The document discusses the differences between the center of mass and geometric center of objects. The center of mass takes into account the mass distribution of an object, while the geometric center only considers the object's geometry. The center of mass is used in physics, while the geometric center is used in design. The document also provides examples of calculating the center of mass and geometric center of different objects and systems.
The document discusses ellipses and their key properties. It defines an ellipse as the set of points where the sum of the distances to two fixed foci is constant. Examples are given showing how to identify the center, vertices, covertices, and foci of ellipses given their equations. It also explains how to write the standard form of the equation of an ellipse and determines whether it has a horizontal or vertical major axis. Activities are provided for students to practice identifying parts of ellipses and writing their standard forms from equations.
The document discusses centroids and centers of gravity. It defines a centroid as the geometric center of an object where density is distributed uniformly throughout. The center of gravity is where gravitational forces balance and is affected by changes in gravitational field. Methods for determining centroids include applying the principle of moments to differential elements and integrating with respect to mass. Key differences between centroids and centers of gravity are also outlined.
The document defines center of mass and discusses methods to calculate the center of mass for one, two, and three-dimensional objects. It provides formulas to find the center of mass of planes and solids of revolution using concepts like mass density, first moment, and partitioning regions into infinitesimal elements. Examples are included to demonstrate calculating the center of mass of planes bounded by curves and parabolas and a solid of revolution.
Combination Forecast using econometrics methodsBonifaceOkuda
This module discusses combining multiple forecasts to create a single forecast. Combining forecasts can reduce risk compared to using a single forecast by averaging out errors. The optimal weights for combining forecasts minimize the mean squared error and are based on the variance-covariance matrix of the individual forecasts' errors. However, estimating this matrix from past data poses challenges when the number of forecasts exceeds the number of observations or when the underlying relationships change over time. Imposing constraints like non-negative weights can address issues like negative optimal weights.
GEOMETRICAL CENTRE AND THE CENTER OF GRAVITY.pptJorielCruz1
This document discusses the concepts of centroid, center of gravity, momentum, and impulse. It begins by defining the centroid as the geometrical center of an area, while the center of gravity is the point where all the mass of a body can be assumed to be concentrated. It then explains how momentum is calculated as mass times velocity, and how impulse is equal to the change in momentum caused by an applied force over time. Several examples are provided to illustrate these concepts, such as how changing the time over which a force is applied affects the force magnitude. The document aims to build an understanding of these foundational physics concepts.
1) The document discusses the concept of center of mass, including how to calculate the coordinates of the center of mass for a system of particles and extended objects.
2) It also covers how the motion of the center of mass relates to the total momentum and Newton's second law, where the net external force acting on a system causes the center of mass to accelerate.
3) An example problem is solved where a rocket separates into two equal mass parts at the top of its trajectory, and the location where the second part lands is calculated based on the motion of the center of mass.
The document discusses the concepts of center of mass and centroid. It provides mathematical definitions and principles for determining the coordinates of the center of mass/centroid of various bodies. The center of mass is the point where the entire weight of a body can be considered to be concentrated. It can be found using the principle of moments, by taking the weighted average of the position vectors of infinitesimal mass elements of the body. The document also discusses finding the centroids of common geometric shapes, composite bodies, and solving sample problems for determining centroids.
The document discusses ellipses and their key properties. It defines an ellipse as the set of points where the sum of the distances to two fixed foci is constant. Examples are given showing how to identify the center, vertices, covertices, and foci of ellipses given their equations. It also explains how to write the standard form of the equation of an ellipse and determines whether it has a horizontal or vertical major axis. Activities are provided for students to practice identifying parts of ellipses and writing their standard forms from equations.
The document discusses centroids and centers of gravity. It defines a centroid as the geometric center of an object where density is distributed uniformly throughout. The center of gravity is where gravitational forces balance and is affected by changes in gravitational field. Methods for determining centroids include applying the principle of moments to differential elements and integrating with respect to mass. Key differences between centroids and centers of gravity are also outlined.
