Have you ever wondered what goes on behind those flashy slot machines in casinos? Slot machines, or “slots” as we call them, are one of the most popular casino games played today. With their bright lights and fun themes, they draw players in hoping to hit the jackpot. But have you ever thought about how they work or why casinos set the payout percentages the way they do? In this post, I’ll explain the inner workings of slot machines and the mathematical formulas that determine payouts.
How Slot Machines Work?
At the heart of every slot machine is a tiny computer called a random number generator, or RNG for short. The RNG is constantly generating random numbers that determine the outcome of each spin. When you hit the spin button, the RNG selects numbers that tell the reels when to stop. Slot online reels may look like they’re mechanically spinning, but today they’re just colourful images on a video screen. The reels stop on different symbol combinations based on the random numbers selected. Most slots also have paylines that run across the reels, like horizontal, diagonal or zigzag patterns. Different symbol combinations along these paylines result in different prizes.
The reels may seem random when you’re playing, but they’re actually carefully programmed behind the scenes. Programmers set the probabilities of different symbols landing on paylines to control the machine’s payout percentage. This brings me to my next topic – the mathematics behind slot payouts.
Mathematics Behind Payout Percentages
Casinos set an ideal return to player percentage, or RTP, for each slot game. The RTP reflects the average percentage of all money wagered that the casino expects to pay back out over time. For example, a 95% RTP means for every $100 wagered, $95 is expected to be paid back out in wins. This 5% difference is the casino’s advantage, or “house edge”.
The RTP is determined using probability theory and expected value calculations. Probability theory allows casinos to precisely set the odds of hitting different prize combinations. Expected value considers not just probability, but also factoring in the potential payback amounts. This allows casinos to balance high and low payouts in a way that controls the long-term hold percentage.Another important mathematical concept for slots is variance. Variance refers to how consistently a game pays out or how “bumpy” the payouts are over time. High variance slots have more unpredictable wins but also higher potential jackpots. Low variance games pay out smaller amounts more regularly. Variance benefits casinos because it keeps players engaged through occasional big wins, even if mathematically they’re likely losing over many spins.
Specific Payout Structures
One popular type of slot is a progressive jackpot machine. Here, a small percentage of each bet goes into a jackpot that grows larger until someone hits the winning combination. Nearby linked machines all contribute to the same rising jackpot amount displayed above the machines. These big progressive jackpots keep players inserting more coins in hopes of striking it rich.Casinos also use payout tables that list the possible winning combinations and corresponding payout amounts. Higher paying symbol combinations require matching more symbols or lining them up a certain way on the paylines. Being able to decipher payout tables is important for players to understand their odds of winning different prizes.
Player Strategies To Win At Slot Games
To make the most of their slot playing time and money, players should have a good bankroll management strategy. This means only risking a small, preset percentage of their total bankroll on each spin. Players should also walk away once they reach their loss limit, rather than chasing losses. Chasing losses is a recipe for depleting one’s funds quickly.
It’s also important for players to consider a machine’s volatility before sitting down to it. High volatility slots are riskier but could result in bigger wins. Low volatility games may pay smaller but more often, which some may prefer. Picking slots aligned with a player’s personal risk tolerance is key to enjoying the experience.
The magic behind slot machines is really advanced mathematics and computer programming. Random number generators, probability calculations and concepts like variance and expected value all come together behind the scenes to determine payouts. While the outcomes may seem random to players, casinos have the long-term advantage through precisely controlling the machines’ maths. Hopefully this post gave you a better understanding of how slots operate and the role mathematics plays in the casino gaming experience. Good luck at the machines may variance be in your favour!


The Mathematical Foundations of Informatics (MFOI) annually organized event brings together the mathematicians, computer scientists and IT professionals and adds synergy to their endeavors to lay down the mathematical foundations for computer science, also known as informatics. The previous editions can be found here http://www.mfoi.eu/conf2016/ and http://www.mfoi.eu/workshop2015/.
This year's edition will be a continuation of the 4th Conference of Mathematical Society of the Republic of Moldova (June 28-July 2, 2017).
In the frame of MFOI-2017 conference are planned the following events:
- A special session dedicated to the 70th anniversary of Florin Gheorghe Filip,
- The IVth edition of "Prof. Yuri Rogozhin" seminar,
- Workshop "Software applications of the type "Self Management Disease": solutions and prospects."
All the accepted papers will be published in the Proceedings of the Conference, which will be handed over to the participants on the first day of the conference. The selected papers will be also published after the conference in the CSJM (Computer Science Journal of Moldova – indexed in Mathematical Reviews, Zentralblatt MATH, MathSciNet, DBLP, DOAJ, EBSCO) or in the Bulletin of Academy of Sciences of Moldova, ser. Mathematics (indexed in Mathematical Reviews, Zentralblatt MATH, SCImago Journal Rank (SJR), Russian RZh Matematika).


Do you want to help your 6th through 9th grade students get excited about math and science in the real world? eCYBERMISSION is a web-based competition from the U.S. Army that encourages students to solve real problems in their communities using Science, Technology, Engineering and Math.
Students identify a problem in their community and then choose a mission challenge that addresses it. They then compete for state, regional and national awards while working to solve the problem.
In the ten years since the contest’s inception, over $8.5 million have been awarded to students across the country.
Get your 3-4 student team together quickly because applications are only being accepted through December 16th. For more information and to apply, go to the eCYBERMISSION website.


Catastrophe models calculate the stochastic distributions of loss originating from events like hurricanes and earthquakes. These models are typically based on a stochastic event catalog. For each event spatial correlation needs to be simulated. The standard approach is based on the evaluation of a copula. However the complexity of the corresponding algorithm is O(n3) and it becomes difficult to execute for n in the thousands.
So as the number n of locations in the footprint of the event grows, this approach quickly becomes infeasible. We propose a slight modification of the well-known Kriging technique, in order to solve this problem. With our solution the creation of simulation data for catastrophe models becomes manageable with the use of Big Data techniques.


My interests encompass phenomena in applied polymer physics, fluid mechanics, wetting and biomimetics. In my thesis, I investigate the fundamental problem of how structured liquid-repellent surfaces, which support a composite liquid-air interface, can reduce skin friction in both viscous laminar and turbulent flows. I have performed experimental characterization and applied mathematical modeling to demonstrate that such a macroscopic drag reduction in turbulent flow is indeed sustainable, thus settling a long standing unresolved question.
My research leads to a number of practical applications such as reducing the drag in marine vehicles and energy savings in pipe flows. In addition to this central theme, I have also worked on a mathematical analysis of the gravity driven deformation of drops on non-wetting surfaces, correlating the wetting dynamics on the barbs and barbules of aquatic birds with diving and wing-spreading behavior and on developing a hierarchical model to design oleophobic woven fabrics. Please take a look at my published work for more details.


