The Four Million
by O. Henry
"The Romance of a Busy Broker"
Additional Information
- Year Published: 1906
- Language: English
- Country of Origin: United States of America
- Source: O. Henry, The Four Million (Doubleday, Page & Company; New York: 1906) Henry, O. (1906). The Four Million. Doubleday, Page & Company; New York.
-
Readability:
- Flesch–Kincaid Level: 5.2
- Word Count: 1,472
- Genre: Realism
- Keywords: fate, fiction, short story
- ✎ Cite This
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Pitcher, confidential clerk in the office of Harvey Maxwell, broker, allowed a look of mild interest and surprise to visit his usually expressionless countenance when his employer briskly entered at half past nine in company with his young lady stenographer. With a snappy "Good–morning, Pitcher," Maxwell dashed at his desk as though he were intending to leap over it, and then plunged into the great heap of letters and telegrams waiting there for him.
The young lady had been Maxwell's stenographer for a year. She was beautiful in a way that was decidedly unstenographic. She forewent the pomp of the alluring pompadour. She wore no chains, bracelets or lockets. She had not the air of being about to accept an invitation to luncheon. Her dress was grey and plain, but it fitted her figure with fidelity and discretion. In her neat black turban hat was the gold–green wing of a macaw. On this morning she was softly and shyly radiant. Her eyes were dreamily bright, her cheeks genuine peachblow, her expression a happy one, tinged with reminiscence.
Pitcher, still mildly curious, noticed a difference in her ways this morning. Instead of going straight into the adjoining room, where her desk was, she lingered, slightly irresolute, in the outer office. Once she moved over by Maxwell's desk, near enough for him to be aware of her presence.
The machine sitting at that desk was no longer a man; it was a busy New York broker, moved by buzzing wheels and uncoiling springs.
"Well—what is it? Anything?" asked Maxwell sharply. His opened mail lay like a bank of stage snow on his crowded desk. His keen grey eye, impersonal and brusque, flashed upon her half impatiently.
"Nothing," answered the stenographer, moving away with a little smile.
"Mr. Pitcher," she said to the confidential clerk, did Mr. Maxwell say anything yesterday about engaging another stenographer?"
"He did," answered Pitcher. "He told me to get another one. I notified the agency yesterday afternoon to send over a few samples this morning. It's 9.45 o'clock, and not a single picture hat or piece of pineapple chewing gum has showed up yet."
"I will do the work as usual, then," said the young lady, "until some one comes to fill the place." And she went to her desk at once and hung the black turban hat with the gold–green macaw wing in its accustomed place.
He who has been denied the spectacle of a busy Manhattan broker during a rush of business is handicapped for the profession of anthropology. The poet sings of the "crowded hour of glorious life." The broker's hour is not only crowded, but the minutes and seconds are hanging to all the straps and packing both front and rear platforms.
And this day was Harvey Maxwell's busy day. The ticker began to reel out jerkily its fitful coils of tape, the desk telephone had a chronic attack of buzzing. Men began to throng into the office and call at him over the railing, jovially, sharply, viciously, excitedly. Messenger boys ran in and out with messages and telegrams. The clerks in the office jumped about like sailors during a storm. Even Pitcher's face relaxed into something resembling animation.
On the Exchange there were hurricanes and landslides and snowstorms and glaciers and volcanoes, and those elemental disturbances were reproduced in miniature in the broker's offices. Maxwell shoved his chair against the wall and transacted business after the manner of a toe dancer. He jumped from ticker to 'phone, from desk to door with the trained agility of a harlequin.
In the midst of this growing and important stress the broker became suddenly aware of a high–rolled fringe of golden hair under a nodding canopy of velvet and ostrich tips, an imitation sealskin sacque and a string of beads as large as hickory nuts, ending near the floor with a silver heart. There was a self–possessed young lady connected with these accessories; and Pitcher was there to construe her.
"Lady from the Stenographer's Agency to see about the position," said Pitcher.

"What position?" he asked, with a frown.
"Position of stenographer," said Pitcher. "You told me yesterday to call them up and have one sent over this morning."
"You are losing your mind, Pitcher," said Maxwell. "Why should I have given you any such instructions? Miss Leslie has given perfect satisfaction during the year she has been here. The place is hers as long as she chooses to retain it. There's no place open here, madam. Countermand that order with the agency, Pitcher, and don't bring any more of 'em in here."
The silver heart left the office, swinging and banging itself independently against the office furniture as it indignantly departed. Pitcher seized a moment to remark to the bookkeeper that the "old man" seemed to get more absent–minded and forgetful every day of the world.
The rush and pace of business grew fiercer and faster. On the floor they were pounding half a dozen stocks in which Maxwell's customers were heavy investors. Orders to buy and sell were coming and going as swift as the flight of swallows. Some of his own holdings were imperilled, and the man was working like some high–geared, delicate, strong machine—strung to full tension, going at full speed, accurate, never hesitating, with the proper word and decision and act ready and prompt as clockwork. Stocks and bonds, loans and mortgages, margins and securities—here was a world of finance, and there was no room in it for the human world or the world of nature.
When the luncheon hour drew near there came a slight lull in the uproar.
 His window was open, for the beloved janitress Spring had turned on a little warmth through the waking registers of the earth.
And through the window came a wandering—perhaps a lost—odour—a delicate, sweet odour of lilac that fixed the broker for a moment immovable. For this odour belonged to Miss Leslie; it was her own, and hers only.
The odour brought her vividly, almost tangibly before him. The world of finance dwindled suddenly to a speck. And she was in the next room—twenty steps away.
"By George, I'll do it now," said Maxwell, half aloud. "I'll ask her now. I wonder I didn't do it long ago."
He dashed into the inner office with the haste of a short trying to cover. He charged upon the desk of the stenographer.
She looked up at him with a smile.  Maxwell leaned one elbow on her desk. He still clutched fluttering papers with both hands and the pen was above his ear.
"Miss Leslie," he began hurriedly, "I have but a moment to spare. I want to say something in that moment. Will you be my wife? I haven't had time to make love to you in the ordinary way, but I really do love you. Talk quick, please—those fellows are clubbing the stuffing out of Union Pacific."
"Oh, what are you talking about?" exclaimed the young lady. She rose to her feet and gazed upon him, round–eyed.
"Don't you understand?" said Maxwell, restively. "I want you to marry me. I love you, Miss Leslie. I wanted to tell you, and I snatched a minute when things had slackened up a bit. They're calling me for the 'phone now. Tell 'em to wait a minute, Pitcher. Won't you, Miss Leslie?"
The stenographer acted very queerly. At first she seemed overcome with amazement; then tears flowed from her wondering eyes; and then she smiled sunnily through them, and one of her arms slid tenderly about the broker's neck.
"I know now," she said, softly. "It's this old business that has driven everything else out of your head for the time. I was frightened at first. Don't you remember, Harvey? We were married last evening at 8 o'clock in the Little Church Around the Corner."