The document defines center of mass and discusses methods to calculate the center of mass for one, two, and three-dimensional objects. It provides formulas to find the center of mass of planes and solids of revolution using concepts like mass density, first moment, and partitioning regions into infinitesimal elements. Examples are included to demonstrate calculating the center of mass of planes bounded by curves and parabolas and a solid of revolution.
Combination Forecast using econometrics methodsBonifaceOkuda
This module discusses combining multiple forecasts to create a single forecast. Combining forecasts can reduce risk compared to using a single forecast by averaging out errors. The optimal weights for combining forecasts minimize the mean squared error and are based on the variance-covariance matrix of the individual forecasts' errors. However, estimating this matrix from past data poses challenges when the number of forecasts exceeds the number of observations or when the underlying relationships change over time. Imposing constraints like non-negative weights can address issues like negative optimal weights.
GEOMETRICAL CENTRE AND THE CENTER OF GRAVITY.pptJorielCruz1
This document discusses the concepts of centroid, center of gravity, momentum, and impulse. It begins by defining the centroid as the geometrical center of an area, while the center of gravity is the point where all the mass of a body can be assumed to be concentrated. It then explains how momentum is calculated as mass times velocity, and how impulse is equal to the change in momentum caused by an applied force over time. Several examples are provided to illustrate these concepts, such as how changing the time over which a force is applied affects the force magnitude. The document aims to build an understanding of these foundational physics concepts.
1) The document discusses the concept of center of mass, including how to calculate the coordinates of the center of mass for a system of particles and extended objects.
2) It also covers how the motion of the center of mass relates to the total momentum and Newton's second law, where the net external force acting on a system causes the center of mass to accelerate.
3) An example problem is solved where a rocket separates into two equal mass parts at the top of its trajectory, and the location where the second part lands is calculated based on the motion of the center of mass.
The document discusses the concepts of center of mass and centroid. It provides mathematical definitions and principles for determining the coordinates of the center of mass/centroid of various bodies. The center of mass is the point where the entire weight of a body can be considered to be concentrated. It can be found using the principle of moments, by taking the weighted average of the position vectors of infinitesimal mass elements of the body. The document also discusses finding the centroids of common geometric shapes, composite bodies, and solving sample problems for determining centroids.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 3: Describing, Exploring, and Comparing Data
3.1: Measures of Center
This document summarizes a study that used the fuzzy TOPSIS method to select the optimal type of spillway for a dam in northern Greece called Pigi Dam. Five alternative spillway types were evaluated based on nine criteria. The criteria were expressed as triangular fuzzy numbers to account for uncertainty. Weights for the criteria were determined using the AHP method and also expressed linguistically as fuzzy numbers. The fuzzy TOPSIS method was then used to rank the alternatives based on their distances from the ideal and negative-ideal solutions. The alternative with the highest relative closeness to the ideal solution was determined to be the optimal spillway type.
The document discusses the center of mass of particle systems and solid objects. It defines the center of mass as the point where the object is balanced horizontally if suspended from that point. For particle systems, the x- and y-components of the center of mass are calculated as the weighted averages of the x- and y-coordinates of the particles, with weights proportional to the particle masses. For solid objects, the calculation involves dividing the object into infinitesimal mass elements and taking integrals over density to compute the center of mass.
Time series decomposition involves breaking down a time series into various components: trend, seasonality, and error/noise. There are different decomposition models such as additive and multiplicative. Smoothing methods like moving averages are used to estimate the trend-cycle component by reducing random variation. Box-Jenkins models combine autoregressive (AR) and moving average (MA) terms to model time series, and involve identification, estimation, and diagnostic stages.
1. The document discusses centroids, which are the geometric centers of objects where density is distributed. It defines centroids for lines, areas, volumes, and composite bodies.
2. Centroids can be determined through integration by applying the principle of moments to gravitational forces acting on particles of a body.
3. There is a distinction made between the center of mass, which is based on mass distribution, and the center of gravity, which is based on weight distribution and affected by changes in gravitational fields.
This document discusses different approaches to calculating measures of central tendency (location) from data. It compares the geometric approach of deriving formulas from histograms and ogives to the numerical/mathematical formula approach. Formulas for mean, median, and mode are presented and derived both geometrically and mathematically. The key findings are that the geometric approach can produce the same results as the mathematical formulas and that both approaches are useful for understanding measures of location.
