base.
The Cornhuskers will be staying at the Ramada Hotel in Altoona, only because their room request was too large for the Ramada on South Atherton Street in State College, where visiting teams normally stay.
Altoona is 45 miles from State College.
"It wasn't our preference," Nebraska sports information director Chris Anderson said Wednesday. "It was a matter of rooms."
According to Karen Terrill, sales director at the Altoona Ramada, Nebraska has taken 100 rooms. That's more than the State College Ramada holds for visiting teams.
Big Ten travel rosters are limited to 62 players.
"I'm not sure what happened, but we're thrilled to have them," Terrill said.
Jennifer Brooks Stahl, marketing manager at the State College Ramada, declined comment.
When Nebraska was reportedly told it couldn't be accommodated in State College, it said it would just stay in Altoona. After later changing its mind, sources say, it was told the rooms in State College had been released.
It's the first time in memory that Penn State's visiting team has stayed in Altoona.
Ohio State flew into Altoona in 1994 before the runway in State College was long enough to accommodate a large plane. The Buckeyes flew to Martinsburg in three separate planes, bused to State College and lost 63-14.
The Huskers are flying into State College Friday evening and will have a brief walkthrough at Beaver Stadium. They'll bus to Altoona and are scheduled to depart around 5 p.m., with the aid of a police escort, for Saturday 8 p.m. kickoff.
"We'll do the best we can," Anderson said.
Nebraska will fly out of University Park Airport after the game.

redpajama_set_name: RedPajamaCommonCrawl


text:
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Pemetrexed: Italian Court reversed the first instance decision ordering a PI on the basis of Eli Lilly's patent
Add this article to your bookmarks
11 December 2018 | Evelina Marchesoni Associate
In the case Fresenius Kabi Oncology Plc and other v Eli Lilly and Company and other, by decision of 15 October 2018, the Court of Milan overturned the first instance decision (reported here) ascertaining the prima facie infringement by equivalence of Eli Lilly's patent EP 1 313 508 ("EP '508"), claiming the use of pemetrexed disodium in combination with vitamin B12 and optionally a folic binding protein agent for inhibiting tumour growth, by Fresenius' product containing pemetrexed diacid in combination with tromethamine to be reconstituted in glucose.
In fact, diverging from the previous decision, the Court pointed out that the relevance of the file history to construe the scope of protection of a patent and then the infringement of equivalence has to be carefully and prudentially evaluated.
In particular, according to the Judges, the file history may narrow the scope of protection only if the patentee has limited its patent during prosecution to overcome novelty/inventive step objections of the EPO and not merely for added matter reasons (deemed by the Court a formal issue concerning the literal wording of the amended claims).
As a consequence, in the case at stake, agreeing on what declared by the Panel of Court Experts who were appointed in the appeal PI proceedings, the Court denied that the amendment of the claim made by Eli Lilly during the prosecution before the EPO can be considered as a limitation implying that EP '508 has to be interpreted as exclusively referring to Pemetrexed disodium and not also to compounds equivalent of it. Neither did the reading of the patent lead to a different conclusion. Indeed, according to the Judges, the invention at issue did not regard the selection of a Pemetrexed salt but the reduction of the toxic effects of the active part of the active ingredient, the anion. At the date of filing of EP '508, the skilled person knew that the active part of Pemetrexed (that was able to penetrate into the cells and carry out its inhibition and toxic activity) is the anion, which may be obtained by different pharmaceutical forms of Pemetrexed. Therefore the skilled person would not have attributed a particular meaning to the fact that Pemetrexed was in the form of disodium salt because he would have interpreted that element as not essential to the invention. In other words, EP '508 has to be construed as referring to the use of Pemetrexed (leaving out of consideration the specific salt) in a combination therapy to inhibit tumour growth.
Therefore the Judges, recalling what was ascertained by the Panel of Experts, concluded prima facie infringement by equivalence of EP '508 by Fresenius' product pointing out that the replacement of Pemetrexed disodium with Pemetrexed diacid was obvious to the skilled person.
As to the validity of EP '508, the Court highlighted that Fresenius contested it late, without filing any prior art documents able to actually support this objection and merely on the basis of the judgment of the German Federal Patent Court of July 17, 2018 declaring the German designation of Eli Lilly's patent invalid in first instance. As a consequence, the Judges considered Fresenius' invalidity objection inadmissible.
In other words the Court did not consider the validity of Eli Lilly's patent. The Judges merely recalled that the Panel of Court Experts criticized the objection of the EPO Examiner on the added matter of EP '508, by saying that in the technical framework of the invention in question "Pemetrexed" and "Pemetrexed disodium" may not be considered two different active ingredients, even if they have different CAS numbers.
On this reasoning the Court denied the interim declaration of non-infringement sought by Fresenius, instead ordering a PI and the recall from the market of the Fresenius' generic product containing Pemetrexed diacid/ditromethamine to be reconstituted in glucose.
Evelina Marchesoni
I'm specialised in multi-jurisdictional contentious patent matters, with a particular focus in life sciences & healthcare and the tech & comms sector.
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text:
DynaMike, juggler
Michael Anthony Bonnici, a.k.a. "DynaMike", is an award-winning busker who performs continuously around the world. He has performed in Dubai, China, Holland, the UK and Oman but is known locally for his performances on Granville Island. Bonnici loves busking. "In a world full of bad news and bad attitudes, busking is a breath of fresh air," says Bonnici. "It makes people feel good in simple ways. It brings people together. It's fun. It's good for people."
Mike, DynaMike, Bonnici has performed on the streets of 14 different countries, busked since the age of 16 and never had an ordinary job. However, he is well dressed, good-looking, self-confident and enjoys life as he puts it "doing shows for the good people of the earth".
Mike was doing gymnastics from a young age. He liked the attention when he was doing the tricks most children couldn't do. "When I was a kid it was like a drug; I was hungry for it. You get all this energy from all sorts of people. Every little baby loves attention and as we grow up some of us become more or less dependent on attention and I was pretty dependent"
Mike Bonnici is the producer of the Vancouver International Busker festival which transforms downtown Vancouver's main street into an arts-rich and large scale public celebration annually on Canada Day Weekend.
Mike doesn't think about life in terms of job and free time, career and personal life. For him it's all life: "I was born, I will die in the end and in between I am just alive". Street performing for DynaMike is the part of who he is. It makes him happy.
BoomBot & Chester, LED Robots
Cory Van Ieperen, Caricaturist
Cosmo, Circus Clown
Go Bonkers, Balloon Artists
Interactive Comedic Characters
Kellie & Co, Ventriloquist
Matt Henry, Juggler
Mike Battie, Juggler
Neezar The Mime
President Trump, Comedic Character
Paintertainment
Rod Boss, Magician
Ruffle Redbird, Family Performer
Stilt walkers
The Ta Daa Lady, Family Performer
Ted Couling, Caricaturist
Will Stroet, Family Performer
Woops-A-Daisy


