Hummingbirds and Chasing Neotropical Birds
BY Ro Wauer
Hummingbirds of Texas and Chasing Neotropical Birds offer the nature reader two super reads! Hummingbirds of Texas, written by three Texas naturalists, Clifford Shackelford, Madge Lindsay, and Mark Klyn, contains up-to-date information about all 18 Texas hummingbirds, along with some excellent photos by Sid and Shirley Rucker and paintings by Clemente Guzman III. This book is generally divided into two sections. The first is full of good information about hummingbird biology; finding hummingbirds; food, water and shelter; feeders; visitors, pests, and predators; overwintering hummingbirds; migratory behavior; and photographing the hummingbirds.
The second section of Hummingbirds of Texas contains a two-page spread on each species that includes identification hints, a range map for Texas as well as adjacent New Mexico and Arizona, and some excellent illustrations. These illustrations include front and back views, including both male and female views when pertinent, and also the tail that can be extremely important for identifying some species. Species include Allen's, Anna's, berylline, black-chinned, blue-throated, broad-billed, broad-tailed, buff-bellied, calliope, Costa's, green violet-ear, green-breated mango, lucifer, magnificent, plain-capped starthroat, ruby-throated, rufous, violet-crowned, and white-eared.
Scattered throughout the text are a variety of comments that any reader will find interesting and useful. Here are a few examples: "Migratory male hummingbirds reach the breeding grounds before the females in order to stake out their claim to the best territory. A hummingbird territory is an amorphous area that ranges in size (often smaller than a football field) and must include ample food sources, which are vigorously defended." "Hummingbirds are polygamous and thus do not form mated pairs. Instead, males attempt to mate with as many females as possible and leave all the nest building and brood rearing to the females." "Spiders are important to adult and nestling hummingbirds in several ways. First, hummingbirds use web fibers as sticky material to hold the nest together. In addition, hummingbirds take prey caught in the web of a spider, and they may also consume spiders and their young or eggs." And finally, "Hummingbirds can be found at cold, high elevations. While roosting through the night, some species of hummingbirds have the ability to slow down their metabolism, which cuts their body temperature in half to conserve energy, a state known as torpor."
Hummingbirds of Texas, published by Texas A&M University Press, is 110 pages and sells for $24.95 in hardback. It is a real bargain for anyone interested in birds, and especially for those of us fascinating by these flying jewels, the hummingbirds.
Chasing Neotropical Birds, by Vera and Bob Thornton, is a very different kind of book, as it highlights the Thornton's efforts of 15 years to photograph a wide variety of neotropical birds in their native habitats south of the U.S.-Mexico border. Besides a vast array of marvelous photographs, the book includes an excellent narrative about their personal adventures while photographing the birds throughout Mexico, Central America, and South America. Many of the bird photographs included in this beautiful book are seldom found in other books of this type.
A few of the most impressive photos, at least for me, included a Baird's trogon, green-crowned brilliant, and three-wattled bellbird from Costa Rica; a tufted coquette from Trinidad; a yellow-tufted woodpecker, speckled tanager, blue-winged mountain-tanager, and golden-tanager from Venezuela; a zigzag heron and toucan barbet from Ecuador; a bicolored antbird and western slaty-antshrike from Panama; a golden-hooded tanager from Honduras; a black jacobin and red-billed scythebill from Brazil; a versicolored barbet from Peru; and a yellow-billed jacamar from Suriname.
The narrative within each chapter includes enough information so that the reader understands the efforts made to capture each of the photos, as well as the details about where the authors traveled on each of their expeditions. This is truly a beautiful book and one that a birder sooner-or-later will want to include in their birding library. Especially for those of us interested in traveling to countries south of the border, it can be a very worthwhile reference. Published by the University of Texas Press, this 160-page hardback, that includes 78 color photos, sells for $34.95. It, as well as the Hummingbirds of Texas, would make for excellent Christmas presents.


An introduction to the history, approaches, and roles in an Agile project management environment. This unit will also provide students with an understanding of the most appropriate situations in which to employ Agile practices, the benefits and challenges of Agile, and the growing use of hybrid forms of project management. In this unit you will study:
- The history of the development of Agile, including the Agile Manifesto and primary principles and values of Agile.
- The key differences and similarities between traditional project management approaches and Agile, and the role of complication and complexity.
- Frameworks that fall under the Agile banner.
- Implementing and managing Agile projects, and interacting with the PMO and organisational governance.
- Resourcing and measuring Agile.
- Using Agile in hybridised project management situations.
- Identify the key elements that differentiates Agile from traditional project management approaches, and discuss the benefits and disadvantages of the various methods in order to apply the best approach to a given situation.
- Understand principles, values, tools and techniques in order to effectively apply Agile to a project.
- Demonstrate understanding of the hybridised way in which Agile and traditional project management approaches are employed.
You can study this unit as part of the Master of Project Management. Please note, unit structure and content are subject to change. Contact your SCU Online student advisor on 1300 589 882 for more information based on your particular circumstances.


Technology is not just about work, computers, Smart phones and i Phones a great way to give children something cool to do. Thanks to the various apps and programs available today, the possibilities are endless. However, below is a list of a few ideas to get parents and children started on cools ways to spend the summer.
Technology provides children with “cool” opportunities to have fun. A prime example is using a Smart phone to discover the stars. With available apps, children can spend their evening learning the names of stars and discovering planets. All they have to do is point the phone skyward to gain an overview of the heavenly bodies shining down on them.
2. Child Safe Websites
The Internet provides thousands of child safe websites for children to chose from. Offering children a fun environment, many site age-appropriate games, art projects and reading goals. Parents should always use parental controls, provided in most Internet packages, to protect their children from inappropriate websites. In addition, cautioning children against signing up for paid websites is advised.
3. Movie Time
A Smart phone or i Phone is a great way for children to make their own home movies. They can write a play and act out the parts with family members and friends, use a computer to edit the play, and put together a fun movie for the family to enjoy.
Children are inquisitive by nature, the idea of using a Smart phone with GPS tracking to find treasures throughout the yard sure to please. A technological twist on an old game, parents can use this to give their children something really fun and cool to do. Parents hide the treasures and children use the Smart phone for their own personal treasure hunt.
A computer and an art program are great ways for children to record their summer vacation. They can use art programs to personalize photo frames, add an artistic twist to captions and create decorative pages for their scrapbook. This is not only a cool way to record those fun summer days but a creative outlet children will love.
6. Keeping In Touch
Summer can become a lonely time for children, but a Smart phone or computer can end those lonely blues. Allowing children to make long distance calls to friends on vacation gives them something to look forward to and someone to share with. Setting up an Instant Message app ensures children keep in touch with friends, too.