This document discusses different approaches to calculating measures of central tendency (location) from data. It compares the geometric approach of deriving formulas from histograms and ogives to the numerical/mathematical formula approach. Formulas for mean, median, and mode are presented and derived both geometrically and mathematically. The key findings are that the geometric approach can produce the same results as the mathematical formulas and that both approaches are useful for understanding measures of location.
This document discusses measurement, physical quantities, dimensions, and dimensional analysis. It defines fundamental and derived physical quantities. Dimension is defined as how physical quantities relate to fundamental quantities of mass, length, and time. Dimensional analysis shows how physical quantities relate to each other and can be used to derive formulas, check the homogeneity of equations, and convert between units. Errors are deviations between measured and exact values. Dimensional analysis has limitations and cannot be used for trigonometric, logarithmic, or exponential formulas or detect dimensionless constants.
Statistical Measures of Location: Mathematical Formulas versus Geometric Appr...BRNSS Publication Hub
This paper illustrates with an example of the comparison of the geometrical and the numerical approaches
of measures of location. A geometrical derivation of the most popular measure of location (mean) was
derived from a histogram by determining the centroid of a histogram. The numerical or mathematical
expression of the other measures of location, median and mode were derived from ogive and histogram,
respectively. Finally, the research establishes that the two approaches produce the same results.
This document provides an outline and summary of key concepts related to data analysis, including measures of central tendency (mean, median, mode), spread of distribution (range, variance, standard deviation), and experimental designs (paired t-test, ANOVA). It explains how to calculate and interpret the mean, median, mode, range, variance, and standard deviation. It also provides brief definitions and examples of paired t-tests and ANOVA.
Here are the steps to find the centroid of each given plane region:
1. Region bounded by y = 10x - x^2, x-axis, x = 2, x = 5:
- Set up the integral to find the area A: ∫2^5 (10x - x^2) dx
- Evaluate the integral: A = 96
- Set up the integrals to find the x- and y-moments: ∫2^5 x(10x - x^2) dx and ∫2^5 (10x - x^2)x dx
- Evaluate the integrals: Mx = 192, My = 960
- Use the formulas for centroid: C
1) The document discusses the distance and midpoint formulas. The distance formula calculates the distance between two points on a coordinate plane using their x and y coordinates, while the midpoint formula calculates the midpoint between two points.
2) An example uses the distance formula to calculate the distance between points (2, -6) and (-3, 6), finding it to be 13.
3) Another example finds the midpoint between points (1, 2) and (-5, 6) to be (-2, 8) using the midpoint formula.
This document discusses measures of central tendency. It defines measures of central tendency as summary statistics that represent the center point of a distribution. The three main measures discussed are the mean, median, and mode. The mean is the sum of all values divided by the total number of values. There are different types of means including the arithmetic mean, weighted mean, and geometric mean. The document provides formulas for calculating each type of mean and discusses their properties and applications.
This presentation covers the basics of calculating midpoints, essential in geometry and real-world applications. It breaks down the formula and steps needed to find the midpoint between two points, providing clear examples for easy understanding.
This presentation covers the basics of calculating midpoints, essential in geometry and real-world applications. It breaks down the formula and steps needed to find the midpoint between two points, providing clear examples for easy understanding.
Visit https://www.omnicalculator.com/math/midpoint to unlock mathematical insights and streamline your numerical solutions today!
Computational Physics - modelling the two-dimensional gravitational problem b...slemarc
The document describes a C++ program that models the two-dimensional gravitational interaction between two point masses. The program uses Newton's laws of motion and gravitation to calculate the forces, accelerations, velocities, and positions of the two masses at each time step. It assumes the masses follow elliptical orbits and uses the orbital period and energy to determine the time step for its calculations. The program outputs the positions of the masses over time to a file that can be used to plot their orbital paths.
This document provides an introduction to Baabtra-Mentoring Partner and some key concepts in statistics including the mean, median, variance, and standard deviation. It includes examples and solutions to problems calculating these measures from data sets. The document was prepared for training purposes by Baabtra.