To prepare female national teams for the World Cup to be hosted by France in June, China is hosting a four-team international tournament, which has seen the ladies of Coach Alain Djeumfa qualify to face China.
The host nation laminated Russia, to book a place in the final while the Lionesses defeated Croatia.
It was Tactician Djeumfa's first game as Head Coach of the Lionesses. In the selection, he positioned Annette Ngo Ndom as goalkeeper, Christine Manie, Aurelle Awona, Yvonne Leuko, Falonne Meffometou as defenders, Grace Ngock Yango, Therese Abena, Genevieve Ngo Mbeleck and at the attacking compartment, Gabrielle Aboudi, Nchout Ajara, Michelle Ngono Mani.
Magdalene Ngono Manie, Lionesses' all-time goal scorer kept doing what she has always been doing, as in the 33rd minute of the game, her goal gave the Lionesses a head start. The girls kept the score till the first half came to an end.
Unfortunately, a 63rd minute blunder by Lionesses shot stopper Annette Ngo Ndom permitted the Croatians to level. 6 minutes to full time, Gabrielle Aboudi Ongene served Michelle Akaba with a beautiful pass as she pushed it into the net of Croatia, offering victory to Cameroon.
Coach Alain Djeumfa and his girls have more to do in preparation for the World Cup in France. The Lionesses will face the host nation on Sunday, April 7, 2019, to see who will carry home the trophy.


text:
O Pizzo Stella é uma montanha dos Alpes de Platta (sub-cordilheira dos Alpes Réticos ocidentais), na Lombardia, norte da Itália. Tem 3163 m de altitude e 585 m de proeminência topográfica. Fica no Cantão dos Grisões.

Ligações externas

Montanhas da Lombardia
Montanhas dos Alpes


text:
Namma Metro – Gathering Speed, all right
By bglr_ravi
September 3rd, 2007 @ 1:03 PM Namma Metro
Recently, I came across this wide-ranging article on Bangalore, excuse me, I meant Bengaluru in Frontline, the Hindu publication. Titled 'Gathering Pace' I feel its one of the best, comprehensive articles that has been written on the (pathetic) state of Bangalore's infrastructure and what's being attempted / done on a ground level to solve all / some of them.
Some of the public agencies which the article covers are the BBMP, BDA, BMRC, BMTC, BWSSB etc. Do check out the article for worthwhile reading.
I'm reproducing a couple of points here about Bangalore's Project Namma Metro which make interesting reading, though they are already in the public domain:
– 2.5 million vehicles on Bangalore Roads
– 900 being added everyday
– The city should have got an MRTS "at least 15 years ago".
– In 1994, a study commissioned by the Bangalore Mass Rapid Transit Limited suggested a public-private
partnership model and even selected a partner. None of these projects took off.
– Fares are expected to be between Rs.7 and Rs.15.
– 6,395 crore, with funding coming by way of equity (Rs.1,918.50 crore) and subordinate debt (Rs.1,598.75
crores) from both the State and Central governments – and senior term debt (Rs.2,877.75 crore). While 17 per
cent of senior term debt will come from a consortium of Indian banks led by UTI Bank, the remaining 28 per cent is
from the Japan Bank for International Cooperation.
– Under the present plan, 138 acres will be acquired from the Centre and 32.22 acres from the State government. In
addition, 27 acres will be acquired from private owners at a cost of Rs.566 crore.
– The underground tunnelling itself is estimated to cost Rs.1,300 crore.
– BMRCL has listed over 6,500 tasks that must be completed before project completion in March 2010
– The BMRCL has appointed a consortium of general consultants at a cost of Rs.212 crore.
– Over the next six years, the consortium will offer 9,000 man months of design and management advice and
supervise the project.
One cannot help but chuckle at this printer's devil within the article. This picture accompanied in the article is described as " A view of Bangalore's Metro station and tracks"
Here are a couple of pictures taken recently on M.G Road, where work – starting with barricading – has increased on Namma Metro. V Madhu, Managing Director of BMRC, said, "The work on M G Road will be completed in around 10 months. The barricades will remain till then."
Article cross-posted here
Sharath (unregistered) on September 3rd, 2007 @ 4:14 pm
If the metro project happens on schedule, you will in some way be responsible for it. You're the Honorary Project Overlooker !!
Ravi (unregistered) on September 3rd, 2007 @ 4:29 pm
Sharath,
Thanks for reading and comments!
The Delhi Metro Rail Corporation (DMRC) began construction on 1 October 1998. On 25 December 2002 ( just 4 years later ) the first section of Line 1 from Shahdara to Tis-Hazari ( equivalent to our Byapannahalli to M G Road) was opened.
By November 2006, all three lines in Phase 1 totalling 65km (40.4 miles) were operational, THREE YEARS ahead of schedule.
Its important to have faith in Namma Metro for 2 reasons. One, we Indians must believe in ourselves and our capabilities, particularly where politicians are NOT involved. Secondly, with Dr. E Sreedharan advising BMRCL & with Mr. V Madhu at the helm, I'm confident Namma Metro will be a huge success.
Just wait and see! Till then, keep checking out http://nammametrotales.com/blog for all things concerning Namma Metro and other news.
vk (unregistered) on September 4th, 2007 @ 12:05 am
Which part of bengaluru is the first pic depicting?
Ravi (unregistered) on September 4th, 2007 @ 8:25 am
VK,
I wouldn't know. The pic and its description is the 'printer's devil' that I referred to!