class: center, middle, inverse, title-slide # ETC3250: Linear Regression ## Semester 1, 2020 ###
Professor Di Cook
Econometrics and Business Statistics
Monash University ### Week 2 (a) --- ## Multiple Regression
 .tip[ `$$Y_i = \beta_0 + \beta_1 X_{1,i} + \beta_2 X_{2,i} + \cdots + \beta_pX_{p,i} + e_i.$$`] - Each `\(X_{j,i}\)` is .orange[numerical] and is called a .orange[predictor]. - The coefficients `\(\beta_1,\dots,\beta_p\)` measure the .orange[effect] of each predictor after taking account of the effect of all other predictors in the model. - Predictors may be .orange[transforms] of other predictors. e.g., `\(X_2=X_1^2\)`. --- ## Multiple Regression - The model describes a .orange[**line**, plane or hyperplane] in the predictor space.     .font_tiny[(Chapter3/3.1.pdf)] --- ## Multiple Regression - The model describes a .orange[line, **plane** or hyperplane] in the predictor space.      .font_tiny[(Chapter3/3.5.pdf)] --- ## Categorical Variables Qualitative variables need to be converted to numeric. `$$x_i = \left\{\begin{array} {ll} 1 & \mbox{if} ~~~ i^{\text{th}} \mbox{ obs is a koala} \\ 0 & \mbox{otherwise} \end{array}\right\}$$` which would result in the model `$$\hat{y}_i = \left\{\begin{array} {ll} \beta_0+\beta_1 & \mbox{if} ~~~ i^{\text{th}} \mbox{ obs is a koala} \\ \beta_0 & \mbox{otherwise} \end{array}\right\}$$` --- ## Categorical Variables More than two categories `$$x_{i1} = \left\{\begin{array} {ll} 1 & \mbox{if} ~~~ i^{\text{th}} \mbox{ obs is a koala} \\ 0 & \mbox{otherwise} \end{array}\right\}$$` `$$x_{i2} = \left\{\begin{array} {ll} 1 & \mbox{if} ~~~ i^{\text{th}} \mbox{ obs is a bilby} \\ 0 & \mbox{otherwise} \end{array}\right\}$$` which would result in the model using .orange[dummy variables]. `$$\hat{y}_i = \left\{\begin{array} {ll} \beta_0+\beta_1 & \mbox{if} ~~~ i^{\text{th}} \mbox{ obs is a koala} \\ \beta_0+\beta_2 & \mbox{if} ~~~ i^{\text{th}} \mbox{ obs is a bilby} \\ \beta_0 & \mbox{otherwise} \end{array}\right\}$$` --- ## Ordinary Least Squares (OLS)
 .orange[Ordinary least squares] is the simplest way to fit the model. Geometrically, this is the sum of the squared distances, parallel to the axis of the dependent variable, between each observed data point and the corresponding point on the regression surface – the .orange[smaller the sum] of differences, the .orange[better] the model fits the data. --- ## Model Diagnostics
 `\(R^2\)` is the proportion of variation explained by the model, and measures the goodness of the fit, close to 1 the model explains most of the variability in `\(Y\)`, close to 0 it explains very little. `$$R^2 = 1 - \frac{RSS}{TSS}$$` where `\(RSS = \sum_{i=1}^n (y_i-\hat{y})^2\)` (read: Residual Sum of Squares), and `\(TSS=\sum_{i=1}^n (y_i-\bar{y})^2\)` (read: Total Sum of Squares). --- ## Model Diagnostics
 .orange[Residual Standard Error (RSE)] is an estimate of the standard deviation of `\(\varepsilon\)`. This is meaningful with the assumption that `\(\varepsilon \sim N(0, \sigma^2)\)`. `$$RSE = \sqrt{\frac{1}{n-p-1}RSS}$$` --- ## Model Diagnostics
 F statistic tests whether any predictor explains response, by testing `\(H_0: \beta_1=\beta_2=...=\beta_p=0\)` vs `\(H_A:\)` at least one is not 0 --- ## Think about ...
 - Is at least one of the predictors useful in predicting the response? - Do all the predictors help to explain `\(Y\)`, or is only a subset of the predictors useful? - How well does the model fit the data? - Given a set of predictor values, what response value should we predict and how accurate is our prediction? --- ## Example - Wages Wage and other data for a group of 3000 male workers in the Mid-Atlantic region. .orange[Goal:] .green[Predict wage based on worker characteristics.] ``` ## Observations: 3,000 ## Variables: 11 ## $ year <int> 2006, 2004, 2003, 2003, 2005, 2008, 2009, 2008, 200... ## $ age <int> 18, 24, 45, 43, 50, 54, 44, 30, 41, 52, 45, 34, 35,... ## $ maritl <fct> 1. Never Married, 1. Never Married, 2. Married, 2. ... ## $ race <fct> 1. White, 1. White, 1. White, 3. Asian, 1. White, 1... ## $ education <fct> 1. < HS Grad, 4. College Grad, 3. Some College, 4. ... ## $ region <fct> 2. Middle Atlantic, 2. Middle Atlantic, 2. Middle A... ## $ jobclass <fct> 1. Industrial, 2. Information, 1. Industrial, 2. In... ## $ health <fct> 1. <=Good, 2. >=Very Good, 1. <=Good, 2. >=Very Goo... ## $ health_ins <fct> 2. No, 2. No, 1. Yes, 1. Yes, 1. Yes, 1. Yes, 1. Ye... ## $ logwage <dbl> 4.318063, 4.255273, 4.875061, 5.041393, 4.318063, 4... ## $ wage <dbl> 75.04315, 70.47602, 130.98218, 154.68529, 75.04315,... ``` --- class: split-two .column[.pad50px[ ## Take a look
 - What do the following pairwise comparisons of the variables `year`, `age` and, `education` against `wage` show us? ]] .column[.content.vmiddle.center[  ]] --- class: split-two .column[.pad50px[ ## Take a look