This document discusses measures of central tendency, which provide a single value to represent the center of a data set. There are three main types: mean, median, and mode. The mean is the average and there are different types of means depending on the data, including arithmetic mean and weighted mean. The median is the middle value of the data when sorted. The mode is the most frequently occurring value. Calculating these measures allows analysis and comparison of data sets.
PRESENTATION ON INTRODUCTION TO SEVERAL VARIABLES AND PARTIAL DERIVATIVES Mazharul Islam
This document provides an introduction to partial derivatives and several examples of calculating them. It begins by defining partial derivatives as the rate of change of a function with respect to one variable, holding other variables constant. Several examples are then provided of calculating partial derivatives of multivariable functions. The document concludes by stating the chain rule for partial derivatives, which relates the derivative of a composite function to its constituent partial derivatives.
This document describes a project to compute the volume of a section of the Atlantic Ocean off the coast of Florida using data obtained from a GeoMapApp. It includes mathematical derivations of interpolation formulas and integration methods to discretize the ocean area into rectangles and compute the volume. Computational details are provided on obtaining depth data from the app and implementing the volume calculation in MATLAB. A verification method using a rectangular prism approximation is also described and matches the computed volume result. Contributions of the four authors to various aspects of the project are outlined.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 3: Describing, Exploring, and Comparing Data
3.1: Measures of Center
This document summarizes a study that used the fuzzy TOPSIS method to select the optimal type of spillway for a dam in northern Greece called Pigi Dam. Five alternative spillway types were evaluated based on nine criteria. The criteria were expressed as triangular fuzzy numbers to account for uncertainty. Weights for the criteria were determined using the AHP method and also expressed linguistically as fuzzy numbers. The fuzzy TOPSIS method was then used to rank the alternatives based on their distances from the ideal and negative-ideal solutions. The alternative with the highest relative closeness to the ideal solution was determined to be the optimal spillway type.
The document discusses the center of mass of particle systems and solid objects. It defines the center of mass as the point where the object is balanced horizontally if suspended from that point. For particle systems, the x- and y-components of the center of mass are calculated as the weighted averages of the x- and y-coordinates of the particles, with weights proportional to the particle masses. For solid objects, the calculation involves dividing the object into infinitesimal mass elements and taking integrals over density to compute the center of mass.
Time series decomposition involves breaking down a time series into various components: trend, seasonality, and error/noise. There are different decomposition models such as additive and multiplicative. Smoothing methods like moving averages are used to estimate the trend-cycle component by reducing random variation. Box-Jenkins models combine autoregressive (AR) and moving average (MA) terms to model time series, and involve identification, estimation, and diagnostic stages.
1. The document discusses centroids, which are the geometric centers of objects where density is distributed. It defines centroids for lines, areas, volumes, and composite bodies.
2. Centroids can be determined through integration by applying the principle of moments to gravitational forces acting on particles of a body.
3. There is a distinction made between the center of mass, which is based on mass distribution, and the center of gravity, which is based on weight distribution and affected by changes in gravitational fields.
This document discusses different approaches to calculating measures of central tendency (location) from data. It compares the geometric approach of deriving formulas from histograms and ogives to the numerical/mathematical formula approach. Formulas for mean, median, and mode are presented and derived both geometrically and mathematically. The key findings are that the geometric approach can produce the same results as the mathematical formulas and that both approaches are useful for understanding measures of location.
This document discusses different approaches to calculating measures of central tendency (location) from data. It compares the geometric approach of deriving formulas from histograms and ogives to the numerical/mathematical formula approach. Formulas for mean, median, and mode are presented and derived both geometrically and mathematically. The key findings are that the geometric approach can produce the same results as the mathematical formulas and that both approaches are useful for understanding measures of location.
This document discusses measurement, physical quantities, dimensions, and dimensional analysis. It defines fundamental and derived physical quantities. Dimension is defined as how physical quantities relate to fundamental quantities of mass, length, and time. Dimensional analysis shows how physical quantities relate to each other and can be used to derive formulas, check the homogeneity of equations, and convert between units. Errors are deviations between measured and exact values. Dimensional analysis has limitations and cannot be used for trigonometric, logarithmic, or exponential formulas or detect dimensionless constants.