|Version 11 (modified by jarrod.millman, 6 years ago)|
CAUTION: This page is in the process of being migrated from an older page. Until this process is completed, information found here may be incorrect.
SciPy uses the NumPy testing system, which is based on the unit testing framework offered by unittest.py. Our goal is that every module and package in SciPy should have a thorough set of unit tests. These tests should exercise the full functionality of a given routine as well as its robustness to erroneous or unexpected input arguments. The best time to write the tests is when the module itself is being written. In addition, whenever a new bug is found in a routine, a new test for that specific case should be written and added to the test suite to prevent that bug from creeping back in unnoticed.
To run SciPy's full test suite, use the following:
>>> import scipy >>> scipy.test()
The test method may take two arguments; the first specifies the level of testing and the second the verbosity. The higher the level the more test will be run. If the verbosity is 1 or less, the tests will just show information messages about the tests that are run; but if it is greater than 1, then the tests will also provide warnings on missing tests. So if you want to run every test and get messages about which modules don't have tests:
>>> scipy.test(level=10, verbosity=2) # or >>> scipy.test(10, 2)
Finally, if you are only interested in testing a subset of SciPy, for example, the integrate module, use the following:
The rest of this page will give you a basic idea of how to add unit tests to modules in SciPy. It is extremely important for us to have extensive unit testing since this code is going to be used by scientists and researchers and is being developed by a large number of people spread across the world. So, if you are writing a package that you'd like to become part of SciPy, please write the tests as you develop the package. Also since much of SciPy is legacy code that was originally written without unit tests, there are still several modules that don't have tests yet. To see what modules still lack tests use:
>>> scipy.test(level=0, verbosity=2) # or >>> scipy.test(0, 2)
Please feel free to choose one of these modules to develop test for either after or even as you read through this introduction.
Writing your own tests
Ideally, every Python code, extension module, or subpackage in the SciPy package directory should have a corresponding test_<name>.py file. This file should define classes derived from the NumpyTestCase (or the unittest.TestCase) class and have names starting with test_. The methods of these classes whose names start with bench_, check_, or test_, are passed on to the unittest machinery. In addition, the value of the first optional argument of these methods determines the level of the corresponding test. (Default level is 1.)
Suppose you have a SciPy module scipy/xxx/yyy.py containing a function zzz(). To test this you would start by creating a test module called test_yyy.py. This test file should include a class that tests zzz(). The test class has test methods that test various aspects of zzz(). Within these test methods, assert() is used to test whether some case is true. If the assert fails, the test fails. The NumpyTest().run() function actually runs the test suite. A minimal example of a test_yyy.py file that implements tests for a Scipy package module scipy.xxx.yyy, is shown below:
import sys from numpy.testing import * set_package_path() # import xxx symbols from xxx.yyy import zzz restore_path() #Optional: set_local_path() # import modules that are located in the same directory as this file. restore_path() class test_zzz(NumpyTestCase): def check_simple(self, level=1): assert zzz()=='Hello from zzz' #... if __name__ == "__main__": NumpyTest().run()
NumpyTestCase is derived from unittest.TestCase and it basically only implements an additional method measure(self, code_str, times=1). Note that all classes that are inherited from TestCase class, are picked up by the test runner when using testall. For more detailed information on defining test classes see the official documentation for the Python Unit testing framework.
The tests/ directory
Rather than keeping the code and the tests in the same directory, we put all the tests for a given subpackage in a tests/ subdirectory. For our example, if it doesn't all ready exist you will need to create a tests/ directory in scipy/xxx/. So the path for test_yyy.py is scipy/xxx/tests/test_yyy.py.
Once the scipy/xxx/tests/test_yyy.py is written, its possible to run the tests by going to the tests/ directory and typing:
Or if you add scipy/xxx/tests/ to the Python path, you could run the tests interactively in the interpreter like this:
>>> import test_yyy.py >>> test_yyy.test()
The __init__.py file
Usually however, adding the tests/ directory to the python path isn't desirable. Instead it would better to invoke the test straight from the module xxx. To this end, simply place the following two lines at the end of your package's __init__.py file:
from numpy.testing import NumpyTest test = NumpyTest().test
Now you can do the following to test your module:
>>> import scipy >>> scipy.xxx.test()
Also, when invoking the entire SciPy test suite, your tests will be found and run:
>>> import scipy >>> scipy.test() # your tests are included and run automatically!
This package holds a few helper routines. Several were discussed in the previous section. A couple of others are something of a verbose assert() function that give feedback as to what error occured. One of them, assert_array_equal(), is useful for comparing values in two arrays. The library of assert functions should grow to test for other general cases.
- Speed testing: It'd be nice to have some speed testing facilities so that we could keep track of whether new code hurts or helps our speed. It would provide some simple benchmarking facilities also. To do this correctly, we'd need something like start_timer() and stop_timer() calls that could be placed in the check_xxx() methods. This would allow the set up and error checking code at the beginning and end of check methods to be ignored for timing purposes. If these functions weren't used in the check method, then the entire check method would be timed.
- Regression testing and database: If each test could be stored in a database, including timing information, that would allow us to see how speed is changing over time (using a reference machine).
- Web output of test results: Haven't looked much at the TestRunner stuff, but I imagine it is possible to grab info from the test suite and write it out to an HTML file. We should do this on several architectures every evening on the SciPy site.


trusted.pgp is a keyring containing all keys which PRZ has signed, all keys which those have signed, and so on. trusted.sig is a detached PGP signature for trusted.pgp. trusted.dat.gz is a gzipped listing of those keys in order of distance from the email@example.com key. For instance, those with distance 1 are signed by prz.acm. trusted.kv.gz is the output of pgp -kv "" trusted.pgp .
strong.pgp is a keyring containing all keys which are reachable to and from PRZ. That is, they have signed or have been signed by (possibly indirectly) firstname.lastname@example.org. strong.sig is a detached PGP signature for strong.pgp and strong.kv.gz is the output of pgp -kv "" strong.pgp .