through a client-centered approach to technology and branding.
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Kanye West finally released his new album The Life of Pablo in CDQ on TIDAL, so drop everything and stream or download the record now. After all, this is an effort that Kanye boldly described as "not the album of the year. This is the album of the life." Similarly, waiting for the album has seemed like an effort of the year.
The Life of Pablo (previously known as WAVES, SWISH, and So Help Me God before that) is Kanye's seventh studio album and has been a project of the rapper's for over a year now. Paul McCartney, Sia, Vic Mensa, Kendrick Lamar, Ty Dolla $ign and Post Malone are among people who've collaborated on tracks for the the album–but not all of them made the cut. That's the kind of perfectionist attitude you can expect from Yeezy.
Update–3/31/2016: Sources have told The Verge that they expect The Life of Pablo to hit major music streaming service tomorrow, including Apple Streaming and Google Play.
The Life of Pablo is now available for streaming on all major streaming sites, not just TIDAL. That means Spotify and Apple Music. We've included the Spotify embed for you below.
If you want to create a TIDAL account, you'll need to also choose a type of account you'd like to have if you don't cancel within thirty days. Sadly that requires a credit card.
One person who didn't work with Kanye was Wiz Khalifa, who along with Amber Rose, joined Kanye in one of the greatest Twitter arguments of all time. While a lot of Kanye's vitriol–which stemmed from a misunderstanding–has since been removed by the rapper himself, the beef did do a fantastic job of promoting TLOP.
TLOP was released through GOOD Music, Roc-A-Fella Records, and Def Jam Recordings on February 11th, 2016. Before the entire album's release, though, Kanye had offered tracks that didn't make the cut (with exception to "No More Parties in LA") to the public.
LISTEN: Kanye West Kendrick Lamar "No More Parties In LA"
Kanye is certainly proud of T.L.O.P, but it's important to note that the record is just one part of his attempt to take over Spring 2016. He debuted the album at a listening party in Madison Square Garden, which was then streamed via TIDAL to movie theaters, computers and mobile devices around the world. He also took this opportunity to debut his fashion line, "Yeezy Season 3." That effort features clothing and shoes made by Adidas, all of which are made of neutral tones.
Many fans of Kanye are wondering why the album is called The Life of Pablo. Although Kanye hasn't come out to explain just yet his reasoning, some of his fans have a pretty solid theory. Check it out below, via Reddit's /r/Kanye.
In "No More Parties in LA" Kanye says: "I feel like Pablo when I'm working on my shoes. I feel like Pablo when I see me on the news. I feel like Pablo when I'm workin' on my house".
When he's working on his shoes, he is Picasso (artist). When he is on the news, he is Escobar (criminal) When he is working on his house, he is the stereotypical Spanish male working hard. So his new album title is his way of expressing his multiple personalities.
But enough about the man. Let's the get to the music.
Kanye has famously changed a lot of things about The Life of Pablo, presumably because he's such a perfectionist. Originally he had shared a notepad with some tracks he had laid out to be his tracklist, but slowly that list grew along with the amount of signatures and names associated with the project.
You can see the final instance of that notepad below.
Except he didn't use that at all, switching up the album, the track list, and plenty more before it turned into The Life of Pablo.
Kanye released the album art for the Life of Pablo one hour before his MSG Yeezus Season 3 listening party. The cover is by artist Peter de Potter. We think it looks like garbage, but whatever.


« New research from Newcastle School.
Within a paper published in the journal Frontiers in Neuroscience, the team proposes a fresh theory for how uncommon gene mutations cause Alzheimer's disease. A theory that, if established, could help out with obtaining ways to avoid the crippling disease. The experts caution people against building options about diet plan or health supplements predicated on this fundamental idea, as their theory just pertains to how cells deal with iron, not just how much iron is within a person's diet plan. Background 'For twenty years most researchers have believed a small proteins fragment, amyloid beta, causes Alzheimer's disease,' says Affiliate Teacher Michael Lardelli, College of Biological Sciences, the University or college of Adelaide.This protein is actually a tumour suppressor, since it stops cells turning cancerous. Nevertheless OPCML is normally lost in malignancy patients. Researchers have finally discovered that when OPCML is certainly place back to cancer tumor cells, it deactivates a kind of proteins called AXL cleverly. Earlier research shows the AXL protein accelerates the distributed and growth of ovarian cancer cells. Medical tests are underway for remedies that stop AXL currently, known as AXL inhibitors. But this new early stage analysis demonstrates prospect of minimising treatment with AXL inhibitors, and subsequently reducing unwanted effects for girls being treated for ovarian cancers. When AXL turns into activated, it makes cancer tumor cells more aggressive, increasing their capability to move and pass on to other areas of your body.