Statistical Measures of Location: Mathematical Formulas versus Geometric Appr...BRNSS Publication Hub
This paper illustrates with an example of the comparison of the geometrical and the numerical approaches
of measures of location. A geometrical derivation of the most popular measure of location (mean) was
derived from a histogram by determining the centroid of a histogram. The numerical or mathematical
expression of the other measures of location, median and mode were derived from ogive and histogram,
respectively. Finally, the research establishes that the two approaches produce the same results.
This document provides an outline and summary of key concepts related to data analysis, including measures of central tendency (mean, median, mode), spread of distribution (range, variance, standard deviation), and experimental designs (paired t-test, ANOVA). It explains how to calculate and interpret the mean, median, mode, range, variance, and standard deviation. It also provides brief definitions and examples of paired t-tests and ANOVA.
Here are the steps to find the centroid of each given plane region:
1. Region bounded by y = 10x - x^2, x-axis, x = 2, x = 5:
- Set up the integral to find the area A: ∫2^5 (10x - x^2) dx
- Evaluate the integral: A = 96
- Set up the integrals to find the x- and y-moments: ∫2^5 x(10x - x^2) dx and ∫2^5 (10x - x^2)x dx
- Evaluate the integrals: Mx = 192, My = 960
- Use the formulas for centroid: C
1) The document discusses the distance and midpoint formulas. The distance formula calculates the distance between two points on a coordinate plane using their x and y coordinates, while the midpoint formula calculates the midpoint between two points.
2) An example uses the distance formula to calculate the distance between points (2, -6) and (-3, 6), finding it to be 13.
3) Another example finds the midpoint between points (1, 2) and (-5, 6) to be (-2, 8) using the midpoint formula.
This document discusses measures of central tendency. It defines measures of central tendency as summary statistics that represent the center point of a distribution. The three main measures discussed are the mean, median, and mode. The mean is the sum of all values divided by the total number of values. There are different types of means including the arithmetic mean, weighted mean, and geometric mean. The document provides formulas for calculating each type of mean and discusses their properties and applications.
This presentation covers the basics of calculating midpoints, essential in geometry and real-world applications. It breaks down the formula and steps needed to find the midpoint between two points, providing clear examples for easy understanding.
This presentation covers the basics of calculating midpoints, essential in geometry and real-world applications. It breaks down the formula and steps needed to find the midpoint between two points, providing clear examples for easy understanding.
Visit https://www.omnicalculator.com/math/midpoint to unlock mathematical insights and streamline your numerical solutions today!
Computational Physics - modelling the two-dimensional gravitational problem b...slemarc
The document describes a C++ program that models the two-dimensional gravitational interaction between two point masses. The program uses Newton's laws of motion and gravitation to calculate the forces, accelerations, velocities, and positions of the two masses at each time step. It assumes the masses follow elliptical orbits and uses the orbital period and energy to determine the time step for its calculations. The program outputs the positions of the masses over time to a file that can be used to plot their orbital paths.
This document provides an introduction to Baabtra-Mentoring Partner and some key concepts in statistics including the mean, median, variance, and standard deviation. It includes examples and solutions to problems calculating these measures from data sets. The document was prepared for training purposes by Baabtra.
This document discusses measures of central tendency, which provide a single value to represent the center of a data set. There are three main types: mean, median, and mode. The mean is the average and there are different types of means depending on the data, including arithmetic mean and weighted mean. The median is the middle value of the data when sorted. The mode is the most frequently occurring value. Calculating these measures allows analysis and comparison of data sets.
PRESENTATION ON INTRODUCTION TO SEVERAL VARIABLES AND PARTIAL DERIVATIVES Mazharul Islam
This document provides an introduction to partial derivatives and several examples of calculating them. It begins by defining partial derivatives as the rate of change of a function with respect to one variable, holding other variables constant. Several examples are then provided of calculating partial derivatives of multivariable functions. The document concludes by stating the chain rule for partial derivatives, which relates the derivative of a composite function to its constituent partial derivatives.
