--- title: Boosted Tree author: 'Alexis Adams-Clark' date: '2020-12-03' slug: boosted-tree categories: [] tags: [] ---
In this post, we will outline how we fit a boosted tree model to our data. Most of our information on boosted trees comes from our lovely instructors Daniel Anderson and Joe Nese, who taught a Machine Learning as part of the Educational Data Specialization at the UO.
What does the Boosted Tree Model do? “Boosting” is similar to “bagging,” (see post on bagging here) in that it’s a general algorithm that can theoretically be used with many types of models. However, it’s super common with tree models (see posts on bagging here and random forest models here that introduce the concepts of “decision trees”). In this post, we are going to use it here to make a Boosted Tree Model. Unlike “bagging” (which uses bootstrap resampling), boosted models are built in a sequential and iterative manner, and each subsequent model is fit to the residuals (error term) of the previous model. In other words, each tree builds upon information from the prior tree. The steps to creating a decision tree are below: (courtesy of Daniel Anderson and Joe Nese).
First, a decision tree is fit the the data, which looks like: \(f_1\left(x\right) = y\)
Then, a second decision tree is fit to the residuals of the first, which looks like: \(h_1\left(x\right) = y - f_1\left(x\right)\)
The trees are then added together to obtain an ensemble algorithm, which looks like: \(f_2\left(x\right) = f_1\left(x\right) + h_1\left(x\right)\)
A new decision tree is then fit to the residuals of this model, which looks like: \(h_2\left(x\right) = y - f_2\left(x\right)\)
This tree is then added to our ensemble again, which looks like: \(f_3\left(x\right) = f_2\left(x\right) + h_2\left(x\right)\)
The process is repeated over and over again until we meet some specified stopping rule and we have our final model, which can be represented as: \[ f\left(x\right) = \sum^B_{b=1}f^b\left(x\right) \]
Hyperparameters to be optimized Boosted trees have SO MANY hyperparameters that can be tuned for model optimization. There are 4 primary hyperparameters for standard Boosted Tree Models, including:
Tree Depth - Like other tree models, tree depth is something to consider when designing a model. However, boosted trees typically involve shallow trees (3-6 splits). Boosted tree models with stumps (1 split) are also common. We probably want to stick with shallow trees because these are more computationally efficient, and deeper trees with a lot of splits put us at risk for overfitting to our training data.
Minimum n for terminal nodes - Although this is indeed a hyperparameter that can be optimized in our boosted tree model, it’s rarely necessary to tune because boosted tree models usually do not grow trees that are very deep. Right now, we are not going to worry about this hyperparameter.
n trees - Unlike some other tree models, we want to really think about the number of trees that we fit in boosted tree models. This will determine how much our model learns overall. If we have too few trees, the model will underfit. If there are too many trees, the opposite will occur, and we will overfit. This often depends on what our tree depth is set to, so we want to pay close attention to the relationship between these two hyperparameters. If we have shallow trees that learn less, we might need a large number of total trees to balance that out.
Learning Rate - Our learning rate (which is also called shrinkage) maps onto the size of the step taken down our gradient during each iteration (see below for a discussion of gradient descent). These values vary from 0-1, but rarely are above 0.3. Smaller values tend to be more helpful because we learn less from each individual tree, leading to more optimal generalizations that avoid overfitting to our data. However, like most things in machine learning, there is a delicate balance that must be struck. If we have too small of a learning rate, the model can be more computationally intensive and require a large number of trees. It is also sill subject to local minima that might exist across our gradient decent landscape.
There are three additional hyperparameters for stochastic Boosted Tree Models that we can also consider (particularly if we want to add more randomness), including:
Subsample of rows - We can actually tune our model to select a subsample of cases in the training data for each tree (usually between 50-80%)
Subsample of columns for each tree - We can also tune our model to select a sample of features (columns) to be used by each tree.
Subsample of columns for each level - We can also tune our model to select a sample of features (columns) to be used by each level.
Subsample of columns for each node/split - We can also tune our model to select a sample of features (columns) to be used by each split.
If you can believe it, there are even MORE hyperparameters we can consider if we use XGBoost
through tidymodels
to run our boosted tree model.
Loss Reduction - controls the tree depth and specifies a minimum loss required for further splits. In other words, it stops growing the tree if the change in the cost function doesn’t surpass a given threshold (see below for discussion of cost function). Although values range from 0 to infinity, values 1-20 are common.
L1 and L2 penalties - also possible to add if model is continuing to overfit
AND we can also vary our early stopping value. Although not a technical hyperparameter, it denotes a specific number of iterations that the algorithm will stop after if it’s not improving predictions. This is another way to control the algorithm speed and protect against overfitting of the data.
Because we are using XGBoost
through tidymodels
, we will specify a large number of trees and an early stopping rule. We will then tune the learning rate, then the tree specific parameters, and then the stochastic components.
Why did we select this model? We selected this model because, unlike random forests, which aggregate predictions across many deep trees (with a lot of splits), boosted models are slow learners. Boosted models start with a base learner that’s a shallow decision tree with only a few splits. These models may be considered “weak learners” because they learn very slowly. However, they are far from weak in the literal sense of the word! For model prediction, slow learning can have a lot of benefits; usually, it helps us find the most optimal solution.
Because of this, Boosted Tree Models are usually the best Out-Of-Box (OOB) models (and they win a lot of Kaggle data prediction competitions!).
Boosted Tree Model Assumptions Boosted Tree Models rely on an optimization algorithm known as Gradient Descent. Although gradient descent is a rather complex concept, it can be thought of as a way to evaluate our predictions against a “cost function,” which then will move us in the direction with the “steepest descent.” In a tree model, gradient descent is used to iteratively build each tree from the prior trees in the most optimal way. Models that use gradient descent specify a cost function, such as mean squared error. Then, the partial derivative of each model parameter is calculated according to the cost function, which will define our gradient landscape and allow us to move downhill in the steepest direction.
The distance that our predictions move is influenced by the learning rate of our model, which is multiplied by these derivative values and then subtracted from the current parameter estimates. This learning rate is essentially the speed at which the algorithm steps downhill.
The model continues to move down the gradient in an iterative fashion to minimize our model error until our model can’t improve any more! During each iteration, we will take a step along the gradient in the most optimal direction to reduce the error in our predictions. Luckily, we don’t have to do any of this by hand ourselves because tidymodels
will handle this for us! Bless you, tidymodels
!
Model Fitting Procedures
A description of our data preparation procedures can be found here. The model fit was completed using the University of Oregon’s “Talapas” Supercomputer, so while some example code will be included with this post, computations were conducted elsewhere, and the full code can be found on our GitHub here. Unfortunately, although I would love to share the .RDS file that Talapas provides us with after the model is done running, I couldn’t upload it to github because it was too big, and github wanted me to upgrade my storage.
We will start out with a default Boosted Tree Model with no tuned hyperparameters. The arguments defined are based on recommendations from Daniel Anderson and Joe Nese.
set.seed(500)
#default model without tuning
mod <- boost_tree() %>%
set_engine("xgboost", nthreads = parallel::detectCores()) %>%
set_mode("regression") %>%
set_args(trees = 5000, #number of trees in the ensemble
stop_iter = 20, #the number of iterations without improvement before stopping
validation = 0.2,