- TikTok’s most popular videos typically consist of choreography, skits, or memes, but a niche group of math influencers is going viral.
- The #algebra and #mathematics pages on TikTok have hundreds of millions of views.
- Math educators and students said it’s an exciting, fun, and helpful way of learning.
- Visit Business Insider’s homepage for more stories.
Josh Martin, a junior at the State University of New York at Albany studying physics and math, has always known that math was a specialty of his. “I’d always been told that I kind of had a little bit of a gift for teaching, because I would be teaching people in my class,” he told Insider. So in 2018, as a college freshman, he started a YouTube channel to help his fellow students understand their math classes.
But in December 2019, he decided to try out TikTok, the new short-form video app that started booming last summer.
While a whiteboard filled with algebra equations isn’t the backdrop of a typical TikTok video, Martin soon found a huge audience on the app. His YouTube channel has 60,000 subscribers, but his TikTok currently boasts 336,000 followers and three million likes.
TikTok’s most popular videos consist of choreography, skits, or memes, but Martin is part of a niche group of math influencers that’s gained traction on the app. “I’ve been able to help millions of students and it’s just been crazy to see,” Martin said.
A niche group of TikTokers are filling the app with equations, personal finance, and SAT help.
The small but mighty…


To Learn Boats and figure out all the currents related Math 5 short strategies
In favor of a stream of 10 km per hour And two kilometers against the stream .. What is the velocity of the stream?
01. technique: = stream velocity (the velocity of the flow in favor of the boat – the boat against the stream velocity) / 2
= (10 – 2) / 2
= 4 km
Rule II: A boat in favor of a stream of 8 km per hour And 4 km per hour against the stream Said. What is the velocity of the boat?
02. technique: the boat speed = (+ currents in favor of the speed of the boat, the boat against the stream velocity) / 2
= (8 + 4) / 2
= 6 km
Rule 3: Boats and stream velocity of 10 km per hour, respectively, And 5 km .. River 45 km How much time does it take to go on the way back?
Answer: in favor of the bloodstream naukarabega = (10 + 5) = 15 km Boats against the stream velocity = (10-5) = 5 km
03. technique: Total Time = [(total distance / in favor of the velocity) + (total distance / upstream speed)] = [(45/15) + (45/5)] 3 + 9 =
= 1 hour
Rule 4: A boatman in favor of the running 5 kilometers in two hours 4 hours and returned to the first position. Its total
What is the average speed per trip?
04. technique: average speed = (total distance / total time)
= (5 + 5) / (2 + 4)
= 5/3 mile
Lets see details


Following on from my last blog, the table below shows the raw scoring rates on two additional items, H and K. Item H is twice as hard as item I, and is only addressed by Student B. Item K is twice as easy as Item J and is only addressed by Student A. This scenario synthesises one which might be generated by a computer based adaptive arithmetic test, which presents more difficult items to more able students and easier items to less able students.
|Raw Rates||Item H||Item I||Item J||Item K||Session Mean|
From the table, the effect of the adaptive component of the computer based arithmetic test has been similar to that of a very good handicapper in a horse race. By presenting more difficult items to the more able student and easier items to the less able student, it has produced a dead heat in the result. An examiner looking at the raw rates might be misled into thinking that both students had the same ability. Hence the need to adjust the results to take into account the difficulty of the items presented to each student.
Similarly the adaptive component of the test has distorted the item mean scores of those items presented to only one student. Take Item H. The item mean scoring rate is shown as 4. However, had the item been presented to Student A, from the stated assumptions of the example, one might have expected the scoring rate to have been 2 capm, and the item mean scoring rate would then have been 3, not 4. In the case of Item K, the item mean scoring rate is shown as 16. From the stated assumptions of the example, had this item been presented to Student B, one might have expected the scoring rate to have been 32 capm, and the item mean scoring rate would then have been 24. Item H has been made to look relatively easier than it is, because it was only presented to the more able student, and Item K has been made to look relatively harder than it is, because it was only presented to the less able student.
The scoring rate quotients calculate out as follows:
|Quotients||Item H||Item I||Item J||Item K||Session Mean|
The session rates can then be adjusted, using the item quotients to calculate the adjusted rate. I am reversing the order here from my previous blog. In the previous blog I adjusted the item rates first, but in this example, it is the session rates which most clearly "need" adjusting, and the item rates in fact cannot be adjusted.
|Adjusted Rates||Item H||Item I||Item J||Item K||Session Mean|
The adjusted mean scoring rate for Student B is now higher than that for Student A, but not by a factor of 2. Just looking at the numbers for this example, it very clear that the data set is "incomplete". The "missing" data from Student A on item H and from Student B on Item K is distorting the results. The session quotient method of transforming the data offsets the distortion partially, but not completely.
And in this example, adjusting the item rates, using the session mean quotients, is not very useful, as the session means were identical, and the session mean quotients were all unity. It follows that iterations would not achieve much, because no matter how many times you divide by one, you move no further forward.


I am currently a Biostatistician at the Australian Proteome Analysis Facility at Macquarie University, where I help people generate biological insights out of their proteomics data, especially in the context of complex experiments.
Working in a proteomics facility, our focus has been on generating reliable methods of interpreting and analysing data from a variety of platforms, lately emphasizing SWATH and TMT, and wherever possible incorporating them into software workflows. Areas of particular relevance to us have been plasma proteomics, and plant proteomics of agriculturally important species. Our work has benefitted from interactions with researchers, students and the APAF team of mass spectrometry specialists and analytical chemists.
I come from a mathematical and computational background, having completed a bachelor degree in Mathematics and Computer Science at Dartmouth College in the US, followed by a PhD in Mathematics at MIT, and a brief stint of teaching at Purdue. In Sydney I took a more practical turn and worked in the industry in the area of speech recognition, before settling into biostatistics for the past 13 years, both in the industry and research environment.
Abstracts this author is presenting:


Successful maths learning is linked to a range of skills involving processing of numbers. When a child finds learning maths tricky these number skills are often focused on as the source of the difficulty. Learning maths also relies on having good thinking skills such as efficient memory. Much less focus though has been given to the importance of these thinking skills. Understanding this relationship is particularly important for helping children with ADHD learn maths as many of these children show difficulties with these thinking skills.
We recently published a systematic review, which is a ‘study of studies’ on this topic. Our review looked at published papers of studies that had researched the relationship between thinking skills and maths learning in children with ADHD. This review revealed that there are very few studies that have looked at this important relationship – only 4 studies matched the criteria we had set. These studies differed from one another in a number of ways such as the tasks they used to assess thinking skills. However we were able to systematically review them and come to some conclusions about what research is telling us on this topic.
First, as expected, thinking skills were positively related to children’s maths performance. This means that better thinking scores were related to higher maths test scores in children with ADHD. Memory emerged as playing an important role in maths learning. There are different types of memory such as short-term memory where we hold information in a short term store and more strategic aspects of memory which involve not just holding the numbers in our mind but also when we have to update the information we are holding in memory. Updating is far more than just holding the numbers in memory but involved when you have to for example add or delete numbers and really actively update the information you are holding in your mind. Our review showed that this latter type of memory known as ‘working memory’ was particularly important for maths attainment in children with ADHD. Our own research studies has shown that many children with ADHD have difficulties in their working memory and it is therefore important that there is understanding that this may be contributing to any problems that the child is having with maths. Our EPIC intervention focuses on improving understanding and supporting this type of memory and ideas from our booklets may be useful in supporting children’s educational learning.
In our review we found differences between what is known as ‘verbal’ and ‘non-verbal’ types of memory and their relationship with maths. ‘Verbal memory’ involves memory processes such as those involved in rehearsal of number facts (e.g. repeating 5+5=10, 6+6 =12). What is referred to as ‘visuospatial memory’ in a nutshell involves seeing and representing information ‘spatially’. What is meant by that? The best way to understand this is with an example. So when working out what 6+7 adds up to – one way is to visually see 6+6=12 while at the same time working out 12+1=13 in your mind.
Our review seemed to suggest that verbal memory (such as being able to repeat numbers in your mind) was linked to numerical calculation skills (i.e. being able to add numbers). Quite differently, what is known as ‘visuospatial memory’ (described in the example above) was linked to children’s conceptual understanding which means being able to understand the rules of maths while calculating numbers. What does this all mean for children who have memory difficulties? This means that the type of memory difficulties children with ADHD (or other condition) have will impact the type of maths they have difficulties with. If they have typical verbal working memory they may not have difficulties with learning to add numbers. If they have reduced ‘visuospatial memory capacity’ though (see example above of visually holding numbers in mind) they may not be able to use conceptual rules that help us do maths as easily as their peers.
The key question is how can we help children with ADHD (or others with these thinking difficulties) with their learning? Reducing memory load is a really useful technique to support children with this type of difficulty, such as using a mini-whiteboard so they can see a visual breakdown of sums or using items like lego pieces to represent numbers. The child being active in their learning, such as using the lego pieces themselves to work out sums, can make a transformational impact on them wanting to participate as well as their understanding.
Much more research is needed on this important topic. We know that maths attainment can be an important predictor of many life outcomes such as career attainment. It is really important we understand the thinking difficulties that underlie being able to confidently engage in maths learning and develop strategies to support these children. This is not only important for them being able to access learning like their peers but also that they understand the everyday strategies they need to have a happy school experience.
Visual photo credit: Photographer valentinrussanov via Getty images.