S: Bill of Rights
FC: The First 10 Ammendments of the Constitution
1: 1st Ammendment Freedom of religion, speech, press,and assembly
3: 2nd Ammendment Right to bear arms and maintain state militias
5: 3rd Ammendment Troops may not be quartered in homes in peacetime
7: 4th Amendment No unreasonable searches and seizures.
9: 5th Ammendment Grand jury indictment required to prosecute a person for a serious crime.No double jeopardy.
11: 6th Ammendment Right to speedy,public, impartial trial with defense counsel, and right to cross examine witnesses.
13: 7th Ammendment Jury trials in civil suits where value exceeds twenty dollars
15: 8th Ammendment No excessive bail or fines, no cruel unusual punishments.
17: 9th Ammendment Unlisted rights are not necessarily denied.
19: 10th Ammendment Powers not delegated to the United States or denied to states are reserved to the states or to the people
20: Although 12 ammendments were originally proposed, the 10 that were ratified became the Bill of Rights in 1791. They defined citizens rights in relation to newly established government under the constitution.
21: TEKS Social Studies, Grade 8 (1) In Grade 8, students study the history of the United States from the early colonial period through Reconstruction. The knowledge and skills in subsection (b) of this section comprise the first part of a two-year study of U.S. history. The second part, comprising U.S. history from Reconstruction to the present, is provided in 113.41 of this title (relating to United States History Studies Since 1877 (One Credit), Beginning with School Year 2011-2012). The content in Grade 8 builds upon that from Grade 5 but provides more depth and breadth. Historical content focuses on the political, economic, religious, and social events and issues related to the colonial and revolutionary eras, the creation and ratification of the U.S. Constitution, challenges of the early republic, the Age of Jackson, westward expansion, sectionalism, Civil War, and Reconstruction. Students describe the physical characteristics of the United States and their impact on population distribution and settlement patterns in the past and present. Students analyze the various economic factors that influenced the development of colonial America and the early years of the republic and identify the origins of the free enterprise system. Students examine the American beliefs and principles, including limited government, checks and balances, federalism, separation of powers, and individual rights, reflected in the U.S. Constitution and other historical documents. Students evaluate the impact of Supreme Court cases and major reform movements of the 19th century and examine the rights and responsibilities of citizens of the United States as well as the importance of effective leadership in a constitutional republic. Students evaluate the impact of scientific discoveries and technological innovations on the development of the United States. Students use critical-thinking skills, including the identification of bias in written, oral, and visual material.


(intransitive) (archaic) to happen (to)
1520s, from be- + chance. Related: Bechanced; bechancing.
to charm; bewitch; captivate.
to clasp all around or on all sides.
to darken or obscure with clouds. to make confused: Angry words beclouded the issue. Historical Examples This decision made both Northern and Southern agents anxious and the latter took steps further to becloud the status of the Rams. Great Britain and the American Civil War Ephraim Douglass Adams These only becloud, they do not help […]


Select a young TURKEY; remove all the feathers carefully, singe it over a burning newspaper on the top of the stove; then “draw” it nicely, being very careful not to break any of the internal organs; remove the crop carefully; cut off the head, and tie the neck close to the body by drawing the skin over it. Now rinse the inside of the TURKEY out with several waters, and in the next to the last, mix a teaspoonful of baking soda; oftentimes the inside of a fowl is very sour, especially if it is not freshly killed. Soda, being cleansing, acts as a corrective, and destroys that unpleasant taste which we frequently experience in the Dressing when FOWLS have been killed for some time.
Now, after washing, wipe the TURKEY dry, inside and out, with a clean cloth, rub the inside with some salt, then stuff the breast and body [Pg 83]with “Dressing for FOWLS.” Then sew up the TURKEY with a strong thread, tie the legs and wings to the body, rub it over with a little soft butter, sprinkle over some salt and pepper, dredge with a little flour; place it in a dripping-pan, pour in a cup of boiling water, and set in the oven. Baste the TURKEY often, turning it around occasionally so that every part will be uniformly baked. When pierced with a fork and the liquid runs out perfectly clear, the bird is done. If any part is likely to scorch, pin over it a piece of buttered white paper. A fifteen pound TURKEY requires between three and four hours to bake. Serve with cranberry Sauce.
Gravy for TURKEY.—When you put the TURKEY in to roast , put the neck, heart, liver and gizzard into a stewpan with a pint of water; boil until they become quite tender; take them out of the water, chop the heart and gizzard, mash the liver and throw away the neck; return the chopped heart, gizzard and liver to the liquor in which they were stewed; set it to one side, and when the TURKEY is done it should be added to the gravy that dripped from the TURKEY, having first skimmed off the fat from the surface of the dripping-pan; set it all over the fire, boil three minutes and thicken with flour. It will not need brown flour to color the gravy. The garnishes for TURKEY or CHICKEN are fried Oysters, thin slices of HAM, slices of lemon, fried PIGEONSs, or force meat balls, also parsley.
The Whitehouse Cookbook, by Mrs. F.L. Gillette (Year 1887)


Start your own project
A Project on awarenet is also called collaborative content. It means that a person or a group of people can work together on the same project online, but have the possibility to work individually at different times from any computer with Internet access. The topic of the project can be chosen freely, but must not be offending, aggressive, or discriminating. The project can consist of text and pictures. In the future, audio and video files can be used as well. Projects are meant to be educational for the project members as well as for future researchers. They will be stored in the awarenet's database for other users to read and learn.
To join a project, go to "Projects" in the main menu. The project's front page displays several ongoing projects. You can read through existing projects to get an idea which project interests you. You can work together with other people in your school, at a different school and even internationally to share ideas and safe work, because you won't have to do the same research again. Further, working in a team with foreign learners is an important skill to learn for your future.
Click on the project that interests you to get access to the full project. In the side column you will see the members of that project. If you want to join that specific project, click on "ask" in the box "Ask To Join The Project". The box will open and present a text box with the following text: "Message to Project Admins: Hi! I'd like to join your project. I think it's interesting and would like to participate.", followed by your name. If you like you can edit the text, but you don't have to. Then, you click on the "Make Request" button to send your request to join the project. If you don't want to join the project, you click on the "Hide Form" button. The project administrator has to first confirm your request before you will be a member of the project. His decision will be sent to you in "My Feed", your notifications.
To start a project, go to "Projects" in the main menu. The project's front page displays several ongoing or finished projects. You can read through existing projects to get an idea what a project could be about and how to create a good project. Have a look if there is already a similar project to the one that you would like to start. It is more interesting and follows the nature of awarenet if you join an existing project with the same topic rather than starting a new one.
If you can't find an existing project similar to the project you planned, you can start your own project. In the side column of project's home page is a box called "Create New Project". You can open the box by clicking on the "+" and close it by clicking on the dot in the little circle. Type a title for your project into the text field and click on the >> button next to it.
To edit your project you can either open your project from the main project page by clicking on the project title and then on "Edit This Project" in the submenu, or by clicking on the little  link under the abstract of your project on the the main project page. You will only be able to edit your abstract and title. To edit a section of your project, click on the little  link under a specific section of your project. You can change the title, add, edit or delete text, up- and download pictures and drag and drop them into the text box, and edit the text with the little icons on top of the text box. Remember to save your changes by clicking on the "Save Changes" button under the text box.
To structure your project, you can add sections that will appear below your title and abstract. To add a section to your project, scroll down until you see the button "Add Section". Click on the "+" to type a section title in the little text field and click on the "Add Section >>" button. Sections can be edited as projects. All section titles will appear below your project title at the top. You can change the order of the sections by clicking on the little up or down arrows next to the section titles in your Sections box.
You can work on a project together with a group of other awarenet users. To add new members to your project you have to click on the link  to edit your project. Below your project description you will find the project members list where all current members are listed and the button "Add Members". By clicking on the "+" it will open a text field where you can type in the name. You can determine if that person will be a member or an administrator of that project. Usually, there is only one administrator per project. They have special rights that regular members don't need, e.g. deleting the project. Regular members can edit the project the same way as administrators. Click on the button "Add to project". Done.
What is a tag?
A tag is a keyword assigned to a piece of information, e.g. disease would be one key word for a text about HIV/AIDS. It helps describe an item and allows it to be found again by browsing or searching, which means if you would like your project to be found easier by other awareNet members then it is a good method to add tags to your project. Tags will appear next to your project, which describe your project in key words. A tag cloud describes all projects that have been tagged by using larger text sizes for frequently used key words and smaller text sizes for rarely used key words.
To add a tag to your project click on the link  to edit your project. You will find the button "Edit tags" below your project description. By clicking on it you will see a little text field to type the tag before you click on the >> button. You can add as many tags as you like. They will appear under your project and describe your project in key words.
A tag cloud will appear next to all projects which gives you an overview of all topics of all tagged projects. A larger text size means that there are relatively more projects about that specific topic. By clicking on a specific key word in the tag cloud you will see all projects that were tagged with that key word.