Hines is planning a rare feat on Chicago's Goose Island: a wooden building.
A shuttered Goose Island lumberyard could soon become home to a type of construction not seen in Chicago since the 1800s: a wood-structured office building.
Real estate developer Hines said it plans to build a six-story, 270,000-square-foot office building on Division Street that would become the largest mass timber structure built in the United States in the modern construction era.
The city known for the Great Chicago Fire of 1871 may seem like an unexpected location for an all-wood building, but the project continues a recent trend of similar projects in North America.
Improved technology and manufacturing methods allow today's developers to work with large, densely engineered pieces of mass timber that are fire-resistant and can be quickly pieced together to form an entire building. The building process is much faster and more environmentally friendly than traditional steel-and-concrete construction, according to Hines.
"I think it's something we're definitely going to see a lot more of in Chicago," said Chicago-based architect Todd Snapp, a principal at Perkins + Will who is part of a team working to design wood skyscrapers for future generations.
Hines' project, called T3 Goose Island, is planned on a site at the center of the North Side industrial island at Division and North Branch streets. Big Bay Lumber closed there in 2015.
Partners on the project include Big Bay Realty, owned by the Ciral family that formerly ran the lumberyard on the site, and Diversified Real Estate Capital, whose founders include longtime entrepreneur and restaurateur Larry Levy.
Houston-based Hines is known more for building glass-and-steel towers in large cities, including high-end office towers along the Chicago River such as the recently completed 52-story River Point and the 60-story tower at 300 N. LaSalle St. Hines is developing the three-tower Wolf Point project along the river with the Kennedy family and is working on the Lincoln Common — the mixed-use project on the site of the former Children's Memorial Hospital in Lincoln Park — with partner McCaffery Interests.
Last year Hines completed a seven-story, 220,000-square foot T3 building — which stands for timber, technology and transit — in Minneapolis. The building is fully leased to tenants including Amazon.
Hines also plans a mass timber office building in Atlanta, and the firm wants to develop similar buildings throughout the country, said Hines senior managing director Steve Luthman, who oversaw the Minneapolis project. Timber buildings are outfitted with an exterior of Coreten steel.
The Chicago project comes after the city recently made major zoning changes in a former industrial corridor near the river, which is expected to launch a wave of big, multiuse developments just north of downtown.
"We're reacting to demand in the market for unique, authentic office environments," said Brian Atkinson, a Hines managing director. "Timber has an appearance, texture and smell — a warmth to it that you don't get with concrete and steel.
T3 Goose Island will have ground-floor retail, 275 parking spaces, bike storage, a fitness center with locker rooms, a rooftop deck and tenant balconies on each floor. Hines plans to begin construction after pre-leasing some space. If there's more demand from smaller tenants, the design could be changed to two thinner, connected towers on the site, Atkinson said.
Construction will take about a year, which is several months faster than traditional construction would take, according to Hines.
As the legacy of the 1871 fire that ravaged the city, many types of wood construction are prohibited in a downtown fire district bounded by Halsted and Division streets, Lake Michigan and Roosevelt Road. But because T3 Goose Island is an office building less than 80 feet tall, and is made of mass timber, Hines' use is allowed, according to the city's building code.
Snapp, the architect, is working on a model of an 80-story wood tower called the River Beech Tower that would hypothetically be built along the Chicago River. Snapp's efforts are part of a collaboration with the University of Cambridge in England and Chicago-based structural engineers at Thornton Tomasetti.
While the viability of wood high-rises is being studied, smaller structures — previously more common in Europe — have begun making their way to North America in recent years. That includes Hines' project in Minneapolis, a 12-story mixed-use building in Portland, Ore., and an 18-story student housing building at the University of British Columbia.
Adaptation of the building process by Hines, a firm associated with gleaming trophy towers, "is good validation that it makes sense," Snapp said.
A rendering of a high-rise made of wood, planned for Chicago's Goose Island. A similar building was built in Minneapolis, and is known as T3 (Timber, Technology and Transit).
The lobby area of a wooden building planned for Goose Island by Hines. A similar building was built in Minneapolis, and is known as T3 (Timber, Technology and Transit).


text:
More than 480 Million Animals Perish in Australia's Wildfires
The fires in Australia—which have so far burned more than 12 million acres—are the worst the continent has seen in its history.
An estimated 480 million animals have been killed by the deadly bushfires in Australia since the fires began in September 2019, according to University of Sydney professor and biodiversity expert Chris Dickman. The figure was calculated using the same formula that Dickman used for his 2007 report for the World Wild Fund for Nature on the impacts of land clearing on Australian wildlife, which included an estimation of mammal population density in New South Wales (NSW). The estimated animal loss due to the fires relates only to animals in NSW and not to other areas now affected by the fires, such as Victoria. A statement released by the University of Sydney says that many of these animals have been killed directly by fires, while others have succumbed later because of the lack of food and shelter and the introduction of predators such as feral cats and red foxes. "The figure includes mammals, birds, and reptiles and does not include insects, bats, or frogs," the University of Sydney stated. "The true loss of animal life is likely to be much higher than 480 million. NSW's wildlife is seriously threatened and under increasing pressure from a range of threats, including land clearing, exotic pests, and climate change." Australia's fires have so far burned more than 12 million acres—an area approximately the size of Vermont and New Hampshire combined—and are being considered the worst wildfires in its history.
Beef Burgers are Destroying the Amazon. Here's Why.
Will This Cultured Palm Oil Be the Solution to Saving the Orangutans?
The Meat Industry Is Still Destroying the Amazon for Soy, Study Finds
What Is Factory Farming, Exactly? Here's Why It's Time To Change the Food System
Top 15 Meat and Dairy Producers Emit Nearly as Much Methane as the Entire EU, Study Finds
Idris Elba Backs $560 Million Vegan Shake Brand to Promote 'Low Carbon' Food