 - If we examine `logwage` instead of `wage` as response variable - what changes? ]] .column[.content.vmiddle.center[  ]] --- ## Model for wage data
 .tip[ `\(Y = \beta_0 + \beta_1X_1 + \beta_2X_2 + \beta_3X_3 + e\)` ] where `\(Y=\)` `logwage`, `\(X_1=\)` `year`, `\(X_2=\)` `age`, and `\(X_3=\)` `education`. --- ## Fitting the model in R ``` lm(formula = logwage ~ year + age + education, data = Wage) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.745e+01 5.469e+00 -3.191 0.00143 year 1.078e-02 2.727e-03 3.952 7.93e-05 age 5.509e-03 4.813e-04 11.447 < 2e-16 education2. HS Grad 1.202e-01 2.086e-02 5.762 9.18e-09 education3. Some College 2.440e-01 2.195e-02 11.115 < 2e-16 education4. College Grad 3.680e-01 2.178e-02 16.894 < 2e-16 education5. Advanced Degree 5.411e-01 2.362e-02 22.909 < 2e-16 Residual standard error: 0.3023 on 2993 degrees of freedom Multiple R-squared: 0.2631, Adjusted R-squared: 0.2616 F-statistic: 178.1 on 6 and 2993 DF, p-value: < 2.2e-16 ``` --- ## Interpretation - The ideal scenario is when the predictors are uncorrelated. - Each coefficient can be interpreted and tested separately. - Correlations amongst predictors cause problems. - The variance of all coefficients tends to increase, sometimes dramatically. - Interpretations become hazardous -- when `\(X_j\)` changes, everything else changes. - Predictions still work provided new `\(X\)` values are within the range of training `\(X\)` values. - Claims of causality should be avoided for observational data. --- ## Interactions - An interaction occurs when the one variable changes the effect of a second variable. (e.g., spending on radio advertising increases the effectiveness of TV advertising). - To model an interaction, include the product `\(X_1X_2\)` in the model in addition to `\(X_1\)` and `\(X_2\)`. - **Hierarchy principle**: If we include an interaction in a model, we should also include the main effects, even if the p-values associated with their coefficients are not significant. (This is because the interactions are almost impossible to interpret without the main effects.) --- ## Interactions    .font_tiny[(Chapter3/3.7.pdf)] --- ## Residuals
 - If a plot of the residuals vs any predictor in the model shows a pattern, then the .orange[relationship is nonlinear.] - If a plot of the residuals vs any predictor **not** in the model shows a pattern, then .orange[the predictor should be added to the model.] - If a plot of the residuals vs fitted values shows a pattern, then there is .orange[heteroscedasticity in the errors]. (Could try a transformation.) --- ## Residuals      .font_tiny[(Chapter3/3.9.pdf)] --- ## Model .green[ `$$Y_i = \beta_0 + \beta_1 X_{1,i} + \beta_2 X_{2,i} + \cdots + \beta_pX_{p,i} + e_i.$$` ] Let `\(Y = (Y_1,\dots,Y_n)'\)`, `\(e = (e_1,\dots,e_n)'\)`, `\(\beta = (\beta_0,\dots,\beta_p)'\)` and `$$X = \begin{bmatrix} 1 & X_{1,1} & X_{2,1} & \dots & X_{p,1}\\ 1 & X_{1,2} & X_{2,2} & \dots & X_{p,2}\\ \vdots & \vdots & \vdots & & \vdots\\ 1 & X_{1,n} & X_{2,n} & \dots & X_{p,n} \end{bmatrix}.$$` Then .green[ `$${Y} = {X}{\beta} + {e}$$`] --- ## Least squares estimation Minimize: `\((Y - X\beta)'(Y - X\beta)\)` Differentiate wrt `\(\beta\)` and equal to zero gives `\(\hat{\beta}=(X'X)^{-1}X'Y\)` (The "normal equation".) `$$\hat{\sigma}^2 = \frac{1}{n-p-1}({Y} - {X}\hat{{\beta}})' ({Y} - {X}\hat{{\beta}})$$` **Note:** If you fall for the dummy variable trap, `\(({X}'{X})\)` is a singular matrix. --- ## Likelihood If the errors are iid and normally distributed, then `$${Y} \sim \mathcal{N}_n({X}{\beta},\sigma^2{I})$$` So the likelihood is `$$L = \frac{1}{\sigma^n(2\pi)^{n/2}}\exp\left(-\frac1{2\sigma^2}({Y}-{X}{\beta})'({Y}-{X}{\beta})\right)$$` which is maximized when `\(({Y}-{X}{\beta})'({Y}-{X}{\beta})\)` is minimized. .orange[So MLE `\\(\equiv \\)` OLS.] --- ## Predictions .green[Optimal predictions] `$$\hat{Y}^* = \text{E}(Y^* | {X}^*, {Y},{X}) = {X}^*\hat{{\beta}} = {X}^*({X}'{X})^{-1}{X}'{Y}$$` where `\({X}^*\)` is a row vector containing the values of the regressors for the predictions (in the same format as `\({X}\)`). .green[Prediction variance] `$$\text{Var}(Y^* | {X}^*, {Y},{X}) = \sigma^2 \left[1 + {X}^* ({X}'{X})^{-1} ({X}^*)'\right]$$` - This ignores any errors in `\({X}^*\)`. - 95% prediction intervals assuming normal errors: `\(\hat{Y}^* \pm 1.96 \sqrt{\text{Var}(Y^*| {Y},{X},{X}^*)}\)`. --- layout: false # 👩💻 Made by a human with a computer ### Slides at [https://monba.dicook.org](https://monba.dicook.org). ### Code and data at [https://github.com/dicook/Business_Analytics](https://github.com/dicook/Business_Analytics).
 ### Created using [R Markdown](https://rmarkdown.rstudio.com) with flair by [**xaringan**](https://github.com/yihui/xaringan), and [**kunoichi** (female ninja) style](https://github.com/emitanaka/ninja-theme).