Machine learning has emerged as a transformative force in the world of technology and data-driven decision-making. At its core, machine learning is about enabling computers to learn and make predictions or decisions without being explicitly programmed. However, this remarkable capability hinges on a critical foundation: data processing. Here, we will delve into the pivotal role of data processing in machine learning. We will explore what data processing is, its significance, and the various techniques and methods involved in this crucial aspect of the machine learning pipeline.
Unlock the True Potential of Data.
Data processing in machine learning refers to the series of operations and transformations applied to raw data to prepare it for analysis and model training. It is the fundamental step that precedes any machine learning endeavor and can significantly impact the performance and reliability of machine learning models.
Key Aspects of Data Processing
Data processing involves several key activities
1. Data Collection: This initial step involves gathering data from various sources, which could be structured or unstructured. Data can come from sensors, databases, text documents, images, videos, and more. The quality and quantity of data collected are crucial factors that influence the outcome of machine learning models.
2. Data Cleaning: Raw data often contains errors, missing values, outliers, and inconsistencies. Data cleaning involves identifying and rectifying these issues to ensure that the dataset is accurate and reliable.
3. Data Transformation: Data must be transformed into a suitable format for analysis. This includes encoding categorical variables, scaling numerical features, and applying other transformations that make the data compatible with the chosen machine learning algorithms.
4. Feature Engineering: Feature engineering is the process of creating new features from existing data or domain knowledge to improve the model’s performance. It requires a deep understanding of the problem domain and the data itself.
5. Data Splitting: To evaluate the machine learning model’s performance, the dataset is typically divided into training, validation, and test sets. This separation ensures that the model can generalize well to unseen data.
6. Data Preprocessing: Data preprocessing encompasses various techniques such as normalization, standardization, and handling imbalanced datasets. These steps aim to further prepare the data for training and make it suitable for the chosen machine learning algorithm.
7. Data Augmentation (for image and text data): In certain cases, additional data samples can be generated through techniques like image rotation, flipping, or text augmentation. This can help improve model performance, especially when the dataset is limited.
Understanding Data Processing in Machine Learning
Understanding the intricacies of data processing in machine learning is paramount. Data is often hailed as the lifeblood of machine learning, and its quality and preparation significantly impact the success of any ML project. Let’s delve into the crucial role of data processing, starting with the point of origin: raw data.
Raw Data: Where It All Begins
Raw data is the starting point for any machine learning endeavor. It is the unrefined, often messy collection of information gathered from various sources. While raw data may hold invaluable insights, it is far from ready for machine learning algorithms. Here’s where data processing enters the scene.
The Need for Data Preprocessing
Data preprocessing serves as the bridge between raw data and machine learning models. Its importance can be understood through several key dimensions:
- Validity: Raw data can be riddled with errors, outliers, or inaccuracies. Data preprocessing techniques such as outlier detection and error correction ensure that the data used for training and testing models is valid and reliable.
- Accuracy: Accuracy in data processing involves rectifying inaccuracies, inconsistencies, and discrepancies in the data. This step ensures that the data faithfully represents the real-world phenomena it intends to model.
- Completeness: Incomplete data, with missing values or gaps, can impede model training and lead to biased results. Data preprocessing techniques like imputation fill in these gaps, making the dataset complete and suitable for analysis.
- Consistency: Consistency ensures that data values are uniformly represented and follow a common format. Inconsistent data may confuse machine learning algorithms, leading to suboptimal performance.
- Uniformity: Data processing transforms variables into a consistent range, making it easier for machine learning models to learn patterns. Techniques like normalization and standardization achieve uniformity.
Here’s how Data Processing fits into the Machine Learning Workflow
- Data processing is not a one-time task; it is an ongoing and iterative process that permeates the entire machine learning workflow:
- Data Collection: During this initial phase, understanding data processing is essential to decide what data to collect and how to store it efficiently.
- Data Cleaning: Raw data is often messy, and data cleaning is the first step in transforming it into a usable format.
- Feature Engineering: Feature engineering relies on an in-depth understanding of the data to create meaningful variables that enhance model performance.
- Data Splitting: When splitting data into training, validation, and test sets, knowledge of data processing helps ensure that each subset is representative and unbiased.
- Data Preprocessing: Throughout model development, preprocessing techniques are continually applied to maintain data quality and integrity.
- Model Evaluation: Understanding data processing is crucial when assessing a model’s performance. Misleading results can often be traced back to inadequate data processing.
Data Cleaning: The First Step
Data cleaning is the critical initial phase in the data processing pipeline, aimed at improving the quality and reliability of the dataset for machine learning. It involves identifying and rectifying various issues within the data, ensuring that it is ready for analysis. Here are some of the key aspects of data cleaning:
Identifying Missing Data
Missing data is a common issue in real-world datasets and can severely impact the performance of machine learning models. Identifying missing data is a crucial step in data cleaning. Techniques for identifying missing data include:
- Visual Inspection: Visualizing data using plots or heatmaps can reveal missing values as blank spaces or irregular patterns.
- Summary Statistics: Computing summary statistics like mean, median, or count of missing values for each feature can help quantify the extent of missing data.
- Data Profiling Tools: Specialized data profiling tools can automate the process of identifying missing values and provide detailed reports.
Handling Missing Data (Imputation, Removal, or Prediction)
Once missing data is identified, it must be handled appropriately. There are several strategies for dealing with missing data:
- Imputation: Imputation involves filling in missing values with estimated or calculated values. Common imputation methods include mean imputation (replacing missing values with the mean of the feature), median imputation, mode imputation, or more advanced techniques like regression imputation or k-nearest neighbors imputation.
- Removal: In some cases, if the amount of missing data is minimal and doesn’t significantly impact the dataset, rows or columns containing missing values can be removed. However, this should be done judiciously to avoid losing valuable information.
- Prediction: For more complex scenarios, predictive modeling can be used to predict missing values based on the relationships within the data. This is particularly useful when missing data is dependent on other variables in the dataset.
Detecting and Managing Outliers
Outliers are data points that significantly deviate from the majority of the data and can distort the results of machine learning models. Detecting and managing outliers is an integral part of data cleaning. Methods for identifying and managing outliers include:
- Visualizations: Box plots, scatter plots, and histograms can reveal the presence of outliers.
- Statistical Methods: Z-scores, IQR (Interquartile Range), or modified Z-scores can be used to identify outliers.
- Transformation: Applying mathematical transformations (e.g., log transformation) to data can sometimes mitigate the impact of outliers.
Fix Structural Errors
Structural errors in data refer to issues related to data format, representation, or encoding. These errors can include:
- Inconsistent Formatting: Ensuring consistent formatting for categorical data (e.g., “Male” vs. “male”) is essential.
- Data Type Errors: Ensuring that data types match the expected format (e.g., dates represented as strings instead of datetime objects).
- Encoding Issues: Handling character encoding problems that may arise in text data, especially when dealing with multilingual datasets.
Data transformation is a critical step in the data processing pipeline that involves converting and shaping data into a suitable format for machine learning algorithms. This step is essential because different algorithms have various requirements, and the quality of transformation can significantly impact model performance. Here are some key aspects of data transformation:
Numeric Encoding: Many machine learning algorithms work with numerical data. Therefore, categorical variables (those with distinct categories or labels) need to be encoded into numerical format. There are two primary techniques for numeric encoding:
- Label Encoding: Assigns a unique integer to each category. This is suitable for ordinal data where there is a meaningful order among categories.
- One-Hot Encoding: Creates binary columns for each category, where a ‘1’ indicates the presence of the category, and ‘0’ represents its absence. This is suitable for nominal data with no inherent order.
Handling Text Data: Text data often requires specialized preprocessing techniques such as tokenization (breaking text into words or tokens), stemming/lemmatization (reducing words to their root form), and vectorization (converting text to numerical representations, e.g., using techniques like TF-IDF or word embeddings).
Scaling and Normalization
Scaling and normalization are essential for numerical features to ensure that they have similar scales, as many machine learning algorithms are sensitive to feature scaling. Common techniques include:
- Min-Max Scaling (Normalization): Scales data to a specific range, typically between 0 and 1. It preserves the relative relationships between data points and is suitable when the data follows a uniform distribution.
- Z-Score Standardization: Standardizes data by subtracting the mean and dividing by the standard deviation. It results in a distribution with mean 0 and standard deviation 1. This method is appropriate when data follows a Gaussian (normal) distribution.
- Robust Scaling: Scales data by subtracting the median and dividing by the interquartile range (IQR). It is less affected by outliers compared to min-max scaling and is useful when data contains outliers.
The Impact on Model Performance
Data transformation can have a significant impact on model performance:
- Improved Convergence: Properly scaled and normalized data can help machine learning algorithms converge faster during training, especially for gradient-based methods like neural networks.
- Reduced Model Bias: In models that rely on distance-based metrics (e.g., K-nearest neighbors), scaling can prevent features with larger scales from dominating the prediction.
- Enhanced Model Interpretability: Scaling and encoding can make model results more interpretable because coefficients or feature importance can be compared directly.
- Model Stability: Data transformation can improve model stability, making it less sensitive to changes in input data.
Feature engineering is the process of creating new and meaningful features (variables) from existing data to improve the performance of machine learning models. It is a crucial step in the data preprocessing pipeline because the quality and relevance of features have a direct impact on the model’s ability to learn and make accurate predictions. Effective feature engineering requires domain knowledge, creativity, and a deep understanding of the problem you are trying to solve.
Feature engineering involves several key aspects:
- Feature Extraction: This involves extracting relevant information from raw data to create new features. For example, in natural language processing, features can be extracted from text data by counting the frequency of specific words or using techniques like TF-IDF to measure term importance.
- Feature Transformation: Transformation techniques modify existing features to make them more suitable for modeling. Common transformations include scaling, normalization, and log transformations to handle skewed distributions.
- Feature Creation: Creating entirely new features based on domain knowledge or patterns observed in the data. For example, in a real estate prediction model, you could create a “price per square foot” feature by dividing the price by the square footage of a property.
- Feature Selection: Selecting the most relevant features from the existing set to reduce dimensionality and focus on the most informative attributes. This helps in improving model efficiency and interpretability.
Techniques for Creating New Features
- Binning/Discretization: Grouping continuous numerical data into discrete bins or categories. For example, age can be discretized into age groups such as “young,” “middle-aged,” and “senior.”
- Polynomial Features: Creating new features by raising existing features to different powers. This is particularly useful for capturing non-linear relationships in the data.
- Interaction Features: Combining two or more existing features to create new ones. For example, in a recommendation system, you might create an interaction feature between “user rating” and “item popularity” to capture user-item interactions.
- Time-Based Features: Extracting information from timestamps, such as day of the week, month, or season, which can be valuable in time series analysis or forecasting.
- Feature Encoding: Encoding categorical variables into numerical form, such as one-hot encoding or label encoding.
- Text-Based Features: Creating features from text data, such as word counts, term frequency-inverse document frequency (TF-IDF), or word embeddings.
Feature Selection and Its Significance
Feature selection is the process of choosing a subset of the most relevant features from the original set of features. It is essential for several reasons:
- Dimensionality Reduction: By selecting the most informative features, you can reduce the dimensionality of the data, which can lead to faster training times and less overfitting.
- Improved Model Performance: Removing irrelevant or redundant features can improve a model’s predictive accuracy because it focuses on the most critical information.
- Enhanced Interpretability: Models with fewer features are easier to interpret and explain to stakeholders.
Feature selection can be done using various techniques, such as:
- Univariate Feature Selection: Selecting features based on statistical tests like chi-squared or mutual information.
- Recursive Feature Elimination (RFE):Iteratively removing the least important features until a desired number is reached.
- Feature Importance from Models: Some machine learning models (e.g., decision trees, random forests) provide feature importance scores that can guide feature selection.
- L1 Regularization (Lasso): Adding a penalty term to the model’s loss function to encourage sparsity in the feature set.
In this dynamic field, where the quality of data and the ingenuity of feature engineering can tip the scales, understanding and mastering these processes are critical for success. We hope this guide will serve as a valuable resource for readers looking to deepen their understanding of data processing’s pivotal role in the machine learning workflow, complete with practical examples and ethical considerations.
As we navigate the exciting landscape of machine learning, it is essential to recognize that the journey begins with raw data and evolves through a series of transformative steps. The art of data processing, transformation, and feature engineering is a passion for Google Cloud Partners such as Niveus. Our work empowers businesses to unlock the potential within their data and harness the true capabilities of machine learning.