This document describes a project to compute the volume of a section of the Atlantic Ocean off the coast of Florida using data obtained from a GeoMapApp. It includes mathematical derivations of interpolation formulas and integration methods to discretize the ocean area into rectangles and compute the volume. Computational details are provided on obtaining depth data from the app and implementing the volume calculation in MATLAB. A verification method using a rectangular prism approximation is also described and matches the computed volume result. Contributions of the four authors to various aspects of the project are outlined.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
3. Center of Mass
- Is the point where the whole mass of
the body/system is concentrated
- The point where the gravitational
force, weight of the body, acts
generally in any orientation of the
body
4. Center of Mass
It is calculated using the following formula:
Center of mass
= (m1r1 + m2r2 + ... + mn rn) / (m1 +
m2 + ... + mn)
5. Geometric Center or Centroid
The geometric center is the
point where all the sides or
edges of an object intersect or
converge.
6. Geometric Center or Centroid
It is calculated using the following formula:
Geometric center
= (x1 + x2 + ... + xn) / n, (y1 + y2 + ... +
yn) / n
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8. Center of Mass vs Geometric Center
The center of mass and geometric center may
not always coincide. The geometric center is a
purely geometric property, whereas the center
of mass depends on the mass distribution of the
object. The center of mass takes into account
the mass of each object and its position, while
the geometric center is only based on the
position of each point.
9. Center of Mass vs Geometric Center
The center of mass is the point in a system
where the mass is evenly distributed, and it
behaves as if all the mass were concentrated at
that point. It takes into account the mass of
each object and its position, and it is used in
physics and engineering to analyze the motion
of systems, such as objects in freefall,
projectiles, and collisions.
10. Center of Mass vs Geometric Center
On the other hand, the geometric center is
the point where all the sides or edges of an
object intersect or converge. It is a purely
geometric property, and it is used in design,
architecture, and graphics to locate the
position of an object, such as a shape, a
building, or an image.
11. Center of Mass vs Geometric Center
While the center of mass depends on the
mass distribution of the object, the
geometric center is only based on the
position of each point or edge.
Therefore, the center of mass and geometric
center may not always coincide.
13. Find the center of mass of a system of two
masses: 2 kg located at (1,1) and 3 kg located
at (5,3).
Center of Mass
Difficulty Level: Easy
14. Solution:
Center of mass = (2(1,1) + 3(5,3)) / (2+3) = (4,2.2)
Center of Mass
Difficulty Level: Easy
15. Explanation:
In this case, we have a system of two masses, one of
2 kg at point (1,1) and the other of 3 kg at point (5,3).
To find the center of mass, we need to use the
formula:
Center of mass = (m1r1 + m2r2) / (m1 + m2)
where m is the mass of each object and r is the
position vector of each object.
Center of Mass
Difficulty Level: Easy
16. Explanation:
Substituting the given values, we get:
Center of mass = (2(1,1) + 3(5,3)) / (2+3)
= (2, 2) + (15, 9) / 5
= (17, 11) / 5
= (3.4, 2.2)
Therefore, the center of mass of the system of two
masses is located at (3.4, 2.2).
Center of Mass
Difficulty Level: Easy
17. Find the geometric center of a rectangle with
vertices at (0,0), (0,4), (6,4), and (6,0).
Geometric Center
Difficulty Level: Easy
19. Explanation:
In this case, the opposite corners of the rectangle are
(0,0) and (6,4), and (0,4) and (6,0).
To find the geometric center, we take the average of
the x-coordinates of the corners and the average of
the y-coordinates of the corners separately.
Geometric Center
Difficulty Level: Easy
20. Explanation:
The average of the x-coordinates is
(0+0+6+6)/4 = 3, and the average of the y-
coordinates is (0+4+4+0)/4 = 2.
Therefore, the geometric center of the
rectangle is located at (3, 2).
Geometric Center
Difficulty Level: Easy
21. Find the center of mass of a system of
four masses: 2 kg located at (0,0), 4 kg
located at (4,0), 6 kg located at (4,3), and
8 kg located at (0,3).
Center of Mass
Difficulty Level: Medium
22. Explanation:
In this case, we have a system of four masses, each
with a different mass and position.