learn_rate = 0.1) #the rate at which boosting algoirithm adapts at each iteration
#translate model, so we get a sense of what it looks like
translate(mod_boost)
#create workflow for default boosted model
wf_df <- workflow() %>%
add_recipe(rec) %>%
add_model(mod)
#fit model
fit_default <- fit_resamples(
wf_df,
train_cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(verbose = TRUE,
save_pred = TRUE,
extract = function(x) extract_model(x)))
#collect metrics
bt_def_met <- fit_default %>%
collect_metrics()
bt_def_met %>%
write.csv("./BTTuneMetricsDefault.csv", row.names = FALSE)
The metrics for the default model are:
| .metric | .estimator | mean | n | std_err | .config |
|---|---|---|---|---|---|
| rmse | standard | 84.9435403 | 10 | 0.2966425 | Preprocessor1_Model1 |
| rsq | standard | 0.4603229 | 10 | 0.0018735 | Preprocessor1_Model1 |
If we were happy with this (and didn’t want to tune hyperparameters), we could then apply this our workflow to our last split with the following code:
test_fit <- last_fit(wf_df, edu_split)
test_metrics <- test_fit$.metrics
test_metrics %>%
write.csv("./BTTestMetrics1.csv", row.names = FALSE)
When our default boosted model is applied to our full training set, we get the following results:
| .metric | .estimator | .estimate | .config |
|---|---|---|---|
| rmse | standard | 85.0943206 | Preprocessor1_Model1 |
| rsq | standard | 0.4660012 | Preprocessor1_Model1 |
And then we can make our predictions on unseen data, and submit that to Kaggle with the following code:
#make predictions on test.csv using this final workflow
full_test <- read_csv("data/test.csv",
col_types = cols(.default = col_guess(),
calc_admn_cd = col_character()))
#str(full_test)
#join with ethnicity data
full_test_eth <- left_join(full_test, ethnicities) #join with FRL dataset
#str(full_test_eth)
#join with frl
full_test_FRL <- left_join(full_test_eth, frl)
nrow(full_test_FRL)
#workflow
fit_workflow <- fit(wf_df, frl_fulltrain)
#use model to make predictions for test dataset
preds_final <- predict(fit_workflow, full_test_FRL) #use model to make predictions for test dataset
head(preds_final)
pred_frame <- tibble(Id = full_test_FRL$id, Predicted = preds_final$.pred)
head(pred_frame, 20)
nrow(pred_frame)
#create prediction file
write_csv(pred_frame, "fit_bt.csv")
THEN SUBMIT TO KAGGLE!
(Note that for computational efficiency, since this model had to be run on Talapas, we generated these predictions after fitting and tuning each of our models, but only submitted the predictions for the testing set on Kaggle that corresponded to the model with the best rmse based on our validation set. For more information, see our final fit post.)
But wait……
What if we really wanted to try and reduce the rmse by tuning the hyperparameters?
Let’s try to tune the learning rate of the model to reduce the rmse even more using the following code:
set.seed(500)
#tuned model
tune_lr <- mod %>%
set_args(trees = 5000, #number of trees in the ensemble
stop_iter = 20, #the number of iterations without improvement before stopping
validation = 0.2,
learn_rate = tune()) #the rate at which boosting algoirithm adapts at each iteration
#let's get a sense of what this tuned model looks like
translate(tune_lr)
#create workflow for tuned model
wf_tune_lr <- wf_df %>%
update_model(tune_lr)
#grid for tuning learning rate
grd <- expand.grid(learn_rate = seq(0.0001, 0.3, length.out = 30))
#fit model w/ tuned learning rate
tune_tree_lr <- tune_grid(
wf_tune_lr,
train_cv,
grid = grd,
metrics = metric_set(rmse, rsq),
control = control_resamples(verbose = TRUE,
save_pred = TRUE,
extract = function(x) extract_model(x)))
#let's collect metrics
#collect metrics
bt_grid_met <- tune_tree_lr %>%
collect_metrics()
bt_grid_rsq <- bt_grid_met %>%
filter(.metric == "rsq") %>%
arrange(.metric, desc(mean)) %>%
slice(1:5)
bt_grid_rmse <- bt_grid_met %>%
filter(.metric == "rmse") %>%
arrange(.metric, mean) %>%
slice(1:5)
bt_grid_metrics <- rbind(bt_grid_rsq, bt_grid_rmse)
bt_grid_metrics %>%
write.csv("./BTTuneMetricsGrid.csv", row.names = FALSE)
It looks like we got the following metrics for our model:
| learn_rate | .metric | .estimator | mean | n | std_err | .config |
|---|---|---|---|---|---|---|
| 0.0828310 | rsq | standard | 0.4605314 | 10 | 0.0019007 | Preprocessor1_Model09 |
| 0.0518069 | rsq | standard | 0.4603968 | 10 | 0.0019518 | Preprocessor1_Model06 |
| 0.1138552 | rsq | standard | 0.4603554 | 10 | 0.0019941 | Preprocessor1_Model12 |
| 0.0311241 | rsq | standard | 0.4601135 | 10 | 0.0017949 | Preprocessor1_Model04 |
| 0.0931724 | rsq | standard | 0.4601047 | 10 | 0.0020148 | Preprocessor1_Model10 |
| 0.0828310 | rmse | standard | 84.9262587 | 10 | 0.2920822 | Preprocessor1_Model09 |
| 0.0518069 | rmse | standard | 84.9354897 | 10 | 0.2925249 | Preprocessor1_Model06 |
| 0.1138552 | rmse | standard | 84.9397766 | 10 | 0.3012949 | Preprocessor1_Model12 |
| 0.0311241 | rmse | standard | 84.9563442 | 10 | 0.2769502 | Preprocessor1_Model04 |
| 0.0931724 | rmse | standard | 84.9614124 | 10 | 0.3004549 | Preprocessor1_Model10 |
It looks like a learn rate of 0.0828310 gives us our best rmse and rsq values! Let’s create a plot using the following code:
#Let's plot
to_plot <- tune_tree_lr %>%
unnest(.metrics) %>%
group_by(.metric, learn_rate) %>%
summarize(mean = mean(.estimate, na.rm = TRUE)) %>%
filter(learn_rate != 0.0001)
highlight <- to_plot %>%
filter(.metric == "rmse" & mean == min(mean)) %>%
ungroup() %>%
select(learn_rate) %>%
semi_join(to_plot, .)
ggplot(to_plot, aes(learn_rate, mean)) +
geom_point() +
geom_point(color = "#de4f69", data = highlight) +
facet_wrap(~.metric, scales = "free_y")
{{% figure src="/plots/BTMetricsGrid_Tuned.pdf" alt="Tuned Metrics Visualization" %}}
If we wanted to now finalize our model and apply it to our test data to make predictions, we would use the following code.
#Now let's select best model and finalize our workflow
best_rmse <- tune_tree_lr %>%
select_best(metric = "rmse")
bt_wf_final <- finalize_workflow(
wf_tune_lr,
best_rmse)
#fit to test set
test_fit <- last_fit(bt_wf_final, edu_split)
test_metrics <- test_fit$.metrics
test_metrics %>%
write.csv("./BTTestMetrics_Tuned.csv", row.names = FALSE)
Model Fitting Results
Our final workflow applied to the full training dataset gives us the following results:
| .metric | .estimator | .estimate | .config |
|---|---|---|---|
| rmse | standard | 85.2073125 | Preprocessor1_Model1 |
| rsq | standard | 0.4645603 | Preprocessor1_Model1 |
Then we would follow a similar procedure as above to submit to predict on our unseen data and submit to Kaggle!
SPECIAL NOTE: However……
If we wanted to tune other hyperparameters because we are really ambitious, we could use the following code (although we did not execute this code for our model predictions because it just took way too long!!!!).
set.seed(500)
#new hyperparameters to tune
tune_depth <- tune_lr %>%
finalize_model(select_best(tune_tree_lr, "rmse")) %>%
set_args(tree_depth = tune(), #tree depth - the maximum depth of a tree
min_n = tune()) #min_n - the minimum number of data points in a node thst is required for the node to be split further
#update workflow
wf_tune_depth <- wf_df %>%
update_model(tune_depth)
#update grid
grd <- grid_max_entropy(tree_depth(), min_n(), size = 30)
#fit model
tune_tree_depth <- tune_grid(
wf_tune_depth,
train_cv,
grid = grd,
metrics = metric_set(rmse, rsq)
#plot
autoplot(tune_tree_depth)
#show best
show_best(tune_tree_depth, "rmse")
If we wanted to tune regularization, randomness, and learning rate one more time, we could use this code (although we did not do this for our model predictions because it just took way too long!)
tune_reg <- tune_depth %>%
finalize_model(select_best(tune_tree_depth, "rmse")) %>%
set_args(loss_reduction = tune()) #loss_reduction - the reduction in the loss function required to split further
#update workflow
wf_tune_reg <- wf_df %>%
update_model(tune_reg)
#update grid
grd <- expand.grid(loss_reduction = seq(0, 100, 5))
#run model again
tune_tree_reg <- tune_grid(
wf_tune_reg,
train_cv,
grid = grd)
autoplot(tune_tree_reg)
#show best
show_best(tune_tree_reg, "rmse")
#tune randomness
tune_rand <- tune_reg %>%
finalize_model(select_best(tune_tree_reg, "rmse")) %>%
set_args(mtry = tune(), #mtry - the number of predictors that will be randomly sampled at each split when creating the tree models
sample_size = tune()) #sample_size - the amount of data exposed to the fitting routine
#update workflow
wf_tune_rand <- wf_df %>%
update_model(tune_rand)
#update grid
grd <- grid_max_entropy(finalize(mtry(),
juice(prep(rec))),
sample_size = sample_prop(),
size = 30)
tune_tree_rand <- tune_grid(
wf_tune_rand,
train_cv,
grid = grd)
#plot
autoplot(tune_tree_rand)
#show bet metrics
show_best(tune_tree_rand, "rmse")
#check learning rate one more time
check_lr <- tune_rand %>%
finalize_model(select_best(tune_tree_rand, "rmse")) %>%
set_args(learn_rate = tune())
#update workflow
wf_final_lr <- wf_df %>%
update_model(check_lr)
#update grid
final_lr <- tune_grid(wf_final_lr, cv, grid = 30)
#plot
autoplot(final_lr)
#show best
show_best(final_lr, "rmse")