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OPM - Overriding a Private Method
A subclass should not contain a method with the same name and signature as in a superclass if these methods are declared to be private.
Wrong
private void func () {}
}
class Monkey extends Animal {
private void func () {}
}
Tip: Rename descendand class' method
Right
private void func () {}
}
class Monkey extends Animal {
private void extFunc () {}
}


A funny thing happened to me as I was working on my memoir. The story I thought I was writing has morphed into something else. Honestly, my first draft was a temper tantrum on paper and my second draft was not much better. I realize now that those first drafts had to be written to reach the vulnerability beneath. I also realize my mental household is shifting. I am shedding the skin of abuse and neglect on a whole different level.
I am grateful for the process; I have faith in the process; I will rise through the process. I am thankful.


China’s auto sales fall 5.8% in October
BEIJING (AP) - China’s auto sales fell 5.8% from a year earlier in October as demand for electric cars plunged, an industry group reported Monday, extending a painful squeeze in the global industry’s biggest market.
The Chinese auto market is on track to contract for second year, dragged down by weak demand in the face of cooling economic growth and a tariff war with Washington.
Drivers bought 1.9 million sedans, SUVs and minivans, according to the China Association of Automobile Manufacturers.
Sales growth has been in negative territory every month since June 2018.
Total vehicle sales, including trucks and buses, shrank 0.6% to 2.3 million.
Sales of electric and gasoline-electric hybrid vehicles tumbled 45.6% to 75,000. Demand has been hurt by the end of government subsidies in mid-2019.
Beijing has spent billions of dollars promoting electrics sales. Regulators are shifting the burden to automakers by eliminating subsidies and imposing minimum sales quotas. That raises the cost to buyers.
The October figures were a small improvement over September’s 6.3% contraction and better than double-digit contractions in previous months.
Still, sales were off 11% in the first 10 months of 2019 compared with a year earlier.
Electrics sales still are up 10.1% for the year to date, reflecting strong first-half demand before the end of subsidies.
Sales of SUVs, usually a bright spot for the industry, were down 8.3% in the first 10 months of the year but CAAM gave no total. That was slight improvement over the 9.3% decline for the nine months through September.
Sales by Chinese brands were down 9.6% from a year earlier at 770,000. They lost 1.7 percentage points of market share to 39.9%.
CAAM: http://www.caam.org.cn/
Auto sales data: http://www.auto-stats.org.cn/xxkd.asp
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Below are some empt inputes to fill in some of your favourite stuff that you love to do;
Fill in appropriately, Have Fun


Just adding a few paragraphs to show you that they are ALL styled, not just one
See? Doesn't this look beautiful? Both of the paragraphs are green.


IGV.js locuschange
Event Example
In this example we want to update the page URL whenever the IGV viewer locus changes.
To do this, we subscribe to the locuschange
.
Try it out:
- Zoom or pan the canvas in the IGV viewer.
- Notice that the page URL updated to match the new locus.


- e.g. '1. Execute a function var result = doSomething()'
The parentheses in the examples are used when calling function.
Function is some peace of codes which is defined to do small task which can be
executed as many times. It looks like this :
function functionName(variable) { code }
Some variables that is needed to run functions go in the parentheses. If
no variable is needed, leave it empty.
When calling the function, required variable needs go inside.
functionName(variable)
Brackets usually defines arrays.
array = [a, b, c]
Array is a list which contains anything such as string, integer, and even other arrays.
In order to access array, index number is used starting from 0, like this :
variable = array[index];
Braces are used for Function or Objects. For the Function, function code is inside braces.
function f1() { code }
And for the Object, object properties are inside.
var object = {"prop1":"a","prop2":"b" }
Object is a list of properties. It differs from arry because object must contain
PropertyName:property.
The single quotes and double quotes are used for strings.
var string = "Hello"
If quotes are not used, computer doesn't understand it as a string, hence it will
make error.


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full: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p774f/fs/
page: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p774f/single-page/
thumbnail: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p774f/thumbnail/
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---
{% raw %}CHAPTER XVI.{% endraw %}
{% raw %}CONDUCTING SLAVES TO CANADA.{% endraw %}
{% raw %}SYMPATHY FOR THE SLAVES.—JAMES LIGHTFOOT.—MY" FIRST{% endraw %}
{% raw %}MISSION TO THE SOUTH.—A KENTUCKY* COMPANY OF FUGITIVES.{% endraw %}
{% raw %}—SAFE AT HOME.{% endraw %}
{% raw %}The degraded and hopeless condition of a slave{% endraw %}
{% raw %}can never be properly felt by him while he{% endraw %}
{% raw %}remains in such a position. After I had tasted the{% endraw %}
{% raw %}blessings of freedom, my mind reverted to those{% endraw %}
{% raw %}whom I knew were groaning in captivity, and I at{% endraw %}
{% raw %}once proceeded to take measures to free as many as{% endraw %}
{% raw %}I could. I thought that, by using exertion, num¬{% endraw %}
{% raw %}bers might make their escape as I did, if they had{% endraw %}
{% raw %}some practical advice how to proceed.{% endraw %}
{% raw %}I was once attending a very large meeting at Fort{% endraw %}
{% raw %}Erie, at which a great many coloured people were{% endraw %}
{% raw %}present. In the course of my preaching, I tried to{% endraw %}
{% raw %}impress upon them the importance of the obliga¬{% endraw %}
{% raw %}tions they were under ; first, to God, for their de¬{% endraw %}
{% raw %}liverance ; and then, secondly, to their fellow-men,{% endraw %}
{% raw %}to do all that was in their power to bring others out{% endraw %}
{% raw %}of bondage. In the congregation was a man named{% endraw %}
{% raw %}James Lightfoot, who was of a very active tempera¬{% endraw %}
{% raw %}ment, and had obtained his freedom by fleeing to{% endraw %}
{% raw %}Canada, but had never thought of his family and{% endraw %}
{% raw %}friends whom he had left behind, until the time he{% endraw %}


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An asynchronous operation has an associated executor satisfying the Executor
requirements.
If not otherwise specified by the asynchronous operation, this associated
executor is an object of type system_executor
.
All asynchronous operations in this library have an associated executor object that is determined as follows:
— If the initiating function is a member function, the associated executor
is that returned by the get_executor
member function on the
same object.
— If the initiating function is not a member function, the associated executor
is that returned by the get_executor
member function of the
first argument to the initiating function.
Let Executor1
be the type of the associated executor. Let
ex1
be a value of type Executor1
, representing
the associated executor object obtained as described above.