Exploring the Mean Squared Logarithmic Error Loss in Data Analysis
Learn about the Mean Squared Logarithmic Error Loss, its importance in data analysis, and how to apply it effectively. Discover insights from experts and practical examples.
In the world of data analysis, there exists a wide range of metrics to quantify the performance of predictive models. One such crucial metric is the Mean Squared Logarithmic Error Loss (MSLE), which serves as an essential tool for evaluating the accuracy of logarithmic predictions. In this comprehensive guide, we’ll delve deep into the concept of MSLE, its significance, practical applications, and how it can enhance your data analysis endeavors.
Table of Contents
|Understanding Mean Squared Logarithmic Error||Basics of Mean Squared Logarithmic Error|
|Formula and Calculation|
|Importance of MSLE||Why MSLE Matters|
|Applications of Mean Squared Logarithmic Error||Forecasting and Time Series Analysis|
|Advantages and Limitations of MSLE||Pros of Using MSLE|
|Considerations and Drawbacks|
|How to Calculate MSLE||Step-by-step Guide|
|Python Implementation Example|
|Key Differences: MSE vs. MSLE|
Data analysis has become the cornerstone of decision-making across various industries. As the complexity of data-driven models increases, the need for accurate evaluation metrics also rises. The Mean Squared Logarithmic Error Loss (MSLE) steps in as a specialized metric tailored to assess logarithmic predictions, providing a nuanced perspective on model performance.
Understanding Mean Squared Logarithmic Error
Basics of Mean Squared Logarithmic Error
MSLE, an extension of the Mean Squared Error (MSE) metric, focuses on the logarithmic differences between predicted and actual values. While MSE measures the average of squared differences, MSLE emphasizes the logarithmic nature of these differences. This logarithmic transformation is particularly valuable when dealing with data that spans multiple orders of magnitude, as it mitigates the influence of outliers.
Formula and Calculation
The formula for calculating MSLE is as follows:
MSLE = (1/n) Σ(log(y_pred + 1) – log(y_true + 1))^2
- n is the total number of data points
- y_pred represents the predicted values
- y_true represents the actual ground truth values
Importance of MSLE
Why MSLE Matters
MSLE’s significance lies in its ability to penalize underestimation and overestimation proportionately, giving more balanced insights into model accuracy. This is especially valuable in scenarios where predicting small and large values are equally crucial. MSLE provides a fair evaluation of the model’s performance across the entire range of predictions.
Imagine a stock market forecasting model. If the model underestimates a stock’s future price by a factor of 10, the MSLE would still consider this prediction to be more accurate than a 10x overestimation. This realistic evaluation aligns well with practical decision-making processes.
Applications of Mean Squared Logarithmic Error
Forecasting and Time Series Analysis
MSLE finds extensive use in time series analysis, such as predicting future sales, stock prices, or weather patterns. Its logarithmic nature makes it suitable for scenarios where predicting values across different scales is essential.
Anomaly detection often involves identifying rare events or outliers in datasets. MSLE’s balanced approach ensures that anomalies of varying magnitudes are treated fairly, improving the accuracy of anomaly detection systems.
In image processing, MSLE can be employed to assess the performance of algorithms aimed at enhancing image quality or reducing noise. Its ability to handle diverse scales of pixel values makes it a valuable tool in this context.
Advantages and Limitations of MSLE
Pros of Using MSLE
- Robustness to Outliers: MSLE’s logarithmic transformation reduces the impact of outliers, making it suitable for datasets with extreme values.
- Balanced Evaluation: It provides a balanced assessment of model accuracy across the entire prediction range.
- Real-world Relevance: MSLE aligns with real-world decision-making by penalizing underestimation and overestimation proportionally.
Considerations and Drawbacks
- Logarithmic Transformation: While the logarithmic transformation helps with outliers, it may downplay errors in predictions near zero.
- Domain-specific: MSLE’s effectiveness varies based on the nature of the data and the specific problem domain.
How to Calculate MSLE
- Calculate the natural logarithm of each predicted value and actual value.
- Square the difference between the logged predicted and actual values.
- Average the squared differences across all data points.
- Take the square root of the average to obtain the final MSLE score.
Python Implementation Example
import numpy as np
def calculate_msle(y_pred, y_true):
log_pred = np.log(y_pred + 1)
log_true = np.log(y_true + 1)
squared_diff = (log_pred – log_true)**2
msle = np.mean(squared_diff)
Key Differences: MSE vs. MSLE
Both Mean Squared Error (MSE) and Mean Squared Logarithmic Error (MSLE) are vital metrics in data analysis, but they serve distinct purposes. MSE is sensitive to large errors, while MSLE focuses on the logarithmic differences between predictions and actual values, making it more suitable for scenarios involving multiple scales of data.
Q: How does MSLE compare to other evaluation metrics? A: MSLE offers a unique perspective by accounting for logarithmic differences, providing a more balanced view of model accuracy across scales.
Q: Can MSLE be negative? A: No, MSLE is always non-negative, representing the squared differences between logarithmic predictions and actual values.
Q: When should I prefer using MSLE over MSE? A: Use MSLE when your data spans various orders of magnitude and a balanced evaluation of model predictions is crucial.
Q: Is there a threshold for MSLE interpretation? A: Interpretation varies based on the problem domain, but lower MSLE values generally indicate better model accuracy.
Q: Can MSLE be applied to classification problems? A: MSLE is primarily designed for regression tasks and may not be directly applicable to classification.
Q: Are there alternative logarithmic metrics to MSLE? A: Yes, Root Mean Squared Logarithmic Error (RMSLE) is another metric that considers the square root of the logarithmic differences.
The Mean Squared Logarithmic Error Loss (MSLE) is a powerful tool in the arsenal of data analysts and machine learning practitioners. By addressing the challenges posed by varying scales of data, MSLE offers a nuanced evaluation of model accuracy, making it particularly valuable in scenarios where both small and large predictions are equally important. As you navigate the intricate landscape of data analysis, remember that MSLE can provide the balanced insights needed to make informed decisions.


Our goal in the math department is to develop each mathematician through collaborative problem solving, in-depth discussions, and a deep understanding of how the properties of math are present in real-world applications and examples. Upper School students have the opportunity to earn high school credit for Algebra and Geometry.
At ACE Academy, we use multiple standards, sources, and approaches to best meet the needs of our gifted learners. This includes, but is not limited to Texas Essential Knowledge and Skills (TEKS), Beast Academy and the Art of Problem Solving (AoPS) resources and texts, and the Number Lab philosophy
and structure to guide our curriculum.


Comparison of strategies for validating binary logistic regression models russian dating moscow
Conference presentation about the colorspace toolbox for manipulating and assessing color palettes at use R! Read more » Estimates of population parameters based on samples are not exact: there is always some error involved.2019 in Toulouse: Slides, video, replication materials, and working paper. In principle, one can estimate a population parameter with any estimator, but some...Here is an opportunity to try predictive analytics in identifying the employees most likely to get promoted. It is used to predict a binary outcome (1 / 0, Yes / No, True / False) given a set of independent variables.
Let’s understand it further using an example: We are provided a sample of 1000 customers.Read more » In the previous three posts I used multiple linear regression, decision trees, gradient boosting, and support vector machine to predict miles per gallon for 2019 vehicles. Every machine learning algorithm works best under a given set of conditions.Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.Visit Stack Exchange Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Sign up to join this community I have a question regarding calibration plot for a binary logistic regression model (calibrate) in the rms(regression modelling strategies) package.