Books That Have Taught Me Product Management
tl;dr: I recently saw a LinkedIn post that asked for a list of books helpful for upping one’s game as a technical product manager. This post is the result.
What is Technical Product Management?
I think of it as the combo/intersection of product management and technical work, so books that help me sharpen either of those are really valuable to me.
Here’s a list of some of the books that have been most pivotal in my experience and have really helped me in my roles, especially as a PM.
I’m not including anything technical in this post, because it’s gonna vary widely with what you’re interested in? APIs? Databases? JS libraries? Search algorithms? Google books about those things. But here’s a list of books about product management and related skills and ideas that have really influenced me. I may revisit and revise in the future.
About negotiation, collaboration, and management
Crucial Conversations: Talking When the Stakes are High
The Unicorn Project - a followup to The Phoenix Project.
Project Management for Humans: Helping People Get Things Done
Getting Things Done: The Art of Stress-Free Productivity
About product management as such
Inspired: Create Tech Products that Customers Love
About business and careers in software development
Unlocking the Customer Value Chain: How Decoupling Drives Consumer Disruption
The First 90 Days: Proven Strategies for Getting Up to Speed Faster and Smarter


(a)Determine the voltage and current across the each diode in Fig. 1(a(i)) and Fig. 1(a(ii)) assuming that they are germanium diodes with a reverse resistance of 47MΩ. (b)Determine the peak and average power delivered to
RL in Fig. 1(b).
(20 Marks)
(a)Determine the ripple factor for the filtered bridge rectifier with a load as indicated in Fig.2(a). (b) A loaded zener regulator is shown in Fig.2(b) Vz = 5.1V at Iz = 49mA, IZK=1 MA, Zz= 7Ω, and IZM= 70 mA. Determine the minimum and maximum permissible load currents.
(20 Marks)
(a)Determine the IB, IE, IC, VBE, VCE and VCB in the circuit of Fig. 3(a). If VCE (sat) = 0.2V, determine whether or not this transistor is in saturation. Assume βDC= 100. (b) Determine the value of RB required to ensure saturation when VIN is 5V in Fig.3(b). What must VIN be to cut off the transistor? Assume VCE(sat) = 0V. (20 Marks)
(a)Find IC and VEC for the pnp tranistor circuit in Fig. 4(a). (b)What is the minimum value of βDC in Fig. 4(b) that makes R
IN(BASE) = 10R2? The bias resistor R2 in Fig.4(b) is replaced by a 15kΩ potentiometer. What minimum resistance setting causes saturation?
(20 Marks)
(a)Select a minimum value for the emitter bypass capacitor, C2, in Fig. 5(a) if the amplifier must operate over a frequency range from 200Hz to 10 kHz. (b) Determine the total input resistance of the emitter-follower in Fig.5(b). Also find the voltage gain, current gain, and , and power gain in terms of power delivered to the load, RL.Assme βDC= 175 and that the capacitance reactance are negligible at the frequencty of operations.
(20 Marks)


Data fitting example
using Random, DataFitting, DataFittingBasics
# Prepare the model
model = Model(
:offset => SimpleParam(4),
:line1 => DataFittingBasics.Gaussian(1.1 , 4.5, 0.5),
:line2 => DataFittingBasics.Gaussian(0.52, 5 , 1.0))
# Prepare data domain and compile the model
x = Domain(1:0.05:10)
prepare!(model, x, :(offset + line1 + line2))
# add noise and and create a Measure object
rng = MersenneTwister(0);
noise = maximum(model(1)) * 0.01
data = Measures(model(1) + noise * randn(rng, length(model(1))), noise)
# Fit model to data
@time bestfit = fit!(model, data)


This project's source code is freely available on github.com/onebusaway/onebusaway-iphone under the Apache 2.0 license, with the exception of the third party modules listed below.
Contributions are welcome!
View list of contributors on GitHub
Galuca Project
Galuca is a special project to develop FREE high-quality, high-resolution icons for iOS, Android and Windows Phone apps. Galuca is aimed at helping professional and freelance developers when designing and choosing stunning icons for their apps with minimum time and no cost.
Size and formats
Galuca Project now include 300 icons perfect for iPhone 4 and iPad 3 with Retina (@2x) and those icons can be easily adapted for Android and Windows Phone apps.
All Galuca project's icons are designed, developed and distributed freely according to Creative Commons Attribution license v3.
Galuca Project by Galuca Project is licensed under a Creative Commons Attribution 3.0 Unported License.
Based on a work at www.galuca.com.
Permissions beyond the scope of this license may be available at www.galuca.com.
Authored by Troy Brant, and found at http://troybrant.net/blog/2010/01/set-the-zoom-level-of-an-mkmapview.
A drop-in replacement for UILabel, which automatically adds a scrolling marquee effect when the label's text is larger than the specified frame.
Authored by Charles Powell, and found at https://github.com/cbpowell/MarqueeLabel.
The MIT License (MIT)
Copyright (c) 2013 Matteo Lallone
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Copyright © 2009 bdferris
Copyright © 2009-2013 University of Washington
Copyright © 2009-2013 Individual Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.


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How to create a simple slider
How to build a simple slider
Markup
Markup for slider is very simple. We are going to create div with id slider as main container, and then div with class slides-container which is going to hold all the slides. We then just need another div that’s going to hold arrows and another that will act as pager. Markup for slider is very simple. We are going to create div with id slider as main container, and then div with class slides-container which is going to hold all the slides. We then just need another div that’s going to hold arrows and another that will act as pager. Markup for slider is very simple. We are going to create div with id slider as main container, and then div with class slides-container which is going to hold all the slides. We then just need another div that’s going to hold arrows and another that will act as pager.
Styles
It will be all very messed up. Another important thing here is postion: relative
because we
need to set slides to position: absolute
. That way they are one beneath another and we will
have smooth fade in-out transition.
WWF's goal is to: Build a future where people live in harmony with nature.


Verified suppliers
Category description
Category description
A pump is a machine used to move liquid through a piping system and to raise the pressure of the liquid. A pump can be further defined as a machine that uses several energy transformations to increase the pressure of a liquid. Pumps can be classified according to their function, their conditions of service, materials of construction, etc. The pump industry trade association is the Hydraulic Institute, which has classified pumps as shown below:
Kinetic
In a kinetic pump, energy is continuously added to the liquid to increase its velocity. When the liquid velocity is subsequently reduced, this produces a pressure increase. Although there are several special pumps that fall into this classification, for the most part this classification consists of centrifugal pumps.
A centrifugal pump stripped of all nonessential details, consists of an impeller attached to and rotating with the shaft, and a casing that encloses the impeller. Liquid is forced into the inlet side of the pump casing atmospheric pressure or some upstream pressure. As the impeller rotates, liquid moves towards the discharge side of the pump.
This creates a void or reduced pressure area at the impeller inlet. The pressure at the pump casing inlet, which is higher than this reduced pressure at the impeller inlet, forces additional liquid into the impeller to fill the void.
Centrifugal pumps usually have fewer moving pumps, have no check valves associated with the pumps, produce minimal pressure pulsations, do not have rubbing contact with the pump rotor, and are not subject to the fatigue loading of bearings and seals that the periodic aspect of positive displacement pumps produce.
Positive Displacement
In a positive displacement pump, energy is periodically added to the liquid by the direct application of a force to one or more movable volumes of liquid. This causes an increase in pressure up to the value required to move the liquid through ports in the discharge line. The important points here are that the energy addition is periodic (i.e. not continuous) and that there is a direct application of force to the liquid.
The preference for a centrifugal pump over a positive displacement pump is not always the case, and, in fact, there are certain applications criteria that demand the use of a positive displacement pump. The following are some key application criteria that would lead to the selection of a positive displacement pump over a centrifugal pump:
- High viscosity
- Self-priming
- High pressure
- Low flow
- High efficiency
- Low velocity
- Low shear
- Fragile solids handling capability
- Sealless pumping
- Accurate, repeatable flow measurement
- Constant flow/variable system pressure
- Two-phase flow
Risks & Opportunities
Risks & Opportunities
A pump is a machine used to move liquid through a piping system and to raise the pressure of the liquid. A pump can be further defined as a machine that uses several energy transformations to increase the pressure of a liquid. Pumps can be classified according to their function, their conditions of service, materials of construction, etc. The pump industry trade association is the Hydraulic Institute, which has classified pumps as shown below:
Kinetic
In a kinetic pump, energy is continuously added to the liquid to increase its velocity. When the liquid velocity is subsequently reduced, this produces a pressure increase. Although there are several special pumps that fall into this classification, for the most part this classification consists of centrifugal pumps.
A centrifugal pump stripped of all nonessential details, consists of an impeller attached to and rotating with the shaft, and a casing that encloses the impeller. Liquid is forced into the inlet side of the pump casing atmospheric pressure or some upstream pressure. As the impeller rotates, liquid moves towards the discharge side of the pump.
This creates a void or reduced pressure area at the impeller inlet. The pressure at the pump casing inlet, which is higher than this reduced pressure at the impeller inlet, forces additional liquid into the impeller to fill the void.
Centrifugal pumps usually have fewer moving pumps, have no check valves associated with the pumps, produce minimal pressure pulsations, do not have rubbing contact with the pump rotor, and are not subject to the fatigue loading of bearings and seals that the periodic aspect of positive displacement pumps produce.
Positive Displacement
In a positive displacement pump, energy is periodically added to the liquid by the direct application of a force to one or more movable volumes of liquid. This causes an increase in pressure up to the value required to move the liquid through ports in the discharge line. The important points here are that the energy addition is periodic (i.e. not continuous) and that there is a direct application of force to the liquid.
The preference for a centrifugal pump over a positive displacement pump is not always the case, and, in fact, there are certain applications criteria that demand the use of a positive displacement pump. The following are some key application criteria that would lead to the selection of a positive displacement pump over a centrifugal pump:
- High viscosity
- Self-priming
- High pressure
- Low flow
- High efficiency
- Low velocity
- Low shear
- Fragile solids handling capability
- Sealless pumping
- Accurate, repeatable flow measurement
- Constant flow/variable system pressure
- Two-phase flow


https://peaceful-refuge-97525.herokuapp.com/api/search/funny?offset=3 https://peaceful-refuge-97525.herokuapp.com/api/latest/imagesearch/