OLIVETTE FARM IS A PROJECT THAT AFFIRMS OLIVETTE COMMUNITY'S COMMITMENT TO RE-LOCALIZING FOOD PRODUCTION IN WESTERN NORTH CAROLINA.
The farm grows year round salad greens, microgreens, specialty produce, flowers, fruit and honey for local farmers markets and restaurants, as well as CSA memberships for the Olivette and Asheville community.
Learn more about our veggies, sign up for a CSA, or schedule a visit via our farm website.


For those of you who are not familiar with the Conference, this is actually our sixth biennial gathering. Since 2001, the volunteers of The Auxiliary to Texas Children's Hospital have organized, marketed and staffed a conference on the subject of Spirituality in Pediatrics. This much-needed conference began because members of the auxiliary recognized that pediatric patients have specific spiritual needs as they face their illnesses and they have a very unique way of expressing those needs. Through the years, we have seen our attendance grow to include pediatric professionals from across the country and now from around the world. Our attendees span the scope of health care — their will be physicians, nurses, social workers, child life specialists and chaplains in attendance.
Each year, the conference chair has had the opportunity to choose the theme for the conference. This year's conference is titled Spirituality in Pediatrics: Providing Joyful Spiritual Abundance in Lean Times.
Throughout my association with Texas Children's, both as an employee and as a volunteer, I am frequently asked, "How can you work around sick kids?" or "Isn't it totally depressing to be around sick children all the time?" It is so hard to make those not familiar with the pediatric environment understand that, in direct contrast to it such as being a depressing place, Texas Children's is actually quite a joyful place. I am constantly amazed and uplifted by our patients and their families and am always awed by their strength, their outlook, their determination, and their spirit. Yes, there are frequently sad stories or heart-wrenching moments, but all-in-all, it is a joyful environment. Our conference speakers this year will address this seeming contradiction and will discuss the role of spirituality in medicine, methods of measuring spiritual awareness, and the connection between spirituality and joyfulness as they relate to the care and treatment of pediatric patients and their families. They will also explore various approaches to enhance the spiritual care programs at different institutions.
Check out our web site at texaschildrens.org/spirituality for an overview of the speakers and their topics. I look forward to seeing you there!


MILWAUKEE — The Milwaukee Bucks have assigned Sterling Brown to the Wisconsin Herd of the NBA G League.
According to a news release from the Bucks, Brown is averaging 22.5 points, 3.0 rebounds, 2.0 assists and 1.5 steals per game with the Herd this season.
Brown will be in uniform tonight for the Herd as they take on the Windy City Bulls at Menomonie Arena in Oshkosh. He will be joined by fellow Bucks player Christian Wood, who has also been assigned to the Herd.


The EPC project including four substations(33kV New Balaka Substation, extension and upgrading of 66kV Chingeni, 132kV Golomoti and 132kV Nkhotakota Substation) constructed by Sieyuan Electric Co.,Ltd. for Electricity Supply Corporation of Malawi Ltd. (ESCOM) had been successfully obtained completion certificate on 4th June, 2018. The project will strengthen and expand Electricity Network in Malawi.


Polaris Shipping CEO, Kim Wan-joong and four others are facing arrest warrants over the sinking of VLOC Stellar Daisy 22 months back.
In addition to Kim, the other four people are – Head of Maritime Affairs at Polaris Shipping, an Inspector of Korean Register of Shipping (KR), and two persons of a private inspection company responsible for inspecting the hull thickness of Stellar Daisy.
The charges pressed against the five people are illegal modification of vessel for Polaris Shipping, illegitimate inspection of KR, and forged document by the inspection company regarding thickness of the hull.
The 266,000-ton ore carrier carrying eight South Korean and 16 Filipino sailors went missing in waters near Uruguay on April 1, 2017. Only two Filipino crew members were rescued and the rest remain unaccounted for.
No cause for the sinking has been determined. The South Korean Coast Guard has been probing the case.
The police plan to press further charges, including negligent homicide, against the chairman after conducting a deep-sea search for the vessel. They plan to wrap up the investigation by the end of next month.
The Marshal Islands-flagged ship had departed Rio de Janeiro on March 26 and was sailing off Uruguay when it put out a distress call.
The ship operator and victims' families later agreed on a settlement, the detailed terms of which were never disclosed to public.