Welcome to year two of the WildBarry Halloween Party!!!
This year we've decided to have a little more scary fun with clowns, lol. As you can see, there are
pictures being changed up periodically.
This is happening through a series of events triggered from
a simple button push. The button kicks off a blast of cold air from a leafblower (held by the clown you see
above), lights up a creepy clown with glowing eyes, triggers a scream, and takes a little snapshot to remember the occasion.
Then, the picture gets pushed to source control.
I have a jenkins server that is polling my repository every minute for changes. If there is a change,
this website is rebuilt.
LOVE YOU GUYS!!! Happy Halloween!!!


Hello ! My Name is Amit Pariyar . I am an Associate Professor at the Kyoto College of Graduate Studies for Informatics. I graduted with a PhD in Social Informatics from Kyoto University in 2015. My research interests are in the areas of Knowledge Management, ICT4D and Social Informatics.
I like to play musical instruments and listening to music.
I am teaching following courses this semester:
Hyakumanben Campus
The Kyoto College of Graduate Studies for Informatics
Sunday: 5-6 pm or (Appointment by email)
Email<EMAIL_ADDRESS>Here is the video of my office:
| Course | Weekdays | |||||
| Sunday | Monday | Tuesday | Wednesday | Thursday | Friday | |
|---|---|---|---|---|---|---|
| Web Programming | 1:30 to 4:40 | |||||
| E-Commerce Methodology | 1:30 to 4:40 | |||||
| Project Management | 4:50 to 6:20 |
Near Doshisha University
The Kyoto College of Graduate Studies for Informatics


Rugby: East Kilbride boss hails best-ever display as they reach shield final
Allan Steel's side smashed Strathaven to set up a showdown with Allan Glens next month
by Paul Thomson, https://www.dailyrecord.co.uk/authors/paul-thomson/Strathaven...12 East Kilbride...50
Allan Steel saluted a ‘best-ever’ performance from his East Kilbride players as they marched into the West Regional Shield final.
The Torrance House outfit beat Strathaven 50-12 on Saturday to set up a final with league rivals Allan Glens on December 7.
EK dominated their lower-league opponents from start to finish and gaffer Steel said: “It’s the best performance in the whole time I’ve been at the club.
“The level of focus and commitment was spot on and for the first 70 minutes we didn’t give Strathaven a look in.
“We prepared excellently and stood up well to a potential banana skin.

EK are now hoping to land their first trophy in 17 years.
“We’ve not lifted silverware since around 2002 when we last won the shield, so this is a big incentive for the boys to do that,” Steel added.
“We’ve had a few promotions, but it’s always been as runners-up, so this is a chance to get our hands on a trophy.”
Competitive fixtures between the neighbours have been rare over the years, and with Strathaven currently sitting top of the division below East Kilbride there could have been concern in the Torrance House camp going into the tie.
However, a week off from competition had left EK, in the words of captain, Mark Ellison, “buzzin” beforehand and they blew their hosts away pretty much from the off.
An early Grant Dryburgh penalty was followed by tries from Chris Mclellan, Robbie Greenfield and Ellison to leave the visitors home & hosed by the half.
 Either that or he is faster, much faster, than anyone previously thought and was actually onside.
The second half carried on in a similar vein, with Jonno Ford and Chris O’Neil both touching down twice before a late Strathaven rally brought them some reward with a brace of scores.
It was appropriate that both Ford and O’Neill went over as they were amongst the more prominent members of a dominant EK pack.
Strathaven carry some big laddies up front but they were unable to cope with the physicality and technique of the EK forwards.
Credit, though, to Allan Steel’s side in that they did not seek just to play, ‘up the jumper’ rugby, and instead spun the ball wide at every opportunity.
Wingers Iain Brown and Dryburgh were both on fire and despite neither touching down wrought havoc in the Strathaven defence and created space for colleagues to exploit.
East Kilbride will now face Allan Glens in early December at a venue to be confirmed. Scotstoun, home of Glasgow Warriors, was a potential venue but there now seems to be some conjecture over this. Details will be announced in due course.
Steel added: "I'm very happy with the win and the performance.
"We knew going into the game it would be really tricky because Strathaven are a team who haven't lost in a long time this year.
"We showed them respect because of that with our preparation and that preparation was key. We knew if we got our set-pieces right and defending then we'd win the game and that's exactly what happened.
"The boys knocked Starthaven back well and didn't give them an inch. We were totally dominant at the set-pieces and they couldn't do much about it."
This weekend EK return to league action when they host South Lanarkshire rivals Cambuslang at Torrance House.
Kick-off is scheduled for 2pm.
EK TEAM: 1. Mclellan, 2. Ford, 3. E. Beaton, 4. McLean, 5. Rosie, 6. O’Neill, 7. R. Beaton, 8. Middleton, 9. Ellison, 10. Henderson, 11. Dryburgh, 12. Greenfield, 13. Stott, 14. Brown, 15. Murray.
Replacements: Jaap, Dalgleish, Mason and Haggerty.