The Fine Art of Getting Ahead Sleepy Hollow
By Ross Anthony
Tim Burton's films are oft anticipated for his boldly off-center and usually dark art direction. "Sleepy Hollow" is no exception. The warm colors have bled from the footage leaving the flesh stark, cold and clammy, and the darks -- soot black. Burton also enjoys painting a "small world" set with which to stage his sideways stories. Frequently, these worlds are neither live action looking nor quite animated. I find them cartoony, claustrophobic and distracting, but I'm probably one of only a handful; others seem to enjoy them.
Gadgetry, on the other hand, Burton excels in -- gadgetry and freakish characters. Ichabod Crane (Johnny Depp) wields coroner's tool that look as if they were designed for handicapped piranhas. And Burton's Headless Horseman is simply magnificent; it's the heart and soul (though not brains) that keeps this "Sleepy Hollow" charging mightily through the foggy twisted forest. This powerful black torso with limbs spinning sword and hatchet from atop a pounding steed is as splendid as a bolt of lightening from the heavens.
Unfortunately, sleepy and hollow all too appropriately describe the first half of the production (prior to any significant appearance of the horseman). Wrought with exposition and an elusively wry sense of humor giving even the artfully apt Depp a larger foe to over come than the one of high shoulders; the horseman charges on, breathing life (from it's severed esophagus) into this paling production.
Sleepy Hollow. Copyright © 1999. Rated R. Starring Johnny Depp, Christina Ricci and Christopher Walken, Marc Pickering. Directed by Tim Burton. Screenplay by Andrew Kevin Walker. Produced by Scott Rudin and Adam Schroeder at Mandalay/Lion's Gate/Paramount/American Zoetrope.
Grade..........................B
-- Copyright © 1999 Ross Anthony, currently based in Los Angeles, has scripted and shot documentaries, music videos, and shorts in 35 countries across North America, Europe, Africa and Asia. For more reviews visit: http://RossAnthony.com
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
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Setting Up MySQL Replication as a back-up solution
Category: sysadmin
MySQL replication is a good thing to have. It provides greater resilience and removes a single point of failure. By following these steps, you will create a replica of a master server, acting as a permanent backup in the case of a DR scenario.
Introduction
MySQL servers can be a single point of failure if you have not set up a good back-up infrastructure. It is difficult to get a good back-up infrastructure in place, without some time spent out of service. However, a MySQL replication server avoids this downtime.
One server will act as the master, and the other will act as slave. The slave will be paused and then backed up.
Benefits of running a replicated MySQL server:
- Remove single point of failure adding high availability
- Improved performance if applications can use the read-only server (think data querying)
Conifgure the Master MySQL server
The master is the live database that is serving your users. To back this up on it’s own, you will have to stop transactions on the server and then carry out the backup. In a lot of high availability environments, this is not an option. However, you can prepare your master MySQL server to send those transactions to a different server.
In /etc/my.conf
add the following lines:
log-bin = master-bin
log-bin-index = master-bin.index
server-id = 1
The log-bin
directive tells mysql to store a binary log. This binary log is what makes replication easy and quick. We also have a log-index
. The binary log tells the other server the events that are happening on the master server.
You can watch the binary log file with the mysql prompt with SHOW BINLOG EVENTS\G;
. However, to find out which binlog file is currently being written to you should enter SHOW MASTER STATUS;
and then SHOW BINLOG EVENTS IN 'master-bin.000002'\G;
(or whatever file was returned from the master status command.
If you added the replication config lines above after creating the server, as opposed to when you first set the server up, you will need to restart the mysql server:
service mysql-server restart
Then you will need to login to the MySQL command prompt:
mysql -u root
Next, it is good practise to create a user for replication that only has access rights for the IP range for the subnet where the slave server is.
CREATE USER repl_user;
GRANT REPLICATION SLAVE ON *.*
TO repl_user IDENTIFIED BY 'password';
This user will have access to retrieve the binary log from the master server.
Configuring the MySQL slave Server
Again, we need a user that has certain privileges on the slave server.
GRANT REPLICATION, SLAVE, RELOAD, CREATE USER, SUPER ON *.* TO<EMAIL_ADDRESS>WITH GRANT OPTION;
Next, you will have to log in to the slave mysql server and issue the following command:
CHANGE MASTER TO MASTER_HOST = '<IP_ADDRESS>', MASTER_PORT = 3306, MASTER_USER = 'repl_user', MASTER_PASSWORD = 'password';
START SLAVE;
Any changes for new databases on the master server will now be replicated on the slave server.
Enabling replication on an existing database
If you already have a database on the master server, that you also want to replicate, you will need to copy it over to the slave database.
If the database is relatively small (i.e. less than 50MB), then the mysqldump
command will work. Shutdown the database first to ensure data integrity.
mysqldump -u root -p databasename > file.sql
If the dataset is large, then it will be better to create an archive of the files and then transfer that over to the slave machine and restart the server there. Firstly, you should shutdown the mysql server.
Then, on the slave server, copy the files in the database directory (usually /var/lib/mysql
). You can then tar that and then copy to the slave server and extract there. If there are large differences between the configuration files between the servers, then this might not work.
Carry out the backup
Now that we have a slave, we can take point in time back-ups by stopping the slave and pausing transactions (although transactions are still ongoing on the master server).
-
Shut down the server
sudo service mysql-server stop
-
Copy the data files to a secure off-site location (in this case, Amazon S3)
sudo aws s3 sync --delete /var/lib/mysql s3://bucket-name/
-
Restart the server
sudo service mysql-service start


Weather Station with ESP32 - Part II
In the previous post, we began the process of building a weather station using an ESP32 board. We selected the BMP180 sensor due to its precise and dependable temperature and pressure measurements.
To make it easy to check the readings from the sensor, we created a web-based dashboard using the ESP32’s built-in WiFi capabilities. This dashboard allows us to view the current temperature and pressure readings, as well as historical readings over time.
However, we also wanted to have a physical display that we could use to check the sensor readings without having to access the web-based dashboard. To achieve this, we decided to integrate a 0.96 OLED module to display the sensor readings directly on the screen.
With this integration, we can now view the sensor readings both on the web-based dashboard and on the OLED screen. This makes it easy to check the weather conditions at a glance, whether we are sitting at our desk or out and about.
Aditional Materials
- 0.96 inch OLED module
Setup
Here are the steps to connect the OLED module to your ESP32 board:
- Connect the OLED module’s VCC pin to the 3.3V pin on the ESP32 board.
- Connect the OLED module’s GND pin to the GND pin on the ESP32 board.
- Connect the OLED module’s SDA pin to the SDA pin on the ESP32 board (GPIO 21).
- Connect the OLED module’s SCL pin to the SCL pin on the ESP32 board (GPIO 22).
Once we have made these connections, we can use the Adafruit_SSD1306 library to display text and graphics on the OLED module.
Programming
In order to continue with our weather station project, we need to add the Adafruit_GFX
and Adafruit_SSD1306
libraries. These libraries provide the necessary functions to display sensor data on a small OLED module:
- Open the PlatformIO IDE and navigate to the “Libraries” tab on the left-hand side.
- In the search bar, search for “Adafruit GFX Library” and click on the first result.
- Click on the “Install” button and wait for the installation process to complete.
- Repeat steps 2 and 3 for the “Adafruit SSD1306” library.
- Once the libraries are installed, open the ‘PlatformIO.ini’ file and add the following lines of code to the [env:d1_mini] section:
lib_deps =
...
...
Adafruit GFX Library
Adafruit SSD1306
- Save the file and the libraries should now be added to the project.
With the required libraries installed and added to the project, we can now move on to integrating the OLED module and displaying the sensor values on the screen.
#include <Wire.h>
#include <Adafruit_GFX.h>
#include <Adafruit_SSD1306.h>
#include <Adafruit_Sensor.h>
#include <Adafruit_BMP085.h>
#include <WiFi.h>
#include <WebServer.h>
#include <SPI.h>
#include <WiFiClientSecure.h>
#define BMP085_ADDRESS 0x76
// Replace with your network credentials
const char* ssid = "xxx";
const char* password = "xxx";
// Create an instance of the BMP180 sensor
Adafruit_BMP085 bmp;
// Create an instance of the WebServer
WebServer server(80);
// Create an instance of the OLED display
Adafruit_SSD1306 display(128, 64, &Wire, -1);
// Function prototype for handleRoot()
void handleRoot();
void setup() {
// Start the serial communication
Serial.begin(115200);
// Connect to Wi-Fi network
WiFi.begin(ssid, password);
while (WiFi.status() != WL_CONNECTED) {
delay(1000);
Serial.println("Connecting to WiFi...");
}
// Print the IP address of the board
Serial.print("IP address: ");
Serial.println(WiFi.localIP());
// Initialize the BMP180 sensor
if (!bmp.begin(BMP085_ADDRESS)) {
Serial.println("Could not find a valid BMP085 sensor, check wiring!");
while (1);
}
// Initialize the OLED display
if (!display.begin(SSD1306_SWITCHCAPVCC, 0x3C)) {
Serial.println("SSD1306 allocation failed");
for (;;);
}
// Serve the web page
server.on("/", handleRoot);
server.begin();
// Display a message on the OLED screen
display.clearDisplay();
display.setTextSize(1);
display.setTextColor(WHITE);
display.setCursor(0, 0);
display.println("Weather Station");
display.println("with ESP32 WROOM");
display.println("and BMP180 Sensor");
display.display();
delay(2000);
display.clearDisplay();
}
void loop() {
// Handle web requests
server.handleClient();
// Read the temperature and pressure from the BMP180 sensor
float temperature = bmp.readTemperature();
float pressure = bmp.readPressure() / 100.0F;
// Print the readings to the serial monitor
Serial.print("Temperature = ");
Serial.print(temperature, 1);
Serial.println(" °C");
Serial.print("Pressure = ");
Serial.print(pressure);
Serial.println(" hPa");
// Display the readings on the OLED screen
display.clearDisplay();
display.setTextSize(1);
display.setTextColor(WHITE);
display.setCursor(0, 0);
display.print("Temp: ");
display.print(temperature, 1);
display.println(" C");
display.print("Pressure: ");
display.print(pressure);
display.println(" hPa");
display.display();
// Wait for a second before taking the next reading
delay(1000);
}
void handleRoot() {
// Build the HTML page
String html = "";
html += "
Weather Station with an ESP32 WROOM 32D and a BMP180 Sensor
";
html += "
";
html += "
";
html += "
Temperature
";
html += "
" + String(bmp.readTemperature(), 1) + " °C
";
html += "
";
html += "
";
html += "
Pressure
";
html += "
" + String(bmp.readPressure() / 100.0F) + " hPa
";
html += "
";
html += "
";
html += "";
// Send the HTML page to the client
server.send(200, "text/html", html);
}
In the loop function, we also use the display
object of the Adafruit_SSD1306 class so we can display the readings of the sensor on our little screen, however we can definitelly improve it by implementing some designing and making fonts bigger etc by using the methods on this class:
void setup() {
// Start the serial communication
Serial.begin(115200);
// Connect to Wi-Fi network
WiFi.begin(ssid, password);
while (WiFi.status() != WL_CONNECTED) {
delay(1000);
Serial.println("Connecting to WiFi...");
}
// Print the IP address of the board
Serial.print("IP address: ");
Serial.println(WiFi.localIP());
// Initialize the BMP180 sensor
if (!bmp.begin(BMP085_ADDRESS)) {
Serial.println("Could not find a valid BMP085 sensor, check wiring!");
while (1);
}
//xzvtx 0.96 inch OLED Module display added
Wire.begin();
Adafruit_SSD1306 display(SSD1306_SWITCHCAPVCC, 0x3C); // 0x3C is the I2C address of the OLED module
display.clearDisplay();
display.setTextSize(3); // Set the text size to 3
display.setTextColor(WHITE);
display.setCursor((display.width() - 12 * 3) / 2, (display.height() - 24) / 2); // Position the text in the center
display.println("Hello world!");
display.display();
}
In the above code, we set the text size to 3 by calling display.setTextSize(3) and position the text in the center of the screen by calculating the X and Y coordinates of the center of the screen using the width() and height() methods of the Adafruit_SSD1306 class, respectively.
Conclusion
we have successfully built a weather station using an ESP32 board and a BMP180 sensor, and we have added a 0.96 inch OLED module to display the temperature and pressure readings on the screen. By integrating the capabilities of the ESP32 board, BMP180 sensor, and the OLED display, we have created a custom weather station that is tailored to our specific needs. Additionally, we have created a web-based dashboard that allows us to remotely monitor the sensor values from anywhere with an internet connection. This project is a great example of how the combination of hardware and software can be used to create innovative solutions to real-world problems. With some basic knowledge of electronics and programming, anyone can create their own custom weather station or similar projects