The medical cannabis industry in Lesotho has begun taking root with one of the companies operating in the industry, Medigrow set for the first sale of its commercial produce this year, expects to sell its first commercial produce by the second quarter of 2019.
The company, which has been operating for over a year now, says it is 24 months away from completing the construction of its full-scale production facilities.
Lesotho is the first African country to legalise the growing of medical marijuana (cannabis) amid high expectations that the country would immensely benefit from the world-wide industry which is projected by forbes.com to grow from US$7, 7 billion in 2017 to $31, 4 billion in 2021.
The marijuana plant contains more than 100 different chemicals called cannabinoids. Each one has a different effect on the body. Delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) are the main chemicals used in medicine. Recreational cannabis has higher content of THC while medicinal has higher content of CBD.
In an interview with Lesotho Times this week, chief executive Andre Bothma said a lot of work has already been laid down with over M250 million already spent on the project.
"We have already installed the critical infrastructure, water, electricity, tunnels, acquisition of essential expertise and employment of 300 people to date," Mr Bothma said.
He said their staff compliment includes people with high-end science skills like biochemistry, agricultural sciences and pharmacists among others.
While the majority of licence holders are yet to make any movement, Medigrow is already miles ahead and Mr Bothma attributes their achievements to sheer determination.
Out of 33 companies that have been granted operating licences, only 13 were reported to have been able to raise the previously required M540 000 as licence fees by the end of 2018.
He however, said it has not been smooth sailing as they have encountered a number of challenges including scarcity of relevant skills and expertise.
Health minister Nkaku Kabi recently spoke of the need for the country to deliberately develop its human resource capital base to take advantage of being a first mover to open the industry.
"We can benefit a lot from the first movers' advantage as we also have favourable altitude and climatic conditions which significantly lower the production costs," Mr Kabi said during his recent tour of Medigrow production facilities at Marakabei.
Mr Bothma said his vision is to see Medigrow Lesotho developing into a market leader in cannabis products.
Asked why Lesotho is regarded as a suitable base for production of medical marijuana, he said the country has a favourable legislative framework, conducive climate with an abundance of clean water and low input costs.
Apart from the health benefits to the country, Mr Bothma said other benefits include "economic impact through direct employment, indirect employment of people in industries supporting and ancillary to cannabis industry as well as multiplier effect of extended families that will be taken care of by those directly or industry employed in the industry".
He said other possible spin-off ventures include the development of a pharmaceutical industry and professional services like attorneys, financial institutions, security export agencies, other suppliers and vendors like catering and food supplies.
Cannabis Products Expected to Grow from $6.6 bn in 2016 to $24.1 bn in 2025.


text:
The Adventures of Gerard (play 1903-1904)
James O'Neill as Captain Gerard.
(Wilkes-Barre Times, 28 november 1903, p. 8)
(The Sunday Leader (Wilkes-Barre), 29 november 1903, p. 2)
Ad in Newport Daily News (13 november 1903, p. 5)
The Adventures of Gerard is a 3-acts American play, written by Arthur Conan Doyle and adapted by Eugene W. Presbrey, produced by Liebler & Co., premiered at the Smith's Theatre (Bridgeport, CT, USA) on 9 november 1903, then toured in several towns and states until (around) 15 january 1904 (see performances), starring James O'Neill (often mispelled O'Neil in the newspapers) as Captain (sic) Gerard.
Note that Conan Doyle's play was performed in USA (1903) before UK (1905). See a 1905 interview of Arthur Conan Doyle in The New-York Times about his difficulties to place his play to the London theatre managers : Conan Doyle's Hard Luck as a Playwright.
2 Crew
3 Known performances
Captain Gerard : James O'Neill
Talleyrand : Creston Clarke
Napoléon : Edmund Breese
Countess : Selene Johnson
Director : Unknown
Playwright : Arthur Conan Doyle, Eugene W. Presbrey
Known performances
9 november 1903 : Smith's Theatre (Bridgeport, CT) Premiere
10 november 1903 : Pera House (Holyoke, CT)
11 november 1903 : Metropolitan Theatre (Springfield, MA)
12 november 1903 : Broadway Theatre (Norwich, CT)
13-14 november 1903 : Hyperion Theatre (New Haven, CT)
16 november 1903 : Lyceum Theatre (New London, CT)
18 november 1903 : Opera House (Rhode Island, RI)
19 november 1903 : Taunton's Theatre (Taunton, MA)
23 november 1903 : Parson's Theatre (Hartford, CT)
24 november 1903 : Poli's Theatre (Waterbury, CT)
25 november 1903 : Empire Theatre (Glens Falls, NY)
26 november 1903 : Empire Theatre (Albany, NY)
27 november 1903 : Majestic Theatre (Utica, NY)
28 november 1903 : Lyceum Theatre (Elmira, NY)
30 november 1903 : Lyceum Theatre (Ithaca, NY)
1 december 1903 : Lycoming Opera House (Williamsport, PA)
2 december 1903 : The Nesbitt (Wilkes-Barre, PA)
3 december 1903 : Lyceum Theatre (Scranton, PA)
4-5 december 1903 : Lyceum Theatre (Rochester, NY)
7-9 december 1903 : Star Theatre (Buffalo, NY)
12 december 1903 : English's Theatre (Indianapolis, IN)
11 december 1903 : Grand Opera House (Anderson, IN)
13-14 december 1903 : Olympic Theatre (St. Louis, MO)
29 november 1903 : Faurot Opera House (Lima, OH)
31 december 1903 : Powers Theatre (Grand Rapids, MI)
31 december 1903 : Davidson Theatre (Milwaukee, WI)
1-2 january 1904 : Davidson Theatre (Milwaukee, WI)
5 january 1904 : Belle City Opera House (Racine, WI)
7 january 1904 : The Clinton Theatre (Clinton, IA)
8 january 1904 : Illinois Theatre (Rock Island, IL)
9 january 1904 : Grand Opera House (Rockford, IL)
Last performance in Chicago between 11-14 january 1904.
Review in Wilkes-Barre Times (28 november 1903, p. 8)
Review in The Sunday Leader (Wilkes-Barre) (29 november 1903, p. 2)
Review in The Buffalo Courier (8 december 1903, p. 8)
Review in The Daily Times (Davenport) (7 january 1907, p. 8)
Other source : New York Dramatic Mirror archives 1903-1904 and local American newspapers
Conan Doyle on stage
Retrieved from "https://www.arthur-conan-doyle.com/index.php?title=The_Adventures_of_Gerard_(play_1903-1904)&oldid=90628"