As GED math curriculum is always changing, a lot of people find the math part of the GED test really hard. It’s really important for test-takers to have the most recent and complete study materials. That’s why many people use study guides to help them get ready for the test.
This article will serve as a guide for those looking to take the GED math test in 2023. It will provide the key concepts and strategies necessary to excel in the math section of the GED test.
2023 GED Math Curriculum Changes :
- Real-world problem solving focus
- Integration of technology in math concepts
- Increased emphasis on financial literacy
- Introduction of data analysis and statistics
- Incorporation of geometry and measurement skills
- Use of real-life scenarios in math questions
Key Concepts and Formulas to Remember
In order to excel on the GED math exam, it is essential to have a firm grasp of key mathematical concepts and formulas.
Some Important Concepts :
- Master Arithmetic Operations: Understand addition, subtraction, multiplication, and division, and apply them to solve word problems effectively.
- Comprehend Fractions, Decimals, and Percentages: Grasp the concepts and learn how to convert between fractions, decimals, and percentages.
- Basic Geometric Principles: Familiarize yourself with properties of triangles, circles, and polygons to solve geometry problems.
- Algebraic Mastery: Learn to solve equations, inequalities, and work with variables for algebraic problem-solving.
- Remember Formulas: Memorize key formulas like area, perimeter of shapes, the Pythagorean theorem, and the quadratic formula for efficient problem-solving in the GED math exam.
Important Math Formulas :
- Pythagorean theorem: a² + b² = c²
- Order of operations: PEMDAS (Parentheses, Exponents, Multiplication, Division, Addition, Subtraction)
- Area of a rectangle: length x width
- Slope: rise/run
- Percentages: part/whole x 100
- Volume of a cube: side length³
List of Topics that Appear on the GED Math Test
- Algebraic expressions and equations
- Measurement and data analysis
- Geometry concepts and constructions
- Functions and linear equations
- Proportions and percentages
- Probability and statistics
- word problems
Strategies for Solving Word Problems
Break Into Steps: Divide the problem into smaller, manageable steps to enhance clarity.
Visualize with Diagrams: Create diagrams or charts to visualize the problem and organize information effectively.
Translate into Equations: Convert problem words into math equations or inequalities and solve for the unknown variable.
Check and Verify: Ensure the answer aligns with the problem’s context; verify its accuracy and relevance.
Practice Regularly: By employing these strategies, students can improve their proficiency in solving word problems effectively.
Tips for Mastering Algebraic Equations
To master algebraic equations, it is important to have a solid understanding of the fundamental principles and techniques.
Here are some tips for solving GED math tests algebraic equations:
- Understand the Basics of Algebra: Before you can start solving equations, you need to have a strong foundation in the basics of algebra. This includes understanding concepts like variables, coefficients, and exponents.
- Identify the Variable: The variable is the unknown quantity that you are trying to solve for. It is usually represented by a letter, such as x, y, or z.
- Isolate the Variable: To isolate the variable, you need to manipulate the equation so that the variable is by itself on one side of the equation. This can be done by adding, subtracting, multiplying, or dividing both sides of the equation by the same number.
- Simplify the Equation: Once the variable is isolated, you can simplify the equation by combining like terms. For example, 2x + 3 + 4x = 6x + 3.
- Solve for the Variable: To solve for the variable, divide both sides of the equation by the coefficient of the variable. For example, if 6x = 18, then x = 3.
Here are some additional tips for solving GED math tests algebraic equations:
- Use the distributive property to multiply expressions. For example, 2(x + 3) = 2x + 6.
- Use the order of operations to simplify expressions. For example, 2 + 3 x 4 = 2 + 12 = 14.
- Use a calculator to check your work.
Understanding Data and Statistics
Understanding data and statistics involves a range of skills, including the ability to identify and collect relevant data, analyze it using appropriate statistical methods, and draw accurate and meaningful conclusions.
It also requires a solid understanding of concepts such as probability, sampling techniques, and statistical inference.
By developing proficiency in these areas, students can navigate the vast amount of information available and make sound judgments and predictions based on data-driven evidence.
Navigating Geometry and Measurements
Navigating the intricacies of geometry and measurements requires a solid understanding of fundamental concepts and principles.
Geometry encompasses the study of shapes, angles, lines, and their properties.
While measurements involve quantifying physical attributes such as length, area, volume, and angles.
Mastery of these topics involves :
-Recognizing geometric shapes,
-Understanding their properties
-Applying appropriate formulas and techniques to solve problems.
Additionally, being able to accurately measure and interpret data using appropriate units and tools is essential.
Interpreting Graphs and Charts Accurately
Graphs and charts show information in a visual way, which makes it easier to understand complicated data. They can show trends, patterns, and connections between different things in a clear and attractive way.
But in order to get useful information from these visuals, it’s crucial to know about the different parts of graphs and charts, like the axes, labels, legends, and scales.
By getting good at interpreting graphs and charts, people can handle lots of information, make smart choices, and explain their findings to others effectively.
GED Calculator Lessons
It’s highly recommended that you become familiar with the TI-30XS calculator. Knowing how to solve basic problems and even some more complex equations can help you earn extra points.
GED Math Test Tips
- Familiarize yourself with the calculator provided on the test.
- Understand and practice problems from algebra, geometry, and basic arithmetic topics.
- Review probability, statistics, and data analysis concepts.
- Develop robust problem-solving skills.
- Speed is important but accuracy is key; avoid rush mistakes.
- Refresh your understanding of mathematical notations used in the test.
- Practice plenty of past GED Math exam questions for familiarity.
- Don’t waste too much time on difficult questions; move on and come back later if possible.
- Use your scrap paper for calculations and note-taking to help clarity and accuracy.
- Rest well before the exam day to ensure optimal mental performance during the test.
Is the GED math Test Hard?
The difficulty of the GED math test varies from person to person. For some individuals, the math test may be challenging, especially if they have not studied or practiced math concepts in a while. However, with proper preparation and study, many find the GED math test manageable.
How Many Questions Can I Miss on the GED Math Test?
The number of questions that you can miss on the GED Math test depends on the total number of questions on the test and the passing score. The GED Math test has 46 questions, and you need to earn a minimum of 145 points to pass. The scoring is based on a scale of 100-200, with 145 being the passing score. Therefore, you can miss a maximum of 46 – (145-100) = 1 question and still pass the GED Math test.
How long is the math GED test?
The math GED test typically takes around 90 minutes to complete.
How many times can you fail the GED Test?
There is no limit to the number of times you can attempt the GED test. You can retake any or all of the sections as many times as needed until you pass. However, there may be waiting periods between retakes and certain fees associated with each attempt. It is recommended to adequately prepare and study before each attempt to increase the chances of passing.


Youll find the famous Mario and Sonic as well as characters from newer games like Fortnite Angry Birds Skylander. You can use our amazing online tool to color and edit the following Coloring Pages For 6th Graders.
Winter Multiplication Color By Number Free By Glitter In Third Teachers Pay Teachers Winter Math Math Coloring Multiplication Free
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Printable Coloring Pages. Download and print these 6th Grade coloring pages for free. Coloring Squared will try to get you a new math fact coloring page often.
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Use 5 different colors to color in the sailboat and lighthouse. This entry was posted in Coloring Pages Grade 1 and tagged 1st grade Coloring Worksheets Worksheets on May 6 2021 by wahyudc. Coloring is a fun way to develop your creativity your concentration and motor skills while forgetting daily stress.
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Combine fun and learning with this printable Halloween Addition Worksheet Generator!
With this special PDF, you can create an endless number of addition math problems, perfect for early elementary school kids.
With one click of a button, a new set of math problems will be created. You can use it again and again, year after year.
The Halloween Addition Worksheet Generator comes in full color and also black & white. An answer key is also included.
The instructions on the worksheet are as follows: Help the Trick-or-Treaters run through the haunted cemetery to the bowl of candy. Solve the math problems along the way.
Create infinite addition worksheets, with a delightfully spooky Halloween theme!


can find a shortest route between points by using the generated
routing points as supporting points for its route search.
Finds routing points that are close to every geometry coordinate but not intersecting with the geometry and
only within the envelope.
Creates evenly distributed points within the defined envelope.
Randomly generates points within the geometry envelope and discards those that intersect with the geometries.
To efficiently find a route within a cartesian area, we need routing points of a mesh to build a graph for the route search.