What is a "Comprehensive School Health Approach"?
Comprehensive school health (CSH) is an approach rather than a program or initiative. An internationally recognized framework, CSH addresses school health in a planned, integrated, and holistic way in order to support improvements in student achievement and well-being.
This approach to student health and learning outcomes involves the entire school community, not only what happens in the classroom. It is a way of thinking and acting that takes a subject like mathematics and applies it to work being done in the school garden, or an issue like bullying and involves parents and community partners as well as a peer-led approach to healthy relationships and self-regulation. CSH is the mechanism through which issues such as physical activity, positive mental health, or injury prevention are addressed in school.
Different Terminology, Same Ideas
The term “comprehensive school health” is widely used in Canada. In some provinces and territories, and in other countries, the approach may be also known as ‘health promoting school’, ‘healthy schools’, or ‘coordinated school health’, and its components may be expressed in different ways. However, the underlying concepts are the same; they are all based on the World Health Organization’s Ottawa Charter for Health Promotion (1986).
Comprehensive School Health in Canada
Effective, sustainable progress in comprehensive school health depends on a common vision, shared responsibilities and harmonized actions among health, education and other sectors. The challenge is to coordinate these efforts so that partners pool resources and develop action plans together with, and in support of schools. In Canada, the Pan-Canadian Joint Consortium for School Health models, supports and encourages the partnerships between health and education that are essential to comprehensive school health. It works across provincial, territorial and federal governments to better coordinate and integrate efforts that champion improved health and learning for children and youth. For more information on comprehensive school health in Canada, contact the Consortium at firstname.lastname@example.org , or visit www.jcsh-cces.ca.