My current hobby: Making #pokemon-based themes for @vivaldibrowser.
Currently: Absol, Pikachu and Zoroark. t.co/Ohvworr9m…


transparent-test-block
is rendered
transparent-test-block
u-color-background-gray-100
u-color-background-gray-300
u-color-background-gray-400
u-color-background-gray-500
u-color-background-gray-600
u-color-background-gray-700
u-color-background-gray-900


University of Portsmouth Room Booking
Select the room you wish to book
Lion gate
Anglesea
Buckingham
Burnaby
Dennis Sciama Building
Eldon
Hampshire Terrace
Institute of Marine Sciences
James Watson West
King Henry
Mildam
Park
Portland
Richmond
Rotunda
Spinnaker Building
St. Andrew's Court
St. George's
St. Michael's
The University Library
Wiliam Beatty
Wiltshire


What is represented by dashboard?
The URL for the admin shall be : www.yourwebsitename.com/index.php/admin
Top of the dashboard admin can monitor the last 24 hours statistics of stock, sale & destroy.
Admin also can monitor monthly statistics of stock, sale, destroy and grand profit in a smart animated chart.
Register your Domain
After installation, please go to activeitzone.com/activate link and fill the form with your codecanyon purchase code, your registering domain, e-mail and license type. Once you activate, the purchase key will be attached with your domain. You can change the domain of your script from the same link by providing the activation code. You can also find the link in the admin panel.
If you try to use the script in an unregistered online domain, you'll not be able to do the functions of the system properly. The frontend will be distorted and notifations will appear frequently.
How to manage your products from admin?
Category:Create your product Category. Can Edit / Delete the category (Step - 1)
Sub-category:Create Sub-category according to Category. Can Edit / Delete the created sub-category (Step - 2).
Brands:Create Brand according to category. Can Edit / Delete the created Brand (Step - 3).
All Products:
Products shall be managed from here:
Create Product:Now upload your product according to Category, Sub-Category and Brand by filling the upload form.Admin shall provide purchase price and sale price of the product. If applicable, admin can provide product tax and product discount.Admin can add unlimited product color option from “Add more color”. Admin can also add more fields in product upload form as needed.You can reset the form by clicking ‘Reset’ (Step - 4).
After create the product from the product table admin will get these for every product in the product list:
Stock:Admin can increase stock from here. (Step - 5)
View:The uploaded product has been viewed with smart user interface.
Discount:Easily give discount with percentage (%) or fixed amount from here for the uploaded products.
Destroy:Admin can log destroy of products.The destroyed product number will be reduced from the product stock. Reason of such reduction can be saved.
Edit:Admin can edit the specifications of the product from this option.
Delete:Admin can delete product.
Publish/Unpublish:Admin can publish/unpublishany product.
Feature/Unfeature: Admin can Feature/Unfeature any product.
Today's Deal: Admin can add any product to today's deal.
Product Stock: Product stock quantity can be seen and changed from here. Admin will also see the product quantity reduction by sale.
Create Stock: Admin can increase stock of a product by selecting the product and quantity.
Destroy: Admin can decrease the stock quantity of a product by selecting the product, quantity of product unit and the reason (optional) of such destruction.
From the Product Stock List the Admin can:
Delete: If the Admin deletes a ‘Destroy’ entry, the product quantity previously reduced will be added to total stock of the product. In case of an ‘Add’ entry, the product quantity previously increased will be reduced from the total stock of the product.
How to manage your products from admin?
Upload:The admin and vendors can upload digital products as other products. In the product creation form, admin shall find a switch to determine if it is digital product. Once he selected it to be digital, he'll get a file choosing button beneath. He can select files (*.zip,*.rar) here and upload product with other required fields.
Edit:Once a digital product, always a digital product. So, while editing it, the admin shall find the digital product file choosing button directly. He can change the digital file here.
Delete:The Admin can delete the digital product.
File Security:For file security, the product name is generated with long random names. Moreover, for making it difficult to trace the upload folder of digital products in the server, the admin can change the digtal product folder name from front settings -> site settings.
How to manage your sales from admin?
All the sales by the customers shall be listed in the sales list. From here the Admin can:
View Invoice: The Admin can view the detailed invoice of the sale. In the invoice, the Admin will be able to see all the purchased products with quantity and price. Tax and Shipping costs shall also be added with it. Moreover, if the customer provides his/her shipping location in the Google map, Admin will also see it in the invoice page.
Delivery Status: By clicking ‘Delivery Status’ the Admin will get a form where he/she can change the payment and delivery status along with payment details.
Delete: The Admin shall be able to delete the sale completely and the sale will also disappear from the customers list. The reduced quantities of products due to that sale shall also be added back to the respective product stocks.
What will get from Reports?
Product Compare: The Admin can view the sale of a product by dates. The graph will show both sold quantity and amount. The admin can also compare sale graph of two or more products by selecting other products from the left bar of the graph.
Product Stock: The Admin can select a product by category and subcategory to get a graph of product stock by date.
Product Wishes: The Admin shall be able to see the most wished products in a descending order form this graph.
How to manage the Customers?
All the customers registered from the frontend shall be listed in the Customer list.
From here the Admin can:
Profile: The Admin can view the customer profile by clicking this button.
Delete: The Admin can delete the customer from here.
Blog Categories
Just like product categories, the admin can add, edit and delete blog categories.
Blogs
The admin can add, edit and delete blogs and provide information in that regard. The blog commenting shall be either facebook or, Disqus as defined for the product reviews.
How does vendors register?
1. At first, the interested vendor will register as a vendor. Vendor registration button is in the top of the homepage. He’ll provide necessary information required.
2. After successful registration, the vendor will get a email with his credentials. But, he’ll have to wait until the Admin approves his vendorship.
3. Once the vendor is approved, he’ll get confirmation e-mail from the Admin. Now, the vendor can login with his credentials (e-mail and password) in the following URL:
www.your-site-name.com/index.php/vendor
4. Initially the vendorship type is: Default. This vendorship type is for lifetime.
How the vendors upload products?
1. In the vendor panel, he’ll get a product panel. He can upload products here. But the number of uploaded products for the specific vendorship is defined by the admin (in the vendorship type section in the Admin Panel).
2. Once the vendor uploaded maximum number of products, he’ll have to unpublish his previous products to upload new. He cannot have more than the maximum limit number of products visible in the frontend at a time.
How the vendors upgrade vendorship type?
1. Initially the vendorship type is: Default
2. Vendor can upgrade his vendorship from the ‘My Packages’ section in the vendor panel.
3. In the section, he’ll see the available packages, their specifications with price. Once he is to upgrade, he’ll have to pay the amount, which is specified by the Admin (in the vendorship type section in the Admin Panel).
4. After the timespan (i.e. 30 days), is end for the vendorship type, the vendor will return to ‘Default’.
5. During upgrade, the timespan will count from the day of upgrade.
How the sales payment and delivery goes?
1. If there is a product in any sale of the vendor, he’ll see it in the sale section of the vendor panel. He can see his part of invoice in the sale.
2. All the online payments from customers are paid to the admin first. All the ‘Cash on Delivery’ payments are counted as paid to the vendor directly.
3. For all the sales (in the sales section in Admin panel), the Admin will see the portions of the vendors as well as the admin.
4. As all the online payments are paid to the admin, amounts will be due to the vendors. From the ‘Vendors’ section in the admin panel, the admin can see the stats of the vendor and pay him the amount (via the payment methods specified by the respective vendors).
How the discount coupons work?
1. Both the Admin and Vendor can set discount coupons. Vendor can set discount coupons only for his uploaded products. Admin can set discount coupons for all products, category-wise products and also selected products.
2. The customer can use only one coupon per cart. Once he applies a coupon code, if it is validated, he’ll get the specified discount for that cart.
How the customer choice options work?
1. Both admin and vendor can upload products with customer choice options. Along with the color choice options, more custom choice options can also be introduced to it.
2. During creating new product or editing one, admin or vendor can add choice options of different types (text input: the customer can write something, single choice input: the customer can choose from a drop-down list like sizes, multiple choices, radio etc.)
3. Once a customer provides a choice in it, the vendor and admin will get to see in beside the product in their invoice.
How to Manage the stuffs?
Admin can manage all his staffs that will have access to the admin panel.
Create Admin: The Admin can create other admin for the admin panel, who will be the staffs of his shop. The Admin will provide the staff’s name, email etc. and also provide the Role the staff is going to accomplish as an admin. Role means the set of permissions to make changes in the admin panel.On creation of admin, he’ll get confirmation e-mail with an auto-generated password, which can be changed later.
Edit: The admin can edit the details of a staff except email from here.
Delete: Admin can delete all admins except the master admin.
Staff Permissions :
Admin can manage Role (set of account permissions) for the staff.
Create Role: The Admin can create role by providing name, description and switching on the features the Admin want the role-holder to get. For example, an admin can set permission for ‘Product Managing Staff’ to change products and brands. If any staff is given this role, he’ll only see products and brand in his admin panel nothing more.
Edit: The Admin can edit the created roles.
Delete: The Admin can delete the roles he things not needed. But, if any admin is already added to the role and it’s deleted, it’ll make fatal errors.
The Admin can manage banners in the Homepage from here. There are tabs for each page of frontend. For each position, where there are four banners given, the Admin can publish 1,2,3 or 4 banners. It’ll take the full width always. For example, if 2 banners are published, each of the banners will be half of the full width. If only one is published, it’ll take full width. Admin can provide link for each of the banners.