The Offaly Vocational Educational Committee has come up with the idea of using video conferencing technology to help Irish schools cope with tighter resources and budget cuts.
The new device, which connects students and teachers through video link, means that schools can still offer the same number of subjects, despite having fewer staff and reduced funding.
With figures from the Association of Secondary Teachers Ireland showing that 83% of secondary schools have lost full-time and part-time teaching jobs since 2009 and research from Trends in International Mathematics and Science Study (TIMSS ) last year revealing that a smaller proportion of time allocated to science and maths in schools, video conferencing might be the answer.
Edward McEvory, CEO of Offaly VEC, said it had helped rural students to engage with specialised subjects and other students from different backgrounds.
"Students that previously had no access to applied mathematics are now studying it twice a week. Additionally, classes in rural Ireland are having regular face-to-face interaction with a school in the United States. The school’s faculty couldn’t be happier with the technology and the advantages it has afforded the students," he said
You can also record classes, which means that even if students miss a class they can catch up.
But I wonder if there are there any hurdles. What about connectivity? Is there high-speed Internet access available from all geographical locations? And can it really be a subsitute for real-life communication?
Let us know your thoughts.


The Master’s Degree in Complex Systems Modeling is an interdisciplinary and innovative graduate program that aims to train professionals capable of employing quantitative and computational techniques in problems applied to health, management, public policy, social and environmental sciences, and to reinterpret these problems in the light of complexity science.
The program’s target audience is quite diverse, consisting of:
a) Students from management and applied social, biological, environmental and health sciences interested in developing projects in quantitative modeling;
b) Students from quantitative areas, such as mathematics, physics, statistics, computing, engineering, etc., interested in theoretical research and modeling applications in complex systems and data science.
As is the case with similar programs in Europe (for instance, Erasmus Mundus Masters in Complex Systems Science and European Master Programme in System Dynamics), Complex Systems graduates are not only able to follow an academic career in interdisciplinary doctorates, but also capable to work as differentiated and highly innovative professionals in the market.
The main research areas of the program are:
– Data Science
– Fundamentals of Complex Systems
– Complex Networks
– Complex Systems Applied to Social, Biological, Environmental and Health Sciences
Click here for information about admission ways and application dates.
Prof. Dr. Carlos Molina Mendes – Coordinator
Profa. Dra. Ana Amélia Benedito Silva – Alternate coordinator


X11 (also called Xlib or X Windows), is the 2D graphics and event library available on most Unix systems, and which has been used to develop Full Metal Jacket's development environment.
Here, we use it to develop two simple programs, a damped harmonic oscillator and a sketch pad.
Damped Harmonic Oscillator
In general, a damped harmonic oscillator has the equations
y' = y + v * dt v' = v + a * dt where m * a = spring force + damping force = - k * (y - y0) - b * v
The spring force is proportional to the distance from the equilibrium value, and the damping force is proportional to the velocity, opposite in direction to them. The mass oscillates back and forth through the equilibrium point, each oscillation smaller than the last.
In the system modelled below,
dt = 0.01 y0 = 300 m = 1 k = b = 0.1 a = - k * (y - y0) - b * v = - 0.1 * (y - 300) - 0.1 * v = 0.1 * (300 - y) - 0.1 * vso we have
y' = y + v * 0.01 v' = v + a * dt = v + (0.1 * (300 - y) - 0.1 * v) * 0.01
It is necessary to have separate loops for y and v.
Values of v are updated from their initial value of 0.0 in the master
feedback loop (left), and values of y are updated from their initial value
of 100.0 in the slave feedback loop (right). To model the system
graphically, we have a circle move up and down on a canvas. The canvas,
To run, the inputs to the slave loop, the master loop and
any input of
It is possible to change values of the constants while the system is running, with immediate effect. At present, input is by typing in the new value, but in future, input of "constants" using sliders and other GUI components will be added.
The output is:
Keyboard and mouse events can be captured by
Here, we implement a simple sketch pad, which allows us
to draw on a canvas. If the event is
We can then draw on the canvas by pressing the left mouse button and dragging the mouse:
© Copyright Donald Fisk 2015, 2016


In most renewable energy systems low voltage DC (direct current) electricity is generated. When this is from a wind turbine generator or a hydro electricity generator there is usually quite a distance between the location of the generator and the battery bank or power inverter.
If the wire (cable) taking this electricity is not thick enough you may potentially lose vast amounts of expensively generated power in resistive line losses – basically speaking, you will be heating the wire rather than charging your batteries. However, with the recent jump in the price of copper, if you choose wire which is unnecessarily thick, the extra costs incurred will seriously get in the way of your generating free (not that such a thing exists) electricity.
In an ideal situation line losses should be kept at or below 10% – i.e. no more than 10% of the power you generate should be lost in the wires you use to transmit it to your battery bank or inverter.
What You Need to Know to Calculate Line Losses
Calculating your likely line losses is quite a simple process requiring no more than a standard calculator and a little knowledge – supplied here.
NOTE – If you are not interested in the mathematics then scroll down to the final equations so you can start making some calculations for your system.
NEW You can also click here to go to our new Line Losses Calculator which will automatically calculate the size of wire you require for your system, or calculate the line losses for a given wire size.
The one constant you need is the resistivity of copper (rho) = 0.000000017 Ohms per metre at 20 degrees Celcius.
You should know the charging voltage (peak) and current (peak) of your generator. The charging voltage is always higher than the rated voltage of a generator – e.g. a 12 Volt solar panel will actually have an open circuit voltage of at least 18 Volts. You need to use the higher value in your calculations.
The cross-sectional area of your wire is given by multiplying the radius (half the diameter) of the wire squared by PI (3.14159).
If the wire is made up of multple strands then the cross-sectional area is given by squaring the radius of one strand and multiplying it by PI and then by the number of strands.
Once you know these values you are ready to calculate the resistance of your power lines and therefore your power losses.
Calculating Line Losses
The resistance of a length of wire is equal to 1,000,000 multiplied by the length of the wire (in metres) multiplied by rho and then divided by the cross sectional area of the wire (in square mm). Note that 1,000,000 square mm is equal to one square metre – hence the factor of 1,000,000 in the equation.
A wire 200 metres long with a cross sectional area of 2.25 square mm has a resistance of:
The power loss is given by the square of the current in amps multiplied by the resistance of the wire, so with a 30 amp peak power output wind turbine generator and the example wire above, the power loss is:
If the example wind turbine has a charging voltage of 65 volts (typical of a 48 Volt rated wind turbine) then the peak power output of this wind turbine would be equal to the charging voltage multiplied by the current = 65 x 30 = 1,950 Watts.
Therefore of the 1.95kW generated by the wind turbine, 1.36kW would be lost as line losses = 70%!
Using the Equation to Select wire for Your System
All the mathematics above can be simplified into one easy (relatively) equation:
|Power Loss (%) =||
In order to choose wire for your own renewable energy system you just need to choose the amount of line losses you are willing to accept (e.g. 10%), feed the numbers into the equation below, and out comes the minimum cross-sectional area of wire you will need.
|Wire X-Sectional Area =||
So with the example 48V wind turbine generator which had a 65 Volt charging voltage and 30 amp maximum current, a 200 metre run of wire required, and no more than 10% line losses acceptable – wire with a cross sectional area of 15.7 square mm is required:
Looking at the wires available on the market around this size there is 6 AWG (13.29 sq.mm) and 5 AWG (16.76 sq.mm). To keep line losses under 10% the 5 AWG would have to be selected giving 9.3% resistive line losses in this example.
Click here to visit our new automatic Line Losses Calculator.
Converting and Understanding Wire Sizes
To convert wire sizes from American Wire Gauge (AWG) to cross sectional area, mm diameter, or inches diamter click here to view our Wire Size Conversion Table