An issue of The Amazing Spider-Man give out on Free Comic Book Day 2007.
Marvel's own Milwaukee-based Misfits are back-in their very own, double sized X-mas special! Sure, they survived the GLA mini-series, but can Squirrel Girl, Mr. Immortal, and pals survive... the HOLIDAYS? 6 all-new features of mockery, morbidity, and merriment including the return of Grasshopper, Deathurge...and the Yuletide tyranny of Dr. Tannenbaum?!
- Just in time for Halloween, it's a FREE all-ages flip-book comic reprinting classic BATMAN and SCOOBY-DOO tales!- In his side of the comic, The Dark Knight meets Gotham City's new vigilante, the Cavalier, and battles the evil of Ra's Al Ghul in stories from BATMAN ADVENTURES #1 and #4.- And in the other side, the Mystery Inc. Gang tries to get to the bottom of "The Costume Caper," in a story from SCOOBY-DOO, WHERE ARE YOU? #2.
Thing's second solo series. Unfortunately the series was canceled after issue 8, but already had become a book with a small but very loyal fan base. Numerous petitions to let the book continue did not have any effect.
Peter Parker, Eddie Brock and Flash Thompson have one major thing in common the Venom symbiote changed all of their lives. The inky black alien life-form is doubling down on our heroes and they're not the only ones about to be put through the ringer. VENOM INC. kicks off here and rolls into AMAZING SPIDER-MAN & VENOM as a familiar face rises to new levels of villainy!


text:
Q: ModuleNotFoundError: No module named '__main__.models' This is the project skeleton.
Directory: users. testing.py and models.py are in users directory.
For the following code, the mentioned error is generated:
from .models import CustomUser
username='Jason'
userLL = CustomUser.objects.get(username=username)
print(userLL)
last_login = userLL.last_login
print(last_login)

Error is
Traceback (most recent call last):
  File "C:/Users/lenovo/Desktop/vidyaConnect/users/testing.py", line 1, in <module>
    from .models import CustomUser
ModuleNotFoundError: No module named '__main__.models'; '__main__' is not a package

If i remove the dot (.) before models, I get the following error
Traceback (most recent call last):
  File "C:/Users/lenovo/Desktop/vidyaConnect/users/testing.py", line 1, in <module>
    from models import CustomUser
  File "C:\Users\lenovo\Desktop\vidyaConnect\users\models.py", line 1, in <module>
    from django.contrib.auth.models import AbstractUser
  File "C:\Users\lenovo\Desktop\projectVC\venv\lib\site-packages\django\contrib\auth\models.py", line 2, in <module>
    from django.contrib.auth.base_user import AbstractBaseUser, BaseUserManager
  File "C:\Users\lenovo\Desktop\projectVC\venv\lib\site-packages\django\contrib\auth\base_user.py", line 47, in <module>
    class AbstractBaseUser(models.Model):
  File "C:\Users\lenovo\Desktop\projectVC\venv\lib\site-packages\django\db\models\base.py", line 103, in __new__
    app_config = apps.get_containing_app_config(module)
  File "C:\Users\lenovo\Desktop\projectVC\venv\lib\site-packages\django\apps\registry.py", line 252, in get_containing_app_config
    self.check_apps_ready()
  File "C:\Users\lenovo\Desktop\projectVC\venv\lib\site-packages\django\apps\registry.py", line 134, in check_apps_ready
    settings.INSTALLED_APPS
  File "C:\Users\lenovo\Desktop\projectVC\venv\lib\site-packages\django\conf\__init__.py", line 79, in __getattr__
    self._setup(name)
  File "C:\Users\lenovo\Desktop\projectVC\venv\lib\site-packages\django\conf\__init__.py", line 64, in _setup
    % (desc, ENVIRONMENT_VARIABLE))
django.core.exceptions.ImproperlyConfigured: Requested setting INSTALLED_APPS, but settings are not configured. You must either define the environment variable DJANGO_SETTINGS_MODULE or call settings.configure() before accessing settings.