To find the center of mass, we need to use the
formula:
Center of mass
= (m1r1 + m2r2 + m3r3 + m4r4) / (m1 + m2 + m3 + m4)
where m is the mass of each object and r is the
position vector of each object.
Center of Mass
Difficulty Level: Medium
23. Explanation:
Substituting the given values, we get:
Center of mass
= (2(0,0) + 4(4,0) + 6(4,3) + 8(0,3)) / (2+4+6+8)
= (0, 0) + (16, 0) + (24, 18) + (0, 24) / 20
= (40, 42) / 20
= (3.2, 1.8)
Therefore, the center of mass of the system of four masses is
located at (3.2, 1.8).
Center of Mass
Difficulty Level: Medium
24. Find the geometric center of a regular octagon with side length
10.
The midpoint of AB is ((A + B)/2) = ((0, 5) + (5, 5))/2 = (2.5, 5).
The midpoint of BC is ((B + C)/2) = ((5, 5) + (5, 0))/2 = (5, 2.5).
The midpoint of CD is ((C + D)/2) = ((5, 0) + (0, 0))/2 = (2.5, 0).
The midpoint of DE is ((D + E)/2) = ((0, 0) + (0, 5))/2 = (0, 2.5).
The midpoint of EF is ((E + F)/2) = ((0, 5) + (-5, 5))/2 = (-2.5, 5).
The midpoint of FG is ((F + G)/2) = ((-5, 5) + (-5, 0))/2 = (-5, 2.5).
The midpoint of GH is ((G + H)/2) = ((-5, 0) + (0, 0))/2 = (-2.5, 0).
The midpoint of HA is ((H + A)/2) = ((0, 0) + (0, 5))/2 = (0, 2.5).
Geometric Center
Difficulty Level: Medium
26. Explanation:
To find the geometric center of this regular octagon,
we can start by finding the midpoints of each of its
sides. We can use the formula for finding the
midpoint of a line segment, which is ((x1 + x2)/2, (y1 +
y2)/2), where (x1, y1) and (x2, y2) are the coordinates
of the endpoints of the segment.
Geometric Center
Difficulty Level: Medium
27. Explanation:
Once we have found the midpoints of all eight sides
of the octagon, we can take the average of these
midpoints to find the geometric center. This average
will give us a point that is equidistant from all the
midpoints and therefore lies at the intersection of all
the lines of symmetry of the octagon.
Geometric Center
Difficulty Level: Medium
28. Explanation:
Using this method, we can calculate that the
geometric center of this regular octagon with side
length 10 is located at the
point (0, 2.5).
Geometric Center
Difficulty Level: Medium
Editor's Notes
Where m is the mass of each object and r is the position vector of each object.
Where x and y are the coordinates of each point and n is the total number of points.
The problem asks us to find the geometric center of a regular octagon with side length 10. To understand what that means, we need to know what a regular octagon is and what a geometric center is.
A regular octagon is a polygon with eight sides of equal length and eight angles of equal measure. It looks like a stop sign, but with more sides. The side length of this particular octagon is given as 10.
The geometric center of a shape is the point at which all the lines of symmetry intersect. This means that if we fold the shape along all its lines of symmetry, the geometric center will be the point where all the folds intersect.
The problem asks us to find the geometric center of a regular octagon with side length 10. To understand what that means, we need to know what a regular octagon is and what a geometric center is.
A regular octagon is a polygon with eight sides of equal length and eight angles of equal measure. It looks like a stop sign, but with more sides. The side length of this particular octagon is given as 10.
The geometric center of a shape is the point at which all the lines of symmetry intersect. This means that if we fold the shape along all its lines of symmetry, the geometric center will be the point where all the folds intersect.
The problem asks us to find the geometric center of a regular octagon with side length 10. To understand what that means, we need to know what a regular octagon is and what a geometric center is.
A regular octagon is a polygon with eight sides of equal length and eight angles of equal measure. It looks like a stop sign, but with more sides. The side length of this particular octagon is given as 10.
The geometric center of a shape is the point at which all the lines of symmetry intersect. This means that if we fold the shape along all its lines of symmetry, the geometric center will be the point where all the folds intersect.