Chapter 5 in-class exercise merchandising 1 the following events pertain to downtown toy shop for october 2016 the company uses. 115 chapter exercises the following exercises give you practice creating and using messages in the virtual viewer before you start these exercises, make sure you have the notepadjss script file open within the script manager. Answers to exercises in chapter 5 - markov processes repeat exercise 5-8 under the assumption that each detector is equally likely to expected time is 5 hours. Chapter 5 exercises a guest nov 12th, 2012 268 never not a member of pastebin yet sign up, it unlocks many cool features raw download clone. Programming logic and design 8th chapter 5 exercise 3 design the logic for a program that outputs every number from 1 through 20. Financial accounting (chapter # 5) – exercise & problems exercise 52 (financial statement preparation) tutors for rent, inc, performs adjusting entries every month, but closes its accounts only at year end.
Chapter 5, exercise answers, principles of econometrics, 4e 3 exercise 58 (a) equations describing the marginal effects of nitrogen and phosphorus on yield are. 1 chem 1411, chapter 5 exercises chem 1411, chapter 5 exercises 1 a gas-filled balloon with a volume of 125 l at 090 atm and 21°c is allowed to rise to the stratosphere where the temperature is –5°c and the pressure is 10 millibar. These videos contain solutions to exercises from chapter 5 of gerald folland's textbook, real analysis: modern techniques and their applications for some. Chapter 5 answers to exercises exercise 31 this is something of a trick question, for which i apologize the trick comes from the unix ® definition of a suffix.
242 chapter 5 normal probability distributions exercises building basic skills and vocabulary 1 find three real-life examples of a continuous variable which do you think. I need the solutions to: 5-17 (cost hierarchy) on p 165 5-20 (process costing) on p 167 5-26 (activity-based costing, job-costing system) on p 170 there aren't any solutions to the ch 5 exercises so can someone please help me out - 423175. Solutions in algebra 1: a common core curriculum (9781608408382) exercises: p278: chapter 5 review: p282: chapter 5 test: p285: chapter 5 assessment: p286. Exercise 52: a common programming task is computing statistics of a set of numbers (a statistic is a number that summarizes some property of a set of data.
Chapter 5 exercises 1 researchers at farrlandia u are interested in measuring the occurrence of insomnia among newly enrolled students at the farrlandia school of public health during the first two weeks of courses. Chapter 1  see below for chapter 1 exercises exercise 14  if you run a 10 kilometer race in 43 minutes 30 seconds, what is your average time per mile. Grammar chapter 5 exercises 3rd person pronoun (αὐτος) opposite endings personal pronouns (1st & 2nd person. Chapter 5 exercises (1) a random variable x has the distribution: x 0 1 4 6 p(x) 1 4 3 16 5 16 1 4 find the expected value, variance, and standard deviation of x.
Study 80 chapter 5: exercises and answers (pages 193-198) flashcards from shhhhhh s on studyblue. Course 1 of 5 in the specialization python for everybody this course aims to teach everyone the basics of programming computers using python we cover the basics of how one constructs a program from a series of simple instructions in python the course has no pre-requisites and avoids all but the. 438 chapter 5 analytic trigonometry section 51 using fundamental identities you should know the fundamental trigonometric identities (a) reciprocal identities. Answers to chapter 5 review questions 1 the movement of water through the water cycle involves evaporation, primarily from the ocean, transport via the atmosphere, and eventually precipitation back to the surface.
Chapter 5 exercises and answers answers are in blue for exercises 1- 16, match the power of ten to its name or use a 10-12 b 10 -9 c 10 -6. Financial reporting and analysis chapter 5 solutions essentials of financial statement analysis exercises exercises e5-1 inventory turnover (aicpa adapted) inventory turnover. Answers to exercises61 answers to exercises chapter 5 • chapter chapter 5 • chapter lesson 51 1 see table below 2 see table below 3.
Answers to selected exercises chapter 5 1 they are ordered so that the last item added to the stack is the first one removed (lifo) 4 a. 52 (repeat additions) listing 54, subtractionquizloopjava, generates five random subtraction questions revise the program to generate ten random addition questions for two integers between 1 and 15. Naming conventions the html page names are constructed as follows: krx - prefix (kernighan and ritchie exercises) c - chapter number (1 to 8. Quizlet provides exercise chapter 5 activities, flashcards and games start learning today for free.
Chapter 5 programming exercise #7 python fiddle python cloud ide follow @python_fiddle.


Lets anticipate critical transitions in complex systems !
Maintaining a level of abstraction, human intelligence, and decision-making can be understood as analogies to mathematical functions . The inference that we make is essentially a function that processes a set of input sensory signals, mapping it to some meaningful target domain. For example, when one infers that is an apple, it can be understood as a function that maps visual sensory signals to a known space of objects. What constitutes knowledge and intelligence then is learning such functions, and human beings learn from experience.
On similar lines, one way to enable machines to make inferences is to model the task using a parameterized function that maps data in some input space to a desired target space. Machine 'learning' then refers to finding the optimal parameters for the function that best explains the data for the task at hand. Utilizing samples of data in the input and target spaces, one can learn the function that maps the input domain to the target domain. This enables performing inference on a new/unseen data point. This is popularly known as supervised learning and constitutes the base for the functioning of EWSNet.
Deep neural networks are essentially parameterized functions, proven to be universal function approximators. Efficiently learning from data using classical machine learning models often required handcrafting efficient feature representations for the data. The advent of convolutional neural networks enabled stacking multiple neural network layers and going deep, to automatically extract features that are important for learning, from raw input data.
In their original form, neural networks were built to process IID data samples in fixed dimensional spaces. On the contrary, most real-life data such as time-series, text, audio, video, etc., are sequential in nature and of variable length. To be able to process such sequential data and capture the inherent dependence that is present, novel neural network architectures have been proposed, the most popular among them being recurrent neural networks (RNN). RNNs process sequential data in an iterative manner, keeping a memory of data at the previous time steps. However, capturing long-term dependencies is difficult with RNNs. An LSTM is a sophisticated RNN architecture that addresses this issue by enabling the addition and deletion of information to a common memory that is propagated across time steps.
Thus, EWSNet is a parameterized function that employs an LSTM to capture long-term dependencies in the sequential time series data, in addition to a fully convolutional submodule that helps automatically extract complex non-linearities from the data, requisite to learn the characteristics indicative of a future transition.
Here you can load the time series data and visualize them prior to making predictions on them using EWSNet. We provide the following two options for generating data.
You can chose to load real world data or simulate stochastic time series using the options given in pane below.
Click on the buttons provided below to load the corresponding real world data. For further details, see the references provided.


Math and science have never been my most favorite classes. Though some of those classes I do enjoy, I prefer other ways of entertainment. That is, unless it's going to the Math and Science Center . I always find the experiments extremely enjoyable and I learn a few things as well.
These pictures are from our latest trip.
By tilting a spinning bike wheel, Titus becomes part of a gyroscope.
My grandma experimenting with bubbles?
Right here I am controlling a mechanical arm.
Our friend Jacob and I are seeing the odds of where most of the balls land.
I had a ton of fun at the Math and Science Center. I also picked up a few scientific and mathematical facts.
Thank you for your notice, Cameron.