redpajama_set_name: RedPajamaStackExchange


The 4.52� Super AMOLED™ Plus Screen on the Samsung Galaxy S II is more vivid than I've ever seen on a smartphone, even when sitting next to the iPhone. I hate to say it, but it is like the commercial, anytime I pull it out of my pocket jaws drop of people standing around me. It really is that bright and the colors really are that rich. I don't like gushing so much during reviews, but compared side by side against any smartphone it will shine. Pulling out my old EVO feels like using an LCD phone from early 2000.
One of the things that always annoys me about smartphones are the included apps. NASCAR, Amazon, and who knows what else, they're annoying, not useful, and you can't get rid of them. Not that this has a wealth of useful added apps, but having a Samsung TV and really liking it's DLNA integration, I do appreciate the addition of the allshare on the Galaxy S II. It let's me clear the phone memory of iMediaShare and Skifta. DLNA streaming of music, pictures, and videos to our Samsung TV via allshare was flawless, with zero setup, which I love about DLNA. In did surprise me a little bit how slow the streaming was however, especially when considering the wireless performance shown below. Over Christmas I'll attempt to DLNA stream my phone contents to the new Sony TV my retired parents just purchased, and we'll see how well that works.
One thing that REALLY impressed me about the Samsung Galaxy S II is its speed. Yeah, speed in applications with the dual core processor, but that's expected. Wireless speed was what far exceeded my expectations. With the Samsung Galaxy S II I was able to get the exact wireless WAN speeds as I did on a quadcore desktop on a wire, blew me away. I used Speedtest.net for these tests as my usual standby SpeakEasy does not have an Android app.
Galaxy S II, even the latency is right on!
I really only had 3 complaints with the phone. The first was the power button on the side versus the top. While not a huge deal, it's hard to get used to, but even more I often end up changing volume as opposing fingers to the power button need a place to rest, and that happens to be right where the volume controls are. For a powerful, expensive, and very nice smart phone, the plastic on the back just feels and looks cheap. It's also very slippery, often sliding off surfaces like the couch where my charger usually is. Lastly, one thing that is a bummer about the Android OS not being platform-specific, are the subtle differences you find between manufacturers. A few features I liked on my EVO were not available, or simply different, on the Galaxy S II. Conversely I found a few features on the Galaxy S II that I liked better than my old EVO.
All in all, the Galaxy S II is a great phone, and on your home network it will be blazingly fast. The screen is bright and beautiful. Samsung's apps like allshare are great and work flawlessly. The wireless performance will rival about anything you have wirelessly on the network. The things I found to gripe about were admittedly rather superficial and small peanuts compared to its many benefits.


@juhongtan is on Carousell - Follow to stay updated!
1) 3 sided wooden toy with Xylophone 2) Suction based wooden toy Like brand new condition. Kindly message for video.
Bought this clutch purse for dinner, but hardly used. Only used once or twice maximum. Still in extremely good condition like brand new.


text:
Sununu Requests State-Federal Offshore Wind Task Force; Advocates Court Supply Business
By Annie Ropeik • Jan 7, 2019
Credit WPS Geography
Business support for offshore wind is building in New Hampshire.
Governor Chris Sununu said Monday he'd requested a federal task force that would be a first step to development.
But some advocates aren't waiting to try and court the growing industry.
Michael Behrmann works with the trade group Clean Energy New Hampshire – the new name for the state's former Sustainable Energy Association and Clean Tech Council.
Last fall, Behrmann led a delegation of lobbyists and energy developers to Denmark, which he says is an offshore wind hotspot. The group met with industry leaders who may want to expand to the Northeast.
"My whole emphasis was not – pun intended – missing the boat on this," Behrmann says, "and making sure that New Hampshire can actually participate now, rather than having to compete harder in the future when more of that infrastructure for the country is kind of established."
Behrmann says likely wind growth in nearby states like Maine and Massachusetts means New Hampshire could attract related businesses to Pease International Tradeport, in Portsmouth.
It has a deep-water port and easy transportation access, Behrmann says, which could supply potential wind projects elsewhere -- even before any active wind development directly off the Seacoast.
The task force Sununu requested this month will let state and local authorities weigh in as federal regulators study whether to set up leasing areas for potential wind development.
(Click here to see more about the comparable task force set up in Massachusetts in 2009.)
Only a few states that have a task force, including Massachusetts and Rhode Island, have actually reached the point of selling leases to specific developers or projects.
But Behrmann says there's still business to be gained now.
"You're looking at what's going to become one of the largest markets in the world for offshore wind," Behrmann says of the East Coast. "We do not currently, in the U.S., have a lot of manufacturers that can supply this type of rapid development."
Behrmann says New Hampshire's business commissioner, Taylor Caswell, supported the Denmark trip as a way to find new opportunities in clean technology.
A recent auction in Massachusetts nearly doubled past records for the sale price of federal offshore wind leasing areas. And projects off Cape Cod and Rhode Island are already in the works.
Meanwhile, Maine's new Democratic governor, Janet Mills, has pledged to support offshore wind, potentially ending her Republican predecessor Paul LePage's moratorium on development.
Pease International Tradeport
Sununu Will Ask for Federal Offshore Wind Study, A First Step To Development
By Annie Ropeik • Dec 20, 2018
BOEM.gov
Governor Chris Sununu says he plans to request a federal study of potential offshore wind development in New Hampshire.
Speaking on NHPR's The Exchange Thursday, Sununu said offshore wind may have a role to play in how the state responds to human-driven climate change:
"Sure, if you want to set up a task force to look at wind, I don't have any problem with that at all," Sununu said.
After Years of Debate, Construction to Begin on Antrim Wind Project
By Britta Greene • Jul 31, 2018
Saratoga Associates
After years of planning and debate, construction on a wind farm in the town of Antrim is scheduled to begin next week.
Walden Green Energy, the company behind the project, has notified state and local officials that it intends to break ground on or around August 6.
N.H.'s Local Energy Solutions Conference Marks 10 Years
By Annie Ropeik • Nov 15, 2018
Courtesy ReVision Energy
New Hampshire towns will trade ideas for local energy projects – and talk with policymakers – at a conference Friday in Concord.
The state Sustainable Energy Association has held this expo for the last 10 years.
At the start, local energy solutions director Henry Herndon says, it was mainly residents trading tips for home energy upgrades. Now, the event attracts hundreds of stakeholders in local energy development, including businesses, municipal leaders and lawmakers.
Frontier Airlines To Begin Flights Out of Portsmouth
By Todd Bookman • Sep 12, 2018
Cubbie_n_Vegas via Flickr
For the first time since the 1990s, Portsmouth International Airport at Pease will be served by two commuter airlines.