You can easily manage your front site from “Site Settings”. Please follow the following step:
General Settings: Admin can set ‘system name’, ‘system email’, ‘system title’, ‘language’ and ‘notification sound control’ from here. You can set the Homepage Cache Time (in Munites) from here. You can also disable/enable Whole Vendor System from here. Disabling vendor system shall turn off vendor login and vendor pages. But the vendor uploaded published products will be shown on the homepage freely.
Header/Footer Scheme: Admin can set ‘Header/footer color scheme from 6 different color sets in here.
Logo upload: Admin can fix website logo from here as you need. You can select homepage and admin logo separately from here.
Favicon: Upload your favicon from here.
Social Media: You can upload all your social media links (Facebook,Google-plus, twitter, Skype, Pinterest, Youtube), which will be used in the front site.
Facebook login: Admin need to give his/her Facebook App ID and secret ID for manage the user Facebook login/registration system.
Google login: Admin need to give his/her Google Application Name, Client ID, Client Secret, Redirect_uri and API key for manage the user Google login/registration system.
Comment Settings: Admin will provide 'Disqus ID' or 'Facebook App ID' for opening commenting in the product page. The Admin can set which of the comment is to be shown in the product page.
Captcha Settings: Admin will provide Public Key and Private Key for Contact form’s Google Captcha.
Terms and Condition, Privacy Policy can be provided to show them up in the frontend.
Texts and column orientations of Home Page, Category Page, Contact Page and Footer can be provided and updated from the respective tabs.
New pages can be added to the system from the admin and can be customized to a complete static page.
Create new page: The Admin can create new page from here. The Admin will set the name of the page name and the permalink of the page will be set automatically. Than sizes of the columns of the page shall also be selected by the admin. Maximum of four columns can be added to a page. For each column Admin can set widgets or texts and images. Clicking ‘upload’ will set the page to the homepage and there will be a new menu on the homepages.
Preview: Click preview to view the page you created. You can view it even if it is unpublished.
Edit: Admin can edit the added pages as like as it was added.
Publish: Admin can publish/unpublished page on the pages’ list.
The Admin can manage Homepage sliders from here.
The Admin can disable Layer Slider from the switch at top left of the table.
Create Slider: The Admin can create a slider from here. On clicking Create slider, he’ll get a list of slider template, from where he’ll choose one to work with. After clicking on the choose button, he’ll get a form for creating the slider. The Admin shall provide slider title and background image. The admin shall provide the texts to the appropriate fields and images for the places fixed. Admin can see the preview by clicking ‘Enter Preview’ or can ‘Publish’ it.
In the ‘Slider List’, the admin can get the following for each slider:
Edit: On edit, Admin will see the slide preview and all the images and texts added. He/she can change text/image or, remove them. He/she can also change the background and title of the slide. Like create product, Admin can both ‘Enter Preview’ or ‘Publish’ the slide.
Delete: The Admin can delete the slide.
Publish/unpublish: The Admin can publish/unpublish the slide.
Slider Serial: The Admin can set the sliders’ serial of appearance from here.
The Admin can manage Homepage top sliders from here.
The Admin can disable Top Slides from the switch at top left of the table.
Add Slide: The Admin can add slide from here. On clicking Create slides, he’ll get a form for creating the slide. The Admin shall provide slide image and Upload.
Edit Slide: On edit, Admin will see the slide image and can change the slide image here.
Delete: The Admin can delete the slide.
SEO Settings: Admin will input Meta tag, Meta content, Meta Description, Revisit After & keywords from here. The Sitemap link for SEO is here. You can get the sitemap form this link.
Social Network Reach: You'll get to have a look on how your website is shared and interacted in the most popular social media (i.e. Facebook, Google Plus etc.).
Alexa Traffic Rank: You'll see your global nd country specific Alexa Traffic Rank here.
Alexa Traffic Graphs: You'll see your Daily Traffic Trends, clients' Time on Site, Bounce Rate etc. in Alexa graphs here.
Search Index: You'll have a look over Google Websearch Index, backlink numbers and other graphs here.
How to manage Newsletters?
The Admin can send newsletters from the admin panel. He’ll see all the customer’s email addresses on the top. He can remove some of them. He’ll also get the list of subscribers’ email addresses from which he can remove. He’ll provide the ‘from’ Email Address. Subject of the newsletter and the HTML of the newsletter in the HTML Editor shall be given. For providing raw html code to the newsletter, the Admin can go to the code view of the editor and then paste the code there. The Admin shall get confirmation once the newsletter is sent.
How to manage Contact Messages
The Admin can read and reply the messages sent form the home contact page. The Admin will see a list of latest messages, read them and reply them. The reply will go as a email to the message sender.
The System has the feature of using multiple languages. From here the Admin can manage this.
Add Language: The Admin can add new language For the system. Once He added a new language, he’ll have to put all the translations to that language on the table.
Add Word: If needed, the admin can add new word. Once he added a new word, he’ll have to provide translations of the word of all the languages separately from the list.
Select Language: On the dropdown the Admin will see all the added languages. On his selection from the dropdown, the list will be changed to that language’s word list with translation.
Delete Language: Once a language is selected, the admin can delete that language.
Save Translation: The Admin, from the list, will get an input box and a save button for each word. The admin shall give the translation of the word on the input box and press save button. This will save the translation of that word only.
Delete: This is important! If the Admin delete a word from any list, it’ll be deleted from every list, so it is better not to use this delete.
The Admin shall change the business settings of the admin panel:
Paypal Payment: If the admin want to provide the customers with the Paypal payment system, he can switch it on. Provide Paypal Email (Payment Receiver) and the Paypal Account Type from the dropdown. If it is made off, customers will not see the paypal button on the cart checkout.
Stripe Payment: If the admin want to provide the customers with the Stripe payment system, he can switch it on. Provide Stripe Secret Key and Stripe Publisher Key. If it is made off, customers will not see the Stripe button on the cart checkout.
Currency: The Admin shall provide Currency Name and Currency Symbol of the system. He’ll also provide the exchange rate of the currency with US Dollar ($). This is important!
Shipping Cost: The Admin will provide the way of calculating shipping cost. It can be fixed for every purchase or it can be product wise. If it is fixed, the Admin shall provide with the fixed amount.
Shipment Info: The Admin will provide the shipment related information, which will be viewed to the customers when they’ll browse the product page.
FAQs: The Admin shall provide a list of questions and answers, which are frequently asked. This list will be viewed on the cart checkout page.
The Admin can manage his own profile.
He can change the details of his own and can change his password.
Slider: The Homepage contains a slider which is customizable by the Admin
Languages: From the language list on the top left, users can choose between available languages to view the whole homepage in that language.
Featured Products: The Admin can choose products to be featured to view them just after the Slider
Banners: There are many banners in the homepage which are controlled by the admin.
Category wise Product view: Selected categories in the site settings of the Admin Panel will be shown in the homepage.
Brands: Selected brands in the site settings of the Admin Panel will be shown in the homepage.
Add to Wish List: The customer can add products to his/her wish list to remember what he/she wants to purchase later. The coustomer will have to login if he want to wish a product. He can see all his wished products in his profile
Subscribe: For the regular newsletter from the system, any customer can subscribe.
Product Search Filter: The customers can search products by category and subcategory from the search product page.
How to Purchase Products?
The following steps are to be taken to purchase a product:
1. Adding a Product to cart:
For every product on show, there is an Add to Cart button. On clicking on thatbutton, the product will be added to the cart. The quantity of the product will be 1 unit. The customer will see the added product to the top right of the homepage. He can hover the cursor over the cart icon and see all the products with price and then total amount. The customer can remove the product from the cart from here.
2. Checkout: In Checkout, the customer shall have to pass through 3 steps:
3. Product Orders:The Customer, when want to checkout, can click on the checkout button and go to the checkout page. In the checkout page, the customer can change the product quantity, color of the product (if any). The price and total cost will change accordingly. After it is set, he can go to the next step. If the customer is not logged in, he’ll have to login or signup before going to next step.
4. Provide Delivery Address: The logged in customer will see his name, email and address placed in the input boxes with the information he provided during sign up. He can change it. In here, he can move the Google map marker pointer to the delivery address. Then, he can click on next step.
5. Payment and Finalize Order: The customer will then choose between the payment options (Pay on Delivery or, PayPal). If chooses PayPal, he’ll have to go to PayPal website to pay the cost.After payment, he’ll see the invoice.
Customer Profile: The Customer shall have access to his/her profile page after logging in.
All Customer Purchases: The customer can see all his purchases along with invoices.
All Wished Products: The Customer will view all his wished products in a list.
Edit Info: The Customer can change profile details.
Update Password: The Customer can also change his password.
Registration: The Customer can Register as a Customer providing the required information. a confirmation email will be sent to the provided email
Social Registration: The Customer can register using Facebook or Google account.
Login: The Customer can login using the email and password he registered with.
Forget Password: The Customer will get new password once he provides his email address to the forget password form. New password will be sent to his email address.
Social login: The Customer can login using Facebook or Google account. In this case, for setting password to his profile, he’ll have to get new password by clicking new password. A new password will go to his/her email address.