A calendar with every country in the world: national holidays, religions, world time zones, dialing codes, international weather.
Your COMPANY LOGO printed on the front cover and throughout the inside pages.
Metric/U.S. Standard Conversion Tables
Metric ConversionMetric is a standard decimal-based system of weights and measures based on the meter as a standard of measurement. The system is approximately 200 years old, contains 7 basic measures and was created to meet the requirements of science and trade. However, two older systems still exist: the Imperial and the USA systems of measurement. Both of these systems are based on the older English units of measurement. These older systems contain more than 300 different measures so efforts have been made to adopt one system worldwide. Metric conversion is the process of converting the older systems of measurement to the more recent metric system which is less complicated.
Metric System ConversionThe metric system is currently the international standard for measurement and most countries since the mid-20th century have been slowing replacing the imperial system with the newer metric standard. Currently, the only 3 countries that have retained a non-metric system are the USA, Liberia and Burma. The modern metric system is called the International System of Units and was established in 1960. Metric system conversion involves converting the older units of measurement into the newer metric units.
Metric Conversion ChartA metric conversion chart will show you the newer metric equivalent of different types of measurements. There are thousands of units and measurements to convert, but most of the charts will allow you to convert the more common ones such as temperature, length, distance, area, volume, mass and weight. After you enter a specific measurement into one section of the table, the equivalent of that measurement will be displayed in metric form.
Conversion TableA conversion table will allow you to enter a specific measurement from the older Imperial or USA system of measurement and convert it to the more recent metric standard or vice-versa. You can easily convert almost any form of unit or measurement. Conversion tables can be found online or in various documents. For example, the International Planner produced by A Global World contains a conversion table near the back of the document.
If you want to convert an older standard of measurement from the Imperial or USA system, you should consult a metric conversion table. These tables will help you to easily determine the converted number. The results will often be shown as standard or US units to SI or Metric units.
US Metric ConversionThe US is one of the few remaining countries in the world that continues to use the US system of measurements instead of the worldwide standard metric system. Therefore, a US metric conversion chart will convert a measurement from the current American system of measurements to the more recent metric system.
If you wish to convert a metric measurement such as a centimeter, meter, kilometer, kilogram or liter, you will need to find conversion metric to US chart. The chart will show you the equivalent US system measurements such as an inch, foot, mile, pound or gallon.
Standard Metric Conversion ChartA standard metric conversion chart is very useful when you need help converting measurements from metric to the US or Imperial systems or vice-versa. The standards of measurement for the metric system involve multiplying by 10 or dividing by 10 to obtain larger or smaller units. A chart will perform these mathematical calculations for you and produce an accurate result in seconds.
Online Metric ConversionYou can obtain online metric conversion tables by conducting internet searches on search engines such as Yahoo, AltaVista or Google. Just enter the words "online metric conversion" and you will receive a long list of websites containing this information. The sites will contain charts which allow you to quickly and easily convert measurements to or from metric by typing in the measurement unit you wish to convert.
If you want to perform metric to inch conversion, you should consult a metric conversion chart which will show you both measurements. You can enter a number in the metric column of the chart and you will receive the equivalent measurement in inches almost instantly.
A metric system conversion chart is necessary to convert the newer system to measurements in the older US and Imperial measurement systems. Charts can be found in many reference materials as well as online.
In 1916, a non-profit organization called the US Metric Association or USMA was created to promote converting the current US system of measurements to the modern metric system. However, until the US has agreed to convert their current system, you can use a metric system conversion table to convert measurements between the two systems.
US Metric Conversion TableA US metric conversion table is often necessary when you are cooking or baking. Many of the ingredients listed in recipes or cookbooks are listed in the older US or Imperial systems of measurements. However, boxes and cans containing food products often contain metric measurements. Therefore, you need to use a conversion table to determine the correct metric equivalent of the ingredient such as teaspoons, tablespoons, cups, pints and ounces. You may even need to convert the size of your baking dish or pan to metric!
Metric To Standard Conversion TableYou can locate a metric to standard conversion table online by conducting an internet search. However, many companies also produce a printed conversion table that you can keep by your desk as a handy reference. You can even purchase a special laminated conversion table that is meant to be hung on the inside of your kitchen cupboards to help you convert measurements when cooking or baking.
Metric Unit Conversion TableA metric unit conversion table is very helpful when you need to convert an older US or Imperial system measurement to the newer, standard metric system. The process of converting units to the metric system is called metricating or metrification.
The older Imperial and US systems of measurement are based on the historical English units and earlier systems developed in England by combining the Roman and Anglo-Saxon systems. Therefore, the two systems may be referred to as the English system or British Imperial System. You can use an English metric conversion table to convert between the 2 older systems and the more recent metric system.
English Metric Conversion ChartYou can find an English metric conversion chart online on many different websites. However, these documents are also available in many books, maps, or international documents such as the International Planner produced by A Global World.
An older style or printed metric system conversion chart will tell you the exact number by which you need to manually multiply or divide the metric system measurement to convert to or from the older US or Imperial systems. The advantage to an online metric system conversion chart is the fact that it will automatically perform the conversion for you. You just enter in a number and the computer will show you the equivalent conversion in a matter of seconds.
Metric Liter Conversion TableIf you want to convert a metric measurement such as a liter to a non-metric measurement such as a gallon, quart, cup, ounce or pint, you need to use a metric liter conversion table. Standard tables will tell you how to multiply or divide the metric or standard measurement or they will show you a detailed list of the various conversion rates. Online conversion tables will automatically convert the number of liters to a non-metric unit or vice-versa.
You can also use conversion tables for converting measurements such as centimeters, inches, feet or yards. You will often need to know the equivalent measurement for a metric inch. This is where a conversion table metric inch will help. You can look up the information on a printed chart or enter the number of inches in an online table and receive the equivalent non-metric unit.
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They reached the figure using an advanced mathematical model which they believe is the most accurate ever.
Previous estimates ranged from three million to 100million.
The Canadian researchers hope their ‘telephone book’ for the planet will help find undiscovered species and quantify how many are being lost.
Zoologists and botanists have been cataloguing species for 250 years, but so far only 1.2million have been logged. Each year 6,000 are discovered, but even at that rate, it would take another 1,200 years to find them all.
The authors reached their total using a model based on known species groups. Each was divided into categories, then re-divided again and again into theoretical sub-categories. Read More


Slides of this plenary presentation, please click here
Understanding strengths and weaknesses of optimization algorithms with new visualization tools and methodologies
Objective assessment of optimization algorithm performance is notoriously difficult, with conclusions often inadvertently biased towards the chosen test instances. Rather than reporting average performance of algorithms across a set of chosen instances, we discuss a new methodology to enable the strengths and weaknesses of different optimization algorithms to be compared across a broader instance space. Results will be presented on TSP, timetabling and graph coloring to demonstrate: (i) how pockets of the instance space can be found where algorithm performance varies significantly from the average performance of an algorithm; (ii) how the properties of the instances can be used to predict algorithm performance on previously unseen instances with high accuracy; (iii) how the relative strengths and weaknesses of each algorithm can be visualized and measured objectively; and (iv) how new test instances can be generated to fill the instance space and provide desired insights into algorithmic power.
About the Speaker
Kate Smith-Miles is a Professor of Mathematical Sciences at Monash University, Australia, where she is also Director of MAXIMA (the Monash Academy for Cross & Interdisciplinary Mathematical Applications). She has published 2 books and over 220 refereed journal and international conference papers in the areas of neural networks, combinatorial optimization, intelligent systems and data mining. She serves as an Area Editor for Computers & Operations Research, and has been program chair for several international conferences including the 2012 International Joint Conference on Neural Networks. From 2007-2008 she was Chair of the IEEE Technical Committee on Data Mining (IEEE Computational Intelligence Society). She is an elected Fellow of the Institute of Engineers Australia and the Australian Mathematical Society. She was awarded the Australian Mathematical Society Medal in 2010 for distinguished research. In addition to her academic activities, she also regularly acts as a consultant to industry in the areas of optimisation, data mining, and intelligent systems