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5.YIELD
5.1. Description
The central objective of Yield (computer animation and installation) [Figure 16] was to develop the iconography of The Virtual Harvester project and to test the design credibility of the digital models. Because both productions, Yield and Data Field (to be described later), make use of the same 3D digital information, it creates the possibility for one project to grow into the other. Portraying a more oppressive vision of our digital world, I have set out to project an environment wherein the technologies of freedom and information threaten to turn into machines of dominance and control. "While the tourist hovers past in his private helicopter / we are down here / trying to figure out / what will be the weapon / that we will one day wield." (Groenewegen 1997) [16]
5.2. Cyberpunk
The iconography of Yield, developing from the research done for An Electronic Laager (as described earlier), was influenced by the writings of the cyberpunk movement in '80s science fiction (SF). Although cyberpunk is the product of the 1980's milieu, it is rooted in the tradition of popular SF literature, especially those with a more dystopian message. Of special importance are the '60s and '70s New Wave writers such as Samuel R. Delany, Philip K. Dick, and J. G. Ballard, as well as the postmodern fragmentations of William Burroughs and apocalypticism of Thomas Pynchon.
Also influential were the "postmodern" movies of the last decade (TRON, Blade Runner, the Mad Max films, [Figure 17] Brazil, Robocop, [Figure 18] Terminator 2, The Lawnmower Man etc.), portraying material conditions at the end of the century in a catastrophic state of decline. Other noted influences are musicians and punk bands (Laurie Anderson, Lou Reed and the Velvet Underground, Sonic Youth, The Sex Pistols, Pink Floyd, The Clash, U2), and performance and installation artists (Nam June Paik, Demers and Vorn, Stelarc, Mark Pauline and the Survival Research Laboratories). Unlike the Star Wars and Indiana Jones movies, with their romantic reaffirmation of the righteous empire and neo-colonial adventurism, the spaces described in cyberpunk fiction were not the expansive realms of far-away empires or enormous spaceships. Cyberpunk rather described a street level science fiction that foregrounded a perverse urbanism, marked by its visual intensity. [17]
This highly-stylized narrative form can be read as a reflection of the information overload that characterizes today's urban societies, and McLuhan's metaphor of the implosion of the media. (Expansive SF was based on historical analogies of colonialism and exploration.) The implosive SF of cyberpunk is based on analogies of the invasion and transformation of the body (prosthetic limbs, cosmetic surgery) and mind (brain-computer interfaces, neuro-chemistry) by the 'alien' technologies of our own making. It draws from the history of the development of robotics and computers as Manuel De Landa describes: "In this chart the robot would see the evolution of armies as machines (clockworks, motors and networks), the different forms in which intelligence 'migrated' from human bodies to become incarnated in physical contraptions, and the processes through which artificial forms of perception (vision, hearing) came to be synthesized and embodied in computers." (1991: 10)
Cyberpunk unveils how these technologies are redefining the nature of humanity and the nature of self as the distinction between organic and non-organic life is becoming blurred. From these writers point of view humans have served only as "machines' surrogate reproductive organs until robots acquired their own self-replication capabilities." (1991: 7) The cyberpunk artists' vision is that the technological dreams and nightmares spoken of in SF literature are already part of reality. A collapse of the future on the present has occurred with the advent of post-industrialism.
5.3. Wasteland
In his provocative book (which I will refer to extensively during this study) The Ends of the Earth: A journey to the frontiers of Anarchy reporter Robert Kaplan relates a story he was told by a foreign ambassador while he dined with him in Abidjan. I am referring to this account because it sounds like a description from a modern cyberpunk novel and hence it relates very well to the helicopter sequence in Yield. [Figure 19] In these scenes a helicopter transports the viewers across a virtual wasteland towards lush 'data fields of poverty'.
The ambassador told about a visit he had made to the extreme northwest of the Ivory Coast to observe an international aid project supported by the United Nations, implemented to eradicate river blindness. Black flies accumulating over and alongside the rivers of interior Africa causes the disease when they sting African people as they bathe and wash their clothes in the rivers.
It was a difficult and dangerous operation. The helicopters doing the spraying had to deal with flying upwind at high speeds, near the water, over and in between small side streams not much higher than their rotor blades, with trees on either side. Landsat satellites beamed down computerized pictures showing the state of each river and side stream. Highly sensitive computers monitored the chemicals content of the river water, requiring constant readjustments in the ratio of the six chemical comprising the spray repellent. The pilots came from the U.S., Canada, Peru, Portugal, and the former Yugoslavia. The ambassador told Kaplan how impressed he was by their military discipline.
Though the pilots succeed in eradicating river blindness from the immediate region, the disease could easily return to nearby Liberia and Nigeria if the project was ever ended. "This is what it takes," said the ambassador, full of awe. "The highest levels of technology, attention to the finest details, and the most extraordinary talent and work ethic" are required to halt one disease, in one part of Africa, temporarily. The ambassador went on to describe how the pilots had been rising before dawn every day for a few years to be briefed and to study their flight plans and computer printouts. Western food was flown from Abidjan and trucked to their compounds. The pilots had their own generators for electricity. The whole set-up was like a base on the moon. (Kaplan 1996: 87-88)
5.4. Virtual Reality
In their imaginations they saw the cinema as the total and complete representation of reality; they saw in a trice the reconstruction of a perfect illusion of the outside world in sound, color and relief. - André Bazin, 1946 (Bukatman 1993: 77)
But in the computer country, they dream of eliminating the interface, of breaking down the barrier of the screen and eliminating the distinctions made between this side and that side. - Karrie Jacobs, 1990 (Bukatman 1993: 77)
Virtual reality (VR) [18] technologies have attracted much attention at the beginning of the 1990's in attempting to simulate cyberspace. At this stage, the talk surrounding the 'immersive interface' of virtual reality far outstrips the achievements. The VR apparatus, bulky and inconvenient, is far from the desired ideal of a 'transparent interface.' At the same time the computational requirements to run these systems are so intense as to put the experience out of reach for all but the wealthiest, such as the military and entertainment clients. However, it can be assumed that the technology will improve to the point of widespread feasibility and affordability within a decade.
Yet, the mere existence of virtual reality raises important questions. "Such ontological and epistemological structures as the nature of the human, the real, experience, sensation, cognition, identity and gender are all placed, if not under erasure then certainly in question around the (still largely) discursive object of virtual reality and the postulated existence of perfect, simulated, environment. Virtual reality has become the very embodiment of postmodern disembodiment." (Bukatman 1993: 78)
For me virtual reality becomes a powerful metaphor by which to question the Western world's isolation from the social and economic realities of the rest of the developing world. As the helicopter story in the previous section illustrates, the Western world's involvement with places like Africa is becoming more and more detached and disembodied, not unlike an exploration into cyberspace.
5.5. Cyberspace
What the implosive SF of William Gibson's book Neuromancer (1984) deals with is the invisible spaces of electronic circulation or microscopic nanotechnologies. As such cyberpunk is concerned with the intimacy of the contemporary interfaces that exist between human subjects and electronic technology. [Figure 20]
In cyberpunk literature 'urban space' depicts more than just a realistic milieu in which the punk protagonists fight out their battles. It is the central metaphor used to describe the phenomenologically relevant 'other space' of information circulation and data control of today's electronic culture, coined by Gibson as cyberspace. "Urban space and cyberspace become reciprocal metaphors – each enables an understanding and negotiation of the other." (Bukatman 1993: 145) Gibson's defined cyberspace as a consensual hallucination -- a software meditation -- whose function is precisely to provide that vision of, and entry into, the invisible -- but actual -- world within the computer.
Cyberspace is thus an abstraction that provides a narrative compensation of McLuhan's idea of the loss of visibility and space in the technological, cybernetic world of today. It is a metaphor attempting to recognize and overcome the technological estrangements of the electronic age. In this way, cyberpunk becomes the defining movement / element of contemporary society. Indeed, as Frederick Jameson puts it, "cyberpunk is the supreme literary expression, if not of postmodernism, then of late capitalism itself". (Jameson 1991: 419)
5.6. Design
"Sometimes the design is the statement." -- Ridley Scott, director of Blade Runner. (Sammon 1996) [Figure 21]
For the styling and design of Yield, I deliberately set out to portray the banal 'hyper-reality' of the electronic 'cyberspace' within the journey towards the data fields takes place. Unlike big-budget Hollywood productions of the '90s (Jurrasic Park, Speed, Titanic), wherein it becomes impossible to discern computer generated images (CGI) from 'real' footage, Yield draws from '80s films like TRON [Figure 22] and The Last Starfighter. TRON was the first commercial film to rely on entirely computer generated images and sequences. These effects were characteristically too perfect to be accepted as 'facsimiles' of the real world -- the surfaces were too smooth and undetailed, the reflections were perfectly focussed, movements were unswerving and non-inertial. The perfect coordinate space of the terminal field allowed a precision of perspective that could not escape the ordinary eye. "The body, with its analogic imperfections, is exempted from this digital world -- contra TRON's narrative strategies, this is precisely a disembodied space." (Bukatman1993: 88)
The slick, perfectly stylized machines appearing in Yield, stood in sharp contrast to the organic, worked shapes and surfaces of my sculptural objects. As one friend remarked: "...I just miss the absolute raw power that is so present in your sculptures -- rough hewn stone and welded steel. Your threeD [sic] world is very clean and antiseptic."