auditd
This responds to requests from the audit utility and notifications from the kernel.
It should not be invoked manually.
More information: home page
- Start the daemon:
auditd
- Start the daemon in debug mode:
auditd -d
- Start the daemon on-demand from launchd:
auditd -l


President Umaru Musa Yar'Adua born 16th August 1951.Yar'Adua was born into an aristocratic Fulani family in Katsina; his father, a former Minister for Lagos during the First republic, held the chieftaincy title of Matawalle (or custodian of the royal treasury) of the Katsina Emirate, a title which Yar'Adua inherited.He started his education at Rafukka Primary School in 1958, and moved to Dutsinma Boarding Primary School in 1962. He attended the Government College at Keffi from 1965 until 1969. In 1971 he received a Higher School Certificate from Barewa College.He attended Ahmadu Bello University in Zaria from 1972 to 1975, obtaining a B.Sc. degree in Education and Chemistry, and then returned in 1978 to achieve an M.Sc. degree in Analytical Chemistry.
Yar'Adua's first employment was at Holy Child College in Lagos (1975–76). He later served as a lecturer at the College of Arts, Science, and Technology in Zaria, Kaduna State, between 1976 and 1979. In 1979 he began working as a lecturer at College of Art Science, remaining in this position until 1983, when he began working in the corporate sector. Yar'Adua worked at Sambo Farms Ltd in Funtua, Katsina State, as its pioneer General Manager between 1983 and 1989. He served as a Board Member of Katsina State Farmers' Supply Company between 1984 and 1985, Member of the Governing Council of Katsina College of Arts, Science and Technology Zaria and Katsina Polytechnic between 1978 and 1983, Board Chairman of Katsina State Investment and Property Development Company (KIPDECO) between 1994 and 1996. He served as a director of many companies, including Habib Nigeria Bank Ltd, 1995–99; Lodigiani Nigeria Ltd, 1987–99, Hamada Holdings, 1983–99; and Madara Ltd, Vom, Jos, 1987–99. He was Chairman of Nation House Press Ltd, Kaduna, from 1995 to 1999.
President Yar'Adua left Nigeria on 23 November 2009, and was reported to be receiving treatment for pericarditis at a clinic in Saudi Arabia. He was not seen in public again, and his absence created a dangerous power vacuum in Nigeria.[27] In December 2009 Oluwarotimi Odunayo Akeredolu, president of the Nigerian Bar Association (NBA), stated that Yar'Adua should have handed over power to Vice-President Goodluck Jonathan in an acting capacity during his illness, a statement that was backed up by the NBA national executive committee.On 22 January 2010, the Supreme Court of Nigeria ruled that the Federal Executive Council (FEC) had fourteen days to decide a resolution on whether Yar'Adua was "incapable of discharging the functions of his office". The ruling also stated that the Federal Executive Council should hear testimony of five doctors, one of whom should be Yar'Adua's personal physician. On 10 February 2010, the Senate controversially used the "doctrine of necessity" to transfer Presidential Powers to Vice President Goodluck Jonathan, and declared him Acting President, with all the accompanying powers, until Yar'Adua returned to full health. The power transfer, considered illegal by some, has been called a "coup without the word" by opposition lawyers and lawmakers. However, there are others that felt the power vacuum would lead to instability and a possible military takeover. On 24 February 2010, Yar'Adua returned to Abuja under the cover of darkness.His state of health was unclear, but there was speculation that he was still on a life support machine.Various political and religious figures in Nigeria had visited him during his illness saying he would make a recovery.
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A Bayesian network is a probabilistic graphical model (a type of statistical model) that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG).
Here are a series of simple examples to show what Bayesian Networks look like and how to use them to prodict the answer you want. The Bayes Networks are really useful (such as the bayes rnn)— but only if you know how to build one.
Before diving in: if you haven’t encountered Bayes algorithms before, here’s what you need to know about the math behind it. Formally, Bayesian networks are DAGs whose nodes represent random variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies; nodes that are not connected represent variables that are conditionally independent of each other. Each node is associated with a probability function that takes, as input, a particular set of values for the node's parent variables, and gives (as output) the probability of the variable represented by the node.
Besides the graph, the Bayesian Networks include a Conditional Probability Table (CPT) which defined for a set of discrete (not independent) random variables to demonstrate marginal probability of a single variable with respect to the others. Here are some math fundamentals:
Conditional Probability : P(B|A) = P(B,A)/P(A)
Product Rule : P(B,A) = P(B|A)·P(A)
If two nodes are marginal independent , it means: P(A|B) =P(A) ,P(B|A)=P(B). If three nodes are conditional independent ,such as A and B are independent but C is not, it means P(A|B,C) = P(A|C)
If we can know all different node sets' joint distribution, we can solve all the statisc problems, but the truth is it is too difficult to calculate all the probabilities with a large number of nodes.We need find some ways to simplify the question.
Let’s start with the Naive Bayes.In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
We assume that the x,y attributes are independent. For every pix, we use its x values to find nearest K points and use the ratio of each category as the possibility of this category.
Here is a small example of Naive Bayes. We use each pix to calculate the possibilities of the number.
The train data comes from MNIST database so the answer may not be acurate enough for every number you write
The offset of the number will strongly affect the answer.
So far, so good. But what if there are two attributes that are not independent? Are the result of naive bayes still convincing ? Below are Bayesian Networks for a mixture of connections in plane with different network structures.
Differ from the top 3D Bayesian Network which only can show the category that has the highest possibility, the Bayesian Network can calculate the possibility of each category.
There are three different relation between those points.
Head To Head:
which means a and b are independent P(a,b)=P(a)*P(b)
Tail To Tail:
which means a and b are independent under c P(a,b,c)=P(b|c)P(a|c)P(c)
Head to Tail:
which means P(a,b,c)=P(a)*P(c|a)*P(b|c)
We have considered two different ways to compute the parameter, the MLE and the EM.
Parameter means the conditional possibilities of each point.
The math foundation of these algorithms are too complex to be shown in pictures so we only show some examples of these algorithms.
An animation demonstrating the EM algorithm fitting a two component Gaussian mixture model to the Old Faithful dataset. The algorithm steps through from a random initialization to convergence.
A more fully Bayesian approach to parameters is to treat parameters as additional unobserved variables and to compute a full posterior distribution over all nodes conditional upon observed data, then to integrate out the parameters. This approach can be expensive and lead to large dimension models, so in practice classical parameter-setting approaches are more common.
In the simplest case, a Bayesian network is specified by an expert and is then used to perform inference. In other applications the task of defining the network is too complex for humans.
Actually the structure learning of bayesian network is a NP-hard problem,but we have no choice and the network structure and the parameters of the local distributions must be learned from data.
Although the Artificial Neural Networks are so popular today, the uninterpretion and mystery confuse a bunch of people about applying them to some field. We ,human-beings ,sometimes prefer reasons that can be easily understanded.The bayesian networks are one type of good structures that can provide logical structure.There are some algorithms that combine the bayesian networks with the Artificial Neural Networks.
We hope this page can give you some useful information about the bayesian networks.
We are grateful to Professor Wang for giving us this opportunity, and for excellent editorial.


API Keys
New API Key
A Cannlytics API key identifies a particular application, granting programmatic use with a selected subset of a user's permissions.
API Key Created
You can store your API key now
Your API key is not stored in any other place and cannot be retrieved or re-created. We strongly recommend that you encrypt your API keys in your data store and in memory when working with them, except when you need to access them to interface with the API. Your API key is observable if you use HTTP, so, please use HTTPS when you use the API.