A new study from Stanford University economist Caroline Hoxby suggests that students who pursue degrees through distance learning typically don’t increase their professional earnings by enough to offset the costs of the degree itself. According to the data, students do increase earnings while enrolled and immediately after…
- A new study from Stanford University economist Caroline Hoxby suggests that students who pursue degrees through distance learning typically don't increase their professional earnings by enough to offset the costs of the degree itself.
- According to the data, students do increase earnings while enrolled and immediately after completing programs which are exclusively or substantially offered online by more than $16,000 annually. But the total costs of loans and grants frequently exceeds $10,000 per student annually, and yields loan default rates in excess of 10% for online learners.
- The research also shows that online degree programs often do not offer the professional mobility which allows graduates to move into high-earning jobs over the course of a career.
The data in this report suggests a common theory advanced by the Obama Administration on the subject of online learning: the majority of schools and programs are seemingly swindling learners out of money and career opportunities. Putting conversations about individual choice and convenience aside, the research doesn't appear to readily separate earnings generated by degrees earned from for-profit and non-profit online programs, and doesn't parse the variety of industries in which these programs may or may not focus.
For college leaders, the question is one of industrial value. Offer degrees in fields that are rapidly producing jobs, and market them in areas where the jobs are being created. This is the way to ensure that continuing learners and new students have access to training and credentialing that matters in the workplace and in social mobility.
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The discovery of a new type of supernova may shed light on some universal mysteries
Not all explosions are created equal: It’s as true for film effects as it is for the stars. Yet, until now, scientists had only observed two basic kinds of exploding stars, known as supernovae. Now, scientists at the Weizmann Institute of Science, in collaboration with others around the world, have identified a third type of supernova. Their findings appeared this week in Nature.
The first two types of supernova are either hot, young giants that go out in a violent display as they collapse under their own weight, or old, dense white dwarves that blow up in a thermonuclear explosion. The new supernova appeared in telescope images in early January, 2005 and scientists, seeing that it had recently begun the process of exploding, started collecting and combining data from different telescope sites around the world, measuring both the amount of material thrown off in the explosion and its chemical makeup. But Dr. Avishay Gal-Yam, Hagai Perets, (now at the Harvard-Smithsonian Center for Astrophysics), Iair Arcavi and Michael Kiewe of the Weizmann Institute’s Faculty of Physics, together with Paolo Mazzali of the Max-Planck Institute for Astrophysics, Germany, and the Scuola Normale Superiore, Pisa, and INAF/Padova Observatory in Italy, Prof. David Arnett from the University of Arizona, and researchers from across the USA, Canada, Chile and the UK, soon found that the new supernova did not fit either of the known patterns.
On the one hand, the amount of material hurled out from the supernova was too small for it to have come from an exploding giant. In addition, its location, distant from the busy hubs where new stars form, implied it was an older star that had had time to wander off from its birthplace. On the other hand, its chemical makeup didn’t match that commonly seen in the second type. ‘It was clear,’ says the paper’s lead author Perets, ‘that we were seeing a new type of supernova.’ The scientists turned to computer simulations to see what kind of process could have produced such a result.
The common type of exploding white dwarf (a type Ia supernova) is mainly made up of carbon and oxygen, and the chemical composition of the ejected material reflects this. The newly-discovered supernova had unusually high levels of the elements calcium and titanium; these are the products of a nuclear reaction involving helium, rather than carbon and oxygen. ‘We’ve never before seen a spectrum like this one,’ says Mazzali. ‘It was clear that the unique chemical composition of this explosion held an important key to understanding it.’ Where did the helium come from? The simulations suggest that a pair of white dwarves are involved; one of them stealing helium from the other. When the thief star’s helium load rises past a certain point, the explosion occurs. ‘The donor star is probably completely destroyed in the process, but we’re not quite sure about the fate of the thief star,’ says Gal-Yam.
The scientists believe that several other previously observed supernovae may fit this pattern. In fact, these relatively dim explosions might not be all that rare; if so, their occurrence could explain some puzzling phenomena in the universe. For example, almost all the elements heavier than hydrogen and helium have been created in, and dispersed by supernovae; the new type could help explain the prevalence of calcium in both the universe and in our bodies. It might also account for observed concentrations of particles called positrons in the center of our galaxy. Positrons are identical to electrons, but with an opposite charge, and some have hypothesized that the decay of yet unseen ‘dark matter’ particles may be responsible for their presence. But one of the products of the new supernova is a radioactive form of titanium that, as it decays, emits positrons. ‘Dark matter may or may not exist,’ says Gal-Yam, ‘but these positrons are perhaps just as easily accounted for by the third type of supernova.’
Dr. Avishay Gal-Yam’s research is supported by the Nella and Leon Benoziyo Center for Astrophysics; the Yeda-Sela Center for Basic Research; the Peter and Patricia Gruber Awards; the Legacy Heritage Fund Program of the Israel Science Foundation; the Minerva Foundation with funding from the Federal German Ministry for Education and Research; and Miel de Botton Aynsley, UK.
The Weizmann Institute of Science in Rehovot, Israel, is one of the world's top-ranking multidisciplinary research institutions. Noted for its wide-ranging exploration of the natural and exact sciences, the Institute is home to 2,600 scientists, students, technicians and supporting staff. Institute research efforts include the search for new ways of fighting disease and hunger, examining leading questions in mathematics and computer science, probing the physics of matter and the universe, creating novel materials and developing new strategies for protecting the environment.