GROSSE FATIGUE A film review by James Berardinelli Copyright 1995 James Berardinelli
RATING (0 TO 10): 8.3
France, 1994 U.S. Availability: limited release 7/95 Running Length: 1:24 MPAA Classification: R (Double entendres, profanity) Theatrical Aspect Ratio: 1.85:1
Cast: Michel Blanc, Carole Bouquet, Philippe Noiret Director: Michel Blanc Producer: Patrice Ledoux Screenplay: Michel Blanc Cinematography: Eduardo Serra Music: Rene-Marc Bini U.S. Distributor: Miramax Zoe In French with English subtitles
There are two types of satire: high concept and low concept. Both lampoon specific targets, but while the latter does so with more concern for belly-laughs than subtle, thought-provoking amusement, the former is often characterized by wit and intelligence. GROSSE FATIGUE, a quirky, unique product of French writer/director/actor Michel Blanc, is a high concept farce. During most showings of this film, smiles and chuckles - - not uncontrollable laughter -- will be in abundance.
One common disclaimer reads: "The names have been changed to protect the innocent." In GROSSE FATIGUE, names are precisely what have not been altered. The personalities portrayed by Blanc, Carole Bouquet, and Philippe Noiret retain the actors' real names. In GROSSE FATIGUE as in life, Blanc is a famous French movie maker/actor. Bouquet is a glamorous actress and Noiret is the suave, cultured man who graced international screens in CINEMA PARADISO and THE POSTMAN. Only a special sort of director would dare take such obvious jabs at his own celebrity status, and part of GROSSE FATIGUE's appeal is that this film plays off egos rather than pandering to them.
As has been expected of satires since THE PLAYER, the movie features several cameos. Actresses Mathilda May and Charlotte Gainsbourg, and director Roman Polanski all make appearances. And, while they never show up in the flesh, icons Gerard Depardieu and Emanuelle Beart are the targets of several verbal ripostes. Yet, even though many of the in-jokes refer to French cinema, familiarity with France and its movies is not mandatory to enjoy GROSSE FATIGUE.
The premise, which demands a fair (but not unreasonable) level of suspended disbelief, postulates what might happen if a famous person had a double, and that double chose to masquerade as the star. In this case, the celebrity victim is Blanc, and the man cashing in on the resemblance is a nobody named Patrick Olivier (also played by Blanc). The impostor is so good that Blanc's relatives and friends (like ex-007 girl Carole Bouquet, who co-starred with Roger Moore in FOR YOUR EYES ONLY) doubt the identity of the true man. By the time Blanc figures out what's going on and decides to take action, it may already be too late.
Blanc has been referred to as France's Woody Allen, and the script for GROSSE FATIGUE is sprinkled with the kind of comedy and insight the New York based film maker is famous for. Although the movie is occasionally hamstrung by technical limitations (a poor score being the most obvious example), superb writing and solid acting make up for the few faults. Thematically, the picture delves into such diverse (and weighty) topics as the nature of fame and personal identity, and the decline of the French film industry in the face of an all-out onslaught from Hollywood.
In the way it toys with reality and fantasy, GROSSE FATIGUE is a fascinating exploration of life as seen through the eyes of a perceptive and humorous director. Blanc never allows his film to degenerate into a series of pointless jokes, but the serious side is also kept at bay. GROSSE FATIGUE is smart in both script and approach, and this makes for a lighthearted ninety minutes where turning off the thinking portions of the brain is not a prerequisite to enjoyment.
- James Berardinelli<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
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References
- A. Berger. Long-term variations of daily insolation and quaternary climatic changes. Journal of Atmospheric Science, 35(12):2362–2367, 1978.
- A. Berger and M. F. Loutre. Insolation values for the climate of the last 10 million years. Quaternary Science Reviews, 10(4):297–317, 1991.
- M. I. Budyko. The effect of solar radiation variations on the climate of the earth. Tellus, 21(5):611–619, 1969.
- Ken Caldeira and James F. Kasting. Susceptibility of the early earth to irreversible glaciation caused by carbon dioxide clouds. Nature, 359:226–228, 1992.
- J. Laskar, P. Robutel, F. Joutel, M. Gastineau, A. C. M. Correia, and B. Levrard. A long-term numerical solution for the insolation quantities of the earth. Astronomy & Astrophysics, 428:261–285, 2004.


Two types of pairwise genome alignment are available in Ensembl Genomes, based on LastZ [1] (or its predecessor BlastZ [2]) and translated BLAT (tBLAT) [3]. LastZ is typically used for closely related species, and tBLAT for more distant species. The method of alignment affects the coverage of the genomes, with tBLAT expected to mostly find homologies in coding regions.
The raw results from LastZ or tBLAT are alignment blocks, which are 'chained' according to their location in both genomes, then 'netted' to choose the best sub-chain in each region for the reference species [4]. The resultant LastZ-net and tBLAT-net alignments are displayed in Ensembl Genomes for selected fungal, metazoan, protist and plant species.