|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object javax.print.SimpleDoc
public final class SimpleDoc
This class is an implementation of interface Doc
that can
be used in many common printing requests.
It can handle all of the presently defined "pre-defined" doc flavors
defined as static variables in the DocFlavor class.
In particular this class implements certain required semantics of the Doc specification as follows:
If the print data is a stream, or a print job requests data as a
stream, then SimpleDoc
does not monitor if the service
properly closes the stream after data transfer completion or job
termination.
Clients may prefer to use provide their own implementation of doc that
adds a listener to monitor job completion and to validate that
resources such as streams are freed (ie closed).
| Constructor Summary | |
|---|---|
SimpleDoc(Object printData,
DocFlavor flavor,
DocAttributeSet attributes)
Constructs a SimpleDoc with the specified
print data, doc flavor and doc attribute set. |
| Method Summary | |
|---|---|
DocAttributeSet |
getAttributes()
Obtains the set of printing attributes for this doc object. |
DocFlavor |
getDocFlavor()
Determines the doc flavor in which this doc object will supply its piece of print data. |
Object |
getPrintData()
Obtains the print data representation object that contains this doc object's piece of print data in the format corresponding to the supported doc flavor. |
Reader |
getReaderForText()
Obtains a reader for extracting character print data from this doc. |
InputStream |
getStreamForBytes()
Obtains an input stream for extracting byte print data from this doc. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public SimpleDoc(Object printData, DocFlavor flavor, DocAttributeSet attributes)
SimpleDoc
with the specified
print data, doc flavor and doc attribute set.
printData
- the print data objectflavor
- the DocFlavor
objectattributes
- a DocAttributeSet
, which can
be null
IllegalArgumentException
- if flavor
or
printData
is null
, or the
printData
does not correspond
to the specified doc flavor--for example, the data is
not of the type specified as the representation in the
DocFlavor
.| Method Detail |
|---|
public DocFlavor getDocFlavor()
getDocFlavor
in interface Doc
public DocAttributeSet getAttributes()
getAttributes
in interface Doc
public Object getPrintData() throws IOException
Doc
getPrintData()
method returns an instance of
the representation class whose name is given by getDocFlavor()
.getRepresentationClassName()
, and the return value can be cast
from class Object to that representation class.
getPrintData
in interface Doc
IOException
- Thrown if the representation class is a stream and there was an I/O
error while constructing the stream.public Reader getReaderForText() throws IOException
Doc
implementation is required to support this
method if the DocFlavor
has one of the following print
data representation classes, and return null
otherwise:
char[]
java.lang.String
java.io.Reader
Reader
for reading the print data as a stream
of characters from the print data representation object.
However, if the print data representation object is itself a
Reader
then the print data representation object is
simply returned.
getReaderForText
in interface Doc
Reader
for reading the print data
characters from this doc.
If a reader cannot be provided because this doc does not meet
the criteria stated above, null
is returned.
IOException
- if there was an I/O error while creating
the reader.public InputStream getStreamForBytes() throws IOException
Doc
implementation is required to support this
method if the DocFlavor
has one of the following print
data representation classes; otherwise this method
returns null
:
byte[]
java.io.InputStream
getStreamForBytes
in interface Doc
InputStream
for reading the print data
bytes from this doc. If an input stream cannot be
provided because this doc does not meet
the criteria stated above, null
is returned.
IOException
- if there was an I/O error while creating the input stream.
|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


Ulysses Figueroa Gonzalez
About
|
Portfolio
(current)
|
Contact
Portfolio
Channel 4 News
Ballet
Nightlife
Aerobics
Professional Wrestling


FabAcademy: accelerometer data with D3.js
Data samples
Url:
Reload data
Charts contols
Start time:
sec.
Time window:
sec.
Update charts
Play ⏵
Pause ⏸


__ __
/\ \ \ _ __/ /______
/ \/ / / / / __/ ___/
/ /\ / /_/ / /_(__ )
\_\ \/\__,_/\__/____/
nuts, aka Network Updatable Things Services, is a Java™ Package Manager that helps
discovering, downloading, assembling and executing local and remote artifacts (packages) in a very handy way.
Unlike maven which resolves dependencies at compile time, nuts solves dependencies and
builds
the classpath at install time and, as a result, saves disk and bandwidth by downloading and caching only libraries
required for the current environment and share them between multiple installed applications.
nuts is unique in that it reuses maven and other build tool descriptor formats to solve dependency graph, and does not, whatsoever, have any requirement on existing maven created packages.
nuts is the ultimate solution to get rid of the ugly lib jars, fat-jars, uber-jars and
one-jars used for deploying java applications.


Scopes
since 2017
DevOps
Git, Ansible, Docker, IaaS Cloud, Vagrant, Jenkins,
ELK
Stack
since 2014
Web Development
Backend: Python Django/Flask, Ruby RoR/Jekyll,
Java
Spring/JSP&Servlet
Frontend: HTML5, SCSS, Javascript(ES,
JQuery,
TypeScript, Angular), Bootstrap
since 2013
Database
MySQL, OracleDB, SQLite, PostgreSQL
since 2012
Linux Operating System
Services Management, System Administration,
Tools Deployment,
Virtualization + HA Clusters
since 2010
Software Development
Python, Ruby, .NET(C#, VB, C/C++), Java, Bash,
Powershell,
PHP
Interests
Scripting
Ruby2, Python3, Bash5, Powershell7
Hacking
Programming, OS, RaspberryPi, Adobe Ps+Dw
Life
Bring blessings and wisdom home


Check Out the Latin Music Exhibit at the Grammy Museum
Latin music has become a worldwide influence. To celebrate this growing legacy in the U.S., the Grammy Museum opened a Latin music gallery which highlights historical moments and accomplishments of Latin Grammy Award-winning and nominated artists-including Shakira, Mana, Los Tigres del Norte, Juanes and more. Visitors can explore the costumes and looks from their favorite Latin artist show stopping moments at the Latin Grammy Awards. The exhibits are rolling and will change over time, so be on the look for Bad Bunny's next red carpet look to hit the exhibit! For more info visit: Grammy Museum


--- layout: post title: Semicolons in JavaScript and life summary: The best explanations I've read for when to use semicolons. tags: [JavaScript] ---
The best explanations I've read for when to use semicolons.
For life: http://theoatmeal.com/comics/semicolon
For JavaScript: http://blog.alexanderdickson.com/javascript-semicolons


PIL filed against excess ticket fare in buses
by Staff ReporterCoimbatore Consumer Cause (CCC) has filed a public interest litigation (PIL) in Madras High Court against excess ticket fare allegedly levied in government and private buses.
In a release on Tuesday, K. Kathirmathiyon, CCC secretary, said that Justice M. Sathyanarayanan and Justice N. Seshasayee directed the State government to file its reply within two weeks and posted the matter to December 6.
Mr. Kathirmathiyon alleged that buses operated by Tamil Nadu State Transport Corporation (TNSTC) and private operators collected excess ticket fare, higher than the fare approved by Regional Transport Authority, who is the District Collector, or State Transport Authority. “Such approved fare tables must be exhibited inside the buses for the information of passengers,” he said.
He further alleged that the authorities did not penalise those buses that did not comply despite the provisions available in Central Motor Vehicles Rules to “suspend and cancel the permits of the buses.”
Recalling the observations made by the Madras High Court in 2018 against the excess fare by private bus operators, Mr. Kathirmathiyon claimed that “the problem of excess fares is much more” in TNSTC buses.


Class EnVector3IntExtensions
Inheritance
System.Object
EnVector3IntExtensions
Assembly: ECHTNICE.EnUnityEngineExtensions.dll
Syntax
public static class EnVector3IntExtensions
Methods
|
Improve this Doc
View Source
AddX(Vector3Int, Int32)
Declaration
public static Vector3Int AddX(this Vector3Int vec, int x)
Parameters
| Type |
Name |
Description |
| UnityEngine.Vector3Int |
vec |
|
| System.Int32 |
x |
|
Returns
| Type |
Description |
| UnityEngine.Vector3Int |
|
|
Improve this Doc
View Source
AddY(Vector3Int, Int32)
Declaration
public static Vector3Int AddY(this Vector3Int vec, int y)
Parameters
| Type |
Name |
Description |
| UnityEngine.Vector3Int |
vec |
|
| System.Int32 |
y |
|
Returns
| Type |
Description |
| UnityEngine.Vector3Int |
|
|
Improve this Doc
View Source
AddZ(Vector3Int, Int32)
Declaration
public static Vector3Int AddZ(this Vector3Int vec, int z)
Parameters
| Type |
Name |
Description |
| UnityEngine.Vector3Int |
vec |
|
| System.Int32 |
z |
|
Returns
| Type |
Description |
| UnityEngine.Vector3Int |
|
|
Improve this Doc
View Source
Invert(Vector3Int)
Declaration
public static Vector3Int Invert(this Vector3Int vec)
Parameters
| Type |
Name |
Description |
| UnityEngine.Vector3Int |
vec |
|
Returns
| Type |
Description |
| UnityEngine.Vector3Int |
|
|
Improve this Doc
View Source
InvertX(Vector3Int)
Declaration
public static Vector3Int InvertX(this Vector3Int vec)
Parameters
| Type |
Name |
Description |
| UnityEngine.Vector3Int |
vec |
|
Returns
| Type |
Description |
| UnityEngine.Vector3Int |
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InvertY(Vector3Int)
Declaration
public static Vector3Int InvertY(this Vector3Int vec)
Parameters
| Type |
Name |
Description |
| UnityEngine.Vector3Int |
vec |
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Returns
| Type |
Description |
| UnityEngine.Vector3Int |
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InvertZ(Vector3Int)
Declaration
public static Vector3Int InvertZ(this Vector3Int vec)
Parameters
| Type |
Name |
Description |
| UnityEngine.Vector3Int |
vec |
|
Returns
| Type |
Description |
| UnityEngine.Vector3Int |
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ToVector2(Vector3Int)
Declaration
public static Vector2 ToVector2(this Vector3Int v)
Parameters
| Type |
Name |
Description |
| UnityEngine.Vector3Int |
v |
|
Returns
| Type |
Description |
| UnityEngine.Vector2 |
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ToVector3(Vector3Int)
Declaration
public static Vector3 ToVector3(this Vector3Int v)
Parameters
| Type |
Name |
Description |
| UnityEngine.Vector3Int |
v |
|
Returns
| Type |
Description |
| UnityEngine.Vector3 |
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WithX(Vector3Int, Int32)
Declaration
public static Vector3Int WithX(this Vector3Int vec, int x)
Parameters
| Type |
Name |
Description |
| UnityEngine.Vector3Int |
vec |
|
| System.Int32 |
x |
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Returns
| Type |
Description |
| UnityEngine.Vector3Int |
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WithY(Vector3Int, Int32)
Declaration
public static Vector3Int WithY(this Vector3Int vec, int y)
Parameters
| Type |
Name |
Description |
| UnityEngine.Vector3Int |
vec |
|
| System.Int32 |
y |
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Returns
| Type |
Description |
| UnityEngine.Vector3Int |
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WithZ(Vector3Int, Int32)
Declaration
public static Vector3Int WithZ(this Vector3Int vec, int z)
Parameters
| Type |
Name |
Description |
| UnityEngine.Vector3Int |
vec |
|
| System.Int32 |
z |
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Returns
| Type |
Description |
| UnityEngine.Vector3Int |
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