Vedic Maths is a collection of techniques/sutras to solve mathematical problem sets in a fast and easy way. These tricks introduce wonderful applications of Arithmetical computation, theory of numbers, mathematical and algebraic operations, higher-level mathematics, calculus, and coordinate geometry, etc.
Benefits of Vedic Maths:
- 1. It helps students to solve mathematical problems many times faster
- 2. It helps in making intelligent decisions to both simple and complex problems
- 3. It reduces the burden of memorizing difficult concepts
- 4. It increases the concentration of a student and his determination to learn and develop his/her skills
- 5. It helps in reducing silly mistakes which are often created by students


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The history of discrete mathematics has involved several challenging problems that have focused attention within regions of the area. The expression finite mathematics can be applied to regions of the area of discrete mathematics that addresses finite sets, particularly those areas applicable to business. Find online algebra tutors or internet math tutors in a few clicks.
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If you examine this step, you’re likely to find that both solutions actually https://www.paramountessays.com/ get the job done. There’s a business that supplies road data for satnav systems. As an example, spectral methods are increasingly utilized in graph algorithms for handling massive data sets.
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It may appear strange that these sets have the exact same cardinality. Now, among the things you’re likely to observe whenever we do these radical equations is we wish to isolate a minumum of one of the radicals. Trigonometry is about triangles. In the other direction, it is exceedingly desirable in order to construct random-like graphs. Look here to find out more on matrices and parametric equations, amongst others.
A little pink-tiled bathroom was located between both. Any one of these things would require me to show the impossibility of an extremely elaborate activity which nobody has truly attempted to date. In the event the radicand is 1, then the answer is going to be 1, regardless of what the root is.
Throughout this site, we link to different outside sources. Numerical analysis offers an important example. Statistical software is going to be used.
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The educational facet of discrete mathematics is just as important and deserves extended coverage alone. Discrete mathematics can play a central part within this connection. Utilize our service to work out a trigonometry tutor.
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But a lot of events aren’t fully predictable or explicable. This set can be denoted by N. For the building of a frequency-distribution of a variable, we want to experience the subsequent steps.
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For this issue, we’re likely to solve it in two ways. You’re likely to find that it works. I will live there too.
If you examine this step, you’re likely to find that both solutions actually get the job done. Numerical analysis offers an important example. As an example, spectral methods are increasingly utilized in graph algorithms for handling massive data sets.
In reality, the gap between here and there may be non-existent. Sadly, this is the point where the lecture started turning downhill. So, it’s vital to supply your eyes rest for some time by taking rests after specific time intervals.
It may appear strange that these sets have the exact same cardinality. Now, among the things you’re likely to observe whenever we do these radical equations is we wish to isolate a minumum of one of the radicals. In this we’ll see how to do addition operation on the exponents. As we go, bear in mind that we must square the 2 sides of an equation, in place of the individual terms in those 2 sides. Then regroup the aspects to make fractions equivalent to one.
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The consequent diagram appears like Fig. 5. If it isn’t, then you’ve got to multiply the base and make them same then execute the operation. This kind of equation is known as an extraneous solution.
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She’ll be made to figure it out, just enjoy the remainder of the world. If you would like to elevate everybody’s attention to detail, teach them of the visual arts. But there are certainly other approaches to consider time, each with their own intent.


The Elementary Algebra test measures your skills in three main categories:
This includes addition, subtraction, multiplication and division with integers
and negative rational numbers along with the use of absolute values and
This includes evaluations of simple formulas and expressions as well as
adding and subtracting monomials and polynomials. Also covered is
evaluation of positive rational roots and exponents, simplifying algebraic
fractions and factoring.
This includes solving verbal problems presented in algebraic context,
geometric reasoning, the translation of written phrases into algebraic
expressions and graphing.
(A total of 12 questions are administered in this test.)


How can Math and science skills contribute to the long-term sustainability of a state?
How can math and science skills contribute to the long-term sustainability of a state?
Best Solver (Answer):
Answer by joyousnorth
As populations increase past the point of small villages, it becomes critical for the welfare of all involved that the state’s available resources are used efficiently. This demand for efficient use continues to increase as the population increases. This efficient use requires math and science skills. For example, while a village of 100 may all get their drinking water from a village, and all live close enough to get it themselves, a city of 100,000 cannot. Thus, the water must be distributed from the river among the inhabitants which requires the construction of canals, pumps, etc. Besides the obvious resources like water, food, timber, etc. this need for efficiently managing resources impacts many resources we often take for granted in small communities (the ability to communicate, the ability to travel, governance, etc.).


Originally posted by ijg
Training question #2:
How exactly are the decimals carried over?
Premise: It appears decimal values - whether displayed (multi) or not (normal train) in the expected training value are rounded when applied to your training bar. Is this correct and is it working as intended?
1) In multi train - where decimals are shown - the training bars seem to go up by the rounded up value displayed, e.g. if it's 16.7% the bar goes up 17% and even if it's 16.3%, the bar tends to go up 17% too. It will go up 17% each train, so after 3 trains, you get 51%, not 50% (16.7% * 3) as best I can tell. So is it correct that the decimal is not stored in the training bar?
2) In daily training, there seems to be a similar decimal expected value, though it's not shown. Is this correct? This becomes apparent at high skill values, so when a skill is at 80, the light expected value will be 1% and normal may be 3% whereas at 90, the light value is still 1% but normal may be 2%.
When you train, your bar goes up by those displayed 1%, 2%, or 3% each time, again suggesting no stored decimal. However, I am guessing when you get 3% normal, 1% light it's because the expected values are say 2.8% and 1.4% whereas at 2% norm, 1% light, it's because the expecteds are 1.8% and 0.9%. Again, is this roughly correct?
Bottom line: The reason I am asking all this is there are funny situations when you're trying to fill out a bar (say speed is at 82 and 94% full) and light takes 6 trains at 1% vs only 2 norm trains at 3%. However, if I wait til 88 to fill the bar, it now takes 3 norm trains at 2%, but I can still do 6 light trains at 1%, so there is a bit of an arbitrage to knowing where norm is of value and where light is of value. Is that disparity WAI? I would have thought there would always be a linear relationship preserved there, but if decimal values aren't stored, then inefficiencies get created.
Note: It isn't just apparent at high values. I've noticed at lower values sometimes as well, usually around the caps.
Originally posted by Bort
Decimals are rounded/chopped off when inserted into the database, as we just store the total gains as integers. No, there's no plans to change that.


Politeconometrics: The Regulatory Confluence Theory as a Mathematical Framework
An academic discussion on a new attempt to mathematically interpret the dynamics of the space industry and its policies’ impact respective to time and jurisdiction.
Have an initial look on Politeconometrics and Regulatory Confluence Theory,dubbed as “a burgeoning theory poised to revolutionize our understanding ofspace regulations” (International Space Convention, 2023);-
Better understand how we can mathematically observe the role of spacepolicies in the overall system dynamics of the space sector;-
Understand how physics especially Newton’s Laws of Motion can be used toobserve the system dynamics of the space sector and its policies; and-
Learn how this approach can help policy-makers create better programs for the space industry


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These free printable kindergarten number worksheets range in numbers from 0 to 20, 1 to 10, 11 to 20, 1 to 30, 1 to 50 and 1 to 100. These free printables are created for coloring and number recognition activities. FREE Printable Worksheets. Kindergarten Worksheets Printable Charts Math Worksheets on Graph Paper Most Popular Worksheets Halloween Worksheets.
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List of Number Activities. subitising, numbers and basic addition and subtraction sums.