Important Information:
The development process of this package is ongoing at eduAlgo GITHUB. The modules are incomplete and as soon as the development phase ends the stable version will be released, which will contain a lot of functionalities and algorithms.


Index
A B C D E F G L M N O P R S T U V Z
All Classes All Packages
All Classes All Packages
All Classes All Packages
C
- calculateElapsedDays() - Method in class OneNightCar.Rental.OneNightCar.Rental
-
Calculates the duration of a rental
- calculateRentalPriceForCombustion(CombustionCar) - Method in class OneNightCar.Rental.FuelRental
-
Takes the price of a car and multiplies it with the elapsed hours
- calculateRentalPriceForElectric(ElectricCar) - Method in class OneNightCar.Rental.ElectricRental
- cancelElectricRental(ElectricRental) - Method in class OneNightCar.Person.Customer
-
Ask the Admin to Cancel an existing Electrical OneNightCar.Rental made by this Customer
- cancelFuelRental(FuelRental) - Method in class OneNightCar.Person.Customer
-
Ask the Admin to Cancel an existing Fuel OneNightCar.Rental made by this Customer
- OneNightCar.Car - package OneNightCar.Car
- OneNightCar.Car - Class in OneNightCar.Car
-
Represents a OneNightCar.Car
- OneNightCar.Car(OneNightCar.Car.Type, String, String, OneNightCar.Car.State, double, double, long, Customer.CustomerLevel, float) - Constructor for class OneNightCar.Car.OneNightCar.Car
-
Creates an OneNightCar.Car Constructor with Parameters to use it in the Child Class.
- OneNightCar.Car.State - Enum in OneNightCar.Car
- OneNightCar.Car.Type - Enum in OneNightCar.Car
- cardNumber - Variable in class OneNightCar.Person.PaymentMethod
- cardType - Variable in class OneNightCar.Person.PaymentMethod
- CardType() - Constructor for enum OneNightCar.Person.PaymentMethod.CardType
- carID - Variable in class OneNightCar.Rental.OneNightCar.Rental
- carIsBeingUsed(CombustionCar) - Method in class OneNightCar.ParkingArea.OneNightCar.ParkingArea
-
sets a Combustion OneNightCar.Car in the state notAvailableCars and removes it from availableCars
- carIsNoLongerBeingUsed(CombustionCar) - Method in class OneNightCar.ParkingArea.OneNightCar.ParkingArea
-
sets a Combustion OneNightCar.Car in the state availableCars and removes t from notAvailableCars
- CarManagementSystem - Class in OneNightCar.Car
-
Represents a OneNightCar.Car Management System
- CarManagementSystem() - Constructor for class OneNightCar.Car.CarManagementSystem
-
Create a CarManagementSystem with empty lists a list for the Combustion cars and a list for the Electric cars
- carsInStation - Variable in class OneNightCar.ParkingArea.OneNightCar.ParkingArea
- CCV - Variable in class OneNightCar.Person.PaymentMethod
- changeCarState(OneNightCar.Car.State) - Method in class OneNightCar.Car.OneNightCar.Car
-
Change the OneNightCar.Car state .
- chargePercent - Variable in class OneNightCar.Car.ElectricCar
- chargePercentAfter - Variable in class OneNightCar.Rental.ElectricRental
- chargePercentBefore - Variable in class OneNightCar.Rental.ElectricRental
- city - Variable in class OneNightCar.ParkingArea.ParkingAreaAddress
- city - Variable in class OneNightCar.Person.PersonAddress
- combustionCar - Variable in class OneNightCar.Rental.FuelRental
- CombustionCar - Class in OneNightCar.Car
-
Represents a Combustion OneNightCar.Car extent OneNightCar.Car
- CombustionCar(OneNightCar.Car.Type, String, String, OneNightCar.Car.State, double, double, long, Customer.CustomerLevel, float, double, double, double, CombustionCar.Transmission, CombustionCar.FuelType, CarManagementSystem) - Constructor for class OneNightCar.Car.CombustionCar
-
Creates an ElectricCar with specified ElectricCar Parameters.
- CombustionCar(CarManagementSystem) - Constructor for class OneNightCar.Car.CombustionCar
-
Creates a Combustion OneNightCar.Car with default Values.
- CombustionCar.FuelType - Enum in OneNightCar.Car
- CombustionCar.Transmission - Enum in OneNightCar.Car
- combustionCarsList - Variable in class OneNightCar.Car.CarManagementSystem
- COMPACT - OneNightCar.Car.OneNightCar.Car.Type
- consumption - Variable in class OneNightCar.Car.CombustionCar
- CREDIT - OneNightCar.Person.PaymentMethod.CardType
- Customer - Class in OneNightCar.Person
-
Represents a PaymentMethod
- Customer(String, String, GregorianCalendar, PersonAddress, PersonManager, Customer.CustomerLevel, PaymentMethod) - Constructor for class OneNightCar.Person.Customer
-
Creates a Customer with specified Customer Parameters.
- Customer(PersonManager) - Constructor for class OneNightCar.Person.Customer
-
Creates a Customer with default Values.
- Customer.CustomerLevel - Enum in OneNightCar.Person
- customerCounter - Variable in class OneNightCar.Person.PersonManager
- customerDamagesAFuelCar(CombustionCar) - Method in class OneNightCar.Person.Customer
-
Simulates the Situation in which a customer damages an Electric OneNightCar.Car
- customerDamagesAnElectricCar(ElectricCar) - Method in class OneNightCar.Person.Customer
-
Simulates the Situation in which a customer damages an Electric OneNightCar.Car
- customerID - Variable in class OneNightCar.Person.Customer
- customerID - Variable in class OneNightCar.Rental.OneNightCar.Rental
- customerLevel - Variable in class OneNightCar.Person.Customer
- CustomerLevel() - Constructor for enum OneNightCar.Person.Customer.CustomerLevel
- customerNeedHelp(Employee) - Method in class OneNightCar.Person.Customer
-
Represents the situation in which the customer needs Support from an Employee
- customers - Variable in class OneNightCar.Person.PersonManager
- CUSTOMERSUPPORT - OneNightCar.Person.Employee.TypeOfActivity
All Classes All Packages


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Pick & Pack
Pick
Pack
Today
Yesterday
7 days
30 days
B.C.
0
Items
0
Products
Print
Product
Variation
Quantity
Left in stock
0
Orders
0
Value
Print
OrderNr
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Products
OrderDate
ShippingMethod
Notes