Cuba is celebrating the International Literacy Day today with the recognition for its educational achievements, particularly the campaign that declared the country in 1961 as first territory free of illiteracy in the region.
The United Nations Educational, Scientific and Cultural Organization (UNESCO) described this initiative as a pioneering example of the values promoted by the agency for the Education 2030 Agenda.
The campaign marked the subsequent future of the nation where it had some 979 200 illiterate people in 1961, and more than 800 000 children aged 5-15 did not attend school before the revolutionary victory.
With the premise of expanding educational guarantees, more than 100 000 students were called by the leader of the Cuban Revolution Fidel Castro to participate in the campaign that started on January 1, 1961.
As a result, about 707 200 illiterate people learned to read and write, reducing illiteracy rate to 3.9% and the free and universal access to different levels of education was facilitated.
Almost six decades later and amid the complex situation caused by Covid-19, UNESCO recognizes Cuba’s strategies in terms of inclusive and quality education, as it was stated in the most recent 2020 Global Education Monitoring (GEM) Report.
Cuba has shown significant signs of its commitment to global efforts to eliminate illiteracy through international cooperation, the statement noted.
The best example of this is the teaching method developed by Cuban pedagogues “Yes I Can”, winner of the 2006 UNESCO King Sejong Literacy Prize by its contribution to the teaching of reading and writing, which is successfully applied in 30 countries in Latin America and the Caribbean, Africa, Oceania and Europe.


Umbilical hernias occur in or around the naval, or umbilicus. An umbilical hernia occurs when the abdominal lining, or any portion of abdominal organs, protrudes through a small hole in the abdominal wall around the navel area. An umbilical hernia is also known as a belly button hernia.
Umbilical hernias can be acquired as the result of a sudden or repetitive strain, pressure or injury which weakens the abdominal wall. However, umbilical hernias can also be congenital, or present since birth. In some cases, umbilical hernias are caused at birth by a weakness in the navel area where the umbilical cord exited the infant, the umbilical ring never quite heals. What exactly prevents the umbilical ring from closing completely is not known, though it is thought that many aspects affecting the developing fetus are involved. For example, infants born prematurely may not have been given sufficient time in the womb for the abdominal wall to fully develop, thus causing the weakness. This is why umbilical hernias are commonly found in infants, but can occur at any time through late adulthood.
Depending on the severity of the hernia, it may be anywhere from 1 to 5 or more inches in diameter. In infants, hernias may slowly close, possibly delaying surgery until age 3 or 4 unless the hernia causes problems before then. However, umbilical hernias which do not close by this time may require surgery. In adults an umbilical hernia will not heal and go away and is often caused by sudden or repeated strain or stress on the abdominal muscles. As time passes, the hole or weakness increasingly grows larger allowing more abdominal contents to protrude, sometimes to the point that the hernia will distort the navel creating an "outie" instead of an "innie". Often times umbilical hernias in adults can become problematic in that incarceration or strangulation can occur and immediate medical attention may become necessary. This is known as an incarcerated umbilical hernia or a strangulated umbilical hernia.
Umbilical hernia symptoms. As always, signs and symptoms of an umbilical hernia vary from person to person, however, the primary umbilical hernia symptom is often a small, soft bulge under or around the navel area. The bulge may be visible, or it may only be felt when pushed on. Sometimes the umbilical hernia is accompanied by pain, or a burning sensation, in the abdomen and may become more severe when lifting, coughing or sneezing. This area may also become swollen and may appear red or a gray-blue on the surface. It is possible to be able to push part of the bulge from the hernia back into the abdomen temporarily, but again, it will not heal or go away on its own and will eventually require umbilical hernia surgery.
Umbilical Hernia Surgery
Many techniques still in practice today make use of umbilical hernia repair by closing the hole in the abdominal wall with sutures, creating a considerable amount of tension, and overlapping muscle. At the Hernia Center of Southern California, we do not practice this type of umbilical hernia repair, but recommend and practice the "tension free" mesh technique to repair umbilical hernias. We understand by overlapping muscle and repairing the abdominal wall only with sutures causes more tension in an already weak area. If your body could not hold everything together on its own, creating more tension in an area where tension already failed is not the remedy. Rather, strengthening and reinforcing the weakness to withstand further wear and tear on your body is the remedy and is also why we practice the "tension free" mesh technique.
The safest, most effective umbilical hernia surgery is with mesh and the advanced "tension free" technique. Umbilical hernia repair is a one-day, out-patient procedure and is performed under our preferred method of local anesthesia with sedation. In umbilical hernia repair, the mesh is placed underneath the weakness or hole in the abdominal wall. The mesh is tailored to extend past the outside edges of each individual hernia. This ensures the mesh will act as a sturdy platform for all new tissue growth and also as a support for the existing abdominal wall muscle. NO muscles are ever cut and nothing is ever sutured together to cause tension. The polypropylene mesh is thin and incredibly flexible and throughout the healing process will incorporate itself safely into your muscle without you feeling anything during everyday activity. When performing umbilical hernia repair, cosmetic surgery techniques are utilized to ensure the navel returns to its normal appearance after surgery as it is not altered in any way.
We have perfected our surgical techniques in such a way that we are even able to test and maximize the strength of the hernia before surgery is complete. Our proprietary methods of umbilical hernia surgery enable you to return to everyday activity quicker. Not only that, but our surgical methods are:
- Fully Approved
- Designed for Fast Return to Everyday Activity
- Provide Minimum Post-Operative Pain
- Less Physically Restrictive
- Achieve High Patient Satisfaction


Guernsey, British crown dependency and island, second largest of the Channel Islands. It is 30 miles (48 km) west of Normandy, France, and roughly triangular in shape. With Alderney, Sark, Herm, Jethou, and associated islets, it forms the Bailiwick of Guernsey. Its capital is St. Peter Port.
In the south, Guernsey rises in a plateau to about 300 feet (90 metres), with ragged coastal cliffs. It descends in steps and is drained mainly by streams flowing northward in deeply incised valleys. Northern Guernsey is low-lying, although small outcrops of resistant rock form hills (hougues). The soil on lower ground is of blown sand, raised beach deposits, and the fills of old lagoons. The climate is maritime; snow and severe frost are rare, and the annual temperature range is only about 17 °F (9 °C). Annual rainfall varies from 30 to 35 inches (750–900 mm). The somewhat scanty water supplies are supplemented by seawater distillation.
The island was known as Sarnia to the Romans. Early documents (11th century) show that the chief landowners were the lords of Saint-Sauveur (hereditary vicomtes of the Cotentin), the vicomtes of the Bessin, the abbey of Le Mont-Saint-Michel, and the duke of Normandy.
After separation from Normandy in 1204, the Channel Islands were put in the charge of a warden and sometimes granted to a lord. From the end of the 15th century, however, Guernsey (with Alderney and Sark) was put under a captain, later governor, an office abolished in 1835. The duties devolved upon a lieutenant governor. Because the warden could not conduct sessions of the king’s courts regularly on all four of the main Channel Islands, his judicial responsibilities on Guernsey fell to a bailiff. This bailiff came to preside over the Royal Court of Guernsey, in which judgment was given and the law declared by 12 jurats (or permanent jurors). The Royal Court has survived substantially in this medieval form, administering the law of Guernsey founded on the custom of Normandy and local usage.
From the bailiffs’ practice of referring difficult points of law to local notables, Guernsey’s deliberative and legislative assembly, the States of Deliberation, ultimately grew. In the 19th century the States of Deliberation emerged as a legislative assembly administering the island through executive committees. The assembly is presided over by the bailiff of Guernsey. The lieutenant governor is the personal representative of the British sovereign. Governmental and judicial proceedings on Guernsey are conducted in English, the principal language for most of the island’s inhabitants, though a small number of residents speak a version of Norman French known as Guernésiais, or Guernsey French, as their first language.
Guernsey was never dominated by any one great landowning family, and the early growth of commerce in St. Peter Port, with later smuggling and privateering and 19th-century industrial development, weakened what remained of the feudal landlords’ power. During World War II many of Guernsey’s inhabitants were evacuated to England before the Germans occupied the island (July 1940–May 1945)
The population is mainly of Norman descent with an admixture of Breton. St. Peter Port and St. Sampson are the main towns. Dairy farming with the famous Guernsey breed of cattle is largely confined to the high land in the south. Market gardening is concentrated chiefly in the north, where greenhouses produce tomatoes, flowers, and grapes, mostly exported to England.
Tourism became an important part of Guernsey’s economy in the 20th century. The house in St. Peter Port in which the French author Victor Hugo resided from 1855 to 1870 is now a museum. The island relies increasingly on airline services and is served by an airport at La Villaize. There are shipping links with Jersey, Alderney, and Sark; London and Weymouth, England; and Saint-Malo, France Area Guernsey, 24 square miles (62 square km); Bailiwick of Guernsey, 30 square miles (78 square km). Pop. (2001) Guernsey, 59,710; Bailiwick of Guernsey, 62,692.


Standard industrialization and numerical models fail to translate the richness and complexity of traditional crafts into the making of architectural elements. This excludes them from the design and construction industry, and limits the making of architecture in smarter and more sustainable ways.
This project develops a new way of modelling a complex craft fabrication method, namely the English Wheel, that is based on the creation of a cyber-physical system. The cyber-physical system connects a robotic arm and an artificial neural network. The method develops a new understanding of Digital Craft that is beyond mimicry, but rather exploring a unique aesthetic identity.
A 1.5 x 1.2 m demonstrator was built to proof the method and explore the tectonic expression of the assembly and marking.
Find out more!
go to www.gabriellarossi.tech


Sleep Dentistry: Assessment and Referral
Improve Your Sleep for a Better Life!
Do you snore? Or suffer from sleep apnoea? These are signs of disorders in your body you may not be aware of. So, what are you going to do about them?
Throat weakness and a mispositioned jaw are just some of the causes of snoring. Even sleeping on your back causes your tongue to drop to your mouth’s back!
Snoring deprives you of sleep and those around you. It also causes daytime drowsiness, irritability, lack of focus and even decreased libido! Studies reveal a positive link between loud snoring and risk of heart attack. And that’s only for snoring!
With sleep apnoea, you have shallow, infrequent breathing during sleep. Each pause can last for several seconds to minutes and may occur 5 to 30 times or more in an hour! Untreated, it’s strongly linked to heart disease and hypertension.
Sleep apnoeacan go for years undetected. The first signs may be chronic daytime fatigue and a constant need for stimulants (like caffeine) during the day. Other common effects include a slower reaction time and vision problems. Sleep apnoea affects some children, too!
Yet surprisingly, snoring and sleep apnoea are common. A study from the University of Sydney revealed:
“Out of men tested, 81% snored with 25% having breathing problems.”
Chronic blocked sinuses, enlarged tonsils or collapsed airway cause insufficient oxygen resulting in sleep apnoea.
At Happy Teeth
We correctly identify the causes of snoring and sleep apnoea. We also apply Australia’s standard of care and turn around contributing factors to reduce or eliminate these conditions. When required, we refer to ENT specialists for orthopaedic jaw expansion and elimination of chronic airway allergic conditions.
Call or visit us today if you snore and suffer from sleep apnoea!


Data on the incidence of and prognosis for epilepsy in sub-Saharan Africa are scarce, but prevalence data show that epilepsy is two or three times more common than in industrialise countries in non-tropical areas. The high prevalence of epilepsy and low life expectancy indicate that incidence is high. Relative contributions of each cause of epilepsy are difficult to determine. Only a few case-control studies have been done in sub-Saharan Africa. Infections, in particular cysticercosis in its endemic areas, cause most cases of epilepsy. The implementation of studies to accurately determine the causes of epilepsy in sub-Saharan Africa is urgently required. Such studies will help to lower the incidence of epilepsy in this region and better understand the aetiology of epilepsy in other areas.
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Since 1995 there is a project called “SmartRoad” running at TUDelft. The goal of the project was to design ICT solutions for for traffic control and warning systems. In this paper we focus on dynamic routing systems.
The basic idea is that car drivers communicate with each other via an intelligent system implemented on the network of lamppost. By tracking individual car drivers the system gets real time information of traffic speed along the roads. Based on that information dynamic routing algorithm has been designed, which is based on concepts of artificial life.
The Ant Based Control (ABC-) routing algorithm computes the shortest traveling time from start to destination. A simulation environment has been implemented and experimental results of static and dynamic routing will be presented.